Douglass returns to his previous practice of rhyming for this entire conclusion, truly giving the audience his thesis in a way that sticks in their minds more than the rest of his speech would.

A lot of people forget that the point of these games are not extreme anything. It is a closed-minded and stubborn issue. People caring about to much about stuff that doesn’t affect them. In the closed-minded aspect, people can not wrap their head around a different stylistic choice. Not everything needs to be black and white, wet and dry. If you do not allow ambiguity, you can cause hateful environments with that separation. The separation can then come on though during difficulty of the game and how people wish to play. But perhaps there are others who don’t enjoy the fast play and wish to play for a calming enjoyment. The hateful community can move in again with diction like, “wuss mode;” This creates a harmful, unhappy and toxic environment. The author should go into the toxicity this environment creates in the gaming community because it would help explain why Gone Home was hated on so much. Also some opposing perspectives created by the author would be nice describing or putting a perspective in of someone who doesn't like the slow pace game.

The writer summarizes a few of the scenes from the first two episodes. This supports her claim that this sitcom is an appealing show for those who don't like marvels. I've never been a Marvel fan but I can attest that after reading this I would potentially watch the show.

In some ways the American dream is a way to cover and ignore the fact that there are such big inequalities between different people and different areas and this idea allows you to ignore those disparities by saying well if you work hard anything is possible.

This paragraph highlights how parental success affects children's opportunities. School aim to create but those with more resources can give their kids advantages, leading to an uneven playing field. I agree because this reflects systemic inequality factor like income and education reinforce each other over generations. Without policies like school funding reform, the gap will continue to widen, making it harder for future generations to achieve true equality.

I think this is interesting because the deeper I dive into imagination or trying to create the life I want in my head the easier it is to getting there. I wonder how many people in the past studied these techniques and actually used their imagination as a tool... or I guess everyone has becuase it all starts with an idea. Right?

The passage shows the cruelness of an animal and also illustrates the behavior of animals being skilled at imitation. Though it did it unconsciously, it’s also harmful.

I wonder how did the murderer cut the Madame L’Espanaye's neck so deeply that her head fell off when people tried to raise her corpse. Also, what knid of weapon did the murderer use? I guess the weapon is probably big or long, but how did the murderer take away and nobody saw it?

This part surprised me a lot. I also find it creepy as the first time I read it, for all the narrator’s movement were observed and memorized by Dupin. It feels like the narrator stayed with a monitor. What’s more, Dupin can even follow up the narrator’s mind.

I was horrified by this scene, but also curious at the same time. I couldn’t understand how the daughter could be “head downward and was dragged”. Was she trying to hide in the fire-place but fell down? Or was she trying to run but being caught by the animal?

I'm surprised that Poe, as the pioneer of detective literature, can come up with such a deliberate and coherent process of thinking.

I know that the author wants to create an image of Dupin as a detective who is good at reasoning; however, I wondered, how could he link all these details together and never miss one action or facial expression from our narrator? If the author had cut some of the details, would it be more convincing to most people? Since most of us could barely do that, we might not be able to think of it and resonate with it.

1.  How flexible are these category heads in exocentric compounds?

Could we argue that the right-hand component is, in some cases, not only a category head but also holds some metaphorical or implicit interpretive meaning?

VERY important element in both stories. In poe's story in word choice makes so easy for you to imagine it in your head as you read. But in TV for the haunting of hill house, the choice of colors and adding in darkness is scenes makes it more eerie and scary

I personally believe that Plato was able to crystalize his answer on how to live subject to a critical examination because he saw himself in the fantastic imagination in The Republic from the 3rd perspective, as a reader. When I was traveling Japan I came across this book which I forgot the name of, however, the book gave me an inspirational insight: to be the person watching your life as a movie in a theater. We often feel sentimental or jealous when we see others living their everyday life from a third perspective. From washing dishes in a dimmed light kitchen to just having a casual family dinner. Seeing yourself and your life as a movie lets you to see yourself objectively. Seeing the movie (your life) objectively allows the audience, who is only yourself, to critically analyze your actions, emotions and thoughts. Another method is to see yourself from an above, just like a drone flying above your head and seeing yourself as one of a player in a game. This may be more effective than imagining seeing your life as a movie in a theater, however, this method's risk becomes uncorrelated to the reward as you get older, because you have more responsibilities in your actions.

I personally believe that Plato was able to crystalize his answer on how to live subject to a critical examination because he saw himself in the fantastic imagination in The Republic from the 3rd perspective, as a reader. When I was traveling Japan I came across this book which I forgot the name of, however, the book gave me an inspirational insight: to be the person watching your life as a movie in a theater. We often feel sentimental or jealous when we see others living their everyday life from a third perspective. From washing dishes in a dimmed light kitchen to just having a casual family dinner. Seeing yourself and your life as a movie lets you to see yourself objectively. Seeing the movie (your life) objectively allows the audience, who is only yourself, to critically analyze your actions, emotions and thoughts. Another method is to see yourself from an above, just like a drone flying above your head and seeing yourself as one of a player in a game. This may be more effective than imagining seeing your life as a movie in a theater, however, this method's risk becomes uncorrelated to the reward as you get older, because you have more responsibilities in your actions.

It is amazing that she chose to listen to the commands spoken in her head

Does he really think George Washington is King George?

This feels like a gross way to describe people

These people shouldn't have married. This feels like "I hate my wife!" humor when I would bet that Rip puts literally no effort into maintaining the household (and sounds like a deadbeat dad). But noooooo, the tone is "she's so awful". I'd be upset too if I were stuck with him.

he's cooked

he kind of found an escape? or a secret oasis?

???? what does that even mean?

As the new Department Head of a fairly new Dual Language program in Stratford, CT (this is the seventh year of implementation) versus the 22 years spent in Norwalk where I was part of the program that scaled up to an entire dual language school, my goal is to improve this program and make it a highly effective dual language program based on the criteria from the book, The Guiding Principles of Dual Language Education. Therefore, this article is both relevant and meaningful to me.

Note: This response was posted by the corresponding author to Review Commons. The content has not been altered except for formatting.

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Reply to the reviewers

Response to Reviewer Comments:

Reviewer #1 (Evidence, reproducibility and clarity (Required)):

*Glaucoma-associated optineurin mutations increase transmitophagy in vertebrate optic nerve.

Summary In Jeong et al., the authors perform live imaging of the X. laevis optic nerve to track neuronal mitochondrial movement and expulsion in an intact nervous system. The authors observe similar mitochondrial dynamics in vivo as previously described in other systems. They find that stationary mitochondria are more likely to be associated with OPTN, suggestive of mitochondria undergoing mitophagy. Forced expression of OPTN mutations results in a larger pool of stationary mitochondria that colocalize withLC3B, and OPTN. Finally, the authors argue that extra-axonal mitochondria are observed more frequently in OPTN mutants, suggesting that mutations in OPTN that are associated with disease can lead to an increase in the expulsion of mitochondria through exopher-like structures.

Major Findings and impact: • The authors establish that mitochondria dynamics can be tracked in the X. laevis optic nerve. • OPTN mutations increase the stationary pool of mitochondria and likely result in increased rates of mitophagy. • Exopher-like structures containing mitochondria and LC3 can be expelled from the optic nerve and increase in the presence of OPTN mutations. These structures were observed in a living system and have interesting implications in the context of disease.

Concerns: • The authors state in their results that the secreted blebs are exophers. While these initial observations are consistent with exophers, additional data are needed to strengthen this claim. For example: what are the sizes of secreted vesicles? Do all express LC3? How frequently do these occur? From where are they expelling? Alternatively, the discussion of exophers could be moved to the discussion.*

We agree that calling the axon shedding intermediates “exophers” was an overreach on our part. While we believe that in all probability time will demonstrate this to be the case, reviewers are correct in stating that putting our work in the context of exophers is best left to the discussion. We have removed all mention of exophers from the results and graphical abstract and now use the term only once in the discussion. We do provide detail as to the frequency of the structures, what fraction contain mitochondria, and morphological parameters of the contained mitochondria. And while all of these new data support them being exophers, the point remains that the use of the nomenclature “exopher” in the results section was inappropriate.

• Quantifications in sparse labeling experiments seem quite surprising and concerns related to these findings should be addressed. As the authors used LC3b expression to represent axonal volume, the authors should demonstrate that this is the case using an axonal fill or membrane marker in both the wt and E50K conditions. This is important as it is unclear whether LC3b expression is consistent between the wild type and the E50K conditions. Lower expression of LC3b in E50K could account for the large changes in axonal width that seem to be observed and could confound the measured amount of expelled mitochondria.*
• *

We agree that using EGFP-LC3b as a “cell fill” was problematic in a situation where the interventions likely perturb autophagy/mitophagy and therefore might have also perturbed LC3b. We do provide some axon width and LC3b-EGFP intensity data for a partial dataset that had been imaged side-by-side, showing that expression of LC3b is not different in the two conditions. We also provide independent measures of extra-axonal mitochondria based on a membrane-GFP reporter. While in principle there would be value to repeat the studies of Wt vs. E50K in the context of the membrane-GFP reporter, these experiments would involve new constructs and new breedings, and would likely take months to years to complete.

• • Could large amounts of exogenous mitochondria in explant experiments be from cells that died during the plantation?* The concern that some of the exogenous mitochondria signal might derive from degenerating axons is one that we worry much about, and not only in the transplantation experiments. In our sparse labeling experiments we do occasionally see axons undergoing Wallerian degeneration, but it is rare and does not appear to be more common in the expression of the mutated OPTN, at least not at the stage after transgene expression that the analyses were performed. We do provide new data that expression of E50K OPTN does not compromise vision at the time that experiments were carried out, ruling out that extra-axonal mitochondria are the result of large-scale degeneration. However, from other data we know that axon loss would likely need to be very extensive to manifest itself in functional vision loss in our behavioral assay, so milder axon loss contributing some noise to the measures cannot be excluded. But, the point raised is heard, and now we include a sentence in the discussion acknowledging that some of the signal outside of axons could have been due to degenerating axons, but still contend that our documentation of shedding intermediates support the view that many of the axonal mitochondria outside of axons were shed from otherwise intact axons.

Suggested experiments/quantifications: • In OPTN/MITO/LC3b trafficking experiments, does flux/number of events change? Representative kymograph in Figure 2D seems to show far more OPTN-positive mitochondria which is opposite of what is shown in Figure 2C.

Multiple reviewers rightfully point out that we did not carry out the flux experiments which would be necessary to make definitive statements regarding the amount of mitophagy. New experiments show that inhibiting lysosomal activity through chloroquine does increase the amount of astrocytic autophagosomes not yet acidified as expected, and that they contain axonal mitochondria signal, supporting the idea that astrocytes are involved in the degradation of axonal mitochondria. However, they did not show changes in the amount of stopped mitochondria, supporting the view that the co-localization of OPTN and mitochondria in axons is not conventional autophagy. This is a very important point that affects the interpretation of our results, and we thank reviewers for suggesting this experiment.

• Demonstrate that axonal width measured with LC3B is representative of axonal fill/membrane marker in wt and E50K. Axonal area appears to change, is this accurate? This appears to be the case for both figure 3 and figure 4.* Addressed above.

• Raw images in addition to the reconstruction would be beneficial.* Now include raw images beside the reconstruction at the first use of reconstructions.

• Further characterization of exopher-like structures.**

* Addressed above.

***Referees cross-commenting**

I agree with the concerns of the other reviewers, and perhaps was over-optimistic about a timeline for revision. However, I do think the work is worth the effort, and I hope to see a revised manuscript published somewhere, as these observations are novel

Reviewer #1 (Significance (Required)):

This work reports potentially novel biology, and thus will be of interest to the field. The strength of the study is that it is an initial description of this biology, rather than a complete analysis. The work raises many more questions than it answers, and much further work on this topic is required to support these initial findings, but the manuscript will likely be of interest to many. Revisions are required to improve the rigor and clarity of the work, but following these revisions we recommend publication to facilitate follow-up work.*

Fully agree that our study raises far more questions than it answers. Believe that the revisions made to address reviewer comments go a long way to improve rigor and clarity of the work. We hope that the reviewers agree and deem the changes sufficient.

*Reviewer #2 (Evidence, reproducibility and clarity (Required)):

Summary: This article studied transmitophagy in xenopus optic nerves in the context of overexpressing glaucoma-associated optineurin mutations. Using a series of labeling, imaging and transplantation techniques, the authors found that overexpressing mutated optineurins stops mitochondria movements and potentially induces transmitophagy, and that astrocytes are responsible for taking up the extra-axonal mitochondria. Below are my comments on this article.

Major comments: 1. Identifying extra-axonal mitochondria is key to this research. In Figure 3, the authors used EGFP-LC3B as a marker for RGC boundaries. However, it is unconvincing how perfect LC3B is as a cell membrane marker. Particularly in the case of OPTN E50K OE, it seems that the optic nerve is thinner than the WT condition, which makes the quantification of extra-axonal OPTN less convincing. The authors should detect extra-axonal mitochondria with an RGC membrane marker or cytosolic marker. In addition, in Figure 3, the extra-axonal mitochondria seem to localize mostly on the dorsal surface. Why is there such a polarity?*

As stated above, we acknowledge that the use of LC3b as both an autophagosome marker and a cell fill was somewhat problematic and now provide additional experiments ruling out that the LC3b expression or axon thickness in our sparse axon labeling experiments, or that E50K might affect the thickness of the optic nerve. In addition, we also provide additional new data using a bona fide membrane marker together a transgenic labeling or RGC mitochondria that also shows under the “baseline state” extensive mitochondria signal outside the axons on the surface of the optic nerve (New Fig. 6A and new Suppl Fig. 3D). All the new data are consistent with the previous data and support the view that using LC3b potentially could have been problematic, for the reasons reviewers state, but in practice it was not.

The reviewer observes that the E50K optic nerve appears thinner--this observation is not a consistent difference in optic nerves across the experimental groups. The images we show are always near the mean values for the quantitative results presented, and we rather not include prettier nerves that are not representative of the whole datasets.

As for why the extra-axonal mitochondria localize mostly to the dorsal surface, it remains undetermined. There are dorsoventral differences in the optic nerve established during development, as developmental Sonic hedgehog signaling emanating from the midline appears to affect dorsoventral aspects of the optic nerve differentially. Early axon loss in humans and some models of glaucoma do show a dorsal bias, and there may be optic nerve lymphatic structure reported in mice that also may be preferentially dorsal. However, it is not known whether any of these observations are connected, so we did not want to speculate beyond what the data say. We do now explicitly mention the dorsoventral difference in the discussion, and state why we think it may be worth further study.

• The experiment in Figure 5 is very important as it gives direct evidence of transmitophagy. However, one caveat is that the mitotracker injection is done after the transplantation. If in rare cases the dye is leaky after injection and is taken up by astrocytes directly, then the conclusion that mitochondria from RGCs are phagocytosed by astrocytes will be flawed. The authors should either use a transgene in the donor to label mitochondria or inject mitotracker into the donor before the transplantation and repeat the experiments. In addition, in Figure 5E, what is the large membranous structure inside the highlighted astrocyte? Is it associated with phagocytosis?*

We fully agree that MitoTracker is an imperfect tool, both for the reason stated here that the dye may get into the astrocytes directly (or may label astrocyte mitochondria after it is released from degrading RGC mitochondria), and, also as stated by reviewer 3, that it requires healthy mitochondria for labeling. For this reason, we have added new datasets that rely on RGC mitochondria labeling not by Mitotracker but through a genetic reporter. As to identity of the conspicuous structure shown inside the astrocytes, it remains an open question, and we are avidly pursuing what astrocytic organelles are involved through additional transgenic reporters and correlated-light-EM studies, but those are complicated experiments that are beyond the scope of the current manuscript.

• This research is entirely based on overexpression of OPTN. Since overexpressing WT OPTN does seem to affect mito trafficking (Figure S2G, and the description in the manuscript is often inconsistent with this result), it is unclear what the increased stalled mitochondria really mean when overexpressing mutated OPTN. Similarly, the authors examined extra-axonal mitochondria in Figures 3 and 4 all in overexpressing conditions, and made the connection that increased stalled mitochondria lead to transmitophagy. However, this conclusion will be better supported by using mutant animals rather than overexpression. The authors should consider using OPTN mutant xenopus if available or using CRISPR to introduce the specific mutations and repeat mitochondria trafficking and transmitophagy.*

• *

We thank this reviewer by pointing out an important detail that we failed to highlight, namely that transgenic overexpression of Wt OPTN (and/or Wt LC3B) does have a small but significant effect on mitochondria trafficking. Interestingly, it is affecting just the speed of retrogradely transported mitochondria, which based on the elegant work of Holzbaur and colleagues, include mitochondria destined for degradation. So, we now acknowledge more explicitly that, since our studies involve expression of OPTN and LC3b transgenes (fluorophore tagged human genes, no less), that some caution should be exercised in not overinterpreting the results. Nonetheless, since we show that expression of Wt OPTN behaves similarly to expression of a mitochondria reporter (Tom20-mCherry) in not affecting either stopped mitochondria or extra-axonal mitochondria, we believe that our results still stand. Nonetheless, we now make mention of the effect Wt OPTN on retrograde mitochondria movement. We have embarked on OPTN loss-of-function studies and have some founder animals carrying CRISPR-generated mutations; however, these experiments will take additional time, and based on the results in mammals may or may not show any measurable effects in our assays, not only because of possible redundancy by the other damaged mitochondria adaptors that we mention in the introduction, but also because the mutations that affect the shedding process (as well as cause glaucoma) are thought to be gain-of-function mutations. However, we decided not to dwell on these complexities in the discussion, as the discussion was previously quite extensive and now is even moreso with the added discussion on how our studies relate to those of exophers.

• On Page 12, the authors claim that even overexpressing WT OPTN causes extra-axonal mitochondria in the optic nerve. However, there is no control condition without OE to support this conclusion. It is thus unclear to what extent extra-axonal mitochondria occur at baseline and how many extra-axonal mitochondria can be induced by overexpression. The authors should include, in Figure 3 and 4, controls without overexpression.*

We acknowledge that our language was confusing and somewhat misleading on this point. With the caveat mentioned above that WT OPTN expression does perturb the system somewhat (by increasing the speed of mitochondria retrograde transport, perhaps by increasing the proportion of retrograde moving mitochondria tagged for degradation), we still contend that the state observed after WT OPTN expression is close to the “baseline” state. In support of that, in the new data included in response to the LC3b concern, we observe plentiful shedding events in the absence of any OPTN or LC3b transgenes. Indeed, what may be the most surprising finding of our studies is that in the absence of any significant perturbation of OPTN, there is already a large fraction of axonal mitochondria that are outside of axons and inside of astrocytes, which is consistent with what we previously observed in the optic nerve head of mice; however, the current studies provide much more rigorous quantification of the process and live imaging of intermediates, but also provide for an intervention that increases the process. While there are many more questions to answer, we do believe our studies contribute mechanistic insights.

• A technical question regarding kymographs: Based on Figure 2C, it looks that OPTN and LC3B labeling are pretty diffuse in axons and this makes sense since they may only be associated with damaged mitos. But this raises a question about how accurate the kymograph assay is. It may significantly underestimate the fraction of OPTN/LC3B that is stationary since they appeared diffusedon the kymograph. This may explain why the percentage of stationary OPTN/LC3B is so small when the authors OE WT OPTN in Figure 2E and 2E', compared to the percentage of moving mitochondria shown in Figure 1E.*

We fully agree that the kymograph studies likely underestimate the amounts of stationary mitochondria for the reasons stated. However, we interpret the discrepancy between Figure 1E and 2E and 2E’ differently. We believe that the value of stopped mitochondria in the sparse labeling experiments are actually more accurate, as the value of stopped mitochondria in the whole nerve experiments likely include mitochondria stopped within the axons, but also mitochondria recently shed either by those or nearby axons which are perceived to be in axons due to limitations of imaging resolution. In the discussion we now make very explicit that all the measures we provide need should be interpreted as estimates, as every experiment relies on assumptions and is subject to technical limitations.

Minor: 1. Figure 2E and 2E' do not agree with the text on page 7 and page 8. Not only F178A, but also H486R and D474N have no effect on OPTN trafficking. The authors should make their conclusions more accurate.

F178 was the only mutation that had no effect on either OPTN or LC3b in either F0 or F1 experiments. However, we agree that our language should have been clearer, and now we have made our description of the results (and conclusions) more accurate.

• Figure S2E-F: why does OE of mutated OPTN in F1s but not in F0s reduce trafficking speed compared to WT?*

We do not know the reason for this discrepancy. Though it does not wholly agree with the rest of the story, we felt it important to include all relevant data, not only that which perfectly fit our interpretation. One possible reason may be that the F1 data derives from a single integration event, which is the reason why we trust more the F0 data that derive from multiple integrations, in what are essentially outbred animals, which is the reason we present the F0 data as the primary results where possible.

* In movie 5, fusion of exopher with other structures is not clear and also the GFP signal does not disappear, which is in contrast to the statement in the text that the GFP signal is quenched in acidified environment. To confirm that LC3B leaves RGC axons in exophers, the authors should consider switching the fluorophores and examine LC3B localization during exopher formation.*

This too is a valid point, and we have amended our description of these results. While swapping fluorophores between OPTN and LC3b is a highly worthy experiment, for technical reasons it likely would take many months to carry out just because of how involved it is to make the relevant constructs (recombineering details provided in the methods section).

• In figure 6, to better show exopher formation and the pinching-off step, the authors should consider labeling the membrane and mitochondria instead of using the LC3B and OPTN marker.*

This arguably was the biggest weakness of our initial submission, and now provide new experiments using a bona fide membrane marker. We have not yet captured a pinching-off event with these better reporters, but that is not surprising given how rare they are, which we now quantify. Indeed, a membrane reporter and a mitochondria transgene in sparsely labeled axons are the ideal tool for figuring out the frequency of these structures and what fraction contain mitochondria, data which we now provide.

***Referees cross-commenting**

Generally agree with the criticisms voiced by the other reviewers; in aggregate the reviews indicate the manuscript needs more than just a quick fix.

Reviewer #2 (Significance (Required)):

Previous literature has already described the transmitophagy process in the optic nerve. The significance of this paper lies in the observation that overexpressing glaucoma-associated OPTN mutants can induce increased transmitophagy through astrocytes, which points to a potential role of OPTN in glaucoma. A highlight of this paper is the use of correlated light SBEM to directly show transmitophagy in astrocytes. However, the significance of this paper may be limited for the following reasons: 1. everything is based on overexpression of mutated OPTN, which makes it hard to translate the results to real disease conditions; 2. The consequence of increased transmitophagy on RGC survival or visual functions is unclear.

*

While we agree that much of the paper is based on OPTN overexpression, we did have experiments and now provide more that that were not based on OPTN overexpression. Some of these still involve expression of a different transgene (Tom20-mCherry) that might in principle perturb the system, though we show that expression of Tom20-mCherry does not affect mitochondria movement parameters as measured by Mitotracker. As to “the consequence of increased transmitophagy”, we do now provide data showing that there is no vision loss suggestive of axon loss or severe dysfunction at the time that the imaging studies were carried out. Whether longer term expression of these OPTN transgenes lead to axon degeneration and visual dysfunction are studies that are ongoing, but those studies involve extensive characterizations and controls that are beyond what could be included in this study.

*Reviewer #3 (Evidence, reproducibility and clarity (Required)):

Summary In this work, Jeong et al describe the effect of Optineurin (OPTN) mutations in the transcellular degradation of retinal ganglion cell (RGC) mitochondria by astrocytes at the Optic Nerve (ON), a process previously described this group and referred as "transmitophagy" (Davis et al 2014). Here, authors use Xenopus laevis animal model to image the optic nerve of animals carrying different OPTN mutations associated to disease or with compromised function and explore its effect in mitochondria dynamics at the RGC axons. They find that OPTN mutants lead to increased stationary mitochondria in the nerve and affect their co-localization with mitophagy-related markers, suggesting alterations in this pathway. Finally, they found that mitochondria co-localizing with OPTN can be found in the periphery of the ON under different conditions and this is particularly increased in glaucoma-associated E50K mutation. This extracellular mitochondria are transferred in vesicles to astrocytes, as they previously described in mice (Davis 2014), where they are presumably degraded. Major comments - OPTN levels at a given time point cannot be used as readout for mitophagy level/flux. Both OPTN and LC3b are degraded upon fusion with acidic compartment (i.e. lysosomes, PMID: 33783320, 33634751) and that is the reason why the field of autophagy /mitophagy blocks lysosomal activity to measure autophagy/mitophagy flux (PMID: 33634751). In this document, authors claim that there is low levels of mitophagy in RGC axons at baseline and increased levels of mitophagy in glaucoma associated perturbations just based on increased presence of OPTN+ mitochondria in this condition. This could be also interpreted as an accumulation of non-degraded defective mitochondria due to a mitophagy block in neurons carrying the glaucoma associated mutation, which is the opposite of what they propose. If authors want to evaluate mitophagy levels in this system, mitophagy/autophagy flux experiments should be performed.*

In response to reviewers, we do now include “lysosome inhibition” experiment, using chloroquine at doses modestly above those used in aquaculture as an anti-parasitic. After testing various chemical means to inhibit lysosome activity, it was the only one that did not adversely affect the animals. We know the chloroquine intervention works because we see the expected increase in autophagosomes using the standard LC3b-tandem reporter, and in those unacidified astrocytic autophagosomes we do indeed find axonal mitochondria signal. However, since the amount of mitochondria signal there is small relative to the total amount of axonal mitochondria in the astrocytes, we do not feel it would be appropriate to make mechanistic claims, for example claiming this to be related to LC3b associated phagocytosis; much more work would be needed to make that claim. However, we were surprised to find no alteration in either stopped mitochondria in axons or axonal mitochondria material within the astrocytes. There are technical reasons why this result might be difficult to interpret, but now having done it (as we should have before), we are even more careful in describing the process as transcellular degradation rather than transmitophagy. We elaborate further on this point in the next response.

- I find inappropriate the use of the term "transmitophagy". Although this term transmits very well the message that the authors try to strength, the term "mitophagy" refers to the specific elimination of mitochondria through autophagy (PMID: 21179058). There are many reasons why I think that "transmitophagy" is not adequate to describe this phenomena but I will just refer to these three: First, authors do not provide data showing that this mechanism is specific for mitochondria as they have never checked for the presence of other type of cargo in the vesicles produced by RGCs. If these are related to exophers as they suggest in the document, is very probable that they contain other type of cargo; Second, if the final destiny for those particles is the acidic compartment of astrocytes, this process may have nothing to do with autophagy/mitophagy and just share some molecular mediators with those pathways; Third, they should explore if other canonical mitophagy molecular mediators (i.e. Parkin/Pink) are regulating the production or the mitochondria recruitment to this extracellular particles.

We too struggle with our own “transmitophagy” term, for the very reasons stated. To address this concern, we now refer to the process as “transcellular degradation of mitochondria”, which is how we described it initially in mice as well. We do present new data that show that while the majority of axonal outpocketings contain mitochondria, not all do. This suggests that the others may contain other cargo, which supports the view that what we are dealing with in axons are indeed exophers. And yet, since what we measure is mitochondria, we think most appropriate to describe the process narrowly and not extrapolate to other types of exophers. We agree that what we originally discovered in mice and now live image and perturb in frog, may not be “autophagy” according to the strict definition of the term, but rather a process that uses some of the same molecular machinery, which given the evolutionary link between autophagy and phagocytosis that should be no surprise. Terminology can be tricky, and we thank the reviewer for calling us out on this point. We now use the term “transmitophagy” only once in the discussion section making the link between our work and the emerging field of exopher biology, and use that occasion to elaborate the point that the more descriptive term “transcellular degradation of mitochondria” is more appropriate in our case.

*- In several experiments, authors use Mitotracker instead of genetic tools to quantify the amount of mitochondria co-localizing with OPTN (Fig2, Fig3) or being transferred to astrocytes (Fig4). A problem here is that Mitotracker needs the mitochondria to be active at the time of injection in order to label them (PMID: 21807856) and it has a clear effect in mitochondria dynamics in their setting, as pointed by the authors. Since most mitochondria transferred to astrocytes would be presumably damaged and not able to import Mitotracker, I am concern about how this is affecting their quantifications and the conclusions.

*

We agree. The use of Mitotracker to label the RGC mitochondria can be problematic for the reasons stated by reviewers 1 and 3. Indeed, our opinion is that many of the studies out there that claim to demonstrate transfer of mitochondria between cells likely are just showing the transfer of the dye rather than the mitochondria. While the previous submission included a number of controls to address this concern, we now provide multiple new experiments that measure the transfer of mitochondria through a transgene rather than Mitotracker. The provided experiments use a new Tom20-mCherry transgene which is highly specific to mitochondria due to the use of an SOD2 UTR. We have similar data using RGC-expressed Mito-mCherry and Mito-EGFP-mCherry (using the commonly used Cox8 mitochondria matrix targeting sequence); we do not include such data because we find the provided data sufficiently compelling, and the story is already sufficiently long and complicated.

- Some conclusions are based on single images with no quantifications or statistics. This is the case for: 1) Page 6) "Most of the mCherry and Mitotracker objects colocalized with each other both in the merged images (Fig. S1C) and kymographs (Fig. S1D), indicating that the mitochondria-targeted transgene and Mitotracker similarly label the RGC axonal mitochondria".

That is a fair comment. After reanalyzing the original dataset used, it would be very difficult to quantify that statement, largely because the Tom20-mCherry expression was relatively weak in those particular animals. We are confident that we could generate a new dataset to provide support for this statement, but instead chose to just provide side-by-side movies of mitochondria labeled by Mitotracker or the Tom20-mCherry transgenes, which we believe is far more compelling than any quantification we could provide.

2) Page 8) "In the nerves labeled by Mitotracker, visual inspection of the raw images (Fig. 2C) and the derived kymographs (Fig. 2D) showed that OPTN and the Mitotracker labeled mitochondria often co-localized, particularly in the stopped populations, and more so in the animals expressing E50K OPTN, further suggesting that at least a fraction of the stopped LC3b, OPTN and mitochondria might represent mitophagy occurring in the axons".

While we have made a minor change to this sentence, we feel that it is appropriate given that it serves just as a justification to carry out the quantitative studies that follow. We would not have quantified the process had it not been obvious to the eye. However, we do not interpret the results as supporting that mitophagy occurs in axons, for the reasons explained above.

3) Page 14) "We also observed similar axonal dystrophies and exopher-like structures in E50K OPTN under similar imaging settings, but with 2-min intervals and additional Mitotracker labeling (Mov. 6), demonstrating that these structures not only contain OPTN but also mitochondria or mitochondria remnants". Image in video is not clear and there is not quantification for OPTN or OPTN+ mitochondria.*

*

We have removed Mov. 6.

*Minor comments

• In Figures showing the reconstruction of OPTN+ mitochondria outside nerve (Fig.3 and Fig.4), those seem to be present only in one lateral of the nerve. Is this process polarized in any way (i.e. faced to astrocytes) or is the result of a technical issue (i.e. difference in laser penetration for blue vs Yellow lasers)? I think it will be important to include this in the discussion.*

This was also pointed out by reviewer 1, and we agree that it is worth including in the discussion, which we now do. While we do not believe it to be a light penetration issue (based on fluorescence intensities and apparent spatial resolution), we also do not yet have an explanation. Having studied dorsoventral differences in the visual pathway both during my graduate and post-doctoral years, I am very interested in this asymmetry, and we have some theories that might explain it, mentioned above. The asymmetry is obvious and thus we think it would have been inappropriate not to show, but it also be inappropriate to be overly speculative.

- In Pag.13 authors claim "OPTN and mitochondria leave RGC axons in the form of exophers". After "exophers" were coined by the Driscoll lab in 2017, too few people has adopted this terminology and the molecular machinery involved in this process is still under research. It is clear that the particles described here share some similarities with exophers like size (in the range of microns) and cargo (mitochondria), but you have not demonstrated if they share the same origin or are part of the same phenomena. For that reason, I recommend to be more cautious with this statement and point these limitations in the discussion. Additionally, since Exophers are not a consensus or well defined particles, authors should include an introductory paragraph at the beginning of this section for readers to understand what they are talking about.

We wholly agree with all points. We now have moved all mention of exophers to just the discussion.

- Exophers described by Monica Driscoll and Andres Hidalgo laboratories are presented as "garbage bags" that help cells to stay fit through elimination of unwanted material. If the extracellular vesicles presented here are part of the same mechanism and potentially beneficial for the RGCs, why are they increased in OPTN mutants? Is it part of RGCs response to a proteomic stress generated by malfunctioning OPTN? I think that is critical to understand this to figure out the relevance of your findings.

• *

Our personal opinion is that the OPTN mutants most likely lead to stress focally in the axons, thus triggering exopher generation. We are carrying additional experiments to determine whether too much exopher generation or their insufficient degradation by astrocytes might be deleterious (by causing inflammation). However, those are big stories that would not stand on their own were we not able to first rigorously demonstrate that certain OPTN mutants increase exopher generation, which I believe our study demonstrates, albeit now without calling them exophers.

- Related to Fig.5G, authors say "The soma of the astrocytes were located at the optic nerve periphery but had processes that extended deep into the parenchyma". This is very interesting and opens the possibility that many mitochondria are directly transferred to astrocytes through that processes instead of the lateral of the nerve, meaning that your quantifications of "transmitophagy" may be underestimated.

* *We also agree that this. Our limited optical resolution, and limitations intrinsic to carrying out quantifications with Imaris software, are likely the main reasons for the discrepancy between the whole nerve and sparse-labelled-axon estimates of how much axonal material is outside of axons. Our view is that most of the transcellular degradation occurs within fine astrocyte processes, and that only in the case of failure to degrade material in these fine processes that significant amounts accumulate in the cell body (optic nerve periphery), and that in the cell body additional or different degradative pathways are utilized. Experiments using various transgenes and correlated EM as well as perturbation experiments are ongoing attempting to firmly establish what organelles are used in processes versus soma. However, we believe that such studies are well beyond the scope of this manuscript..

- Reference to Fig. S2G is missing. Now mentioned twice. Thank you.

- I cannot find in Fig.5 E-I legends what are the cells/structures labelled in Green and Red. Thank you.

***Referees cross-commenting**

In agreement with my colleagues, I think that a revision is needed to support some important points of the paper. The the work is interesting and I think it deserves a chance for revision. Having that said, I am not familiar with the breeding and experimental times when working with Xenopus but, considering the amount of work requested, it may require more than 3 months to have the work done.

*

*Reviewer #3 (Significance (Required)):

Until not very long ago, it was thought that mitochondria could not cross cell barriers. In recent years however, there has been an explosion in the number of works showing mitochondria transfer between different cell types in vivo. This may happen either as an organelle donation to improve energy production or as a quality control mechanism to get rid of damaged mitochondria, as it is the case in this work. The laboratory of Nicholas Marsh-Armstrong was pioneer in this field with a foundational work in 2014 where they show how RGC-derived mitochondria are captured and eliminated by astrocytes in mice (PMID: 24979790). This work was particularly relevant because it proposed for the first time that mitochondrial degradation can occur in RGC axons far from the cell soma, and surrogated in a different cell type, something that changed completely the view of how quality control is maintained in neurons and other cell types. In the present study, Jeong and collaborators explore how Glaucoma-associated Optineurin mutations affect this process, which is of potential interest for the broad cell biologist community due to its possible implications in other tissues and cell types (OPTN is broadly expressed), but especially for those researchers interested in neurobiology, quality control mechanisms and mitochondria biology. Since some OPTN mutations studied here cause disease, they are also relevant for the clinic. This work provides a thorough characterization of how relevant Optineurin mutations affect mitochondria dynamics in RGCs and their transference to astrocytes, as fairly claimed in the title. However, the mechanism by which they result in pathology is not either explored or carefully discussed, making this a descriptive work with no much conceptual insight. In addition, conclusions are often not unambiguously stated and the results part contains a lot of large sentences and unnecessary technical data that hinders reading and difficult the transmission of the key messages. Even if it stands as a descriptive work, the physiological and clinical relevance of these findings is not clear. There are some claims related with mitophagy activity that may require more sophisticated experiments (mitophagy flux with lysosomal inhibitors). Please see comments above. A critical point to understand the relevance of this work would be to demonstrate if alterations in transmitophagy are either causing or involved in the disease generated by these OPTN mutations in any way, or just a correlative phenomenon. To help authors contextualize my point of view, my field of expertise includes cell biology, imaging, quality control pathways, mitochondria biology and phagocytosis, among others. I am not familiar with Xenopus Laevis genetics or the limitations to work with this animal model.*

• *

We appreciate both the complements and the critiques. To a fault, we rather undersell than oversell. We are actively pursuing the possibility that dysregulation of this process is disease causing, and not just for glaucoma. However, those studies will not stand without a strong foundation, which we believe this study provides.

Reviewer #1 (Public review):

Summary:

The authors of this valuable study use linearly polarized UV light rotating at different angular velocities to stimulate photoreceptors in bumblebees and study the response of TL3 neurons to polarized light. Previous work has typically used a single constant rotation velocity of the polarized light, while the authors of this study explore a range of constant rotational velocities spanning from 30deg/s to 1920deg/s. The authors also use linearly polarized UV light rotating at continuously varying velocities following the angular velocity of the head of a flying bumblebee. 

Strengths:

The authors investigate the neuronal responses of TL3 neurons to a variety of rotational velocities. This approach has the potential to reveal the neuronal response to dynamically changing stimuli experienced by the animal as it moves around its environment.

The authors make good use of physiology and modeling to validate their hypotheses and findings.  If done right, this line of investigation has the potential to provide a very useful methodology for utilizing more complex stimuli in studies of the visual pathway and central complex than traditionally. 

Weaknesses: 

The attempt of the authors to use more naturalistic stimuli than previous studies is very important, but the stimulus they use, i.e. linearly polarized UV light projected on the whole dorsal rim of the animal's eyes, is very different from the circular pattern of UV light polarization coming through the sky. In particular, as a bumblebee turns under the sky, the light projected on each ommatidium of the dorsal rim area will not smoothly change like the rotating linearly polarized light used in the experiments. The authors need to discuss this and other limitations of their study. 

The authors should also commend the light intensity confound common in polarized light setups as discussed by Reinhard Wolf et al, J. Comp. Physiol. 1980 and in the thesis of Peter Weir, California Institute of Technology, 2013. It is unclear whether the authors performed measurements to quantify the intensity pattern and if they took measures to compensate and make the polarized light intensity uniform. 

The authors show that the neuronal responses of TL3 neurons depend on the recent history of the polarized light stimulus. They use as evidence, the different neuronal firing rates measured when arriving at the same polarization stimulus by following two different preceding stimulus sequences. It would have been worthwhile to investigate to what extent the difference in neuronal response is due to the history alone and to what extent it is due to spike timing stochasticity inherent in the neurons. According to the raster plots in Figure 2F, there is substantial stochasticity in the timing of the action potential firing events.

The authors appear to base their delay calculations and analysis on the response of one single neuron (Figures 2 and 3) even though they have recorded the responses of several TL3 neurons. There is no reason for the authors not to use all neuron recordings in their calculations and analysis.

Another concern is that while the authors make good use of modeling, like any model, the presented models only partially explain the observed phenomena. However, a discussion about the limitations of their model needs to be provided.  Actually, observing the discrepancies between the model's output and the intracellular recordings reveals what the model is missing. That is, careful consideration of the discrepancies would have led the authors to try adding some noise in their model, which would partially resolve the differences observed at the lower rotational speeds (see stars deviating from the fitted line in Figure 2A) and to consider that introducing an asymmetry between the post-stimulus inhibition and excitation time constants could result in a model not deviating as much at the higher rotation velocities during counter-clockwise rotation of the polarized light (see stars deviating from the fitted line in Figure 2A). 

In the end, the authors use the observation that during saccades, the average activity in their model-with-history increases to claim that when the animal does not turn, it uses less neuronal activity and energy. This is not a convincing line of reasoning. To make a claim about energy efficiency, the authors must instead compare their model with alternatives and show that the neuronal activity of their model during straight flight is indeed lower than those alternative models. Note that such a comparison would be meaningful only if the alternative models compared against capture physiology equally well in all other respects. However, the evident deviations of the presented model from the physiology measurements and the short duration of the test stimulus used would make any such claims difficult to substantiate. 

Finally, for most experiments, the models are stimulated with a single short yaw sequence lasting a few seconds to measure responses. Given the dependence of the model on history, using such a small sample, we cannot see how generalizable the observations are. The authors need to show that the same effect is produced using multiple different trajectories.

Reviewer #3 (Public review):

This manuscript reports the temporal history dependence of central complex TL/ring neuron spiking activity to polarized light patterns. Using sharp recording in tethered bumblebees with synthetic and natural visual stimulation, the authors nicely measured activities to rotating polarized UV light, and made the interesting finding that spiking activity depends on not just current stimulus but also recent activity.

(1) History dependence has been reported before in ring neurons in Drosophila (Sun et al., Nature Neuroscience, 2017; Shiozaki et al., Nature Neuroscience, 2017). While there are differences in the nature of the visual stimulation used, the basic phenomenology of temporal history dependence bears some resemblance. Where are the differences in the physiological properties of ring/TL neurons between different insect species in relevance to history dependence? What are the structural similarities and differences in the circuits that may help to explain history dependence? Just to name a few. To gain further insight into this question, the manuscript may benefit from putting the findings here into context.

(2) Figure 3b serves as a critical evidence for history-dependence. However, it is unclear from this data if this is history dependence, or other physiological processes such as OFF response to sensory stimulation, or sensory adaptation. One way to test this is to examine whether such an effect can be detected after a delay period. For example, history dependence in fly ring neurons is mediated by delay period activity present for several seconds. This can be easily tested here as well.

(3) The properties of the history dependence can be better characterized to help understand its nature. What are the statistical characteristics of post-stimulus inhibition to preferred AoP and post-stimulus excitation to anti-preferred AoP? What are the temporal dynamics of such an effect, e.g., how long does it take to return to baseline? Are the differences in these properties recorded across the TL neuron population? Is it possible to categorize these TL neurons based on these properties and morphology? These properties are important to under the physiological basis of such effect. The authors only presented two traces in Figure 3b, beautiful example traces, but without any further population data and statistical analysis.

(4) A major point of the manuscript is energy efficiency via reduction of firing rate. However, the only evidence comes from simulation, and it seems to be a weak effect of 0.5 APs/s.

(5) Another major point of the manuscript is "increases sensitivity for course deviations during straight flight". However, this again is supported by simulation only. To validate these claims, empirical support of behavioral experiments is highly desired. Otherwise, it is recommended to minimize emphasizing such behavioral predictions.

(6) A substantial portion of the text emphasizes the importance of natural stimulation. While natural stimulation is indeed a desirable experimental approach, it is unclear if natural stimulation is exploited to its full in this manuscript. History dependence can be explored with synthetic stimulation.

(7) A phenomenological model was used to account for the history effect, by assuming a linear integration process and a linear history effect. However, such an assumption is not adequately backed up by rigorous statistical analysis of experiment data or at least proper conceptual discussion.

(8) Population responses, as in Figure 4, are based on strong assumptions of neuronal properties without clear experimental support, thus seeming to be quite a stretch.

(9) There are interesting observations in simulation results from Figure 5; it would be nice to experimentally test at least some of these ideas.

(10) "anticipate future head directions" seems to be quite a stretch to me without mechanistic explanations.

(11) The visual stimulation design used can be improved and expanded. The synthetic stimulation used in Figure 1c follows a stereotyped order, according to angular velocities. As the focus of the manuscript is to probe the history effect and to test again the findings made with this stimulation, randomized stimulation should ideally be examined.

(12) State dependence was observed in ring neurons in Drosophila (Sun et al., Nature Neuroscience, 2017) which might be related to ongoing neural activity and history dependence. While I realize that the animal is tethered, I was wondering if there was any signature of neural activity state dependence observed in this study.

Q,K,V 被复制为N路，每一路都是一个注意力头(Attention Head)的输入，N 个注意力头的输出被合并为最终的 Attention Score。

Eliot’s reference to Philomena comments on the notion of a voice, in particular, the connection between a voice and humanity. In “The Metamorphises of Ovid”, Philomena, the princess of Athens, is raped by Tereus, king of Thrace. After Philomena seeks revenge by serving Tereus his own son to eat, she is pursed, and is changed into a nightingale as she escapes. Eliot references this change and the rape, calling it “so rudely forced” on line 100. He then continues, referring to the nightingale, who had an involiable voice”. Eliots use of the conjuction “yet” while focusing on the nightingale and her voice suggests a contrast between the rape of Philomena, and her new form as a bird. When regarding the “The Metamorphosis of Ovid”, one comes across the line, “she [Philomena] tries to swear, to call upon the gods as witnesses, that she had been a prey of violence, but after all, she had no voice-- just gestures”. In the original mythology, Philomena is rendered speechless, seeking divine security and affirmation of what had happened to her. Though much more elaborate and descriptive of the intensity of the rape, this line recalls the line “so rudely forced”, and further clarifies Philomena’s perspective and expression. Eliot’s contrast between Philomena’s silenced state as her state as a bird is further sharpened by the “involiable voice” of the nightingale. This description of Philomena’s voice suggests that she is now able to express herself in an invincible manner as a bird. Despite Elliot’s seemingly optimistic understanding of Philomena’s story, I see her transformation as an event of further silencing and death. She has lost her voice, as well as her humanity, as she is now a bird. Adding onto this theme of death through her transformation, Eliot describes how her nightingale’s cries “filled all the desert”. The mention of the desert is interesting, as it possibly refers to the idea of a wasteland, a place for Eliot of conceptual death. This twisted understanding of death and a loss of humanity is also reflected in the story of Sybill, who is left to age into nothing but a voice through divine cruelty. Finally, the idea of the feminine voice after death is also portrayed in “A Martyred Woman” by Charles Baudelaire. As the narrator speaks to a dead corpse, they demand, “Tell me, ghastly head, did he glue on your cold teeth the kisses of the last farewell?”. Again the notion of male violence is brought up, furthered by the mention of “cold teeth” which could imply the male actor kissed the deceased woman after her death, or refer to a silent mouth, as in one that doesn’t move to generate heat. Despite this gruesome description, the narrator still asks the woman to speak, as if her voice still remained with her corpse. These stories describe acts of male violence against women, and subsequent dehumanization and death of those women. Despite the violence and death, the female voice strangely persists. Ultimately, this persistence seems cruel and draws attention from the horrific experiences and silencing of these women.

This line recalls the language and setting of the room where Dido is infected by Cupid to love Aeneas, which inevitably leads to her demise. A moment marked by Dido losing her autonomy and unwillingly being transformed into a pawn, is described not by the woman herself, but the candles that surround her. As she becomes a passive agent, the inorganic flames become the subject, described with an active tense. Even they have autonomy as to where they fling their smoke, but as Dido “burns” with love sickness, she has no control over where her smoke goes.

This language depicts Dido as a chess piece in Venus’s cosmic plan, which she describes, “Wherefore I purpose to outwit the queen with guile and encircle her with love’s flame, that so no power may change her, but on my side she may be held fast in strong love for Aeneas” (Aeneid 1). The word “encircle” is indicative of predators circling around their prey before going in for the kill. Similarly, a powerful chess piece is typically killed by being surrounded, meaning no move will result in a safe square. Furthermore, “on my side” almost equates Dido as a tool in a belt, meant to be taken out when the user is ready.

Like in a game of chess, where the player is like a God, moving pieces around and sacrificing them for the safety of the King, in this same way Venus plays with Dido. This queen, once a pawn in her brother's quest for power, traveled across the ocean, across the chess board, to become a queen in a new land, like a pawn becoming a new queen. Yet in the end, her power, her ability to become a queen and then move anywhere across the board, is reduced once more as an agent for the player to use, protecting the King, Aeneas. When the game finishes, Dido is still the pawn she once came from.

The women of A Game of Chess are all similar pawns, victims of the use of divine power to serve a more powerful figure's needs. This pattern appears in all of the referenced women: Philomela becomes an object of lust for the King Tereus, and when she breaks from her role in the game, she is turned into a bird, doomed to sing her song of sorrow with a mute voice. The head being chopped off the cadaver woman brings a queen back down to the size of a pawn, and Madame Sosostris uses his “divine” powers to force women into his plan.

And it is in this repetition that these women lose even more of their individuality; they join a long list of examples, an array of chess boards with the same goal. Unless the game itself is rewritten, their fate remains final.

Last year, Nate Sidenstein and Naima Johnson both noticed the water motif intrinsic in the verb “flowed”, connecting it to the river Acheron, which Dante and Virgil must cross in order to enter hell. I see, however, one more layer to be peeled back. In The Inferno, like in Greek and Roman mythology, souls must cross the river separating the realm of the living from the realm of the dead via Charon’s ferry. In Ancient tradition, if a soul did not have a coin to afford passage, they became stuck in a liminal space neither dead nor alive. In Dante, these entrapped souls shift to become those who were neither good nor evil, instead more focused with themselves. All this is to track the various interpretations of souls trapped on the banks of a river. Now one must determine what river motif actually represents the border between living and dead: is it the river implied to run beneath the London Bridge? Or is it the river implied by the language of the crowd crossing the bridge? It is important to note that the word “river” is never used by Eliot, making it difficult to discern what he intended the answer to be. If it is the former, the souls cross into the afterlife, the city of London, and head towards Saint Mary Woolnoth, a church on the London side of the bridge. This church could represent salvation, or, in the context of Dante and The Underworld, judgment. It is marked by “a dead sound on the final stroke of nine” (68), which Eliot remarks in his notes as “A phenomenon which I have often noticed.” Does this mean the souls are actually returning to a world of living, their corpses sprouting new life? Is Eliot saying that all living in London are actually dead, including himself? And if so dead because of industrialization or dead because we must all die eventually?

If it is the latter, the souls become a river and are therefore unable to cross it themselves, trapping them between the world of life and death and, as Nate pointed out, insignificant and not individualized. This flow of people must be a torrent, for Eliot comments, “I had not thought death had undone so many” (63). River force is typically measured in cubic feet per second (cfs), the volume of water that passes through any given point in one second. A cubic foot is approximately one frozen turkey, but a similar value can be attributed to the flow of bodies passing through (adjusting for scale). Essentially, these souls lose meaning in an arbitrary flow of so many others like themselves. This explanation is bolstered when considered in the context of an earlier line, “I was neither living nor dead” (39-40).

Regardless of which interpretation one takes, the bridge and river dynamic always creates a cross, a visual representation of contradiction. These contradictions culminate in a paradox indicative of the liminal space Dante, and seemingly the Wasteland, inhabit, not just at the river. For Dante is a living man experiencing the world of the dead, the earlier Sybil is a dying woman trapped in the world of the living, and a dead soul buries a corpse which may or may not give life to new plants. Here, the line between life and death blurs further.

This quotation from "Tristan und Isolde", in English translates to, "The wind blows fresh, to the homeland. My Irish child, where are you dwelling?". Elliot's choice to begin on a line about the wind stresses the importance of this element of nature. Alongside the rest of the lines, the wind is associated with feelings of longing and homesickness. The wind is also portrayed as a voyager in this way, something that comes from afar and reminds the narrator of his homeland. <br /> The subsequent mention of hyacinths in Elliot's poem also evokes the role of the wind. Lempriere states, "Hyacinthus, a beautiful youth of Amyclæ, beloved by Apollo. Some legends relate that Zephyrus, enraged by the preference Hyacinth showed for Apollo over himself, blew the discuss... against the head of the youth, and killed him". Thus, in the story of Hyacinth, the wind is something that is violent and jealous, somewhat contradicting with Eliot's previous reference from "Tristan und Isolde". Ultimately, these two references of the wind create an understanding of the natural world as gentle and reminding, but also occasionally harsh and displeased. The ocean, or "das Meer", mentioned on the forty second line of this poem, is also understood in this dichotomous way. Another important interpretation of the wind, especially in regards to land and human nature, is the wind's ability to travel great distances, and its association with love in through references. Like love, the wind is for the most part unconstrained by the laws of man and division of land. I believe the wind can be interpreted as a symbol of hope and the subversion of human restraints in "The Waste Land", as Eliot fears a continuingly privatized and industrialized world.

ABAB rhyme scheme head said bare hair the poem also talks about the lost of innocence

personification of earth throughout the poem -- makes the poem feel animated

Reviewer #1 (Public review):

Summary:

This manuscript addresses an important problem of the uncoupling of oxidative phosphorylation due to hypoxia-ischemia injury of the neonatal brain and provides insight into the neuroprotective mechanisms of hypothermia treatment.

Strengths:

The authors used a combination of in vivo imaging of awake P10 mice and experiments on isolated mitochondria to assess various key parameters of the brain metabolism during hypoxia-ischemia with and without hypothermia treatment. This unique approach resulted in a comprehensive data set that provides solid evidence for the derived conclusions.

Weaknesses:

(1) The experiments were performed acutely on the same day when the surgery was performed. There is a possibility that the physiology of mice at the time of imaging was still affected by the previously applied anesthesia. This is particularly of concern since the duration of anesthesia was relatively long. Is it possible that the observed relatively low baseline OEF (~20%) and trends of increased OEF and CBF over several hours after the imaging start were partially due to slow recovery from prolonged anesthesia? The potential effects of long exposure to anesthesia before imaging experiments were not discussed.

(2) The Methods Section does not provide information about drugs administered to reduce the pain. If pain was not managed, mice could be experiencing significant pain during experiments in the awake state after the surgery. Since the imaging sessions were long (my impression based on information from the manuscript is that imaging sessions were ~4 hours long or even longer), the level of pain was also likely to change during the experiments. It was not discussed how significant and potentially evolving pain during imaging sessions could have affected the measurements (e.g., blood flow and CMRO2). If mice received pain management during experiments, then it was not discussed if there are known effects of used drugs on CBF, CMRO2, and lesion size after 24 hr.

(3) Animals were imaged in the awake state, but they were not previously trained for the imaging procedure with head restraint. Did animals receive any drugs to reduce stress? Our experience with well-trained young-adult as well as old mice is that they can typically endure 2 and sometimes up to 3 hours of head-restrained awake imaging with intermittent breaks for receiving the rewards before showing signs of anxiety. We do not have experience with imaging P10 mice in the awake state. Is it possible that P10 mice were significantly stressed during imaging and that their stress level changed during the imaging session? This concern about the potential effects of stress on the various measured parameters was not discussed.

(4) The temperature of the skull was measured during the hypothermia experiment by lowering the water temperature in the water bath above the animal's head. Considering high metabolism and blood flow in the cortex, it could be challenging to predict cortical temperature based on the skull temperature, particularly in the deeper part of the cortex.

(5) The map of estimated CMRO2 (Fig. 4B) looks very heterogeneous across the brain surface. Is it a coincidence that the highest CMRO2 is observed within the central part of the field of view? Is there previous evidence that CMRO2 in these parts of the mouse cortex could vary a few folds over a 1-2 mm distance?

(6) The justification for using P10 mice in the experiments has not been well presented in the manuscript.

(7) It was not discussed how the observations made in this manuscript could be affected by the potential discrepancy between the developmental stages of P10 mice and human babies regarding cellular metabolism and neurovascular coupling

Reviewer #2 (Public review):

Summary:

In this study, authors have hypothesized that mitochondrial injury in HIE is caused by OXPHOS-uncoupling, which is the cause of secondary energy failure in HI. In addition, therapeutic hypothermia rescues secondary energy failure. The methodologies used are state-of-the art and include PAM technique in live animal , bioenergetic studies in the isolated mitochondria, and others.

Strengths:

The study is comprehensive and impressive. The article is well written and statistical analyses are appropriate.

Weaknesses:

(1) The manuscript does not discuss the limitation of this animal model study in view of the clinical scenario of neonatal hypoxia-ischemia.

(2) I see many studies on Pubmed on bioenergetics and HI. Hence, it is unclear what is novel and what is known.

(3) What are the limitations of ex-vivo mitochondrial studies?

(4) PAM technique limits the resolution of the image beyond 500-750 micron depth. Assessing basal ganglia may not be possible with this approach.

(5) Hypothermia in present study reduces the brain temperature from 37 to 29-32 degree centigrade. In clinical set up, head temp is reduced to 33-34.5 in neonatal hypoxia ischemia. Hence a drop in temperature to 29 degrees is much lower relative to the clinical practice. How the present study with greater drop in head temperature can be interpreted for understanding the pathophysiology of therapeutic hypothermia in neonatal HIE. Moreover, in HIE model using higher temperature of 37 and dropping to 29 seems to be much different than the clinical scenario. Please discuss.

(6) NMR was assessed ex-vivo. How does it relate to in vivo assessment. Infants admitted in Neonatal intensive Care Unit, frequently get MRI with spectroscopy. How do the MRS findings in human newborns with HIE correlate with the ex-vivo evaluation of metabolites.

Reviewer #1 (Public Review):

Summary:

In this paper the authors provide a thorough demonstration of the role that one particular type of voltage-gated potassium channel, Kv1.8, plays in a low voltage activated conductance found in type I vestibular hair cells. Along the way, they find that this same channel protein appears to function in type II vestibular hair cells as well, contributing to other macroscopic conductances. Overall, Kv1.8 may provide especially low input resistance and short time constants to facilitate encoding of more rapid head movements in animals that have necks. Combination with other channel proteins, in different ratios, may contribute to the diversified excitability of vestibular hair cells.

Strengths:

The experiments are comprehensive and clearly described, both in text and in the figures. Statistical analyses are provided throughout.

Weaknesses:

None.

Author response:

The following is the authors’ response to the original reviews.

We thank the reviewers for their constructive reviews.  Taken together, the comments and suggestions from reviewers made it clear that we needed to focus on improving the clarity of the methods and results.  We have revised the manuscript with that in mind.  In particular, we have restructured the results to make the logic of the manuscript clearer and we have added details to the methods section.

Public Reviews: 

Reviewer #1 (Public Review): 

Summary: 

The work of Muller and colleagues concerns the question of where we place our feet when passing uneven terrain, in particular how we trade-off path length against the steepness of each single step. The authors find that paths are chosen that are consistently less steep and deviate from the straight line more than an average random path, suggesting that participants indeed trade-off steepness for path length. They show that this might be related to biomechanical properties, specifically the leg length of the walkers. In addition, they show using a neural network model that participants could choose the footholds based on their sensory (visual) information about depth. 

Strengths: 

The work is a natural continuation of some of the researchers' earlier work that related the immediately following steps to gaze [17]. Methodologically, the work is very impressive and presents a further step forward towards understanding real-world locomotion and its interaction with sampling visual information. While some of the results may seem somewhat trivial in hindsight (as always in this kind of study), I still think this is a very important approach to understanding locomotion in the wild better. 

Weaknesses: 

The manuscript as it stands has several issues with the reporting of the results and the statistics. In particular, it is hard to assess the inter-individual variability, as some of the data are aggregated across individuals, while in other cases only central tendencies (means or medians) are reported without providing measures of variability; this is critical, in particular as N=9 is a rather small sample size. It would also be helpful to see the actual data for some of the information merely described in the text (e.g., the dependence of \Delta H on path length). When reporting statistical analyses, test statistics and degrees of freedom should be given (or other variants that unambiguously describe the analysis).

There is only one figure (Figure 6) that shows data pooled over subjects and this is simply to illustrate how the random paths were calculated. The actual paths generated used individual subject data. We don’t draw our conclusions from these histograms – they are instead used to generate bounds for the simulated paths.  We have made clear both in the text and in the figure legends when we have plotted an example subject. Other plots show the individual subject data. We have given the range of subject medians as well as the standard deviation for data illustrated in Figure (random vs chosen), we have also given the details of the statistical test comparing the flatness of the chosen paths versus the randomly generated paths.  We have added two supplemental figures to show individual walker data more directly: (Fig. 14) the per subject histograms of step parameters, (Fig. 18) the individual subject distributions for straight path slopes and tortuosity.

The CNN analysis chosen to link the step data to visual sampling (gaze and depth features) should be motivated more clearly, and it should describe how training and test sets were generated and separated for this analysis.

We have motivated the CNN analysis and moved it earlier in the manuscript to help clarify the logic the manuscript. Details of the training and test are now provided, and the data have been replotted. The values are a little different from the original plot after making a correction in the code, but the conclusions drawn from this analysis are unchanged. This analysis simply shows that there is information in the depth images from the subject’s perspective that a network can use to learn likely footholds. This motivates the subsequent analysis of path flatness.

There are also some parts of figures, where it is unclear what is shown or where units are missing. The details are listed in the private review section, as I believe that all of these issues can be fixed in principle without additional experiments. 

Several of the Figures have been replotted to fix these issues.

Reviewer #2 (Public Review): 

Summary: 

This manuscript examines how humans walk over uneven terrain using vision to decide where to step. There is a huge lack of evidence about this because the vast majority of locomotion studies have focused on steady, well-controlled conditions, and not on decisions made in the real world. The author team has already made great advances in this topic, but there has been no practical way to map 3D terrain features in naturalistic environments. They have now developed a way to integrate such measurements along with gaze and step tracking, which allows quantitative evaluation of the proposed trade-offs between stepping vertically onto vs. stepping around obstacles, along with how far people look to decide where to step. 

Strengths: 

(1) I am impressed by the overarching outlook of the researchers. They seek to understand human decision-making in real-world locomotion tasks, a topic of obvious relevance to the human condition but not often examined in research. The field has been biased toward well-controlled studies, which have scientific advantages but also serious limitations. A well-controlled study may eliminate human decisions and favor steady or periodic motions in laboratory conditions that facilitate reliable and repeatable data collection. The present study discards all of these usually-favorable factors for rather uncontrolled conditions, yet still finds a way to explore real-world behaviors in a quantitative manner. It is an ambitious and forward-thinking approach, used to tackle an ecologically relevant question. 

(2) There are serious technical challenges to a study of this kind. It is true that there are existing solutions for motion tracking, eye tracking, and most recently, 3D terrain mapping. However most of the solutions do not have turn-key simplicity and require significant technical expertise. To integrate multiple such solutions together is even more challenging. The authors are to be commended on the technical integration here.

(3) In the absence of prior studies on this issue, it was necessary to invent new analysis methods to go with the new experimental measures. This is non-trivial and places an added burden on the authors to communicate the new methods. It's harder to be at the forefront in the choice of topic, technical experimental techniques, and analysis methods all at once. 

Weaknesses: 

(1) I am predisposed to agree with all of the major conclusions, which seem reasonable and likely to be correct. Ignoring that bias, I was confused by much of the analysis. There is an argument that the chosen paths were not random, based on a comparison of probability distributions that I could not understand. There are plots described as "turn probability vs. X" where the axes are unlabeled and the data range above 1. I hope the authors can provide a clearer description to support the findings. This manuscript stands to be cited well as THE evidence for looking ahead to plan steps, but that is only meaningful if others can understand (and ultimately replicate) the evidence. 

We have rewritten the manuscript with the goal of clarifying the analyses, and we have re-labelled the offending figure.

(2) I wish a bit more and simpler data could be provided. It is great that step parameter distributions are shown, but I am left wondering how this compares to level walking.  The distributions also seem to use absolute values for slope and direction, for understandable reasons, but that also probably skews the actual distribution. Presumably, there should be (and is) a peak at zero slope and zero direction, but absolute values mean that non-zero steps may appear approximately doubled in frequency, compared to separate positive and negative. I would hope to see actual distributions, which moreover are likely not independent and probably have a covariance structure. The covariance might help with the argument that steps are not random, and might even be an easy way to suggest the trade-off between turning and stepping vertically. This is not to disregard the present use of absolute values but to suggest some basic summary of the data before taking that step. 

We have replotted the step parameter distributions without absolute values. Unfortunately, the covariation of step parameters (step direction and step slope) is unlikely to help establish this tradeoff.  Note that the primary conclusion of the manuscript is that works make turns to keep step slope low (when possible). Thus, any correlation that might exist between goal direction and step slope would be difficult to interpret without a direct comparison to possible alternative paths (as we have done in this paper). As such we do not draw our conclusions from them.  We use them primarily to generate plausible random paths for comparison with the chosen paths.  We have added two supplementary figures including distributions (Fig 15) and covariation of all the step parameters discussed in the methods (Fig 16).

(3) Along these same lines, the manuscript could do more to enable others to digest and go further with the approach, and to facilitate interpretability of results. I like the use of a neural network to demonstrate the predictiveness of stepping, but aside from above-chance probability, what else can inform us about what visual data drives that?

The CNN analysis simply shows that the information is there in the image from the subject’s viewpoint and is used to motivate the subsequent analysis.  As noted above, we have generally tried to improve the clarity of the methods.

Similarly, the step distributions and height-turn trade-off curves are somewhat opaque and do not make it easy to envision further efforts by others, for example, people who want to model locomotion. For that, clearer (and perhaps) simpler measures would be helpful. 

We have clarified the description of these plots in the main text and in the methods.  We have also tried to clarify why we made the choices that we did in measuring the height-turn trade-off and why it is necessary in order to make a fair comparison.

I am absolutely in support of this manuscript and expect it to have a high impact. I do feel that it could benefit from clarification of the analysis and how it supports the conclusions. 

Reviewer #3 (Public Review): 

Summary: 

The systematic way in which path selection is parametrically investigated is the main contribution. 

Strengths: 

The authors have developed an impressive workflow to study gait and gaze in natural terrain. 

Weaknesses: 

(1) The training and validation data of the CNN are not explained fully making it unclear if the data tells us anything about the visual features used to guide steering. It is not clear how or on what data the network was trained (training vs. validation vs. un-peeked test data), and justification of the choices made. There is no discussion of possible overfitting. The network could be learning just e.g. specific rock arrangements. If the network is overfitting the "features" it uses could be very artefactual, pixel-level patterns and not the kinds of "features" the human reader immediately has in mind. 

The CNN analysis has now been moved earlier in the manuscript to help clarify its significance and we have expanded the description of the methods. Briefly, it simply indicates that there is information in the depth structure of the terrain that can be learned by a network. This helps justify the subsequent analyses.  Importantly, the network training and testing sets were separated by terrain to ensure that the model was being tested on “unseen” terrain and avoid the model learning specific arrangements.  This is now clarified in the text.

(2) The use of descriptive terminology should be made systematic. 

Specifically, the following terms are used without giving a single, clear definition for them: path, step, step location, foot plant, foothold, future foothold, foot location, future foot location, foot position. I think some terms are being used interchangeably. I would really highly recommend a diagrammatic cartoon sketch, showing the definitions of all these terms in a single figure, and then sticking to them in the main text. 

We have made the language more systematic and clarified the definition of each term (see Methods). Path refers to the sequence of 5 steps. Foothold is where the foot was placed in the environment. A step is the transition from one foothold to the next.

(3) More coverage of different interpretations / less interpretation in the abstract/introduction would be prudent.  The authors discuss the path selection very much on the basis of energetic costs and gait stability. At least mention should be given to other plausible parameters the participants might be optimizing (or that indeed they may be just satisficing). That is, it is taken as "given" that energetic cost is the major driver of path selection in your task, and that the relevant perception relies on internal models. Neither of these is a priori obvious nor is it as far as I can tell shown by the data (optimizing other variables, satisficing behavior, or online "direct perception" cannot be ruled out). 

The abstract has been substantially rewritten.  We have adjusted our language in the introduction/discussion to try to address this concern.

Recommendations for the authors:

Reviewing Editor comments 

You will find a full summary of all 3 reviews below. In addition to these reviews, I'd like to highlight a few points from the discussion among reviewers. 

All reviewers are in agreement that this study has the potential to be a fundamental study with far-reaching empirical and practical implications. The reviewers also appreciate the technical achievements of this study. 

At the same time, all reviewers are concerned with the overall lack of clarity in how the results are presented. There are a considerable number of figures that need better labeling, text parts that require clearer definitions, and the description of data collection and analysis (esp. with regard to the CNN) requires more care. Please pay close attention to all comments related to this, as this was the main concern that all reviewers shared. 

At a more specific level, the reviewers discussed the finding around leg length, and admittedly, found it hard to believe, in short: "extraordinary claims need strong evidence". It would be important to strengthen this analysis by considering possible confounds, and by including a discussion of the degree of conviction. 

We have weakened the discussion of this finding and provided some an additional analyses in a supplemental figure (Figure 17) to help clarify the finding.

Reviewer #1 (Recommendations For The Authors): 

First, let me apologize for the long delay with this review. Despite my generally positive evaluation (see public review), I have some concerns about the way the data are presented and questions about methodological details. 

(1) Representation of results: I find it hard to decipher how much variability arises within an individual and how much across individuals. For example, Figure 7b seems to aggregate across all individuals, while the analysis is (correctly) based on the subject medians.

Figure 7b That figure was just one subject. This is now clarified.

It would be good to see the distribution of all individuals (maybe use violin plots for each observer with the true data on one side and the baseline data on the other, or simple histograms for each). To get a feeling for inter-individual and intra-individual variability is crucial, as obviously (see the leg-length analysis) there are larger inter-individual differences and representations like these would be important to appreciate whether there is just a scaling of more or less the same effect or whether there are qualitative differences (especially in the light of N=9 being not a terribly huge sample size). 

The medians for the individual subjects are now provided with the standard deviations between subjects to indicate the extent of individual differences. Note that the random paths were chosen from the distribution of actual step slopes for that subject as one of the constraints. This makes the random paths statistically similar to the chosen paths with the differences only being generated by the particular visual context. Thus the test for a difference between chosen and random is quite conservative

Similarly, seeing \DeltaH plotted as a function of steps in the path as a figure rather than just having the verbal description would also help. 

To simplify the discussion of our methods/results we have removed the analyses that examine mean slope as a function of steps.  Because of the central limit theorem the slopes of the chosen paths remain largely unchanged regardless of the choice path length.  The slopes of the simulated paths are always larger irrespective of the choice of path length.

(2) Reporting the statistical analyses: This is related to my previous issue: I would appreciate it if the test statistics and degrees-of-freedom of the statistical tests were given along with the p-values, instead of only the p-values. This at some points would also clarify how the statistics were computed exactly (e.g., "All subjects showed comparable difference and the difference in medians evaluated across subjects was highly significant (p<<0.0001).", p.10, is ambiguous to me). 

Details have been added as requested.

(3) Why is the lower half ("tortuosity less than the median tortuosity") of paths used as "straight" rather than simply the minimum of all viable paths)?

The benchmark for a straight path is somewhat arbitrary. Using the lower half rather than the minimum length path is more conservative.

(4) For the CNN analysis, I failed to understand what was training and what was test set. I understand that the goal is to predict for all pixels whether they are a potential foothold or not, and the AUC is a measure of how well they can be discriminated based on depth information and then this is done for each image and the median over all images taken. But on which data is the CNN trained, and on which is it tested? Is this leave-n-out within the same participant? If so, how do you deal with dependencies between subsequent images? Or is it leave-1-out across participants? If so, this would be more convincing, but again, the same image might appear in training and test. If the authors just want to ask how well depth features can discriminate footholds from non-footholds, I do not see the benefit of a supervised method, which leaves the details of the feature combinations inside a black box. Rather than defining the "negative set" (i.e., the non-foothold pixels) randomly, the simulated paths could also be used, instead. If performance (AUC) gets lower than for random pixels, this would confirm that the choice of parameters to define a "viable path" is well-chosen. 

This has been clarified as described above.

Minor issues: 

(5) A higher tortuosity would also lead a participant to require more steps in total than a lower tortuosity. Could this partly explain the correlation between the leg length and the slope/tortuosity correlation? (Longer legs need fewer steps in total, thus there might be less tradeoff between \Delta H and keeping the path straight (i.e., saving steps)). To assess this, you could give the total number of steps per (straight) distance covered for leg length and compare this to a flat surface.

The calculations are done on an individual subject basis and the first and last step locations are chosen from the actual foot placements, then the random paths are generated between those endpoints. The consequence of this is that the number of steps is held constant for the analysis.  We have clarified the methods for this analysis to try to make this more clear.

(6) As far as I understand, steps happen alternatingly with the two feet. That is, even on a flat surface, one would not reach 0 tortuosity. In other words, does the lateral displacement of the feet play a role (in particular, if paths with even and paths with odd number of steps were to be compared), and if so, is it negligible for the leg-length correlation? 

All the comparisons here are done for 5 step sequences so this potential issue should not affect the slope of the regression lines or the leg length correlation.

(7) Is there any way to quantify the quality of the depth estimates? Maybe by taking an actual depth image (e.g., by LIDAR or similar) for a small portion of the terrain and comparing the results to the estimate? If this has been done for similar terrain, can a quantification be given? If errors would be similar to human errors, this would also be interesting for the interpretation of the visual sampling data.

Unfortunately, we do not have the ground truth depth image from LIDAR.  When these data were originally collected, we had not imagined being able to reconstruct the terrain.  However, we agree with the reviewers that this would be a good analysis to do. We plan to collect LIDAR in future experiments. 

To provide an assessment of quality for these data in the absence of a ground truth depth image, we have performed an evaluation of the reliability of the terrain reconstruction across repeats of the same terrain both between and within participants.  We have expanded the discussion of these reliability analyses in the results section entitled “Evaluating Terrain Reconstruction”, as well as in the corresponding methods section (see Figure 10).

(8) The figures are sometimes confusing and a bit sloppy. For example, in Figure 7a, the red, cyan, and green paths are not mentioned in the caption, in Figure 8 units on the axes would be helpful, in Figure 9 it should probably be "tortuosity" where it now states "curviness". 

These details have been fixed.

(9) I think the statement "The maximum median AUC of 0.79 indicates that the 0.79 is the median proportion of pixels in the circular..." is not an appropriate characterization of the AUC, as the number of correctly classified pixels will not only depend on the ROC (and thus the AUC), but also on the operating point chosen on the ROC (which is not specified by the AUC alone). I would avoid any complications at this point and just characterize the AUC as a measure of discriminability between footholds and non-footholds based on depth features. 

This has been fixed.

(10) Ref. [16]is probably the wrong Hart paper (I assume their 2012 Exp. Brain Res. [https://doi.org/10.1007/s00221-012-3254-x] paper is meant at this point) 

Fixed

Typos (not checked systematically, just incidental discoveries): 

(11) "While there substantial overlap" (p.10) 

(12) "field.." (p.25) 

(13) "Introduction", "General Discussion" and "Methods" as well as some subheadings are numbered, while the other headings (e.g., Results) are not. 

Fixed

Reviewer #2 (Recommendations For The Authors): 

The major suggestions have been made in the Public Review. The following are either minor comments or go into more detail about the major suggestions. All of these comments are meant to be constructive, not obstructive. 

Abstract. This is well written, but the main conclusions "Walkers avoid...This trade off is related...5 steps ahead" sound quite qualitative. They could be strengthened by more specificity (NOT p-values), e.g. "positive correlation between the unevenness of the path straight ahead and the probability that people turned off that path." 

The abstract has been substantially rewritten.

P. 5 "pinning the head position estimated from the IMU to the Meshroom estimates" sounds like there are two estimates. But it does not sound like both were used. Clarify, e.g. the Meshroom estimate of head position was used in place of IMU? 

Yes that’s correct.  We have clarified this in the text.

Figure 5. I was confused by this. First, is a person walking left to right? When the gaze position is shown, where was the eye at the time of that gaze? There are straight lines attached to the blue dots, what do they represent? The caption says gaze is directed further along the path, which made me guess the person is walking right to left, and the line originates at the eye. Except the origins do not lie on or close to the head locations. There's also no scale shown, so maybe I am completely misinterpreting. If the eye locations were connected to gaze locations, it would help to support the finding that people look five steps ahead of where they step. 

We have updated the figure and clarified the caption to remove these confusions.  There was a mistake in the original figure (where the yellow indicated head locations, we had plotted the center of mass and the choice of projection gave the incorrect impression that the fixations off the path, in blue, were separated from the head).

The view of the data is now presented so the person is walking left to right and with a projection of the head location (orange), gaze locations (blue or green) and feet (pink).

Figure 6. As stated in the major comments, the step distributions would be expected to have a covariance structure (in terms of raw data before taking absolute values). It would be helpful to report the covariances (6 numbers). As an example of a simple statistical analysis, a PCA (also based on a data covariance) would show how certain combinations of slope/distance/direction are favored over others. Such information would be a simple way to argue that the data are not completely random, and may even show a height-turn trade-off immediately. (By the way, I am assuming absolute values are used because the slopes and directions are only positive, but it wasn't clear if this was the definition.) A reason why covariances and PCA are helpful is that such data would be helpful to compute a better random walk, generated from dynamics. I believe the argument that steps are not random is not served by showing the different histograms in Figure 7, because I feel the random paths are not fairly produced. A better argument might draw randomly from the same distribution as the data (or drive a dynamical random walk), and compare with actual data. There may be correlations present in the actual data that differ from random. I could be mistaken, because it is difficult or impossible to draw conclusions from distributions of absolute values, or maybe I am only confused. In any case, I suspect other readers will also have difficulty with this section. 

This has been addressed above in the major comments.

p. 9, "average step slope" I think I understand the definition, but I suggest a diagram might be helpful to illustrate this.

There is a diagram of a single step slope in Figure 6 and a diagram of the average step slope for a path segment in Figure 12.

Incidentally, the "straight path slope" is not clearly defined. I suspect "straight" is the view from above, i.e. ignoring height changes. 

Clarified

p. 11 The tortuosity metric could use a clearer definition. Should I interpret "length of the chosen path relative to a straight path" as the numerator and denominator? Here does "length" also refer to the view from above? Why is tortuosity defined differently from step slope? Couldn't there be an analogue to step slope, except summing absolute values of direction changes? Or an analogue to tortuosity, meaning the length as viewed from the side, divided by the length of the straight path? 

We followed the literature in the definition of tortuosity.  We have clarified the definition of tortuosity in the methods, but yes, you can interpret the length of the chosen path relative to a straight path, as the numerator and denominator, and length refers to 3D length.  We agree that there are many interesting ways to look at the data but for clarity we have limited the discussion to a single definition of tortuosity in this paper.

Figure 8 could use better labeling. On the left, there is a straight path and a more tortuous path, why not report the metrics for these? On the right, there are nine unlabeled plots. The caption says "turn probability vs. straight path slope" but the vertical axis is clearly not a probability. Perhaps the axis is tortuosity? I presume the horizontal axis is a straight path slope in degrees, but this is not explained. Why are there nine plots, is each one a subject? I would prefer to be informed directly instead of guessing. (As a side note, I like the correlations as a function of leg length, it is interesting, even if slightly unbelievable. I go hiking with people quite a bit shorter and quite a lot taller than me, and anecdotally I don't think they differ so much from each other.) 

We have fixed Figure 8 which shows the average “mean slope” as a function of tortuosity.  We have added a supplemental figure which shows a scatter plot of the raw data (mean slope vs. tortuosity for each path segment).  

Note that when walking with friends other factors (e.g. social) will contribute to the cost function. As a very short person my experience is that it is a problem. In any case, the data are the data, whatever the underlying reasons. It does not seem so surprising that people of different heights make different tradeoffs. We know that the preferred gait depends on individual’s passive dynamics as described in the paper, and the terrain will change what is energetically optimal as described in the Darici and Kuo paper.

Figure 9 presumably shows one data point per subject, but this isn't clear. 

The correlations are reported per subject, and this has been clarified. 

p. 13 CNN. I like this analysis, but only sort of. It is convincing that there is SOME sort of systematic decision-making about footholds, better than chance. What it lacks is insight. I wonder what drives peoples' decisions. As an idle suggestion, the AlexNet (arXiv: Krizhevsky et al.; see also A. Karpathy's ConvNETJS demo with CIFAR-10) showed some convolutional kernels to give an idea of what the layers learned. 

Further exploration of CNN’s would definitely be interesting, but it is outside the scope of the paper. We use it simply to make a modest point, as described above.

p. 15 What is the definition of stability cost? I understand energy cost, but it is unclear how circuitous paths have a higher stability cost. One possible definition is an energetic cost having to do with going around and turning. But if not an energy cost, what is it? 

We meant to say that the longer and flatter paths are presumably more stable because of the smaller height changes. You are correct that we can’t say what the stability cost is and we have clarified this in the discussion.

p. 16 "in other data" is not explained or referenced.

Deleted 

p. 10 5 step paths and p. 17 "over the next 5 steps". I feel there is very little information to really support the 5 steps. A p-value only states the significance, not the amount of difference. This could be strengthened by plotting some measures vs. the number of steps ahead. For example, does a CNN looking 1-5 steps ahead predict better than one looking N<5 steps ahead? I am of course inclined to believe the 5 steps, but I do not see/understand strong quantitative evidence here. 

We have weakened the statements about evidence for planning 5 steps ahead.

p. 25 CNN. I did not understand the CNN. The list of layers seems incomplete, it only shows four layers. The convolutional-deconvolutional architecture is mentioned as if that is a common term, which I am unfamiliar with but choose to interpret as akin to encoder-decoder. However, the architecture does not seem to have much of a bottleneck (25x25x8 is not greatly smaller than 100x100x4), so what is the driving principle? It's also unclear how the decoder culminates, does it produce some m x m array of probabilities of stepping, where m is some lower dimension than the images? It might be helpful also to illustrate the predictions, for example, show a photo of the terrain view, along with a probability map for that view. I would expect that the reader can immediately say yes, I would likely step THERE but not there. 

We have clarified the description of the CNN. An illustration is shown in Figure 11.

Reviewer #3 (Recommendations For The Authors): 

(This section expands on the points already contained in the Public Review). 

Major issues 

(1) The training and validation data of the CNN are not explained fully making it unclear if the data tells us anything about the visual features used to guide steering. A CNN was used on the depth scenes to identify foothold locations in the images. This is the bit of the methods and the results that remains ambiguous, and the authors may need to revisit the methods/results. It is not clear how or on what data the network was trained (training vs. validation vs. un-peeked test data), and justification of the choices made. There is no discussion of possible overfitting. The network could be learning just for example specific rock arrangements in the particular place you experimented. Training the network on data from one location and then making it generalize to another location would of course be ideal. Your network probably cannot do this (as far as I can tell this was not tried), and so the meaning of the CNN results cannot really be interpreted. 

I really like the idea, of getting actual retinotopic depth field approximations. But then the question would be: what features in this information are relevant and useful for visual guidance (of foot placement)? But this question is not answered by your method. 

"If a CNN can predict these locations above chance using depth information, this would indicate that depth features can be used to explain some variation in foothold selection." But there is no analysis of what features they are. If the network is overfitting they could be very artefactual, pixel-level patterns and not the kinds of "features" the human reader immediately has in mind. As you say "CNN analysis shows that subject perspective depth features are predictive of foothold locations", well, yes, with 50,000 odd parameters the foothold coordinates can be associated with the 3D pixel maps, but what does this tell us? 

See previous discussion of these issues.

It is true that we do not know the precise depth features used. We established that information about height changes was being used, but further work is needed to specify how the visual system does this. This is mentioned in the Discussion.

You open the introduction with a motivation to understand the visual features guiding path selection, but what features the CNN finds/uses or indeed what features are there is not much discussed. You would need to bolster this, or down-emphasize this aspect in the Introduction if you cannot address it. 

"These depth image features may or may not overlap with the step slope features shown to be predictive in the previous analysis, although this analysis better approximates how subjects might use such information." I do not think you can say this. It may be better to approximate the kind of (egocentric) environment the subjects have available, but as it is I do not see how you can say anything about how the subject uses it. (The results on the path selection with respect to the terrain features, viewpoint viewpoint-independent allocentric properties of the previous analyses, are enough in themselves!) 

We have rewritten the section on the CNN to make clearer what it can and cannot do and its role in the manuscript. See previous discussion.

(2) The use of descriptive terminology should be made systematic. Overall the rest of the methodology is well explained, and the workflow is impressive. However, to interpret the results the introduction and discussion seem to use terminology somewhat inconsistently. You need to dig into the methods to figure out the exact operationalizations, and even then you cannot be quite sure what a particular term refers to. Specifically, you use the following terms without giving a single, clear definition for them (my interpretation in parentheses): 

foothold (a possible foot plant location where there is an "affordance"? or a foot plant location you actually observe for this individual? or in the sample?) 

step (foot trajectory between successive step locations) 

step location (the location where the feet are placed) 

path (are they lines projected on the ground, or are they sequences of foot plants? The figure suggests lines but you define a path in terms of five steps. 

foot plant (occurs when the foot comes in contact with step location?) 

future foothold (?) 

foot location (?) 

future foot location (?) 

foot position (?) 

I think some terms are being used interchangeably here? I would really highly recommend a diagrammatic cartoon sketch, showing the definitions of all these terms in a single figure, and then sticking to them in the main text. Also, are "gaze location" and "fixation" the same? I.e. is every gaze-ground intersection a "gaze location" (I take it it is not a "fixation", which you define by event identification by speed and acceleration thresholds in the methods)? 

We have cleaned up the language. A foothold is the location in the terrain representation (mesh) where the foot was placed. A step is the transition from one foothold to the next. A path is the sequences of 5 steps. The lines simply illustrate the path in the Figures. A gaze location is the location in the terrain representation where the walker is holding gaze still (the act of fixating). See Muller et al (2023) for further explanation.

(3) More coverage of different interpretations / less interpretation in the abstract/introduction would be prudent. You discuss the path selection very much on the basis of energetic costs and gait stability. At least mention should be given to other plausible parameters the participants might be optimizing (or that indeed they may be just satisficing). Temporal cost (more circuitous route takes longer) and uncertainty (the more step locations you sample the more chance that some of them will not be stable) seem equally reasonable, given the task ecology / the type of environment you are considering. I do not know if there is literature on these in the gait-scene, but even if not then saying you are focusing on just one explanation because that's where there is literature to fall back on would be the thing to do. 

Also in the abstract and introduction you seem to take some of this "for granted". E.g. you end the abstract saying "are planning routes as well as particular footplants. Such planning ahead allows the minimization of energetic costs. Thus locomotor behavior in natural environments is controlled by decision mechanisms that optimize for multiple factors in the context of well-calibrated sensory and motor internal models". This is too speculative to be in the abstract, in my opinion. That is, you take as "given" that energetic cost is the major driver of path selection in your task, and that the relevant perception relies on internal models. Neither of these is a priori obvious nor is it as far as I can tell shown by your data (optimizing other variables, satisficing behavior, or online "direct perception" cannot be ruled out). 

We have rewritten the abstract and Discussion with these concerns in mind.

You should probably also reference: 

Warren, W. H. (1984). Perceiving affordances: Visual guidance of stair climbing. Journal of Experimental Psychology: Human Perception and Performance, 10(5), 683-703. https://doi.org/10.1037/0096-1523.10.5.683 

Warren WH Jr, Young DS, Lee DN. Visual control of step length during running over irregular terrain. J Exp Psychol Hum Percept Perform. 1986 Aug;12(3):259-66. doi: 10.1037//0096-1523.12.3.259. PMID: 2943854. 

We have added these references to the introduction.

Minor point 

Related to (2) above, the path selection results are sometimes expressed a bit convolutedly, and the gist can get lost in the technical vocabulary. The generation of alternative "paths" and comparison of their slope and tortuousness parameters show that the participants preferred smaller slope/shorter paths. So, as far as I can tell, what this says is that in rugged terrain people like paths that are as "flat" as possible. This is common sense so hardly surprising. Do not be afraid to say so, and to express the result in plain non-technical terms. That an apple falls from a tree is common sense and hardly surprising. Yet quantifying the phenomenon, and carefully assessing the parameters of the path that the apple takes, turned out to be scientifically valuable - even if the observation itself lacked "novelty". 

Thanks.  We have tried to clarify the methods/results with this in mind.

Author response:

The following is the authors’ response to the original reviews.

Reviewer #1 (Public Review):

[...] Based on these results, the authors support a model whereby kinetic regimes are encoded in the cis-regulatory sequences of a gene instead of imposed by an evolving trans-regulatory environment.

The question asked in this manuscript is important and the eve locus represents an ideal paradigm to address it in a quantitative manner. Most of the results are correctly interpreted and well-presented. However, the main conclusion pointing towards a potential "unified theory" of burst regulation during Drosophila embryogenesis should be nuanced or cross-validated.

Our results and those of others suggest that different developmental genes follow unified—yet different—transcriptional control strategies whereby different combinations of bursting parameters are regulated to modulate gene expression: burst frequency and amplitude for eve (Berrocal et al., 2020), and burst frequency and duration for gap genes (Zoller et al., 2018). In light of the aforementioned works, we can only claim that our results suggest a unified strategy for eve, our case of study, as we observe that eve regulatory strategies are robust to disruption of enhancers and binding sites. In the Discussion section of our revised manuscript, we will emphasize that the bursting control strategy we uncovered for eve does not necessarily apply to other genes, and speculate in more detail that genes that employ the same strategy of transcriptional bursting may be grouped in families that share a common molecular mechanism of transcription.

Manuscript updates:

We have emphasized in the Discussion section that our claim of unified strategies pertains exclusively to the bursting behavior of the gene even-skipped, and do not necessarily extend to other genes. To clarify this point, we referenced the findings of (Zoller, Little, and Gregor 2018) and (Chen et al. 2023), who observed that the bursting control strategy of Drosophila gap genes relies on the modulation of burst frequency and duration. Additionally, we cited the findings of (Syed, Duan, and Lim 2023), who reported a decrease in bursting amplitude and duration upon disruption of Dorsal binding sites on the snail minimal distal enhancer. Both examples describe bursting control strategies that differ from the modulation of burst frequency and amplitude observed for even-skipped.

In addition to the lack of novelty (some results concerning the fact that koff does not change along the A/P axis/the idea of a 'unified regime' were already obtained in Berrocal et al 2020),...

Unfortunately, we believe there is a misunderstanding in terms of what we construe as novelty in our work. In our previous work (Berrocal et al., 2020), we observed that the seven stripes of even-skipped (eve) expression modulate transcriptional bursting through the same strategy—bursting frequency and amplitude are controlled to yield various levels of mRNA synthesis, while burst duration remains constant. We reproduce that result in our paper, and do not claim any novelty. However, what was unclear is whether the observed eve bursting control strategy would only exist in the wild-type stripes, whose expression—we reasoned—is under strong selection due to the dramatic phenotypic consequences of eve transcription, or if eve transcriptional bursting would follow the same strategy under trans-regulatory environments that are not under selection to deliver specific spatiotemporal dynamics of eve expression. Our results—and here lies the novelty of our work—support the second scenario, and point to a model where eve bursting strategies do not result from adaptation of eve activity to specific trans-regulatory environments. Instead, we speculate that a molecular mechanism constrains eve bursting strategy whenever and wherever the gene is active. This is something that we could not have known from our first study in (Berrocal et al., 2020) and constitutes the main novelty of our paper. To put this in other words, the novelty of our work does not rest on the fact that both burst frequency and amplitude are modulated in the endogenous eve pattern, but that this modulation remains quantitatively indistinguishable when we focus on ectopic areas of expression. We will make this point clearer in the Introduction and Discussion section of our revised manuscript.

Manuscript updates:

We have clarified this point in both the Introduction and Discussion sections. In the updated Introduction, we state that while our previous work (Berrocal et al. 2020) examined bursting strategies in endogenous expression regions that are, in principle, subject to selection, the present study induced the formation of ectopic expression patterns to probe bursting strategies in regions presumably devoid of evolutionary pressures. In the Discussion section, we highlight that the novelty of our work lies in the insights derived from the comparative analysis between ectopic and endogenous regions of even-skipped expression, an aspect not addressed in our previous work.

… note i) the limited manipulation of TF environment;...

We acknowledge that additional genetic manipulations would make it possible to further test the model. However, we hope that the reviewer will agree with us that the manipulations that we did perform are sufficient to provide evidence for common bursting strategies under the diverse trans-regulatory environments present in wild-type and ectopic regions of gene expression. In the Discussion section of our revised manuscript, we will elaborate further on the kind of genetic manipulations (e.g., probing transcriptional strategies that result from swapping promoters in the context of eve-MS2 BAC; or quantifying the impact on eve transcriptional control after performing optogenetic perturbations of transcription factors and/or chromatin remodelers) that could shed further light on the currently undefined molecular mechanism that constrains eve bursting strategies, as a mean to motivate future work.

Manuscript updates:

In our Discussion section, we elaborated on proposed manipulations of the transcription factor environment to elucidate the molecular mechanisms behind even-skipped bursting control strategies. We began by listing studies linking transcription factor concentration to bursting control strategies, such as (Hoppe et al. 2020), who observed that the natural BMP (Bone Morphogenetic Protein) gradient shapes bursting frequency of target genes in Drosophila embryos. And (Zhao et al. 2023), who used the LEXY optogenetic system to modulate Knirps nuclear concentration and observed that this repressor acts on eve stripe 4+6 enhancer by gradually decreasing bursting frequency until the locus adopts a reversible quiescent state. Then, we proposed performing systematic LEXY-mediated modulation of critical transcription factors (Bicoid, Hunchback, Giant, Kruppel, Zelda) to understand the extent of their contribution to the unified even-skipped bursting strategies.

To better frame the hypothesis that the even-skipped promoter defines strategies of bursting control, we added a reference to the work of (Tunnacliffe, Corrigan, and Chubb 2018). This study surveyed 17 actin genes with identical sequences but distinct promoters in the amoeba Dictyostelium discoideum, and found that all genes display different bursting strategies. Their findings, together with the previously cited work by (Pimmett et al. 2021) and (Yokoshi et al. 2022), suggest a critical role of gene promoters in constraining the bursting strategies of eukaryotic genes.

… ii) the simplicity with which bursting is analyzed (only a two-state model is considered, and not cross-validated with an alternative approach than cpHMM) and…

Based on our previous work (Lammers et al., 2020), and as described in the SI Section of the current manuscript: Inference of Bursting Parameters, we selected a three-state model (OFF, ON1, ON2) under the following rationale: transcription of even-skipped in pre-gastrulating embryos occurs after DNA replication, and promoters on both sister chromatids remain paired. Most of the time these paired loci cannot be resolved independently using conventional microscopy. As a result, when we image an MS2 spot, we are actually measuring the transcriptional dynamics of two promoters. Thus, each MS2-fluorescent spot may result from none (OFF), one (ON1) or two (ON2) sister promoters being in the active state. Following our previous work, we analyzed our data assuming the three-state model (OFF, ON1, ON2), and then, for ease of presentation, aggregated ON1 and ON2 into an effective single ON state. As for the lack of an alternative model, we chose the simplest model compatible with our data and our current understanding of transcription at the eve locus. With this in mind, we do not rule out the possibility that more complex processes—that are not captured by our model—shape MS2 fluorescence signals. For example, promoters may display more than two states of activity. However, as shown in (Lammers et al., 2020 - SI Section: G. cpHMM inference sensitivities), model selection schemes and cross-validation do not give consistent results on which model is more favorable; and for the time being, there is not a readily available alternative to HMM for inference of promoter states from MS2 signal. For example, orthogonal approaches to quantify transcriptional bursting, such as smFISH, are largely blind to temporal dynamics. As a result, we choose to entertain the simplest two-state model for each sister promoter. We appreciate these observations, as they point out the need of devoting a section in the supplemental material of our revised manuscript to clarify the motivations behind model selection.

Manuscript updates:

We have devoted the new Supplemental Material section “Selection of a three-state model of promoter activity and a compound Hidden Markov Model for inference of promoter states from MS2 fluorescent signal” to clarify the rationale behind our selection of a three-state promoter activity model. Since transcription in pre-gastrulating Drosophila embryos occurs after DNA replication, each MS2-active locus contains two unresolvable sister promoters that can either be inactive (OFF), one active (ON1), or both active (ON2).

Next, we elaborated on the conversion of a three-state model into an effective two-state model for ease of presentation and described how the effective two-state model parameters—kon (burst frequency), koff-1 (burst duration), and r (burst amplitude)—were calculated.

Additionally, we acknowledged that while the three-state model of promoter activity is the simplest model compatible with our current understanding of transcription in the even-skipped locus, we do not rule out the possibility that even-skipped transcription may be described by more complex models that include multiple states beyond ON and OFF. Finally, we referenced (Lammers et al. 2020) who asserted that while all inferences of promoter states computed from confocal microscopy of MS2/PP7 fluorescence data rely on Hidden Markov models, cross-comparisons between one, two, or multiple-state Hidden Markov models do not yield consistent results regarding which is more accurate. We close the new section by proposing that state-of-the-art microscopy and deconvolution algorithms to improve signal-to-noise-ratio may offer alternatives to the inference of promoter states.

… iii) the lack of comparisons with published work.

We thank the reviewer for pointing this out. In the current discussion of our manuscript, we compare our findings to recent articles that have addressed the question of the origin of bursting control strategies in Drosophila embryos (Pimmett et al., 2021; Yokoshi et al., 2022; Zoller et al., 2018). Nevertheless, we acknowledge that we failed to include references that are relevant to our study. Thus, our revised Discussion section must include recent results by (Syed et al., 2023), which showed that the disruption of Dorsal binding sites on the snail minimal distal enhancer results in decreased amplitude and duration of transcription bursts in fruit fly embryos. Additionally, we have to incorporate the study by (Hoppe et al., 2020), which reported that the Drosophila bone morphogenetic protein (BMP) gradient modulates the bursting frequency of BMP target genes. References to thorough studies of bursting control in other organisms, like Dictyostelium discoideum (Tunnacliffe et al., 2018), are due as well.

Manuscript updates:

As mentioned in the updates above, our revised manuscript now includes long due references to studies by (Syed, Duan, and Lim 2023), (Hoppe et al. 2020), (Tunnacliffe, Corrigan, and Chubb 2018), and (Chen et al. 2023). All of which are relevant for our current workk.

Reviewer #2 (Public Review):

The manuscript by Berrocal et al. asks if shared bursting kinetics, as observed for various developmental genes in animals, hint towards a shared molecular mechanism or result from natural selection favoring such a strategy. Transcription happens in bursts. While transcriptional output can be modulated by altering various properties of bursting, certain strategies are observed more widely. As the authors noted, recent experimental studies have found that even-skipped enhancers control transcriptional output by changing burst frequency and amplitude while burst duration remains largely constant. The authors compared the kinetics of transcriptional bursting between endogenous and ectopic gene expression patterns. It is argued that since enhancers act under different regulatory inputs in ectopically expressed genes, adaptation would lead to diverse bursting strategies as compared to endogenous gene expression patterns. To achieve this goal, the authors generated ectopic even-skipped transcription patterns in fruit fly embryos. The key finding is that bursting strategies are similar in endogenous and ectopic even-skipped expression. According to the authors, the findings favor the presence of a unified molecular mechanism shaping even-skipped bursting strategies. This is an important piece of work. Everything has been carried out in a systematic fashion. However, the key argument of the paper is not entirely convincing.

We thank the reviewer, as these comments will enable us to improve the Discussion section and overall logic of our revised manuscript. We agree that the evidence provided in this work, while systematic and carefully analyzed, cannot conclusively rule out either of the two proposed models, but just provide evidence supporting the hypothesis for a specific molecular mechanism constraining eve bursting strategies. Our experimental evidence points to valuable insights about the mechanism of eve bursting control. For instance, had we observed quantitative differences in bursting strategies between ectopic and endogenous eve domains, we would have rejected the hypothesis that a common molecular mechanism constrains eve transcriptional bursting to the observed bursting control strategy of frequency and amplitude modulation. Thus, we consider that our proposition of a common molecular mechanism underlying unified eve bursting strategies despite changing trans-regulatory environments is more solid. On the other hand, while our model suggests that this undefined bursting control strategy is not subject to selection acting on specific trans-regulatory environments, it is not trivial to completely discard selection for specific bursting control strategies given our current lack of understanding of the molecular mechanisms that shape the aforesaid strategies. Indeed, we cannot rule out the hypothesis that the observed strategies are most optimal for the expression of eve endogenous stripes according to natural selection, and that these control strategies persist in ectopic regions as an evolutionary neutral “passenger phenotype” that does not impact fitness. We recognize the need to acknowledge this last hypothesis in the updated Introduction and Discussion sections of our manuscript. Further studies will be needed to determine the mechanistic and molecular basis of eve bursting strategies.

Manuscript updates:

In this work, we compared strategies of bursting control between endogenous and ectopic regions of even-skipped expression. Different strategies between both regions would suggest that selective pressure maintains defined bursting strategies in endogenous regions. Conversely, similar strategies in both ectopic and endogenous regions would imply that a shared molecular mechanism constrains bursting parameters despite changing trans-regulatory environments.

In our updated Discussion section, we acknowledge that while our work provides evidence supporting the second hypothesis, we cannot conclusively rule out the possibility that the observed strategies were selected as the most optimal for endogenous even-skipped expression regions and that ectopic regions retain such optimal bursting strategies as an evolutionary neutral “passenger phenotype”.

Reviewer #3 (Public Review):

In this manuscript by Berrocal and coworkers, the authors do a deep dive into the transcriptional regulation of the eve gene in both an endogenous and ectopic background. The idea is that by looking at eve expression under non-native conditions, one might infer how enhancers control transcriptional bursting. The main conclusion is that eve enhancers have not evolved to have specific behaviors in the eve stripes, but rather the same rates in the telegraph model are utilized as control rates even under ectopic or 'de novo' conditions. For example, they achieve ectopic expression (outside of the canonical eve stripes) through a BAC construct where the binding sites for the TF Giant are disrupted along with one of the eve enhancers. Perhaps the most general conclusion is that burst duration is largely constant throughout at ~ 1 - 2 min. This conclusion is consistent with work in human cell lines that enhancers mostly control frequency and that burst duration is largely conserved across genes, pointing to an underlying mechanistic basis that has yet to be determined.

We thank the reviewer for the assessment of our work. Indeed, evidence from different groups (Berrocal et al., 2020; Fukaya et al., 2016; Hoppe et al., 2020; Pimmett et al., 2021; Senecal et al., 2014; Syed et al., 2023; Tunnacliffe et al., 2018; Yokoshi et al., 2022; Zoller et al., 2018) is coming together to uncover commonalities, discrepancies, and rules that constrain transcriptional bursting in Drosophila and other organisms.

Additional updates to the manuscript

(1) In our current study, we observed the appearance of a mutant stripe of even-skipped expression beyond the anterior edge of eve stripe 1, which we refer to as eve stripe 0. This stripe appeared in embryos with a disrupted eve stripe 1 enhancer. In a previous study, (Small, Blair, and Levine 1992) reported a “head patch” of even-skipped expression while assaying the regulation of reporter constructs carrying the minimal regulatory element of eve stripe 2 enhancer alone. In our updated manuscript, we state that it is tempting to identify our eve stripe 0 with the previously reported head patch. (Small, Blair, and Levine 1992) speculated that this head patch of even-skipped expression appeared as a result of regulatory sequences present in the P-transposon system they used for genomic insertions. However, P-transposon sequences are not present in our experimental design. Thus, the appearance of eve stripe 0 indicates a repressive role of the eve stripe 1 enhancer at the anterior end of the embryo and may imply that the minimal regulatory element of the eve stripe 2 enhancer, as probed by (Small, Blair, and Levine 1992), can drive the expression of the head patch/eve stripe 0 when the eve stripe 1 enhancer is not present.

(2)  In our current analysis, we observed that the disruption of Gt-binding sites on the eve stripe 2 enhancer synergizes with the deletion of the eve stripe 1 enhancer, as double mutant embryos display more ectopic expression in their anterior regions than embryos with only disrupted Gt-binding sites. While this may indicate that the repressive activity of eve stripe 1 enhancer synergizes with the repression exerted by Giant, other unidentified transcription factors may be involved in this repressive synergy. In the updated manuscript we clarified that unidentified transcription factors may bind in the vicinity of Gt-binding sites. The hypothesis that Gt-binding sites recognize other transcription factors was proposed by (Small, Blair, and Levine 1992), as they observed that the anterior expansion of eve stripe 2 resulting from Gt-binding site deletions was “somewhat more severe” than expansion observed in embryos carrying null-Giant alleles.

It doesn't seem like he necessarily cared she was leaving and just wanted her gone, and found any excuse to get her off the Google team. Like Buolamwini, she talked to important people to try to get things changed and bettered and it seemed like there wasn't some interest in anything she had to say.

Author response:

The following is the authors’ response to the original reviews.

eLife assessment

This study presents valuable findings on the potential of short-movie viewing fMRI protocol to explore the functional and topographical organization of the visual system in awake infants and toddlers. Although the data are compelling given the difficulty of studying this population, the evidence presented is incomplete and would be strengthened by additional analyses to support the authors' claims. This study will be of interest to cognitive neuroscientists and developmental psychologists, especially those interested in using fMRI to investigate brain organisation in pediatric and clinical populations with limited fMRI tolerance.

We are grateful for the thorough and thoughtful reviews. We have provided point-bypoint responses to the reviewers’ comments, but first, we summarize the major revisions here. We believe these revisions have substantially improved the clarity of the writing and impact of the results.

Regarding the framing of the paper, we have made the following major changes in response to the reviews:

(1) We have clarified that our goal in this paper was to show that movie data contains topographic, fine-grained details of the infant visual cortex. In the revision, we now state clearly that our results should not be taken as evidence that movies could replace retinotopy and have reworded parts of the manuscript that could mislead the reader in this regard.

(2) We have added extensive details to the (admittedly) complex methods to make them more approachable. An example of this change is that we have reorganized the figure explaining the Shared Response Modelling methods to divide the analytic steps more clearly.

(3) We have clarified the intermediate products contributing to the results by adding 6 supplementary figures that show the gradients for each IC or SRM movie and each infant participant.

In response to the reviews, we have conducted several major analyses to support our findings further:

(1) To verify that our analyses can identify fine-grained organization, we have manually traced and labeled adult data, and then performed the same analyses on them. The results from this additional dataset validate that these analyses can recover fine-grained organization of the visual cortex from movie data.

(2) To further explore how visual maps derived from movies compare to alternative methods, we performed an anatomical alignment control analysis. We show that high-quality maps can be predicted from other participants using anatomical alignment.

(3) To test the contribution of motion to the homotopy analyses, we regressed out the motion effects in these analyses. We found qualitatively similar results to our main analyses, suggesting motion did not play a substantial role.

(4) To test the contribution of data quantity to the homotopy analyses, we correlated the amount of movie data collected from each participant with the homotopy results. We did not find a relationship between data quantity and the homotopy results. 

Public Reviews:

Reviewer #1 (Public Review):

Summary:

Ellis et al. investigated the functional and topographical organization of the visual cortex in infants and toddlers, as evidenced by movie-viewing data. They build directly on prior research that revealed topographic maps in infants who completed a retinotopy task, claiming that even a limited amount of rich, naturalistic movie-viewing data is sufficient to reveal this organization, within and across participants. Generating this evidence required methodological innovations to acquire high-quality fMRI data from awake infants (which have been described by this group, and elsewhere) and analytical creativity. The authors provide evidence for structured functional responses in infant visual cortex at multiple levels of analyses; homotopic brain regions (defined based on a retinotopy task) responded more similarly to one another than to other brain regions in visual cortex during movie-viewing; ICA applied to movie-viewing data revealed components that were identifiable as spatial frequency, and to a lesser degree, meridian maps, and shared response modeling analyses suggested that visual cortex responses were similar across infants/toddlers, as well as across infants/toddlers and adults. These results are suggestive of fairly mature functional response profiles in the visual cortex in infants/toddlers and highlight the potential of movie-viewing data for studying finer-grained aspects of functional brain responses, but further evidence is necessary to support their claims and the study motivation needs refining, in light of prior research.

Strengths:

- This study links the authors' prior evidence for retinotopic organization of visual cortex in human infants (Ellis et al., 2021) and research by others using movie-viewing fMRI experiments with adults to reveal retinotopic organization (Knapen, 2021).

- Awake infant fMRI data are rare, time-consuming, and expensive to collect; they are therefore of high value to the community. The raw and preprocessed fMRI and anatomical data analyzed will be made publicly available.

We are grateful to the reviewer for their clear and thoughtful description of the strengths of the paper, as well as their helpful outlining of areas we could improve.

Weaknesses:

- The Methods are at times difficult to understand and in some cases seem inappropriate for the conclusions drawn. For example, I believe that the movie-defined ICA components were validated using independent data from the retinotopy task, but this was a point of confusion among reviewers. 

We acknowledge the complexity of the methods and wish to clarify them as best as possible for the reviewers and the readers. We have extensively revised the methods and results sections to help avoid potential misunderstandings. For instance, we have revamped the figure and caption describing the SRM pipeline (Figure 5).

To answer the stated confusion directly, the ICA components were derived from the movie data and validated on the (completely independent) retinotopy data. There were no additional tasks. The following text in the paper explains this point:

“To assess the selected component maps, we correlated the gradients (described above) of the task-evoked and component maps. This test uses independent data: the components were defined based on movie data and validated against task-evoked retinotopic maps.” Pg. 11

In either case: more analyses should be done to support the conclusion that the components identified from the movie reproduce retinotopic maps (for example, by comparing the performance of movie-viewing maps to available alternatives (anatomical ROIs, group-defined ROIs). 

Before addressing this suggestion, we want to restate our conclusions: features of the retinotopic organization of infant visual cortex could be predicted from movie data. We did not conclude that movie data could ‘reproduce’ retinotopic maps in the sense that they would be a replacement. We recognize that this was not clear in our original manuscript and have clarified this point throughout, including in this section of the discussion:

“To be clear, we are not suggesting that movies work well enough to replace a retinotopy task when accurate maps are needed. For instance, even though ICA found components that were highly correlated with the spatial frequency map, we also selected some components that turned out to have lower correlations. Without knowing the ground truth from a retinotopy task, there would be no way to weed these out. Additionally, anatomical alignment (i.e., averaging the maps from other participants and anatomically aligning them to a held-out participant) resulted in maps that were highly similar to the ground truth. Indeed, we previously23 found that adult-defined visual areas were moderately similar to infants. While functional alignment with adults can outperform anatomical alignment methods in similar analyses27, here we find that functional alignment is inferior to anatomical alignment. Thus, if the goal is to define visual areas in an infant that lacks task-based retinotopy, anatomical alignment of other participants’ retinotopic maps is superior to using movie-based analyses, at least as we tested it.” Pg. 21

As per the reviewer’s suggestion and alluded to in the paragraph above, we have created anatomically aligned visual maps, providing an analogous test to the betweenparticipant analyses like SRM. We find that these maps are highly similar to the ground truth. We describe this result in a new section of the results:

“We performed an anatomical alignment analog of the functional alignment (SRM) approach. This analysis serves as a benchmark for predicting visual maps using taskbased data, rather than movie data, from other participants. For each infant participant, we aggregated all other infant or adult participants as a reference. The retinotopic maps from these reference participants were anatomically aligned to the standard surface template, and then averaged. These averages served as predictions of the maps in the test participant, akin to SRM, and were analyzed equivalently (i.e., correlating the gradients in the predicted map with the gradients in the task-based map). These correlations (Table S4) are significantly higher than for functional alignment (using infants to predict spatial frequency, anatomical alignment > functional alignment: ∆Fisher Z M=0.44, CI=[0.32–0.58], p<.001; using infants to predict meridians, anatomical alignment > functional alignment: ∆Fisher Z M=0.61, CI=[0.47–0.74], p<.001; using adults to predict spatial frequency, anatomical alignment > functional alignment: ∆Fisher Z

M=0.31, CI=[0.21–0.42], p<.001; using adults to predict meridians, anatomical alignment > functional alignment: ∆Fisher Z M=0.49, CI=[0.39–0.60], p<.001). This suggests that even if SRM shows that movies can be used to produce retinotopic maps that are significantly similar to a participant, these maps are not as good as those that can be produced by anatomical alignment of the maps from other participants without any movie data.” Pg. 16–17

Also, the ROIs used for the homotopy analyses were defined based on the retinotopic task rather than based on movie-viewing data alone - leaving it unclear whether movie-viewing data alone can be used to recover functionally distinct regions within the visual cortex.

We agree with the reviewer that our approach does not test whether movie-viewing data alone can be used to recover functionally distinct regions. The goal of the homotopy analyses was to identify whether there was functional differentiation of visual areas in the infant brain while they watch movies. This was a novel question that provides positive evidence that these regions are functionally distinct. In subsequent analyses, we show that when these areas are defined anatomically, rather than functionally, they also show differentiated function (e.g., Figure 2). Nonetheless, our intention was not to use the homotopy analyses to define the regions. We have added text to clarify the goal and novelty of this analysis.

“Although these analyses cannot define visual maps, they test whether visual areas have different functional signatures.” Pg. 6

Additionally, even if the goal were to define areas based on homotopy, we believe the power of that analysis would be questionable. We would need to use a large amount of the movie data to define the areas, leaving a low-powered dataset to test whether their function is differentiated by these movie-based areas.

- The authors previously reported on retinotopic organization of the visual cortex in human infants (Ellis et al., 2021) and suggest that the feasibility of using movie-viewing experiments to recover these topographic maps is still in question. They point out that movies may not fully sample the stimulus parameters necessary for revealing topographic maps/areas in the visual cortex, or the time-resolution constraints of fMRI might limit the use of movie stimuli, or the rich, uncontrolled nature of movies might make them inferior to stimuli that are designed for retinotopic mapping, or might lead to variable attention between participants that makes measuring the structure of visual responses across individuals challenging. This motivation doesn't sufficiently highlight the importance or value of testing this question in infants. Further, it's unclear if/how this motivation takes into account prior research using movie-viewing fMRI experiments to reveal retinotopic organization in adults (e.g., Knapen, 2021). Given the evidence for retinotopic organization in infants and evidence for the use of movie-viewing experiments in adults, an alternative framing of the novel contribution of this study is that it tests whether retinotopic organization is measurable using a limited amount of movie-viewing data (i.e., a methodological stress test). The study motivation and discussion could be strengthened by more attention to relevant work with adults and/or more explanation of the importance of testing this question in infants (is the reason to test this question in infants purely methodological - i.e., as a way to negate the need for retinotopic tasks in subsequent research, given the time constraints of scanning human infants?).

We are grateful to the reviewer for giving us the opportunity to clarify the innovations of this research. We believe that this research contributes to our understanding of how infants process dynamic stimuli, demonstrates the viability and utility of movie experiments in infants, and highlights the potential for new movie-based analyses (e.g., SRM). We have now consolidated these motivations in the introduction to more clearly motivate this work:

“The primary goal of the current study is to investigate whether movie-watching data recapitulates the organization of visual cortex. Movies drive strong and naturalistic responses in sensory regions while minimizing task demands12, 13, 24 and thus are a proxy for typical experience. In adults, movies and resting-state data have been used to characterize the visual cortex in a data-driven fashion25–27. Movies have been useful in awake infant fMRI for studying event segmentation28, functional alignment29, and brain networks30. However, this past work did not address the granularity and specificity of cortical organization that movies evoke. For example, movies evoke similar activity in infants in anatomically aligned visual areas28, but it remains unclear whether responses to movie content differ between visual areas (e.g., is there more similarity of function within visual areas than between31). Moreover, it is unknown whether structure within visual areas, namely visual maps, contributes substantially to visual evoked activity. Additionally, we wish to test whether methods for functional alignment can be used with infants. Functional alignment finds a mapping between participants using functional activity – rather than anatomy – and in adults can improve signal-to-noise, enhance across participant prediction, and enable unique analyses27, 32–34.” Pg. 3-4

Furthermore, the introduction culminates in the following statement on what the analyses will tell us about the nature of movie-driven activity in infants:

“These three analyses assess key indicators of the mature visual system: functional specialization between areas, organization within areas, and consistency between individuals.” Pg. 5

Furthermore, in the discussion we revisit these motivations and elaborate on them further:

[Regarding homotopy:] “This suggests that visual areas are functionally differentiated in infancy and that this function is shared across hemispheres31.” Pg. 19

[Regarding ICA:] “This means that the retinotopic organization of the infant brain accounts for a detectable amount of variance in visual activity, otherwise components resembling these maps would not be discoverable.” Pg. 19–20

[Regarding SRM:] “This is initial evidence that functional alignment may be useful for enhancing signal quality, like it has in adults27,32,33, or revealing changing function over development45.” Pg. 21

Additionally, we have expanded our discussion of relevant work that uses similar methods such as the excellent research from Knapen (2021) and others:

“In adults, movies and resting-state data have been used to characterize the visual cortex in a data-driven fashion25-27.” Pg. 4

“We next explored whether movies can reveal fine-grained organization within visual areas by using independent components analysis (ICA) to propose visual maps in individual infant brains25,26,35,42,43.” Pg. 9

Reviewer #2 (Public Review):

Summary:

This manuscript shows evidence from a dataset with awake movie-watching in infants, that the infant brain contains areas with distinct functions, consistent with previous studies using resting state and awake task-based infant fMRI. However, substantial new analyses would be required to support the novel claim that movie-watching data in infants can be used to identify retinotopic areas or to capture within-area functional organization.

Strengths:

The authors have collected a unique dataset: the same individual infants both watched naturalistic animations and a specific retinotopy task. These data position the authors to test their novel claim, that movie-watching data in infants can be used to identify retinotopic areas.

Weaknesses:

To claim that movie-watching data can identify retinotopic regions, the authors should provide evidence for two claims:

- Retinotopic areas defined based only on movie-watching data, predict retinotopic responses in independent retinotopy-task-driven data.

- Defining retinotopic areas based on the infant's own movie-watching response is more accurate than alternative approaches that don't require any movie-watching data, like anatomical parcellations or shared response activation from independent groups of participants.

We thank the reviewer for their comments. Before addressing their suggestions, we wish to clarify that we do not claim that movie data can be used to identify retinotopic areas, but instead that movie data captures components of the within and between visual area organization as defined by retinotopic mapping. We recognize that this was not clear in our original manuscript and have clarified this point throughout, including in this section of the discussion:

“To be clear, we are not suggesting that movies work well enough to replace a retinotopy task when accurate maps are needed. For instance, even though ICA found components that were highly correlated with the spatial frequency map, we also selected some components that turned out to have lower correlations. Without knowing the ground truth from a retinotopy task, there would be no way to weed these out. Additionally, anatomical alignment (i.e., averaging the maps from other participants and anatomically aligning them to a held-out participant) resulted in maps that were highly similar to the ground truth. Indeed, we previously23 found that adult-defined visual areas were moderately similar to infants. While functional alignment with adults can outperform anatomical alignment methods in similar analyses27, here we find that functional alignment with infants is inferior to anatomical alignment. Thus, if the goal is to define visual areas in an infant that lacks task-based retinotopy, anatomical alignment of other participants’ retinotopic maps is superior to using movie-based analyses, at least as we tested it.” Pg. 21

In response to the reviewer’s suggestion, we compare the maps identified by SRM to the averaged, anatomically aligned maps from infants. We find that these maps are highly similar to the task-based ground truth and we describe this result in a new section:

“We performed an anatomical alignment analog of the functional alignment (SRM) approach. This analysis serves as a benchmark for predicting visual maps using taskbased data, rather than movie data, from other participants. For each infant participant, we aggregated all other infant or adult participants as a reference. The retinotopic maps from these reference participants were anatomically aligned to the standard surface template, and then averaged. These averages served as predictions of the maps in the test participant, akin to SRM, and were analyzed equivalently (i.e., correlating the gradients in the predicted map with the gradients in the task-based map). These correlations (Table S4) are significantly higher than for functional alignment (using infants to predict spatial frequency, anatomical alignment < functional alignment: ∆Fisher Z M=0.44, CI=[0.32–0.58], p<.001; using infants to predict meridians, anatomical alignment < functional alignment: ∆Fisher Z M=0.61, CI=[0.47–0.74], p<.001; using adults to predict spatial frequency, anatomical alignment < functional alignment: ∆Fisher Z

M=0.31, CI=[0.21–0.42], p<.001; using adults to predict meridians, anatomical alignment < functional alignment: ∆Fisher Z M=0.49, CI=[0.39–0.60], p<.001). This suggests that even if SRM shows that movies can be used to produce retinotopic maps that are significantly similar to a participant, these maps are not as good as those that can be produced by anatomical alignment of the maps from other participants without any movie data.” Pg. 16–17

Note that we do not compare the anatomically aligned maps with the ICA maps statistically. This is because these analyses are not comparable: ICA is run within-participant whereas anatomical alignment is necessarily between-participant — either infant or adults. Nonetheless, an interested reader can refer to the Table where we report the results of anatomical alignment and see that anatomical alignment outperforms ICA in terms of the correlation between the predicted and task-based maps.

Both of these analyses are possible, using the (valuable!) data that these authors have collected, but these are not the analyses that the authors have done so far. Instead, the authors report the inverse of (1): regions identified by the retinotopy task can be used to predict responses in the movies. The authors report one part of (2), shared responses from other participants can be used to predict individual infants' responses in the movies, but they do not test whether movie data from the same individual infant can be used to make better predictions of the retinotopy task data, than the shared response maps.

So to be clear, to support the claims of this paper, I recommend that the authors use the retinotopic task responses in each individual infant as the independent "Test" data, and compare the accuracy in predicting those responses, based on:

-  The same infant's movie-watching data, analysed with MELODIC, when blind experimenters select components for the SF and meridian boundaries with no access to the ground-truth retinotopy data.

-  Anatomical parcellations in the same infant.

-  Shared response maps from groups of other infants or adults.

-  (If possible, ICA of resting state data, in the same infant, or from independent groups of infants).

Or, possibly, combinations of these techniques.

If the infant's own movie-watching data leads to improved predictions of the infant's retinotopic task-driven response, relative to these existing alternatives that don't require movie-watching data from the same infant, then the authors' main claim will be supported.

These are excellent suggestions for additional analyses to test the suitability for moviebased maps to replace task-based maps. We hope it is now clear that it was never our intention to claim that movie-based data could replace task-based methods. We want to emphasize that the discoveries made in this paper — that movies evoke fine-grained organization in infant visual cortex — do not rely on movie-based maps being better than alternative methods for producing maps, such as the newly added anatomical alignment.

The proposed analysis above solves a critical problem with the analyses presented in the current manuscript: the data used to generate maps is identical to the data used to validate those maps. For the task-evoked maps, the same data are used to draw the lines along gradients and then test for gradient organization. For the component maps, the maps are manually selected to show the clearest gradients among many noisy options, and then the same data are tested for gradient organization. This is a double-dipping error. To fix this problem, the data must be split into independent train and test subsets.

We appreciate the reviewer’s concern; however, we believe it is a result of a miscommunication in our analytic strategy. We have now provided more details on the analyses to clarify how double-dipping was avoided. 

To summarize, a retinotopy task produced visual maps that were used to trace both area boundaries and gradients across the areas. These data were then fixed and unchanged, and we make no claims about the nature of these maps in this paper, other than to treat them as the ground truth to be used as a benchmark in our analyses. The movie data, which are collected independently from the same infant in the session, used the boundaries from the retinotopy task (in the case of homotopy) or were compared with the maps from the retinotopy task (in the case of ICA and SRM). In other words, the statement that “the data used to generate maps is identical to the data used to validate those maps” is incorrect because we generated the maps with a retinotopy task and validated the maps with the movie data. This means no double dipping occurred.

Perhaps a cause of the reviewer’s interpretation is that the gradients used in the analysis are not clearly described. We now provide this additional description:  “Using the same manually traced lines from the retinotopy task, we measured the intensity gradients in each component from the movie-watching data. We can then use the gradients of intensity in the retinotopy task-defined maps as a benchmark for comparison with the ICA-derived maps.” Pg. 10

Regarding the SRM analyses, we take great pains to avoid the possibility of data contamination. To emphasize how independent the SRM analysis is, the prediction of the retinotopic map from the test participant does not use their retinotopy data at all; in fact, the predicted maps could be made before that participant’s retinotopy data were ever collected. To make this prediction for a test participant, we need to learn the inversion of the SRM, but this only uses the movie data of the test participant. Hence, there is no double-dipping in the SRM analyses. We have elaborated on this point in the revision, and we remade the figure and its caption to clarify this point:

We also have updated the description of these results to emphasize how double-dipping was avoided:

“We then mapped the held-out participant's movie data into the learned shared space without changing the shared space (Figure 5c). In other words, the shared response model was learned and frozen before the held-out participant’s data was considered.

This approach has been used and validated in prior SRM studies45.” Pg. 14

The reviewer suggests that manually choosing components from ICA is double-dipping. Although the reviewer is correct that the manual selection of components in ICA means that the components chosen ought to be good candidates, we are testing whether those choices were good by evaluating those components against the task-based maps that were not used for the ICA. Our statistical analyses evaluate whether the components chosen were better than the components that would have been chosen by random chance. Critically: all decisions about selecting the components happen before the components are compared to the retinotopic maps. Hence there is no double-dipping in the selection of components, as the choice of candidate ICA maps is not informed by the ground-truth retinotopic maps. We now clarify what the goal of this process is in the results:

“Success in this process requires that 1) retinotopic organization accounts for sufficient variance in visual activity to be identified by ICA and 2) experimenters can accurately identify these components.” Pg. 10

The reviewer also alludes to a concern that the researcher selecting the maps was not blind to the ground-truth retinotopic maps from participants and this could have influenced the results. In such a scenario, the researcher could have selected components that have the gradients of activity in the places that the infant has as ground truth. The researcher who made the selection of components (CTE) is one of the researchers who originally traced the areas in the participants approximately a year prior to the identification of ICs. The researcher selecting the components didn’t use the ground-truth retinotopic maps as reference, nor did they pay attention to the participant IDs when sorting the IC components. Indeed, they weren’t trying to find participants-specific maps per se, but rather aimed to find good candidate retinotopic maps in general. In the case of the newly added adult analyses, the ICs were selected before the retinotopic mapping was reviewed or traced; hence, no knowledge about the participant-specific ground truth could have influenced the selection of ICs. Even with this process from adults, we find results of comparable strength as we found in infants, as shown in Figure S3. Nonetheless, there is a possibility that this researcher’s previous experience of tracing the infant maps could have influenced their choice of components at the participant-specific level. If so, it was a small effect since the components the researcher selected were far from the best possible options (i.e., rankings of the selected components averaged in the 64th percentile for spatial frequency maps and the 68th percentile for meridian maps). We believe all reasonable steps were taken to mitigate bias in the selection of ICs.

Reviewer #3 (Public Review):

The manuscript reports data collected in awake toddlers recording BOLD while watching videos. The authors analyse the BOLD time series using two different statistical approaches, both very complex but do not require any a priori determination of the movie features or contents to be associated with regressors. The two main messages are that 1) toddlers have occipital visual areas very similar to adults, given that an SRM model derived from adult BOLD is consistent with the infant brains as well; 2) the retinotopic organization and the spatial frequency selectivity of the occipital maps derived by applying correlation analysis are consistent with the maps obtained by standard and conventional mapping.

Clearly, the data are important, and the author has achieved important and original results. However, the manuscript is totally unclear and very difficult to follow; the figures are not informative; the reader needs to trust the authors because no data to verify the output of the statistical analysis are presented (localization maps with proper statistics) nor so any validation of the statistical analysis provided. Indeed what I think that manuscript means, or better what I understood, may be very far from what the authors want to present, given how obscure the methods and the result presentation are.

In the present form, this reviewer considers that the manuscript needs to be totally rewritten, the results presented each technique with appropriate validation or comparison that the reader can evaluate.

We are grateful to the reviewer for the chance to improve the paper. We have broken their review into three parts: clarification of the methods, validation of the analyses, and enhancing the visualization.

Clarification of the methods

We acknowledge that the methods we employed are complex and uncommon in many fields of neuroimaging. That said, numerous papers have conducted these analyses on adults (Beckman et al., 2005; Butt et al., 2015; Guntupalli et al., 2016; Haak & Beckman, 2018; Knapen, 2021; Lu et al., 2017) and non-human primates (Arcaro & Livingstone, 2017; Moeller et al., 2009). We have redoubled our efforts in the revision to make the methods as clear as possible, expanding on the original text and providing intuitions where possible. These changes have been added throughout and are too vast in number to repeat here, especially without context, but we hope that readers will have an easier time following the analyses now. 

Additionally, we updated Figures 3 and 5 in which the main ICA and SRM analyses are described. For instance, in Figure 3’s caption we now add details about how the gradient analyses were performed on the components: 

“We used the same lines that were manually traced on the task-evoked map to assess the change in the component’s response. We found a monotonic trend within area from medial to lateral, just like we see in the ground truth.” Pg. 11

Regarding Figure 5, we reconsidered the best way to explain the SRM analyses and decided it would be helpful to partition the diagram into steps, reflecting the analytic process. These updates have been added to Figure 5, and the caption has been updated accordingly.

We hope that these changes have improved the clarity of the methods. For readers interested in learning more, we encourage them to either read the methods-focused papers that debut the analyses (e.g., Chen et al., 2015), read the papers applying the methods (e.g., Guntupalli et al., 2016), or read the annotated code we publicly release which implements these pipelines and can be used to replicate the findings.

Validation of the analyses

One of the requests the reviewer makes is to validate our analyses. Our initial approach was to lean on papers that have used these methods in adults or primates (e.g., Arcaro,

& Livingstone, 2017; Beckman et al., 2005; Butt et al., 2015; Guntupalli et al., 2016; Haak & Beckman, 2018; Knapen, 2021; Moeller et al., 2009) where the underlying organization and neurophysiology is established. However, we have made changes to these methods that differ from their original usage (e.g., we used SRM rather than hyperalignment, we use meridian mapping rather than traveling wave retinotopy, we use movie-watching data rather than rest). Hence, the specifics of our design and pipeline warrant validation. 

To add further validation, we have rerun the main analyses on an adult sample. We collected 8 adult participants who completed the same retinotopy task and a large subset of the movies that infants saw. These participants were run under maximally similar conditions to infants (i.e., scanned using the same parameters and without the top of the head-coil) and were preprocessed using the same pipeline. Given that the relationship between adult visual maps and movie-driven (or resting-state) analyses has been shown in many studies (Beckman et al., 2005; Butt et al., 2015; Guntupalli et al., 2016; Haak & Beckman, 2018; Knapen, 2021; Lu et al., 2017), these adult data serve as a validation of our analysis pipeline. These adult participants were included in the original manuscript; however, they were previously only used to support the SRM analyses (i.e., can adults be used to predict infant visual maps). The adult results are described before any results with infants, as a way to engender confidence. Moreover, we have provided new supplementary figures of the adult results that we hope will be integrated with the article when viewing it online, such that it will be easy to compare infant and adult results, as per the reviewer’s request. 

As per the figures and captions below, the analyses were all successful with the adult participants: 1) Homotopic correlations are higher than correlations between comparable areas in other streams or areas that are more distant within stream. 2) A multidimensional scaling depiction of the data shows that areas in the dorsal and ventral stream are dissimilar. 3) Using independent components analysis on the movie data, we identified components that are highly correlated with the retinotopy task-based spatial frequency and meridian maps. 4) Using shared response modeling on the movie data, we predicted maps that are highly correlated with the retinotopy task-based spatial frequency and meridian maps.

These supplementary analyses are underpowered for between-group comparisons, so we do not statistically compare the results between infants and adults. Nonetheless, the pattern of adult results is comparable overall to the infant results. 

We believe these adult results provide a useful validation that the infant analyses we performed can recover fine-grained organization.

The reviewer raises an additional concern about the lack of visualization of the results. We recognize that the plots of the summary statistics do not provide information about the intermediate analyses. Indeed, we think the summary statistics can understate the degree of similarity between the components or predicted visual maps and the ground truth. Hence, we have added 6 new supplementary figures showing the intensity gradients for the following analyses: 1. spatial frequency prediction using ICA, 2. meridian prediction using ICA, 3. spatial frequency prediction using infant SRM, 4.

meridian prediction using infant SRM, 5. spatial frequency prediction using adult SRM, and 6. meridian prediction using adult SRM.

We hope that these visualizations are helpful. It is possible that the reviewer wishes us to also visually present the raw maps from the ICA and SRM, akin to what we show in Figure 3A and 3B. We believe this is out of scope of this paper: of the 1140 components that were identified by ICA, we selected 36 for spatial frequency and 17 for meridian maps. We also created 20 predicted maps for spatial frequency and 20 predicted meridian maps using SRM. This would result in the depiction of 93 subfigures, requiring at least 15 new full-page supplementary figures to display with adequate resolution. Instead, we encourage the reader to access this content themselves: we have made the code to recreate the analyses publicly available, as well as both the raw and preprocessed data for these analyses, including the data for each of these selected maps.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

(1) As mentioned in the public review, the authors should consider incorporating relevant adult fMRI research into the Introduction and explain the importance of testing this question in infants.

Our public response describes the several citations to relevant adult research we have added, and have provided further motivation for the project.

(2) The authors should conduct additional analyses to support their conclusion that movie data alone can generate accurate retinotopic maps (i.e., by comparing this approach to other available alternatives).

We have clarified in our public response that we did not wish to conclude that movie data alone can generate accurate retinotopic maps, and have made substantial edits to the text to emphasize this. Thus, because this claim is already not supported by our analyses, we do not think it is necessary to test it further.

(3) The authors should re-do the homotopy analyses using movie-defined ROIs (i.e., by splitting the movie-viewing data into independent folds for functional ROI definition and analyses).

As stated above, defining ROIs based on the movie content is not the intended goal of this project. Even if that were the general goal, we do not believe that it would be appropriate to run this specific analysis with the data we collected. Firstly, halving the data for ROI definition (e.g., using half the movie data to identify and trace areas, and then use those areas in the homotopy analysis to run on the other half of data) would qualitatively change the power of the analyses described here. Secondly, we would be unable to define areas beyond hV4/V3AB with confidence, since our retinotopic mapping only affords specification of early visual cortex. Thus we could not conduct the MDS analyses shown in Figure 2.

(4) If the authors agree that a primary contribution of this study and paper is to showcase what is possible to do with a limited amount of movie-viewing data, then they should make it clearer, sooner, how much usable movie data they have from infants. They could also consider conducting additional analyses to determine the minimum amount of fMRI data necessary to reveal the same detailed characteristics of functional responses in the visual cortex.

We agree it would be good to highlight the amount of movie data used. When the infant data is first introduced in the results section, we now state the durations:

“All available movies from each session were included (Table S2), with an average duration of 540.7s (range: 186--1116s).” Pg. 5

Additionally, we have added a homotopy analysis that describes the contribution of data quantity to the results observed. We compare the amount of data collected with the magnitude of same vs. different stream effect (Figure 1B) and within stream distance effect (Figure 1C). We find no effect of movie duration in the sample we tested, as reported below:

“We found no evidence that the variability in movie duration per participant correlated with this difference [of same stream vs. different stream] (r=0.08, p=.700).” Pg. 6-7

“There was no correlation between movie duration and the effect (Same > Adjacent: r=-

0.01, p=.965, Adjacent > Distal: r=-0.09, p=.740).” Pg. 7

(5) If any of the methodological approaches are novel, the authors should make this clear. In particular, has the approach of visually inspecting and categorizing components generated from ICA and movie data been done before, in adults/other contexts?

The methods we employed are similar to others, as described in the public review.

However, changes were necessary to apply them to infant samples. For instance, Guntupalli et al. (2016) used hyperalignment to predict the visual maps of adult participants, whereas we use SRM. SRM and hyperalignment have the same goal — find a maximally aligned representation between participants based on brain function — but their implementation is different. The application of functional alignment to infants is novel, as is their use in movie data that is relatively short by comparison to standard adult data. Indeed, this is the most thorough demonstration that SRM — or any functional alignment procedure — can be usefully applied to infant data, awake or sleeping. We have clarified this point in the discussion.

“This is initial evidence that functional alignment may be useful for enhancing signal quality, like it has in adults27,32,33, or revealing changing function over development45, which may prove especially useful for infant fMRI52.” Pg. 21

(6) The authors found that meridian maps were less identifiable from ICA and movie data and suggest that this may be because these maps are more susceptible to noise or gaze variability. If this is the case, you might predict that these maps are more identifiable in adult data. The authors could consider running additional analyses with their adult participants to better understand this result.

As described in the manuscript, we hypothesize that meridian maps are more difficult to identify than spatial frequency maps because meridian maps are a less smooth, more fine-grained map than spatial frequency. Indeed, it has previously been reported (Moeller et al., 2009) that similar procedures can result in meridian maps that are constituted by multiple independent components (e.g., a component sensitive to horizontal orientations, and a separate component sensitive to vertical components). Nonetheless, we have now conducted the ICA procedure on adult participants and again find it is easier to identify spatial frequency components compared to meridian maps, as reported in the public review.

Minor corrections:

(1) Typo: Figure 3 title: "Example retintopic task vs. ICA-based spatial frequency maps.".

Fixed

(2) Given the age range of the participants, consider using "infants and toddlers"? (Not to diminish the results at all; on the contrary, I think it is perhaps even more impressive to obtain awake fMRI data from ~1-2-year-olds). Example: Figure 3 legend: "A) Spatial frequency map of a 17.1-monthold infant.".

We agree with the reviewer that there is disagreement about the age range at which a child starts being considered a toddler. We have changed the terms in places where we refer to a toddler in particular (e.g., the figure caption the reviewer highlights) and added the phrase “infants and toddlers” in places where appropriate. Nonetheless, we have kept “infants” in some places, particularly those where we are comparing the sample to adults. Adding “and toddlers” could imply three samples being compared which would confuse the reader.

(3) Figure 6 legend: The following text should be omitted as there is no bar plot in this figure: "The bar plot is the average across participants. The error bar is the standard error across participants.".

Fixed

(4) Table S1 legend: Missing first single quote: Runs'.

Fixed

Reviewer #2 (Recommendations For The Authors):

I request that this paper cite more of the existing literature on the fMRI of human infants and toddlers using task-driven and resting-state data. For example, early studies by (first authors) Biagi, Dehaene-Lambertz, Cusack, and Fransson, and more recent studies by Chen, Cabral, Truzzi, Deen, and Kosakowski.

We have added several new citations of recent task-based and resting state studies to the second sentence of the main text:

“Despite the recent growth in infant fMRI1-6, one of the most important obstacles facing this research is that infants are unable to maintain focus for long periods of time and struggle to complete traditional cognitive tasks7.”

Reviewer #3 (Recommendations For The Authors):

In the following, I report some of my main perplexities, but many more may arise when the material is presented more clearly.

The age of the children varies from 5 months to about 2 years. While the developmental literature suggests that between 1 and 2 years children have a visual system nearly adult-like, below that age some areas may be very immature. I would split the sample and perhaps attempt to validate the adult SRM model with the youngest children (and those can be called infants).

We recognize the substantial age variability in our sample, which is why we report participant-specific data in our figures. While splitting up the data into age bins might reveal age effects, we do not think we can perform adequately powered null hypothesis testing of the age trend. In order to investigate the contribution of age, larger samples will be needed. That said, we can see from the data that we have reported that any effect of age is likely small. To elaborate: Figures 4 and 6 report the participant-specific data points and order the participants by age. There are no clear linear trends in these plots, thus there are no strong age effects.

More broadly, we do not think there is a principled way to divide the participants by age. The reviewer suggests that the visual system is immature before the first year of life and mature afterward; however, such claims are the exact motivation for the type of work we are doing here, and the verdict is still out. Indeed, the conclusion of our earlier work reporting retinotopy in infants (Ellis et al., 2021) suggests that the organization of the early visual cortex in infants as young as 5 months — the youngest infant in our sample — is surprisingly adult-like.

The title cannot refer to infants given the age span.

There is disagreement in the field about the age at which it is appropriate to refer to children as infants. In this paper, and in our prior work, we followed the practice of the most attended infant cognition conference and society, the International Congress of Infant Studies (ICIS), which considers infants as those aged between 0-3 years old, for the purposes of their conference. Indeed, we have never received this concern across dozens of prior reviews for previous papers covering a similar age range. That said, we understand the spirit of the reviewer’s comment and now refer to the sample as “infants and toddlers” and to older individuals in our sample as “toddlers” wherever it is appropriate (the younger individuals would fairly be considered “infants” under any definition).

Figure 1 is clear and an interesting approach. Please also show the average correlation maps on the cortical surface.

While we would like to create a figure as requested, we are unsure how to depict an area-by-area correlation map on the cortical surface. One option would be to generate a seed-based map in which we take an area and depict the correlation of that seed (e.g., vV1) with all other voxels. This approach would result in 8 maps for just the task-defined areas, and 17 maps for anatomically-defined areas. Hence, we believe this is out of scope of this paper, but an interested reader could easily generate these maps from the data we have released.

Figure 2 results are not easily interpretable. Ventral and dorsal V1-V3 areas represent upper or lower VF respectively. Higher dorsal and ventral areas represent both upper and lower VF, so we should predict an equal distance between the two streams. Again, how can we verify that it is not a result of some artifacts?

In adults, visual areas differ in their functional response properties along multiple dimensions, including spatial coding. The dorsal/ventral stream hypothesis is derived from the idea that areas in each stream support different functions, independent of spatial coding. The MDS analysis did not attempt to isolate the specific contribution of spatial representations of each area but instead tested the similarity of function that is evoked in naturalistic viewing. Other covariance-based analyses specifically isolate the contribution of spatial representations (Haak et al., 2013); however, they use a much more constrained analysis than what was implemented here. The fact that we find broad differentiation of dorsal and ventral visual areas in infants is consistent with adults (Haak & Beckman, 2018) and neonate non-human primates (Arcaro & Livingstone, 2017). 

Nonetheless, we recognize that we did not mention the differences in visual field properties across areas and what that means. If visual field properties alone drove the functional response then we would expect to see a clustering of areas based on the visual field they represent (e.g., hV4 and V3AB should have similar representations). Since we did not see that, and instead saw organization by visual stream, the result is interesting and thus warrants reporting. We now mention this difference in visual fields in the manuscript to highlight the surprising nature of the result.

“This separation between streams is striking when considering that it happens despite differences in visual field representations across areas: while dorsal V1 and ventral V1 represent the lower and upper visual field, respectively, V3A/B and hV4 both have full visual field maps. These visual field representations can be detected in adults41; however, they are often not the primary driver of function39. We see that in infants too: hV4 and V3A/B represent the same visual space yet have distinct functional profiles.” Pg. 8

The reviewer raises a concern that the MDS result may be spurious and caused by noise. Below, we present three reasons why we believe these results are not accounted for by artifacts but instead reflect real functional differentiation in the visual cortex. 

(1) Figure 2 is a visualization of the similarity matrix presented in Figure S1. In Figure S1, we report the significance testing we performed to confirm that the patterns differentiating dorsal and ventral streams — as well as adjacent areas from distal areas — are statistically reliable across participants. If an artifact accounted for the result then it would have to be a kind of systematic noise that is consistent across participants.

(2) One of the main sources of noise (both systematic and non-systematic) with infant fMRI is motion. Homotopy is a within-participant analysis that could be biased by motion. To assess whether motion accounts for the results, we took a conservative approach of regressing out the framewise motion (i.e., how much movement there is between fMRI volumes) from the comparisons of the functional activity in regions. Although the correlations numerically decreased with this procedure, they were qualitatively similar to the analysis that does not regress out motion:

“Additionally, if we control for motion in the correlation between areas --- in case motion transients drive consistent activity across areas --- then the effects described here are negligibly different (Figure S5).” Pg. 7

(3) We recognize that despite these analyses, it would be helpful to see what this pattern looks like in adults where we know more about the visual field properties and the function of dorsal and ventral streams. This has been done previously (e.g., Haak & Beckman, 2018), but we have now run those analyses on adults in our sample, as described in the public review. As with infants, there are reliable differences in the homotopy between streams (Figure S1). The MDS results show that the adult data was more complex than the infant data, since it was best described by 3 dimensions rather than 2. Nonetheless, there is a rotation of the MDS such that the structure of the ventral and dorsal streams is also dissociable. 

Figure 3 also raises several alternative interpretations. The spatial frequency component in B has strong activity ONLY at the extreme border of the VF and this is probably the origin of the strong correlation. I understand that it is only one subject, but this brings the need to show all subjects and to report the correlation. Also, it is important to show the putative average ICA for retinotopy and spatial frequencies across subjects and for adults. All methods should be validated on adults where we have clear data for retinotopy and spatial frequency.

The reviewer notes that the component in Figure 3 shows strong negative response in the periphery. It is often the case, as reported elsewhere (Moeller et al., 2009), that ICA extracts portions of visual maps. To make a full visual map would require combining components into a composite (e.g., a component that has a high response in the periphery and another component that has a high response in the fovea). If we were to claim that this component, or others like it, could replace the need for retinotopic mapping, then we would want to produce these composite maps; however, our conclusion in this project is that the topographic information of retinotopic maps manifest in individual components of ICA. For this purpose, the analysis we perform adequately assesses this topography.

Regarding the request to show the results for all subjects, we address this in the public response and repeat it here briefly: we have added 6 new figures to show results akin to Figure 3C and D. It is impractical to show the equivalent of Figure 3A and B for all participants, yet we do release the data necessary to see to visualize these maps easily.

Finally, the reviewer suggests that we validate the analyses on adult participants. As shown in Figure S3 and reported in the public response, we now run these analyses on adult participants and observe qualitatively similar results to infants.

How much was the variation in the presumed spatial frequency map? Is it consistent with the acuity range? 5-month-old infants should have an acuity of around 10c/deg, depending on the mean luminance of the scene.

The reviewer highlights an important weakness of conducting ICA: we cannot put units on the degree of variation we see in components. We now highlight this weakness in the discussion:

“Another limitation is that ICA does not provide a scale to the variation: although we find a correlation between gradients of spatial frequency in the ground truth and the selected component, we cannot use the component alone to infer the spatial frequency selectivity of any part of cortex. In other words, we cannot infer units of spatial frequency sensitivity from the components alone.” Pg. 20

Figure 5 pipeline is totally obscure. I presumed that I understood, but as it is it is useless. All methods should be clearly described, and the intermediate results should be illustrated in figures and appropriately discussed. Using such blind analyses in infants in principle may not be appropriate and this needs to be verified. Overall all these techniques rely on correlation activities that are all biased by head movement, eye movement, and probably the dummy sucking. All those movements need to be estimated and correlated with the variability of the results. It is a strong assumption that the techniques should work in infants, given the presence of movements.

We recognize that the SRM methods are complex. Given this feedback, we remade Figure 5 with explicit steps for the process and updated the caption (as reported in the public review).

Regarding the validation of these methods, we have added SRM analyses from adults and find comparable results. This means that using these methods on adults with comparable amounts of data as what we collected from infants can predict maps that are highly similar to the real maps. Even so, it is not a given that these methods are valid in infants. We present two considerations in this regard. 

First, as part of the SRM analyses reported in the manuscript, we show that control analyses are significantly worse than the real analyses (indicated by the lines on Figure 6). To clarify the control analysis: we break the mapping (i.e., flip the order of the data so that it is backwards) between the test participant and the training participants used to create the SRM. The fact that this control analysis is significantly worse indicates that SRM is learning meaningful representations that matter for retinotopy. 

Second, we believe that this paper is a validation of SRM for infants. Infant fMRI is a nascent field and SRM has the potential to increase the signal quality in this population. We hope that readers will see these analyses as a proof of concept that SRM can be used in their work with infants. We have stated this contribution in the paper now.

“Additionally, we wish to test whether methods for functional alignment can be used with infants. Functional alignment finds a mapping between participants using functional activity -- rather than anatomy -- and in adults can improve signal-to-noise, enhance across participant prediction, and enable unique analyses27,32-34.” Pg. 4

“This is initial evidence that functional alignment may be useful for enhancing signal quality, like it has in adults27,32,33, or revealing changing function over development45.” Pg. 21

Regarding the reviewer’s concern that motion may bias the results, we wish to emphasize the nature of the analyses being conducted here: we are using data from a group of participants to predict the neural responses in a held-out participant. For motion to explain consistency between participants, the motion would need to be timelocked across participants. Even if motion was time-locked during movie watching, motion will impair the formation of an adequate model that can contain retinotopic information. Thus, motion should only hurt the ability for a shared response to be found that can be used for predicting retinotopic maps. Hence, the results we observed are despite motion and other sources of noise.

What is M??? is it simply the mean value??? If not, how it is estimated?

M is an abbreviation for mean. We have now expanded the abbreviation the first time we use it.

Figure 6 should be integrated with map activity where the individual area correlation should be illustrated. Probably fitting SMR adult works well for early cortical areas, but not for more ventral and associative, and the correlation should be evaluated for the different masks.

With the addition of plots showing the gradients for each participant and each movie (Figures S10–S13) we hope we have addressed this concern. We additionally want to clarify that the regions we tested in the analysis in Figure 6 are only the early visual areas V1, V2, V3, V3A/B, and hV4. The adult validation analyses show that SRM works well for predicting the visual maps in these areas. Nonetheless, it is an interesting question for future research with more extensive retinotopic mapping in infants to see if SRM can predict maps beyond extrastriate cortex.

Occipital masks have never been described or shown.

The occipital mask is from the MNI probabilistic structural atlas (Mazziotta et al., 2001), as reported in the original version and is shared with the public data release. We have added the additional detail that the probabilistic atlas is thresholded at 0% in order to be liberally inclusive. 

“We used the occipital mask from the MNI structural atlas63 in standard space -- defined liberally to include any voxel with an above zero probability of being labelled as the occipital lobe -- and used the inverted transform to put it into native functional space.” Pg. 27–28

Methods lack the main explanation of the procedures and software description.

We hope that the additions we have made to address this reviewer’s concerns have provided better explanations for our procedures. Additionally, as part of the data and code release, we thoroughly explain all of the software needed to recreate the results we have observed here.

What a tragedy did Ted had done to her😮‍💨😥

The electric shock therapy had wiped off Esther's mania

Thats crazy

Reviewer #2 (Public review):

Summary:

This manuscript by Hosack and Arce-McShane examines the directional tuning of neurons in macaque primary motor (MIo) and somatosensory (SIo) cortex. The neural basis of tongue control is far less studied than, for example, forelimb movements, partly because the tongue's kinematics and kinetics are difficult to measure. A major technical advantage of this study is using biplanar video-radiography, processed with modern motion tracking analysis software, to track the movement of the tongue inside the oral cavity. Compared to prior work, the behaviors are more naturalistic behaviors (feeding and licking water from one of three spouts), although the animals were still head-fixed.

The study's main findings are that:

• A majority of neurons in MIo and a (somewhat smaller) percentage of SIo modulated their firing rates during tongue movements, with different modulations depending on the direction of movement (i.e., exhibited directional tuning). Examining the statistics of tuning across neurons, there was anisotropy (e.g., more neurons preferring anterior movement) and a lateral bias in which tongue direction neurons preferred that was consistent with the innervation patterns of tongue control muscles (although with some inconsistency between monkeys).

• Consistent with this encoding, tongue position could be decoded with moderate accuracy even from small ensembles of ~28 neurons.

• There were differences observed in the proportion and extent of directional tuning between the feeding and licking behaviors, with stronger tuning overall during licking. This potentially suggests behavioral context-dependent encoding.

• The authors then went one step further and used a bilateral nerve block to the sensory inputs (trigeminal nerve) from the tongue. This impaired the precision of tongue movements and resulted in an apparent reduction and change in neural tuning in Mio and SIo.

Strengths:

The data are difficult to obtain and appear to have been rigorously measured, and provide a valuable contribution to this under-explored subfield of sensorimotor neuroscience. The analyses adopt well-established methods, especially from the arm motor control literature, and represent a natural starting point for characterizing tongue 3D direction tuning.

Weaknesses:

There are alternative explanations for some of the interpretations, but those interpretations are described in a way that clearly distinguishes results from interpretations, and readers can make their own assessments. Some of these limitations are described in more detail below.

One weakness of the current study is that there is substantial variability in results between monkeys, and that only one session of data per monkey/condition is analyzed (8 sessions total). This raises the concern that the results could be idiosyncratic. The Methods mention that other datasets were collected, but not analyzed because the imaging pre-processing is very labor-intensive. While I recognize that time is precious, I do think in this case the manuscript would be substantially strengthened by showing that the results are similar on other sessions.

This study focuses on describing directional tuning using the preferred direction (PD) / cosine tuning model popularized by Georgopoulous and colleagues for understanding neural control of arm reaching in the 1980s. This is a reasonable starting point and a decent first-order description of neural tuning. However, the arm motor control field has moved far past that viewpoint, and in some ways, an over-fixation on static representational encoding models and PDs held that field back for many years. The manuscript benefits from drawing the readers' attention (perhaps in their Discussion) that PDs are a very simple starting point for characterizing how cortical activity relates to kinematics, but that there is likely much richer population-level dynamical structure and that a more mechanistic, control-focused analytical framework may be fruitful. A good review of this evolution in the arm field can be found in Vyas S, Golub MD, Sussillo D, Shenoy K. 2020. Computation Through Neural Population Dynamics. Annual Review of Neuroscience. 43(1):249-75

Can the authors explain (or at least speculate) why there was such a large difference in behavioral effect due to nerve block between the two monkeys (Figure 7)?

Do the analyses showing a decrease in tuning after nerve block take into account the changes (and sometimes reduction in variability) of the kinematics between these conditions? In other words, if you subsampled trials to have similar distributions of kinematics between Control and Block conditions, does the effect hold true? The extreme scenario to illustrate my concern is that if Block conditions resulted in all identical movements (which of course they don't), the tuning analysis would find no tuned neurons. The lack of change in decoding accuracy is another yellow flag that there may be a methodological explanation for the decreased tuning result.

The manuscript states that "Our results suggest that the somatosensory cortex may be less involved than the motor areas during feeding, possibly because it is a more ingrained and stereotyped behavior as opposed to tongue protrusion or drinking tasks". Could an alternative explanation be more statistical/technical in nature: that during feeding, there will be more variability in exactly what somatosensation afferent signals are being received from trial to trial (because slight differences in kinematics can have large differences in exactly where the tongue is and the where/when/how of what parts of it are touching other parts of the oral cavity)? This variability could "smear out" the apparent tuning using these types of trial-averaged analyses. Given how important proprioception and somatosensation are for not biting the tongue or choking, the speculation that somatosensory cortical activity is suppressed during feedback is very counter-intuitive to this reviewer.

for - incredible plasticity of life - Michael Levin

Signifying the emptiness, the indifference and hopelessness of her situation. And yet there is still light, still a hope that can be salvaged -- hence the title being Desperation

Post-modernism, shows that everything has been passed through her head and her perspective and that none of this is anything but a story. It is no truth, only a story, that she is constantly reflecting upon and rebuilding, just as the voice memos have been rearranged to make the most sense, to create meaning.

Very creepy for our society now

Odd metaphors being used here to comment on his lover's physical shortcomings. I find the language of "If __ be __" strange in this context

blood comes arms and the head and put blood to the top of the heart blood comes from legs and goes to the bottom of the heart vein = toward Artery = away both start with letter A Tricuspid valve (three flaps) mitral valve- make sure there is foward flow between the ventricles

Contributing to the formation of DWB as a response.

Countless funerals. The purpose of describing the women is to set the scene of a funeral and how she would prepare for it. You can hear this stanza. From the strings to the bells and voices. You hear the suffering and the lamentations of those that have lost people they love.

When I pictured this in my head I imagined someone on their way to meet death or their creator but they don't even know it yet. They still believe they are alive but when they look at the "white road," which could be a symbolism for the pathway to heaven, they see someone they know but they don't seem as familiar anymore as they are in a different place.

So like shape rotating?

I wonder what problem being Scitzo solves, or if it is a specific type of problem solving taken too far by the brain?

A mind has to judge what is worth keeping in memory

I remember that Adam Savage Quote, I got all the lyrics of every ___ type of sone stuck in my head competing with useful spy knowledge or something

I can envision this in my head. A dog under a table waiting for food and a little girl giving it to the dog.

When I read this line I got an image in my head. I can see that the nurse was really trying to help her and be there for her. I also see that the nurse was like a mother figure or just a great figure. That's what I got from digging deeper and deeper.

I feel like this shows that Rowlandson did acclimate to captivity after a while.

sass.

She's getting more bold and snarky with her responses.

She is being greedy and searching for more than she was given (+ more than what she stole from that poor child); ungrateful. I think she also forgets that she is a representation of her master, she can't just do whatever she wants.

I fear her abrasive attitude will have negative consequences for all the English currently in captivity

Ironic how she feels that way. They've held her captive for (at least) over a month now; if they were actually trying to starve her to death by now, she would be dead.

Well she won't starve to death stealing from children :) /sarcastic

Crazy thing to say to your captive, isn't she scared?

She was fed why is she talking about the native starving them

It's very interesting how so much is shared although we live completely different lives in different counties, even though the idea of skaters wearing "baggy pants and crop tops" is so familiar.

This is funny because as a kid i felt i had a whole map of where i lived in my head but in reality there was so many places i never knew about and had never been so i can relate to these kids having traced their own city map.

This stand out to me or more gave me a vision on maybe how the early 2000s were just because this is what i think they we're doing back in the early 2000s.

Author response:

The following is the authors’ response to the original reviews.

Reviewer #1 (Public Review):

Summary:

The study made fundamental findings in investigations of the dynamic functional states during sleep. Twenty-one HMM states were revealed from the fMRI data, surpassing the number of EEG-defined sleep stages, which can define sub-states of N2 and REM. Importantly, these findings were reproducible over two nights, shedding new light on the dynamics of brain function during sleep.

Strengths:

The study provides the most compelling evidence on the sub-states of both REM and N2 sleep. Moreover, they showed these findings on dynamics states and their transitions were reproducible over two nights of sleep. These novel findings offered unique information in the field of sleep neuroimaging.

Weaknesses:

The only weakness of this study has been acknowledged by the authors: limited sample size.

We thank the reviewer for the overall enthusiasm for this study.

Reviewer #1 (Recommendations For The Authors):

(1) Were there differences in the extent of head motion during sleep among sleep stages? How was the potential motion parameter differences handled during the statistical analyses?

If there were large head motions that continued for a long time (e.g., longer than 1 minute), how did the authors deal with that scanning session? For an extremely long scanning session (3 hours), how was motion correction conducted? It would be great if the authors could provide more details.

We found that N3 sleep stage had lowest head motion, followed by REM, N2, N1, and lastly Wake. In other words, participants have lower head motion during sleep than during Wakefulness. We added this information to the Supplemental Results, copied below.

We performed standardized motion correction during preprocessing using AFNI regardless of the duration of the scans. We did not include motion parameters in the HMM model. Time frames with Excessive head motion (any of 6 head motion parameters exceeding 0.3 mm or degree) was censored. Previous analysis of the same data indicated that motion during extended sleep scans is comparable to the motion observed in shorter resting-state scans (Moehlman et al., 2019).

In Supplemental Results, “Motion parameters with sleep stages.

Averaged motion across six motion parameters decreased from wake to light sleep to deep sleep at night 2. For example, mean (standard deviation) motion for each sleep stage is as follows, N1: 0.043 (0.37); N2: 0.039 (0.033); N3: 0.035 (0.031); REM: 0.035 (0.032); Wake: 0.057 (0.052).

Similarly, the percentage of timepoints retained after censoring decreased from wake to light sleep to deep sleep at night 2. N1: 91%; N2: 93%; N3: 96%; REM: 89%; Wake 90%.”

In the method section, “Previous analysis of the same data indicated that motion during extended sleep scans is comparable to the motion observed in shorter resting-state scans (Moehlman et al., 2019). We also found that motion is lower during deep sleep compared to wake, see Supplemental Results.”

(2) It is possible that the data input for the HMM analyses might vary among participants and between the two nights, how did the authors deal with this issue during statistical analyses?

This is a great question. We standardized BOLD timecourses for each participant and each night to avoid differences among participants and between two nights. We revised the description in the method section to make this point clear.

In the method section, “To prepare the data for analysis, we first standardized the participant-specific sets of 300 ROI timecourses (scaled to a mean of 0, and a standard deviation of 1), which were then concatenated across all participants. This standardization was performed separately for each night. ”

(3) Figures 2 and 4, the top part seems to be missing, e.g., "Night 2" in Figure 2, and "N2-related" in Figure 4.

Thank you for pointing out these errors. We fixed them.

(4) Figure 3 seems to be more stretched vertically than horizontally.

We revised the figure to ensure it appears balanced on both sides.

Reviewer #2 (Public Review):

Summary:

Yang and colleagues used a Hidden Markov Model (HMM) on whole-night fMRI to isolate sleep and wake brain states in a data-driven fashion. They identify more brain states (21) than the five sleep/wake stages described in conventional PSG-based sleep staging, show that the identified brain states are stable across nights, and characterize the brain states in terms of which networks they primarily engage.

Strengths:

This work's primary strengths are its dataset of two nights of whole-night concurrent EEG-fMRI (including REM sleep), and its sound methodology.

Weaknesses:

The study's weaknesses are its small sample size and the limited attempts at relating the identified fMRI brain states back to EEG.

We thank the reviewer for the positive feedback and helpful suggestions for this study.

General appraisal:

The paper's conclusions are generally well-supported, but some additional analyses and discussions could improve the work.

The authors' main focus lies in identifying fMRI-based brain states, and they succeed at demonstrating both the presence and robustness of these states in terms of cross-night stability. Additional characterization of brain states in terms of which networks these brain states primarily engage adds additional insights.

A somewhat missed opportunity is the absence of more analyses relating the HMM states back to EEG. It would be very helpful to the sleep field to see how EEG spectra of, say, different N2-related HMM states compare. Similarly, it is presently unclear whether anything noticeable happens within the EEG time course at the moment of an HMM class switch (particularly when the PSG stage remains stable). While the authors did look at slow wave density and various physiological signals in different HMM states, a characterization of the EEG itself in terms of spectral features is missing. Such analyses might have shown that fMRI-based brain states map onto familiar EEG substates, or reveal novel EEG changes that have so far gone unnoticed.

We thank the reviewer for this great suggestion. We performed EEG spectral analysis on each HMM state. Results were added to Suppementary Results and Supplementary Figure 10 and 11 (Copied below). Specifically, we confirmed that N3-related states had highest Delta power and that the Deep-N2 module showed different spectral profiles compared to Light-N2 module.

In Supplemental Results: “We conducted spectral analysis for each TR and calculated the average power spectrum for each common EEG brainwave—Delta (0.5-4 Hz), Theta (4-8 Hz), Alpha (8-13 Hz), Beta (13-30 Hz), and Gamma (30-100 Hz)—across the 21 HMM states. See Supplementary Figure 10 and 11 for night 2 and night 1 data, respectively. As expected, we found that N3-related states 8 and 10 had highest Delta power in both nights. In addition, the Deep-N2 module had higher power in Theta and Alpha bands compared to the Light-N2 module.”

It is unclear how the presently identified HMM brain states relate to the previously identified NREM and wake states by Stevner et al. (2019), who used a roughly similar approach. This is important, as similar brain states across studies would suggest reproducibility, whereas large discrepancies could indicate a large dependence on particular methods and/or the sample (also see later point regarding generalizability).

This is a great question. There are some similarities and differences between the current study and Stevner et al. (2019). We discussed this in the Supplementary Discussion. Copied below.

In the Supplementary Discussion: “Both studies demonstrated that HMM states can be effectively divided into meaningful modules solely based on transition probabilities. Furthermore, both studies indicated that pre-sleep wakefulness differs from post-sleep wakefulness.

However, despite the similar approaches used, key differences in data acquisition and analysis make it challenging to directly compare HMM states between these two studies. Firstly, Stevner et al. (2019) collected only 1-hour-long sleep data from 18 participants, whereas our current study includes 8-hour-long sleep data from 12 participants for two consecutive nights. As discussed in the main text, full sleep cycling cannot be obtained from 1-hour long sleep due to the lack of REM stage and incomplete sleep cycles. Secondly, in Stevner et al. (2019) (Figure 4e), the four wake-NREM stages had roughly the same duration. In contrast, in our current study (Night 2, Figure 2A), the N2 stage comprises 43% of total sleep, which aligns with the natural N2 composition of nocturnal sleep stages. This discrepancy might explain the different number of N2-related states found in the two studies, with 3 out of 19 in Stevner et al. (2019) versus 13 out of 21 in our current study.”

More justice could be done to previous EEG-based efforts moving beyond conventional AASM-defined sleep/wake states. Various EEG studies performed data-driven clustering of brain states, typically indicating more than 5 traditional brain states (e.g., Koch et al. 2014, Christensen et al. 2019, Decat. et al 2022). Beyond that, countless subdivisions of classical sleep stages have been proposed (e.g., phasic/tonic REM, N2 with/without spindles, N3 with global/local slow waves, cyclic alternating patterns, and many more). While these aren't incorporated into standard sleep stage classification, the current manuscript could be misinterpreted to suggest that improved/data-driven classifications cannot be achieved from EEG, which is incorrect.

We agree with the reviewer that previous EEG-based efforts should be mentioned. We now added this in the manuscript. Copied below.

In the Discussion section, “Third, we chose to not include EEG features in our data-driven model. However, the current method is not limited to fMRI data and can be applied to EEG data. Given that previous data-driven studies based on EEG data have suggested that there might be more than five traditional sleep stages (Christensen et al., 2019; Decat et al., 2022; Koch et al., 2014), as well as subdivisions within these traditional sleep stages (Brandenberger et al., 2005; Decat et al., 2022; Simor et al., 2020), future studies may apply data-driven models on both fMRI and EEG data. ”

More discussion of the limitations of the current sample and generalizability would be helpful. A sample of N=12 is no doubt impressive for two nights of concurrent whole-night EEG-fMRI. Still, any data-driven approach can only capture the brain states that are present in the sample, and 12 individuals are unlikely to express all brain states present in the population of young healthy individuals. Add to that all the potentially different or altered brain states that come with healthy ageing, other demographic variables, and numerous clinical disorders. How do the authors expect their results to change with larger samples and/or varying these factors? Perhaps most importantly, I think it's important to mention that the particular number of identified brain states (here 21, and e.g. 19 in Stevner) is not set in stone and will likely vary as a function of many sample- and methods-related factors.

We thank the reviewer for the great suggestions. We now included these points when discussing limitations in the Discussion section. We think that a HMM model with larger sample size might produce more fine-grained results, but this remains to be investigated when a more extensive dataset becomes available.

In the Discussion section, “Secondly, while our study involved a relatively small number of participants (12), it included a large amount of fMRI data (~16 hours scan) per participant. Although the HMM trained on data from 12 participants was robust, the generalizability of the current results to different populations—such as healthy aging individuals and clinical populations—needs to be demonstrated in future studies, particularly with larger sample sizes and more diverse populations.”

“Fourth, while we selected 21 HMM brain sleep states based on model evaluation parameters in the current study, the exact number of sleep states is not fixed and likely depends on various sample- and methods-related factors, such as sample size and model setups.”

Retrieved from :https://hyp.is/go?url=https%3A%2F%2Fcanadaehx.com%2F2022%2F04%2F23%2Fthe-richard-riot%2F&group=world

Retrieved from :https://hyp.is/go?url=https%3A%2F%2Fcanadaehx.com%2F2022%2F04%2F23%2Fthe-richard-riot%2F&group=world

Reviewer #1 (Public Review):

Summary:

The authors aimed to develop and validate an automated, deep learning-based system for scoring the Rey-Osterrieth Complex Figure Test (ROCF), a widely used tool in neuropsychology for assessing memory deficits. Their goal was to overcome the limitations of manual scoring, such as subjectivity and time consumption, by creating a model that provides automatic, accurate, objective, and efficient assessments of memory deterioration in individuals with various neurological and psychiatric conditions.

Strengths:

Comprehensive Data Collection: The authors collected over 20,000 hand-drawn ROCF images from a wide demographic and geographic range, ensuring a robust and diverse dataset. This extensive data collection is critical for training a generalizable and effective deep learning model.

Advanced Deep Learning Approach: Utilizing a multi-head convolutional neural network to automate ROCF scoring represents a sophisticated application of current AI technologies. This approach allows for detailed analysis of individual figure elements, potentially increasing the accuracy and reliability of assessments.

Validation and Performance Assessment: The model's performance was rigorously evaluated against crowdsourced human intelligence and professional clinician scores, demonstrating its ability to outperform both groups. The inclusion of an independent prospective validation study further strengthens the credibility of the results.

Robustness Analysis Efficacy: The model underwent a thorough robustness analysis, testing its adaptability to variations in rotation, perspective, brightness, and contrast. Such meticulous examination ensures the model's consistent performance across different clinical imaging scenarios, significantly bolstering its utility for real-world applications.

Appraisal and discussion:

By leveraging a comprehensive dataset and employing advanced deep learning techniques, they demonstrated the model's ability to outperform both crowdsourced raters and professional clinicians in scoring the ROCF. This achievement represents a significant step forward in automating neuropsychological assessments, potentially revolutionizing how memory deficits are evaluated in clinical settings. Furthermore, the application of deep learning to clinical neuropsychology opens avenues for future research, including the potential automation of other neuropsychological tests and the integration of AI tools into clinical practice. The success of this project may encourage further exploration into how AI can be leveraged to improve diagnostic accuracy and efficiency in healthcare.

However, the critique regarding the lack of detailed analysis across different patient demographics, the inadequacy of network explainability, and concerns about the selection of median crowdsourced scores as ground truth raises questions about the completeness of their objectives. These aspects suggest that while the aims were achieved to a considerable extent, there are areas of improvement that could make the results more robust and the conclusions stronger.

Comments on revised version:

I appreciate the opportunity to review this revised submission. Having considered the other reviews, I believe this study presents an important advance in using AI methods for clinical applications, which is both innovative and has implications beyond a single subfield.

The authors have developed a system using fundamental AI that appears sufficient for clinical use in scoring the Rey-Osterrieth Complex Figure (ROCF) test. In human neuropsychology, tests that generate scores like this are a key part of assessing patients. The evidence supporting the validity of the AI scoring system is compelling. This represents a valuable step towards evaluating more complex neurobehavioral functions.

However, one area where the study could be strengthened is in the explainability of the AI methods used. To ensure the scores are fully transparent and consistent for clinical use, it will be important for future work to test the robustness of the approach, potentially by comparing multiple methods. Examining other latent variables that can explain patients' cognitive functioning would also be informative.

In summary, I believe this study provides an important proof-of-concept with compelling evidence, while also highlighting key areas for further development as this technology moves towards real-world clinical applications.

Author response:

The following is the authors’ response to the original reviews.

Reviewer #1:

Comment #1: Insufficient Network Analysis for Explainability: The paper does not sufficiently delve into network analysis to determine whether the model's predictions are based on accurately identifying and matching the 18 items of the ROCF or if they rely on global, item-irrelevant features. This gap in analysis limits our understanding of the model's decision-making process and its clinical relevance.

Response #1: Thank you for your comment. We acknowledge the importance of understanding the decision-making process of AI models is crucial for their acceptance and utility in clinical settings. However, we believe that our current approach, which focuses on providing individual scores for each of the 18 items of the Rey-Osterrieth Complex Figure (ROCF), inherently offers a higher level of explainability and practical utility for clinicians than a network analysis could. Our multi-head convolutional neural network is designed with a dedicated output head for each of the 18 items in the ROCF, and thus provides separate scores for each of the 18 items in the ROCF. This architecture helps that the model focuses on individual elements rather than relying on global, item-irrelevant features.

This item-specific approach directly aligns with the traditional clinical assessment method, thereby making the results more interpretable and actionable for clinicians. The individual scores for each item provide detailed insights into a patient's performance. Clinicians can use these scores to identify specific areas of strength and weakness in a patient's visuospatial memory and drawing abilities.

Furthermore, we evaluated the model's performance on each of the 18 items separately, providing detailed metrics that show consistent accuracy across all items. This item-level performance analysis offers clear evidence that the model is not relying on irrelevant global features but is indeed making decisions based on the specific characteristics of each item. We believe that our approach provides a level of explainability that is directly useful and relevant to clinical practitioners.

Comment #2: Generative Model Consideration: The critique suggests exploring generative models to model the joint distribution of images and scores, which could offer deeper insights into the relationship between scores and specific visual-spatial disabilities. The absence of this consideration in the study is seen as a missed opportunity to enhance the model's explainability and clinical utility.

Response #2: Thank you for your thoughtful comment and the suggestion to explore generative models. We appreciate the potential benefits that generative models to model the joint distribution of images and scores. However, we chose not to pursue this approach in our study for several reasons: First, our primary goal was to develop a model that provides accurate and interpretable scores for each of the 18 individual items in the ROCF figure. Second, generative models, while powerful, would add a layer of complexity that might diminish the clarity and immediate clinical applicability of our results. Generative models, (particularly deep learning-based) can be challenging to interpret in terms of how they make decisions or why they produce specific outputs. This lack can be a concern in critical applications involving neurological and psychiatric disorders. Clinicians require tools that provide clear insights without the need for additional layers of analysis. Our current model provides detailed, item-specific scores that clinicians can directly use to assess visuospatial memory and drawing abilities. Initially, we explored using generative models (i.e. GANs) for data augmentation to address the scarcity of low-score images compared to high-score images. Moreover, for the low-score images, the same score can be achieved by numerous combinations of figure elements. However, due to our extensive available dataset, we did not observe any substantial performance improvements in our model. Nevertheless, future studies could explore generative models, such as Variational Autoencoders (VAEs) or Bayesian Networks, and test them on the data from the current prospective study to compare their performance with our results.

In the revised manuscript, we have included additional sentences discussing the potential use of generative models and their implications for future research.

“The data augmentation did not include generative models. Initially, we explored using generative models, specifically GANs, for data augmentation to address the scarcity of low-score images compared to high-score images. However, due to the extensive available dataset, we did not observe any substantial performance improvements in our model. Nevertheless, Future studies could explore generative models, such as Variational Autoencoders (VAEs) or Bayesian Networks, which can then be tested on the data from the current prospective study and compared with our results.”

Comment #3: Lack of Detailed Model Performance Analysis Across Subject Conditions: The study does not provide a detailed analysis of the model's performance across different ages, health conditions, etc. This omission raises questions about the model's applicability to diverse patient populations and whether separate models are needed for different subject types.

Response #3: Thank you for your this important comment. Although the initial version of our manuscript already provided detailed “item-specific” and “across total scores” performance metrics, we recognize the importance of including detailed analyses across different patient demographics to enhance the robustness and applicability of our findings. In response to your comment, we have conducted additional analyses that provide a comprehensive evaluation of model performance across various patient demographics, such as age groups, gender, and different neurological and psychiatric conditions. This additional analysis demonstrates the generalizability and reliability of our model across diverse populations. We have included these analyses in the revised manuscript.

“In addition, we have conducted a comprehensive model performance analysis to evaluate our model's performance across different ROCF conditions (copy and recall), demographics (age, gender), and clinical statuses (healthy individuals and patients) (Figure 4A). These results have been confirmed in the prospective validation study (Supplementary Figure S6). Furthermore, we included an additional analysis focusing on specific diagnoses to assess the model's performance in diverse patient populations (Figure 4B). Our findings demonstrate that the model maintains high accuracy and generalizes well across various demographics and clinical conditions.”

Comment #4: Data Augmentation: While the data augmentation procedure is noted as clever, it does not fully encompass all affine transformations, potentially limiting the model's robustness.

Response #4: We appreciate your feedback on our data augmentation strategy. We acknowledge that while our current approach significantly improves robustness against certain semantic transformations, it may not fully cover all possible affine transformations.

Here, we provide further clarification and justification for our chosen methods and their impact on the model's performance: In our study, we implemented a data augmentation pipeline to enhance the robustness of our model against common and realisitc geometric and semantic-preserving transformations. This pipeline included rotations, perspective changes, and Gaussian blur, which we found to be particularly effective in improving the model's resilience to variations in input data. These transformations are particularly relevant for the present application since users in real-life are likely to take pictures of drawings that might be slightly rotated or with a slightly tilted perspective. With these intuitions in mind, we randomly transformed drawings during training. Each transformation was a combination of Gaussian blur, a random perspective change, and a rotation with angles chosen randomly between -10° and 10°. These transformations are representative of realistic scenarios where images might be slightly tilted or photographed from different angles. We intentionally did not explicitly address all affine transformations, such as shearing or more complex geometric transformations because these transformations could alter the score of individual items of the ROCF and would be disruptive to the model.

As noted in our manuscript and demonstrated in supplementary Figure S1, the data augmentation pipeline significantly improved the model's robustness against rotations and changes in perspective. Importantly, our tablet-based scoring application can further ensure that the photos taken do not exhibit excessive semantic transformations. By leveraging the gyroscope built into the tablet, the application can help users align the images properly, minimizing issues such as excessive rotation or skew. This built-in functionality helps maintain the quality and consistency of the images, reducing the likelihood of significant semantic transformations that could affect model performance.

Comment #5: Additionally, the rationale for using median crowdsourced scores as ground truth, despite evidence of potential bias compared to clinician scores, is not adequately justified.

Response #5: Thank you for this valuable comment. Clarifying the rationale behind using the median score of crowdsourcing as the ground truth is indeed important. To reach high accuracy in predicting individual sample scores of the ROCFs, it is imperative that the scores of the training set are based on a systematic scheme with as little human bias as possible influencing the score. However, our analysis (see results section) and previous work (Canham et al., 2000) suggested that the scoring conducted by clinicians may not be consistent, because the clinicians may be unwittingly influenced by the interaction with the patient/participant or by the clinicians factor (e.g. motivation and fatigue). For this reason and the incomplete availability of clinician scores for all figures (i.e. for 19% of the 20’225 figures), we did not use the clinicians scores as ground truth scores. Instead, we have trained a large pool (5000 workers) of human internet workers (crowdsourcing) to score ROCFs drawings guided by our self-developed interactive web application. Each element of the figure was scored by several human workers (13 workers on average per figure). We have obtained the ground truth for each drawing by computing the median for each item in the figure, and then summed up the medians to get the total score for the drawing in question. To further ensure high-quality data annotation, we identified and excluded crowdsourcing participants that have a high level of disagreement (>20% disagreement) with this rating from trained clinicians, who carefully scored manually a subset of the data in the same interactive web application.

We chose the median score for several reasons: (1) the median score is less influenced by outliers compared to the mean. Given the variability of scoring between different clinicians and human workers (see human MSE and clinician MSE), using the median ensures that the ground truth is not skewed by extreme values, leading to more stable and reliable scores. (2) Crowdsource data do not always follow a normal distribution. In cases where the distribution is skewed or not symmetric, the median can be a more representative measure of the center. (3) The original scoring system involves ordinal scales (0,0.5,1,2). For ordinal scales, the median is often more appropriate than the mean. Finally, by aggregating multiple scores from a large pool of crowdsourced raters, the median provides a consensus that reflects the most common assessment. This approach mitigates the variability introduced by individual rater biases and ensures a more consistent ground truth. In clinical settings, the consensus of multiple expert opinions often serves as the benchmark for assessments. The use of median scores mirrors this practice, providing a ground truth that is representative of collective human judgment.

Canham, R. O., S. L. Smith, and A. M. Tyrrell. 2000. “Automated Scoring of a Neuropsychological Test:

The Rey Osterrieth Complex Figure.” Proceedings of the 26th Euromicro Conference. EUROMICRO 2000. Informatics: Inventing the Future. https://doi.org/10.1109/eurmic.2000.874519.

Reviewer #2:

Comment #1: There is no detail on how the final scoring app can be accessed and whether it is medical device-regulated.

Response #1: We appreciate the opportunity to provide more information about the current status and plans for our scoring application. At this stage, the final scoring app is not publicly accessible as it is currently undergoing rigorous beta testing with a select group of clinicians in real-world settings. The feedback from these clinicians is instrumental in refining the app’s features, interface, and overall functionality to improve its usability and user experience. This ensures that the app meets the high standards required for clinical tools. Following the successful completion of the beta testing phase, we aim to seek FDA approval for the scoring app. Achieving this regulatory milestone will guarantee that the app meets the stringent requirements for medical devices, providing an additional layer of confidence in its safety and efficacy for clinical use. Once FDA approval is obtained, we plan to make the app publicly accessible to clinicians and healthcare institutions worldwide. Detailed instructions on how to access and use the app will be provided at that time on our website (https://www.psychology.uzh.ch/en/areas/nec/plafor/research/rfp.html).

Comment #2: No discussion on the difference in sample sizes between the pre-registration of the prospective study and the results (e.g., aimed for 500 neurological patients but reported data from 288). Demographics for the assessment of the representation of healthy and non-healthy participants were not present.

Response #2: Thank you for your comment. We believe there might have been a misunderstanding regarding our preregistration details. In the preregistration, we planned to prospectively acquire ROCF drawings from 1000 healthy subjects. Each subject should have drawn two ROCF drawings (copy and memory condition). Consequently, 2000 samples should have been collected. In addition, in our pre-registration plan, we aimed to collect 500 drawings from patients (i.e. 250 patients), not 500 patients as the reviewer suggested (https://osf.io/82796). Thus in total, the goal was to obtain 2500 ROCF figures. The final prospective data set, which contained 2498 ROCF images from 961 healthy adults and 288 patients very closely matches the sample size, we aimed for in the the pre-registration. We do not see a necessity to discuss this negligible discrepancy in the main manuscript. The prospective data set remains substantial and sufficient to test our model on the independent prospective data set. Importantly, we want to highlight that the test set in the retrospective data set (4045 figures) was also never seen by the model. Both the retrospective and prospective data sets demonstrate substantial global diversity as the data has been collected in 90 different countries. Please note, that Supplementary Figures S2 & S3 provide detailed demographics of the participants in the prospectively collected data, present their performance in the copy and (immediate) recall condition across the lifespan, and the worldwide distribution of the origin of the data.

Comment #3: Supplementary Figure S1 & S4 is poor quality, please increase resolution.

Response #3: We apologize for the poor quality of Supplementary Figures S1 and S4 in the initial submission. In the revised version of our submission, we have increased the resolution of both Supplementary Figure S1 and Supplementary Figure S4 to ensure that all details are clearly visible and the figures are of high quality.

Comment #4: Regarding medical device regulation; if the app is to be used in clinical practice (as it generates a score and classification of performance), I believe such regulation is necessary - but there are ways around it. This should be detailed.

Response #4: We agree that regulation is essential for any application intended for use in clinical practice, particularly one that generates scores and classifications of performance. As discussed in response #1, the final scoring application is currently undergoing intensive beta testing in real-world settings with a limited group of clinicians and is therefore not publicly accessible at this time. We are fully committed to obtaining the necessary regulatory approvals before the app is made publicly accessible for clinical use. Once the beta testing phase is complete and the app has been refined based on clinician feedback, we will prepare and submit a comprehensive regulatory dossier. This submission will include all necessary data on the app's development, testing, validation, and clinical utility. We are adhering to relevant regulatory standards and guidelines, such as ISO 13485 for medical devices and the FDA's guidance on software as a medical device (SaMD).

Comment #7: Need to clarify that work was already done and pre-printed in 2022 for the main part of this study, and that this paper contributes to an additional prospective study.

Response #7: We would like to clarify that the pre-print the reviewer is referring to is indeed the current paper submitted to ELife. The submitted paper includes both the work that was pre-printed in 2022 and the additional prospective study, as you correctly identified.

Reviewer #3:

Comment #1: The considerable effort and cost to make the model only for an existing neuropsychological test.

Response #1: We acknowledge that significant effort and resources were dedicated to developing our model for the Rey-Osterrieth Complex Figure (ROCF) test. Below, we provide a detailed rationale for this investment and the broader implications of our work. The ROCF test is one of the most widely used neuropsychological assessments worldwide, providing critical insights into visuospatial memory and executive function. While the initial effort and cost are substantial, the long-term benefits of an automated, reliable, objective, fast and widely applicable neuropsychological assessment tool justify the investment. The scoring application will significantly reduce the time for scoring the test and thus provide more efficient use of clinical resources, and the potential for broader applications makes this a worthwhile endeavor. The methods and infrastructure developed for this model can be adapted and scaled to other neuropsychological tests and assessments (e.g. Taylor Figure).

Comment #2: I was truly impressed by the authors' establishment of a system that organizes the methods and fields of diverse specialties in such a remarkable way. I know the primary purpose of ROCFT. However, beyond the score, neuropsychologically, these are observed by specialists while ROCFT and that is attractive of the test: the turn of each stroke (e.g., from right to left, from the main structure to the margin or small structure), the process to total completeness as a figure, e.g., confidential speed and concentration, the boldness of strokes, unnatural fragmentation of strokes, the not deviated place in a paper, turning of the figure itself (before the scanning level), the total size, the level compared with the age, education, and experiences of the patient. Those are reflected by the disease, visuospatial intelligence, executive function, and ability to concentrate. Scores are crucial, but by observing the drawing process, we can obtain diverse facts or parts of symptoms that imply the complications of human behavior.

Response #2: Thank you for your insightful comments and observations regarding our system for organizing diverse specialties within the ROCFT methodology. We agree that beyond the numerical scores, the detailed observation of the drawing process provides invaluable neuropsychological insights. How strokes are executed, from their direction and placement to the overall completion process, offers a nuanced understanding of factors like spatial orientation, concentration, and executive function. In fact, we are working on a ROCF pen tracking application, which enables the patient to draw the ROCF with a digital pen on a tablet. The tablet can 1) assess the sequence order of drawing the items and the number of strokes, 2) record the exact coordinate of each drawn pixel at each time point of the assessment, 3) measure the duration for each pen stroke as well as total drawing time, and 4) assess the pen stroke pressure. Through this, we aim to extract additional information on processing speed, concentration, and other cognitive domains. However, this development is outside the scope of the current manuscript.

By them saying that attention layers have different "attention heads" makes it so different words can match up but still also have many different terms used within the paragraph.

what does this mean?

This seems exceptionally cruel.

Wow...

Are they trying to remind her she isn't safe? Was there any interactions prior to this statement that could have led them there? Why threaten her?

I feel like its an empty threat, as they were being kind earlier.

what does this even mean?

What do they mean with "your master will knock your child in the head"?

Her belief of the Natives brutal nature is wrong and is proven wrong in this instance. She also once again takes the good nature of the Natives as an act of God.

Wow

That is nice of them to offer and almost take care of her. I wonder what the other Native Americans in the tribe thought of her.

Her pov is making them seem bipolar

Again she is treated kindly and even given food when we have learned how hard it is for all of them to find food, and yet she still isn't understanding or seeing them as people.

who did she say this to a why? her initial capturers or the new people who just showed her kindness?

interesting how the Indians of (I assume) different tribes treat her differently

she seems to be very vocal about her discomfort, which probably irritated the natives. She is almost teasing them in a way.

This sentence shows that WandaVision features funny and light-hearted moments similar to classic sitcoms.

she's borderline slamming her head into a realization and she's still missing it

I think this image is one of the most important sections for understanding the topic that is being discussed. It may not seem like it but I think it's important because it is able to create a visualization of the title and what the article is discussing. In the image we see that there is a cat, surrounded in a blue tape square box on top of a tapestry with an illusion like pattern. This visualization helps us understand the rest of the text because it created that image in our head. Now when I read the rest of the text, I have an idea of what the researchers are trying to study.

Welcome back, this is part two of this lesson.

We're going to continue immediately from the end of part one, so let's get started.

Now we talked about the source and destination IP address of these packets, so now let's focus on IP addressing itself.

IP addressing is what identifies a device which uses layer 3 IP networking.

Now I'll talk more about how IP addressing is decided upon and assigned in another video, for now I want you to fully understand the structure of an IP address.

In this video I'll be focusing on IP version 4, because I have a separate video which will cover IP version 6 in depth.

This is an IP address, 133.33.3.7.

From a pure network connectivity point of view, if you have a valid IP version 4 address, you can send packets to 133.33.3.7 and they will at least start on the journey of getting to this destination.

Now there might be blocks in the way, so firewalls or other security restrictions, all the IP could be offline, but packets will move from you over the internet on their way to this IP address.

Now this format is known as dotted decimal notation.

It's four decimal numbers from 0 to 255 separated by dots.

So 133.33.3.7.

Now all IP addresses are actually formed of two different parts.

There's the network part which states which IP network this IP address belongs to, and then the host part which represents hosts on that network.

So in this example the network is 133.33, and then the hosts on that network can use the remaining part of the IP.

In this case 3.7 is one device on that network, a laptop.

A really important part of understanding how your data gets from your location to a remote network is the given two IP addresses.

How do you tell if they're on the same IP network or different IP networks?

If the network part of the IP address matches between two different IP addresses, then they're on the same IP network.

If not, they're on different IP networks.

So you need to be able to calculate where you've an IP address, which part of that address is the network, and which part is the host.

And by the end of this lesson you will know how to do that.

Now IP addresses are not actually dotted decimal.

That's how they're represented for humans.

They're actually binary numbers.

Each decimal part of the IP address is an 8-bit binary number.

There are four of these per IP version 4 address, and this means that an entire IP address is 32 bits in size.

So four sets of 8 bits, and each of these 8 bits is known as an octet.

You might hear somebody refer to say the first and second octet of an IP address, and this is always read left to right.

The first octet in this example is 1, 3, 3, or in binary 1, 0, 0, 0, 1, 0, 1.

And the second octet is 33, which in binary is 0, 0, 1, 0, 0, 0, 0, 1.

Now this binary conversion, this is not something which I'm going to cover in this lesson, but I will make sure there's a link attached to the lesson which shows you how to do it.

It's just decimal to binary maths, and once you know how it's done, it's really easy to do, even in your head.

Now I'm going to talk about how you can determine which IPs are on the same network next, but I wanted to introduce the format of IP addresses first.

In this example, this IP address has what's known as a /16 prefix.

This means that the first 16 bits represent the network, and the rest are for hosts.

Now I don't really talk about how this works in detail coming up next.

Because the first 16 bits are network, it means that the second IP address is 1, 3, 3, .33, .33, .37, because the network part of that matches is 1, 3, 3, .33, and it's on the same IP network.

I'm going to detail coming up next how this calculation is done.

For now, I want you to be comfortable knowing that if the network component of two IP addresses match, then devices are local.

If they don't match, then devices are remote.

That matters when we start covering IP routing.

Now IP addresses are networks.

These are either statically assigned by humans, and this is known as a static IP, or they're assigned automatically by machines.

So service on your network running DHCP service software.

Now DHCP stands for Dynamic Host Configuration Protocol, and this is something I'll be covering in detail in a separate video.

On a network, IP addresses need to be unique, or bad things happen.

Globally, in most cases, IP addresses need to be unique, or also bad things happen.

So keep that in mind.

Generally, when you're dealing with IP addresses, you want them to be unique, especially on your local network.

Now let's talk about subnet masks, because these are what helps us determine if IP addresses are local to each other or remote.

Subnet masks are a critical part of IP networking.

They're configured on layer 3 interfaces, along with IP addresses.

What's also configured on most network interfaces is a default gateway.

This is an IP address on a local network, which packets are forwarded to, generally, if the intended destination is not a local IP address.

Subnet masks are what allow an IP device to know if an IP address which it's communicating with is on the same network or not, and that influences if the device attempts to communicate directly on the local network, or if it needs to use the default gateway.

On your home network, for example, your internet router is likely set as your default gateway, so when you browse to Netflix.com or interact with AWS because the IP addresses that you're talking to are not local, then packets from your machine are passed to your router, which is the default gateway.

So let's say that we have an IP address, 133.33.3.7.

Now this alone is just a single address.

We don't know which part of it is the network and which part of it is the host component.

I just finished talking about how IP addresses can match binary numbers.

This IP address in binary is 1-0-0-0-1-0-1, so that's the first octet, and then 0-0-1-0-0-1, that's the second octet, and then 0-0-0-0-0-0-1-1, that's the third octet, and then finally 0-0-0-0-0-1-1-1, and that's the fourth octet, and that represents 133.33.3.7.

So as a reminder, if we're dealing manually with subnet masks, and remember this is something that's generally performed in software by your networking stack, the first thing we need to do is convert the dotted decimal notation into a binary number.

Now along with this IP address, we would generally also configure either statically or using DHCP, a subnet mask.

In this example, the subnet mask that we have is 255.255.0.0 or /16, and these mean the same thing, and I'll show you why over the next few minutes.

A subnet mask represents which part of the IP is for the network.

It helps you, or more often a machine, know which part of an IP address is which.

To use a subnet mask, you first have to convert it into binary, so 255.255.0.0 is this in binary.

We convert it just like an IP address.

So the first octet is all 1s, the second octet is all 1s, the third and fourth octet are all 0s.

The /16, which is known as the prefix, this is just shorthand.

It's the number of 1s in the subnet mask starting from the left.

So /16 simply means 16 1s, which is the same as 255.255.0.0 when you convert that into binary.

Now when you have the subnet mask in binary, anything with a 1 represents the network, anything with a 0 represents the host component.

So if you overlay a subnet mask and an IP address, both of them in binary, it becomes really easy to tell which part is which.

Something else which is really cool is that for a given network, you can calculate the start and end IP addresses of that network.

Take for example, the IP address that's on screen now, so 133.33.3.7.

Well we've converted that into binary and we've also converted the subnet mask of 255.255.0.0 also into binary.

So that's in blue, right below the binary IP address.

To calculate the start of the network, we begin with the network part of the IP address and then for the host part, we have all 0s.

So let's look at what we've done.

The subnet mask, where there are 1s, this is the network part.

So we take the original IP address and where the subnet mask has 1s, that's the network part, so 133.33.

Then for the part which is hosts, which is where the subnet mask shows 0s, then we have all 0s.

This means that the network starting point is 133.33.0.0.

Now to find the end, we take the network component of the IP address again, so where the subnet mask is all 1s, that's what we start with.

And to work out the end of the network, we take the host component, so where the subnet mask is 0s, and we have all 1s in the IP address.

So the ending part of this network is 133.33.255.255.

So the starting address of a network is the network component of the IP address, identified with the subnet mask, and then all 0s for the host part of the IP address, and the ending address of the network is the network part of the IP address to start with, and then for the host component, we have all 1s.

So this is how subnet masks work.

They're used to identify which part of an IP address is the network part and which is the host part.

As long as the network part for two different IP addresses is the same, then we know that both of those IP addresses are on the same IP network, and this is essential so that the machine can identify when it can send data directly on the same local network, or when IP routing needs to be used to transfer packets across different intermediate networks.

So it's how your local device, your local laptop, knows to send packets to your internet router for Netflix or AWS, rather than trying to look for both of those systems locally on your local area network.

And that's how a router makes that decision too, when it's looking where to forward packets to.

So using subnet masks and IP addresses, it's how a lot of the intelligence of layer 3 is used.

Now next, I want to spend some time looking at route tables and routes.

Let's step through an example of data moving from you to AWS, and I want to keep focus for now on how a router makes a decision where to send data.

Packets that you create for AWS will move from your house into your internet provider across the internet, potentially even between countries, and then finally arrive at AWS.

Let's step through a simple example.

So we start with our house on the left.

Next, we have our internet provider known as an ISP or Internet Service Provider, and let's call this Meow ISP, and then we have three destination networks.

We have AWS, our ISP's upstream provider, and then Netflix.

Now we want to communicate with AWS, and so we create a packet on our local device, which has our IP address 1.3.3.7 as the source IP address, and it has a destination IP address of 52.217.13.37.

Now you're going to have an internet router within your home, and this is where your device will send all of its data through.

That router has what's known as a default route, which means all IP traffic is sent to it on its way to Meow ISP.

Now I'll explain what a default route is in a second.

For now, just assume that all data that you generate within your local network by default is sent through to your internet service provider.

So now the packet that you've generated is inside your internet service provider on a router, and this router has multiple network interface cards connecting to all of those remote networks.

Now let's assume in those remote networks is another router, and each of these routers uses the dot 1 IP address in each of those networks.

So how does the ISP router inside Meow ISP know where to forward your data to?

Well, it uses routes and route tables.

Every router will have at least one route table.

It could have more, which are attached to individual network interfaces, but for now let's keep things simple and assume that the router within our ISP has a single route table, and it will look something like this.

A route table is a collection of routes.

Each row in this table is an example route.

It will have a destination field, and it will have a next hop or a target field.

What happens is that every packet which arrives at this router, the router will check the packet's destination.

What IP address is this packet destined for?

And in this example, it's 52.217.13.37.

Now at this point, the router will look for any routes in the route table which match the destination IP address of this packet.

If multiple routes match, then it will prefer ones which are more specific.

The two routes in yellows at the top and the bottom, these are examples of fairly specific routes.

The one in blue in the middle is the inverse, this is not a specific route.

The larger the prefix, so the higher the number after the slash, the more specific the route.

So a slash 32 is the most specific, and a slash 0 is the least specific.

A slash 32 actually represents one single IP address, and a slash 0, well this represents all IP addresses.

A slash 24 means that the first 24 bits are for the network, and the last 8 bits are for the host.

So this matches a network of 256 IP addresses.

So for this packet that we have with the destination of 52.217.13.37, we've got two routes which match.

The top route, which is 52.217.13.0/24, that network contains the IP address which our packet is destined for.

So this matches.

But also the middle route, 0.0.0/0, this matches, because this matches all IP addresses.

The middle route is known as a default route.

I mentioned before the packets from our home network on the left arrive at our ISP because there's a default route.

Well this 0.0.0/0 is an example of a default route.

This will match if nothing else does.

Because we have two more specific routes in this route table, so the top and bottom, if either of those match, they will be selected rather than the default route in the middle.

In this case the bottom route doesn't match our particular packet, only the top one matches.

And so the top route will be selected because it's more specific than the default route.

Now for the route that's selected, so the top route, it has a next hop or target field.

This is the IP address which the packet is going to be forwarded to, to get one step closer through to its destination.

Or in this case to arrive at the actual destination.

And so the packet is forwarded through to this address.

Routing as a process is where packets are forwarded or routed hop by hop across the internet from source to destination.

Route tables are the thing which enables this.

Route tables can be statically populated, or there are protocols such as BGP or the border gateway protocol, which allow routers to communicate with each other to exchange which networks they know about.

And this is how the core of the internet functions.

One important thing that you need to understand though, is that when our ISP router is forwarding the packet through to the AWS router, it's forwarding it at layer 2.

It wraps the packet in a frame.

The packet doesn't change.

The frame though, it has the AWS routers MAC address as its destination.

That's how the packet gets to the AWS router.

But how do we determine the MAC address of the AWS router in this example?

For that we use something called the address resolution protocol, and that's what I'm going to be covering next.

This is the end of part 2 of this lesson.

It's a pretty complex lesson, and so I wanted to give you the opportunity to take a small break, maybe stretch your legs, or make another coffee.

Part 3 will continue immediately from this point, so go ahead, complete this video, and when you're ready, I look forward to you joining me in part 3.

This truly gives and insight to Paul's character. Everyone sees him as the kid that is always smiling and is just a bit off. However in reality, whenever he get's to home he feels the depression weigh on him.

Love is political because collective political action requires solidarity. A movement can only succeed with love and care for others. Orpheus doesn't succeed not because he's weak, but because Hades destroys this solidarity.

did he get drunk?

Reviewer #3 (Public review):

Summary:

Redman and colleagues analyze grid cell data obtained from public databases. They show that there is significant variability in spacing and orientation within a module. They show that the difference in spacing and orientation for a pair of cells is larger than the one obtained for two independent maps of the same cell. They speculate that this variability could be useful to disambiguate the rat position if only information from a single module is used by a decoder.

Strengths:

The strengths of this work lie in its conciseness, clarity, and the potential significance of its findings for the grid cell community, which has largely overlooked this issue for the past two decades. Their hypothesis is well stated and the analyses are solid.

Weaknesses:

On the side of weaknesses, we identified two aspects of concern. First, alternative explanations for the main result exist that should be explored and ruled out. Second, the authors' speculation about the benefits of variability in angle and spacing for spatial coding is not particularly convincing, although this issue does not diminish the importance or impact of the results.

Major comments:

(1) One possible explanation of the dispersion in lambda (not in theta) could be variability in the typical width of the field. For a fixed spacing, wider fields might push the six fields around the center of the autocorrelogram toward the outside, depending on the details of how exactly the position of these fields is calculated. We recommend authors show that lambda does not correlate with field width, or at least that the variability explained by field width is smaller than the overall lambda variability.

(2) An alternative explanation could be related to what happens at the borders. The authors tackle this issue in Figure S2 but introduce a different way of measuring lambda based on three fields, which in our view is not optimal. We recommend showing that the dispersions in lambda and theta remain invariant as one removes the border-most part of the maps but estimating lambda through the autocorrelogram of the remaining part of the map. Of course, there is a limit to how much can be removed before measures of lambda and theta become very noisy.

(3) A third possibility is slightly more tricky. Some works (for example Kropff et al, 2015) have shown that fields anticipate the rat position, so every time the rat traverses them they appear slightly displaced opposite to the direction of movement. The amount of displacement depends on the velocity. Maps that we construct out of a whole session should be deformed in a perfectly symmetric way if rats traverse fields in all directions and speeds. However, if the cell is conjunctive, we would expect a deformation mainly along the cell's preferred head direction. Since conjunctive cells have all possible preferred directions, and many grid cells are not conjunctive at all, this phenomenon could create variability in theta and lambda that is not a legitimate one but rather associated with the way we pool data to construct maps. To rule away this possibility, we recommend the authors study the variability in theta and lambda of conjunctive vs non-conjunctive grid cells. If the authors suspect that this phenomenon could explain part of their results, they should also take into account the findings of Gerlei and colleagues (2020) from the Nolan lab, that add complexity to this issue.

(4) The results in Figure 6 are correct, but we are not convinced by the argument. The fact that grid cells fire in the same way in different parts of the environment and in different environments is what gives them their appeal as a platform for path integration since displacement can be calculated independently of the location of the animal. Losing this universal platform is, in our view, too much of a price to pay when the only gain is the possibility of decoding position from a single module (or non-adjacent modules) which, as the authors discuss, is probably never the case. Besides, similar disambiguation of positions within the environment would come for free by adding to the decoding algorithm spatial cells (non-hexagonal but spatially stable), which are ubiquitous across the entorhinal cortex. Thus, it seems to us that - at least along this line of argumentation - with variability the network is losing a lot but not gaining much.

(5) In Figure 4 one axis has markedly lower variability. Is this always the same axis? Can the authors comment more on this finding?

(6) The paper would gain in depth if maps coming out of different computational models could be analyzed in the same way.

(7) Similarly, it would be very interesting to expand the study with some other data to understand if between-cell delta_theta and delta_lambda are invariant across environments. In a related matter, is there a correlation between delta_theta (delta_lambda) for the first vs for the second half of the session? We expect there should be a significant correlation, it would be nice to show it.

Author response:

We thank the reviewers for their time and thoughtful comments. We are encouraged that all reviewers found our work novel and clear. We will submit a full revision to address all the points the reviewers made. Below, we briefly highlight a few clarifications and planned analyses to address major concerns; all other concerns raised by the reviewers will also be addressed in the revision.

Reviewers #1 and #3 asked whether the variability in grid properties emerged with experience/time in the environment. We agree that this is an interesting question, and we will re-analyze the data to explore whether between-cell variability increases with time within a session. However, we note that since the rats were already familiarized to the environment for 10-20 sessions prior to the recordings, there may be limited additional changes in between-cell variability between recording sessions. In one case, two sessions from the same rat were recorded on consecutive days (R11/R12 and R21/R22) - these sessions did not show any difference in variability. 

Reviewer #2 noted that the variability in grid properties is known to experimentalists. We will tone down our discussion on the current assumptions in the field to accurately reflect this awareness in the community. However, we would like to emphasize that the lack of work carefully examining the robustness of this variability has prevented a firm understanding of whether this is an inherent property of grid cells or due to noise. The impact of this can be seen in theoretical neuroscience work where a considerable number of articles (including recent publications) start with the assumption that all grid cells within a module have identical properties, with the exception of phase shift and noise. In addition, since grid cells are assumed to be identical in the computational neuroscience community, there has been little work on quantifying how much variability a given model produces. This makes it challenging to understand how consistent different models are with our observations. We believe that making these limitations of previous work clear is important to properly conveying our work’s contribution. 

Reviewer #3 asked whether the variability in grid properties could be driven by cells that were conjunctively tuned with head direction. We agree that this is an interesting hypothesis and will explore this by performing new analysis. We note that, as reported by Gardner et al. (2022), only 19 of the 168 cells in recording session R12 are conjunctive. Even if these cells are included in the same proportion as pure grid cells by our inclusion criteria (which appears unlikely, given that conjunctive cells may be less reliable across splits of the data), then approximately 9 out of the 82 cells we analyzed would be conjunctive. Therefore, we expect it to be unlikely that they are the main source of the variability we find. However, we will test this in our revised manuscript.

Reviewer #3 asked whether the “price” paid in having grid property variability was too high for the modest gain in ability to encode local space. We agree that losing the continuous attractor network (CAN) structure, and the ability to path integrate, would be a very large loss. However, we do not believe that the variability we observe necessarily destroys either CAN or path integration. We argue this for two reasons. First, the data we analyzed [from Gardner et al. (2022)] is exactly the data set that was found to have toroidal topology and therefore viewed to be in line with a major prediction of CANs. Thus, the amount of variability in grid properties does not rule out the underlying presence of a continuous attractor. Second, path integration may still be possible with grid cells that have variable properties. To illustrate this, and to address another comment from Reviewer #3, we have begun to analyze the distribution of grid properties in a recurrent neural network (RNN) model trained to perform path integration (Sorscher et al., 2019). This RNN model, in addition to others (Banino et al., 2018; Cueva and Wei, 2018), has been found to develop grid cells and there is evidence that it develops CANs as the underlying circuit mechanism (Sorscher et al., 2023). We find that the grid cells that emerge from this model exhibit variability in their grid spacings and orientations. This illustrates that path integration (the very task the RNN was trained to perform) is possible using grid cells with variable properties.

This paragraph as a whole summarizes a common misconception or idea on feminism, believing that all men are bad or that woman should be the only ones in rule, when feminism is about equality, not out balancing the system. this is also another reason why so many people are afraid to speak up and be apart of the movement as its seen as a negative "drive" in most cases.

Kin, is used here to mean relation but as she stated previously not in a familial or genealogical sense. She wants to create a new frame, a new narrative, an ontology of not being but being as kin. To be kin is to be modular to the environment. When she says assemblages she wants us to get out of thinking of ourselves as just individualized subjects, only able to think in one head, ,but that actively we are connected to the world.

It is great to incorporate quotes from different people in the essay as it enhances credibility and delivers emotion to the reader.

“O Machine! O Machine!” and raised the volume to her lips. Thrice she kissed it, thrice inclined her head, thrice she felt the delirium of acquiescence

literally or in her head?

even more dehumanizing language towards natives, racist and just generally makes you wonder how much of this is affected by her own personal views (a lot)

Her beliefs are definitely playing a role in her writing. I am still confused about the wounded babe.

I can't tell what makes me more sad: the fact that a child is even there, or the fact that the "inhumane creatures" aren't even processing how must distress they're subjecting this woman to

I really liked learning about Helen Keller's involvement in politics and hope to talk more about disability through history and the development of the ADA. Growing up with epilepsy I liked reading books about Helen Keller but they never discussed her involvement in politics (likely to remain neutral and get a variety of readers) but I think it's important to acknowledge that disabled people are often at the head of movements for change.

Chekhov, when looking at dead neighbor's head, does not see death of neighbor, but sees life for self.

Note: This response was posted by the corresponding author to Review Commons. The content has not been altered except for formatting.

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Reply to the reviewers

Thank you very much for your editorial handling of our manuscript entitled 'A conserved fungal Knr4/Smi1 protein is vital for maintaining cell wall integrity and host plant pathogenesis'. We have taken on board the reviewers' comments and thank them for their diligence and time in improving our manuscript.

Please find our responses to each of the comments below.

Reviewer(s)' comments

Reviewer #1

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Major comments:

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__1.1. As a more critical comment, I find the presentation of the figures somewhat confusing, especially with the mixing of main figures, supplements to the main figures, and actual supplemental data. On top of that, the figures are not called up in the right order (e.g. Figure 4 follows 2D, while 3 comes after 4; Figure 6 comes before 5...), and some are never called up (I think) (e.g. Figure 1B, Figure 2B). __

__Response: __The figure order has been revised according to the reviewer's suggestion, while still following eLife's formatting guidelines for naming supplementals. Thank you.

1.2. I agree that there should be more CWI-related genes in the wheat module linked to the FgKnr4 fungal module, or, vice-versa, CW-manipulating genes in the fungal module. It would at least be good if the authors could comment further on if they find such genes, and if not, how this fits their model.

Response: Thank you for your insightful suggestion regarding the inclusion of more CWI-related genes in the wheat module linked to the FgKnr4 fungal module F16, or vice versa. We did observe a co-regulated response between the wheat module W05 which is correlated to the FgKnr4 module F16. Namely, we observed an enrichment of oxidative stress genes including respiratory burst oxidases and two catalases (lines 304 - 313) in the correlated wheat module (W05). Early expression of these oxidative stress inducing genes likely induces the CWI pathway in the fungus, which is regulated by FgKnr4. Knr4 functions as both a regulatory protein in the CWI pathway and as a scaffolding protein across multiple pathways in S. cerevisiae (Martin-Yken et al., 2016, https://onlinelibrary.wiley.com/doi/10.1111/cmi.12618 ). Scaffolding protein-encoding genes are typically expressed earlier than the genes they regulate to enable pre-assembly with their interacting partners, ensuring that signaling pathways are ready to activate when needed. In this context, the CWI integrity MAPKs Bck1 and Mkk1 are part of module F05, which includes two chitin synthases and a glucan synthase. This module is highly expressed during the late symptomless phase. The MAPK Mgv1, found in module F13, is expressed consistently throughout the infection process, which aligns with the expectation that MAPKs are mainly post-transcriptionally regulated. Thank you for bringing our attention to this, this is now included in the discussion (lines 427 - 443) along with eigengene expression plots of all modules added to the supplementary (Figure 3 - figure supplement 1).

To explore potential shared functions of FgKnr4 with other genes in its module, we re-analyzed the high module membership genes within module F16, which includes FgKnr4, using Knetminer (Hassani-Pak et al., 2021; https://onlinelibrary.wiley.com/doi/10.1111/pbi.13583 ). This analysis revealed that 8 out of 15 of these genes are associated with cell division and ATP binding. Four of the candidate genes are also part of a predicted protein-protein interaction subnetwork of genes within module F16, which relate to cell cycle and ATP binding. In S. cerevisiae, the absence of Knr4 results in cell division dysfunction (Martin-Yken et al., 2016, https://onlinelibrary.wiley.com/doi/10.1111/cmi.12618 ). Accordingly, we tested sensitivity of ΔFgknr4 to microtubule inhibitor benomyl (a compound commonly used to identify mutants with cell division defects; Hoyt et al., 1991 https://www.cell.com/cell/pdf/0092-8674(81)90014-3.pdf). We found that the ΔFgknr4 mutant was more susceptible to benomyl, both when grown on solid agar and in liquid culture. This data has now been added Figure 7, and referred to in lines 338-348.

__Specific issues: __

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1.3. In the case of figure 5, I generally find it hard to follow. In the text (line 262/263), the authors state that 5C shows "eye-shaped lesions" caused by ΔFgknr4 and ΔFgtri5, but I can't see neither (5C appears to be a ΔFgknr4 complementation experiment). The figure legend also states nothing in this regard.

__Response: __Thank you for your suggestion. We have amended the manuscript to include an additional panel that shows the dissected spikelet without its outer glumes, making the eye shaped diseased regions more visible in Figure 5.

__1.4. Figure 5D supposedly shows 'visibly reduced fungal burden' in ΔFgknr4-infected plants, but I can't really see the fungal burden in this picture, but the infected section looks a lot thinner and more damaged than the control stem, so in a way more diseased. __

Response: __Thank you for your insight. We have revised our conclusions based on this image to state that while ΔFgknr4 can colonise host tissue, it does so less effectively compared to the wild-type strain as we are unable to quantitatively evaluate fungal burden using image-colour thresholding due to the overlapping colours of the fungal cells and wheat tissues. Decreased host colonisation is evidenced by (i) reduced fungal hyphae proliferation, particularly in the thicker adaxial cell layer, (ii) collapsed air spaces in wheat cells, and (iii) increased polymer deposition at the wheat cell walls, indicating an enhanced defence response. __Figure 5 has been amended to include these observations in the corresponding figure legend and the resin images now include insets with detailed annotation.

__1.5. The authors then go on to state (lines 272-273) that they analyzed the amounts of DON mycotoxin in infected tissues, but don't seem to show any data for this experiment. __

Response: __We have amended this to now include the data in __Figure 5 - figure supplement 2B, thank you.

Reviewer #2

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__Major issues: __

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2.1 If Knf4 is involved in the CWI pathway, what other genes involved in the CWI pathway are in this fungal module? one of the reasons for developing modules or sub-networks is to assign common function and identify new genes contributing to the function. since FgKnr4 is noted to play a role in the CWI pathways, then genes in that module should have similar functions. If WGCN does not do that, what is the purpose of this exercise?

Response: __Thank you for raising this point regarding the role of FgKnr4 in the CWI pathway and the expectations for genes of shared function within the FgKnr4 module F16. We did observe that the module containing FgKnr4 (F16) was also correlated to a wheat module (W05) which was significantly enriched for oxidative stress genes. This pathogen-host correlated pattern led us to study module F16, which otherwise lacks significant gene ontology term enrichment, unique gene set enrichments, and contains few characterised genes. This is now highlighted in __lines 233-246. This underscores the strength of the WGCNA. By using high-resolution RNA-seq data to map modules to specific infection stages, we identified an important gene that would have otherwise been overlooked. This approach contrasts with other network analyses that often rely on the guilt-by-association principle to identify novel virulence-related genes within modules containing known virulence factors, potentially overlooking significant pathways outside the scope of prior studies. Therefore, our analysis has already benefited from several advantages of WGCNA, including the identification of key genes with high module membership that may be critical for biological processes, as well as generating a high-resolution, stage-specific co-expression map of the F. graminearum infection process in wheat. This point is now emphasised in lines 233-252. As discussed in response to reviewer 1, Knr4 functions as both a regulatory protein in the CWI pathway and as a scaffolding protein across multiple pathways in S. cerevisiae (Martin-Yken et al., 2016, https://onlinelibrary.wiley.com/doi/10.1111/cmi.12618 ) which would explain its clustering separate from the CWI pathway genes. The high module membership genes within module F16 containing FgKnr4 were re-analysed using Knetminer (Hassani-Pak et al., 2021; https://onlinelibrary.wiley.com/doi/10.1111/pbi.13583 ), which found that 8/15 of these genes were related to cell division and ATP binding. Four of the candidate genes are also part of a predicted protein-protein interaction subnetwork of genes within module F16, which relate to cell cycle and ATP binding. In S. cerevisiae, the absence Knr4 leads to dysfunction in cell division. Accordingly, we tested sensitivity of ΔFgknr4 to the microtubule inhibitor benomyl (a compound commonly used to identify mutants with cell division defects; Hoyt et al., 1991 https://www.cell.com/cell/pdf/0092-8674(81)90014-3.pdf). We found that the ΔFgknr4 mutant was more susceptible to benomyl, both when grown on solid agar and in liquid culture. This data has now been added as Figure 7 and referred to in lines 338-348.

2.2. Due to development defects in the Fgknr1 mutant, I would not equate to as virulence factor or an effector gene.

__Response: __We are in complete agreement with the reviewer and are not suggesting that FgKnr4 is an effector or virulence factor, we have been careful with our wording to indicate that FgKnr4 is simply necessary for full virulence and its disruption results in reduced virulence and have outlined how we believe FgKnr4 participates in a fungal signaling pathway required for infection of wheat.

2.3. What new information is provided with WGCN modules compared with other GCN network in Fusarium (examples of GCN in Fusarium is below) ____https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5069591/ https://doi.org/10.1186/s12864-020-6596-y____ DOI: 10.1371/journal.pone.0013021. The GCN networks from Fusarium have already identified modules necessary/involved in pathogenesis.

Response: __The 2016 New Phytologist gene regulatory network (GRN) by Guo et al. is large and comprehensive. However, only three of the eleven datasets are in planta, with just one dataset focusing on F. graminearum infection on wheat spikes. The other two in planta datasets involve barley infection and Fusarium crown rot. By combining numerous in planta and in vitro datasets, the previous GRNs lack the fine resolution needed to identify genetic relationships under specific conditions, such as the various stages of symptomatic and symptomless F. graminearum infection of mature flowering wheat plants. This limitation is highlighted in the 2016 paper itself. This network is expanded in the Guo et al., 2020 BMC genomics paper where it includes one additional in planta and nine in vitro datasets. However, the in planta dataset involves juvenile wheat coleoptile infection, which serves as an artificial model for wheat infection but is not on mature flowering wheat plants reminiscent of Fusarium Head Blight of cereals in the field. This model differs significantly in the mode of action of F. graminearum, notably DON mycotoxin is not essential for virulence in this context (Armer et al. 2024, https://pubmed.ncbi.nlm.nih.gov/38877764/ ). The Guo et al., 2020 paper still faces the same issues in terms of resolution and the inability to draw conclusions specific to the different stages of F. graminearum infection. Additionally, these GRNs use Affymetrix data, which miss over 400 genes (~ 3 % of the genome) from newer gene models. In contrast, our study addresses these limitations by analysing a meticulously sampled, stage- and tissue-specific in planta RNA-seq dataset using the latest reference annotation. Our approach provides higher resolution and insights into host transcriptomic responses during the infection process. The importance of our study in the context of these GRNs is now addressed in the introduction (__lines 85-92).

2.4. Ideally, the WGCN should have been used identify plant targets of Fusarium pathogenicity genes. This would have provided credibility and usefulness of the WGCN. Many bioinformatic tools are available to identify virulence factors and the utility of WGCN in this regard is not viable. However, if the authors had overlapped the known virulence factors in a fungal module to a particular wheat module, the impact of the WGCN would be great. The module W12 has genes from numerous traits represented and WGCN could have been used to show novel links between Fg and wheat. For example, does tri5 mutant affect genes in other traits?

__Response: __Thank you for your suggestions. In this study we have shown the association between the main fungal virulence factor of F. graminearum, DON mycotoxin, with wheat detoxification responses. Through this we have identified a set of tri5 responsive genes and validated this correlation in two genes belonging to the phenylalanine pathway and one transmembrane detoxification gene. Although we could validate more genes in this tri5 responsive wheat module, our paper aimed to investigate previously unstudied aspects of the F. graminearum infection process and how the fungus responded to changing conditions within the host environment. We accomplished this by characterising a gene within a fungal module that had limited annotation enrichment and few characterised genes. Tri5 on the other hand is the most extensively studied gene in F. graminearum and while the network we generated may offer new insights into tri5 responsive genes, this is beyond the scope of our current study. In addition to the tri5 co-regulated response, we have also demonstrated the coordinated response between the fungal module F16, which contains FgKnr4 that is necessary for tolerance to oxidative stress, and the wheat module W05, which is enriched for oxidative stress genes.

While our co-expression network approach can be used to explore and validate other early downstream signaling and defense components in wheat cells, several challenges must be considered: (a) the poor quality of wheat gene calls, (b) genetic redundancy due to both homoeologous genes and large gene families, and (c) the presence of DON, which can inhibit translation and prevent many transcriptional changes from being realised within the host responses. Additionally, most plant host receptors are not transcriptionally upregulated in response to pathogen infection (most R gene studies for the NBS-LRR and exLRR-kinase classes), making their discovery through a transcriptomics approach unlikely. These points will be included in our discussion (lines 408-413), thank you.

Specific issues

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2.5. Since tri5 mutant was used a proof of concept to link wheat/Fg modules, it would have been useful to show that TRI14, which is not involved DON biosynthesis, but involved in virulence ( https://doi.org/10.3390/applmicrobiol4020058____) impact the wheat module genes.

Response: __Our goal was to show that wheat genes respond to the whole TRI cluster, not just individual TRI genes. Therefore, the tri5 mutant serves as a solid proof-of-concept, because TRI5 is essential for DON biosynthesis, the primary function of the TRI gene cluster, thereby representing the function of the cluster as a whole. This is now clarified in __lines 217-219. Additionally, the uncertainties surrounding other TRI mutants would complicate the question we were addressing-namely, whether a wheat module enriched in detoxification genes is responding to DON mycotoxin, as implied by shared co-expression patterns with the TRI cluster. For instance, the referenced TRI14 paper indicates that DON is produced in the same amount in vitro in a single media. Although the difference is not significant, the average DON produced is lower for the two Δtri14 transformants tested. Therefore, we cannot definitively rule out that TRI14 is involved in DON biosynthesis and extrapolate this to DON production in planta. Despite this, the suggestion is interesting, and would make a nice experiment but we believe it does not contribute to the overall aim of this study.

2.6. Moreover, prior RNAseq studies with tri5 mutant strain on wheat would have revealed the expression of PAL and other phenylpropanoid pathway genes?

__Response: __We agree that this would be an interesting comparison to make but unfortunately no dataset comparing in planta expression of the tri5 mutant within wheat spikes exists.

2.7. Table S1 lists 15 candidate genes of the F16 module; however, supplementary File 1 indicates 74 genes in the same module. The basis of exclusion should be explained. The author has indicated genes with high MM was used as representative of the module. The 59 remaining genes of this module did not meet this criteria? Give examples.

Response: __The 15 genes with the highest module membership were selected as initial candidates for further shortlisting from the 74 genes within module F16. In WGCNA, genes with high module membership (MM) (i.e. intramodular connectivity) are predicted to be central to the biological functions of the module (Langfelder and Horvath, 2008; https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-9-559 ) and continues to be a metric to identify biologically significant genes within WGCN analyses (https://bmcplantbiol.biomedcentral.com/articles/10.1186/s12870-024-05366-0 Tominello-Ramirez et al., 2024; https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9151341/ ;Zheng et al., 2022; https://www.nature.com/articles/s41598-020-80945-3 Panahi and Hejazi et al 2021). Following methods by Mateus et al. (2019) (https://academic.oup.com/ismej/article/13/5/1226/7475138 ) key genes were defined as those exhibiting elevated MM within the module, which were also strongly correlated (R > |0.70|) with modules of the partner organism (wheat). We have clarified this point in the manuscript. Thank you for the suggestion. (__Lines 253-263).

2.____8. A list from every module that pass this criteria will be useful resource for functional characterization studies.

__Response: __A supplementary spreadsheet has been generated which includes full lists of the top 15 genes with the highest module membership within the five fungal modules correlated to wheat modules and a summary of shared attributes among them. Thank you for this suggestion.

2.9. Figure 3 indicates TRI genes in the module F12; your PHI base in Supp File S2 lists only TRI14. Why other TRI genes such as TRI5 not present in this File?

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Response: For clarity, the TRI genes in module F12 are TRI3, TRI4, TRI11, TRI12, and TRI14 which was stated in Table 1. TRI5 clusters with its neighboring regulatory gene TRI6 in module F11, which exhibits a similar but reduced expression pattern compared to module F12. To improve clarity on this the TRI genes in module F12 are also listed in-text in line 168 and added to Figure 4. The enrichment and correlated relationship of W12 to a cluster's expression still imply a correlated response of the wheat gene to the TRI cluster's biosynthetic product (DON), which is absent in the Δtri5 mutant.

TRI14 and TRI12 are listed in PHI-base. TRI12 was mistakenly excluded due to an unmapped Uniprot ID, which were added separately in the spreadsheet. We will recheck all unmapped ID lists to ensure all PHI-base entries are included in the final output. Thank you for pointing out this error.

2.10. What is purpose of listing the same gene multiple times? Example, osp24 (a single gene in Fg) is listed 13 times in F01 module.

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__Response: __This is a consequence of each entry having a separate PHI ID, which represents different interactions including inoculations on different cultivar. Cultivar and various experimental details were omitted from the spreadsheet to reduce information density, however the multiple PHI base ID's will be kept separate to make the data more user friendly when working with the PHI-base database. An explanation for this is now provided in the file's explanatory worksheet, thank you.

Reviewer #3:

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3.1. Why only use of high confidence transcripts maize to map the reads and not the full genome like Fusarium graminearum? I have never analyzed plant transcriptome.

__Response: __ In the wheat genome, only high-confidence gene calls are used by the global community (Choulet et al., 2023; https://link.springer.com/chapter/10.1007/978-3-031-38294-9_4 ) until a suitable and stable wheat pan-genome becomes available.

3.2. The regular output of DESeq are TPMs, how did the authors obtain the FPKM used in the analysis?

Response: FPKM was calculated using the GenomicFeatures package and included on GitHub to enhance accessibility for other users. However, the input for WGCNA and this study as a whole was normalised counts rather than FPKM. The FPKM analysis was done to improve interoperability of the data for future users and made available on Github. To complement this, the information regarding FPKM calculation is now included in the methods section of the revised manuscript (line 491).

3.3. Do the authors have a Southern blot to prove the location of the insertion and number of insertions in Zymoseptoria tritici mutant and complemented strains?

__Response: __No, but the phenotype is attributed to the presence or absence of ZtKnr4, as the mutant was successfully complemented in multiple phenotypic aspects. This satisfies Koch's postulates which is the gold standard for reverse genetics experimentation (Falkow 1988; https://www.jstor.org/stable/4454582 ).

__3.4. Boxplots and bar graphs should have the same format. In Figures 5 B and F and supplementary figure 6.3 the authors showed the distribution of samples but it is lacking in figure 3 B and all bar graphs. __

__Response: __Graphs have been modified to display the distribution of all samples, thank you.

3.5. Line 247 FGRAMPH1_0T23707 should be FGRAMPH1_01T23707

__Response: __Thank you this has now been amended.

Reviewer #2 (Public Review):

Summary:

Ning and colleagues present studies supporting a role for breast carcinoma amplified sequence 2 (Bcas2) in positively regulating primitive wave hematopoiesis through amplification of beta-catenin-dependent (canonical) Wnt signaling. The authors present compelling evidence that zebrafish bcas2 is expressed at the right time and place to be involved in primitive hematopoiesis, that there are primitive hematopoietic defects in hetero- and homozygous mutant and knockdown embryos, that Bcas2 mechanistically positively regulates canonical Wnt signaling, and that Bcas2 is required for nuclear retention of B-cat through physical interaction involving armadillo repeats 9-12 of B-cat and the coiled-coil domains of Bcas2. Overall, the data and writing are clean, clear, and compelling. This study is a first-rate analysis of a strong phenotype with highly supportive mechanistic data. The findings shed light on the controversial question of whether, when, and how canonical Wnt signaling may be involved in hematopoietic development. We detail some minor concerns and questions below, which if answered, we believe would strengthen the overall story and resolve some puzzling features of the phenotype. Notwithstanding these minor concerns, we believe this is an exceptionally well-executed and interesting manuscript.

Strengths:

(1) The study features clear and compelling phenotypes and results.

(2) The manuscript narrative exposition and writing are clear and compelling.

(3) The authors have attended to important technical nuances sometimes overlooked, for example, focusing on different pools of cytosolic or nuclear b-catenin.

(4) The study sheds light on a controversial subject: regulation of hematopoietic development by canonical Wnt signaling and presents clear evidence of a role.

(5) The authors present evidence of phylogenetic conservation of the pathway.

Weaknesses:

(1) The authors present compelling data that Bcas2 regulates nuclear retention of B-cat through physical association involving binding between the Bcas2 CC domains and B-cat arm repeats 9-12. Transcriptional activation of Wnt target genes by B-cat requires physical association between B-cat and Tcf/Lef family DNA binding factors involving key interactions in Arm repeats 2-9 (Graham et al., Cell 2000). Mutually exclusive binding by B-cat regulatory factors, such as ICAT that prevent Tcf-binding is a documented mechanism (e.g. Graham et al., Mol Cell 2002). It would appear - based on the arm repeat usage by Bcas2 (repeats 9-12)-that Bcas2 and Tcf binding might not be mutually exclusive, which would support their model that Bcas2 physical association with B-cat to retain it in the nucleus would be compatible with co-activation of genes by allowing association with Tcf. It might be nice to attempt a three-way co-IP of these factors showing that B-cat can still bind Tcf in the presence of Bcas2, or at least speculate on the plausibility of the three-way interaction.

(2) A major way that canonical Wnt signaling regulates hematopoietic development is through regulation of the LPM hematopoietic competence territories by activating expression of cdx1a, cdx4, and their downstream targets hoxb5a and hoxa9a (Davidson et al., Nature 2003; Davidson et al., Dev Biol 2006; Pilon et al., Dev Biol 2006; Wang et al., PNAS 2008). Could the authors assess (in situ) the expression of cdx1a, cdx4, hoxb5a, and hoxa9a in the bcas2 mutants?

(3) The authors show compellingly that even heterozygous loss of bcas2 has strong Wnt-inhibitory effects. If Bcas2 is required for canonical Wnt signaling and bcas2 is expressed ubiquitously from the 1-cell stage through at least the beginning of gastrulation, why do bcas2 KO embryos not have morphological axis specification defects consistent with loss of early Wnt signaling, like loss of head (early), or brain anteriorization (later)? Could the authors provide some comments on this puzzle? Or if they do see any canonical Wnt signaling patterning defects in het- or homozygous embryos, could they describe and/or present them?

Author response:

The following is the authors’ response to the previous reviews.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

Du et al. report 16 new well-preserved specimens of atiopodan arthropods from the Chengjiang biota, which demonstrate both dosal and vental anatomies of a pothential new taxon of atiopodans that are closely related to trolobites. Authors assigned their specimens to Acanthomeridion serratum, and proposed A. anacanthus as a junior subjective synonym of Acanthomeridion serratum. Critially, the presence of ventral plates (interpreted as cephalic liberigenae), together with phylogenic results, lead authors to conclude that the cephalic sutures originated multiple times within the Artiopoda.

Strengths:

New specimens are highly qualified and informative. The morphology of dorsal exoskeleton, except for the supposed free cheek, were well illustrated and described in detail, which provide a wealth of information for taxonmic and phylogenic analyses.

Weaknesses:

The weaknesses of this work is obvious in a number of aspects. Technically, ventral morphlogy is less well revealed and is poorly illustrated. Additional diagrams are necessary to show the trunk appendages and suture lines. Taxonomically, I am not convinced by authors' placement. The specimens are markedly different from either Acanthomeridion serratum Hou et al. 1989 or A. anacanthus Hou et al. 2017. The ontogenetic description is extremely weak and the morpholical continuity is not established. Geometric and morphomitric analyses might be helpful to resolve the taxonomic and ontogenic uncertainties. I am confused by author's description of free cheek (libragena) and ventral plate. Are they the same object? How do they connect with other parts of cephalic shield, e.g. hypostome and fixgena. Critically, homology of cephalic slits (eye slits, eye notch, doral suture, facial suture) not extensivlely discussed either morphologically or functionally. Finally, authors claimed that phylogenic results support two separate origins rather than a deep origin. However, the results in Figure 4 can be explain a deep homology of cephalic suture in molecular level and multiple co-options within the Atiopoda.

Comments on the revised version:

I have seen the extensive revision of the manuscript. The main point "Multiple origins of dorsal ecdysial sutures in atiopoans" is now partially supported by results presented by the authors. I am still unsatisfied with descriptions and interpretations of critical features newly revealed by authors. The following points might be useful for the author to make further revisions.

(1) The antennae were well illustrated in a couple of specimens, while it was described in a short sentence.

Some more details of the changing article shape and overall length of antennae has been added to the description.

(2) There are also imprecise descriptions of features.

Measurements, dimensions and multiple figures are provided for many features in the text and the supplement includes more figures. In total, 11 figures are provided with details (photographs or measurements) of the material.

(3) Ontogeny of the cephalon was not described.

A sentence has been added to the description to note the changing width:length of the cephalon during ontogeny, with a reference to Figure 6.

(3) The critical head element is the so called "ventral plate". How this element connects with the cephalic shield is not adequately revealed. The authors claimed that the suture is along the cephalic margin. However, the lateral margin of cephalon is not rounded but exhibit two notches (e.g. Fig 3C) . This gives an indication that the supposed ventral plates have a dorsal extension to fit the notches. Alternatively, the "ventral plate" can be interpreted as a small free cheek with a large ventral extension, providing evidence for librigenal hypothesis.

As noted in the diagnosis for the genus, these notches are interpreted to accommodate the eye stalks. The homology of the ventral plates is discussed at length in the manuscript, and is the focus of the three sets of phylogenetic analyses performed.

Reviewer #3 (Public Review):

Summary:

Well-illustrated new material is documented for Acanthomeridion, a formerly incompletely known Cambrian arthropod. The formerly known facial sutures are proposed be associated with ventral plates that the authors homologise with the free cheeks of trilobites (although also testing alternative homologies). An update of a published phylogenetic dataset permits reconsideration of whether dorsal ecdysial sutures have a single or multiple origins in trilobites and their relatives.

Strengths:

Documentation of an ontogenetic series makes a sound case that the proposed diagnostic characters of a second species of Acanthomeridion are variation within a single species. New microtomographic data shed light on appendage morphology that was not formerly known. The new data on ventral plates and their association with the ecdysial sutures are valuable in underpinning homologies with trilobites.

I think the revision does a satisfactory job of reconciling the data and analyses with the conclusions drawn from them. Referee 1's valid concerns about whether a synonymy of Acanthomeridion anacanthus is justified have been addressed by the addition of a length/width scatterplot in Figure 6. Referee 2's doubts about homology between the librigenae of trilobites and ventral plates of Acanthomeridion have been taken on board by re-running the phylogenetic analyses with a coding for possible homology between the ventral plates and the doublure of olenelloid trilobites. The authors sensibly added more trilobite terminals to the matrix (including Olenellus) and did analyses with and without constraints for olenelloids being a grade at the base of Trilobita. My concerns about counting how many times dorsal sutures evolved on a consensus tree have been addressed (the authors now play it safe and say "multiple" rather than attempting to count them on a bushy topology). The treespace visualisation (Figure 9) is a really good addition to the revised paper.

Weaknesses:

The question of how many times dorsal ecdysial sutures evolved in Artiopoda was addressed by Hou et al (2017), who first documented the facial sutures of Acanthomeridion and optimised them onto a phylogeny to infer multiple origins, as well as in a paper led by the lead author in Cladistics in 2019. Du et al. (2019) presented a phylogeny based on an earlier version of the current dataset wherein they discussed how many times sutures evolved or were lost based on their presence in Zhiwenia/Protosutura, Acanthomeridion and Trilobita. The answer here is slightly different (because some topologies unite Acanthomeridion and trilobites). This paper is not a game-changer because these questions have been asked several times over the past seven years, but there are solid, worthy advances made here.

I'd like to see some of the most significant figures from the Supplementary Information included in the main paper so they will be maximally accessed. The "stick-like" exopods are not best illustrated in the main paper; their best imagery is in Figure S1. Why not move that figure (or at least its non-redundant panels) as well as the reconstruction (Figure S7) to the main paper? The latter summarises the authors' interpretation that a large axe-shaped hypostome appears to be contiguous with ventral plates.

We have moved these figures from the supplementary information to the main text, and renumbered figures accordingly. Fig S1 has now been split – panels a and b are in the main text (new Fig. 4), with the remainder staying as Fig S1. Fig S7 is now Fig. 8 in the main text.

The specimens depict evidence for three pairs of post-antennal cephalic appendages but it's a bit hard to picture how they functioned if there's no room between the hypostome and ventral plates. Also, a comment is required on the reconstruction involving all cephalic appendages originating against/under the hypostome rather the first pair being paroral near the posterior end of the hypostome and the rest being post-hypostomal as in trilobites.

A short comment has been added to the caption.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

I have seen the extensive revision of the manuscript. The main point "Multiple origins of dorsal ecdysial sutures in atiopoans" is now partially supported by results presented by the authors. I am still unsatisfied with descriptions and interpretations of critical features newly revealed by authors. The following points might be useful for the author to make further revisions.

(1) The antennae were well illustrated in a couple of specimens, while it was described in a short sentence.

(2) There are also imprecise descriptions of features (see my annotations in submitted ms).

(3) Ontogeny of the cephalon was not described.

(3) The critical head element is the so called "vental plate". How this element connects with the cephalic shield is not adequately revealed. The authors claimed that the suture is along the cephalic margin. However, the lateral margin of cephalon is not rounded but exhibit two notches (e.g. Fig 3C) . This gives a indication that the supposed ventral plates have a dorsal extension to fit the notches. Alternatively, the "ventral plate" can be interpreted as a small free cheek with a large ventral extension, providing evidence for librigenal hypothesis.

Reviewer #3 (Recommendations For The Authors):

The references swap back and forth between journal titles being abbreviated or written out in full. Please standardise this to journal format rather than alternating between two different styles.

Line 145: Perez-Peris et al. (2021) should be cited as the source for the Anacheirurus appendages.

Added, thank you.

Line 310: The El Albani et al (2024) paper on ellipsocephaloid appendages should be noted in connection with an A+4 (rather than A+3) head in trilobites.

Added.

Minor or trivial corrections:

Line 51: move the three citations to follow "arthropods" rather than following "artiopodans", as none of these papers are specifically about Artiopoda.

Changed thank you

Caption to Figure 1 and line 100: Acanthomeridion appears in Figure 1 and in the text with no context. Please weave it into the text appropriately.

Line 136: The data were...

Corrected

Line 164: upper case for Morphobank.

Corrected

Line 183: spelling of "Village" (not "Vallige").

Corrected

Line 197: I suggest using "articles" rather than "podomeres" for the antenna (as you did in line 232).

Changed thank you

Line 269: "gnathobasal spine (rather than "spin").

Changed thank you

Line 272: "Exopods" is used here but elsewhere "exopodites" is used.

Exopodites is now used throughout

Line 359: "can been seen" is awkward and, as evolutionary patterns are inferred rather than "seen", could be reworded as "... loss of the eye slit has been inferred...".

Reworded as suggested

Line 422 and 423: As two referees asked in the first round of review, delete "iconic" and "symbolic".

Deleted as suggested

Line 467: "librigena-like".

Corrected

Author response:

The following is the authors’ response to the original reviews.

We would like to thank the reviewers and editor for their helpful comments and suggestions. In response, we have revised the manuscript in two main ways:

(1) To address the comments about rearranging figures and tables, we added a new Figure 3 that summarizes neurotransmitter assignments across all neuron classes. Our rationale for this change is detailed below.

(2) To address the comment on clarifying neurotransmitter synthesis versus uptake, we analyzed two additional reporter alleles that tag the monoamine uptake transporters for 5-HT and potentially tyramine. These results are now presented in a new Figure 8 and corresponding sections in the manuscript. Related tables have been updated to include this expression data. Two more authors have been added due to their contributions to these experiments.

For more detailed changes, please see our responses to the specific reviewer's comments as well as the revised manuscript.

Public Reviews:

Reviewer #1 (Public Review): 

Wang and colleagues conducted a study to determine the neurotransmitter identity of all neurons in C. elegans hermaphrodites and males. They used CRISPR technology to introduce fluorescent gene expression reporters into the genomic loci of NT pathway genes. This approach is expected to better reflect in vivo gene expression compared to other methods like promoter- or fosmid-based transgenes, or available scRNA datasets. The study presents several noteworthy findings, including sexual dimorphisms, patterns of NT co-transmission, neuronal classes that likely use NTs without direct synthesis, and potential identification of unconventional NTs (e.g. betaine releasing neurons). The data is well-described and critically discussed, including a comparison with alternative methods. Although many of the observations and proposals have been previously discussed by the Hobert lab, the current study is particularly valuable due to its comprehensiveness. This NT atlas is the most complete and comprehensive of any nervous system that I am aware of, making it an extremely useful tool for the community. 

Reviewer #2 (Public Review):

Summary: 

Together with the known anatomical connectivity of C. elegans, a neurotransmitter atlas paves the way toward a functional connectivity map. This study refines the expression patterns of key genes for neurotransmission by analyzing the expression patterns from CRISPR-knocked-in GFP reporter strains using the color-coded Neuropal strain to identify neurons. Along with data from previous scRNA sequencing and other reporter strains, examining these expression patterns enhances our understanding of neurotransmitter identity for each neuron in hermaphrodites and the male nervous system. Beyond the known neurotransmitters (GABA, Acetylcholine, Glutamate, dopamine, serotonin, tyramine, octopamine), the atlas also identifies neurons likely using betaine and suggests sets of neurons employing new unknown monoaminergic transmission, or using exclusively peptidergic transmission. 

Strengths: 

The use of CRISPR reporter alleles and of the Neuropal strain to assign neurotransmitter usage to each neuron is much more rigorous than previous analysis and reveals intriguing differences between scRNA seq, fosmid reporter, and CRISPR knock-in approaches. Among other mechanisms, these differences between approaches could be attributed to 3'UTR regulatory mechanisms for scRNA vs. knockin or titration of rate-limited negative regulatory mechanisms for fosmid vs. knockin. It would be interesting to discuss this and highlight the occurrences of these potential phenomena for future studies.  

We recognize that readers of this study may be interested in understanding the differences between the three approaches. Therefore, in the Introduction, we addressed the potential risk of overexpression artifacts associated with multicopy transgenes, such as fosmid-based reporters, which can affect rate-limiting negative regulatory mechanisms. Additionally, in the Discussion, we included a section titled 'Comparing approaches and caveats of expression pattern analysis' to further explore these comparative methods and their associated nuances.

Weaknesses: 

For GABAergic transmission, one shortcoming arises from the lack of improved expression pattern by a knockin reporter strain for the GABA recapture symporter snf-11. In its absence, it is difficult to make a final conclusion on GABA recapture vs GABA clearance for all neurons expressing the vesicular GABA transporter neurons (unc-47+) but not expressing the GAD/UNC-25 gene e.g. SIA or R2A neurons. At minima, a comparison of the scRNA seq predictions versus the snf-11 fosmid reporter strain expression pattern would help to better judge the proposed role of each neuron in GABA clearance or recycling. 

The snf-11 fosmid-based reporter data shows very good overlap with scRNA seq predictions (now included in Supp. Table S1). 

But there are two much stronger reasons why we did not seek to further the analysis of expression of the snf-11 GABA uptaker:

(1) Due to available anti-GABA staining data, we do know which neurons have the potential to take up GABA (via SNF-11).

(2) Focusing on SNF-11 function rather than expression, we can ask which neurons lose anti-GABA staining in snf-11 mutants.

Both of these types of analyses have been done in an earlier study from our lab (Gendrel et al., 2016, PMID 27740909), which, among other things, investigated GABA uptake mechanisms via SNF-11. Apart from analyzing the expression of a fosmid-based snf-11 reporter, we immunostained worms for GABA in both snf-11 mutant and wild type backgrounds (results summarized in Tables 1 and 2 of Gendrel et al.). Of the neurons that typically stain for GABA (Table 1, Gendrel et al.), two neuron classes (ALA and AVF) lost the staining in snf-11 mutants, suggesting that these neurons likely uptake GABA via SNF-11. Importantly, one of the neurons the reviewer mentioned, R2A, stains for GABA in both wild type and snf-11 mutants, indicating that it likely does not uptake GABA via SNF-11. The other neuron mentioned, SIA, does not stain for GABA in wild type (Table 2, Gendrel et al.), hence not a GABA uptake neuron. In cases like SIA and other neurons, where a neuron does not express unc-25 but does express unc-47 reporters (either fosmid or CRISPR reporter alleles), we speculate that UNC-47 transport another neurotransmitter.

Considering the complexities of different tagging approaches, like T2A-GFP and SL2-GFP cassettes, in capturing post-translational and 3'UTR regulation is important. The current formulation is simplistic. e.g. after SL2 trans-splicing the GFP RNA lacks the 5' regulatory elements, T2A-GFP self-cleavage has its own issues, and the his-44-GFP reporter protein does certainly have a different post-translational life than vesicular transporters or cytoplasmic enzymes. 

Yes, agreed, these points are mentioned in the Introduction and discussed in "Comparing approaches and caveats of expression pattern analysis" in the Discussion.

Do all splicing variants of neurotransmitter-related genes translate into functional proteins? The possibility that some neurons express a non-functional splice variant, leading to his-74-GFP reporter expression without functional neurotransmitter-related protein production is not addressed. 

We thank the reviewer for bringing up this really interesting point, which we had not considered. First and foremost, with the exception of unc-25 (discussed in the next point), for all other genes that produce multiple splice forms, we made sure to append our tag (at 5’ or 3’ end) such that the expression of all splice forms is captured. The reviewer raises the interesting point that in an alternative splicing scenario, some of the cells that express the primary transcript may “switch” to an inactive form. While we cannot exclude this possibility, we have confirmed by sequence analysis in WormBase that in five of the six cases where there is alternative splicing, the alternatively spliced exon lies outside the conserved, functionally relevant (enzymatic or structural) domain. In one case, unc-25, a shorter isoform is produced that does cut into the functionally relevant domain; however, since all unc-25 reporter allele expression cells are also staining positive for GABA, this may not be an issue. 

Also, one tagged splice variant of unc-25 is expected to fail to produce a GFP reporter, can this cause trouble? 

Yes, there is indeed a third splice variant of unc-25 with an alternative C-terminus. To address potential expression of this isoform, we CRISPR-engineered another reporter, unc-25(ot1536[unc-25b.1::t2a::gfp::h2b]), in which the inserted t2a::gfp::h2b sequences are fused to the C-terminus of the alternative splice form, but we did not observe any expression of this reporter. Now included in the manuscript.

Reviewer #3 (Public Review): 

Summary: 

In this paper, Wang et al. provide the most comprehensive description and comparison of the expression of the different genes required to synthesize, transport, and recycle the most common neurotransmitters (Glutamate, Acetylcholine, GABA, Serotonin, Dopamine, Octopamine, and Tyramine) used by hermaphrodite and male C. elegans. This paper will be a seminal reference in the field. Building and contrasting observations from previous studies using fosmid, multicopy reporters, and single-cell sequencing, they now describe CRISPR/Cas-9-engineered reporter strains that, in combination with the multicolor pan-neuronal labeling of all C. elegans neurons (NeuroPAL), allows rigorous elucidation of neurotransmitter expression patterns. These novel reporters also illuminate previously unappreciated aspects of neurotransmitter biology in C. elegans, including sexual dimorphism of expression patterns, cotransmission, and the elucidation of cell-specific pathways that might represent new forms of neurotransmission. 

Strengths: 

The authors set out to establish neurotransmitter identities in C. elegans males and hermaphrodites via varying techniques, including integration of previous studies, examination of expression patterns, and generation of endogenous CRISPR-labeled alleles. Their study is comprehensive, detailed, and rigorous, and achieves the aims. It is an excellent reference for the field, particularly those interested in biosynthetic pathways of neurotransmission and their distribution in vivo, in neuronal and non-neuronal cells. 

Weaknesses: 

No weaknesses were noted. The authors do a great job linking their characterizations with other studies and techniques, giving credence to their findings. As the authors note, there are sexually dimorphic differences across animals and varying expression patterns of enzymes. While it is unlikely there will be huge differences in the reported patterns across individual animals, it is possible that these expression patterns could vary developmentally, or based on physiological or environmental conditions. It is unclear from the study how many animals were imaged for each condition, and if the authors noted changes across individuals during development (could be further acknowledged in the discussion?)  

We have updated the Methods section to specify the number of animals used for imaging. We agree with the reviewer that documenting the developmental dynamics of neurotransmitter expression would be interesting. However, except for one gene (tph-1, Fig. S2), we did not analyze the expression during different developmental stages for most genes in this study. Following the reviewer's suggestion, we have included this as a potential future direction in "Conclusions" at the end of the revised manuscript.

Recommendations for the authors:

After the consultation session, a common suggestion from the reviewers is to bring the tables more upfront, perhaps even in the form of legible main Figures and in alphabetical order of neurons; since we believe that the study will be in the long-term often used for these data; while the Figures with fluorescent expression patterns could be moved to the supplemental information. 

We appreciate the reviewers' and editor's acknowledgment of the tables' possibly frequent usage by the field. We have considered carefully how to order the data presentation. We prefer to keep most of the fluorescent figures in the main text because they convey important subtleties that we want the reader to be aware of.

To address the suggestions to bring key data more upfront, we have added an entirely new figure (Figure 3) before the ensuing data figures that summarized expression patterns of the fluorescent reporters. This new figure (A) summarizes the neurotransmitter use for all neuron classes and (B) illustrates this information within worm schematics, showing the position of neurons in the whole worm. This figure serves as a good overview of neurotransmitter assignments but also specifically refers to the more extensive data and supplementary tables with detailed notes. We believe this solution effectively balances the need for comprehensive information and ease of reference.

Reviewer #1 (Recommendations for The Authors):

Suggestions: 

(1) The study contains up to 10 Figures with gene expression patterns; however, I believe the community will use this paper mostly in the future for its summarizing tables. I wonder if it would be more useful to edit the tables and move them to the main figures while most fluorescent reporter images could be moved to the supplementary part. 

Yes, as mentioned above, we made new summary table & schematic upfront. We do prefer to keep primary data in main figure body. Please see above (Public Review & Response).

(2) In the section titled 'Neurotransmitter Synthesis versus Uptake', the author's wording could be more careful. The data rather suggests functions for individual neuronal classes, such as clearance neurons or signaling neurons. However, these functions remain hypotheses until further detailed studies are conducted to test them. 

These are fair points. We have made several improvements: 

(1) In the referenced section, we added a sentence at the end of the paragraph on betaine to suggest the importance of future functional studies.

(2) We analyzed reporter allele expression for two additional genes: the known uptake transporter for 5-HT (mod-5, reporter allele vlc47) and the predicted uptake transporter for tyramine (oct-1, reporter allele syb8870). The results from these experiments are presented in the new Figure 8 and discussed in Results and Discussion correspondingly. We also collaborated with Curtis Loer, who conducted anti-5-HT staining in wild type and mod-5 mutant animals (results shown in Figure 12). These experiments have enhanced our understanding of 5-HT uptake mechanisms and potential tyramine uptake mechanisms.

(3) At the end of the Conclusions, we emphasized the need for future detailed studies to test the functions of neurotransmitter synthesis and uptake.

(3) Page 21; add to the discussion: neurons could use mainly electrical synapses for communication. Especially for RMG neurons, this might be the case (in addition to neuropeptide communication). 

“Main usage” is a difficult term to use. If there were neurons that are clearly devoid of any form of synaptic vesicle (small or DCV; note that RMG has plenty of DCVs), but show robust and reproducible electrical synapses, we would agree that such neurons could primarily be a “coupling” neuron. But this call is very hard to make for any C. elegans neuron (RMG included) and hence we prefer to not add further to an already quite long Discussion section.

(4) Page 23: I believe that multi-copy promoter-based transgenes (despite array suppression mechanisms) could be potentially more sensitive than single-copy insertion of fluorescent reporters. In our lab, we observed this a couple of times. This could be discussed. 

We discuss this in "Comparing approaches and caveats of expression pattern analysis" in the Discussion.

We have also added a third possibility (i.e. technical issues related to neuron-ID) in the revised manuscript.   

Reviewer #2 (Recommendations For The Authors): 

Comment during consultation session: As for my feedback on the lack of an SNF-11 reporter strain, exercising more caution in their conclusions would suffice for me. Other comments are simple edits/discussion.  

Please see above.  

Several neurotransmitter symporters exist in the C. elegans genome, does any express specifically in the "orphan" UNC-47+ neurons? 

Yes, good point, we considered this possibility, but of the >10 SLC6-family of neurotransmitter reporters, only the classic, de-orphanized ones that we discuss here in the paper show robust scRNA signals (as discussed in the paper) and none of those give clues about the orphan unc-47(+) neurons.

Based on UNC-47+ expression the article suggests a "Novel inhibitory neurotransmitter". Why would any new neurotransmitter using UNC-47 be necessarily inhibitory? The presence of one potential glycine-gated anion channel and one GPCR in C. elegans genome sounds poor evidence to suggest a sign of glycine or b-alanine transmission. 

Yes, agreed, it does not need to be inhibitory. Fixed in Results and Discussion. 

To help readers the expression of the knocked in GFP in neurons should not be reported as binary in table S1 which leads to a feeling of strong discrepancy between scRNA seq and CRISPR GFP, which is not the case.  

There might be some misunderstanding regarding the coloring in this table. To clarify, the green-filled Excel cells denote the expression of reporters utilized in prior studies, rather than the CRISPR reporter alleles. Expression of the CRISPR alleles is instead indicated on the left side of the neuron names, marked as "CRISPR+" in green font. For signifying absence of expression, we used "no CRISPR" in red font in the first submission. We have now changed it into "CRISPR-" for greater clarity.

The variable expression of reporter GFP between individuals for the same neuron is intriguing. It is unclear if this is observed only for dim neurons or can be more of an ON/OFF expression. 

Variability only occurs for dim expression. We have now clarified this point in Discussion, "Comparing approaches and caveats of expression pattern analysis".

The multiple occurrences of co-transmission, especially in male neurons, are interesting. It will be interesting in the future to establish whether the neurotransmitters are synaptically segregated or coreleased. As the section on sexual dimorphism of neurotransmitter usage does not discuss novel information coming from this study, it is not very necessary. 

Agreed. We added this perspective to the Discussion, "Co-transmission of multiple neurotransmitters".  

In the abstract, dopamine is missing in the main known transmitter.  

Fixed. Thanks for spotting this.

Reviewer #3 (Recommendations For The Authors): 

Great article. Minor suggestions to strengthen presentation: 

Figure 1B is hard to interpret. There could be more intuitive ways of representing the data and the methodologies that support a given expression pattern. Neurons should also be reordered by alphabetical order rather than expression levels to facilitate finding them.  

We considered alternative ways of presenting this data, but, regrettably, did not come up with a better approach. To clarify, the primary focus of Fig. 1B is to compare expression of previously reported reporters and scRNA data, which was quite literally the initial impetus for our analysis, i.e. we noted strong scRNA signals that had not previously been supported by transgenic reporter data. For a comprehensive version of the table that includes more details on the expression of CRISPR reporter alleles, please refer to Table S1, which we referenced in the figure legend.   

GFP-only channel images in Figures 3, 4, 5, and 9 sometimes show dim signals that the authors are highlighting as new findings. We recommend using the inverted grayscale version of that channel since the contrast of dim signals is more noticeable to the human eye rather than when the image is colorized. 

Good point, we implemented these suggestions in the figures the reviewer mentioned, now re-numbered Figures 4, 5, 6, and 12. For Figure 6 (tph-1, bas-1, and cat-1 expression in hermaphrodites), we used a new cat-1 head image to reflect the newly identified ASI and AVL expression that wasn’t readily visible in the original projection used in the earlier version of this manuscript. We also added grayscale images in Figure 13 to reflect dim tbh-1 expression in IL2 neurons more clearly.

A plan to integrate this new information into WormAtlas. The C. elegans community is characterized by the open sharing of information on platforms that are user-friendly and accessible. Ideally, the new information would not just 'erase' what was observed before but will describe the new observations and will let the community reach their own conclusions since there is no perfect method and even these CRISPR/Cas9 reporter strains are only proxy for gene expression that subject to post-transcriptional regulation since they depend on T2A and SL2 sequences. 

We completely agree with the reviewer’s suggestion. We will coordinate with WormAtlas on integrating this new information. 

In the case of neurons that were removed from using a specific neurotransmitter, like PVQ. What do the authors conclude overall, if it does not use glutamate, are there any new hypotheses to what it could be using?

Since all neurons express multiple neuropeptides, we hypothesize neurons such as PVQ may be primarily peptidergic. This is included in Discussion, "Neurons devoid of canonical neurotransmitter pathway genes may define neuropeptide-only neurons".  

In Table S5, the I4 neuron is listed as a variable for eat-4 expression but in Table S1 it says that there was no CRISPR expression detected. Which one is correct? 

Thanks for spotting this. Table S5 is correct, we saw very dim and variable expression of the eat-4 reporter allele in I4. Table S1 is fixed now.

Additional discussion points that might be important for the community: 

CRIPSR strains used here should be deposited in the CGC. 

Yes, all strains generated in this study have already been deposited to CGC. 

It would be great to have an additional discussion point on how the neural clusters in CenGEN were defined based on the fosmid reporter expression, so in a way using the defining factor as one that was already defined by it might make results confusing. 

Neural cluster definition in CeNGEN did not rely on isolated data points but on the combination of many expression reagents, each with its own shortcomings, but in combination providing reliable identification. Since one feedback we have gotten from many readers of our manuscript is that it is already very long as is, we prefer not to dilute the discussion further.

It would be important to discuss the rate of neurotransmitter genes that have variable expression patterns. Are any of those genes used in NeuroPAL to define specific neuronal classes? This is important to describe as NeuroPAL labeling is being used to define neuronal identity. 

All the reporters used in NeuroPAL are promoter-based, very robust and do not include the full loci of genes, so they are not directly comparable with the CRISPR reporter alleles in this study. However, we recognize that some expression pattern variability could be confusing. We have discussed this more in the section "Comparing approaches and caveats of expression pattern analysis" in the Discussion.

I personally think when writers are able to paint a picture in the readers head instead of giving a vague description, it makes it easier for the reader to understand what the writer is talking about. Giving the reader chances to make personal connections .

Reviewer #1 (Public review):

Summary:

The present study addresses whether physiological signals influence aperiodic brain activity with a focus on age-related changes. The authors report age effects on aperiodic cardiac activity derived from ECG in low and high-frequency ranges in roughly 2300 participants from four different sites. Slopes of the ECGs were associated with common heart variability measures, which, according to the authors, shows that ECG, even at higher frequencies, conveys meaningful information. Using temporal response functions on concurrent ECG and M/EEG time series, the authors demonstrate that cardiac activity is instantaneously reflected in neural recordings, even after applying ICA analysis to remove cardiac activity. This was more strongly the case for EEG than MEG data. Finally, spectral parameterization was done in large-scale resting-state MEG and ECG data in individuals between 18 and 88 years, and age effects were tested. A steepening of spectral slopes with age was observed particularly for ECG and, to a lesser extent, in cleaned MEG data in most frequency ranges and sensors investigated. The authors conclude that commonly observed age effects on neural aperiodic activity can mainly be explained by cardiac activity.

Strengths:

Compared to previous investigations, the authors demonstrate the effects of aging on the spectral slope in the currently largest MEG dataset with equal age distribution available. Their efforts of replicating observed effects in another large MEG dataset and considering potential confounding by ocular activity, head movements, or preprocessing methods are commendable and valuable to the community. This study also employs a wide range of fitting ranges and two commonly used algorithms for spectral parameterization of neural and cardiac activity, hence providing a comprehensive overview of the impact of methodological choices. Based on their findings, the authors give recommendations for the separation of physiological and neural sources of aperiodic activity.

Weaknesses:

While the aim of the study is well-motivated and analyses rigorously conducted, the overall structure of the manuscript, as it stands now, is partially misleading. Some of the described results are not well-embedded and lack discussion.

Reviewer #3 (Public review):

Summary:

Schmidt et al., aimed to provide an extremely comprehensive demonstration of the influence cardiac electromagnetic fields have on the relationship between age and the aperiodic slope measured from electroencephalographic (EEG) and magnetoencephalographic (MEG) data.

Strengths:

Schmidt et al., used a multiverse approach to show that the cardiac influence on this relationship is considerable, by testing a wide range of different analysis parameters (including extensive testing of different frequency ranges assessed to determine the aperiodic fit), algorithms (including different artifact reduction approaches and different aperiodic fitting algorithms), and multiple large datasets to provide conclusions that are robust to the vast majority of potential experimental variations.

The study showed that across these different analytical variations, the cardiac contribution to aperiodic activity measured using EEG and MEG is considerable, and likely influences the relationship between aperiodic activity and age to a greater extent than the influence of neural activity.

Their findings have significant implications for all future research that aims to assess aperiodic neural activity, suggesting control for the influence of cardiac fields is essential.

Weaknesses:

Figure 4I: The regressions explained here seem to contain a very large number of potential predictors. Based on the way it is currently written, I'm assuming it includes all sensors for both the ECG component and ECG rejected conditions?

I'm not sure about the logic of taking a complete signal, decomposing it with ICA to separate out the ECG and non-ECG signals, then including these latent contributions to the full signal back into the same regression model. It seems that there could be some circularity or redundancy in doing so. Can the authors provide a justification for why this is a valid approach?

I'm not sure whether there is good evidence or rationale to support the statement in the discussion that the presence of the ECG signal in reference electrodes makes it more difficult to isolate independent ECG components. The ICA algorithm will still function to detect common voltage shifts from the ECG as statistically independent from other voltage shifts, even if they're spread across all electrodes due to the referencing montage. I would suggest there are other reasons why the ICA might lead to imperfect separation of the ECG component (assumption of the same number of source components as sensors, non-Gaussian assumption, assumption of independence of source activities).

The inclusion of only 32 channels in the EEG data might also have reduced the performance of ICA, increasing the chances of imperfect component separation and the mixing of cardiac artifacts into the neural components, whereas the higher number of sensors in the MEG data would enable better component separation. This could explain the difference between EEG and MEG in the ability to clean the ECG artifact (and perhaps higher-density EEG recordings would not show the same issue).

In addition to the inability to effectively clean the ECG artifact from EEG data, ICA and other component subtraction methods have also all been shown to distort neural activity in periods that aren't affected by the artifact due to the ubiquitous issue of imperfect component separation (https://doi.org/10.1101/2024.06.06.597688). As such, component subtraction-based (as well as regression-based) removal of the cardiac artifact might also distort the neural contributions to the aperiodic signal, so even methods to adequately address the cardiac artifact might not solve the problem explained in the study. This poses an additional potential confound to the "M/EEG without ECG" conditions.

Literature Analysis, Page 23: was there a method applied to address studies that report reducing artifacts in general, but are not specific to a single type of artifact? For example, there are automated methods for cleaning EEG data that use ICLabel (a machine learning algorithm) to delete "artifact" components. Within these studies, the cardiac artifact will not be mentioned specifically, but is included under "artifacts".

Statistical inferences, page 23: as far as I can tell, no methods to control for multiple comparisons were implemented. Many of the statistical comparisons were not independent (or even overlapped with similar analyses in the full analysis space to a large extent), so I wouldn't expect strong multiple comparison controls. But addressing this point to some extent would be useful (or clarifying how it has already been addressed if I've missed something).

Methods:

Applying ICA components from 1Hz high pass filtered data back to the 0.1Hz filtered data leads to worse artifact cleaning performance, as the contribution of the artifact in the 0.1Hz to 1Hz frequency band is not addressed (see Bailey, N. W., Hill, A. T., Biabani, M., Murphy, O. W., Rogasch, N. C., McQueen, B., ... & Fitzgerald, P. B. (2023). RELAX part 2: A fully automated EEG data cleaning algorithm that is applicable to Event-Related-Potentials. Clinical Neurophysiology, result reported in the supplementary materials). This might explain some of the lower frequency slope results (which include a lower frequency limit <1Hz) in the EEG data - the EEG cleaning method is just not addressing the cardiac artifact in that frequency range (although it certainly wouldn't explain all of the results).

It looks like no methods were implemented to address muscle artifacts. These can affect the slope of EEG activity at higher frequencies. Perhaps the Riemannian Potato addressed these artifacts, but I suspect it wouldn't eliminate all muscle activity. As such, I would be concerned that remaining muscle artifacts affected some of the results, particularly those that included high frequency ranges in the aperiodic estimate. Perhaps if muscle activity were left in the EEG data, it could have disrupted the ability to detect a relationship between age and 1/f slope in a way that didn't disrupt the same relationship in the cardiac data (although I suspect it wouldn't reverse the overall conclusions given the number of converging results including in lower frequency bands). Is there a quick validity analysis the authors can implement to confirm muscle artifacts haven't negatively affected their results? I note that an analysis of head movement in the MEG is provided on page 32, but it would be more robust to show that removing ICA components reflecting muscle doesn't change the results. The results/conclusions of the following study might be useful for objectively detecting probable muscle artifact components: Fitzgibbon, S. P., DeLosAngeles, D., Lewis, T. W., Powers, D. M. W., Grummett, T. S., Whitham, E. M., ... & Pope, K. J. (2016). Automatic determination of EMG-contaminated components and validation of independent component analysis using EEG during pharmacologic paralysis. Clinical neurophysiology, 127(3), 1781-1793.

make a commitment in helping educate black people to make the world a better place

this is the way it's done in "How to keep your neighbors in order"

hah!

I was reminded of this recently while on a tour of historic Fort George. Life in the military at the end of the 18th century sounded horrible with its ever-abundant risk of disease, death, or dismemberment. But if you weren't a member of the monied elite, it was a guaranteed meal and roof over one's head (in exchange for one's fealty).

If something is doubling faster in small models, how long before they Ive take the larger models? I can’t do the maths in my head

different bodies/structures of government differentiate themselves by the group of people/amt of people who reside at the top

a city is different from a family. cities have an everchanging center of control wherein a family has a permanent head of the household constituted by humans natural instincts.

Author response:

The following is the authors’ response to the original reviews.

Public Reviews: 

Reviewer #1 (Public Review):

Summary:

The question of whether eyespots mimic eyes has certainly been around for a very long time and led to a good deal of debate and contention. This isn't purely an issue of how eyespots work either, but more widely an example of the potential pitfalls of adopting 'just-so-stories' in biology before conducting the appropriate experiments. Recent years have seen a range of studies testing eye mimicry, often purporting to find evidence for or against it, and not always entirely objectively. Thus, the current study is very welcome, rigorously analysing the findings across a suite of papers based on evidence/effect sizes in a meta-analysis.

Strengths:

The work is very well conducted, robust, objective, and makes a range of valuable contributions and conclusions, with an extensive use of literature for the research. I have no issues with the analysis undertaken, just some minor comments on the manuscript. The results and conclusions are compelling. It's probably fair to say that the topic needs more experiments to really reach firm conclusions but the authors do a good job of acknowledging this and highlighting where that future work would be best placed.

Weaknesses:

There are few weaknesses in this work, just some minor amendments to the text for clarity and information.

We greatly appreciate Reviewer 1’s positive comments on our manuscript. We also revised our manuscript text and a figure in accordance with Reviewer 1’s recommendations.

Reviewer #2 (Public Review):

Many prey animals have eyespot-like markings (called eyespots) which have been shown in experiments to hinder predation. However, why eyespots are effective against predation has been debated. The authors attempt to use a meta-analytical approach to address the issue of whether eye-mimicry or conspicuousness makes eyespots effective against predation. They state that their results support the importance of conspicuousness. However, I am not convinced by this.

There have been many experimental studies that have weighed in on the debate. Experiments have included manipulating target eyespot properties to make them more or less conspicuous, or to make them more or less similar to eyes. Each study has used its own set of protocols. Experiments have been done indoors with a single predator species, and outdoors where, presumably, a large number of predator species predated upon targets. The targets (i.e, prey with eyespot-like markings) have varied from simple triangular paper pieces with circles printed on them to real lepidopteran wings. Some studies have suggested that conspicuousness is important and eye-mimicry is ineffective, while other studies have suggested that more eye-like targets are better protected. Therefore, there is no consensus across experiments on the eye-mimicry versus conspicuousness debate.

The authors enter the picture with their meta-analysis. The manuscript is well-written and easy to follow. The meta-analysis appears well-carried out, statistically. Their results suggest that conspicuousness is effective, while eye-mimicry is not. I am not convinced that their meta-analysis provides strong enough evidence for this conclusion. The studies that are part of the meta-analysis are varied in terms of protocols, and no single protocol is necessarily better than another. Support for conspicuousness has come primarily from one research group (as acknowledged by the authors), based on a particular set of protocols.

Furthermore, although conspicuousness is amenable to being quantified, for e.g., using contrast or size of stimuli, assessment of 'similarity to eyes' is inherently subjective. Therefore, manipulation of 'similarity to eyes' in some studies may have been subtle enough that there was no effect.

There are a few experiments that have indeed supported eye-mimicry. The results from experiments so far suggest that both eye-mimicry and conspicuousness are effective, possibly depending on the predator(s). Importantly, conspicuousness can benefit from eye-mimicry, while eye-mimicry can benefit from conspicuousness.

Therefore, I argue that generalizing based on a meta-analysis of a small number of studies that conspicuousness is more important than eye-mimicry is not justified. To summarize, I am not convinced that the current study rules out the importance of eye-mimicry in the evolution of eyespots, although I agree with the authors that conspicuousness is important.

We understand Reviewer 2’s concerns and have addressed them by adding some sentences in the discussion part (L506- 508, L538-L540). In addition, our findings, which were guided by current knowledge, support the conspicuousness hypothesis, but we acknowledge the two hypotheses are not mutually exclusive (L110-112). We also do not reject the eye mimicry hypothesis. As we have demonstrated, there are still several gaps in the current literature and our understanding (L501-553). Our aim is for this research to stimulate further studies on this intriguing topic and to foster more fruitful discussions.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Minor comments

Lines 59/60: "it is possible that eyespots do not involve mimicry of eyes..."

The sentence was revised (L59). To enhance readability, we have integrated Reviewer 1's suggestions by simplifying the relevant section instead of using the suggested sentence.

Line 61: not necessarily aposematism. They might work simply through neophobia, unfamiliarity, etc even without unprofitability

We changed the text in line with the comment from Reviewer 1 (L61-63).

Lines 62/63 - this is a little hard to follow because I think you really mean both studies of real lepidopterans as well as artificial targets. Need to explain a bit more clearly.

We provided an additional explanation of our included primary study type (L64-65).

Lines 93/94 - not quite that they have nothing to do with predator avoidance, but more that any subjective resemblance to eyes is coincidental, or simply as a result of those marking properties being more effective through conspicuousness in their own right.

Line 94 - similarly, not just aposematism. You explain the possible reasons above on l92 as also being neophobia, etc.

We agreed with Reviewer 1’s comments and added more explanations about the conspicuousness hypothesis (L96-97). We have also rewritten the sentences that could be misleading to readers (L428).

Line 96 - this is perhaps a bit misleading as it seems to conflate mechanism and function. The eye mimicry vs conspicuousness debate is largely about how the so-called 'intimidation' function of eyespots works. That is, how eyespots prevent predators from attacking. The deflection hypothesis is a second function of eyespots, which might also work via consciousness or eye mimicry (e.g. if predators try to peck at 'eyes') but has been less central to the mimicry debate.

The explanations and suggestions from Reviewer 1 are very helpful. We revised this part of our manuscript (L103-108) and Figure 1 and its legend to make it clearer that the eyespot hypothesis and the conspicuousness hypothesis explain anti-predator functions from a different perspective than the deflection hypothesis.

There is a third function of eyespots too, that being as mate selection traits. Note that Figure 1 should also be altered to reflect these points.

We wanted to focus on explaining why eyespot patterns can contribute to prey survival. Therefore, we did not state that eyespot patterns function as mate selection traits in this paragraph. Alternatively, we have already mentioned this in the Discussion part (L455-L465) and rewrote it more clearly (L456).

Were there enough studies on non-avian predators to analyse in any way? 

We found a few studies on non-avian predators (e.g. fish, invertebrates, or reptiles), but not enough to conduct a meta-analysis.

Line 171/72 - why? Can you explain, please.

The reason we excluded studies that used bright or contrasting patterns as control stimuli in our meta-analysis is to ensure comparability across primary studies. We added an explanation in the text (L180-181).

Line 177 - can you clarify this?

Without control stimuli, it is challenging to accurately assess the effect of eyespots or other conspicuous patterns on predation avoidance. Control stimuli allow for a comparison of the effect of eyespots or patterns. We added a more detailed explanation to clarify here (L186-188).

Line 309 - presumably you mean 33 papers, each of which may have multiple experiments? I might have missed it, but how many individual experiments in total? 

There were 164 individual experiments. We have now added that information in the manuscript (L320).

Line 320 - paper shaped in a triangle mostly?

We cannot say that most artificial prey were triangular. After excluding the caterpillar type, 57.4% were triangular, while the remaining 43.6% were rectangular (Figure 2b).

Line 406: Stevens.

We fixed this name, thank you (L417).

Discussion - nice, balanced and thorough. Much of the work done has been in Northern Europe where eyespot species are less common. Perhaps things may differ in areas where eyespots are more prevalent.

We appreciate Reviewer 1’s kind words and comments. We agree with your comments and reflected them in our manuscript (L542-545).

Line 477 - True, and predators often have forward-facing eyes making it likely both would often be seen, but a pair of eyes may not be absolutely crucial to avoidance since sometimes a prey animal may only see one eye of a predator (e.g. if the other is occluded, or only one side of the head is visible).

We were grateful for Reviewer 1's comment. We added a sentence noting that the eyespots do not necessarily have to be in pairs to resemble eyes (L490-L492).

This piece gives an ironic statement through this phrase that states while melodies that are heard are sweet, but unheard melodies are sweeter, suggesting that there are things that are better to be unheard thus maker them more sweeter, such as the troubles one goes through so it is better left unheard.

It is also a paradox in how it works as well as we find sweetness in what we hear in music, giving us joy, but this states not knowing what it sounds like provides a grander sense of sweetness. But, is that truly the case? How can we know that such a thing is sweeter if we cannot hear what it is? This results in creating a tension to the structure that is built upon, ironically creating something sweet to the reader's ear by using stressed syllables to play on as the phrase would say. This resulting combination gives a pleasant mix or irony and paradox that had a rhythm that the reader bobs their head along as they enjoyed the moments of tension with the stressed syllables and the moments of peace with the unstressed syllables , making it like music that you can enjoy while you start to see similar problems in language when you compare the two.

I found this sentence so daunting but truthful. If you did not know the backstory of this apology you would have multiple questions and scenarios running through your head. But what i love is once you know what this apology is about, that sentence becomes so minuscule to our feelings (unless you love spiders) and less harmful to our feelings or reaction.

This sentence painted a picture in my head right when I read it. It was descriptive enough that it told the story but also that It had such a worry rushed tone speaking through these words.

This passage is a reflection of autumn. we also see that this passage has metaphysical literary works for example she talks about the leaves, fruit, and trees

This is another example of how we need more women in STEM. There are so many officially desegregated organizations. But segragation is embedded in behavior and that is what needs coaching.

Note: This response was posted by the corresponding author to Review Commons. The content has not been altered except for formatting.

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Reply to the reviewers

Manuscript number: RC-2024-02378

Corresponding author(s): Angelika Böttger

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• *

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1. General Statements [optional]

After we have carefully studied the four reviews we have received, we made some major revisions to the manuscript. These included the following main points:

• Concerns regarding clarity of the manuscript: we have substantially edited the abstract, introduction and discussion part of the manuscript and added many more references to previous work by other authors, especially Cazet 2021, Tursch 2022 and Gahan 2017. We focused our introduction and discussion on organizer function and on the Gierer-Meinhardt-Model for pattern formation. We think that the conclusions are of great general interest because they suggest a function of the Hydra head organizer according to the original definition by Hans Spemann, that is “harmonious interlocking of separate processes which makes up development”. Notch signaling, in our interpretation, is an instrument for this function of the organizer. Comparison with Craspedacusta compellingly illustrates this idea.
• Concerns regarding Craspedacusta experiments: we have isolated four Craspedacusta transcripts (CsSp5, CsWnt3, CsAlx and CsNOWA) and analyzed their response to DAPT during head regeneration in Craspedacusta. This revealed that DAPT did not inhibit CsWnt3 expression, in accordance with it not having an effect on head regeneration in Craspedacusta However, DAPT inhibited expression of the other potential CsNotch target genes, confirming that DAPT generally works in Craspedacusta polyps as Notch-inhibitor.
• Concerns regarding HyKayak function: we have conducted a rescue experiment to show the function of Hykayak as a target for Notch-regulated repressor genes and a local inhibitor of Wnt-3 expression, which revealed that the expected up-regulation of HyWnt3 in DAPT-treated animals was very weak and did not rescue the DAPT-regeneration phenotype-this was discussed, but data were not included.

2. Point-by-point description of the revisions

This section is mandatory. *Please insert a point-by-point reply describing the revisions that were already carried out and included in the transferred manuscript. *

Reviewer #1 (Evidence, reproducibility and clarity (Required)):

Major: • The introduction is lacking a full description of what is known about transcriptional changes during Hydra regeneration and in particular the role of Wnt signalling in this process. Of note the authors do not cite several important studies from recent years including (but not limited to):

*https://doi.org/10.1073/pnas.2204122119 *

*https://doi.org/10.1186/s13072-020-00364-6 *

*https://doi.org/10.1101/587147 *

*https://doi.org/10.7554/eLife.60562 This problem is further compounded later when the authors do not cite/discuss work which has performed the same or similar analyses to their own. The authors should endeavor to give a more comprehensive background knowledge. *

Answer:

Our work focuses on the role of Notch-signalling during Hydra head regeneration, specifically when the head is removed at an apical position. We therefore now have included additional information about transcriptional changes during this process in the introduction. In addition, we have included the suggested citations in the introduction to give a more general background knowledge.

e.g. .Following decapitation, the expression of Hyβ-catenin and HyTcf was upregulated earliest, followed by local activation of Wnt genes. Among these, HyWnt3 and HyWnt11 appeared within 1.5 h of head removal, followed by HyWnt1, HyWnt9/10c, HyWnt16, and HyWnt7, indicating their role in the formation of the Hydra head organizer (Hobmayer et al., 2001; Lengfeld et al., 2009; Philipp et al., 2009; Tursch et al., 2022).

• The authors do not cite or reference at all the study by Cazet et al. which used iCRT14 along with RNAseq and ATACseq to probe the role of Wnt signaling during early regeneration. This is a major issue. Although I appreciate that the authors have done much longer time courses and that their data therefore add something to our understanding it will still be important to discuss here. For example, the authors show that Wnt3 is activated normally in iCRT14 animals. Is this also seen in the RNAseq from Cazet et al.*
• *

Answer:

iCRT14 was used in Hydra regeneration experiments by Gufler et al (which we did cite) and Cazet et al, but the specific aspects of hypostome and tentacle regeneration were not addressed. Cazet et al. have analyzed HyWnt3expression after iCRT treatment during the first 12 hrs of regeneration. Our data show, in addition that HyWnt3 is not controlled by TCF-dependent transcription during Hydra head regeneration after apical cuts throughout the whole regeneration process including the morphogenesis state. Nevertheless, the other Wnt-genes, which are indicated in canonical Wnt-signalling are affected by iCRT14 also in our study.

We have now included comparison of Cazet- and our data, we wrote:

“HyWnt3 and Wnt9/10c expression are swiftly induced by injuries. When HyWnt3 and HyWnt9/10 activities are sustained, organizers can be formed, which induce ectopic heads when the original organizer tissue (the head) is removed (Cazet et al., 2021; Tursch et al., 2022).”

The effect of iCRT14 had been analyzed in previous studies (Cazet et al., 2021; Gufler et al., 2018; Tursch et al., 2022). All showed b-catenin-dependency for down-regulation of head specific genes in foot regenerates at time points up to 12 hrs after head removal, including HyWnt3. They also stated a failure of head regeneration in the presence of iCRT14 but, in accordance with our study, did not reveal that HyWnt3 expression at future heads depended on b-catenin. None of these studies analyzed the regeneration of tentacles and hypostomes separately and they did not report whether* the regeneration of hypostomes 48 hrs after head removal occurred normally upon iCRT14 treatment. *

• The visualizations used in Figure 3 are quite difficult to interpret and do to in all cases match descriptions in the text. The way the same type data is displayed in figure 5 so much nicer. It is also better to treat the same types of data in the same manner consistently throughout the paper. For Hes, for example, the authors state that there is a reduction although the data shows that this is very small and taking into account the 95% confidence interval does not seem to be significant. If this is the case then the positive control is not working in this experiment. This would be much clear if individual time points were compared like in figure 5 and statistical tests shown. The authors then state that Alx is not affected but there is actually a larger effect than what they deemed significant for Hes (the axes are notably different between these two and I think a more consistent axis would make the genes more comparable). Similarly, Gsc is described as being not affected at 8 hours but it appears again to change more that the positive control Hes. Given this I would call into question the validity of this dataset and/or the interpretation by the authors. A more thorough analysis including taking better into account statistical significance would go a long way to increasing confidence in this data. • The same issues in interpretation described for Figure 3 also apply to figure 4. The authors state that Wnt7 is affected less than Wnt1 and 3 but this is not evident in the figure and no comparative analysis is performed to confirm this. The same for Wnt 11 and 9/10c where what the authors description is very difficult to see in the figure. Sp5 is apparently upregulated, but this is not discussed. Again the axes are notably different making it even more difficult to compare between samples. __Answer*____:__

We have now presented the data by simple scatter blots with significance information for every data point. This allows comparison between samples as requested by the reviewer. The GAMs were moved to the supplement. We believe that some readers may appreciate GAM-representation of the data because of the accessibility of the confidence interval over time.

Concerning DAPT:

“We now performed RT-qPCR analysis to compare gene expression dynamics of these genes during head regeneration 0, 8, 24, 36 and 48 hrs after head removal. Animals were either treated with 30 µM DAPT in 1% DMSO, or 1% DMSO as control for the respective time frames. Timepoint 0 was measured immediately after head removal. The results of these analyses revealed that HyHes expression was clearly inhibited by DAPT during the first 36 hrs after head removal (Fig. 3B), confirming previously published data which had indicated HyHes as a direct target for NICD (Münder et al., 2010). HyAlx expression levels were slightly up-regulated after 24 hrs, but later partially inhibited by DAPT (Fig. 3C). CnGsc expression under DAPT treatment initially (8hrs) was comparable to control levels, but then it was strongly inhibited (Fig. 3D). This goes along with the observed absence of organizer activity in regenerating Hydra tips (Münder et al., 2013). Interestingly, a similar result was seen for HySp5 expression, which was also normal at 8 hrs but was then inhibited by DAPT at later time points (Fig. 3E). HyKayak, while expression is normal after 8 hrs, was strongly overexpressed between 24 and 36 hrs of regeneration in DAPT-treated polyps in comparison to control regenerates (Fig. 3F).

Concerning iCRT14

Next, following the same procedure as described for DAPT, we compared the gene expression dynamics of iCRT14-treated regenerates with control regenerates. We found that the expression of HyWnt3 was not inhibited by iCRT14. In fact, it even appeared slightly up-regulated at the 8 hrs time point (Fig. 4A). Normal HyWnt3-expression at the end of the regeneration period was confirmed by in-situ hybridization for HyWnt3 as shown in Fig. 1D, indicating that HyWnt3 expression patterns and expression levels in ecto- and endodermal cells of the hypostome were faithfully regenerated (Fig. 4A). In contrast, HyAlx expression was completely abolished by iCRT14 (Fig. 4B), consistent with the observation that iCRT14-treated head regenerates did not regenerate any tentacles (Fig. 1A). HySp5 expression was not significantly affected by iCRT14 treatment at any time point (Fig. 4C).

Furthermore, we found that CnGsc levels in iCRT14 remained similar to control regenerates up to 24 hrs, but were attenuated at later time points (Fig. 4D), very similar to the expression dynamics of the Notch-target gene HyHes (Fig. 4E). The expression of HyKayak was decreased at 8 hrs after head removal in the presence of iCRT14, but then increased above control levels after 48 hrs (Fig. 4F). There were no significant changes in the expression dynamics of HyBMP2/4 and HyBMP5/8b between iCRT14-treated regenerates and controls (Fig. 4G, H).”

The precise number of biological replicates can be seen in the individual diagrams, they included for most genes three biological replicates, with always three technical replicates for each data point. Biological replicates were obtained over several years by different researchers. For some genes, we obtained very consistent data with high confidence in every experiment (e.g. HyWnt3, HyBMP4). We illustrate this in table 1, where three arrows indicate all such cases. With some genes we observed greater variation, which we interpret as no effect or a minor effect in table 1. Some of these variations may be explained by our observation of wave-like patterns in the expression dynamics. Therefore, we have included the following statement:

“In addition, the gene expression dynamics for many of the analyzed genes appears in wave-like patterns in some experiments (see Figs S3 and S4). As we have only four time points measured, we cannot draw strong conclusions from these observations, except that some of the deviations in our data points (e.g. 48 hrs HyHes)”

• In their description of figure 4 the authors completely omit to discuss the Cazet et al dataset which has the exact same early timepoints for iCRT14 treatment. This must be discussed and compared and any difference noted. * Answer:

We included the iCRT14 results from Cazet et al., in our revised manuscript (see above).

• End of page 11: The authors propose a model thereby the role of Notch in Wnt3 expression may be due to the presence of a repressor. However, I don't see any putative evidence at that stage. The authors also do not cite relevant work from both Cazet et al. and Tursch et al which show that Wnt3 is likely upregulated by bZIP TFs. In both these cases the authors show evidence of bZIP TF binding sites in the Wnt3 promoter along with other analyses. This is very relevantto the model presented by the authors here and must be discussed and compared. - * In particular the authors put forward HyKayak as an inhibitor of Wnt3 and this should be discussed along with the previous work.

Answer:

Tursch et al. 2022 did not claim that HyWnt3 is upregulated by bZiP TFs. They showed that HyWnt3 was strongly upregulated in a position-independent manner upon inhibition of the p38 or JNK (c-Jun N-terminal kinase) pathways (i.e., stress-induced MAPK pathways). This would rather support our hypothesis that HyKayak (AP-1 protein) might be a repressor of Wnt3-expression.

Cazet et al have indicated that injury-responsive bZIP TFs are the most plausible regulators of canonical Wnt-signalling components during the early generic wound response. They identified CRE-elements, which can be bound by bZIP TFs, in the putative regulatory sequences of HyWnt3. However, they focused on the early stage of regeneration (0-12hpa), and showed that bZIP TFs, including jun, fos and creb are transiently upregulated at 3hpa and hypothesise that they could induce the upregulation of HyWnt3 at this stage as an injury response. We have to point out that the Hydra fos-homolog Hykayak, which our work is concerned with, is not identical with the fos-gene described in Cazet’s paper. In addition, the Hykayak gene was downregulated by Notch signalling during the morphogenesis state of regeneration (24-36 hrs), which is not the same stage investigated by Cazet et al. To avoid confusion, we have now included the Cazet-fos-sequence in our sequence comparison in Fig. S1 (fos_Cazet_HYDVU). Moreover, we have included more information about fos_Cazet in the context of a comparison with HyKayak.

• *

Different bZiP transcriptional factors (TFs) may have different effects on the expression of Wnt genes, and these effects are context-dependent. In previous research, Cazet et al. identified another Hydra fos gene (referred to as fos_cazet) and bZiP TF binding sites in the putative regulatory sequences of HyWnt3 and HyWnt9/10c. They showed that bZiP TF-genes, including Jun and fos, were transiently upregulated 3 hrs after amputation, therefore they hypothesized that bZiP TFs could induce TCF-independent upregulation of HyWnt3 during the early generic wound response (Cazet et al., 2021). However, in our study HyKayak expression continuously increased throughout the entire head regeneration process (Fig. 3E and 4E) including the morphogenesis stages (24-48 hrs post-amputation). Another study reported that inhibition of the JNK pathway (which disrupts formation of the AP-1 complex) resulted in upregulation of HyWnt3 expression in both, head and foot regenerates (Tursch et al., 2022). This result might support our hypothesis, but it only included the first 6 hours after amputation, similar to Cazet’s research. Therefore, it appears that HyKayak and fos_Cazet may have opposing roles in the regulation of Wnt-gene expression and are possibly activated by different signaling pathways depending on the stages of regeneration.

• On page 12 the authors conclude based on gene expression in inhibitor treatment that there is a “change in complex composition of the two transcription factors.” This is something which would require biochemical evidence and I therefore suggest they remove this entirely. * Answer:

we have removed this sentence

• The authors use experiments in Craspedacusta to test their hypothesis of the role of Wnt and Notch signaling in Hydra. There is, in my opinion, an incorrect logic here. Regardless of the outcome, the roles of Wnt and Notch could conceivably be different in the two species and therefore testing hypothesis from one is not possible in the other. The authors should reframe their discussion of this to be more of a comparative framework. Moreover, the results do not necessarily indicate what the authors say. In Hydra Notch signaling is required to form the hypostome/mouth and this is not the case in Craspedacusta while Wnt signaling is required. The authors do not cite an important study from another Hydrozoan Hydractinia (Gahan at al.,2017). In that study the authors show that DAPT inhibits tentacles during regeneration but that the hypostome (or at least the arrangement of neurons and cnidocytes around the mouth) forms normally. This would indicate that in Hydractinia the process of head formation is different to in Hydra and would be congruent with what is shown here in Craspedacusta. This should be more thoroughly discussed, and all relevant literature cited.* Answer:

We have concentrated our Craspedacusta work on Notch-signalling now. We only show that DAPT does not inhibit the regeneration of Craspedacusta heads. We have included new data showing that nevertheless it has an effect on the expression of hypothetical Notch target genes, but not on CsWnt3 (new Fig. 7). We have re-written our discussion accordingly and included the Hydractinia-work about Notch (Gahan2017). Although the Hydractinia paper lacks gene expression studies making it difficult to directly compare with the Hydra data, it supports our claim that Notch is required for regeneration of polyps with head and tentacles. We indeed do not know anything about Wnt-signalling in Craspedacusta. Our new results show that it is probably expressed in the head, because we observe very low levels of expression in the polyps after head removal, which increases considerably during regeneration of the head. This was included in the results:

Results:

“Finally, we investigated the expression of the Craspedacusta Wnt3-gene and its response to DAPT treatment during head regeneration. We observed low expression level of CsWnt3 after head removal (t=0), which dramatically increased as the head regenerated, suggesting that Wnt3 is expressed in the head of Craspedacusta polyps as it is in the head of other cnidarians including Hydra, Hydractinia and Nematostella (Hobmayer et al., 2000; Kusserow et al., 2005; Plickert et al., 2006). In accordance with having no effect on head regeneration, DAPT also did not inhibit CsWnt3 expression during this process in Craspedacusta. This is opposite to the situation in Hydra. If CsWnt3 would be involved in the Craspedacusta head regeneration, this could explain the failure of DAPT to interfere with this process”.

Discussion part

“Head regeneration also occurs in the colonial sea water hydrozoan Hydractinia. Colonies consist of stolons covering the substrate and connecting polyps, including feeding polyps, which have hypostomes and tentacles, and are capable of head regeneration, similar to Hydra polyps. Wnt3 is expressed at the tip of the head and by RNAi mediated knockdown it was shown that this gene is required for head regeneration (Duffy et al., 2010). In the presence of DAPT, Gahan et al observed that proper heads did not regenerate, similar to Hydra. However, they observed regeneration of the nerve ring around the hypostome indicating the possibility that hypostomes had been regenerated. Unfortunately, this study did not include gene expression data and therefore it is not clear whether Wnt3 expression was affected or not (Gahan et al., 2017).

…..

An interesting question was whether regeneration of cnidarian body parts, which are only composed of one module, also requires Notch-signalling. This is certainly true for the Hydra foot, which regenerates fine in the presence of DAPT (Käsbauer et al. 2007). Moreover, we tested head regeneration in Craspedacusta polyps, which do not have tentacles, and show that DAPT does not have an effect on this regeneration process. This corroborates our idea that Notch is required for regeneration in cnidarians, when this process involves two pattern forming processes to produce two independent structures, which are controlled by different signalling modules. This would be the case for the Hydra and for the Hydractinia heads, but not for Craspedacusta. ”

…

Moreover, we indicate at the end of our discussion that further studies about head regeneration in Craspedacusta and the genes involved would be desirable. We believe this would be beyond the scope of the current paper and we are working on a new Craspedacusta study.

“Future studies on expression patterns of the genes that control formation of the Hydra head, including Sp5 and Alx in Craspedacusta could provide insights into the evolution of cnidarian body patterns. Sp5 and Alx appear to be conserved targets of Notch-signalling in the two cnidarians we have investigated. Wnt-3, while being inhibited by Notch-inhibition in Hydra head regenerates, is not a general target of Notch signalling. It was not affected by DAPT in our comparative transcriptome analysis (Moneer et al. 2021b) on uncut Hydra polyps, and it was also not affected by DAPT in regenerating heads of Craspedacusta.”

• From reading the manuscript I do not fully understand the model the authors put forward. It is unclear what "coordinating two independent pattern forming systems" really means. It might be beneficial to make a schematic illustration of the model and how it goes wrong in both sets of inhibitor treatments. * Answer:

We have edited the manuscript considerably and explained what we mean with the two pattern forming systems. It starts with the abstract:

“Hydra head regeneration consists of two parts, hypostome/organizer and tentacle development.”

…

Thus, in accordance with regeneration of two head structures we find two signaling and gene expression modules with HyWnt3 and HyBMP4 part of a hypostome/organizer module, and BMP5/8, HyAlx and b-catenin part of a tentacle module. We conclude that Notch functions as an inhibitor of tentacle production in order to allow regeneration of hypostome/head organizer.

…

“Polyps of Craspedacusta do not have tentacles and thus, after head removal only regenerate a hypostome with a crescent of nematocytes around the mouth opening. This corroborates the idea that Notch-signaling mediates between two pattern forming processes during Hydra head regeneration”

We have included the description of the organizer concept in the introduction, because we consider this relevant for our model:

“The “organizer effect” entails a “harmonious interlocking of separate processes which makes up development”, or a side-by-side development of structures independently of each other (Spemann, 1935). In addition to inducing the formation of such structures, the organizer must ensure their patterning (Anderson and Stern, 2016). With reference to Hydra’s hydranth formation after head removal or transplantation, this involves the side-by side induction of hypostome tissue and tentacle tissue. Moreover, it includes the establishment of a regularly organized ring of tentacles with the hypostome doming up in the middle. The function of the Hydra“center of organization” would then be to pattern hypostome and tentacles and to allow for their harmonious re-formation after head removal”.

In the discussion we integrate the organizer concept with the Gierer-Meinhardt reaction-diffusion models which still explain many aspects of Hydra development.

“Is Notch part of the organizer? The organizer is defined as a piece of tissue with inductive and structuring capacity. Notch is expressed in all cells of Hydra polyps (Prexl et al., 2011) and overexpression of NICD does not induce second axes all over the Hydra body column (Pan et al., 2024), as seen with overexpression of stabilized b-catenin (Gee et al., 2010). Moreover, Notch functions differently during regeneration after apical and basal cuts. Phenotypically during head regeneration in DAPT, we clearly recognize a missing inhibition of tentacle tissue after apical cuts and missing inhibition of head induction after basal cuts (Pan et al., 2024). We would thus rather suggest that the organizer activity of Hydra tissue utilizes Notch-signaling as a mediator of inhibition. As our study of transgenic NICD overexpressing and knockdown polyps had suggested, the localization of Notch signaling cells depends on relative concentrations of Notch- and Notch-ligand proteins, which are established by gradients of signaling molecules that define the Hydra body axis (Pan et al., 2024; Sprinzak et al., 2010) . This is in very good agreement with a ”reaction-diffusion-model” provided by Alfred Gierer and Hans Meinhardt (Gierer and Meinhardt, 1972; Meinhardt and Gierer, 1974) suggesting a gradient of positional values across the Hydra body column. This gradient may determine the activities of two activation/inhibition systems, one for tentacles and one for the head. When the polyps regenerate new heads, Notch could provide inhibition for either system, depending on the position of the cut.

We provide a new Fig. 8., which clearly illustrates the effects of DAPT and iCRT14 on hypostome and tentacle regeneration.

Minor: • The abstract could be rewritten to have more of an introduction to the problem rather than jumping directly into results. It would also be beneficial if the abstract followed the logic of the paper.

Answer: We agree and have re-written the abstract.

• In Figure 3 and 4 it is not clear why they are divided into A and B. It appears that the categorization of genes into different groups lacks a clear rationale. This seems totally unnecessary. In addition, the order in which the genes are described in the text does not match what is seen in the figure making it confusing to follow. • In Figure 5 the authors use two different types of charts and I would stick with one. B is much better as it shows the individual data points as well as other information. I would use this throughout including in Figure 3 and 4. *

__Answer: __

We changed Fig. 3, 4 and 5 according to these comments and now present the data in one format over all three figures, in scatterplots (more detailed answer above).

We are now describing the results in the order of the figures, with A and B omitted.

Figure S3 is missing a description of panel C.

In figure S3 it is not clear why the inhibitor was removed and not kept on throughout the experiment. Please discuss. __Answer: __

Fig. S3 was removed.

Figure S4 has no A or B in the figure, only in the legend. __Answer: __

We have included A and B…

*Reviewer #1 (Significance (Required)):

Although some of the authors data appear to be novel I find the study makes only minor progress on the questions. In particular the authors do not properly cite the relevant literature and to put their manuscript into the correct context. The new model proposed by the authors is based entirely on qPCR data which is not thoroughly analyzed and are not strong enough in the absence of information about the spatial expression the genes they discuss. The proposal of HyKayak as a negative regulator of Wnt3 is interesting but the authors do not provide any solid direct evidence for this (ChIP, EMSA etc) and it is somewhat in disagreement with other models of bZIP function in the literature (which again are not discussed).*

The manuscript is of limited general interest. It has a number of interesting observations which would be of interest to the Hydra community and the broader cnidarian community. The study lacks contextualization within a broader framework, whether it be in the context of regeneration or Wnt/Notch signaling. This limitation may narrow the overall interest in it.

Answer:

Our previous analysis of the effect of Notch on head regeneration in Hydra (Münder 2013) had suggested the inhibition model, which is part of Fig. 8. We show now that during head regeneration in Hydra formation of two structures is guided by different signaling/transcription modules, one using Wnt3 and BMP4, but not b-catenin; and one using BMP5/8 and b-catenin. We suggest that Notch functions as an inhibitor “of use” to the organizer when the “two-part” head structure is regenerated.

We agree that our original manuscript was not well enough written to clearly put it into developmental context. We now focus the discussion of our work sharply on the organizer problem and think that the conclusions are of great general interest. In a simple view they suggest that the function of the Hydra head organizer is to allow harmonious development of head and tentacles, which we consider separate, and on a molecular basis independently regulated parts of the Hydra head. Notch signaling, in our interpretation, is an instrument of the organizer. Our comparison with Craspedacusta illustrates this idea. Craspedacusta only regenerates one head structure, which is possible in the absence of this instrument (also see reviewers 3 and 4).

Concerning HyKayak, there is no disagreement with other authors as we analyze a fos-gene different from the one discussed by Cazet et al (see above). We have conducted a rescue experiments as suggested by reviewer 3 with the Kayak-inhibitor and with HyKayak shRNAi knockdown, however, rescue of the phenotype was not achieved although HyWnt3 was upregulated after DAPT treatment in the knockdown group. We attribute this to the very strong effect of DAPT. We have adjusted our hypothesis and only suggest that HyKayak could be a target for the Notch-induced repressor genes (e.g.HyHes). We mentioned this failed rescue in the manuscript (answer for see reviewer 3). Further experiments, e.g Chip/EMSA constitute a new project on the basis of these ideas and should be reserved for further studies of the Kayak-function in Hydra.

Reviewer #2 (Evidence, reproducibility and clarity (Required)):

*The study investigates the role of Notch and beta-catenin signaling in coordinating head regeneration in Hydra. It combines gene expression dynamics, inhibitor treatments, and comparisons with Craspedacusta polyps to propose a lateral inhibition model for Notch function during Hydra head regeneration, mediating between two pattern-forming systems.

Three main concerns arise from this work:*

• Lack of spatial expression data: The study proposes a model based on pattern-forming systems but falls short of providing direct spatial expression data for the genes under consideration in both control and treated scenarios. This gap weakens the empirical support for the proposed model. __Answer:*__

The expression patterns for most of the presented genes including HyAlx and HyWnt3 in the presence and absence of DAPT have been published before (Münder 2013). Expression patterns for all other genes during regeneration (except Hykayak) are already known from literature. For Hykayak we have included expression data from Siebert et al (single cell transcriptome analysis) in the supplementary material. For iCRT14 treatment, we carried out a FISH-experiment and showed that HyWnt3 is expressed in the normal pattern at the hypostome. For further genes after DAPT and iCRT-treatment in situ hybridisation data are indeed lacking (e.g. BMP5/8). However, we have analyzed some very strongly downregulated regulated genes (e.g. HyAlx completely downregulated by iCRT14, all HyWnts and BMP2-4completely downregulated by DAPT) and for those in situ hybridisation could (1) be difficult due to low expression in treated samples and (2) may not be informative.

• Clarity and relevance of Craspedacusta comparisons: The section discussing the regeneration in Craspedacusta polyps appears somewhat disjointed from the main narrative, with its contribution to the overarching story of Hydra regeneration remaining unclear. *

Answer:

We had not intended to explain gene expression during Craspedacusta head regeneration but wanted to prove our hypothesis that Notch is needed to allow side-by-side development of two newly arising structures, which use different signalling modules during head regeneration. That Notch is __not __needed for the regeneration of Craspedacusta, a polyp without tentacles, appears to strengthen our main hypothesis. In order to connect this point more clearly to the narrative we have included new data. We show that CsWnt3 expression lowers after head removal and rises when the head regenerates, indicating CsWnt3-expression in the head of Craspedacusta polyps. Moreover, we show now that Notch in Craspedacusta may have similar target genes as in Hydra (e.g. Sp5 and Alx), might also affect nematocyte differentiation as in Hydra, but does not inhibit Wnt3 expression. We also acknowledge that a precise understanding of the molecular pathways for head regeneration in Craspedacusta requires further work and have removed the results of iCRT14 treatment because of our lack of knowledge about the role of b-catenin in Craspedacusta patterning. Citations from our changed text are found in the answer to reviewer 1.

• Accessibility of the text: The study's presentation, including its title, abstract, and main text, presents challenges in terms of clarity and accessibility, making it difficult for readers to follow and understand the research's scope, methodologies, and conclusions.*

• *

Answer:

We agree and have completely re-written the abstract, and large parts of the introduction and discussion (also see above answer for reviewer 1).

Reviewer #2 (Significance (Required)):

In conclusion, while the study aims to advance our understanding of the complex signaling pathways governing Hydra head regeneration, it necessitates significant revisions. Enhancing the empirical evidence through detailed spatial patterning data, clarifying the comparative analysis with Craspedacusta polyps, and __refining the narrative __to improve accessibility are critical steps needed to solidify the study's contributions to the field.

Answer:

By including Kayak-expression data from Siebert et al and indicating the places of major expression of all analysed genes schematically in the Figs describing the qPCR data we revised our manuscript. We have added new data about Craspedacusta and believe that our re-written manuscript refines the narrative by focusing on the organizer (see answer to reviewer 1).

Reviewer #3 (Evidence, reproducibility and clarity (Required)):

Major comments:

- In the abstract, the authors assert that their findings "indicate competing pathways for hypostome and regeneration." However, the nature of this competition and its resolution is not adequately elucidated within the manuscript. The term "competition" lacks context and clarity, leaving the reader without a clear understanding of what pathways are competing, for what, and how this competition is resolved during regeneration. Furthermore, this concept is not further explored or referenced throughout the remainder of the manuscript, leaving it somewhat disconnected from the main body of the research. It is recommended that the authors either revise the statement in the abstract to provide more clarity on the competing pathways and their implications for regeneration, or alternatively, if the authors believe there is sufficient evidence to support the claim of competing pathways, they should expand upon this point within the main body of the manuscript. Additional argumentation and evidence would be necessary to substantiate such a claim and provide a deeper understanding of the mechanisms underlying regeneration in Hydra.

Answer:

We agree and have removed any reference to “competing” pathways from the abstract and the main text.

- The abstract makes a significant assertion regarding the mechanism by which Notch signaling impacts the expression of HyWnt3, suggesting that it operates by inhibiting HyKayak-mediated repression of HyWnt3 rather than directly activating transcription at the HyWnt3 promoter. This claim is central to the goals outlined in the study, which aim to elucidate the functioning of Notch signaling in HyWnt3 expression. To bolster this assertion, it would be prudent for the authors to conduct experiments demonstrating the mediating role of Kayak. Specifically, demonstrating that downregulation of Kayak through RNAi can rescue the DAPT-mediated downregulation of Wnt3 would provide strong support for the authors' claim. Additionally, while not strictly necessary, it would be beneficial to investigate whether chemical inhibition of Wnt can rescue the phenotype resulting from Kayak RNAi. Conducting and analyzing such experiments within a 2-3-month revision period should be feasible given that the authors already possess all necessary materials and have developed the required methods. These additional experiments would not only strengthen the evidence supporting the authors' claim but also provide further insights into the regulatory mechanisms at play in Notch signaling and HyWnt3 expression.

• *

Answer:

We have conducted the suggested rescue experiments with the kayak-inhibitor, however, rescue was not achieved. We also tried rescue experiments by combining DAPT treatment and Kayak shRNA knockdown. HyWnt3 was slightly upregulated after DAPT treatment in the Kayak knockdown group but the phenotype could not be rescued. We are therefore now only state that HyKayak could be a target for the Notch-induced repressor genes (e.g.HyHes). We mentioned the failed rescue experiments in the manuscript:

Results:

*The up-regulation of HyKayak by DAPT suggests that HyKayak may serve as a potential target gene for Notch-regulated repressors including HyHes and CnGsc, potentially acting as a repressor of HyWnt3 gene transcription. *

Discussion:

We therefore suggest that the Hydra Fos-homolog HyKayak inhibits HyWnt3 expression and can be a target for a Notch-induced transcriptional repressor (like HyHes) in the regenerating Hydra head. Nevertheless, we were not able to rescue the DAPT-phenotype by inhibiting HyKayak, neither by using the inhibitor nor by shRNA-treatment, probably due to the strength of the DAPT effect. Therefore, we cannot exclude that Notch activates HyWnt3 directly, or that it represses unidentified Wnt-inhibitors through HyHes or CnGsc.

- The usage of the term "lateral inhibition" in the title and abstract of the manuscript carries specific implications, as it is commonly associated with distinct mechanisms in the context of Notch signaling and reaction-diffusion systems. Notably, in the Notch signaling context, lateral inhibition typically refers to the amplification of small differences between neighboring cells through direct interactions, facilitated by the limitations of Notch signaling to immediate neighbors. Conversely, in reaction-diffusion systems, such as the Gierer-Meinhardt model, lateral inhibition describes long-range inhibition associated with pattern formation.

Given this discrepancy, it is crucial for the authors to clarify their interpretation of "lateral inhibition" to avoid ambiguity and ensure accurate understanding. If they are referring to Notch-specific lateral inhibition, they should provide evidence of adjacent localization of Notch and Delta cells to support their argument. Alternatively, if they are invoking the concept of long-range inhibition described by the Gierer-Meinhardt model, they must explain how a membrane-tethered ligand like Notch can exert effects beyond one cell diameter from the signaling center.

* Regardless of the interpretation chosen by the authors, addressing this clarification will have significant implications for the subsequent treatment of their arguments. Depending on their chosen interpretation, experimental demonstrations may be necessary to substantiate their claims, which could be laborious and time-consuming. However, such demonstrations are essential for establishing the validity of the term "lateral inhibition" as used in the title and abstract of the manuscript.*

Answer:

We agree with the reviewer concerning the term “lateral inhibition” and have now removed it. Instead, we have emphasized that our data clearly show during apical regeneration a Notch-mediated inhibition of tentacle tissue formation. We also discuss data from our most recent publication (Pan 2024) showing that this is the opposite at basal cuts, where the loss of Notch function leads to the regeneration of two heads. We then discuss this in the context of the Gierer-Meinhardt Model and in the context of the organizer (also see above in answer to reviewer 1):

It is true that it is difficult to reconcile the long-range signaling processes, on which the Gierer-Meinhardt model is based with the cell-cell interactions mediated by Notch-signaling. We have now published a mathematical model to explain our understanding of this for the role of Notch during budding and in steady state animals (Pan2024), which is based on work by Sprinzak et al 2010. For head regeneration, we do not have such a model yet. Here we are looking at expression patterns changing over time. Therefore, we assume waves of gene expression, relying on the autoinhibitory function of the HyHes-repressor. This is included in the discussion:

In addition, the gene expression dynamics for many of the analyzed genes appears in wave-like patterns in some experiments (see Figs S3 and S4). As we have only four time points measured, we cannot draw strong conclusions from these observations, except that some of the deviations in our data points (e.g. 48 hrs HyHes) might be caused by oscillations. Nevertheless, we propose that the dynamic development of gene expression patterns over the time course of regeneration hint at a wave like expression of Notch-target genes (e.g. HyAlx, (Münder et al., 2013; Smith et al., 2000)). Hes-genes have been implicated in mediating waves of gene expression, e.g. during segmentation and as part of the circadian clock (Kageyama et al., 2007). This property is due to the capability of Hes-proteins to inhibit their own promoter. Future models for head regeneration in Hydra should consider the function of Notch to inhibit either module of the regeneration process and the potential for the Notch/Hes system to cause waves of gene expression. Such waves intuitively seem necessary to change the gene expression patterns underlying morphogenesis during the time course of head regeneration.

- The utilization of Craspedacusta as a comparative model in the argumentation of the manuscript appears somewhat unclear. The authors posit that Notch is essential for organizer emergence in Hydra, while Wnt is not necessary, as indicated by the observed effects of iCRT14 beta-catenin/TCF inhibition. However, in Craspedacusta, which lacks tentacles but possesses an organizer, one might anticipate a conserved requirement for organizer formation but not tentacle development. Therefore, it would be reasonable to expect that Craspedacusta would still form an organizer under iCRT14 treatment but would not depend on Notch signaling, as the necessity to separate tentacle formation from organizer formation is absent. The authors' observation that Craspedacusta fails to form an organizer under iCRT14 treatment partially aligns with these expectations. However, the complexity of the results suggests a need for a deeper understanding of the involvement of different pathways in Craspedacusta. Before applying inhibitors, it would be crucial to elucidate the spatiotemporal differences in the expression of relevant Wnt and Notch pathway components between Hydra and Craspedacusta. This knowledge would provide valuable insights into the specific roles of these pathways in organizer formation and tentacle development in both species, helping to clarify the observed differences in response to iCRT14 treatment. Additionally, considering the possibility of differential sensitivity to iCRT14 (see comment below) between Hydra and Craspedacusta would be essential for accurately interpreting the results and drawing meaningful conclusions regarding the involvement of Notch and Wnt signaling pathways in these processes.

Answer:

We have clarified in our re-written manuscript that the organizer functions in Hydra heads and head regeneration to harmonize the development of two independent structures (see answer for reviewer 1) and that Notch-signalling is an instrument to achieve this. Craspedacusta polyps do not have tentacles, thus we do not see two independent structures. Correspondingly, we see that they do not need Notch-signaling. We do not know whether they have organizer tissue, because they are too small to perform transplantation experiments. Similarly, in situ hybridisation experiments to look for CsWnt expression are hard to envisage. What we have now done during the revision of this paper are RT-qPCR experiments to follow the expression of CsWnt3 after head removal until a new head is formed. This indicated the localization of CsWnt3 expression in the head (citations in response to reviewer 1).

We agree that the role of Wnt/b-catenin for Craspedacusta cannot be sufficiently described with our iCRT14 experiment and therefore removed it. To strengthen the DAPT data, we also examined Craspedacusta homologs of the Hydra Notch-target genes that we had previously identified (Moneer2021). We found that expression of CsSp5 and CsAlx were inhibited by DAPT. This was also true for the nematocyte gene NOWA (see new Fig. 7). In Hydra, DAPT blocks one important differentiation step of nematocytes and therefore the expression of all genes expressed in differentiating capsule precursors, including NOWA is inhibited, while the number of mature capsules does not change. To see the same DAPT effect on NOWA-expression in Craspedacusta reassured us that DAPT had entered the animals and might also have a similar effect on nematocytes as in Hydra.

Minor comments - The concentration-dependent effects of iCRT14 on beta-catenin signaling, as demonstrated by Gufler et al. 2018, suggest that the efficacy of inhibition may vary depending on the concentration used. Specifically, Gufler et al. found that a concentration of 10µM was sufficient for efficient inhibition of beta-catenin signaling. However, in the current study, the authors utilized a concentration of 5µM of iCRT14. Given the central role of the observed effects, particularly the persistence of Wnt3 expression, in the argumentation of the manuscript, it is plausible that these effects could be attributed to partial inhibition resulting from the lower concentration of iCRT14 used in the study. To address this potential limitation, the authors could consider conducting a quick examination of the effects of 10µM iCRT14 or utilizing other inhibitors of beta-catenin/TCF interaction, such as iCRT3. By comparing the effects of different concentrations or alternative inhibitors, the authors could ascertain whether the observed effects are indeed attributable to partial inhibition from 5µM iCRT14, or if they persist despite higher concentrations or alternative inhibitors. This additional experimentation would provide valuable insights into the specificity and efficacy of the inhibition and strengthen the validity of the conclusions drawn regarding the role of beta-catenin signaling in the observed phenomena.

Answer:

The iCRT14 concentration was adjusted to 5 µM because the initial 10µM proved to be too toxic. 5µM also produced the same phenotypes and results as seen before. Cazet et al. also used 5 µM iCRT14 in their study.

- The use of Generalized Additive Models (GAMs) in Figures 3 and 4 to present the time series qPCR results may introduce some challenges in interpretation due to the potential for distortion of values at specific time points based on neighboring ones. Given the relatively low time resolution of the data, this approach could lead to a distorted depiction of the temporal dynamics. For instance, in Figure 3B, where Wnt3 peaks at 10 hours, the absence of measurements between 8 and 24 hours introduces uncertainty regarding the accuracy and reliability of this peak at 10 hours.

* To address these concerns and enhance clarity, it may be advisable for the authors to consider presenting the data using simple boxplots instead of GAMs. Boxplots provide a more straightforward visualization of the distribution of data at each time point, allowing for a clearer interpretation of trends and fluctuations over time. This approach would mitigate the potential for distortion introduced by GAMs and provide a more accurate representation of the temporal dynamics observed in the qPCR results*

• *

Answer:

We agree and have changed the data representation to simple scatterplots (see answers for reviewer 1).

- The comparison of the effects of iCRT14 versus DAPT treatments would benefit from having consistent gene expression data across both treatments. However, in Figure 4A, there are fewer genes tested compared to Figure 3A, with Hes and Kayak omitted. While the authors interpretation suggests that these genes may not change after iCRT14 treatment due to their upstream position in the signaling pathways, it is essential to empirically demonstrate this relationship, as it is central to the conclusions drawn. To address this gap in the analysis, it would be valuable for the authors to provide a time series of differential expression for Hes and Kayak following iCRT14 treatment.

Answer:

We have provided a time series for expression of HyHes and HyKayak in responses to iCRT14 treatment during regeneration (see Fig.4).

“We found that the expression the Notch-target gene HyHes remained similar to control regenerates up to 24 hrs, but then was attenuated (Fig. 4A), possibly due to failure of tentacle boundary formation, the tissue where HyHes is strongly expressed…The expression of HyKayak was decreased at 8 hrs after head removal in the presence of iCRT14, came back to normal up to 36 hrs and was suddenly increased after 48 hrs (Fig. 4E), correlating with inhibition of the HyHes repressor. There were no significant changes in the expression dynamics of HyBMP2/4 and HyBMP5/8b between iCRT14-treated regenerates and controls (Fig. 4F, G).”

- The analysis of the impact of chemical inhibition of Notch and Wnt signaling in Figure 7 schematic highlights changes in spatial expression patterns of the target genes. However, the interpretation of their impact primarily relies on qPCR data. As evident from Figure 7, when Notch is inhibited, it is anticipated that Kayak expression will shift from the area of the tentacles to the tip. This spatial shift in expression patterns is a critical aspect of the authors' arguments, especially considering the centrality of Kayak in their findings. Notably, similar spatial expression patterns have been demonstrated for Alx using FISH in Pan et al., available on BioRxiv. Given the importance of Kayak in the presented arguments, it is advisable to also investigate its spatial expression patterns using techniques such as FISH.

• *

Answer:

We have, instead of FISH-experiments, included expression data for HyKayak from Siebert et al 2019 (single cell transcriptome data) in Fig. S1D, which show its expression in head- and battery cells (tentacle cells). This is similar to HyAlx. Therefore, Kayak-FISH would be expected to reveal expression of the gene at the tip of the regenerate the whole time, similar to HyAlx, because tentacle gene inhibition or patterning does not occur (see Münder 2013). Due to the failure of our rescue experiment to demonstrate the function of kayak we have omitted kayak from Fig. 8 and only mention in the discussion that it could be a target for Notch activated transcriptional repressors, like HyHes or CnGsc.

Reviewer #3 (Significance (Required)):

*The paper introduces novelties to the field of regeneration and developmental biology by leveraging Craspedacusta polyp as a novel model system for investigating the evolutionary and developmental dynamics of tentacles. In doing so, it sheds new light on the intricate mechanisms underlying tentacle formation and patterning. Furthermore, the study implicates Kayak in the regulation of Wnt3, adding a fresh perspective to our understanding of the molecular pathways governing Hydra regeneration. Notably, the results of the research challenge the prevailing notion of autoregulation of Wnt3, which has long been considered fundamental to organizer formation in Hydra. While these findings offer intriguing insights, further investigation will be crucial to conclusively ascertain the validity of this assertion. *

• *

Despite the clarity of the data presented, the interpretation and integration of these findings in the manuscript are lacking. The narrative at times feels disjointed, with different storylines loosely connected. While the findings are intriguing and merit publication, a substantial revision of the manuscript is necessary to provide a more coherent and illuminating interpretation of the results. *The implications of this research extend beyond the specific confines of Craspedacusta polyp and Hydra biology. It holds significant relevance for both the Hydra biology community and the broader field of Notch signaling research. *

By highlighting the pivotal role of Notch signaling in regeneration and patterning within Hydra, the study enriches our comprehension of this model organism and its evolutionary adaptations. Moreover, it provides a valuable lens through which the evolution of Notch signalling cascades can be examined. This interdisciplinary approach underscores the interconnectedness of diverse biological systems and underscores the importance of exploring novel model organisms to unravel the complexities of evolution and development.

• *

Answer:

We have edited the manuscript considerably and re-written the introduction and the discussion parts. We are focusing on integrating this work with the organizer concept in developmental biology, and on the Gierer-Meinhardt-model, and point out that Notch-signaling is required for the development of two head structures by inhibiting the development of either one during head regeneration, which is necessary to enable the development of the other one. Which one is inhibited depends on the positional value of the tissue where the cut occurs. Craspedacusta polyps do only have one structure. We suggest that this is why head regeneration does not require Notch-signalling in Craspedacusta. In contrast, as we have included in our discussion now, Hydractinia polyps, again with head/mouth and tentacles, require Notch-signaling for head regeneration (according to Gahan 2019), see also answers for reviewers 1 and 2.

Reviewer #4 (Evidence, reproducibility and clarity (Required)):

Major comments:

The conclusions from the experiments are drawn accurately, not overstating the results. The main conclusion, that in Hydra Notch pathway mediates between two patterning modules, hypostome and tentacle forming modules, is supported by in situ hybridization and qPCR analyses of hypostome and tentacle specific genes.

OPTIONAL. Authors hypothesize, that Notch maintains expression of Wnt3 vie its targets, transcriptional repressors Goosecoid or Hes, which halt the expression of Wnt3 repressor HyKayak. Epistatic relationships between Notch, Goosecoid or Hes and HyKayak could be tested, first, by combining pharmacological inhibition of Notch by DAPT with shRNA-mediated knockdown and second, in double knockdowns generated by electroporating shRNAs for two genes simultaneously. If the proposed in the pathway relationships are correct the repressive effect of DAPT treatment on an organizer regeneration should be rescued in HyKayak shRNA-mediated knockdown. Regeneration of an organizer also should occur in Notch/HyKayak and Goosecoid (Hes)/HyKayak shRNA-mediated double knockdowns. Electroporation of shRNAs for multiple genes is an effective and quick way to generate double and triple knockdowns. The proposed experiments will much strengthen the conclusions drawn from this study. Given that the authors have successfully used shRNA-mediated technique to generate HyKayak knockdown animals, they should be able to complete the proposed experiments within in a couple of months. Answer:

We very much like the suggested strategy to probe the regeneration pathways by shRNA-mediated knockdown experiments- this will be a basis for future investigations.

We conducted the suggested rescue experiment by combining the DAPT treatment and Kayak shRNA knockdown. HyWnt3 was slightly upregulation after DAPT treatment in the Kayak knockdown group. However, this upregulation did not rescue the organizer’s regeneration. We think that the effect of DAPT is too strong. We have included this in the discussion of our results (see answer for reviewer 2).

• The data are presented in a logical and clear manner. The paper is easy to read, and the conclusions are explicit for each experimental section. The methodology is described in detail and should be easy to reproduce.*

• All experiments are done with multiple biological and technical replicates. However, the description of statistical analysis used in each case is missing, p values and error bars are missing in Fig. 2B and Fig. S4. Author should add this information in the main text or in the figure legends.*

Answer:

The statistical information was now added in the methods section.

Minor comments:

• Fig. 1E. It would be more convincing to present tentacle and hypostome regeneration data separately, comparing hypostome regeneration in treated animals with DMSO control, and in a separate analysis comparing tentacle regeneration with control. Provide the description of statistical method, p values and error bars. If authors prefer to stick to the current way of presenting they should also provide description of statistical analysis used and statistical data.*
• *

Answer:

We changed the representation in Fig. 1E. We now use scatter plots in the main text with p-values added, and explained the statistics of the GAM representation in the supplementary material.

• Results, section 4 Kayak. Authors use T5424 inhibitor to block the potential interactions between HyKayak with HyJun. The resulted increase in Wnt3 expression measured by qPCR clearly supports the idea of HyKayak being a repressor of Wnt3. However, authors are going further and present the phenotype of T5424 treatment, shortening of the tentacles. Many factors can influence the length of the tentacles. For example, shortening of tentacles is a strong indication of poisoning or animal being in general unwell. At a concentration double of the one used in the experiment T5424 causes a disintegration of the animals (Fig. 3S). It would be more convincing if the authors could provide an in situ hybridization image showing an expansion of Wnt3 expression domain down the hypostome. This is the result one would expect from the inhibition of HyKayak which, according to the proposed mechanism, restricts Wnt3 spatial expression to the most apical portion of the regenerating tip. Alternatively, authors could try to see if T5424 rescues the inhibition of an organizer formation resulted by DAPT treatment. The latter experiment might be difficult to perform due to a possible toxic effect of multidrug treatment. I suggest that authors either include the proposed experiments or leave the results of the Fig S3 out.*

Answer:

According to this suggestion we have removed the phenotypes of polyps after treatment with T5424.

• Results, section 3.2, paragraph 4. 'This also applies for the suggested Hydra organizer gene CnGsc, and BMP2/4 (Broun, Sokol et al. 1999). Please, insert the citation for BMP2/4.*

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Answer:

We inserted the citation for BMP2-4 (Watanabe 2014).

Reviewer #4 (Significance (Required)):

*Significance:

The current study is a continuation of the author's previous work where they have characterized Notch pathway in Hydra and showed its role in the regeneration of an organizer and patterning of Hydra head. Here, they present the study of Notch pathway in the context of b-catenin pathway, a pathway that has been shown to be essential for the axis induction and patterning in Hydra. The authors challenge this dogma and show, that during head regeneration b-catenin transcriptional activity is not required either to maintain the expression of wnt3 nor to acquire an inductive activity of the regenerating organizer. Second, they show, that transcriptional fos-related factor Kayak is negatively regulated by Notch-signaling and, in turn, represses transcription of Wnt3. Based on those findings authors propose a function of the Notch pathway in Hydra head regeneration, particularly in spatial separation of the hypostome/organizer module from the tentacle module. The role of Notch pathway in lateral inhibition is well documented in bilaterians. However, in Cnidaria, a sister group to Bilateria, the function of Notch was so far restricted to neurogenesis. This study is very important for our understanding of the evolution of morphogenesis as it shows the ancient role that the Notch pathway is playing in axial patterning, possibly, through lateral inhibition.

This study can be of a great interest to both researchers specializing in cnidarian development and to a broader audience interested in the evolution of morphogenesis.*