Reviewer #2 (Public Review):

This manuscript from Chaterjee and colleagues examines head-stabilization reflexes in the hawkmoth. Using light level manipulations and surgical manipulations of the antennae, they show that hawkmoths combine visual and mechanosensory feedback in order to stabilize the head over a wide range of temporal frequencies. Similar to other systems studied, such as flight stablization and antennal positioning, they find that visual feedback makes a stronger contribution at low frequencies while mechanosensory feedback contributes at higher frequencies. Finally they show that loss of head movement during flight contributes to flight instability, suggesting that an inability to stabilize the head might contribute to the effects of antennal manipulation on flight stability. Overall this is a nicely done study with clear findings that support a general principle of multisensory integration for feedback control. One way in which the manuscript could be strengthened is by explicitly modeling the feedback controller shown in Figure 5, and quantitatively comparing results obtained from this model to experimental results. In addition, it might be helpful to further quantify flight trajectories in head-stabilized moths.

Reviewer #3 (Public Review):

Payel Chatterjee et al. investigated how compensatory head movements in the nocturnal hawkmoth Daphnis nerii are controlled. This was done by subjecting tethered moths to open-loop body rotations under different light conditions, while simultaneously measuring their ability to main head angle in the presence or absence (achieved by antennal ablation) of antennal mechanosensory feedback. They find that head stabilization is mediated primarily by visual feedback during roll movements at lower frequencies, while antennal mechanosensory feedback is required when roll occurs at higher frequencies or under dark conditions. The findings add to our understanding of how non-dipteran insects (that lack halteres) stabilize their heads. Compensatory head-movements are essential for stabilizing the visual field on the retina, reducing motion blur and supporting visual self-motion estimation. These are all important parameters to control flight and allow for fast manoeuvres in air. The conclusions of the paper are well supported by the data.

SciScore for 10.1101/2022.05.12.22274989: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:

The limitations of this study include: the sample size is small due to the phase 1 uncontrolled dose-finding design. The exploratory comparative immunogenicity analyses with convalescent sera or Pfizer/BNT vaccinees’ sera are not direct head-to-head comparisons and can contain bias. Convalescent sera were collected during the rise of the Delta variant outbreak, and possibly antibody responses are stronger against Delta than WT. To minimize bias, the convalescent and Pfizer/BNT vaccinees’ serum samples were tested at the same laboratories together with the ChulaCov19 vaccinated samples. In addition, a RCT phase 2 study has commenced and a larger scale immune-bridging, non-inferiority phase 3 study is planned. In summary, ChulaCov19 mRNA vaccine is well tolerated and elicited strong SARS-CoV2 specific B- and T-cell immunogenicity and is currently under Phase 2 and later clinical development.

Results from TrialIdentifier: We found the following clinical trial numbers in your paper:<br><table><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Identifier</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Status</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Title</td></tr><tr><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">NCT04566276</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Active, not recruiting</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">ChulaCov19 Vaccine in Healthy Adults</td></tr></table>

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

Results from rtransparent:

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

Results from scite Reference Check: We found no unreliable references.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

Opinion: the politicization of higher education in Ohio was an ill-conceived and ultimately detrimental decision. Where there was once insulation—the governor appointed members of the board of regents and the regents appointed the chancellor—the ability of the governor to directly appoint the head of higher education in the state injected politics into higher ed, and the higher ed system as a whole lost as a result.

Reviewer #2 (Public Review):

Dalton and colleagues present an interesting and timely manuscript on diffusion weighted imaging analysis of human hippocampal connectivity. The focus is on connectivity differences along the hippocampal long axis, which in principle would provide important insights into the neuroanatomical underpinnings of functional long axis differences in the human brain. In keeping with current models of long-axis organisation, connectivity profiles show both discrete areas of higher connectivity in long axis portions, as well as an anterior-to-posterior gradient of increasing connectivity. Endpoint density mapping provided a finer grained analysis, by allowing visualisation of the spatial distribution of hippocampal endpoint density associated with each cortical area. This is particularly interesting in terms of the medial-lateral distribution with hippocampal head, body and tail. Specific areas map to precise hippocampal loci, and some hippocampal loci receive inputs from multiple cortical areas.

This work is well-motivated, well-written and interesting. The authors have capitalised on existing data from the Human Connectome Project. I particularly like the way the authors try to link their findings to human histological data, and to previous NHP tracing results.

I do, however, have some concerns about the interpretation of the results.

There are some important surprises in the results, particularly the relatively strong connectivity between hippocampus and early visual areas (including V1) and low connectivity with areas highly relevant from functional perspectives, such as the medial prefrontal cortex (rank order by strength of connectivity 7th and 78th of all cortical structures, respectively). This raises a concern that the fibre tracking method may be joining hippocampal connections with other tracts. In particular, given the anatomical proximity of the lateral geniculate nucleus to the body and tail of the hippocampus, the reported V1 connectivity potentially reflects a fusion of tracked fibres with the optic radiation. In visualizing the putative posterior hippocampus-to-V1 projection (Figure 4B, turquoise), the tract does indeed resemble the optic radiation topography. Although care was taken to minimise the hippocampus mask 'spilling' into adjacent white matter, this was done with focus on the hippocampal inferior margin, whereas the different components of the optic radiation lie lateral and superior to the hippocampus.

A second concern pertains to the location of endpoint densities within the hippocampus from the cortical mantle. These are almost entirely in CA1/subiculum/presubiculum. It is, however, puzzling why, in Supp Figure 2, the hippocampal endpoints for entorhinal projections is really quite similar to what is observed for other cortical projections (e.g., those from area TF). One would expect more endpoint density in the superior portions of the hippocampal cross section in head and body, in keeping with DG/CA3 termination. I note that streamlines were permitted to move within the hippocampus, but the highest density of endpoints is still around the margins.

On a related point, the use of "medial" and "lateral" hippocampus can be confusing. In the head, CA2/3 is medial to CA1, but so are subicular subareas, just that the latter are inferior.

Author Response

Reviewer #1 (Public Review):

1) The connectivity patterns along the anterior-posterior hippocampal axis broadly follow an anterior-posterior cortical bias, such that posterior regions, e.g. the visual cortex, are preferentially connected to the hippocampal tail, and anterior regions, e.g. the temporal pole, are preferentially connected to the hippocampal head. The authors focus on the twenty regions with the highest connectivity profiles, which appears to capture the majority of all connections. However, some of the present structural connectivity patterns differ in interesting ways from previously described cortical networks reported in resting-state fMRI studies. Most notably, the medial PFC and orbitofrontal regions combined account for less than 1% of all connections in the present investigation (Table S1 & S2). This is an interesting contrast to functional investigations which tend to find that these regions cluster with the aHPC (e.g., Adnan et al. 2016 Brain Struct Func; Barnett et al. 2021 PLoS Biol; Robinson et al. 2016 NeuroImage). In contrast, the present DWI results suggesting preferential pHPC-medial parietal connectivity dovetail with those observed in fMRI studies. It seems important to discuss why these differences may arise: whether this is a differentiation between structural and functional networks, or whether this is due to a difference in methods.

We thank Reviewer 1 for making this important point and agree that these observations are deserving of further expansion. We have now included additional text where we place the surprising observation of sparse connectivity between PFC regions and the hippocampus more firmly in the context of recent evidence and argue that these observations suggest a potential differentiation between structural and functional networks.

We have included the following text in the discussion (pp. 16-17, lines 439-457);

“While many of our observed anatomical connections dovetail nicely with known functional associations, patterns of anatomical connectivity strength did not always mirror well characterised functional associations between the hippocampus and cortical areas. For example, a surprising observation from our study was that only weak patterns of anatomical connectivity were observed between the hippocampus and the ventromedial prefrontal cortex (vmPFC) and other frontal cortical areas. This lies in contrast to well documented functional associations between these regions (46-48). Our observation, however, supports a growing body of evidence that direct anatomical connectivity between the hippocampus and areas of the PFC may be surprisingly sparse in the human brain. For example, Rosen and Halgren (49) recently reported that long range connections between the hippocampus and functionally related frontal cortical areas may constitute fewer than 10 axons/mm2 and more broadly observed that axon density between spatially distant but functionally associated brain areas may be much lower than previously thought. Our observation of sparse anatomical connectivity between the hippocampus and PFC mirrors this recent work and suggests a potential differentiation between structural and functional networks as they relate to the hippocampus. It remains possible, however, that methodological factors may contribute to these differences. We return to this point later in the discussion. A future dedicated study aimed at assessing whether the well characterised functional associations between the hippocampus and vmPFC are driven by sparse direct connections or primarily by intermediary structures is necessary to address this issue in an appropriate level of detail.”

2) While the analytic pipeline is described in sufficient detail in the Methods, it is somewhat unclear to a non-DWI expert what the major methodological advance is over prior approaches. The authors refer to a tailored processing pipeline and 'an advance in the ability to map the anatomical connectivity (p. 5), but it's not immediately clear what these entail. It would be useful to highlight the key methodological differences or advances in the Introduction to help with the interpretation of the similarities and differences with previous connectivity findings.

We have now included a brief description in the Introduction highlighting the key methodological advances used in the current study.

We have included the following text in the Introduction (pp. 4-5, lines 130-144);

“In typical fibre-tracking studies, we cannot reliably ascertain where streamlines would naturally terminate, as they have been found to also display unrealistic terminations, such as in the middle of white matter or in cerebrospinal fluid (39). While methods have been proposed to ensure more meaningful terminations (40), for example, with terminations forced at the grey matter-white matter interface (gmwmi), this approach is still not appropriate for characterising terminations within complex structures like the hippocampus. A key methodological advance of our approach was to remove portions of the gmwmi inferior to the hippocampus (where white matter fibres are known to enter/leave the hippocampus). This allowed streamlines to permeate the hippocampus in a biologically plausible manner. Importantly, we combined this with a tailored processing pipeline that allowed us to follow the course of streamlines within the hippocampus and identify their ‘natural’ termination points. These simple but effective methodological advances allowed us to map the spatial distribution of streamline ‘endpoints’ within the hippocampus. We further combined this approach with state-of-the-art tractography methods that incorporate anatomical information (40) and assign weights to each streamline (41) to achieve quantitative connectivity results that more faithfully reflect the biological accuracy of the connection’s strength (39).”

3) Related to the point above, it was a bit unclear to me how the present connections map onto canonical white matter tracts. In Fig., 4A, the tracts are shown for a single participant, but it would be helpful to map or quantify know how many of the connections for a given hippocampal subregion are associated with a given tract to provide a link to prior work or clarify the approach. A fairly large body of prior research on hippocampal white matter connectivity has focused on the fornix, but it's a little difficult to align these prior findings with the connectivity density results in the current paper.

We thank Reviewer 1 for this comment and agree this would be an interesting avenue to pursue. However, the reliable segmentation of white matter fibre bundles is currently an area of contention in the DWI community. This pervasive and problematic issue was highlighted in a recently published large multi-site study that revealed a high degree of variability in how white matter bundles are defined, even from the same set of whole-brain streamlines (Schilling et al., 2021, Neuroimage. Nov; 243:118502. https://pubmed.ncbi.nlm.nih.gov/34433094/). This means that, even if we were to choose a particular method to segment white matter bundles, our results would not be readily translatable to those reported in previous DWI studies. This significantly limits meaningful comparison and/or interpretation. Indeed, such an approach may paradoxically take away from the detailed characterisations we have achieved in the current study. As highlighted in that study, it is now paramount that consensus is reached in this field to define criteria to reliably and reproducibly define white matter fibre bundles. Once that is achieved, we plan to conduct a follow-up study to characterise this in more detail, with bundles that will be able to be reliably reproduced by others.

4) Finally, on a more speculative note: based on the endpoint density maps, there seems to be a lot of overlap between the EDMs associated with different cortical regions (which makes sense given the subregion results). Does this effectively mean that the same endpoints may be equally connected with multiple different cortical regions? Part of the answer can be found in Fig. 3D showing the combined EDM for three different regions, but how spatially unique is each endpoint? This is likely not a feasible question to address analytically but it might be helpful to provide some more context for what these maps represent and how they might relate to differences across individuals.

The primary aim of the current analysis was to characterise broad patterns of endpoint density captured by our averaged group level analysis. However, Reviewer 1 is astute in assuming that, although there is overlap in the group averaged endpoint density maps (EDMs) associated with different cortical areas, at the single participant level, there are both overlaps and spatial uniqueness in the location of individual endpoints. For example, while group level analysis revealed that area V1 and area V2 showed preferential connectivity with overlapping regions of the posterior medial hippocampus, when visualising individual endpoints associated with each of these areas at the single participant level, we can see that some endpoints overlap while others display spatially unique patterns (see image below). Although a more in-depth analysis of individual variability in these patterns was beyond the scope of this investigation (as noted on Page14; Lines 379-381), we agree with Reviewer 1 that this is an important point to note in the manuscript. We have, therefore, included additional text touching on this and have included a new Supplementary Figure (Page 42; also see below) to emphasise that, at the single participant level, different cortical areas display both overlapping and spatially unique endpoints within specific regions of the hippocampus (using areas V1 and V2 as an example).

We have included the following text in the Results section (pp. 14, lines 370-379);

“Finally, while we observed clear overlaps in the group averaged EDMs associated with specific cortical areas, a closer inspection of individual endpoints at the single participant level revealed that endpoints associated with different cortical areas displayed both overlapping and spatially unique characteristics within these areas of overlap. For example, at the group level, areas V1 and V2 showed preferential connectivity with overlapping regions of the posterior medial hippocampus (see Supplementary Figure S5) while, at the single participant level, individual endpoints associated with each of these areas display both overlapping and spatially unique patterns (see Supplementary Figure S6). This suggests that, while specific cortical areas display overlapping patterns of connectivity within specific regions of the hippocampus, subtle differences in how these cortical regions connect within these areas of overlap likely exist.”

Reviewer #2 (Public Review):

Dalton and colleagues present an interesting and timely manuscript on diffusion weighted imaging analysis of human hippocampal connectivity. The focus is on connectivity differences along the hippocampal long axis, which in principle would provide important insights into the neuroanatomical underpinnings of functional long axis differences in the human brain. In keeping with current models of long-axis organisation, connectivity profiles show both discrete areas of higher connectivity in long axis portions, as well as an anterior-to-posterior gradient of increasing connectivity. Endpoint density mapping provided a finer grained analysis, by allowing visualisation of the spatial distribution of hippocampal endpoint density associated with each cortical area. This is particularly interesting in terms of the medial-lateral distribution with hippocampal head, body and tail. Specific areas map to precise hippocampal loci, and some hippocampal loci receive inputs from multiple cortical areas.

This work is well-motivated, well-written and interesting. The authors have capitalised on existing data from the Human Connectome Project. I particularly like the way the authors try to link their findings to human histological data, and to previous NHP tracing results.

Many thanks.

1) There are some important surprises in the results, particularly the relatively strong connectivity between hippocampus and early visual areas (including V1) and low connectivity with areas highly relevant from functional perspectives, such as the medial prefrontal cortex (rank order by strength of connectivity 7th and 78th of all cortical structures, respectively). This raises a concern that the fibre tracking method may be joining hippocampal connections with other tracts. In particular, given the anatomical proximity of the lateral geniculate nucleus to the body and tail of the hippocampus, the reported V1 connectivity potentially reflects a fusion of tracked fibres with the optic radiation. In visualizing the putative posterior hippocampus-to-V1 projection (Figure 4B, turquoise), the tract does indeed resemble the optic radiation topography. Although care was taken to minimise the hippocampus mask 'spilling' into adjacent white matter, this was done with focus on the hippocampal inferior margin, whereas the different components of the optic radiation lie lateral and superior to the hippocampus.

We agree with Reviewer 2 that our observations relating to area V1 could be the result of limitations inherent to current tracking methodology. Indeed, probabilistic tracking can result in tracks mistakenly ‘jumping’ between fibre bundles. Unfortunately, primarily due to limitations in image resolution, we do not believe that we can categorically rule this possibility out in the current dataset beyond the measures we have already taken in our analysis pipeline. We have now included additional text in the Discussion acknowledging and emphasising this possible limitation of our study.

We have included the following text in the Discussion section (Page 25; Lines 694-699);

“Also, we cannot rule out that some connections observed in the current study may result from limitations inherent to current probabilistic fibre-tracking methods whereby tracks can mistakenly ‘jump’ between fibre bundles (e.g. for connections between the posterior medial hippocampus and area V1 due to the proximity to the optic radiation), especially in “bottleneck” areas. Again, future work using higher resolution data may allow more targeted investigations necessary to confirm or refute the patterns we observed here.”

Beyond the possibility of tracks jumping between fibre bundles, we feel it is important to emphasise that an integral part of our analysis was the detailed attention we took to minimise mask ‘spillage’ of the entire hippocampus mask. It is not the case that we primarily focussed on inferior portions of the hippocampus as stated by Reviewer 2. Equal focus was paid to medial, lateral and superior portions of the mask which lie adjacent to visual thalamic nuclei, the optic radiation posteriorly and a number of other structures. We can see that our description relating to this lacked the necessary detail to convey this important point clearly and we apologise for the confusion. We have, therefore, included additional text in the Methods section clarifying this further.

We have included the following text in the Methods section (Page 26; Lines 751-755);

“We took particular care to ensure that all boundaries of the hippocampus mask (including inferior, superior, medial and lateral aspects) did not encroach into adjacent white or grey matter structures (e.g., amygdala, thalamic nuclei). This minimised the potential fusion of white matter tracts associated with other areas with our hippocampus mask.”

These points notwithstanding, our results support recently observed structural and functional associations between the posterior hippocampus and early visual processing areas. We agree that these findings are potentially of great conceptual importance for how we think about the hippocampus and its connectivity with primary sensory cortices in the human brain and we have now included a brief comment relating to this in the Discussion.

We have included the following text in the Discussion (Page 23-24; Lines 638-644);

“However, this observation supports recent reports of similar patterns of anatomical connectivity as measured by DWI in the human brain (38) and functional associations between these areas (43, 60). Collectively, these findings are potentially of great conceptual importance for how we think about the hippocampus and its connectivity with early sensory cortices in the human brain and open new avenues to probe the degree to which these regions may interact to support visuospatial cognitive functions such as episodic memory, mental imagery and imagination.”

2) A second concern pertains to the location of endpoint densities within the hippocampus from the cortical mantle. These are almost entirely in CA1/subiculum/presubiculum. It is, however, puzzling why, in Supp Figure 2, the hippocampal endpoints for entorhinal projections is really quite similar to what is observed for other cortical projections (e.g., those from area TF). One would expect more endpoint density in the superior portions of the hippocampal cross section in head and body, in keeping with DG/CA3 termination. I note that streamlines were permitted to move within the hippocampus, but the highest density of endpoints is still around the margins.

We agree with Reviewer 2 that, in relation to the entorhinal cortex, we would expect to see more endpoint density in areas aligning with the dentate gyrus (DG) and CA3 regions of the hippocampus. We noted in the discussion that “Despite the high-quality HCP data used in this study, limitations in spatial resolution likely restrict our ability to track particularly convoluted white-matter pathways within the hippocampus and our results should be interpreted with this in mind”. We believe that this limitation applies to pathways between the entorhinal cortex and DG/CA3. We have now included additional text specifically noting that this limitation likely affects our ability to track streamlines as they relate to DG/CA3. A targeted investigation of this effect using higher resolution diffusion MRI data may help address this issue, and this will be the subject of future work.

We have included the following text in the Discussion (Page 25; Lines 690-693);

“Indeed, this may explain the surprising lack of endpoint density observed in the DG/CA4-CA3 regions of the hippocampus where we would expect to see high endpoint density associated with, for example, the entorhinal cortex which is known to project to these regions. Future dedicated studies using higher resolution data are needed to assess these pathways in greater detail.”

3) On a related point, the use of "medial" and "lateral" hippocampus can be confusing. In the head, CA2/3 is medial to CA1, but so are subicular subareas, just that the latter are inferior.”

We agree that applying the terms ‘medial’ and ‘lateral’ to our three-dimensional representations can lead to some ambiguities and confusion. We have included a new description defining our use of these terms in the Results section.

We have included the following text in the Results section (Page 10; Lines 268-273).

“In relation to nomenclature, our use of the term ‘medial’ hippocampus refers to inferior portions of the hippocampus aligning with the distal subiculum, presubiculum and parasubiculum. Our use of the term ‘lateral’ hippocampus refers to inferior portions of the hippocampus aligning with the proximal subiculum and CA1. In instances that we refer to portions of the hippocampus that align with the DG or CA3/2 we state these regions explicitly by name”.

Reviewer #1 (Public Review):

This manuscript presents novel results which suggest that networks of cortical regions show different patterns of structural connectivity with hippocampal subregions. The results build on prior work, but also provide a spatially precise characterization of whole-brain structural connectivity patterns along the anterior-posterior hippocampal gradient. The paper is well-motivated and well-written. The authors discuss their findings in the context of previous investigations in non-human primates, and draw a number of parallels between these bodies of evidence. However, there were also some interesting differences between the connectivity patterns uncovered in resting-state fMRI and those identified using the present approach. It would also be helpful to highlight the key methodological advances or differences compared to prior work to contextualize the present findings.

1. The connectivity patterns along the anterior-posterior hippocampal axis broadly follow an anterior-posterior cortical bias, such that posterior regions, e.g. the visual cortex, are preferentially connected to the hippocampal tail, and anterior regions, e.g. the temporal pole, are preferentially connected to the hippocampal head. The authors focus on the twenty regions with the highest connectivity profiles, which appears to capture the majority of all connections. However, some of the present structural connectivity patterns differ in interesting ways from previously described cortical networks reported in resting-state fMRI studies. Most notably, the medial PFC and orbitofrontal regions combined account for less than 1% of all connections in the present investigation (Table S1 & S2). This is an interesting contrast to functional investigations which tend to find that these regions cluster with the aHPC (e.g., Adnan et al. 2016 Brain Struct Func; Barnett et al. 2021 PLoS Biol; Robinson et al. 2016 NeuroImage). In contrast, the present DWI results suggesting preferential pHPC-medial parietal connectivity dovetail with those observed in fMRI studies. It seems important to discuss why these differences may arise: whether this is a differentiation between structural and functional networks, or whether this is due to a difference in methods.

2. While the analytic pipeline is described in sufficient detail in the Methods, it is somewhat unclear to a non-DWI expert what the major methodological advance is over prior approaches. The authors refer to a tailored processing pipeline and 'an advance in the ability to map the anatomical connectivity (p. 5), but it's not immediately clear what these entail. It would be useful to highlight the key methodological differences or advances in the Introduction to help with the interpretation of the similarities and differences with previous connectivity findings.

3. Related to the point above, it was a bit unclear to me how the present connections map onto canonical white matter tracts. In Fig., 4A, the tracts are shown for a single participant, but it would be helpful to map or quantify know how many of the connections for a given hippocampal subregion are associated with a given tract to provide a link to prior work or clarify the approach. A fairly large body of prior research on hippocampal white matter connectivity has focused on the fornix, but it's a little difficult to align these prior findings with the connectivity density results in the current paper.

4. Finally, on a more speculative note: based on the endpoint density maps, there seems to be a lot of overlap between the EDMs associated with different cortical regions (which makes sense given the subregion results). Does this effectively mean that the same endpoints may be equally connected with multiple different cortical regions? Part of the answer can be found in Fig. 3D showing the combined EDM for three different regions, but how spatially unique is each endpoint? This is likely not a feasible question to address analytically but it might be helpful to provide some more context for what these maps represent and how they might relate to differences across individuals.

Its possible. One discerns through the sociological imagination, the issue is soon to come to a head within politics in Australia; Indigenous Australian leadership is knocking on the door, and as education around the issue grows to reach more Australians, both Indigenous and non-indigenous, a 'watershed moment' is on the near horizon.

This reminds me of Feynman's observation:"It's a brown-throated thrush, but in Germany it's called a halzenfugel, and in Chinese they call it a chung ling and even if you know all those names for it, you still know nothing about the bird."

Sometimes knowing a name stops our curiosity to push our understanding further because we cheat ourselves into thinking that we know more than the mere name. This is a fallacy.

```html

<html prefix="dct: http://purl.org/dc/terms/ rdf: http://www.w3.org/1999/02/22-rdf-syntax-ns# dcat: http://www.w3.org/ns/dcat# foaf: http://xmlns.com/foaf/0.1/"> <head> <title>DCAT in RDFa</title> </head> <br /> <body> <div typeof="dcat:Dataset" resource="http://gov.example.org/dataset/finances">

Example DCAT Dataset

25th October 2010

10th March 2013

This is the description.

<div property="dct:license" resource="http://reference.data.gov.uk/id/open-government-licence"> <span property="dct:title">UK Open Government Licence (OGL)</span> </div> <div property="dct:publisher" resource="http://example.org/publisher"> <span property="foaf:name">Example Publisher</span> </div> <div> <span property="dcat:keyword">Examples</span>, <span property="dcat:keyword">DCAT</span> </div> <div> Weekly </div> <div property='dcat:distribution' typeof='dcat:Distribution'> <span property="dct:title">CSV download</span>

• Format <span content='text/csv' property='dcat:mediaType'>CSV</span>
• Size <span content='240585277' datatype='xsd:decimal' property='dcat:byteSize'>1024MB</span>
• Issues <span property='dct:issued'>2012-01-01</span>

Download the full dataset

</html> ```

Reviewer #3 (Public Review):

Although it is increasingly realized that cortical neural representations are inherently unstable, the meaning of such "drift" can be difficult or impossible to interpret without knowing how the representations are being read out and used by the nervous system (i.e. how it contributes to what the experimental animal is actually doing now or in the future). Previous studies of representational drift have either ignored or explicitly rejected the contribution of what the animal is doing, mostly due to a lack of high-dimensional behavioural data. Here the authors use perhaps the most extensive open-source and rigorous neural data available to take a more detailed look at how behaviour affects cortical neural representations as they change over repeated presentations of the same visual stimuli.

The authors apply a variety of analyses to the same two datasets, all of which convincingly point to behavioural measures having a large impact on changing neural representations. They also pit models against each other to address how behavioural and stimulus signals combine to influence representations, whether independently or through behaviour influencing the gain of stimuli. One analysis uses subsets of neurons to decode the stimulus, and the independent model correctly predicts the subset to use for better decoding. However, one caveat may be that the nervous system does not need to decode the stimulus from the cortex independently of behaviour; if necessary, this could be done elsewhere in the nervous system with a parallel stream of visual information.

Overall the authors' claims are well-supported and this study should lead to a re-assessment of the concept of "representational drift". Nonetheless, a weakness of all analyses presented here is that they are all based on data in head-fixed mice that were passively viewing visual stimuli, such that it is unclear what relevance the behaviour has. Furthermore, the behavioural measurements available in the open-source dataset (pupil movements and running speed) are still a very low dimensional representation of what the mice were actually doing (e.g. detailed kinematics of all body movements and autonomic outputs). Thus, although the authors here as well as other large-scale neural recording studies in the past decade or so make it clear that relatively basic measures of behaviour can dramatically affect cortical representations of the outside world, the extent to which any cortical coding might be considered purely sensory remains an important question. Moreover, it is possible that lower-dimensional signals are overly represented in visual areas, and that in other areas of the cortex (e.g. somatosensory for proprioception), the line between behaviour parameters and sensory processing is blurred.

I had Fenton for a class once and during a lecture he asked a question of the class. A student raised his hand and answered. Professor Fenton listened and asked the class "Does anyone else agree that his answer is correct?"

About 85% of the students in the large lecture hall raised their hands.

He paused, shook his head, and said "Well, then I'm afraid you're all going to fail." Then he turned around and went back to writing on the chalkboard.

Reign of Terror

linguistic erectile dysfunction?

Sorry, that's what the poet in my head said so I could not resist.

This is especially important for people with motor limitations who may be unable to use standard user interfaces. Because individuals may utilise a mouth stick, head wand, and other devices, having a keyboard friendly site can help assist their motor limitations. This can ultimately be lifesaving for some people.

This webpage follows the perceivable principle from POUR. This is because you are able to distinguish significant text, such as titles, from other texts by comparing the sizes, colours, and placements of the text.

From the four guiding principles learned, this is a great example of understandable. The content is clearly organized as they are given a specific category. The text is also is easily understandable for a wide variety of users. A visual representation for the text is also given. This simple layout makes it easy to access the website

This webpage allows the viewer to easily distinguish the foreground from the background. The white background does not take away from the foreground and the images in the foreground stand out to the viewer. Conflicting colours would cause the background to take away from the foreground or vice versa. This is a good example of web based communication.

In my first draft, I had inserted the research I had done on racial bias with names, but made no effort to connect it to how it possibly influences BC. By including this detail, I point out the possibility for racial bias occurring at BC which I did not do before.

Here Ahrens doesn't say that Luhmann invented the zettelkasten, but he comes pretty close and is heavily implying it rather than delving into the ways which Luhmann may have been taught this practice.

I revamped this paragraph by providing concrete and specific details to illustrate an example of one of my false narratives--receiving a perfect grade. Adding a specific example of a false narrative makes the reader better understand the concept and has a better understanding of the pressure I feel to excel. I also relate back to the introductory paragraph by showing how I was not mentally present in the moment because my head was focused on fantasy rather than reality. I wanted to connect back to my introduction to make the focus of my paper more clear.

To make this paragraph read more clearly, I could adjust the third sentence to "When I write papers for my writing class, I doze off and envision my professor handing me the paper back a few weeks later. A huge smile appears across my face when I read his feedback and see a small “A” enclosed in an oval on the final page of the essay."

In my first draft of this essay, I opened with a broad statement, "Not a day goes by that I don't think about my future." And then I wrote about feeling pressure about not knowing what I want to do for a career which does not set up my focus of this paper, that I'm learning about how to minimize the pressure I place on myself when I feel like a dream should instantly be a reality. To make the focus more clear, and make the focus easier to follow, I opened with an example of me daydreaming about my false narratives which better sets up my paper.

I drafted this new introduction to use more sensory, concrete, and specific details. I wanted to add those details so the reader can better visualize where I'm sitting and what I look like when I'm daydreaming.

This line call back to themes discussed in "What Work Is" and more specifically the poem, "Fire". The connection arises aesthetically, and here the image of a man heading straight for the storm can be likened to the son from "Fire", who as a fire fighter heads directly into the fire storming in the mountains. However, even though the motifs here and setting are somewhat similar, they operate in each respective work in totally opposite ways. In "The Promised Land" this storm and twister thats going to ravage everything in sight, that will blow away both dreams and lies, is something the character embraces as a rebirth which he is dying to run into head first. In "Fire" the all encompassing fire which the son is working in to quell takes away his "sense of time and place" as he works nonstop.

Reminds me of Martin going back into town after working at the laundry springs. Working himself to exhaustion only to further exhaust himself with drink. The only way to relax, but it comes at a cost literally and physically.

Another way of saying going from head to heart is tasting reality or applying the word. Is it just theoretical or is it practical?

Reviewer #3 (Public Review):

Wang and Zhu use a combination of computed tomographic methods and thin sectioning to provide what I think is the most complete characterisation of the articulated squamation of a stem-group gnathostome yet published. They use computed tomography to characterise body scale morphologies across the body of the antiarch placoderm Parayunnanolepis xitunensis, identifying 13 regionalised morphotypes of scale. Thin sectioning of isolated scales from the same locality, assigned to Parayunnanolepis on the basis of these morphotypes, shows that they lack a spongy middle layer, a structural characteristic of antiarch's dermal head armour as well as the dermal skeletons of many other Palaeozoic vertebrates. Based on their inferred position for yunnanolepids, as early-branching members of the antiarchs, themselves the sister-group to all other mandibulate jawed vertebrates, they interpret this data to mean that this absence of a spongy middle layer and regionalised squamation are plesiomorphic both for antiarchs and for mandibulate stem-gnathostomes more generally.

The methods provide a detailed overview of the material the authors describe, and the authors' interpretation of that data is on the whole justified. The real strength of the paper is in identifying all of these morphotypes on an articulated fossil, which preserves the head skeleton, and sufficient phylogenetic information to be able to place confidently in the early vertebrate tree. I'm sure that this will ensure it becomes a key reference work for those interested in the dermal skeleton of Palaeozoic fishes.

The weaknesses mainly lie with the comparison of this data more broadly amongst Palaeozoic jawed vertebrates. There's a conspicuous absence of comparison to jawless stem-group gnathostomes other than osteostracans which would help inform plesiomorphic gnathostome states, despite there being published data on their body squamations. The manuscript would also benefit from considering alternative phylogenetic placements for antiarchs, and the effect that this would have on their conclusions.

The information they provide will doubtless be of great use to other workers interested in the anatomy of placoderm body squamations and in identifying isolated parayunnanolepid scales. As touched on above the fact that this data comes from an animal that is commonly incorporated into phylogenetic analyses means that this new data will be used by those scoring characters for these analyses and perhaps to help formulate new body squamation characters. This means that this new information will also indirectly feed into major areas of investigation in early jawed vertebrate palaeontology such as the evolution of jaws.

revenge!

from Octavia Butler's science fiction novel, Parable of the Sower, published in 1993. This conversation takes place at the beginning of the book between the protagonist, Lauren Oya Olamina, and her stepmother. In the fictional future setting of 2024, the United States has devolved further into authoritarianism, inequality, and chaos. Lauren is a "sharer," which means that she empathizes with others to the extent that she physically feels the emotions of the people around her. If we think of the asterisk as a "sensor", we can see it as a body made entirely of appendages: each spoke a sensitive antenna, or dendrite, or whisker — picking up signals from what is around it.

He has tricked her completely into making a plan to blow him to escape and she is falling for his empty promises.

ricky b like

The expectation on AI seems ideal but to make it possible, there's still some gap and people should feel free to change their long-term plan with the specific development of technology.

interesting double standard

Microsoft Azure has such an extensive array of services and features, how can you wrap your head around them all. We'll try to help you here, by breaking them down into ten main categories.

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Networking these features that you connect your cloud and on-premise infrastructure in order to bring the best possible experience to your customers. VPNs and load balancing are just two examples of these features.

Storage Whether it's disk, file, blob, or archival storage; these services let you scale your data and app storage needs in a secure fashion.

Mobile With the mobile services, you can build and deploy a cross-platform in need of apps for any mobile device. Send notifications, you xamarin to build cloud-powered apps and take advantage of cognitive services to make your apps smarter.

Databases Choose from a variety of proprietary and open source database engines to bring your current databases to the cloud. Use tools to manage your SQL, Cosmos Db, MySQL and other data services.

Web These Services help you build deploy manage and scale your web applications. Create web apps published apis to your services or use Azure maps to provide geo-spatial context to your data.

Internet of Things Use these features to connect Monitor and manage all of your iot assets. Analyze the data, as it arrives from sensors, and then, take meaningful action with it.

Big data When you have large volumes of data, these open source cluster services will help you run analytics at a massive scale and make decisions based off of complex queries.

AI Use your existing data to forecast, future behaviors. Based on these AI services, use machine learning to build train and deploy models to the cloud.

DevOps DevOps brings together. People processes and Technology, by automating software delivery to provide continuous value to your users. with Azure DevOps, you can create build and release pipelines that provide continuous integration delivery and deployment for your applications.

These categories represent just a small fraction of what is available on Azure. Fortunately. It's easy to try out a new service then mix and match them to get exactly what you need. And the best part is you only pay for what you use.

In elementary school, I was much more of a outgoing and confident when speaking out loud. I would always participate when I knew the answer or when I wanted to share or elaborate on a reading that was being discussed. I loved to present my projects and dances. However when I ended up preforming for my fifth grade talent show, my teacher came up to my mother and said that I was going to get bullied in middle school because of how happy of a child I was. Which didn't make sense to me because I though I was going to get bullied because I was weird. But as years went by, I begun to understand that there are many adolescents who are going through difficult times at home and they are miserable. They are not content with there own lives to they choose to target the one's who are happy in what they do for example dancing. Once I got to middle school, I begun to notice who supported and who teased me. I became a much more quite kid to the point where when I joined dance in high school I quit because I did not want to have any eyes on me. Any one talk about me. I stuck to my small group of friends and looking back it was the best decision I made because even the people in the dance team would not be the best teammates. However I do regret making them the reason why I quit dance. Now that I am in college I hope to get back into dancing and put myself out of my comfort zone again! Because putting yourself out of your comfort zone really does help you grow as an individual.

The irony

This is very eye opening because I never thought of checking what the person is verify for. I often see in social media people with blue checks that are not even that famous because I thought only famous people get blue check marks.

I relate to this, because this is something I do. I create a voice for my author in my head and that voice also tells me certain characteristics behind him.

The factual examples are often time the best source in terms of storytellings

This is an important point. I, personally, always have this in my head when I read, or write, about "building community" (or myriad alternatives that ultimately come down to the same idea).

Signs and symptoms may include:

High blood pressure Irregular or slow pulse Severe headache Weakness Cardiac arrest (no pulse) Loss of consciousness, coma Loss of all brainstem reflexes (blinking, gagging, and pupils reacting to light) Respiratory arrest (no breathing) Wide (dilated) pupils and no movement in one or both eyes

Tests may include:

X-ray of the skull and neck CT scan of the head MRI scan of the head Blood tests if an abscess or a bleeding disorder is suspected

To convey the sense of conversation implied by the use of the imperative уймись in the original, I translated болит голова as my head hurts.

What's the difference between a modifier and an adjective ?

so Tuesday morning with be a P with an adjunct head?

before reading the explanation, I thought Russia would be the head because it's where the proposal is coming from

This "mathematical head" sounds to me like they'd be an extraordinary person... maybe even an extraordinary man!

Having further context for the novel here helps but it's so interesting seeing the ideas of the extraordinary man being fleshed out slowly before getting to the conversation we know and love. It seems like this is the hook for an analytical essay about saving mankind. Even though the conversation is yet to happen about Rasko's ideas regarding the extraordinary man, it's easy to see how Dostoevsky it guiding the reader by planting the seed. In a funny way, it's his way of highlighting the absurd aspects of the idea before Rasko offers his own commentary on the issues. It's also key to note how right after there is a mention of a "instinctive dislike of history." This sounds like commentary from the author about the controversial views that many have on whether historical figures are truly "extraordinary" or not. When there is a later mention of Napoleon and others like Muhammad, it's clear that he is nodding towards figures that not all audiences would agree on, just not Rasko makes claims that not everyone is quick to concur with.

This epigraph is taken from the Ballad of Sir Patrick Spence, a popular, oft-anthologized Scottish Ballad. It tells the tale of a king sending the titular talented sailor on an errand to Norway, during which the sailor notices signs that indicate that a large storm is coming. He accepts his duty in anguish, knowing that the trip may very likely be his last. Just as he predicted, a large winter storm overpowers his ship and he and his crew succumb to the powers of nature.

Coleridge uses this reference to set the tone for his Ode. It highlights the part of the ballad in which the sailor's fear is at its peak, and emphasizes how much dominance the power of nature has over mankind (a theme prevalent in Rousseau); even the most talented sailor in all the lands cannot face the forces of nature head-on and emerge victorious.

Prior studies conducted by Pierre et al. have embedded permanent magnets into the soft robot that allow for the generation of a magnetic field. This actuation method allows for faster movement.

Author Response

Reviewer #1 (Public Review):

Previous studies have indicated that neurons in different cortical areas have different intrinsic timescales. However, the functional significance of the difference in intrinsic timescales remains to be established. In this study, Pinto and colleagues addressed this question using optogenetic silencing of cortical areas in an evidence accumulation task in mice. While head-fixed mice performed in an accumulating-towers task in visual virtual reality, the authors silenced specific cortical regions by locally activating inhibitory neurons optogenetically. The weight of sensory evidence from different positions in the maze was estimated using logistic regressions. The authors observed that optogenetic silencing reduced the weight of sensory evidence primarily during silencing, but also preceding time windows in some cases. The authors also performed a wide-field calcium imaging and derived auto-regressive term based on a linear encoding model which include a set of predictors including various task events, coupling predictors from other brain regions in addition to auto-regressive predictors. The results indicated that inactivation of frontal regions reduced the weight of evidence accumulation on longer timescales than posterior cortical areas, and the autoregressive terms also supported the different timescales of integration.

The question that this study addresses is very important, and the authors used elegant experimental and analytical approaches. While the results are of potential interest, some of the conclusions are not very convincing based on the presented data. Some of these issues need to be addressed before publication of this work.

We thank the reviewer for their kind words and constructive feedback. In hindsight, we agree that some conclusions were unwarranted based on the original analysis. We have revamped our analytical approach to address these issues, as detailed below.

Major issues:

1. There are several issues that reduce the strength of the main conclusion regarding the timescale of integration using cortical silencing. 1a. The main analysis relied on the data pooled across multiple animals although individual animals exhibited a large amount of variability in the weights of integration across different time windows. Also, some mice which did not show a flat integration over time were excluded. This might also affect the interpretation of the analysis based on the pooled (and selected) data. How the individual variability affected the main conclusion needs to be discussed carefully.

We have entirely replaced the pooled model for a mixed-effects logistic regression approach in which we explicitly modeled the variability introduced by individual animals (as well as different inactivation conditions). Because of this more principled approach, we added back the previously excluded mice. We also devised a shuffling procedure to further take that variability into account when reporting the statistical significance of the effects, as we now explain in Materials and Methods (line 652):

“For the models in Figure 2, we also computed coefficients for shuffled data, where we randomized the laser-on labels 30 times while keeping the mouse and condition labels constant, such that we maintained the underlying statistics for these sources of variability. This allowed us to estimate the empirical null distributions for the laser-induced changes in evidence weighting terms.”

Finally, we have also added text to be more explicit about this variability and how it informed the new analytical approach (line 169):

“(...) to account for the inter-animal variability we observed, we used a mixed-effects logistic regression approach, with mice as random effects (see Materials and Methods for details), thus allowing each mouse to contribute its own source of variability to overall side bias and sensitivity to evidence at each time point, with or without the inactivations. We first fit these models separately to inactivation epochs occurring in the early or late parts of the cue region, or in the delay (y ≤ 100 cm, 100 < y ≤ 200 cm, y > 200 cm, respectively). We again observed a variety of effect patterns, with similar overall laser-induced changes in evidence weighting across epochs for some but not all tested areas (Figure 2–figure supplement 1). Such differences across epochs could reflect dynamic computational contributions of a given area across a behavioral trial. However, an important confound is the fact that we were not able to use the same mice across all experiments due to the large number of conditions (Figure 1–table supplement 1), such that epoch differences (where epoch is defined as time period relative to trial start) could also simply reflect variability across subjects. To address this, for each area we combined all inactivation epochs in the same model, adding them as additional random effects, thus allowing for the possibility that inactivation of each brain region at each epoch would contribute its own source of variability to side bias; different biases from mice perturbed at different epochs would then be absorbed by this random-effects parameter. We then aligned the timing of evidence pulses to laser onset and offset within the same models, as opposed to aligning with respect to trial start. This alignment combined data from mice inactivated at different epochs together, further ameliorating potential confounds from any mouse x epoch-specific differences. (...) This approach allowed us to extract the common underlying patterns of inactivation effects on the use of sensory evidence towards choice, while simultaneously accounting for inter-subject and inter-condition variability.”

1b. The main conclusion that the frontal areas had longer integration windows largely depends on a few data points which relied on a very small number of samples (n = 4 or 3). This is, in part, because of the use of pooled data and because the number of samples comes from the alignment of the data with different timing of inactivation. This analysis also appears to suffer from the fact that the number of sample is biased toward the time of inactivation (y = 0 which had n = 6) compared to the preceding time windows (y = 50 and 100, which had n = 4 and 3, respectively).

We agree with this assessment. As explained above, our new mixed-effects logistic regression approach explicitly models the variability introduced by mice and conditions, which allows us to focus on the effects that are common across mice and conditions. Because of these changes, we were now able to perform statistical analyses on coefficients using metrics based on their error estimates from the model fitting procedure, such that all estimates come from the same sample size and take into account the full data (t- and z-tests, as explained in more detail in Materials and Methods, line 665). This new analysis approach confirmed, and we believe strengthened, our main conclusions.

1c. The clustering analysis uses only 7 data points corresponding to the cortical areas examined. The conclusions regarding the three clusters appear to be preliminary.

We agree. The clustering analysis was more meant as a way to summarize the data rather than provide a strong statement of area groupings. Because this analysis requires clustering on only 7 data points, as the reviewer points out, and because it is in no way central to our claims, we have decided to drop it. Instead, we now present a direct comparison between frontal and posterior areas, which is more directly related to our claims (Figs. 2C, 3).

1. The authors' conclusion that "the inactivation of different areas primarily affected the evidence-accumulation computation per se, rather than other decision-related processes" can be a little misleading. First, as the authors point out in the Results, the effect can be "the processing and/or memory of the evidence". Given that the reduction in the weight of evidence occurs during the inactivation period, the effect can be an impairment of passing the evidence to an integration process, and not accumulation process itself. Second, as discussed above (1b), the evidence supporting a longer timescale process (characterized as "memory" here) is not necessarily convincing. Additionally, the authors' analysis on "other decision-related processes" is limited (e.g. speed of locomotion), and it remains unclear whether the authors can make such a conclusion. Overall, whether the inactivation affected the evidence accumulation process and whether the inactivation did not affect other cortical functions remain unclear from the data.

We agree with the reviewer that our previous modeling approach did not allow us to adequately separate between these different processes. However, we believe that our new approach addresses some of these shortcomings by being done in time rather than space (thus controlling for running speed effects), and separating evidence occurring before, during or after inactivation within the same model. As we now explain in the main text (line 156):

“We reasoned that changes in the weighting of sensory evidence occurring before laser onset would primarily reflect effects on the memory of past evidence, while changes in evidence occurring while the laser was on would reflect disruption of processing and/or very short-term memory of the evidence. Finally, changes in evidence weighting following laser offset would potentially indicate effects on processes beyond accumulation per se, such as commitment to a decision. For example, a perturbation that caused a premature commitment to a decision would lead to towers that appeared subsequent to the perturbation having no weight on the animal’s choice. Although our inactivation epochs were defined in terms of spatial position within the maze, small variations in running speed across trials, along with the moderate increases in running speed during inactivation, could have introduced confounds in the analysis of evidence as a function of maze location (Figure 1–figure supplement 2). Thus, we repeated the analysis of Figure 1C but now with logistic regression models, built to describe inactivation effects for each area, in which net sensory evidence was binned in time instead of space. (...) We then aligned the timing of evidence pulses to laser onset and offset within the same models, as opposed to aligning with respect to trial start.”

Throughout our description of results, we now more carefully outline whether the findings support a role in sensory-evidence processing, memory, or both, as well as post-accumulation processes manifesting as decreases in the weight of sensory evidence after laser offset. For example, our new analyses have more clearly shown prospective changes in evidence use when M1 and mM2 were silenced, compatible with the latter. We also agree with the reviewer that we cannot completely rule out other untested sources of behavioral deficits beyond the aforementioned decision processes. Thus, we have removed all statements to the effect that only evidence accumulation per se was affected. Importantly, though, we believe the new analyses do support the claims that the inactivation of all tested areas strongly affects the accumulation process, even if not exclusively.

1. Different shapes of the autoregressive term may result from different sensory, behavioral or cognitive variables by which neurons in each brain area are modulated. In other words, if a particular brain area tracks specific variables that change on a slow timescale, the present analysis might not distinguish whether a slow autoregressive term is due to the intrinsic properties of neurons or circuits (as the authors conclude), or neuronal activities are modulated by a slowly-varying variable which was not included in the present model.

We note that many of our task-related predictors, in particular ones related to sensory evidence, had lags that matched the timescales of the auto-regressive coefficients. Along with our regularization procedures, this would argue against variance misattribution to coefficients included in the model. We have now added an analysis of sensory-evidence coefficients to Figure 4–figure supplement 1, which did not reveal any significant differences between areas.

Of course, as the reviewer suggests, it is possible that, despite our extensive parameterization of behavioral events, we failed to model some task component that would display timescale differences across areas. We have added a discussion to acknowledge this possibility (line 332):

“Nevertheless, a caveat here is that the auto-regressive coefficients of the encoding model could conceivably be spuriously capturing variance attributable to other behavioral variables not included in the model. For example, our model parameterization implicitly assumes that evidence encoding would be linearly related to the side difference in the number of towers. Although this is a common assumption in evidence-accumulation models (e.g., Bogacz et al., 2006; Brunton et al., 2013), it could not apply to our case. At face value, however, our findings could suggest that the different intrinsic timescales across the cortex are important for evidence-accumulation computations.”

Reviewer #2 (Public Review):

Pinto et al use temporally specific optogenetic inactivation across the dorsal cortex during a navigation decision task to examine distinct contributions of cortical regions. Consistent with their previous findings (Pinto et al 2019), inactivation of most cortical regions impairs behavioral performance. A logistic regression is used to interpret the behavioral deficits. Inactivation of frontal cortical regions impairs the weighting of prior sensory evidence over longer timescale compared to posterior cortical regions. Similarly, the autocorrelation of calcium dynamics also increases across the cortical hierarchy. The study concludes that distributed brain regions participate in evidence accumulation and the accumulation process of each region is related to the hierarchy of timescales.

Identify the neural substrate of evidence accumulation computation is a fundamentally important question. The authors assembled a large dataset probing the causal contributions of many cortical regions. The data is thus of interest. However, I have major concerns regarding the analysis and interpretation. I feel the results as presented currently do not fully support the conclusion that the behavioral deficit is related to evidence accumulation. Alternative interpretations should be ruled out. Another major concern is the variability of the inactivation effect across conditions. The assumptions for pooling inactivation conditions should be better justified. Finally, some framing in the text should more closely mirror the data. Most notably, the data does not casually demonstrate that the hierarchy of timescales across cortical regions is related to evidence accumulation since the experiments do not manipulate the timescales of cortical regions. The two phenomena might be related, but this is a correlation based on the present findings.

We thank the reviewer for their thorough review and constructive suggestions. As we expand on below, we have changed our modeling approach to better account for data variability, and more explicitly justified the choice to pool across conditions. The modeling approach also allowed us to better pinpoint the different decision processes affected by cortical inactivation. Finally, we have also toned down our claims throughout the manuscript, and removed the claims of causality altogether.

Reviewer #3 (Public Review):

This study examines how the timescale over which sensory evidence is accumulated varies across cortical regions, and whether differences in timescales are causally relevant for sensory decisions. The authors leverage a powerful behavioral paradigm that they have previously described (Pinto et al., 2018; 2019) in which mice make a left vs. right decision in a virtual reality environment based on which side contains the larger number of visual cue "towers" passed by the "running" head-fixed mouse. The probability of tower presentation varies over time/space and between the left and right sides, requiring the mice to integrate tower counts over the course of the trial (several seconds/meters). To examine the contribution of a particular cortical region to sensory evidence accumulation, the authors optogenetically inactivated activity during several sub-epochs of the task, and examined the effect of inhibition on a) behavioral performance (% correct choices) and b) the strength of the contribution of sensory evidence to the decision as a function of time/space from the inhibition onset. Finally, the authors qualitatively compared the timescale of evidence accumulation identified for each region to the autocorrelation of activity in that region, calculated from reanalyzing the author's published calcium imaging data set (Pinto et al., 2019) with a more sophisticated regression model.

The methodology and analyses are leading edge, ultimately allowing for a comparison of evidence accumulation dynamics across multiple cortical regions in a well-controlled behavioral task, and this is a nice extension of the authors' previous studies along these lines. The study can potentially be built on in two broad directions: a) examining how circuits within any of the regions studied here function to accumulate sensory evidence, and b) addressing how these regions coordinate to guide behavior. Overall, while the study is generally strong, addressing some points would increase confidence in the interpretation of the results.

We thank the reviewer for their kind words and very helpful suggestions. As we expand on below, we now fit our model explicitly in the time domain and use mixed-effects regression to account for inter-mouse variability. We also expanded our discussion on interpretation caveats about the inactivation approach.

Specifically:

In describing the contribution of evidence to the decision, and how it is affected by inhibition (primarily Fig. 2), there is a confusing conflation of time and space. These are of course related by the mouse's running speed. But given that inactivation appears to consistently cause faster speeds (Fig. 2-Fig. S1), describing the effect of inhibition on the change of the weight of evidence as a function of space does not seem like the optimal way to examine how inactivation changes the timescale of evidence accumulation. The authors note in Fig. 2-Fig S1 that inactivation does not decrease speed, but it still would confound the results if inactivation increases speed (as appears to be the case; if not, it would be helpful for the authors to state it). Showing the data (e.g., in Fig. 2) as a function of time, and not distance, from laser on would allow the authors to achieve their aim of examining the timescale of evidence accumulation.

Indeed, we do observe significant, though minor, increases in speed. We had originally only considered the confounds of decreases in speed, but we agree that increases could likewise confound the analysis. Following the reviewer’s suggestion, we devised a new model that bins evidence in time rather than in space. Moreover, the time of evidence occurrence is aligned to laser onset or offset within the same model, which allows us to compare more directly the changes in weighting of evidence occurring before, during or after inactivation. The results from these new models are now presented in Figs. 2, 3, 2-S1, 2-S2, and largely confirm the findings from our previous analysis in the space domain.

Performing the analyses mouse by mouse, instead of on data aggregated across mice, would increase confidence in the conclusions and therefore strengthen the study. Mice clearly exhibit individual differences in how they weight evidence (Fig. 1C), as the authors note (line 81). It therefore would make sense to compare the effect of inactivation in a given mouse to its own baseline, rather than the average (flat) baseline. If the analyses must be performed on data aggregated across mice, some justification should be given, and the resulting limitations in how the results should be interpreted should be discussed. For example, perhaps there are an insufficient number of trials for such within-mouse comparisons (which would be understandable given the ambitious number of inactivated regions and epochs)?

As the reviewer suggests, we prioritized the number of conditions and mice per condition rather than the number of trials each mouse had, which complicates a per-mouse analysis of changes in evidence weights. This is particularly true for fitting logistic regressions with multiple coefficients, as was our goal here. Regardless, we still agree that the inter-animal variability should be accounted for in the analysis. Rather than doing a per-mouse regression, however, we implemented a mixed-effects logistic regression, which estimates random effects for all mice together in the same model, accounting for that when estimating the fixed-effects coefficients. Indeed, this approach is recommended for statistical problems such as ours (e.g., Yu et al., Neuron, 2021, In press, https://doi.org/10.1016/j.neuron.2021.10.030). While the overall statistics were still computed from the estimates of the fixed effects, this allowed us to also display per-mouse data when reporting the models (e.g. Figures 2, 3), which hopefully will give readers a greater appreciation for inter-mouse variability in the data, showing variations in their baseline, as the reviewer suggests. Finally, in order to more explicitly account for non-flat baselines, we now report laser-induced changes in evidence weights normalized by the baseline, rather than simply subtracted, as we did previously.

The method of inactivating cortical regions by activating local inhibitory neurons is quite common, and the authors' previous paper (Pinto et al., 2019) performed experiments to verify that light delivery produced the desired effect with minimal rebound or other off-target effects. Since this method is central to interpreting the results of the current study, adding more detail about these previous experiments and results would reassure the reader that the results are not due to off-target effects. Given that the cortical regions under study are interconnected, do the previous experiments (in Pinto et al., 2019) rule out the possibility that inactivating a given target region does not meaningfully affect activity in the other regions? This is particularly important given that activity is inhibited in multiple distinct epochs in this study.

We agree that the issue of off-target effects is important to the interpretation of any inactivation experiment, and one that we have yet to adequately grapple with as a field. Our previous experiments only measured local spread of inactivation effects. Thus, while we did rule out rebound excitation, we cannot rule out possible off-target effects in distal regions that are connected with the region being inactivated. Experiments to measure this would involve measuring from a single area while systematically inactivating distal areas connected to it or not or, more ideally, measuring from multiple areas simultaneously while performing these systematic inactivations. These experiments themselves would constitute a whole project and therefore fall outside the scope of the present manuscript. Following the reviewer’s suggestion, we have expanded the discussion of these experiments and potential caveats.

Line 145, Results: “Although our previous measurements indicate inactivation spreads of at least 2 mm (Pinto et al., 2019), we observed different effects even comparing regions that were in close physical proximity.”

Line 223, Results: “However, the possibility remains that these effects are related to lingering effects of inactivation on population dynamics in frontal regions, which we have found to evolve on slower timescales (see below). Although we have previously verified in an identical preparation that our laser parameters lead to near-immediate recovery of pre-laser firing rates of single units, with little to no rebound (Pinto et al., 2019), these measurements were not done during the task, such that we cannot completely rule out this possibility.”

Line 375, Discussion: “This could be in part due to technical limitations of the experiments. First, the laser powers we used result in large inactivation spreads, potentially encompassing neighboring regions. Moreover, local inactivation could result in changes in the activity of interconnected regions (Young et al. 2000), a possibility that should be evaluated in future studies using simultaneous inactivation and large-scale recordings across the dorsal cortex.”

Line 516, Materials and Methods: “We used a 40-Hz square wave with an 80% duty cycle and a power of 6 mW measured at the level of the skull. This corresponds to an inactivation spread of ~ 2 mm (Pinto et al., 2019). While this may introduce confounds regarding ascribing exact functions to specific cortical areas, we have previously shown that the effects of whole-trial inactivations at much lower powers (corresponding to smaller spatial spreads) are consistent with those obtained at 6 mW. To minimize post-inactivation rebounds, the last 100 ms of the laser pulse consisted of a linear ramp-down of power (Guo et al., 2014; Pinto et al., 2019)”

Reviewer #3 (Public Review):

The authors present a large scale study of connectomics in mouse primary visual cortex. Their study focuses on correlations among the spine head volumes of pairs of dual connections, and specifically those formed between Layer 2/3 pyramidal neurons. They define a "dual connection" as a pair of synapses connecting the same pre- and post-synaptic cells. They report on a novel and very interesting finding that the distribution of sizes among these pairs is bimodal can be fit by a mixture of a binary variable and a log-normal distribution. They draw the conclusion that the binary component could be due to synaptic plasticity rules and that the log-normal component is due to other factors. The authors also relate their novel findings to previous studies of correlations among dual connections.

1) The authors make the claim that the bimodality of the size distribution of "dual connections" is due to plasticity rules while the continuous gradation of the synapse size is dominated by "other influences" besides plasticity rules. But couldn't it be the case that precise synaptic plasticity rules are always at play and that the bimodality is due to the impact of "other influences" on these rules. These influences could be dendrite diameter, synapse density along the dendrite, distance from the soma, branch-point failure of propagation of the pre-synaptic AP or post-synaptic bAP, to name just a few. The continuous log-normal component of the gradation of synapse sizes is not at all inconsistent with the outcome of highly precise synaptic plasticity rules. Indeed, Bartol et al., 2015 showed that synaptic plasticity in rat CA1 stratum radiatum is as precise for small synapses as it is for large synapses over a broad, continuous, log-normal range of synapse sizes. Each individual synapse has its own unique activation history and a resulting, precise synaptic weight. And across the population of synapses there is a continuous distribution of activation histories.

2) The bimodality is a new and very interesting observation in this work and deserves much closer scrutiny to reveal the root cause behind it. The authors have shown that it exists in connections between layer 2/3 PyCs but is absent in other connections traced in their reconstruction of mouse neocortex. Perhaps this reveals something special about information processing in the neural subcircuit of layer 2/3 PyCs. With their highly detailed large-scale reconstruction, the authors have the opportunity to probe this question deeper.

1. I'm sorry, as opposed to eating live animals?? Stop trying to make this something it isn't dude
2. what is this Game of Thrones? are these animals white walkers or something? calm down
3. ah...it's because they're "useful" animals...and thus capitalism rears its ugly head once again

head and tail (respectively) lets you look at the first or last bits of a file

less displays the contents of a file one 'page' at a time

cat dumps the contents of a plain text file. Note can be sub-optimal for large files

Ajouter une bannière en haut des pages

This sounds similar to Candace's trajectory in Severance...trying to escape nostalgia and reflection but feeling it creep in to everything you do...a new kind of haunting

I really like these three words you use to describe peoples emotions when it comes to the moon. It is a pretty accurate representation of how people view the moon and where their head wonders to.

Reviewer #1 (Public Review):

This is a very interesting paper describing membrane potential dynamics of hippocampal principal cells during UP/DOWN transitions and sharp-wave ripples. Using whole-cell in combination with linear LFP recordings in head-fixed awake mice, the authors show striking differences of membrane potential responses in principal cells from the dentage gyrus, CA3 and CA1 sectors. The authors propose that switches between a dominant inhibitory excitable state and a disinhibited non-excitable state control the intra-hippocampal dynamics during UP/DOWN transitions.

Obtaining intracellular recordings in vivo is commendable. The authors provide valuable data and analysis. While data show clear trends and some of the conclusions are well supported, the authors may need to clarify the following potential confounds, which can actually impact their conclusions and interpretation:

1- All the analysis is based in z-scored membrane potential responses but the mean resting membrane potential is never reported. For DG granule cells recorded in awake conditions, the membrane potential is usually hyperpolarized so that most of the effect may be due to reversed GABAa mediated currents. Similarly, for those cells exhibiting the non-expected polarization during UP/DOWN states there may be drifts around reversal potentials explaining their behavior. Moreover, regional trends on passive and active membrane parameters and connectivity can actually explain part of the variability. A longitudinal comparison of state Vm and spikes in fig.5 suggests that some of the largest depolarized responses are not correlated with firing. Authors should evaluate this angle, ideally showing the distribution of membrane potential values across cells and regions and confronting this with the different membrane potential responses.

2- While there are some trends for each hippocampal regions, there is also individual variability across cells during UP/DOWN transitions (fig.5) and near ripples (fig.6). What part of this variability can be explained by proximodistal and/or deep-superficial differences of cell location and identity? Can authors provide some morphological validation, even if in only a subset of cells? For CA3, proximodistal heterogeneity for intrinsic properties and entorhinal input responses are well documented in intracellular recordings both in vitro and in vivo. What is the location of CA3 cell contributing to this study? For CA1 cells, deep-superficial trends of GABAergic perisomatic inhibition and connectivity with input pathways dominate firing responses. Regarding DG cells, are all they from the upper blade?

3- AC-coupled LFP recordings cannot provide unambiguous identification of the sign of phasic CSD signals, because fluctuations accompanying UP/DOWN states alter the baseline reference. This is actually the case, given changes of membrane potential accompanying UP/DOWN transitions. I recommend reading Brankack et al. 1993 doi: 10.1016/0006-8993(93)90043-m. The authors should acknowledge this limitation and discuss how it could influence their results. One potential solution to get rid of this effect is using principal/independent component analysis for blind source separation.

Reviewer #2 (Public Review):

In this manuscript "Inhibition is the hallmark of CA3 intracellular dynamics around awake ripples" the authors obtained Vm recordings from CA1, CA3 and DG neurons while also obtaining local field potentials across the CA1 and DG layers. This enabled them to identify periods of up and down state transitions, and to detect sharp-wave ripples (SWRs). Using these data, they then came to the conclusion that compared to CA1 and DG, the Vm of more CA3 neurons is hyperpolarized at the approximate time of SWRs.

Unfortunately, for the following reasons, the current manuscript does not necessarily support this conclusion:

Recordings are obtained in mice who are recently (same day) recovering from craniotomy surgery/anesthesia and have no training on head fixation. This means that the behavioral state is abnormal, and the animal may have residual anesthesia effects.

Most of the paper is dedicated to dynamics around up-down state transitions, not focused on ripples.

Vm should be examined raw first, then split into fast and slow -the cell lives with the raw Vm.

While some (assumed) CA3 principal cells were hyperpolarized around the time of ripples, saying inhibition is the hallmark of CA3 dynamics around ripples is an exaggeration, especially because it does not seem mechanistically tied to anything else.

The use of ripple onset time is questionable, since the detected onset of the ripple depends on the detector settings, amplifier signal-to-noise ratio, etc. The best and most widely used (including by a subset of these authors) metric is the ripple peak time.

There is not enough raw data (or quality metrics) shown to judge the quality of the data, especially for the whole cell recordings. For instance what was the input resistance of the neurons? Was the access resistance constant?

There is not enough explanation regarding why the reported results on the spiking of CA1 and CA3 neurons in SWRs is so different than previously published. In general, whole cell recording is not the most reliable way to record spike timing, and the presented whole cell data differ from previously published juxtacellular and extracellular recording methods, which better preserve physiological spiking activity.

The number of neurons from each area is not reported.

There is no verification of cell type so it is inappropriate to assume that all neurons are the principal neurons.

Are the fluctuations in the CA3 Vm generally smaller than for CA1 and DG because of physiology or technical reasons?

Reviewer #3 (Public Review):

During slow wave sleep and quiet immobility, communication between the hippocampus and the neocortex is thought to be important for memory formation notably during periods of hippocampal synchronous activity called sharp-wave ripple events. The cellular mechanisms of sharp-wave ripple initiation in the hippocampus are still largely unknown, notably during awake immobility. In this paper, the authors addressed this question using patch-clamp recordings of principal cells in different hippocampal subfields (CA3, CA1 and the dentate gyrus) combined with extracellular recordings in awake head-fixed mice as well as computer modeling. Using the current source density (CSD) profile of local field potential (LFP) recordings in the molecular layer of the dentate gyrus as a proxy of UP/DOWN state activity in the entorhinal cortex they report the preferential occurrence of sharp-wave ripple (recorded in area CA1) during UP states with a higher probability toward the end of the UP state (unlike eye blinks which preferentially occur during DOWN states). Patch-clamp recordings reveal that a majority of dentate granule cells get depolarized during UP state while a majority of CA3 pyramidal cells get hyperpolarized and CA1 pyramidal cells show a more mixed behavior. Closer examination of Vm behavior around state transitions revealed that CA3 pyramidal cells are depolarized and spike at the DOWN/UP transition (with some cells depolarizing even earlier) and then progressively hyperpolarize during the course of the UP state while DGCs and CA1 pyramidal cells tend to depolarize and fire throughout the UP state. Interestingly, CA3 pyramidal cells also tend to be hyperpolarized during ripples (except for a minority of cells that get depolarized and could be instrumental in ripple generation), while DGCs and CA1 pyramidal cells tend to be depolarized and fire. The strong activation of dentate granule cells during ripples is particularly interesting and deserves further investigations. The observation that the probability of ripple occurrence increases toward the end of the UP state, when CA3 pyramidal cells are maximally hyperpolarized, suggests that the inhibitory state of the CA3 hippocampal network could be permissive for ripple generation possibly by de-inactivation of voltage-gated channels thus increasing their excitability (i.e. ability to get excited). Altogether, these results confirm previous work on the impact of slow oscillations on the membrane potential of hippocampal neurons in vivo under anesthesia but also point to specificities possibly linked to the awake state. They also invite to revisit previous models derived from in vitro recordings attributing synchronous activity in CA3 to a global build-up of excitatory activity in the network by suggesting a role for Vm hyperpolarization in preserving the excitability of the CA3 network.

1) In light of recent report of heterogeneity within hippocampal cell types (and notably description of a new CA3 pyramidal cell type instrumental for sharp-wave ripple generation) (Hunt et al., 2018), the small minority of CA3 pyramidal cells depolarized during ripples deserve more attention. These cells are indeed likely key in the generation of sharp wave ripple. Several analyses could be performed in order to decipher whether they have specific intrinsic properties (baseline Vm, firing threshold, burst propensity), whether they are located in specific sub-areas of CA3 (a versus b, deep versus superficial) and whether they are distinctively modulated during UP/DOWN states.

2) The authors use CSD analysis in the DG as a proxy of synaptic inputs coming from the EC to define alternating periods of UP and DOWN states. I have few questions concerning this procedure: 1- It is unclear if only periods when animals was still/immobile were analyzed. 2- How coherent were these periods with slow oscillations recorded in the cortex (which are also recorded with the linear probe?).<br /> 3- How long did these periods last? Did they occur during classically described hippocampal states (LIA/SIA) or do they correspond to a different state (Wolansky et al., J Neurosci 2006).

3) To better characterize hippocampal CSD profiles around ripples and UP/Down states transitions, could you plot ripple and UDS transition-triggered average CSD profiles across hippocampal subfields?

4) The duration of UP states appears longer than that reported in anesthetized animals. To ascertain this fact could the authors quantify and report mean UP and DOWN states durations? Shorter DOWN states would decrease the probability to detect ripple. Could the authors correct for this bias in their analysis of ripple occurrence during UP and DOWN states?

5) The authors report a high coherence between the Vm of an example CA3 pyramidal cells and UP/DOWN state in DG. Was it a general property of a majority of CA3 pyramidal cells? The coherence values should be reported for all CA3 pyramidal cells.

6) Was the high coherence between DG CSD magnitude and CA3 Vm specific to these slow oscillatory periods or a more general feature of the DG/CA3 functional coupling. For example, was it also observed during theta/movement periods?

7) Fig. 6 shows depolarization and increase firing in DGCs up to 150 ms prior to ripple onset. However, ripples sometime occur in bursts with one ripple following others. Could such phenomenon explain the firing prior to ripples? (which would in fact correspond to firing during a previous ripple). What is the behavior of firing rate and Vm of different cells types if analysis is restricted to isolated ripples? This analysis is notably important in CA3 where feedback inhibition following a first ripple could lead to hyperpolarization « during » the next ripple.

this is two or three different sentences in one

Author Response

Reviewer #3 (Public Review):

Martiros et al. investigated whether medium spiny neurons in the striatal component of the olfactory tubercle (OT) acquire conditioned responses to odors that have been paired with unconditioned stimuli (water and airpuff). The authors found that both cells containing D1-type receptors (D1R) and D2-type receptors (D2R) acquire conditioned responses to odors. D1R cells appear to have responded to the valence more readily than the identity of unconditioned stimuli, and vice versa for D2R cells. The authors also found that tones can be used to condition D1R and D2R cells; however, conditioned responses with tones were not as much correlated with the valence as those of odors. The conclusions of the paper should be written with a nuanced manner.

Strength:

The authors used state-of-the-art techniques to monitor changes in cellular activity over multiple days in awake animals engaging in conditioning tasks.

Weaknesses:

1) The grin lens, used to detect cellular activities, was large relative to the mouse brain, causing an extensive brain damage. A 1.0-mm diameter lens was unilaterally placed from the top of the brain to the bottom where the olfactory tubercle is situated. The lateral width of one hemisphere at the level of the olfactory tubercle is approximately 3.5 mm, indicating a large portion of the brain was damaged by the placement of the grin lens. This may be estimated that approximately 20-25% of the brain hemisphere anterior to the thalamus was damaged. The implication of this issue needs to be discussed.

We agree that the damage caused by the GRIN lens is an unfortunate outcome of the experimental procedure. We will attempt to address the issue in two ways. First, to address the degree to which the principal findings related to the OT neuronal activity could be related to this damage, and second to address the degree to which the behavior of the mice may have been affected by it.

The olfactory bulb projects to the olfactory tubercle via the lateral olfactory tract which runs along the ventral portion of the brain and thus would not be damaged by the cannula/GRIN lens insertion. The dopaminergic projections from the VTA to the OT run along a similarly ventral track and do not intersect with the inserted lens. The areas that were primarily damaged by the lens were motor cortex and striatum, neither of which are known to project strongly to the OT. We agree that it is likely that the damage caused to the striatum may have altered some of the indirect inputs to the OT and had this damage not occurred we may have observed some differences in the neuronal activity in the OT. However, the main comparisons we make in our study are likely not a function of the damage caused by the lens. First, the differences in the valence coding of D1 and D2 neurons are unlikely to be a result of lens damage, since there is no reason to suspect that damage caused by the lens will differentially affect D1 and D2 neuronal activity in the OT. D1 and D2 type neurons in the OT and the striatum typically receive inputs from similar upstream structures and their inputs were not generally altered by the lens damage. Second, there is no reason to suspect that the robust valence coding in the absence of the instrumental response and outcomes by D1 neurons could be a result of the lens damage. Third, there is no particular reason to suggest that the distinction between the odor and sound responsive neurons in the last set of experiments would be a result of the damage caused by the lens as the possible auditory cortical projections to the OT arrive from the posterior direction. Finally, the nature of the valence-related responses we observe in the OT are similar to those observed by others using tetrode recordings (Gadziola et al., 2015, Millman and Murthy, 2020) in which there was presumably less damage to the striatum.

Second, we found that after the extensive recovery period of 1 month after the implantation surgery, the mice learned the association tasks extremely rapidly. Most mice began to learn the odor associations within the first day of training, and clearly exhibited anticipatory licking responses to only the rewarded odors by day 2 of training (Fig. 1C and D). This suggests that their behavior relevant to the task was not impaired by the damage caused by the lens. In addition, the observation that OT neurons gain valence coding over learning also suggests that circuitry allowing plasticity is at least partially preserved. We are also able to compare the anticipatory licking rates of unimplanted WT mice trained in the odor-sound task, to the anticipatory licking rates of the mice implanted with the GRIN lens. These mice underwent surgery for the attachment of the head fixation plate to the skull, but did not undergo a craniotomy or GRIN lens implantation. The anticipatory licking of the mice with no GRIN lens implant is similar to that of the implanted mice (Fig. 5B), suggesting that the implanted mice are not impaired in their ability to learn the stimulus-outcome associations. This data is presented in Figure 1 figure supplement 5.

Finally, we made concerted efforts to minimize the damage caused by the implanted cannula. First, the cannula was constructed from highly biocompatible and thin-walled polyamide tubing and quartz floor which have previously been shown to minimize glial scar tissue (Bocarsly et al., 2015). Second, 2mm of the cortex were removed by suction prior to the virus injection and cannula implantation in order to minimize pressure in the brain. Finally, the mice were allowed to recover for at least one month prior to the onset of behavioral training. We have added text in the Discussion section (paragraphs 8 and 9) of the manuscript to summarize these points.

2) The recording of cells may have included non-striatal cells. The olfactory tubercle consists of three major components: striatal, pallidal, and islands of Calleja units. These units are interwoven within the OT. Although the stratal unit is filled with medium spiny GABAergic neurons that the authors was interested in, there are other cells. The pallidal region contains GABAergic, cholinergic, and glutamatergic neurons, and the island Calleja contains granule cells. The authors need to inform readers whether the cells of the pallidal and islands of Calleja units contain D1R or D2R. For example, granule cells of the islands of Calleja have been shown to express D1R (Ridray et al 1998). This fact affects the interpretation of the present study. The implication of this issue needs to be discussed.

We agree that this is a notable concern and have addressed this more explicitly in the paper in the Discussion (paragraph 3) and Results section (Figure 1 figure supplement 4). We will address possible imaging of the two structures separately.

With regards to the ventral pallidum – we believe it to be unlikely that our dataset includes ventral pallidal neurons for two reasons. First, ventral pallidal neurons are dorsal to the tubercle and we assessed the position of each GRIN lens to include only those positioned ventral enough to image the tubercle. In fact, we imaged two Drd1-Cre mice in which we suspect the GRIN lens was positioned at the level of VP (based on histological estimates), and others where the GRIN lens was positioned at the level of NA, which were not included in the data analysis. Second, the Allen Brain atlas demonstrates very low expression of Drd1 and A2A in the portion of VP adjacent to the OT as compared to the OT and striatum (see figure below), which has been also previously observed in anatomical studies (Mansour et al., 1990). Since we focused the imaging on the focal plane with the strongest GCaMP fluorescence, this is unlikely to have been the VP. Certainly, we cannot rule out the possible inclusion of some VP neurons in the dataset, but these are likely to be very rare and unlikely to change the main conclusions of the study.

With regards to the islands of Calleja – the neurons in the IC do express Drd1 but do not express A2A according to the Allen brain atlas and anatomical studies (Barik and de Beaurepaire, 1998, Mengod et al., 1992). Due to this, we don’t believe that IC neurons were included in the D2 neuronal dataset. With regard to the D1 neuronal dataset, it is possible that some IC neurons were included; however, we also believe this to be rare. This is due to the fact that IC neurons are characteristically small in size (~ 8 micron diameter) and densely clustered together likely appearing differently in the imaging field of view than typical OT neurons. In rare cases, we observed such regions in the imaging field of view which may have been IC regions as shown in the figure below (panel B). In these cases, the putative neurons that were clustered in these regions were not included in the data analysis. We further quantified the approximate diameter of the D1 and D2 neurons (see figure below). While this size is a rough estimate based on the number of pixels in the field of view occupied by the footprint of the neuron as selected by the CaImAn analysis tool, and may exceed the size of the neurons’ soma due to scatter of the fluorescence signal, we found that there were very few neurons with diameters of < 10 um, suggesting the absence of the small densely clustered IC neurons in our dataset. Additionally, we chose the imaging focal plane with the brightest GCaMP activity focusing on a layer of OT neurons. Due to the fact that the IC are typically most dense above and below the OT layer, it is also less likely that we focused on the IC neurons. While we cannot rule out the possibility that some IC neurons are included in the D1 neuronal dataset, we don’t believe they are contributing significantly to the main results of the study as the majority of the D1 neuronal population was strongly responsive to odor valence. These results are in strong agreement with previous electrophysiology studies in which authors used criteria such as firing rate, interspike interval distribution, and spike waveforms to classify SPN type neurons and analyze their spiking in response to valenced odors.

Here are notable authors' claims and this reviewer's responses.

Claim 1: The authors state "the OT is likely to be involved in learning about both positive and negative odor associations, rather than the alternative possibilities that the OT is only involved in learning rewarded odor associations, or that it encodes odor salience rather than signed odor valence." I believe that this is a reasonable conclusion.

Claim 2A: The authors state "D1 OT neurons selectively and bidirectionally encode learned odor valence, unlike D2 neurons". This statement should be attenuated. Their data suggest that both D1R and D2R neurons are involved in both valence and identity and that D1R neurons are more likely involved in valence than identity, and vice versa.

We have modified this sentence to “D1 OT neurons are more likely to encode learned odor valence than D2 neurons, and conversely less likely to encode odor identity”.

Claim 2B: The authors state "stimulus valence representation by D1 OT neurons is limited to olfactory stimuli, and does not generalize to multimodal stimuli." This statement is premature, and the authors should provide a more nuanced statement. I note two issues: First, the mice had different experimental histories between odors and sounds; the mice were trained with odors first (4 sessions) and then with sounds (3 sessions). Therefore, differential responses between odors and tones can be attributed to their experimental histories rather than olfactory and auditory modalities. Indeed, the data showed that the mice had not fully learned to discriminate between two tones as the mice displayed anticipatory licks upon the tone paired with airpuff. In addition, it is not warranted to have a sweeping generalization because the authors examined only one reward (water) and one type of sounds (tones). Odors may be better conditioned with water while other rewards may work better with tones. Tones may have affective qualities that may have interfered with water conditioning and were needed additional pairing sessions. Remember that the absence of evidence does not provide a proof. It could simply that it was not done well. Moreover, OT neurons clearly responded to the auditory stimuli. Although the OT is strongly linked with the olfactory system compared to other sensory systems, the OT can receive sensory-related information from the limbic cortical structures that provide afferents, including the medial prefrontal cortex, basolateral nucleus of amygdala, and subiculum. Perhaps, the authors should discuss possible roles of these cortical structures in conditioned signals that were detected in the present study and how the large brain damage caused by the grin lens might have compromised afferent inputs to the OT.

The reviewer is indeed correct, and that statement may not be warranted. Indeed, our own data show that there are D1 neurons that respond positively to rewarded tones. Given the differences in the way we did the tone and odor experiments (which the reviewer highlights), a sweeping generalization is not warranted. We have modified the text to remove such generalizations and discuss the possibility that the OT may represent valence of stimuli from other modalities if those signals reach the OT through polysynaptic pathways.

We however, would like to note that the reviewer is not quite correct in stating that “ the mice had not fully learned to discriminate between two tones”. While it is true that some anticipatory licks remain even for the airpuff predicting tone, the difference in lick rates for the two tones is large and significant. Mice tend to lick more for both tones compared to odors, possibly due to the startling nature of the sound onset. In day 3, the mice clearly and strongly discriminated between sounds 1 and 2. They perform an average of 3.7 anticipatory licks in response to sound 2 and 1.1 licks in response to sound 1 (p = 8.0914e-28, Wilcoxon rank-sum test). The difference between the number of licks between sound 1 and sound 2 is ~2.6 licks. In comparison, the mean number of anticipatory licks for small rewarded odor 4 in the odor-odor task was 2.2 and the number of licks for aversive odor 1 was 0.08, for a smaller difference of 2.1 licks between the two stimuli. Therefore, we are confident that mice are indeed discriminating quite well between sounds 1 and 2. We have now added this text to the Results section when describing the odor-sound experiments.

Claim 3: The authors state "valence representation is not correlational in nature, but likely serves to inform downstream brain regions of the value of odor stimuli". The present study showed that OT responded to conditioned odors in the absence of behavior. Although this result makes it difficult to hypothesize the functional role of those signals, it is reasonable to infer that the acquired conditioned signals influence downstream systems for some unknown function. However, the method used to obtain the data was correlational. The authors should avoid misleading phrases. Additional experiments are needed to understand functional role of the acquires signals.

We have tempered the sentence to “… valence representation could inform downstream brain regions of the …”

Roy inadvertently proposes that the escape from trauma is not an escape at all, but rather a head on approach to reconciliation with the narratives of the past.

This is great about know how to use this tools, because I'm not really good with technology but this had teach me a new thing that diffidently I would use a lot in the future.

SciScore for 10.1101/2022.04.19.22273864: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:

A key limitation of our study is the cohort size. Most of our samples were collected early on in the pandemic before Singapore introduced a slew of effective public health measures to stymie the spread of the virus, including travel restrictions, social distancing, and a lockdown. Due to this unique situation, relatively few people were infected in the first wave from April to August 2020. Limited community transmission between August 2020 and August 2021 prevented further subject recruitment and sample collection. In the context of the present situation, we note two caveats, namely the current dominance of Delta and Omicron over early pandemic variants and the high rate of vaccine uptake by the resident population (∼92% as of 31st March 2022). These two factors preclude meaningful head-to-head comparisons of more recent studies against our cohort in the GIFT study where immunologically naïve individuals were infected with variants possessing lower immunoevasive abilities. Notwithstanding these differences, our studies provide insights into the pathogenesis of COVID-19 in the under-studied demographics of pregnant and lactating women as well as infants born to them.

Results from TrialIdentifier: We found the following clinical trial numbers in your paper:<br><table><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Identifier</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Status</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Title</td></tr><tr><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">NCT04802278</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Recruiting</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Gestational Immunity for Transfer</td></tr></table>

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

Results from rtransparent:

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

Results from scite Reference Check: We found no unreliable references.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

I know learning about these kind of things are extremely important and that it would be so unfair to not allow all these victims to share their story but wow I hate how sad they make me feel. I can not wrap my head around the fact that people from all ages actually experience these kind of things all around the world.

Becker did not want his son to live a life in the streets, or to be starving or without clothes or money.

Becker's son, Clarence, or Booster, liked science. Booster was winning at science fairs, and made it to the University of Pittsburg, even if he was of color. Becker was proud and they were close, always going hunting and fishing in West Virginia. Booster meets a while girl, and her dad was a big shot. They went out together, despite discrimination at the time. She didn't want her dad to know they were a couple. Booster beat her dad almost to death when they met, but didn't know it her her dad. Booster went and killed her after she framed him.

In part because of modalities of studying the brain using methods like fMRI where the images are of an individual's head, we focus too much and too exclusively on single brains bound to individuals rather than on brains working in concert.

Greater flexibilities in tools and methods should help do studies of humans working in concert.

Link this to the anecdote:

I recall a radiology test within a medical school setting in which students were asked to diagnose an x-ray of a human patient's skull. Most either guessed small hairline fractures in the skull or that there was nothing wrong with the patient.

Can you diagnose the patient?

Almost all the students failed the question, and worse felt like idiots when the answer was revealed: the patient must be dead because the spinal column and the rest of the body are not attached. Compare:

figures from tomb indicate not a Buddha Image, but appropriation of Buddhist iconography and synthesis with Chinese immortals

Author Response

Reviewer #1 (Public Review):

When theta phase precession was discovered (O'Keefe & Recce, 1993; place cell firing shifting from late to early theta phases as the rat moves through the firing field, averaged over many runs), it was realized that, correspondingly, firing moves from cells with firing fields that have been run through (early phase) to those whose fields are being entered (late phase), with the consequence that a broader range of cells will be firing at this late phase (Skaggs et al., 1996; Burgess et al., 1993; see also Chadwick et al., 2015). Thus, these sweeps could represent the distribution of possible future trajectories, with the broadening distribution representing greater uncertainty in the future trajectory.

Using data from Pfeiffer and Foster (2013), they examine how neurons could encode the distribution of future locations, including its breadth (i.e. uncertainty), testing a couple of proposed methods and suggesting one of their own. The results show that decoded location has increasing variability at later phases (corresponding to locations further ahead), and greater deviation from the actual trajectory. Further results (when testing the models below) include that population firing rate increased from early to late phases; decoding uncertainty does not change within-cycle, and the cycle-by-cycle variability (CCV) increases from early to late phases more rapidly than the trajectory encoding error (TEE).

They then use synthetic data to test ideas about neural coding of the location probability distribution, i.e. that: a) place cell firing corresponds to the tuning functions on the mean future trajectory (w/o uncertainty); b) the distribution is represented in the immediate population firing as the product of the tuning functions of active cells or c) (DDC) the distribution is represented by its overlap with the tuning curves of individual neurons; d) (their suggestion) that different possible trajectories are sampled from the target distribution in different theta cycles.

The product scheme has decreasing uncertainty with population firing rate, so would have to have maximal firing at early phases (corresponding to locations behind the rat), contradicting what was observed in the data, so this scheme is discarded.

The DDC scheme has an increased diversity of cells firing as the target distribution gets wider within each cycle, whereas the mean and sampling schemes do not have increasing variance within-cycle (representing a single trajectory throughout). The decoding uncertainty in the data did not vary within-cycle, so the DDC scheme was discarded.

The mean and sampling schemes are distinguished by the increase in CCV vs TEE with phase, which is consistent with the sampling scheme.

The analyses are well done and the results with synthetic data (assuming future trajectories are randomly sampled from the average distribution) and real data match nicely, although there is excess variability in the real data. Overall, this paper provides the most thorough analyses so far of place cell theta sweeps in open fields.

We thank the Reviewer for the accurate summary and the encouragement.

I found the framing of the paper confusing in a way that made it harder to understand the actual contribution made here. As noted in the discussion, the field has moved on from the 1990s and cycle-by-cycle decoding of theta sweeps has consistently shown that they correspond to specific trajectories moving from the current trajectory to potential future trajectories, consistent with continuous attractor-based models (in which the width of the activity bump cannot change, e.g. Hopfield, 2010). Thus it seems odd to use theta sweeps to test models of encoding uncertainty - since Johnson & Reddish (2007) we know that they seem to encode specific trajectories (e.g. either going one way or the other at a choice point) rather than an average direction with variance covering the possible alternatives.

We thank the reviewer for emphasising the connections to earlier work on theta sweeps during decision making, which suggests that alternative options before a decision point are assessed individually by hippocampal neuron populations in a simple maze. However, as also noted by the reviewer below, previous analysis of theta sweeps in the hippocampus were limited to discrete decisions in a linear maze, which only permits a limited exploration of the alternative hypotheses an animal might experience in a planning situation.

In particular, the dominant source of future uncertainty in a binary decision task is the chosen option (left or right) providing a distinctly bimodal predictive distribution. Bimodal distributions can not be easily approximated by variational methods (that includes the DDC or product schemes) but can be efficiently approximated by sampling. In contrast, in an open field the available options (changes in direction and speed) are not restricted by the geometry of the environment and the predictive distribution is relatively similar to a Gaussian distribution which can be efficiently approximated by all of the investigated encoding schemes.

Moreover, it has been widely reported that the hippocampal spatial code has somewhat different properties in linear tracks, where the physical movement of the animal is restricted by the geometry of the environment, than in open field navigation. Specifically, in linear tracks most neurons develop unidirectional place fields and the hippocampal population uses different maps to represent the two opposite running directions, whereas a single map and omnidirectional place fields are used in open fields (Buzsaki, 2005). In terms of representing future alternatives, it remains to be an open question if the scheme that is compatible with planning in a 1D environment generalises to two 2D environments. Our detailed comparison of the alternative encoding schemes provides an opportunity to demonstrate that a sampling scheme can be applied as a general computational algorithm to represent quantities necessary for probabilistic planning, while also demonstrating that alternative schemes are incompatible with it.

Moreover, these previous studies did not rule out the possibility that, in addition to alternating between discrete options, specific features of the population activity might also represent uncertainty (conditional to the chosen option) instantaneously as in the product or the DDC schemes.

We added a new paragraph (lines 74-88) to the introduction to clarify that one of the novel contributions of the paper is the generalisation of previous intuitions, largely based on work on binary decision tasks in mazes, to unrestricted open field environments.

The point that schemes that assume varying-width activity distribution might be unfit for modelling hippocampal theta activity is an interesting insight. Let us note that new results have pointed out that the fixed width activity bump is not a necesssary feature of attractor networks. It has recently been shown that in continuous attractors (modelling head direction cells in the fly) the amplitude of the bump can change and the changes can be consistent with the represented uncertainty (Kutschireiter et al., 2021 Biorxiv; https://doi.org/ 10.1101/2021.12.17.473253). We believe that similar principles also apply to higher-dimensional continuous attractor networks and therefore it is entirely possible to represent uncertainty via the amplitude of the bump (equivalent to the population gain) in the hippocampus.

Thus, the main outcomes of the simulations could reasonably be predicted in advance, and the possibility of alternative neural models of uncertainty explaining firing data remains: in situations where it is more reasonable to believe that the brain is in fact encoding uncertainty as the breadth of a distribution.

Having said that, most previous examples of trajectory decoding of theta sweeps have not been for navigation in open fields, and the analysis of Pfeiffer and Foster (2013; in open fields) was restricted to sequential 'replay' during sharp-wave ripples rather than theta sweeps. This paper provides the nicest decoding analyses so far of place cell theta sweeps in open field data. However, there are already examples of theta sweeps in entorhinal cortex in open fields (Gardner et al., 2019) showing the same alternating left/right sweeps as seen on mazes (Kay et al., 2020). Such alternation could explain the additional cycle-by-cycle variability observed (cf random sampling).

We thank the reviewer for encouraging us to more directly test the idea that alternating left right sweeps could explain the increased cycle-to-cylce variability in the data. We thoroughly analysed the data (see our answer to essential revisions 1.) and found that trajectories at subsequent theta cycles are strongly anticorrelated (Fig. 7, Fig. S11, lines 375-415)

Reviewer #2 (Public Review):

This study investigates how uncertainty about spatial position is represented in hippocampal theta sequences. Understanding the neural coding of uncertainty is important issue in general, because computational and theoretical work clearly demonstrates the advantages of tracking uncertainty to support decision-making, behavioural work in many domains shows that animals and humans are sensitive to it in myriad ways, and signatures of the neural representations of uncertainty have been demonstrated in many different systems/ circuits.

We thank the reviewer for the comment.

However, studies of whether and how uncertainty is signalled in the hippocampus has remained understudied. The question of how spatial uncertainty is represented is already interesting but recent interest in interpreting hippocampal sequences as important for planning and decision-making provide additional motivation.

A variety of experimental paradigms such as recordings in light vs. darkness, dual rotation experiments in which different cues are placed in conflict with another, "morph" and "teleportation" experiments and so on, all speak to this issue in some sense (and as I note below, could nicely complement the present study); and a number of computational models of the hippocampus have included some representation of uncertainty (e.g. Penny et al. PLoS Comp Biol 2013, Barron et al. Prog Neurobiol 2020). However, the present study fills an important gap in that it connects a theory-driven approach of when and how uncertainty could be represented in principle, with experimental data to determine which is the most likely scheme.

The analyses rely on the fundamental insight that states/positions further into the future are associated with higher uncertainty than those closer to the present. In support of this idea, the authors first show that in the data (navigation in a square environment, using the wonderful data from Pfeiffer & Foster 2013), decoding error increases within a theta sequence, even after correcting for the optimal time shift.

The authors then lay out the leading theoretical proposals of how uncertainty can be represented in principle in populations of neurons, and apply them to hippocampal place cells. They show that for all of these schemes, the same overall pattern results. The key advance of the paper seems to be enabled by a sophisticated generative model that produces realistic probability distributions to be encoded (that take into account the animal's uncertainty about its own position). Using this model, the authors show that each uncertainty coding scheme is associated with distinct neural signatures that they then test against the data. They find that the intuitive and commonly employed "product" and "DDC" schemes are not consistent with the data, but the "sampling" scheme is.

The final conclusion that the sampling scheme is most consistent with the data is perhaps not surprising, because similar conclusions have been reached from showing alternating representation of left and right at choice points cited by the authors (Johnson and Redish 2007; Kay et al. 2020; Tang et al. 2021) and "flickering" from one theta cycle to the next (Jezek et al. 2011). So, the most novel parts of the work to me are the rigorous ruling out of the alternative "product" and "DDC" schemes.

We thank the reviewer for helping us to clarify the main novelty of our work compared to previous studies. We have updated the introduction (lines ~74–88) to state more clearly how our analysis extends previous work largely restricted to binary decision tasks in mazes and not explicitly considering alternative probabilistic representations.

Overall I am very enthusiastic about this work. It addresses an important open question, and the structure of the paper is very satisfying, moving from principles of uncertainty encoding to simulated data to identifying signatures in actual data. In this structure, the generative model that produces the synthetic data is clearly playing an important role, and intuitively, it seems the conclusions of the paper depend on how well this testbed maps onto the actual data. I think this model is a real strength of the paper and moves the field forward in both its conceptual sophistication (taking into account the agent's uncertainty) and in how carefully it is compared to the actual data (Figures S2, S3).

We thank the reviewer for the encouraging words.

I have two overall concerns that can be addressed with further analyses.

First, I think the authors should test which of the components of this model are necessary for their results. For instance, if the authors simply took the successor representation (distribution of expected future state occupancy given current location) and compressed it into theta timescale, and took that as the probability distribution to be encoded under the various schemes, would the same predictions result? Figuring out which elements of the model are necessary for the schemes to become distinguishable seems important for future empirical work inspired by this paper.

The crucial part of our generative model is its probabilistic nature. Explicit formulation of the generative model under different coding schemes enables us to quantitatively account for the different factors contributing to the variability in the data. Specifically, when we compared sampling and mean codes, we partitioned variability of the represented locations across theta cycles into specific factors related to 1) decoding error; 2) difference between the true position of the animal and its own location estimate; 3) the animal’s own uncertainty about its spatial location; 4) updating this estimate in each theta cycle. This enabled us to derive quantities (CCV, TEE and EVindex) that can discriminate between sampling and mean schemes, and that could be directly measured experimentally. This would not be possible in a simpler model lacking an explicit representation of the animal’s internal uncertainty.

We believe that the assumptions of the model are rather general and those do not limit the scope of the model. Here we list the specific features of the model for clarity (Fig S1a):

1) Planned position (Fig S1a, left): the planned position is required to guide movements in the model. The specific way we generated the planned position was not essential for the simulations but we tuned the movement parameters to generate trajectories matching the real movement of the animal. It is defined as a random walk process for velocity which is the simplest model for smooth trajectories.

2) The inference part (Fig S1a, middle) is crucial for the model since we believe that hippocampal population activity is driven by the animal’s own beliefs about its position, which tells our approach apart from earlier studies (see paragraph around line 466). If the animal represents its predictions optimally then the predictions should be consistent with its movement within the environment. Thus, the consistency of the inference is a critical statistical property of the model, which can be guaranteed if the predictions are generated by the same model that is used for inferring the animal’s position. The simplest model that can be used for inference and predictions is the Kalman filter, which we opted for in our simulations.

3) The assumptions of the encoding model (Fig S1a, right and Fig 1b) are solely determined by the representational scheme being tested. All of the schemes rely on encoding the result of inference in population activity during theta cycles and the scheme determines how this encoding happens. This part of the model is clearly necessary for the analysis.

Alternatively, we could use the above mentioned successor representation (SR) framework (Dayan 1993) to represent possible trajectories and their associated uncertainty in our models of hippocampal population activity. However, this option introduces extra challenges: First, in the SR framework (Stachenfeld et al., 2017) neuronal firing rates are proportional to the discounted expected future number of times a particular location is going to be visited given the current policy and position. Thus, the SR does sum over all possible future visits and does not specify when exactly a particular state might be reached in the future which is inconsistent with the idea that trajectories are represented during theta sequences. Second, the SR represents the probability of occupying all future states in parallel without providing possible trajectories defining specific combinations of future state visits. This property is consistent with the product and the DDC encoding schemes but not with the other two. These two properties of the SR implies that this framework per se does not provide a fine-scale temporal description of how expected future state probabilities are related to the dynamics of the hippocampal population activity during theta oscillation.

Taken together, implementing theta time-scale dynamics using the SR framework would also require several additional model choices to generate consistent temporal trajectories from the expected future state occupancies, and even in this case the subjective uncertainty of the animal would not be consistently represented in the simulated data. Representing the animal’s subjective uncertainty in our model was an important component in contributing to the EV-index and had profound implications on the signatures of generative cycling in a two dimensional arena.

We have to note that on a slower time scale (calculating the average firing rate over multiple theta cycles) all of our encoding schemes are consistent with the SR framework (line 548).

Second, the analyses are generally very carefully and rigorously performed, and I particularly appreciated how the authors addressed bias resulting from noisy estimation of tuning curves (Figure S7). However, the conclusion that the "sampling" scheme is correct relies on there being additional variance in the spiking data. This is reminiscent of the discussions about overdispersion and how "multiple maps" account for it (Jackson & Redish Hippocampus 2007, Kelemen & Fenton PLoS Biol 2010), and the authors should test if this kind of explanation is also consistent with their data. In particular, the task has two distinct behavioral contexts, when animals are searching for the (not yet known) "away" location compared to returning to the known home location, which extrapolating from Jackson & Redish, could be associated with distinct (rate) maps leading to excess variance.

We thank the reviewer for this constructive comment. We note that the signature of the sampling scheme is variability in the decoded trajectory across subsequent theta cycles while overdispersion is usually defined as the supra-Poisson variability in the spiking of individual neurons evaluated across multiple runs or trials. Nevertheless, we tested the existence of multiple maps corresponding to the two distinct task phases and found that the maps representing the two task phases are very similar (Fig S11).

Such an analysis could also potentially speak to an overall limitation of the work (not a criticism, more of a question of scope) which is that there are no experimental manipulations/conditions of different amounts of uncertainty that are analyzed. Comparing random search (high uncertainty, I assume) to planning a path to a known goal (low uncertainty) could be one way to address this and further bolster the authors' conclusions.

We agree with the reviewer that the proposed framework provides additional insights into the way the population activity should change with specific experimental manipulations and can therefore inspire further experiments. In particular, a hallmark of probabilistic computations is that experimental manipulations that control the uncertainty of the animal should be reflected in population responses. In the visual processing such manipulations are indeed reflected in changing response variability, as predicted by sampling (Orban et al, Neuron 2016). In the current experimental paradigm there was no direct manipulation of uncertainty (we discuss this around lines 573-576). While one might argue that there are differences in the planning strategy in trials where the animal was heading for away reward and in those heading for home, this is not a very explicit test of the question. Still, to check if we can find traces of changes in uncertainty in the two conditions, we analysed the EV-index separately on home and away trials (Fig. S11e). We did not find systematic differences in the EV-index across these trial types.

Reviewer #3 (Public Review):

Summary of the goals:

The authors set out to test the hypothesis that neural activity in hippocampus reflects probabilistic computations during navigation and planning. They did so by assuming that neural activity during theta waves represents the animal's location, and that uncertainty about this location should grow along the path from the recent past to the future. They next generated empirical signatures for each of the main four proposals for how probabilities may be encoded in neural responses (PPC, DDC, Sampling) and contrasted them with each other and a non-probabilistic representation (scalar estimate of location). Finally, the authors compared their predictions to previously published neural activity and concluded that a sampling-based representation best explained neural activity.

Impact & Significance: This manuscript can make a significant impact on many fields in neuroscience from hippocampal research studying the functions and neural coding in hippocampus, through theoretical works linking the representation of uncertainty to neural codes, to modeling experimental paradigms using navigation tasks. The manuscript provides the following novel contribution to cognitive neuroscience:

• It exploits the inherent change in uncertainty about a parsimonious internal variable over time during planning to test hypotheses about probabilistic computations.
• A full model comparison of competing hypotheses for the neural implementation of probabilistic beliefs. This is a topic of wide interest and direct comparisons using data have been elusive.
• The study presents substantial empirical evidence for a sampling-based neural representation of the probability distribution over trajectories in the hippocampus, a finding with potential implications for other parts of neural processing. Strengths:
• Creative exploitation of a naturally occurring change in uncertainty over a parsimonious latent variable (location).
• Derivation of three empirical signatures using a combination of analytical and numerical work.
• Novel computational modelling & linking it to neural coding using 4 existing implementational models
• Comprehensive and rigorous data analysis of a large and high-quality neural dataset, with supplemental analyses of a second dataset
• Mostly very clear and high quality presentation We thank the Reviewer for the summary and for the positive feedback on the manuscript. Weaknesses:
• It is unclear to what degree the "signatures" depend on the details of the numerical simulation used by the authors to generate them. At least two of them (gain for the product scheme and excess variability for the sampling scheme) appear very general, but the degree of robustness should be discussed for all three signatures.

The generality of the signatures follows from the fact that we derived them from the fundamental properties of the encoding schemes. We tested their robustness using both idealised test data (Fig S6c-d, Fig S7b) and our simulated hippocampal model (Fig. 4c, Fig5b-c, Fig6b-g).

The reviewer is right that the sensitivity and robustness is a potential issue. These schemes have been originally proposed to encode static distributions ie., the neuronal activity was supposed to encode a specific probability distribution for an extended period of time. Therefore, when we test the signatures we make the simplifying assumption that a static distribution is encoded in the three separate phases of the theta cycle. It is currently unknown whether during theta sequences the trajectories are represented via discrete jumps in positions or as continuously changing locations. Therefore we used our numerical simulations to test whether the proposed signatures are sufficiently sensitive to discriminate the encoding schemes using the limited amount of data available and in the face of biological noise but also robust to the parameter choices and modelling assumptions.

Regarding the product code, the inverse relationship between the gain and the variance has been previously derived analytically for special cases (Ma et al., 2006). In the manuscript we show numerically that the same relationship holds for general tuning curve shapes (Fig. S6d). Finally we demonstrate that the gain is a robust signature that changes systematically along the theta cycles in the case of a product coding scheme.

Second, in the case of the DDC code we used the decoded variance of the posterior as the signature. Since DDC code relies on the overlap between the target distribution and the neuronal basis functions, potentially the most important source of error is if we overestimate the size of the encoding basis functions. To control for this factor, we first explored this effect in an idealised setting (in fig S7) and found that the decoded variance correlates with the encoded uncertainty both if we used the estimated basis functions or the empirical tuning curves for decoding. Next we performed the analysis in our simulated dataset in 4 different ways - either using empirical tuning curves (Fig 5c-d) or the estimated basis functions (Fig S8a-b), focusing on high spike count theta cycles or including all theta cycles. The fact that all these analyses led to similar results confirms the robustness of this signature.

Our third measure, the EV-index measures the variability of the encoded trajectories across theta cycles. The cycle-to-cycle variability is also affected by factors independent of whether a randomly sampled trajectory or the posterior mean is encoded. In particular, the encoded trajectory can start at different distances in the past and can be played at different speeds in different theta cycles. These factors are probably present in the data and all inflate the CCV. Another factor is the start and end time of the trajectories, which we may not be able to accurately find in the real data and confusing the end of a previous trajectory with the start of a new one can also inflate CCV. In our simulations we tested how these potential errors influence our analysis, and found that the EV index is surprisingly robust to such changes (Fig 6fg). An additional factor that the EV-index is sensitive to is the specific sampling algorithm used to sample the posterior: an algorithm that produces correlated samples is hard to distinguish from the MAP scheme. Our newly introduced analysis (Fig 7b) demonstrates this and explores the level of correlation between subsequent trajectories, providing evidence that trajectories decoded during exploration reflect the properties of anticorrelated samples, also a signature of efficient inference.

• The claims about "efficiency" lack a definition of what exactly is meant by that, and empirical support.

We thank the reviewer for pointing out this inconsistency in our terminology. What we generally meant by efficiency was a claim that pertains the computational level, according to Marr’s classification, i.e.that computations are probabilistic, that is, representation in the hippocampus takes into account uncertainty by representing a full posterior distribution. We performed an additional test, which concerns the algorithmic-level efficiency of the computations. We explored the efficiency of the sampling process by assessinga signature of efficientsampling, the expected number of sampled trajectories required to represent the distribution of possible future locations. We found that subsequent samples tended to be anti-correlated which is a signature of efficient sampling algorithms (Fig 7). In the revised manuscript we thus use the word efficient solely when we refer to the anticorrelated samples.

Author Response

Reviewer #1 (Public Review):

Individuals who survive cancer treatment can experience health challenges that accelerate ageing and can lead to the development of frailty and early mortality when compared to others of the same age without a history of cancer. The authors propose that cancer therapy-induced cell senescence contributes to premature ageing in these individuals. The present study investigates whether a brief intervention with drugs that ablate senescent cells (senolytic drugs) or drugs that inhibit the damaging signalling molecules released by senescent cells (senostatic drugs) can block the progression of radiation-induced frailty and disability in a mouse model. The study shows that irradiation-induced frailty and disability can be reduced by a brief exposure to senolytic or senostatic drugs up to a year after the initial radiation exposure and that such therapies are at least partially beneficial even if administered after premature ageing is established.

Strengths:

Although several prior preclinical studies have explored adjuvant senolytic/senostatic drug therapy in the setting of chemotherapy, earlier work used short-term follow-up and focussed on adverse effects on specific body systems. Important advances made by Fielder and colleagues are: 1) the authors have followed mice for a long time after exposure to radiation plus senolytic drug treatment (up to one year); and 2) they have used a diverse array of system-wide and integrative measures (e.g. frailty assessment as well as tests of strength, coordination and cognition) to assess effects on health globally. These data provide strong preclinical evidence that short-term exposure to senolytic/senostatic drugs following radiation therapy can improve health over long time frames.

Weaknesses:

The authors have been careful in their conclusions, and most are well supported by their data. Still, there are some weaknesses to the data reported by Fielder et al.

1) The introduction is lengthy, but it does not provide a rationale for all aspects of the work, and this makes it difficult to follow some of the proposed experiments. For example, the authors spend a lot of time discussing the selection of the senostatic, metformin but reasons for the other specific drugs used have not been provided in the introduction (e.g. navitoclax, dasatinib and quercetin are mentioned in the abstract but first appear in the methods section of the paper). Rapamycin is used in some studies but not discussed. Some relevant information is found in the results section, but this comes too late in the manuscript.

We have now justified the selection of Navitoclax and D+Q in the introduction. We did use rapamycin only for some mechanistic analyses as control and have therefore not referred to it in the introduction.

2) Dose selection is important in studies of senolytic drugs, but the authors did not introduce the rationale for the doses chosen in the introduction. Where they do mention this in the results section, they claim that the doses used are "...comparable to the lower range of therapeutically used doses..." with no references. This should be introduced - with supporting references - and discussed in the discussion.

We have now given the rationales for dose selection (including references) in the results section.

3) The selection of the tissues/cell lines chosen for investigation should be clarified/justified as well as listed in the methods. The authors mention effects of senolytics on liver toxicity and sarcopenia in the introduction. This could be used to justify studies on liver and quadriceps, although this should be made explicit and linked to functional assays where possible. No rationale for studies on the brain and cognition has been provided in the introduction and many other tissues could have been investigated (e.g. kidney, fat etc). Similarly, it would be helpful to know why the authors selected human lung MRC5 fibroblasts.

We have now indicated in the introduction the major adverse outcomes in long-term cancer survivors as rationales for selection of our functional assays and the associated tissues. Specifically, we have cited the high risk for cognitive decline to explain why brain is one of the organs we concentrated our analyses on.

4) The authors emphasize their work on metformin over the other drugs used throughout the manuscript. A more balanced manuscript with more emphasis on the senolytic interventions could address the issues raised here.

The in-vivo intervention studies are actually balanced towards senolytics, as we have performed the late intervention only with these. However, the mechanism of action for Nav and DQ is essentially known based on a large number of published studies comparing these to pharmacogenetic senolytic interventions (which is why we chose these senolytics for our proof-of-principle study). Therefore, we feel that establishing their long-term senolytic capacity together with functional/physiological consequences was sufficient. On the other hand, it was not at all clear how metformin could act as a senostatic at the concentrations that are achievable in vivo, and we feel that our mechanistic work has added significantly to this.

5) The authors have completed their studies using male mice only, so the generalizability of their findings to females is uncertain, as they note in their discussion. They also use only young adult mice subjected to radiation therapy. The authors justify the work in the introduction based, in part, on accelerated ageing seen in long-term survivors of childhood cancers but they do not test their interventions in juvenile mice. Older individuals also experience chemotherapy. The work should be extended, not only to female animals but also to younger and older mice.

We completely agree. We have expanded the discussion on sexual dimorphism. We have also stated the absence of studies in very young and old mice as a limitation of the study in the discussion.

Despite these shortcomings, in general the authors' claims and conclusions are justified by their data.

Reviewer #2 (Public Review):

Strengths of this study include the wide-ranging evaluation of frailty. Measurement of frailty and its effect on brain and liver function.

Weaknesses The lack of head-to-head comparison of the senolytic and senostatic agents in the in--vivo and in-vitro. It would also be helpful to see the effects of specific agonists and antagonists for pathways the authors are targeting to comparatively evaluate the therapeutic activity of the drug treatment being tested.

We do not claim in the paper that changes in functional indicators measured in the in-vivo experiments were mediated through the reduction of SASPs. What we claim and show is that both senolytic and senostatic interventions reduce senescent cell frequencies together with multiple functional outcomes over the lifecourse. We agree that the impact of senescent cell reduction onto these functional improvements could be mediated by different pathways that might be more or less tightly related to the SASP. These pathways are probably tissue- and cell-type specific. Assessing all these would in our opinion go far beyond what can be expected from a single paper.

When assessing the senostatic activity of metformin in vivo, we claim and show that it reduces senescent ROS and the SASP, and we show the pathway that leads to it. We and others have shown previously that reducing ROS and SASP production from senescent cells reduces bystander senescence. Together, this identifies a pathway by which metformin at physiologically achievable concentrations reduces senescent cell frequencies. In order to link our data more closely to SASP, we have measured levels of 18 cytokines/chemokines that are part of the SASP at the end of the experiment in the serum of senolytic- and metformin-treated mice. These data are now integrated into results part 1 and 3. They show that at one year after senolytic intervention, there is no remaining difference in the measured SASP component levels. However, the longer-lasting metformin intervention still results in a persistent tendency for reduction of some SASP components, notably including IL17 and TNFa (albeit at only p=10%), which were also found reduced in vitro (Fig 4E), together with the prominent SASP component CCL2 (at p<0.05).

We did discuss carefully the question of presenting our senolytics vs metformin data in a head-to-head format, e.g. combining the data in Figs 1 and 3 in the same graphs. The outcome is that we do not believe that this is the appropriate presentation for our results. We do show already the Navitoclax vs D+Q data head-to-head, because they were generated using a single common sham control group. However, metformin was given to the animals by a different route (in soaked food instead of gavage) in accordance with widespread practice. This required a separate control group also receiving soaked food, which resulted in higher food intake, greater body weight and somewhat different capabilities in the neuromuscular tests in the metformin control as compared to the senolytics control (most probably due to differences in body weight between the control groups). Therefore, a head-to-head comparison of all groups would distract from the essential information, e.g. the intervention effects. We have tried to make the comparison between the senolytic and senostatic interventions as easy as possible by presenting data in Figs 1 and 3 and their associated supplements as similarly as possible, but do think that a direct head-to-head comparison would not be correct for these two independently designed experiments.

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This paradox needs to be addressed head-on if we want to make progress toward eroding loneliness.

Change the highlighted passage to the following: "most of us have grown up with the story of how Sir Isaac Newton developed the theory of gravity after he was sitting under a tree and hit on the head by a falling apple."

=! Natural inequality for all life forms but equal Natural selection for survival of fittest

• [f] we are born with natural inequalities as natural selection wants us to be survival of the fittest.
• It's not about discrimination, these are selfish man-made ethical rules for which he discriminates earlier and later applies. but character for nature is not like that.

I love this unexpected insight from annoyingly asking questions that might make us look stupid.

initial patients symptoms

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I had never heard of this software, it's amazing! It's great to see that technology is being innovated to be accessible to everyone.

I worked for several years at Lone Star College as a scribe for the Disabilities Center. I would go to the student's classes and write notes for them to have after class. There were some cases, where I would have to type the notes, and they were able to turn it into an audio, to aid students who were visually impaired. I loved to see the various resources students with disabilities had.

However, I know that is not always the case, and some schools may have limiting resources.

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37. </span>02:08:11<span> Cheek – Venus (I:Cube Remix)
38. </span>02:12:15<span> Cassius – La Mouche (Played Live By DJ Falcon)
39. </span>02:16:18<span> Modjo – Music Takes You Back
40. </span>02:19:17<span> Daft Punk – Musique
41. </span>02:22:23<span> Play Paul – Holy Ghostz
42. </span>02:25:56<span> Vinyl Fever – 1h45 A.M on the Floor
43. </span>02:29:30<span> Modjo – On Fire (Archigram’s When What Remix)
44. </span>02:33:48<span> Aloud – Bob O’lean
45. </span>02:36:38<span> Archigram – Mad Joe
46. </span>02:41:04<span> Raw Man – Lovers
47. </span>02:44:15<span> Daft Punk – One More Time
48. </span>02:49:04<span> DJ Falcon – Honeymoon
49. </span>02:52:46<span> Rhythm Masters – Good Times
50. </span>02:56:38<span> Cassius – Nulife
51. </span>02:58:35<span> Jess & Crabbe – Crack Head (Seduction Mix by Deelat)
52. </span>03:03:02<span> Modjo – On Fire
53. </span>03:05:45<span> Together – Together
54. </span>03:11:19<span> Daft Punk – Voyager (Dominique Torti Wild Style Remix)
55. </span>03:15:25<span> Patrick Alavi – How Much That Means To Me</span>

The use of color to describe the frozen words Pantagruel and his crew are encountering can be analyzed as an intentional roundabout way to describe words they have no definitions for. Depicting them as colors can offer a visualization to compare to their modern meanings behind such colors. Alternatively, using colors also turns language on its head as a way "to evoke a symbolic system...in which communication is effected by means of a code of shade and shape, rather than through patterns of sounds and silence", playing into Rabelais' desire in showing how language and the understanding of it changes through not only our social, cultural, and generational environment, but also our perception (Campbell 189). The concept of color as a language descriptor can also be seen with the mention of not knowing the words once they thaw . This showcases sound lacking its previous interpretation advantages, and perhaps explains that as the words become unfrozen, their prior meaning within their respective time has been lost.

Sources: Campbell, Kim. “Of Horse Fish And Frozen Words.” Renaissance and Reformation / Renaissance et Réforme, vol. 14, no. 3, 1990, pp. 183–92, http://www.jstor.org/stable/43444769. Accessed 16 Apr. 2022.

In this context, 'Almain' likely refers to Parisian theologist Jacques Almain from the early 16th century. His beliefs and stances surrounded what the church could and could not do, essentially believing 'the church was able to act in its own defense, even against its visible head, the pope' (Izbicki). Whether or not Rabeleis agreed with any of Almain's beliefs, Rabeleis makes it known that this 'comb' is actually one's hand- taking a dig at Almain.

Izbicki T.M. (2020) Jacques Almain. In: Lagerlund H. (eds) Encyclopedia of Medieval Philosophy. Springer, Dordrecht. https://doi.org/10.1007/978-94-024-1665-7_252

A devout satirist, language and humor are the primary mechanisms by which Rabelais reckons the world. Standing at the cultural intersection, he is an innovator, a virtuoso, a trickster, who is here to tear up the page (formalities) and start all over again. Marie-Luce Demonet makes it plain: “When he published his first novel, Pantagruel, Rabelais offered multiple visions of human language in all its potential” (Demonet 402). His attitude towards life is illuminated in displays that are at once extraordinarily vulgar, like the scene in “How Gargantua paid his welcome to the Parisians, and how he took away the great bells of Our Lady’s Church” where the giant pisses a river that drowns 260,418 people, not counting the women and little children of course. And here, with this exasperating however humorous list of little boy Gargantua’s youthful offences. Rabelais’ constant use of vulgarity, and his extended musing on philosophic issues are evocative of his desire to dismiss all ridged formalities in literature, and create a freer expression that puts everything on the table. In Trickster Makes This World Lewis Hyde writes: “He took this world seriously; then he disrupted it; then he gave it a new form” (p 13).

Works Cited Demonet, Marie-Luce. "Rabelais and Language." A Companion to François Rabelais. Brill, 2021. 402-428. Hyde, Lewis. Trickster Makes This World. Farrar, Straus and Giroux. Kindle Edition. Works Consulted Gray, Hanna H. “Renaissance Humanism: The Pursuit of Eloquence.” Journal of the History of Ideas, vol. 24, no. 4, University of Pennsylvania Press, 1963, pp. 497–514, https://doi.org/10.2307/2707980. This is shown it one of its most vulgar displays in Smith, W. F. “Rabelais on Language by Signs.” The Modern Language Review, vol. 8, no. 2, Modern Humanities Research Association, 1913, pp. 193–98, https://doi.org/10.2307/3713107https://doi.org/10.2307/3713107.

A devout satirist, language and humor are the primary mechanisms by which Rabelais reckons the world. Standing at the cultural intersection, he is an innovator, a virtuoso, a trickster, who is here to tear up the page (formalities) and start all over again. Marie-Luce Demonet makes it plain: “When he published his first novel, Pantagruel, Rabelais offered multiple visions of human language in all its potential” (Demonet 402). His attitude towards life is illuminated in displays that are at once extraordinarily vulgar, like the scene in “How Gargantua paid his welcome to the Parisians, and how he took away the great bells of Our Lady’s Church” where the giant pisses a river that drowns 260,418 people, not counting the women and little children of course. And here, with this exasperating however humorous list of little boy Gargantua’s youthful offences. Rabelais’ constant use of vulgarity, and his extended musing on philosophic issues are evocative of his desire to dismiss all ridged formalities in literature, and create a freer expression that puts everything on the table. In Trickster Makes This World Lewis Hyde writes: “He took this world seriously; then he disrupted it; then he gave it a new form” (p 13).

Works Cited

Demonet, Marie-Luce. "Rabelais and Language." A Companion to François Rabelais. Brill, 2021. 402-428.

Hyde, Lewis. Trickster Makes This World. Farrar, Straus and Giroux. Kindle Edition.

Works Consulted

Gray, Hanna H. “Renaissance Humanism: The Pursuit of Eloquence.” Journal of the History of Ideas, vol. 24, no. 4, University of Pennsylvania Press, 1963, pp. 497–514, https://doi.org/10.2307/2707980. This is shown it one of its most vulgar displays in

Smith, W. F. “Rabelais on Language by Signs.” The Modern Language Review, vol. 8, no. 2, Modern Humanities Research Association, 1913, pp. 193–98, https://doi.org/10.2307/3713107https://doi.org/10.2307/3713107.

Tollerate is a strong word. I prefer the notion of allowing but not "Taking Delivery" of every thought that pops into your mind.

yo, I want the link to this research article

this seems unnecessary

Don Quixote's fantastical view of the world often causes him to see villain where there is only random passerby attempting to do their job. In an article on nostalgia Doctor Avishai Margalit describes the affect that nostalgia can have on memory, and how idealizing the past can affect one's morality in a way that excuses their own violence as "protection" of the innocence they crave a return to. While nostalgia is defined as needing first-hand experience memory, Don Quixote's relationship with the idea of the Knight-errant can be seen as an example of "vicarious nostalgia". Having placed this ideal in his mind as the perfect example of past innocence and the correct order of the world, he is able to validate his actions when those around him see him as someone out of their senses attacking a fellow traveler or in this case stable hand attempting to do their job. What is especially interesting is how he is able to acknowledge the reality when it suits his own interests, such as the giants being "turned into windmills", or acknowledging that Dulcinea is a grandiose fabrication but holding that this doesn't make her less real.

Margalit, Avishai. “Nostalgia.” Psychoanalytic dialogues 21.3 (2011): 271–280. Web.

male gorillas come to power in any of these 3 methods

where and why is it that he chooses to separate a stanza mid-sentence without capitalizing? I know it's a stylistic choice, perhaps even an experiment, but like it's completely absent in the first half of the poem and then suddenly appears out of nowhere, why?

Alarm bells are going off in my head here reminding my of Katherine McKittrick's article "Plantation Futures" where she writes a whole section on the construction of lands as "uninhabitable" and the ways that produces and reifies the naturalization of Black and Indigenous peoples as inferior, nascet, and outside the category of "human" through designating the spaces they occupy as empty, denigrated, dangerous land. McKittrick writes: "Of course there were overlapping geographic experiences and peoples that troubled these seemingly discreet spaces, but this overlap is accompanied by an overarching system wherein particular spaces of otherness—for purposes here, black geographies—were designated as incongruous with humanness"

Author Response:

Reviewer #1:

The manuscript by Wiesinger et al., demonstrates the differentiation of human induced pluripotent stem cells (iPSCs) into pacemaker cardiomyocytes. Authors have shown impressive analyses of sinoatrial node cardiomyocytes (SAN-CM) using scRNA-seq approach followed by a computational method namely Trajectory Inference (TI) to understand the diversification of SAN subtypes. The study further show a key role of Wnt signaling in the critical branching of pacemaker cardiomyocytes and/or pro-epicardial cells. Authors further went on to show the temporal role of Wnt and TGFbeta signaling in the formation of SAN-CM subtypes including SAN-head, SAN-tail and SAN-transitional (TZ) cells.

Strengths:

The manuscript is well written with robust and detailed experimental approach wherein authors study the SAN-CM cell differentiation from iPS cells and reveal the role of specific signaling pathways in directing cell fate choices. The observations may lead to potential targets for disease condition pertaining to defective pacemaker cell activity and also facilitate understanding on cardiac regeneration in general. The results do support the conclusions that the authors made. The methods described in this manuscript can be used for other similar studies and cells types to identify cell fate choices.

Weaknesses:

The study though well-executed do have a lack of conceptual novelty. The generation of SAN-CMs from iPS cells is a well-established method, so is the knowledge about SAN-head, SAN-tail and SAN-TZ subtypes and their general markers. The transcriptomic (mRNA repertoire) of SAN-CM cells of mice already shows the utility of one of the markers (VSNL1) described in the current manuscript, along with GNaO1 (Liang et al., 2021). Authors themselves discuss and agree with most of the published studies that shows the Bone Morphogenic Protein (BMP), Retinoic Acid (RA) and the implication of TGF-beta/BMP signaling in SAN development.

The contribution of this study to our understanding on SAN cells is by pinpointing the role of specific signaling pathways and validating the same in vitro. This can lead to the understanding of how subtype specific differentiation of SAN-CMs can be carried out by fine-tuning these key pathways.

We thank the reviewer for their valuable input. The first description of a method to differentiate pacemaker cells using BMP4, retinoic acid etc. was introduced in 2017 by Protze et al. In 2019, Ren et al described an alternative method utilizing activation of WNT signaling to generate pacemaker cells in vitro. To our knowledge, these protocols have not been reproduced in any other independent studies. Therefore, the generation of SANCMs from human iPS cells is not as well-established as the methods to generate other cardiomyocyte subtypes, for example, atrial cells. Furthermore, the previous studies (Protze et al, 2017 and Ren et al, 2019) did not include a detailed transcriptomic characterization of their differentiated SANCM population, and the existence of the pacemaker subpopulations in vitro remains unknown. Furthermore, we disagree that the existence of SAN-head, tail and TZ subpopulations and their markers is general knowledge. To date, there is only one study in the field (Goodyer et al, 2019) performed in mouse that characterized gene expression of these subpopulations and we believe much remains to be learnt about the molecular and functional properties of these cell types. The other studies mentioned by the reviewer (Liang et al, 2021 and others) indeed performed sequencing of the sinoatrial node but did not identify SAN subpopulations. Thus, we believe our study provides important validation for the aspects discussed above and as also mentioned by the reviewer, our study identifies signaling pathways that can be utilized for differentiation towards SANCM subpopulations in vitro.

Reviewer #2:

In the manuscript titled "A single cell transcriptional roadmap of human pacemaker cell differentiation," the authors seek to delineate the cell fate decisions that occur during the in vitro differentiation of human pacemaker cells (SANCM) from hiPSCs. The authors first compare marker expression and functional properties of differentiated SANCM and VCM cells, and establish that the SANCM cells have the expected characteristics of pacemaker cells. Single cell RNA sequencing was then used to explore the heterogeneity of the differentiated cells and illustrate the separate clustering of VCM and SANCM cells. The scRNAseq data was used to identify and characterize the different SANCM subtypes generated by the differentiation process. scRNAseq was then used to analyze samples from different stages of reprogramming and highlighted the changes in the transcriptome during the differentiation process. In addition, pseudotime analysis was performed in conjugation with pharmacological manipulation to show how WNT and TGF-beta signaling affect the stepwise progression of hiPSCs into the identified different SANCM subtypes. This study provides evidence for the presence of different SANCM subtypes generated by the SANCM differentiation process as well as illustrates the role of the WNT and TGF-beta in generating these different clusters of SANCM cells. Additional validation of the SANCM heterogeneity during the in vitro differentiation process as well as additional evidence of novel mediators of the acquisition of the unique SANCM subtype identity would strengthen the impact of this manuscript.

Specific suggestions:

1. The first scRNAseq experiment highlights the transcriptional differences between VCM and SANCM clusters, however, these differences are to be expected. This data also supports the hypothesis that the SANCM differentiation leads to a heterogeneous population. Additional bioinformatic analyses into the differences between these different clusters may provide more novel insights and could provide molecular targets to explore in vivo during embryonic development. For example, the identification of Vsnl1 and Gnao1 are promising gene candidates that should be further explored during multiple timepoints of heart development and validated with quantification. This data would provide complementary evidence that this differentiation process recapitulates what happens in vivo. Immunofluorescent staining of select markers of different scRNAseq clusters should also be provided to confirm the identified cluster-specific differentially expressed genes.

2. The final portion of the manuscript further establishes the specific roles of the WNT and TGF-beta components of the differentiation protocol, but requires additional experiments to show that the heterogeneity is affected at the single cell level when these pathways are altered (such as immunofluorescence staining to show that fewer cells are expressing that gene of interest rather than a systemic change seen by qPCR). Being that the significant roles of WNT and TGF-beta are to be expected due to the presence of chemical modulators of those pathways are present in the differentiation protocol, this manuscript would benefit from experiments exploring other signaling pathways that increase or decrease the efficiency in the creation of the different subtypes of SANCMs, or a more detailed evaluation of when the hiPSC based strategy begins to overlap with heart development and a characterization of the role of the newly identified genetics target(s) in SANCM subtypes differentiation in vivo.

We thank the reviewer for their suggestions. In response to comment 1, bioinformatic analysis presented in figure 3 as well as accompanying supplement files provide detailed insights into the transcriptional differences between the various SAN subpopulations and additional analysis will not add new information. We agree with the other suggestions provided by the reviewer and are currently working on obtaining additional data to support our conclusions.

Reviewer #1 (Public Review): 

The manuscript by Wiesinger et al., demonstrates the differentiation of human induced pluripotent stem cells (iPSCs) into pacemaker cardiomyocytes. Authors have shown impressive analyses of sinoatrial node cardiomyocytes (SAN-CM) using scRNA-seq approach followed by a computational method namely Trajectory Inference (TI) to understand the diversification of SAN subtypes. The study further show a key role of Wnt signaling in the critical branching of pacemaker cardiomyocytes and/or pro-epicardial cells. Authors further went on to show the temporal role of Wnt and TGFbeta signaling in the formation of SAN-CM subtypes including SAN-head, SAN-tail and SAN-transitional (TZ) cells. 

Strengths:

The manuscript is well written with robust and detailed experimental approach wherein authors study the SAN-CM cell differentiation from iPS cells and reveal the role of specific signaling pathways in directing cell fate choices. The observations may lead to potential targets for disease condition pertaining to defective pacemaker cell activity and also facilitate understanding on cardiac regeneration in general. The results do support the conclusions that the authors made. The methods described in this manuscript can be used for other similar studies and cells types to identify cell fate choices. 

Weaknesses:

The study though well-executed do have a lack of conceptual novelty. The generation of SAN-CMs from iPS cells is a well-established method, so is the knowledge about SAN-head, SAN-tail and SAN-TZ subtypes and their general markers. The transcriptomic (mRNA repertoire) of SAN-CM cells of mice already shows the utility of one of the markers (VSNL1) described in the current manuscript, along with GNaO1 (Liang et al., 2021). Authors themselves discuss and agree with most of the published studies that shows the Bone Morphogenic Protein (BMP), Retinoic Acid (RA) and the implication of TGF-beta/BMP signaling in SAN development. 

The contribution of this study to our understanding on SAN cells is by pinpointing the role of specific signaling pathways and validating the same in vitro. This can lead to the understanding of how subtype specific differentiation of SAN-CMs can be carried out by fine-tuning these key pathways.

Subjective claim implying that the Ukrainian troops shot the Russian POW who had his hands tied behind his back, which may actually be true.

My thoughts, exactly. I’ve had the notice in my head that the only way to fix issue is by, first acknowledging they exist, second charting a course to Elysium.

Creating an ideal world has to come about when idealist are able to not only ponder of their good world, but given a chance to build it. Horace Mann believed in a world of public education and the abolition of slavery in a world which did not offer these liberties. His belief came to pass, thanks to him and the many others who idealized that world.

Books/ stories sometimes can create a false sense of safety. Off top of my head I can think of one story we reference a lot, The Terminator, that we don't have a sense of comparative safety. As technology advances, humans fear technology turning against us because it would have all the knowledge to best us.

This is a thought that I believe goes through an American's head at some point in their life while watching the news. I believe the government purposely shows us dystopian societies to distract us from what is really going on in our own society.

Interesting

First time I've run across the French term fichier boîte (literally 'file box') for index card boxes or files.

As someone looking into note taking practices and aware of the idea of the zettelkasten, the suspense is building for me. I'm hoping this paper will have the payoff I'm looking for: a description of Roland Barthes' note taking methods!

Someone mentioned that this article is from 2018, which is a relevant point but it is still fitting (maybe even more) so in the COVID era school. Teachers who were hesitant to use technology were pushed in head first and now I fear we are too dependent. This article is great because it helps us focus on the learning goals first.

Summary of Chapter 6.2 Locomotion - Key Points The muscular and skeletal systems sustain the body and allow it to move. To demonstrate how these two organ systems cooperate to create movement, we will focus on the relationship between the human skeletal and muscular systems, with references to other animal skeletal and muscular systems. The human skeleton is an endoskeleton with 206 bones in an adult. The skeleton has five primary functions: it supports the body, stores minerals and fats, produces blood cells, protects internal organs, and allows mobility. The axial skeleton, which includes the skull bones, ossicles of the middle ear, hyoid bone of the neck, vertebral column, and thoracic cage, serves as the body's central axis (rib cage). The skull bones protect the brain and support the structures of the face. The skull is made up of cranial and facial bones. The cranial bones create the cranial cavity, which encloses the brain and acts as a place for head and neck muscles to connect. They are securely joined by connective tissue in adults, and neighbouring bones do not move. The auditory ossicles of the middle ear convey vibrations from the air to the fluid-filled cochlea. Facial bones serve as attachment places for facial muscles as well as cavities for sense organs (eyes, mouth, and nose). The hyoid bone is located at the front of the neck, beneath the jaw. It serves as a moveable basis for the tongue and is linked to jaw, larynx, and tongue muscles. The vertebral column, also known as the spinal column, covers and protects the spinal cord, supports the head, and serves as an attachment point for ribs and back and neck muscles. It is made up of 26 bones: 24 vertebrae, a sacrum, and a coccyx. From the second cervical vertebrae to the sacrum, cartilaginous intervertebral discs create slightly moveable joints that absorb shock to allow for activities like walking or running. The ribs, sternum, thoracic vertebrae, and costal cartilages make up the thoracic cage, commonly known as the rib cage. The thoracic cage encloses and protects the thoracic cavity's organs, including the heart and lungs. It also supports the shoulder girdles and upper limbs and acts as an attachment point for the diaphragm, back, chest, neck, and shoulder muscles. Breathing is enabled through changes in the volume of the thorax. The sternum, often known as the breastbone, is a long, flat bone found in the front of the chest. It, like the skull, is produced in the embryo from numerous bones that merge in the adult. The ribs are 12 pairs of long, curved bones that join to the thoracic vertebrae and bend forward to form the ribcage. Costal cartilages attach the anterior ends of the majority of ribs to the sternum. The appendicular skeleton is made up of the upper and lower limb bones. The pectoral, or shoulder girdle, which connects the upper limbs to the body, and the pelvic girdle, which connects the lower limbs to the body, are also included. The pectoral girdle bones carry force generated by upper-limb muscles to the thorax. It is made up of the clavicles (or collarbones) in the front and the scapulae (or shoulder blades) in the back. The upper limb includes the bones of the arm (shoulder to elbow), forearm, and hand. The humerus is the upper limb's biggest and longest bone. It connects to the shoulder and the forearm at the elbow. The forearm is made up of two bones that go from the elbow to the wrist. The pelvic girdle connects to the appendicular skeleton's lower limbs. The pelvic girdle is tightly connected to the axial skeleton by strong ligaments because it bears the weight of the body and is responsible for movement. The pelvic girdle is mostly made up of two big hip bones. The lower limb is made up of the thigh, leg, and foot. Lower limb bones are thicker and stronger than upper limb bones to carry the total weight of the body as well as the stresses of movement. The femur, often known as the thighbone, is the longest, heaviest, and most powerful bone in the body. The hip joint is formed by the femur and pelvis. A joint, or articulation, is the place where two or more bones come together. Joints are responsible for movement, such as limb movement, and stability, such as the stability seen in skull bones. Joints can be classified in two ways: based on their structure or based on their function. Joints are classified as fibrous, cartilaginous, or synovial based on the material that makes up the joint and the presence or absence of a cavity in the joint. Fibrous connective tissue holds the bones of fibrous joints together. Cartilaginous joints have a limited range of motion. The only joints with a gap between the neighbouring bones are synovial joints. This region is known as the joint cavity, and it is filled with fluid. The fluid lubricates the joint, decreasing friction and allowing for better mobility. Synovial joints have the highest range of motion of any joint type. Synovial joints include the knees, elbows, and shoulders. Synovial joints allow for a wide range of mobility, which results in a variety of motions. Flexion is demonstrated by moving the forearm upward at the elbow. Rotation is demonstrated by the movement of the head when saying "no." Humans can only move on two limbs on land or swim with all four limbs on water. Birds are great swimmers and flyers. Some birds, however, are unable to move in both the air and the water.

John struck of the monk's head,

Author Response:

Reviewer #1 (Public Review):

The manuscript by Kim et al. identifies a new role for the F-actin binding protein Rai14 in dendritic spine dynamics. The authors demonstrate both in mice and in culture that Rai14-deficient neurons have decreased dendritic spine density, which corresponds with a reduction in excitatory synapse density and the frequency of miniature excitatory postsynaptic currents (mEPSCs). They also provide convincing evidence that Rai14 is protected from degradation through an interaction with another F-actin binding protein, Tara, and that the two proteins accumulate together in dendritic spines necks when overexpressed in neurons, resulting in enhanced spine maintenance. Characterization of Rai14+/- mice revealed that mice display learning and memory deficits and depressive-like behaviors, and that they have reduced expression of a number of genes identified in major depressive disorder gene set. Finally, the authors show that chronic restraint stress results in a decrease in mRNA and protein expression of Rai14, and that treatment with the antidepressant fluoxetine can rescue depressive-like behavior and reduced spine density in Rai14+/- mice as well as prevent a reduction in Rai14 expression following chronic restraint stress in wild-type (WT) mice. Together, these results identify Rai14 as a novel regulator of dendritic spine dynamics that may play a role in stress-induced depressive-like phenotypes. While the individual conclusions made by the authors are interesting and generally supported by the data (although in some cases missing important details/analyses), the evidence connecting the various findings together to provide proof that Rai14 is involved in regulating dendritic spine dynamics associated with depressive-like behaviors (as the title suggests) is still somewhat lacking and could be further strengthened.

1. In Figure 1, the authors use Golgi staining of WT and Rai14+/- mouse brain slices as well as primary neuron cultures from WT and Rai14-/- mice and shRNA knockdown of Rai14 to demonstrate that Rai14 loss leads to a reduction in dendritic spine density in cortical and hippocampal neurons. From this data, the authors conclude that Rai14 is required to maintain a normal number of dendritic spines. However, some important details and analyses are missing in these experiments. For instance, in Figure 1A and 1B, the authors do not specify which hippocampal or cortical brain regions (or cell types) they are analyzing in the WT or Rai14+/- mice. In Figure 1C-E, the authors claim there is a reduction in mature dendritic spine density in Rai14-/- neurons compared to WT neurons, but they do not detect differences in spine length or spine head width. It would be useful if the authors could include a description of how they are defining "mature spines". The authors also claim that the reduction in spine density on Rai14-deficient neurons is due to a maintenance phenotype, rather than a formation phenotype, but they do not present evidence to differentiate between these two possibilities. Have the authors examined younger Rai14+/- mice (or Rai14-/- neurons) to determine when the spine phenotype is first detected (i.e. do spines form and then are lost, or do they fail to form correctly in the first place)? The authors attempt to address this question in Figure 3 with experiments in neurons overexpressing Rai14 and Tara, but it might also be useful to look at earlier timepoints in Rai14+/- mice and/or time-lapse imaging of Rai14-deficient neurons.

-> In response to the reviewer’s concern, we added information on brain regions, cell types, time points, and spine classification criteria analyzed in both figure legends and the Materials and Method section.

->Regarding the formation vs. maintenance issue, we have tried to address the role of Rai14 in dendritic spine maintenance by observing spine dynamics under naïve condition and spine elimination-induced condition. Spines containing the Rai14 cluster at their neck rarely disappeared during the imaging period (Figure 3D and 3E). Newly formed spines in which Rai14 recruited became stable, whereas newly formed dendritic protrusions in which Rai14 did not gather gradually disappeared (Figure 3F). In addition, dendritic spines from neurons overexpressing Rai14 and Tara were more resistant to spine loss caused by LatA treatment (Figure 3G and 3H), suggesting that Rai14 would take part in dendritic spine maintenance.

-> However, we agree that our data cannot exclude the possibility that the formation phenotype also affected dendritic spine loss in Rai14 deficient neurons. Therefore, we modified some expressions from the text as follows:

• Abstract, line 18: “Rai14-deficient neurons failed to maintain a proper dendritic spine density in the Rai14+/- mouse brain,” -> “Rai14-deficient neurons exhibit reduced dendritic spine density in the Rai14+/- mouse brain,”
• Result, line 64: “Rai14-depleted neurons fail to maintain a normal number of dendritic spines” -> “Rai14-depleted neurons exhibit decreased dendritic spine density”
• Figure 1 title: “Rai14-depleted neurons fail to maintain a normal number of dendritic spines” -> “Rai14-depleted neurons exhibit decreased dendritic spine density”

1. In Figure 3, the authors report the interesting observation that overexpressed Rai14 and Tara accumulate in the necks of dendritic spines, which requires Rai14's ankyrin repeat domains, and that spines containing overexpressed Rai14 are less likely to be eliminated than spines lacking Rai14 clusters, and that neurons overexpressing Rai14 and Tara are resistant to spine loss caused by treatment with the actin destabilizer, latruculin A. Based on these results, the authors suggest in their model (Figure 6) that Rai14 regulates dendritic spine maintenance by stabilizing F-actin in the spine neck. While this is an interesting and feasible possibility, the authors do not directly assess how Rai14 affects F-actin dynamics. They do use RFP-LifeAct in Figure 3G, but only as a neuron fill and not to monitor F-actin dynamics. To better understand how Rai14 might be regulating dendritic spine dynamics, it would be beneficial to assess actin dynamics and/or organization in Rai14-deficient neurons.

-> For the concern regarding the F-actin dynamics, we admit that we did not provide the data on the direct link between Rai14 and F-actin dynamics within the neck of dendritic spines. Therefore, we modified some expressions in the text as follows: - Result, line 144: “indicating that stabilized Rai14 protects F-actin from destruction in dendritic spines.” -> “indicating that Rai14 protects dendritic spines from the pressure of elimination by actin destabilization.” - Fig 6 legend, line 1147: “The Rai14 cluster at the spine neck contributes to maintaining spines, probably by stabilizing F-actin, thereby upregulating dendritic spine density.” -> “The Rai14 cluster at the spine neck contributes to maintaining spines, thereby upregulating dendritic spine density.” -> There are multiple reports that Rai14 stabilizes F-actin (Peng et al., 2000; Qian et al., 2013a; Qian et al., 2013b). Tara is also known to stabilize F-actin (Seipel et al., 2001; Woo et al., 2019). Moreover, we showed that dendritic spines overexpressing Rai14 and Tara were more resistant to spine elimination caused by F-actin destabilizer (Figure 3G and 3H). Therefore, we believe it is plausible that Rai14 and Tara be related to F-actin stabilization in the dendritic spine necks. Therefore, it would be of immediate interest to investigate the direct mechanistic link between Rai14 and F-actin dynamics within the dendritic spine neck for spine stabilization with higher resolution imaging approaches.

1. The authors observe both learning and memory deficits and depressive-like behaviors in Rai14+/- mice compared to WT mice. Treatment with the antidepressant fluoxetine rescues Rai14+/- mouse behavior in the forced swim test to WT levels (i.e. decreases immobility time). Likewise, fluoxetine treatment rescues dendritic spine density in the prefrontal cortex of Rai14+/- mice to a level seen in WT saline-treated mice. Moreover, chronic restraint stress causes downregulation of Rai14 mRNA and protein expression in the prefrontal cortex, which is blocked by fluoxetine treatment. From these data, the authors conclude that Rai14 is important for the remodeling of synaptic connections relevant to depressive-like behaviors (and to the cognitive deficits possibly related to the depressive-like behavior). However, the link the authors are proposing between Rai14's role in regulating spine dynamics and stress-induced depression may be a bit premature. For instance, the analyses done to determine Rai14's role in regulating dendritic spine density and behavior were done using Rai14+/- mice, where Rai14 was deleted throughout development. Thus, it is not clear whether the behavior and spine defects in Rai14+/- mice are developmental, or whether they would arise from Rai14 loss in adulthood (such as in response to chronic stress). The results with fluoxetine treatment are encouraging, but the authors do not show whether fluoxetine treatment would have similar effects on WT mice (they only treated Rai14+/- mice with fluoxetine in their experiments). Since Rai14 is downregulated in the prefrontal cortex of chronic restraint stressed mice, would stabilized Rai14 (i.e. Rai14 948-967) rescue spine loss and/or depressive-like behavior in stressed mice?

-> In response to the reviewer’s suggestion, we modified the expression from the text as follows: - Result, line 191-192: “Taken together, these results support the importance of Rai14 in the plastic changes of neuronal connections relevant to depressive-like behaviors” -> “Taken together, these results support the link between the Rai14-controlled dendritic spine dynamics and depressive-like behaviors.”

-> As the reviewer pointed out, we cannot exclude the possibility that the defects in behavior and dendritic spines in Rai14+/- mice are developmental phenotypes. On the other hand, we would like to note that; 1) Chronic stress reduced the Rai14 expression in adult mice (Figure 5J and 5K), 2) Fluoxetine treatment rescued dendritic spine and behavioral defects in adult Rai14+/- mice, 3) Knockdown of Rai14 starting from DIV15 also led to spine density decrease in primary cultured neurons. These results support the notion that Rai14 can participate in the events related to spine dynamics in the adult brain. We believe that this important question will be better addressed when a mouse model with conditional expression or KO of Rai14 gene becomes available.

Reviewer #1 (Public Review):

The manuscript by Kim et al. identifies a new role for the F-actin binding protein Rai14 in dendritic spine dynamics. The authors demonstrate both in mice and in culture that Rai14-deficient neurons have decreased dendritic spine density, which corresponds with a reduction in excitatory synapse density and the frequency of miniature excitatory postsynaptic currents (mEPSCs). They also provide convincing evidence that Rai14 is protected from degradation through an interaction with another F-actin binding protein, Tara, and that the two proteins accumulate together in dendritic spines necks when overexpressed in neurons, resulting in enhanced spine maintenance. Characterization of Rai14+/- mice revealed that mice display learning and memory deficits and depressive-like behaviors, and that they have reduced expression of a number of genes identified in major depressive disorder gene set. Finally, the authors show that chronic restraint stress results in a decrease in mRNA and protein expression of Rai14, and that treatment with the antidepressant fluoxetine can rescue depressive-like behavior and reduced spine density in Rai14+/- mice as well as prevent a reduction in Rai14 expression following chronic restraint stress in wild-type (WT) mice. Together, these results identify Rai14 as a novel regulator of dendritic spine dynamics that may play a role in stress-induced depressive-like phenotypes. While the individual conclusions made by the authors are interesting and generally supported by the data (although in some cases missing important details/analyses), the evidence connecting the various findings together to provide proof that Rai14 is involved in regulating dendritic spine dynamics associated with depressive-like behaviors (as the title suggests) is still somewhat lacking and could be further strengthened.

1. In Figure 1, the authors use Golgi staining of WT and Rai14+/- mouse brain slices as well as primary neuron cultures from WT and Rai14-/- mice and shRNA knockdown of Rai14 to demonstrate that Rai14 loss leads to a reduction in dendritic spine density in cortical and hippocampal neurons. From this data, the authors conclude that Rai14 is required to maintain a normal number of dendritic spines. However, some important details and analyses are missing in these experiments. For instance, in Figure 1A and 1B, the authors do not specify which hippocampal or cortical brain regions (or cell types) they are analyzing in the WT or Rai14+/- mice. In Figure 1C-E, the authors claim there is a reduction in mature dendritic spine density in Rai14-/- neurons compared to WT neurons, but they do not detect differences in spine length or spine head width. It would be useful if the authors could include a description of how they are defining "mature spines". The authors also claim that the reduction in spine density on Rai14-deficient neurons is due to a maintenance phenotype, rather than a formation phenotype, but they do not present evidence to differentiate between these two possibilities. Have the authors examined younger Rai14+/- mice (or Rai14-/- neurons) to determine when the spine phenotype is first detected (i.e. do spines form and then are lost, or do they fail to form correctly in the first place)? The authors attempt to address this question in Figure 3 with experiments in neurons overexpressing Rai14 and Tara, but it might also be useful to look at earlier timepoints in Rai14+/- mice and/or time-lapse imaging of Rai14-deficient neurons.

2. In Figure 3, the authors report the interesting observation that overexpressed Rai14 and Tara accumulate in the necks of dendritic spines, which requires Rai14's ankyrin repeat domains, and that spines containing overexpressed Rai14 are less likely to be eliminated than spines lacking Rai14 clusters, and that neurons overexpressing Rai14 and Tara are resistant to spine loss caused by treatment with the actin destabilizer, latruculin A. Based on these results, the authors suggest in their model (Figure 6) that Rai14 regulates dendritic spine maintenance by stabilizing F-actin in the spine neck. While this is an interesting and feasible possibility, the authors do not directly assess how Rai14 affects F-actin dynamics. They do use RFP-LifeAct in Figure 3G, but only as a neuron fill and not to monitor F-actin dynamics. To better understand how Rai14 might be regulating dendritic spine dynamics, it would be beneficial to assess actin dynamics and/or organization in Rai14-deficient neurons.

3. The authors observe both learning and memory deficits and depressive-like behaviors in Rai14+/- mice compared to WT mice. Treatment with the antidepressant fluoxetine rescues Rai14+/- mouse behavior in the forced swim test to WT levels (i.e. decreases immobility time). Likewise, fluoxetine treatment rescues dendritic spine density in the prefrontal cortex of Rai14+/- mice to a level seen in WT saline-treated mice. Moreover, chronic restraint stress causes downregulation of Rai14 mRNA and protein expression in the prefrontal cortex, which is blocked by fluoxetine treatment. From these data, the authors conclude that Rai14 is important for the remodeling of synaptic connections relevant to depressive-like behaviors (and to the cognitive deficits possibly related to the depressive-like behavior). However, the link the authors are proposing between Rai14's role in regulating spine dynamics and stress-induced depression may be a bit premature. For instance, the analyses done to determine Rai14's role in regulating dendritic spine density and behavior were done using Rai14+/- mice, where Rai14 was deleted throughout development. Thus, it is not clear whether the behavior and spine defects in Rai14+/- mice are developmental, or whether they would arise from Rai14 loss in adulthood (such as in response to chronic stress). The results with fluoxetine treatment are encouraging, but the authors do not show whether fluoxetine treatment would have similar effects on WT mice (they only treated Rai14+/- mice with fluoxetine in their experiments). Since Rai14 is downregulated in the prefrontal cortex of chronic restraint stressed mice, would stabilized Rai14 (i.e. Rai14 948-967) rescue spine loss and/or depressive-like behavior in stressed mice?

since adjectives describe nouns , it is introduced before a noun and after a determiner or main verb

put in appendix

The typical way of slaughtering a pig is stunning it by bludgeoning it over the head, hanging it upside down over a basin, and severing the major blood vessels in its neck (what is known as “sticking” the pig). The butcher would then bleed the pig, or wait for the blood to drain out of it into the basin so the meat would be clean to eat. The collected blood could be used to make blood or black puddings, a type of sausage made from pork blood, pork fat, oats, and spices.

Other recipes for fowl in this collection follow similar instructions for removing the skin, mincing the body with additional ingredients, filling the skins again, and then stewing them. See “To boile Capons, Pullets,Chickens, Pigeons, Pheasants or Partridges” (pp. 69-70) and “To boil any Land Fowl, as Turky, Bustard, Pheasant, Peacock, Partridge, or the like” (pp. 87-90).

Other recipes for fowl in this collection follow similar instructions for removing the skin, mincing the body with additional ingredients, filling the skins again, and then stewing them. See “To boil a Capon or Chicken with divers compositions” (p. 85) and “To boil any Land Fowl, as Turky, Bustard, Pheasant, Peacock, Partridge, or the like” (pp. 87-90).

Reviewer #3 (Public Review):

The manuscript by Ren et al. seeks to describe a role for endothelial cell (EC) expression of Sirpα playing a role in the importation of hematopoietic progenitors from the circulation into the thymus. Specifically, the authors demonstrate that there is a reduction in the number of the earliest T lineage progenitors (ETPs) in the thymus in mice deficient for Sirpa or CD47 (its ligand), and through a series of elegant in vitro transendothelial migration studies, identify that intracellular Sirpα signaling mediates this process by regulating VE-Cadherin expression and thus EC tight junctions. In particular, the use of transwell assays modified to study TEM is particularly well utilized to tease apart the mechanisms. Overall, I found this to be an excellent manuscript. In fact, every time I had a critique developing in my head, the authors quickly dispensed of it by producing some follow up data that addressed my concern! My biggest concern with the manuscript is that it was difficult to determine exactly how many repeats of each experiment have been performed and what data is being presented in the figures (and being statistically analyzed). This should not change the conclusions of the manuscript but will make reading the figures and matching them with the legends easier. The following are a some major and minor concerns that should be addressed to strengthen the manuscript:

Major:<br /> • My main concern is that there needs to be greater care taken with highlighting the number of repeats done for each individual study as it is not always clear. For instance, in Figure 2 the data are presented as being representative of three independent experiments with an n of 3 in each experiment but in 2B, D, and F there are 4 data points for the Sirpa-/- group. This is likely explained by there being 4 mice in that particular experiment, but that is why the numbers should be presented for each experiment rather than a general statement at the end. Another example of this is that in Figure 2 S1 the authors would like to claim that the only differences are in the DN1 subsets which contains the ETPs. However, it is likely this is just due to low numbers as it seems like there is a real decrease in the number of DN2, DN3, DN4 and even DP thymocytes (as well as total cellularity).<br /> 1. This should not change any conclusions of the paper but will aid in reader interpretation.<br /> 2. In this manuscript the authors show that Sirpa expression by TPECs is critical for their capacity to guide the importation of HPCs, and in their previous work they have shown that lymphotoxin can regulate the importation capacity of these same TPECs. Therefore, it would be extremely interesting to know if LT signaling is regulating the expression of Sirpa. Furthermore, it would be important to at least comment on what may be influencing Sirpa expression. For instance, we know from the work of Petrie and others that DN niche availability can influence the ability of the thymus to import of progenitors. Similarly, after TBI the "gates" are let open and the capacity of the thymus to import progenitors increases. Do the authors know (or could they comment) on what happens to Sipra expression after TBI in ECs?<br /> 3. The use of the in vitro TEM assays in transwell plates are a nifty way of interrogating and manipulating the effect of Sirpa in these conditions, however, the caveat is that these all use EC cell lines that do not correspond to the TPECs being described in vivo. This caveat should be acknowledged in the text.<br /> 4. I am a little confused as to the interpretation of the final experiment looking at tumor clearance. The authors show that this could be clinically relevant as blockade of the CD47-Sirpa axis is becoming an increasingly attractive immunotherapy option but its use could preclude thymic recovery after damage and thus contribute toward poorer T cell responses against tumors. This last study is very interesting but also very hard to interpret given the likely positive effect of Sirpa-CD47 blockade on tumor clearance, in opposition to its potential effects hindering thymic repair. While it is notable that there is reduced clearance of tumor in mice treated with CV1, it is unclear why there does not seem to be any positive effect of CV1 on tumor clearance (is this because there are fewer T cells in the periphery as it is still early after damage?). On the thymic repair and reconstitution front, perhaps a cleaner way would be to look in Sirpa or CD47 deficient mice and without tumors.

Minor Comments:<br /> • In Fig. 2I (and Fig. 2S2I-J), it is difficult to determine how long after the chimera transplant the homing assays were performed. However, this approach has limitations as the process of creating those chimeras (conditioning such as irradiation etc.) will change the function and possibly the mechanisms of progenitor entry into the thymus. There is clearly still an effect of Sirpa in this context but it is possible (even likely) that the importation mechanisms in the thymus change after damage such as that caused by the conditioning required in the initial chimera generation. Furthermore, although using the Tie2-Cre strain will distinguish Sirpa on ECs and TECs, it will not distinguish between expression on other cells such as DCs (Tie2 will delete expression in both endothelial and hematopoietic lineages). Although the optimal experiment to address these concerns would be to delete Sirpa from ECs specifically (such as with Cdh5-CreERT2 mice), I am convinced by the preponderance of in vitro data that there is an EC-specific effect and therefore it is not necessary to perform this time-consuming, albeit interesting, potential experiment. However, these limitations should be acknowledged in the discussion or text.<br /> • As a technical note I am surprised that there was considerable reconstitution of naive T cells at day 21 after TBI (Fig.7G-H). In our experience that is very early for naïve T cells in the periphery which generally take about 4 weeks to start reconstituting in a real sense. Is it possible there are direct effects of this treatment on residual radio-resistant peripheral T cell numbers?

CITATION: THE CIVIL CODE PROCLAMATION OF 1960 (ETHIOPIA) PROCLAMATION NO. 165 OF 1960.

ANALYTIC NOTE: Government may create or empower cabinet ministers to make new rules regulating CSOs on certain matters if the law is enacted or else leave the regulatory regime unchanged.

EXCERPT: *Art. 479. - Ministry of Interior. (1) The Ministry of Interior may prescribe, within the framework of the existing laws, any other measure it thinks fit with a view to placing the offices of associations in a position to exercise an efficient control on the associations.

(2) Such measures may differ according to the object, the importance and the duration of the association.

(3) Particular measures may also be prescribed for the associations which have their head office in a foreign country, or which carry on an activity in a foreign country or the majority of whose members are foreigners, or which have a board of management in which one or more members are foreigners.*

FILENAME OR SOURCE: DeMattee_ETH_Civl_Code_1960

This recipe appears in nearly identical form in Woolley (1675a), p. 122; Woolley (1675b), pp. 287-8; and Woolley (1686), pp. 150-1. A more complex but largely similar recipe appears as “To dress a Codds head the best way” in Rabisha (1661), p. 112. Rabisha’s version of the recipe includes additional ingredients (cockles, crab, white wine, nutmeg) and more involved techniques (adding drawn butter, frying the bay leaf garnish).

The author makes a point to mention that Ted Lavender was shot.

There was a battle that wasn't going well, but Macbeth came on the scene and turned it around.

In my formative years, I use to dance. A technique we were taught to keep from getting dizzy during pirouettes, spins, and twirls was called spotting. It requires the dancer to fixate their gaze on a single spot at eye level and keep their head in the same position for as long as possible, quickly rotating their head around to the same position as they move. Shifts, changes, or loss of this fixed site creates visual instability and dizziness. Vision and one's gaze is an important factor not just for dancers, but for everyone in the navigation of the world around them.

No, because the analyzed data was of employees that had already had a past of leaving the company. It could be postulated that it is leaving out the perspective of those who might have joined if whatever driving factor behind the attrition were addressed head-on, rather than shielding their eyes from the problem.

We are so much more than a physical description, our taste, smell and sight brings the sun to its knees and turn owls heads like cracking leaves. People are walking descriptions of individual personalities in which you do not understand until faced head to head.

I agree that it is not fair for many children. In the race to pursue the American dream, some children have won the starting line compared with those who have no fixed abode because of their good family conditions, access to better educational resources and living environment. Children without these "parental gifts" have to work harder to get the opportunities their heirs compete for.

Wake up call.

I also found this very difficult to wrap my head around

I heavily believe this. Peers can be great role models for kids. I feel that we as humans are naturally competitive (especially myself) and set up a secret competition in their head about who can say... get a better test score than the other. But having that drive from within the classroom can lead to even greater success

This reminds me of an interview last year where a White news reporter interviewed a Black protester. When asking if he dislike any of the rioting he witnessed he simply stated no. He said the lifestyle he lives along with other people of his race results from the "repercussions of the White man ancestors". The same goes with education. We tend to see a lot more Whites at an advance level compared to Blacks simply because they were at an advantage and were given a head start when it comes to quality education.

I think this is a true fact. From the viewpoint of outsider, wealthy families could provide strong background for their child and give them a quite easier track towards success. The child could gain better educational resources and more chances during work searching. We have to admit this is a true and there exists some priority and inequality in our society. We cannot achieve perfectly balance on this thing.