Reviewer #3:

In the current manuscript (De novo learning and adaptation of continuous control in a manual tracking task), Yang et al. aim to demonstrate that motor adaptation to a mirror reversal perturbation to visual feedback is de-novo learning of a movement controller in contrast to the adaptation of an existing controller with rotation to visual feedback. The authors examine two different experimental paradigms (1) continuous tracking of a cursor (trajectories generated by different sum-of-sinusoid functions) and (2) point to point movements under these two different visual manipulations of the cursor feedback: a 90 deg rotation and mirror reversal. Importantly, the authors set the motion of the cursor under the continuous tracking case as a sum of sinusoidal trajectories in order to perform frequency analysis of the motion tracking. The authors then examine the behavior in the time domain, and dissect the responses at individual frequencies in the frequency domain to determine the response of learning observed in each condition to the fast and slow changing components of the perturbation. There are two major reported results: (1) Participants learn both mirror reversal and rotation learning, but mirror reversal learning shows little to no aftereffect, whereas rotation learning shows an ~25º aftereffect from ~70º of learning. The authors argue that this suggests that mirror-reversal learning arises from a de-novo controller that is not engaged during baseline or washout (Lines 199-200) (2) Learning in the continuous tracking task shows a gradation in performance over frequencies (i.e., higher frequencies demonstrate lower learning). These are interesting experiments, with a well-defined motivation/question and (mostly) clear presentation of results. The figures and results largely support the hypothesis. My specific comments are shown below:

1) In the abstract, the last line says 'Our results demonstrate that people can rapidly build a new continuous controller de novo and can flexibly integrate this process with adaptation of an existing controller'. It's not clear if the authors have shown the latter definitively. What is the reasoning for this statement, "flexibly integrate this process with adaptation of an existing controller"? It would seem you would need the same subjects to perform both experimental tasks (mirror reversal and VMR) concurrently to make this claim.

2) It would be helpful if the authors could provide more background/context on their view of de novo learning and explanations on the relationship between de novo learning and the adapted controller model. For example, why does the lack of aftereffects under the mirror-reversal imply that the participants did not counter this perturbation via adaptation and instead engaged the learning by forming a de novo controller (Line 199)? Is the reasoning purely behavioral observations, or is there a physiological basis for this assertion?

3) Details about frequency analysis are buried deep in the methods (around line 711), especially how the hand-target coherence (shown in 4B) is calculated. It would be helpful to include some of these details in the main text. For example, it is currently very difficult to understand the relationship when from moving from Figure 4A to 4B.

4) Lines 197-199: The reason for the lack of after-effects in the mean-squared error analysis is a little vague. It took a few tries to understand the reasoning. It would be good to spell this out a little more clearly.

5) Lines 223-225: The logic behind why coupling across axes is not nonlinear behavior seems to be missing. It's quite unclear and currently difficult to understand. It would be very helpful to spell this out too.

6) Surprisingly, there is no measurement of aiming in the learning to VMR. Several motor learning studies (several the authors cite) show that learning in VMR is a combination of implicit and explicit. I understand that this is not possible in the continuous tracking task, but can certainly be done in the point to point task. Is there a reason this was not done? Wouldn't this have further supported the author's claim of an existing controller?

7) Figure 2C: the data for mirror-reversal seems to have a weird uptick in the error. Why would that be? Is there an explanation for this?

8) Lines 339-342: the results show that mirror-reversal learning is low at high frequencies (Fig 5B). The authors interpret this as reason to believe that this is actually de-novo learning and not adaptation of an existing controller. This seems somewhat unfounded. Could it be that de novo learning performs well at low frequency, through 'catch-up' movements, but not at high frequencies? Do the authors have a counter argument for this explanation?

9) Lines 343 - 350: The authors ascribe the difference between after-effects and end of learning to be due to de-novo learning even in the rotation group. However, that difference would likely be due to the use of explicit strategy during learning and its disengagement afterwards, or perhaps a temporally labile learning. Can the authors rule these possibilities out? What were the instructions given at the end of the block and how much time elapsed?

10) Lines 787: Outlier rejection based on some subjects who had greatly magnified or attenuated data seems like it might be biasing the data. Also, the outlier rejection criteria used (>1.5 IQR) seems very stringent. Furthermore, it appears there was no outlier rejection on the main experiment. It would be good to be consistent across experiments.

11) Figure 4: The authors show the tracking strategies participants applied by investigating the relationship between hand and target movement. The linear relationship would suggest that participants tracked the target using continuous movements. In contrast, a nonlinear relationship would suggest that participants used an alternative tracking strategy. The authors only state this relationship is based on figure 4 but it seems do not provide any proof of the linearity. It would be more convincing to provide an analysis to show that the relationship is indeed linear or nonlinear.

Reviewer #3:

This manuscript is a detailed analysis of the molecular mechanism for ISW2 recruitment in yeast and delineates not only the binding interface between ISW2 and the transcription factor Ume6, but also finds similar interactions between ISW2 and Swi6. The authors take a systematic and rigorous approach in finding that a 27 amino acid region of Ume6 and the WAC domain of Itc1, accessory subunit in ISW2, are responsible for recruiting ISW2 to Ume6 binding sites. The strength of this paper is that they focus on examining these interactions in vivo and using MNase-seq to show changes in nucleosome positioning upon mutation of Itc1, Ume6 and Swi6. The data is well supported and the conclusions are compelling. In addition, they use the Spytag approach to show these regions alone are capable of recruiting Isw2 to genomic target sites. They also show that amino acids 1-73 of Itc1 alone are sufficient for binding to the correct genomics sites and is compelling evidence of their specificity. The authors, by comparing the sequence composition of the WAC domain in ISW2 orthologs from flies to humans, are able to explain a contradiction that has been in this field for a long time about the apparent different role of yeast ISW2 and its Drosophila homolog ACF/ISWI. The Drosophila ISWI complex appears to have a more global role in chromatin organization; whereas yeast ISW2 is more specialized or targeted. The WAC domain in ISWI is defective for recruitment by such transcription factors like Ume6 and Swi6, unlike that observed for ISW2. The other interesting finding or correlation that is derived from their findings is that the recruitment of ISW2 by Ume6 and Swi6 may not only work to recruit ISW2 but may also regulate ISW2 activity as the same region of Itc1 shown to bind to these transcription factors is also shown to regulate the activating function of the H4 tail on Isw2. The paper is well written, clear and nicely organized. I did have one question for the authors, as it seems that this type of recruitment may not be universal as there are only a subset of Ume6 sites that behave as expected in their mutational analysis. Do the authors have any idea why that is the case and what makes this subset of sites behave differently?

Reviewer #3:

The authors report results of an MEG analysis deploying a cognitive paradigm in which participants engage in a source memory task characterized by the appearance of three images in succession and are then tested via a cue (the first of the three images) followed by a choice of responses for a two dimensional pattern and then a choice (out of three images) of a photographic scene.

The principal finding is that (via MEG sensor level data) there is a widespread 8-15 Hz power decrease that is correlated with the number of recalled items (from 0 to 2) on a given trial. In the hippocampus (via MEG source reconstruction), the magnitude of phase amplitude coupling observed as participants are told to associate the items is correlated with memory performance. The 8-15 Hz power decrease/memory correlation (as estimated by beta coefficients in a model described in Figure 1) is larger (across individuals) during moments when subjects are viewing the stimulus items as opposed to during the "associate" period. The novelty in the result is related to the experimental task that attempts to dissociate memory-related effects related to perception from those related to binding which putatively occurs when subjects are given the "associate" instruction.

My main conceptual concern is related to the design of the experimental task. I am not sure that the perception/binding framing is appropriate, since there is no reason to think that subjects are not associating/binding items during the periods when the items are being shown on the screen. I suppose this may partly explain the lack of a significant difference in PAC/memory beta coefficients observed in the hippocampus when contrasting these two epochs (Figure 4). But the corollary is that the alpha power-related beta coefficients are observed while binding is likely also occurring within the paradigm (esp since each image is shown for 1.5 seconds it would seem). Is the alpha power effect seen in the hippocampus? The plots in 3a suggest there is an oscillation present in the relevant frequency range, and the time course of alpha power differences seen in Figure 2 suggests that they occur relatively late after onset of the images, which may fit better with some contribution for this pattern to the forming of associations rather than perception.

I understand that the paradigm was constructed in an attempt to temporally dissociate memory effects attributable to perception versus those attributable to binding. But given the temporal resolution available using EEG, I would imagine that the authors could differentiate an earlier perception-related effect from a later PAC binding effect in the time series if the associated images were presented in conjunction. Is it correct to frame the alpha results as related to "perception?" The beta coefficients used for analysis reflect a "memory related effect observed when visual stimuli are present on the screen," but not necessarily improved memory predicated on more accurate perception to my interpretation. I would think that a perception/binding distinction requires operationalizing perception as activity that doesn't vary with later associative memory success, and binding as activity that does. The notion of perception used by the authors here seems slightly different. The authors can perhaps comment on this concern.

The authors report PAC results for other regions on page 6, but claiming that PAC is a hippocampal-specific effect would require showing that the PAC-related beta coefficients are significantly greater than the other regions, rather than simply the absence of a significant effect in these regions. The authors should also clarify if they combined locally measured PAC over several ROIs into an average for these other regions? It seems unlikely to detect PAC if a single theta/gamma time series were extracted over such a large area of cortex.

The interaction effect reported at the end of the results (ANOVA model) is interpreted such that the cortical alpha effect is stronger when the visual items are presented, while the hippocampal PAC effect is stronger when no items appear on the screen, but these recordings are made in different regions (hippocampus versus the entire cortex). If my understanding is correct, a result in line with the model the authors suggest (cortical alpha power decrease/hippocampal PAC) would show a region (hipp v cortex) x task (images on screen vs "associate" command) x metric (PAC vs alpha) interaction. Can the authors clarify if the cortical data entered into this model includes only those regions that showed a significant effect initially, or just all the sensors? The former would seem to introduce bias.

Similarly, the different visual classes are always presented in the same order, which may give rise to the strong disparity in recall fraction between the pattern and scene images. I understand the linear model incorporates predictor variables for scene/pattern recall, but given that scene recall is driving a significant amount of the overall recall number observed as the main variable of interest, I would wonder if the alpha/beta power effects are related to the relative complexity of the scene images as compared to the patterns. Given the analysis schematic the authors report, I assume the authors have analyzed whether the same effects occur when contrasting scene versus no recollection and pattern vs no recollection. If the same effects are observed regardless of type of image (when compared with no recollection) this may help address this concern.

My second conceptual question is related to MEG data. It appears to me that the authors use MEG sensor-level data for the alpha-related effect in the cortex (Figure 2), but MEG beamformer reconstructed data (localized to the hippocampus) for the PAC effect. Is there a reason the authors did not use MEG data localized to specific cortical regions rather than sensor data? This may reflect confusion on my part, but I don't understand why they would use qualitatively different types of data for these two aspects of the analysis that are then combined (in the ANOVA, for example).

The authors should also engage with concerns regarding the validity of localizing MEG signals (especially for an analysis such as PAC) to deep mesial temporal structures such as the hippocampus. I understand that MEG systems with greater than 300 sensors are more reliable for this purpose, but I think a number of readers would still have doubts about MTL localization of signal. Also, my understanding is that such deep source localization requires around 100 trials per class, which I think fits with what the subjects completed, but the authors may include references related to this issue.

I think the signal processing steps are overall quite reasonable. I would ask the authors to clarify if they limited their analysis of cortical alpha/beta oscillations to those in which a peak exceeded the 1/f background, as they report for the PAC analysis on page 5. Also, it would be helpful to show that the magnitude of the MI values in the hippocampus exceed those observed by chance (using a shuffle procedure) in addition to showing that there is a memory-related association reflected in the beta coefficients.

Moderna’s COVID-19 Vaccine Candidate Meets its Primary Efficacy Endpoint in the First Interim Analysis of the Phase 3 COVE Study | Moderna, Inc. (n.d.). Retrieved 1 December 2020, from https://investors.modernatx.com/news-releases/news-release-details/modernas-covid-19-vaccine-candidate-meets-its-primary-efficacy/

Reviewer #3:

This is a very thorough study giving new insight into a non-cell autonomous mechanism for DCC in axon guidance in midline fusion important for corpus callosum axon guidance.

I have no substantive concerns.

Reviewer #3:

Substantive concerns:

1) Regarding hypothesis 4, the authors test whether or not desiccating species have lower TE loads than non-desiccating species, but in my opinion the logic outlined in lines 114-124 suggests that the relationship between desiccation and TE load may be more nuanced than overall TE load. It could be possible that DSB repair associated with desiccation removes only recent insertions if homologous pairing is involved, or high-copy TEs if ectopic recombination has occurred. The authors already test recent TE activity elsewhere in the manuscript, so they could compare signatures of recent activity in desiccating vs non-desiccating species to see if there are fewer recently active TEs in desiccation species. Similar comparisons could easily be made for abundance of high-copy TEs (regardless of length).

2) Additionally, regarding the signatures of recent transposition, the authors have done a thorough job comparing TE divergences and LTR insertions, but since transcriptomes for some species are available, presence of transcribed TEs could provide further support for recent and ongoing TE activity.

Reviewer #3:

This paper compares two methods for assessing the effect of luminance on visual processing speed. One method represents conventional methodology, using a forced choice button push approach to assess the Pulfrich effect (whereby delayed processing of horizontal motion in one eye creates a percept of motion in depth). The other, more novel method uses a continuous (monocular) tracking task to assess relative delays in signal processing caused by luminance changes. The authors show that the two approaches yield remarkably close agreement (to within a few milliseconds) in their estimates of the relative processing delays caused by luminance differences across eyes. The authors go on to establish Pulfrich-like effects in a binocular tracking task.

The paper is very clearly written, and the experiments and analyses have been meticulously conducted. The technical quality of the work is excellent. Scientifically, the paper does not really contribute any novel insights about the nature of perceptual processing. Rather, the paper represents more of a methodological manifesto advocating for the power of tracking-based psychophysics approaches. The experiments serve as a powerful illustration of how well tracking tasks can work in practice, validated by more conventional approaches. The paper makes a compelling case that tracking tasks are able to reproduce existing findings, and can do so significantly more efficiently (i.e. in much less time).

The novelty of the approach is a bit overstated. On the first page, the authors suggest that continuous target tracking is "a new stimulus-response data collection technique". This is a bit much. People have been doing manual tracking tasks for decades, in many cases with quite sophisticated analysis and an emphasis on elucidating perceptual processing, in a similar spirit to this paper. Studies of eye movement and postural control have also employed related approaches. See, for example, the work of John Jeka, Tim Kiemel, Chris Miall, Otmar Bock, Noah Cowan - as well as the likes of Jex and McRuer in the 70s. Perhaps the authors were not aware of this substantial body of work. It seems appropriate to offer some acknowledgement and discussion of this prior work that has also recognized the power of such methods and employed them very effectively.

A significant weakness of the paper is the small number of participants who performed the tasks - only five, two of which were the authors of the paper. While the within-participant comparisons are compelling, the broader agenda of advocating for wide adoption of these tracking tasks for scientific and potentially clinical applications will need more extensive validation on much broader populations. I do share the authors' optimism about the use of tracking tasks, but broad adoption for probing perceptual processing will require demonstrations that these approaches can be robust across much larger cohorts.

Reviewer #3:

Whole genome sequence data from a geographically large set of 86 Brachypodium distachyon samples is presented and combined with previous data. In addition, flowering time collected from both field and controlled conditions are presented. Overall, the manuscript has many interesting aspects and ideas but overall, the main agenda is not clear. They mention selfing, seed dispersal, coalescence theory, microevolution, plasticity and frequency dependent selection in the abstract but none of those topics are explored in-depth in the manuscript. There were multiple points e.g. in the methods that needed clarification. The manuscript would benefit from focusing on one or two aspects and making strong cases for them.

Main comments:

1) It is an overstatement to claim that this dataset covers the region from Iberia to Iraq, when already previous datasets covered Iberia and Iraq. Here French and Italian samples are added to previous data.

2) The connection between the heterozygosity, structural variation and assembly issues due to paralogy should be more clearly presented. For example, in r. 130-134,it is not obvious what does mapping against BdTR7a to itself and identifying less heterozygous sites prove? In addition, the procedure for masking the fake heterozygosity should be more explicitly described. Inspection by IGB, or defining thresholds by "trial and error" are not reproducible methods. Also, wouldn't one want to take into account the overall level of diversity in a given region instead of putting a threshold as "ten or more SNPs along a distance of at least 300 bp".

3) Sympatry issue: The different lineages are described to be sympatric thus it would be important to be really specific about the sampling locations. How close are the closest sympatric samples representing different lineages? Is that truly a sympatric setting? Further in r. 176-181, how does plotting ancestry components in the map prove that there has not been gene flow between sympatric lineages? There seems to be shared ancestry but it is a known issue that shared ancestry and admixture are not easy to separate. This aspect is central to the paper and would need more rigorous analysis with e.g. forward or coalescence simulations. The reasoning continues in rows 344-352, but is not really backed up by any analysis other than plotting ancestry components on the map. Or if it is, it should be more precisely expressed.

4) R. 301-303 this statement sounds like the authors are suggesting that selfing and dispersal are actively (or as a result of selection) interacting and maintaining the diversity. I did not see convincing evidence that the distribution of lineages is not just a combination of drift, selfing and random dispersal events. Maybe this is what the authors mean, but should be more clearly stated.

Reviewer #3:

This manuscript investigated the interactions of SARS-CoV-2 S protein and its RBD domain with ACE2 protein of host cells using mainly the HDX-MS approach. The results revealed the dynamics information about the interactions and how ACE2 binding at the RBD domain primes enhanced proteolytic processing at the S1/S2 site of S protein, and are potentially useful for the relevant research, e.g., therapeutic development. This is a rather straightforward study, without further biological validation of the major conclusions. Detailed comparison and integration of the HDX-MS results with those from cryo-EM were not provided in the manuscript as well. Some details of the manuscript also need further clarification.

Major comments:

1) Fig. S1: The SDS-PAGE showed around 90 kDa for the molecular weight of RBDisolated, which should be around 25 kDa based on its sequence (318-547). Please check and clarify.

2) It is confusing about the existing forms of the S protein and ACE2 and their binding stoichiometry, regarding the statements such as "we measured dynamics of a trimer of this near-full length S protein..." (Page 4, line 87), "we performed HDXMS experiments of monomeric ACE2..." (Page 10, line 220-222), "......were pre-incubated at 37{degree sign}C for 30 min in a molar ratio of 1:1 to achieve >90% binding......" (Page S2, line 65-66). Please confirm whether the expressed ACE2 is dimeric and S protein is trimeric or not, and their binding stoichiometry is 1:1 or 2:3. Please also provide the concentration and calculation details for ensuring the >90% binding. If only one ACE2 in the ACE dimer and one S protein in the S protein trimer are involved in the binding, how sensitive and accurate could the HDX-MS results reflect the binding, since no HDX difference would be observed for the other ACE2 and other 2 S proteins?

3) Page 2, line 33-35: Other studies (e.g., Ref. 11) have shown that ACE2 binding can enhance S1/S2 cleavage by furin and S1/S2 cleavage site could be possible targets for small molecule inhibitor/antibody development. It would be helpful if further evidence could be provided to support that the stalk hinge regions could also be the targets for that.

Reviewer #3:

Thank you for inviting me to review this manuscript by Guell and colleagues, in which the authors conduct an interesting study into the hemispheric symmetry (or lack thereof) between low-dimensional resting state functional connectivity gradients in key structures within the subcortex. In a large cohort of individuals, the authors demonstrate interesting asymmetries in the thalamus and pallidum, along with the cerebellum and striatum. They then survey a broad anatomical literature in search of a parsimonious explanation for their observed results.

Overall, I found the manuscript to be interesting, well-documented and well-reasoned. I have only minor comments that I hope will help the manuscript.

• My only slightly major concern is in the section titled 'Projection of subcortical functional gradients to cerebral cortex'. Specifically, I'm worried that multiplying each subcortical voxel by the absolute value of its eigenvalue may remove the effects of interest. For instance, in the raw eigenvalue, there is an interpretable (and important) difference between loadings of +1 and -1, however these two scores would be equivalent when the absolute value is taken. The authors mention that "Absolute functional gradient values were used in order to specifically observe the relationship between subcortical regions with strong IHFaS as indexed by asymmetric functional gradients and cerebral cortical connectivity", but I don't see how this follows.

• Is it perhaps surprising that there is strong IHFaS between first order thalamic regions but not between the cortical regions providing modulatory inputs to those regions?

• Do the authors predict that these patterns will be similar for task-based data analyses?

• The thalamic patterns appear to overlap with Ted Jones' concept of 'core' and 'matrix' thalamic nuclei (doi: 10.1016/s0166-2236(00)01922-6). Although these terms loosely overlap with 'first-order' and 'higher-order' thalamus, they are defined by the mode of thalamic projection to the cerebral cortex (targeted, granular vs. diffuse, supragranular, respectively), rather than the projection from cortex (as in the case of first- and higher-order).

• I couldn't find any information about whether the resting state fMRI data were filtered prior to the calculation of voxelwise cosine similarity. It could be interesting to determine whether the observed patterns are associated with broad-band patterns or more specific frequencies.

• The large sample size is a strength of the approach, but I did not see this leveraged anywhere in the manuscript. For instance, was there strong split-half reliability, or were some patterns more variable across subjects?

Reviewer #3:

This work started from the notion that Alzheimer's disease (AD) pathology spreads through connected regions, and investigated whether the level of AD pathology in specific regions relates to the integrity of the fiber bundles connecting them, in 126 elderly with normal cognition at risk of AD. Specifically, AD pathology was quantified by beta-amyloid (Aβ) and tau protein levels from positron emission tomography (PET). Three fiber bundles, the cingulum, the fornix, and the uncinate fasciculus, were a priori selected, and six measures were derived from free-water corrected diffusion tensor imaging. The authors hypothesized that Aβ levels would relate to the integrity of (i) the (anterior) cingulum, and (ii) the uncinate, and (iii) that tau levels would relate to fornix integrity. The direction of the relations was not specified. The authors find support for particularly the second hypothesis (Aβ levels and the uncinate), but also for the first (Aβ levels and anterior cingulum). They also find relations between tau levels and uncinate integrity, and Aβ levels and right fornix integrity. The relations were consistently in a direction the authors refer to as "unanticipated", that is, more restricted diffusion with the presence of pathology. The authors conclude that the result "suggests more restricted diffusion in bundles vulnerable to preclinical AD pathology”.

The work addresses important topics (early detection and spreading of AD pathology) of great interest to people from several disciplines. The sample is interesting with both regional Aβ and tau measurements, and the imaging processing methods used are advanced. The paper is clearly written and nicely illustrated.

My main concern relates to the main conclusion of "more restricted diffusion in bundles vulnerable to preclinical AD pathology". Although this result is discussed as "unanticipated", I think the centrality of this point makes more scrutiny warranted.

1) Direction of relationship. The authors state that "[..]the directionality of the observed pattern of association opposes the classical pattern of degeneration. The classical degeneration pattern accompanying disease progression is characterized by lower anisotropy and higher diffusivity, representing loss of coherence in the white matter microstructure with AD progression", and further: "[..] more restricted diffusion with the presence of pathology was unanticipated [..]".

Indeed, their results were unanticipated based on the literature, as highlighted by the authors. As this is the central point of the work, I believe it is important to do additional analyses to try and enlighten the results and the suggestion of a biphasic relation. I understand that the authors have done a lot of work already, but here are some fairly simple and not too time-consuming suggestions which might be informative (please feel free to ignore these suggestions and instead follow other paths to show the reader more results to evaluate the unexpected direction of the relations):

(i) A simple start could be to assess the relationship with age, how strong this relationship is, and what the residuals look like when regressing out age (and bundle volume).

(ii) As the authors mention, a reduction in crossing fibers might lead to "more restricted diffusion" but be a sign of deterioration. Analyses undertaken to assess this point would be valuable. For instance, one could test if the relations are similar in regions of the bundles where there are little crossing fibers and in regions with more crossing fibers.

(iii) The authors state that "[...] we estimated that 20% of the participants would be considered Aβ-positive". Were a majority of these also tau-positive? If so (or if participants exist in the larger PREVENT-AD sample that were not "cognitively normal at the time they underwent diffusion-weighted MRI»), creating a group of high AD pathology, is the relations between Aβ/tau and diffusivity similar in this group of high Aβ and tau compared to a similar-sized (and, if possible) age-matched group with (very) low Aβ and tau levels?

2) Hypotheses. As mentioned, the authors state in the Discussion that directionality of the observed pattern of association was unanticipated. I was therefore somewhat surprised that the directionally of the hypothesized relations were not included in the hypotheses presented in the Introduction. I think it would increase the readability of the Results section if this point was made explicit earlier in the text, and the non-expected direction mentioned in the Results.

3) Number of tests. The author state that "Associations with a p-value < 0.05 were considered significant, but we also report associations that would survive false-discovery rate (FDR) correction for each bundle with q-value of 0.05, accounting for 6 tests (i.e. the number of diffusion measures assessed per bundle).". I find this somewhat problematic (at least without further justification). First, I think the authors should only consider corrected p-values significant. Second, these 6 measures are tested per hemisphere, and across at least 3 fiber bundles (for cingulum, it seems the authors have done separate analyses for the anterior and posterior part), making the total number of tests higher. Correcting for the number of diffusion measures per bundle might be too strict, but I think the total number to correct for should be higher than 6. Whether any correction has been applied is also difficult to grasp while reading the Result section, as it seems like p-values are not FDR-corrected in Tables 2 and 3 (mentioned only in Table 4). I think the total number of bundles assessed, and the correction should be made explicit when introducing Figure 2 and Table 2.

Reviewer #3:

Carreño-Muñoz et. al. describe an piezoelectric sensor based approach to quantify rodent behavior. Piezoelectric sensors convert pressure, acceleration, strain, and even temperature and sound into an electoral charge. They are exquisitely sensitive and have a wide range of functionalities. The paper describes an open field arena that sits on top of three sensors on an air table that is able to detect animal movement. The authors use several behavioral paradigms and genetic models to validate their system. Overall, the piezo and pressure/force/vibration based systems have been well established for rodent behavior. Some examples of commercial systems are the Laboras (Metris BV) and PeizoSleep (Signal solutions), along with many papers that describe similar systems. The advantage of the system described in this paper (Phenotypix) is that it encompasses a large open field which allows the mouse to carry out naturalistic behavior. It also sits on top of an air table which allows more sensitive measurements. Although the system described has some advantages, the manuscript does not describe a system that leads to a significant enough advance. The manuscript does not offer a thorough solution for any one problem in biology and does not make a convincing case for adaptation of this platform. The figures and experimental description are also lacking leading to unclear interpretation of data.

One of the major issues with this paper is that it does not adequately describe the Phenotypix platform to allow for replication. This may be fine if the platform is commercially available, which seems to be the goal, but when I searched for the "Phenotypix, Roddata", I did not find a commercial supplier. Thus, it is unclear how this data can be replicated. Another major issue is that it is never clear if behavior state determination based on mechanoelectrical signal, video data, or both. Ideally, one would use the video data to train classifiers that only use the mechanoelectrical data. However, it is not clear that this was done in most of the experiments. Without the hardware specifications and classifiers for the behaviors, replicability is an issue. The fact that the apparatus needs to be place on a 250kg air table brings its practical utility and scalability into question. Systems such as Laboras can be obtained with readily available classifiers for numerous behaviors (https://www.metris.nl/en/products/laboras/laboras_specs/) and allow for long term monitoring in home cage environment and questions the claim of "A novel device for behavioural phenotyping of freely moving laboratory animals (rats and mice) now allows to detect behavioural components out of reach of existing systems."

One issue that is not addressed for the various behaviors - how does body weight affect the spectral properties of behaviors. How can we compare the same behavior between two animals of differing sizes? Since this is a pressure sensor, this is important.

Reviewer #3:

The work by Münch et al addresses an important problem of modeling data that originates from multiple channels (100s-1000s) by establishing a Bayesian inference-based framework to extend an existing Kalman filter-based method. They convincingly demonstrate that their approach is much more accurate at quantifying channels using previous, and is impressively able to combine multiple experimental modalities. Most importantly, as a Bayesian method, this approach allows the incorporation of prior information such as the diffusion limit or previous experiments, and also allows one to perform model selection to select the best kinetic model of the data (although this aspect is less developed). In particular, the Bayesian approach of this work is an important advance in the field.

1) The manuscript needs line editing and proofreading (e.g., on line 494, "Roa" should be "Rao"; missing an equals sign in equation 13). Additionally, in many paragraphs, several of the sentences are tangential and distract from communicating the message of the paper (e.g., line 55). Removing them will help to streamline the text, which is quite long.

2) Even more emphasis on the approximation of n(t) as being distributed according to a multivariate normal, and thus being continuous, should be placed in the main text. To my understanding, this limits the applicability of the method to data with > ~100s of channels; although the point is not investigated that I could find. In Fig. 3, it seems the method is only benchmarked to a lower limit of ~500 channels. Although an investigation of performance below that point would be interesting, it is only necessary to discuss the approximate lower bound cutoff.

3) The methods section should include information concerning the parameter initialization choices, HMC parameters (e.g. number of steps) and any burn-in period used in the analyses used in Figs. 3-6

4) In the section on priors, the entire part concerning the use of a beta distribution should be removed or replaced, because it is a probabilistic misrepresentation of the actual prior information that the authors claim to have in the manuscript text. The max-entropy prior derived for the situation described in the text (i.e., an unknown magnitude where you don't know any moments but do have upper and lower bounds; the latter could be from the length from the experiment) is actually P(x) = (ln(x{max}) - ln(x{min}))^{-1} * x^{-1}. I'm happy to discuss more with the authors.

5) Achieving the ability to rigorously perform model selection is a very impressive aspect of this work and a large contribution to the field. However, the manuscript offers too many solutions to performing that model selection itself along with a long discussion of the field (for instance, line 376-395 could be completely cut). Since probabilistic model selection is an entire area of study by itself, the authors do not need to present underdeveloped investigations of each of them in a paper on modeling channel data (e.g., of course WAIC out performs AIC. Why not cover BIC and WBIC?). The authors should pick one, and maybe write a second paper on the others instead of presenting non-rigorous comparisons (e.g., one kinetic scheme and set of parameters). As a side note, it is strange that the authors did not consider obtaining evidences or Bayes factors to directly perform Bayesian model selection - for instance, they could have used thermodynamic integration since they used MC to obtain posteriors anyway (c.f., Computing Bayes Factors Using Thermodynamic Integration by Lartillot and Philippe, Systematic Biology, 2006, 55(2), 195-207. DOI: 10.1080/10635150500433722)

Reviewer #3:

In this manuscript, Robert et al. demonstrated that medial SuM sends glutamatergic projections to the hippocampal CA2 region, and stimulation of these projections exert mixed excitatory and inhibitory responses in CA2 pyramidal neurons. Furthermore, they showed that SuM-CA2 circuits recruit local PV basket cells to provide feedforward inhibition to CA2 pyramidal cells, which increases the precision of action potential firing in conditions of low and high cholinergic tone. Finally, they performed in vivo electrophysiology recording to show that stimulation of SuM-CA2 projections can influence CA1 activity. Overall, this is a well-designed study, and the quality of the data is high. The authors performed an impressive amount of electrophysiology recording in acute slices and provided detailed information on how long-distance SuM projection neurons regulate CA2 pyramidal cell activity. These findings provide insights into how SuM activity directly acts on the local hippocampal circuit to modulate social memory encoding. However, there are some concerns that need to be addressed.

1) The authors performed CAV-based retrograde tracing and demonstrated that medial SuM sends glutamatergic projections to CA2. These results are in contrast to a recent study (Li et al, Elife 2020) showing that lateral SuM neurons send dense projections to both CA2 and DG, and the SuM-DG projections release both glutamate and GABA to dentate granule cells. Based on the results from this study and the study from Li et al. does that mean medial SuM neurons are different from lateral SuM neurons in terms of the neurotransmitters they release? The authors need to clarify this point and provide additional ephys data to show that pyramidal cells do not receive direct GABAergic inputs upon stimulation of SuM-CA2 projections using high-chloride internal solution to reveal the IPSCs.

2) The authors claim that SuM-CA2 circuits recruit local PV basket cells to provide feedforward inhibition to CA2 pyramidal cells. While the data presented are supportive, they are not entirely convincing. Specifically, MOR agonist DAMGO is not specific to PV BCs. Though DAMGO has a preferential effect on PV cells over CCK cells, other interneuron types have been shown to be sensitive to DAMGO manipulation. Therefore, these results are subject to alternative interpretation that other types of CA2 local interneurons may be involved. To show whether PV BCs is the sole interneuron subtype involved, the authors may use a P/Q type calcium channel blocker, ω-agatoxin-TK, as P/Q Ca2+ channels are unique to PV BCs. In addition, chemogenetic inhibition of PV BCs was used, but light-evoked IPSCs are not completely blocked. The authors claimed this could be due to partial silencing of PV BCs. However, there is no evidence showing the efficacy of 10µM CNO application in suppressing CA2 PV basket cell activity. These data should be provided in order to draw such conclusions.

3) CCK basket cells are known to excite PV basket cells (Lee et al 2011) via a pertussin-toxin sensitive pathway. Is it possible that SuM-CA2 mediated excitation of PV basket cells includes a CCK intermediary? This point should be discussed.

4) The in vivo recording data showed that SuM-CA2 circuit stimulation decreases the firing rate of CA1 pyramidal cells followed by increased firing rate in these cells. Then the authors performed slice recording and showed that the reduced firing rate of CA1 neurons in vivo is likely caused by increased inhibitory inputs onto CA1 pyramidal cells. Figure 7G-H seems to explain the reduced events in the first phase of the tetrode recordings, but not the rebound part. Is there some circuit component that is lost when making slices? Furthermore, what does SuM-CA2 circuit stimulation do to theta/gamma rhythms in CA1? These data should be available in the tetrode recordings.

Reviewer #3:

The paper titled: "Auditory detection is modulated by theta phase of silent lip movements" the authors investigate visual entrainment to lip movement using behavioral (exp1) and non-invasive physiology (EEG; exp2).

In the first experiment participants engage in the detection of a brief tone embedded in noise. Critically, the tone appears whilst subjects are viewing a silent movie clip. Tones are critically timed with respect to the phase of the theta rhythm prevalent in the lip action trajectory (and its relation to the original audio track). Each trial includes 0, 1 or 2 tones and subjects provide a speeded response when the tone is detected. Tones are also critically presented either during the first half of the clip or the second half of the clip (or both or neither). This latter timing parameter is designed to probe the possibility of an increasing degree of entrainment to visual lip movement as the clip evolves. In the second experiment the findings demonstrated in the exp 1 are met with an analysis of visual entrainment and its impact on auditory sources using EEG and source estimation on data obtained while observers viewed the same silent movie clips passively. The paper is well written, the premise is clear and the findings are interesting and timely. In what follows I outline some questions and concerns that come to mind when assessing the validity of the interpretation of the findings. Those span the experimental and stimulus design as well as the analysis choices made.

1) The behavioral procedure suggests that the tones were pseudo-randomly positioned w/ respect to the quantified theta phase of the lip movement. It would be interesting to understand whether any care was taken to exhaustively sample different phases of the phase of interest in the lip movement. It might be important, therefore to demonstrate that phases were equivalently sampled by chance in the first and second half trials and over the different clips. An inset in figure 1 would make for a good spot to demonstrate the descriptive statistics of target positioning (as a function of phase).

2) Second and somewhat related, wouldn't it make more sense to quantify accuracy based on phase bins? This way no division to subpopulation would be required since each individual could be aligned to their best phase. The methods leave it somewhat unclear whether this was a possibility in terms of the stimulus design (i.e., were there enough phases to accomplish this in the stimulus/tone timing; see previous point).

In addition the subject mean phase of the correctly detected target provides little insight as to the periodic nature of performance. Analyzing whether there is a periodic modulation of the pattern of responses over phase would provide richer, more nuanced evidence for the claims.

3) It would be important and interesting to learn whether the first and second part of the trial has the same MI profile at theta b/w lip movement and audio track. Currently, The characterization of MI was done on the whole movie clips. This is crucial for both Experiment 1 and Experiment 2 interpretation.

4) The distinction b/w the first and second half -- indicating that entrainment takes time to build up is somewhat overstated in the context of this paper seeing that the literature suggests that by 0.5 s entrainment is fully arrived at (among others -- the authors themselves say so in the TINs piece). Other processes such as calibration to a given speaker might take longer, and those might justify (or account for?) the result showing that early vs. late targets differ in the degree to which the phase of the lip action affects performance.

Important details over the stimuli need to be clarified:

5) Did every clip introduce a new speaker to the subject? Thus, time on cl cip also amounts to degree of familiarity with the speaker?

6) Did each clip have the same degree of MI b/w audio and lip movement or were there better (more pronounced) lip clips than others when considering their link to the audio? Would it make sense to add these measures as covariates in the analysis?

7) Is the same target timing used for the same clip for all subjects? Or are the tones truly randomly placed and matched onto clips such that a given clip could appear w/ tones at different times for different subjects?

At the risk of somewhat repeating point #2 above -- within the analysis the following should be considered:

8) The authors establish that in the second half performance there are, in fact, two subpopulations in the sample. Wouldn't this post hoc grouping factor, which isn't obviously motivated be better described by properly delineating performance as a function of phase? I can readily understand that the authors might not have a clear hypothesis over what might be the better phase for performing on an irrelevant tone probe. Nonetheless, if a periodic process is entraining performance once a best phase is identified adjacent phase bins should demonstrate this circular relationship. This would allow for a direct quantification of ALL data together after aligning performance to the best phase bin, per subject.

Finally, the following points pertain for most for the contextualization of this work and the discussion:

9) While the authors discuss at least two mechanisms relating to how entertainment affects growth by the second part of the clip, it would be nice to relate the concrete reading of this effect to cognitive processes that may evolve within these timescales. In other words, learning that tracking takes 0.5 s or learning that visual inputs to frontal cortex take a given time scale to exert impact on auditory sensory regions is another description of the finding. What might these time scales buy me as a speaker and as a listener? What processes might be reflected by arriving at these states of synchrony and top-down control for speech comprehension?

10) The post hoc description of the subpopulations preferred phases is interesting and could relate interestingly to the entertainment literature (from Spaak 2014 in vision through Hickok 2015 in audition and others). Might the authors speculate on what part of speech is characterized by one phase vs. another?

11) The author's conjecture in the discussion of this topic - an additional one - there are recent papers by Assaneo et al. (Poeppel as PI, Nat Neurosci, 2019) that show bi-modal behavior in a spontaneous synchronization task (motor to auditory), which was found to be related to morphological differences in frontal-to-auditory white matter pathways, functional differences AND better learning in a statistical learning paradigm. How do the two sets of bi-modal populations interact? The author's discussion of the motor cortex suggests they would.

Methods section:

The paper by and large is well written. An exception to this would be the methods section. Currently, the methods do not comply with best practices that would generate the work reproducible by others.

Reviewer #3:

In this manuscript, the authors test the long-standing and long overdue "evolution-on-demand" hypothesis of integrons. Using a combination of genetic construction work, experimental evolution, and WGS the authors present a convincing body of work favoring the presented hypothesis. The paper is clear, well written and the authors should be given credit for including experimental data from an integron containing clinical plasmid including resistance cassettes to the last resort antibiotics carbapenems. This is largely missing in the field.

My overall assessment of the manuscript is very positive. The "evolutionary ramp" approach is an elegant way to test the "evolution on demand" hypothesis and the authors provide compelling evidence favoring the evolutionary effects of an active class 1 integrase. However, reading through the manuscript I have three major questions/comments regarding the mechanistic aspects and conclusions of the paper. Regarding the last two points, I believe a slightly more balanced discussion including other possible explanations (such as experimental conditions) would add more balance to the Conclusion chapter and improve the manuscript.

Major Comments:

1) Based on WGS the authors characterize evolved populations and claim to demonstrate extensive integrase driven rearrangements in combination with chromosomal mutations underpinning the adaptations towards both constant sub-MIC and 2- fold increments of gentamicin concentrations.

My first concern regards the crucial control in Figure S2 where control PCRs confirm data from Illumina short read sequencing on whole populations. It is hard for me to follow and understand this figure. I suggest that a schematic figure of each combination of cassettes, primer positions, and expected band length combined with proper lane descriptions should be prepared.

2) Surprisingly, and contrasting integron structures from environmental and clinical samples, the authors provide evidence for a strong predominance of "copy and paste" as opposed to the emblematic "cut and paste" insertions of the gentamicin resistance cassette during experimental evolution. They argue that their data suggest that intI1 has a bias towards "copy and paste" cassette rearrangements.

First, I find the term "copy and paste" somewhat confusing. I cannot see that the underlying mechanism of cassette excision differs between the two outcomes in integron structure. The cassette is in both cases excised (cut) from the ancestral integron before it is inserted (paste) into either arrays. I may have missed something here- but why "copy" and how is this novel?

Second, I am not convinced that the presented evidence provides sufficient support for the proposed "copy and paste" bias of IntI1. As the authors discuss thoroughly, the presence of multiple copies of the ancestral structure provides more "ancestral" integration targets for the excised cassettes. The authors exclude the alternative hypothesis that a second copy of aadB increased fitness as compared to a single copy (as expected from copy and paste). Fitness effects of different arrays are discussed solely on the basis of retrospective analyses of populations that did not go extinct. I would have been more convinced if this was backed by some measure of fitness, for example MIC values of integron arrays containing two aadB cassettes. From Fig 1C it is not unlikely that it could be increased.

3) The authors highlight in the abstract and in the Conclusion section that they found no evidence of deleterious off-target integrase effects. They suggest that integrase activity, rather purge deleterious chromosomal mutations and enable more targeted beneficial adaptive responses.

The authors present cases where likely beneficial off target recombination events occurred. To what extent do the authors think the absence of deleterious off target effects is due to the experimental conditions (continuous increments in gentamicin concentrations combined with strong bottlenecks)?

Reviewer #3:

The authors showcase results from an experimental pipeline aiming at demonstrating how evolution of in vitro cancer models can be exploited to identify somatic genomic and structural variants associated with the emergence of drug resistance.

To this aim the authors unbiasedly selected 5 widely used chemotherapeutic agents via systematically treating the HAP-1 cell line with 16 different drugs then chose those yielding clinically compatible half-maximal effective concentrations. After generating stably resistant clones ) of the HAP-1 parental cell line, across a number of replicates (by culturing in sublethal doses of the selected compounds), the authors whole-exome/whole-genome sequenced their models and compared variants observed in the resistant clones versus those present in the parental line.

In this way the authors identified recurrent loss of function variants across replicates in the drug resistant clones per each drug and were able to reproduce the increase in drug resistance of the parental line by knocking out the genes found altered in the drug resistant clones or they were able to reproduce the same finding by pharmacologically inhibiting genes found to host gain-of-function mutations in the resistant clones. Thus highlighting a new potential target for combinatorial cancer therapy and chemosensitization.

Briefly, this is a nice piece of work showing for the first time that exploiting in vitro evolution paired with whole genome analysis for identifying targets for combinatorial therapy and elucidate the mechanisms involved in the emergence of drug resistance is practically feasible.

The experimental pipeline and the followup validation experiments are well thought and designed and outcomes convincingly support the authors' final claim. There are no arbitrary nor unjustified choices and the showcased platform seems to be robust enough.

I would like to see the following few points addressed/answered:

1) The authors focused only on chemotherapeutics while composing their initial search basin. Would considering also few targeted therapies worthy? or it is known that no effects would be exerted on HAP-1? This should be briefly mentioned.

2) The title of the manuscript can be improved: the authors are deconvolving genomic alterations whose acquisition is linked to the development of drug resistance, thus potential chemosensitising targets or targets for combinatorial therapies. This could be better reflected by the title. As it is now it reads like the main aim is to identify 'innate/intrinsic' targets/cancer-dependencies.

3) Mutagenesis experiments to identify mutations that are linked to the emergence of drug resistance might be mentioned in the introduction, and the following work cited: PMID: 28179366.

4) When mentioning the 'Genomics of Drug Sensitivity in Cancer' portal (www.cancerrxgene.org) the following two works (describing the online resource) should be cited: PMID: 23180760 and PMID: 27397505

5) Figure 1 nicely describes the experimental pipeline presented in this manuscript however it should be completed with a final panel or a couple of panels illustrating the genomic comparison between parental and drug resistant clones to identify SNV and CNV associated with drug resistance.

6) It is not clear what the numbers in the 'fennel' in figure S3A refer to. Resistant clones within an individual tested drug? individual resistant clone or overall cases? This should be specified.

7) As it is presented, Table 1 is not very informative/clear, I would replace it with a barplot.

Reviewer #3:

This work by Kilroy et al., is a nice study on the role of inactivity on DMD zebrafish and the beneficial impacts of neuromuscular electrical stimulation on muscle structure and function in these fish. The clinical presentation of muscular dystrophies is often variable which makes it difficult to predict the disease severity and progression. The key points of this work are (1) Same genetic defect could lead to phenotypic and functional variability (2) Inactivity in DMD deficiency worsens the disease progression in zebrafish (3) Neuromuscular electrical stimulation improves muscle structure and function. While this study summarizes these key points in a detailed manner, many of the mechanistic details leading to these observations are missing.

1) There have been many published natural history studies as well as longitudinal imaging studies performed in human DMD patients. How does phenotypic data in zebrafish compare with longitudinal phenotypic studies in human patients?

2) For data presented in figure 1: authors describe the birefringence phenotype in mild mutants as increased degeneration for three days and then increased regeneration. Could they provide any experimental evidence of "muscle regeneration" mentioned in this statement?. Similarly, they mention severe dmd mutant regenerated throughout this study, however, no experimental data is provided to support this statement. As myotome contains both normal and degenerating myofibers, could improvement in birefringence be a consequence of the growth of those normal myofibers vs regeneration of sick myofibers? The term regeneration has also been used later in NEMS studies and needs to be supplemented with the experimental evidence of regeneration.

3) DMD is caused by damage in sarcolemma and subsequent myofiber detachment. The authors didn't observe any effect on myofiber structure but still found reduced velocity in mutants that were subjected to intermittent inactivity. Could this be due to a slight increase in sarcolemma damage (not examined here) and/or changes in the calcium in muscle fibers? Similarly, what are the effects of extended inactivity on MTJ structure? While authors make good observations with their animal model (as also seen in human and other animal models previously), mechanistic details underlying these changes are lacking.

4) Authors show few transcripts in figure 10C that were restored to WT level in MT on eNMES treatment. What is the role of these genes in DMD pathology or muscle function? Why do authors think a change in these 5-6 genes out of several hundred genes is important?

5) While authors demonstrate proposed ECM modeling in response to eNMES, it will be helpful to present changes in ECM structure in response to eNMES treatment (EM or IF).

6) Previous studies in humans in other animal models have also shown that physical exertion or mild forms of exercise exacerbates the decline in muscle function in DMD deficiency. How are these results comparable to the previously published studies?

Reviewer #3:

Obstructive sleep apnea (OSA) is a common disease associated with intermittent hypoxia (IH) and is linked to health complications. The lung is the first organ to experience the IH and in this study Wu et al uses a mouse model of OSA to identify transcriptional changes in the lung as a whole organ. The authors then also use single cell RNA sequencing (scRNAseq) to further identify transcriptional changes in different cellular populations of the lung. The authors found changes in circadian and immune pathways and that endothelial cells in the lung specifically showed the greatest transcriptional changes. The data will be useful as a reference for the field in understanding transcriptional responses in lung cells exposed to IH.

scRNASeq is an exciting technique that has the potential to identify how different cell populations respond to a stimulus (in this case intermittent hypoxia). However, it provides an enormous amount of data which requires significant processing and interpretation. This paper contains a huge amount of data generated by scRNASeq, yet the actual data section is very short. Given the complexity of information obtained, I think it warrants a more detailed analysis in the results section and discussion. It would be helpful to me if the authors could distil the very large volumes of information into a more extensive discussion of their findings (particularly discussing the figures in more detail). Is the summary finding of this paper that early changes in hypoxia and circadian gene expression drive later disease in the lungs of OSA patients? The abstract seems to focus on hypoxia, circadian and immune changes but the data text section focuses very little on these pathways. More details on the figures shown and tying the figures to the results text would improve this paper and enable further interpretation by readers.

Reviewer #3:

The manuscript by Dr. Vlachos group has demonstrated many important features as well as mechanisms of RA-induced synaptic plasticity. For example, they demonstrated that RA-induced plasticity happens in human neurons as well as in rodent neurons in vivo; discovery that synapodin as a critical mediator of RA plasticity as well as RA effect on the size of spine head, synaptopodin cluster and spine apparatus. Moreover, the effect of RA on in vivo LTP plasticity is very interesting. The data looks solid and supports the authors' conclusions.

However the manuscript can be significantly improved by discussion of their results, in the context with literature data as well as explaining the possible mechanism of their results.

1) RA effect on AMPAR upregulation has been reported to not share the same SNARE mechanisms as electrical LTP (Synt1/7 independent vs dependent). How does RA have the extra effect on the LTP amplitude? Moreover, RA plasticity is recognized as a form of homeostatic synaptic plasticity, i.e., the effect takes hours to develop as shown by the authors of RA incubation of many hours in their experiment on human neurons. How does this compare with their RA manipulations in LTP exp (Is TA injected shortly before LTP stimulus? What do the author think that LTP stimulus does to RA signaling?)?

What about metaplasticity involves RA? any connections to the present study?

2) The authors conclude that RA have effects on spines with or without spine apparatus, however, the authors' data suggest that RA-plasticity is blocked when spine apparatus is eliminated (with synaptopodin KO). Moreover, there is significant overlap of spine size for spines with or without spine apparatus... How do the authors interpret their results here? Is spine apparatus dynamic? can floating between spines quickly? Any literature on this? The authors need to discuss more on the possible ways, with supporting literature data, of how this spine apparatus can affect RA function.

In short, a discussion of the above points will add significance to the study.

Reviewer #3:

In this manuscript, Sachella et al examine the contributions of the lateral habenula (LHb) to fear conditioning. They use 3 different paradigms: (1) a contextual fear conditioning paradigm, (2) a cued fear conditioning paradigm, (3) a combination paradigm where both context and cues can predict shocks. They also manipulate the LHb in several ways: (1) using muscimol, (2) using inhibitory optogenetics, (3) using excitatory optogenetics. The results are thought-provoking and would represent a novel contribution to the field, but I am left confused about some of the major points. My suggestions for improvement/clarification of the manuscript are as follows:

Major Comments:

1) Some important points need to be brought up in the introduction in order to frame the problem the authors are addressing and motivate the study. First, the introduction needs more background on separate circuits controlling cued vs contextual fear conditioning (hippocampus, amygdala). This only comes up in the discussion. Readers also need more background on connections between known structures for fear conditioning and the LHb. There should also be explicit discussion of the well characterized connections between LHb and dopamine neurons, including how LHb inputs help generate reward prediction errors that may be important for fear conditioning. The idea that prediction errors contribute to the authors' observations could be foreshadowed here.

2) In general, the muscimol experiments are nicely done. However, muscimol is always administered during training. I am left wondering whether LHb activity is required during the initial learning of the association or for consolidation later. It would be ideal to also include a test of muscimol infusion immediately following the FC training, during a memory consolidation period. This is important because the authors at times seem to argue that the LHb is important specifically for memory consolidation, but later in the discussion claim that activity during the training (related to prediction errors) is an explanation for their results.

3) I'm struggling with the interpretation of the experiments in Figures 3 + 4 using the cue + context FC paradigm and talking about "reconsolidation." These are really key to the paper so making sure the experiments are clear is a must. From the cue + context test, it seems that having both cues + contexts available for memory provides a much stronger memory. I am uncertain about why the authors think this is so and whether the effect is independent of the LHb? For the "reconsolidation" experiment, I can't figure out what's new. The no-reconsolidation group should look like Figure 2 muscimol group, and it mostly does. The reconsolidation group should look like the Figure 3 muscimol group, and it mostly does. So this looks to me more like a replication of Figures 2+3 (with no vehicle control) than anything else. What did we learn that could not be learned from the experiments in Figures 1-3? The suggestion is that "FC training under inactivation of the LHb creates a cued memory whose retrieval depends on contextual information." (lines 154-155). I don't disagree with this interpretation necessarily but it seems vague, and there is no circuit-level insight as to the mechanism.

4) The ArchT experiments, as the authors already recognize, are problematic because of potential heating and other artifacts. 25s of continuous 10mW green light is a lot. I am not left with much confidence in interpreting these experiments and therefore I am not sure why they are included in the paper. There are other methods of optogenetic inhibition that would be better suited perhaps, or the results could be replicated with chemogenetics, where the authors could ensure DREADD viruses did not spread into the medial habenula.

5) The oChIEF experiments are interesting, but again very difficult to interpret. There is no data showing what the stimulation does to LHb firing, which is a concern given the very long light stimulation (through the whole experiment). Therefore, it is unclear whether the authors' hypothesis that the light stimulation interferes with normal function is correct. The design here also does not take advantage of the temporal precision of optogenetics.

Reviewer #3:

Verhelst and colleagues presented an interesting work about fibre-specific laterality of white matter in left and right language dominant people. A new fixel-based approach was used. Two main results were reported. First, extensive areas of significant lateralization were found in white matter, and second, a cluster of fixels in the forceps minor showed significant differences between people with the left and right language dominance, but no differences were found in other white matter tracts, including the arcuate fasciculus, which is sometimes considered to be relevant to the language lateralization.

The authors suggest that the lateralization of language functioning and the arcuate fasciculus are driven by independent biases, and that the relationship between forceps minor asymmetry and language dominance could be of interest.

1) Arguments against traditional fiber tractography and DTI-derived metrics. I agree with the authors that it is a great advantage of the fixel-based approach to investigate fiber-specific effects. But some arguments in the current paper seem to be misleading, and are not very convincing. For example, The authors wrote that "it has been established that streamline counts from fibre tractography do not represent an appropriate metric to quantify white matter connectivity (Jones et al., 2013)." In some rare cases, this could be correct, but I don't know robust evidence that could support this absolute statement. No empirical data was found in either the present paper or the cited paper, and relevant discussions were mainly on the usage of the term e.g., 'streamline count' and data interpretation. It may be still fair to assume a monotonic relationship between 'streamline counts' and the actual white matter connectivity. The authors may want to further clarify this point.

A similar problem exists for the argument against DTI-derived metrics. It reads like that we should never use DTI-derived metrics in future studies as 'crossing fibers' widely exist in the brain, and that the DTI model could not provide (as) 'reliable and informative results' (as the fixel-based method). My understanding is that these different approaches/metrics could reflect complementary aspects of white matter fibers. I didn't find relevant data or discussions e.g., about the relationship between DTI-derived metrics and the three metrics in the fixel-based analysis (i.e., FD, FC, and FDC). Actually, if the DTI-derived metrics could reflect unique aspects of white matter, the non-significant results in FD, FC and FDC (e.g., in the arcuate fasciculus) could not simply suggest that no differences in every aspect of one white matter tract. Let alone that there are many other metrics that describe regional properties of white matter. Even so, the authors suggested independent biases repeatedly in the text based on the non-significant results in the arcuate fasciculus.

In addition, it reads strange that, while traditional approaches were simply considered not useful in the Introduction, in the Discussion the consistency with previous results based on these traditional approaches was used to support the current findings. This makes me curious what unique information we could get from the fixel-based approach. Each metric has its own advantages and limitations. I agree that the fixel-based approach could provide great advantage in describing fiber-specific effects. A fair discussion is better for readers to understand the results.

2) Arguments against traditional laterality index. The authors spent several paragraphs to support their proposed log-ratio laterality index. Their main point against the traditional laterality index is that the traditional index lacks additivity property. While I agree that the log-ratio is a potential approach for laterality studies, it seems that such an additivity property is not necessary for the laterality index. The main reference cited is an old paper from Tornqvist et al., (1985), which focused on relative changes, rather than laterality. In this reference, a relative change index H is considered as additive if and only if H(z/x) = H(y/x)+H(z/y) in a two-stage change: x-->y-->z. But for laterality study, it seems not to be in this case. Only left (i.e., x) and right (i.e., y) quantities are used for characterizing laterality, but without the third quantity (i.e., z). The additivity property seems to be meaningless in the context of laterality calculation. Further clarification is needed.

In addition, the authors mentioned that the traditional laterality index is 'bounded and therefore lacks the additivity property'. The authors may want to further explain the reasoning behind this statement.

Finally, although a non-linear relationship between the log-ratio index and the traditional index was showed in the Appendix X, but within the commonly observed range of laterality effect size (i.e., from -0.5 to 0.5 based on the results from this paper), the relationship is almost linear (see Figure 5). Particularly for the most widely used formula (R-L)/((R+L)/2), the results are almost identical to the log-ratio values. Based on this, I guess that if the authors used this traditional laterality index, they would get exactly the same results.

The traditional laterality index e.g., (R-L)/((R+L)/2) is widely used, which also makes results comparable across studies. This further makes me doubt the necessity of promoting a new laterality index while it does not provide additional information. Back to the beginning, my comments were based on the assumption that the additivity issue is not a problem for laterality studies. The authors may want to clarify.

Reviewer #3:

This manuscript presents data in support of a model whereby neurons harboring a YAC bearing 128 CAG repeats of the Huntingtin protein show disrupted Ca2+ handling via the endoplasmic reticulum in axons and nerve terminals. Unfortunately, my enthusiasm for the manuscript is relatively low for the following reasons:

1) It is unclear at this point whether YAC-based models are really appropriate since they lack the appropriate genomic control of transcription. This may be why for example one of the stronger phenotypes, the increase in mEPSC frequency, is greatest at DIV14 and diminishes some by DIV18 and is absent by Div21. This of course is not the same trajectory of the disease impairment itself. The authors speculate that the reversal of the phenomenology with older cultures may be from degeneration but there is no data to back up this claim. There seems little reason at this point in time not to use HD knockin mice.

2) The analysis for synapse "density" (Supplement) was only carried out at Div18, a time point where the impact of the YAC is already diminished. Unfortunately, the high degree of variability associated with measuring all possible puncta on a dendrite is not likely to easily uncover what amounts to a ~30% change in mEPSC frequency. I am not convinced therefore that the data in figure 1 cannot be explained in part by synapse density.

3) The underlying physiological perturbations driven by the YAC are deciphered almost entirely using pharmacological approaches, many of which are in themselves ambiguous in interpretation. Ryanodine is a complex drug as it potentiates receptors at low doses and blocks at higher doses. Confounding all of this is the fact that the literature has incubation times that span tens of minutes to hours (and not specified in this manuscript). I was disappointed that the authors did not at least repeat the pharmacology experiments with different aged neurons (DIV14, 18, 21). If disrupted ER Ca or RyR function lies at the basis for the change in spontaneous exocytosis, the pharmacology experiments should at the very least track this phenomenology. Similarly high/inhibiting doses of ryanodine should presumably lead to opposite effects, and this at the very minimum should have been done in the control and YAC neurons.

4) The reported changes in resting Ca2+ are highly suspect. The use of ionomycin should drive the sensor to saturation, and then from the saturated value and knowledge of the dynamic range of the probe, affinity constant, and the Hill coefficient, one can extrapolate back to what the resting concentration is. This has been done with GCaMPs in the past and predicts resting values in the 100-150 nM range (in broad agreement with many previous Ca measurements in live cells). In the experiments here the ionomycin never convincingly reaches saturation, as the response merely rises and recovers making the data uninterpretable.

5) The central problem with the approach here is that there is a lot of inference with what happens to ER Ca2+ in the YAC cells but no direct measurements were made. There are a number of genetically-encoded probes that have been used in the last 5 years to examine the ER Ca in neurons (CEPA1ER, ER-GCCaMP-150, D1ER), and experiments using one of these probes should be done to inform the science here.

6) The experiments claiming suppression of AP-evoked release are very difficult to interpret as there is no control over the stimulus itself. The authors simply rely on removing TTX to let APs fire randomly, something that will be driven significantly by network density, synaptic connectivity, and the balance of excitatory versus inhibitory drive in the cultures. The authors should simply study evoked release by stimulating the neurons expressing physin-GCaMP6m directly and examining the response sizes in YAC versus control neurons.

7) iGlusnFr is a potentially powerful tool to assess glutamate release, but to be interpretable it too needs to be treated in a quantitative fashion. The size of the signal will be proportional to the fraction of GlusnFr present on the cell surface and the amount of glutamate released. If for some reason expression of the CAG repeat led to a smaller fraction of expressed sensor reaching the surface of the neuron, this would artificially lead to changes in apparent DF/F. In order to use this probe in an interpretable fashion the authors need to carry out experiments whereby they correct for the surface fraction of the probe across experiments.

As it stands, this manuscript reports largely hard to interpret phenomenology owing to the narrow tool kit they have applied to the problem (mostly pharmacology and inference).

Other important details:

• There is no mention in the methods (or anywhere else) regarding the temperature of the experiments.
• A more meaningful graphical representation would be showing median +/- IQR rather than mean +/- SD.
• It would be helpful to show the effects of inhibition of RyR on WT (confirm ability to decrease mEPSC by inhibiting RyR) and YAC128 (additional proof that RyR contributes to YAC128 pathology).
• The data on single bouton physin-GCaMP6m need to be extracted for all boutons and then reported as fraction of boutons showing the fluctuations. As it stands, it is unclear if there is a selection bias.
• What was the percentage decrease in iGluSnFr signal at the last time point?

Reviewer #3:

The authors have conducted a very challenging study. The paper is clearly written and the topic of neural function under anesthesia is interesting. However, a significant limitation is that many of the analyses presented here do not provide clear insights into the processes the authors are studying.

-A key issue is that the authors aim to predict who is more or less sensitive to general anesthesia. However, each individual subject was given a different target plasma concentration of propofol, based on clinical scoring. So any difference in behavior may reflect different dosing rather than different behavioral sensitivity to a particular drug concentration.

-The interpretation of increased functional connectivity is challenging in the context of anesthesia, which modulates vessel dilation and systemic physiology. These analyses would benefit from additional information about the fMRI signal characteristics, e.g. amplitude and physiological signals.

-Fig. 3 is used to portray comparisons of wakefulness vs. sedation, implied in the text, but does not include direct statistical tests of the difference between the two conditions, and contrasting p<0.05 with p>0.05 does not indicate a significant difference. The suggestion of reduced cortical responses to auditory stimuli makes sense given that the participants are sedated, but the analysis does not seem to provide information about which aspect of auditory processing is modulated by sedation.

-The statements about response time not being mediated by age may reflect an underpowered study, as age is a strong modulator of anesthetic sensitivity and one group has an n=6.

-While many interesting MRI studies can be done with quite small n, depending on the question being asked (e.g. Midnight Scan Club, high-resolution individual studies), this study aims to conduct structure-based predictions of individual differences in behavior. This type of analysis requires more than the n=6 slow responders used for Fig. 5, as there are many other features that likely vary in a group this small. I appreciate that the authors have conducted a very challenging study, and it is not easy to collect more data, but while many interesting analyses can be done on this type of data, this is not an appropriate sample size for assessing GMV-individual differences associations. Larger samples sizes or within-subjects analyses are needed for robust GMV effects.

-Cluster correction method in 'Analyses of fMRI data' should be specified (and checked, Eklund et al.). The precise statistical method used to assess FDR corrected activity correlations with individual subject response times is not clear; it seems that the ANOVA resulted in non-significant results that are nevertheless being reported as differences using Hedges d?

-The presented evidence does not sufficiently support the authors' conclusion that they "provided very strong evidence that individual differences in responsiveness under moderate anaesthesia related to inherent differences in brain function and structure within the executive control network, which can be predicted prior to sedation.". I would commend the authors on their interesting and challenging experiment, and recommend refocusing the analyses.

Reviewer #3:

Jack and colleagues report that SARS-CoV-2 interacts with RNA to form phase-separated liquid compartments, similar to P bodies and nucleoli, shown here as blobs. The authors then perturbed the system in numerous ways, showing that: i) different nucleic acids give rise to different blobs; ii) that protein cross-linking and mass spec suggests that the phase-separated N is in a different tertiary or quaternary conformation than the soluble N; iii) that some N domains (e.g., PLD, R2) are important for blob formation, particularly when the protein is phosphorylated (by an unknown kinase); and iv) some small molecules can affect the number and size of the blobs. Overall, this story is at a very early stage phenomenology and lacks clear demonstration of physiological relevance. Certainly, the claim that "nilotinib disrupts the association of the N protein into higher order structures in vivo and could serve as a potential drug candidate against packaging of SARS-CoV-2 virus [sic] in host cells" ought to be tested - it would be easy enough to do, though I don't think this would complete the story.

Major comments:

1) Figure 1 is difficult to interpret with the information provided. In panel A, the colors seem to be important, but readers are not given a clue as to what. In panel B, how were the Y axes calculated? What are we really looking at in Figs. 1C and D? Were these on glass slides? Plastic? Was the surface coated, passivized, or otherwise derivatized in any way? What kind of microscope was used? What do the white signals (blobs) come from? Is there a fluorescent label involved? Is this phase contrast? In panel D, please include a buffer only control (no protein) to demonstrate blobs are not simply a buffer artefact. Finally, what N:RNA molar ratios were used in this Figure?

2) For the polymeric RNAs, what were the average chain lengths?

3) In describing Figure 1, the authors state "The shapes of these asymmetric structures were consistent with remodeling of vRNPs into 'beads on a string', as observed by cryoEM." This is wishful thinking. I see blobs of different shapes, but there is no way to know whether these represent N protein "beads" on RNA "strings." Reference 6 cited in the manuscript and showing "beads on a string" model has a scale bar of 50 nm = 0.05 µm, and even there, the N:RNA complex is very obscure.

4) My greatest concern of this work is that no information was provided about the N protein that was used for the in vitro studies. How pure was it? What steps were taken to remove co-purifying nucleic acids? Was it monodisperse? Aggregated? Please include DLS data and show silver stained SDS-PAGE.

5) Similarly, how did the mutant forms of N (Fig. 3A) behave? Were they properly folded? Did the authors check them by CD or SEC? And what concentrations of mutant proteins were used? Without these data, the rest of Fig. 3 is uninterpretable.

1. B. Could the authors please explain what the numbers on the Y axis are and how they were calculated. Also, their disorder prediction predicts dimerization regions to be highly disordered, would they consider a problem with the prediction method?

7) C, D, E what is the N: RNA molar ratio?

8) Could the authors please explain the calculation method used to calculate the % surface area covered by droplets?

9) Fig, 4A and B. Why is [N] so low? In other experiments the authors usually used 18.5 µM, whereas here the concentration was 7.8 µM, almost invisible blobs as observed in other figures provided by the authors (and below ksat, or very close to it).

10) Fig, 4C. What is 1.5 M N RNA? [N] is set to 57.6 µM, much higher than in Fig. 4 A-B assays. Is there a reason?

11) Fig. 4D is missing control cells transfected with GFP only (no N).

Reviewer #3:

The Aizenman lab has previously demonstrated the utility of Xenopus tectum as a model to examine neuronal, circuit and behavioral manifestations of VPA treatment, a teratogen associated with autism spectrum disorder in humans. In Gore et al., they demonstrate that the deficits induced by VPA treatment, including enhanced spontaneous and evoked neuronal activity, are blocked by pharmacological or morpholino based inhibition of MMP9. Inhibition of MMP9 also reverses the effects of VPA treatment on seizure susceptibility and the startle habituation response. Over-expression of MMP9 pheno-copies the effect of VPA, and inhibition of MMP9 in single tectal neuronal blocks the expression of experience-dependent structural plasticity. The results are convincing and add mechanistic insight into circuit and behavioral dysfunction induced by VPA signaling, as well as an expansion of the repertoire of plasticity mediated by MMP9 signaling.

Minor points:

-The time course for the introduction of VPA and MMP9 inhibitors should be reiterated in the results section.

-Fig 1 Please report the number (or %) of tectal neurons in which MMP9 was over-expressed following whole-brain electroporation.

-Does MMP9 transfection change the E/I ratio, as previously reported for VPA?

-Does VPA or MMP9 inhibition change the initial large amplitude/short latency evoked response?

Figure 2: please report statistics for total number of barrages or barrage distribution across experimental groups (latter also for Fig 3).

Figs 3 and 5: The presentation of the immunoblots should clarify if raw or normalized (to Ponceau Blue) data were quantified.

Fig 4: Please report a post hoc comparison following the repeated measures ANOVA

Fig 5: Total growth and growth rates could also be included in the results section.

Minor comments: -The discussion considers a broad range of potential targets of MMP9, including cell surface receptors, growth factors, adhesive proteins, and extracellular matrix components, many of these are left out of the abstract and introduction.

-The statement of page 6 "Increased synaptic transmission observed in MMP9 over-expression tectal neurons is consistent with dysfunctional synaptic pruning" appears at odds with a body of literature in mouse hippocampus, included many papers cited in the discussion, demonstrating the role of MMP9 in spine elongation, synaptic potentiation and synapse maturation.

Reviewer #3:

Lang and col. used mouse models to address the impact of the light and dark cycle and of myeloid conditional knockout of BMAL1 and CLOCK in susceptibility to endotoxemia. As expected, mortality rate increased in animals housed in constant darkness (DD). The mortality rate remains dependent on the circadian time in DD mice and, more intriguingly, independent on myeloid BMAL1 and CLOCK, with persistent circadian cytokine expression but loss of circadian leukocyte count fluctuations. The study is mainly descriptive without mechanistic explanation, which leaves the reader a bit frustrated.

1) Please revise the result section and the legends (for example legends of Figures 3 and 5) to explicitly mention whether experiments with conditional knockouts were performed with LD or DD mice.

2) Line 15 and 80. Saying that DD mice show a "three-fold increased susceptibility to LPS" is true for very specific conditions only, and should not be used as a general statement.

3) Line 99-. Please be more precise in describing cytokine levels (for example, in LD, TNF peaks at ZT10, IL-18 at ZT14 or ZT22 but not ZT18, and IL-10 but not IL-12 peaks at ZT14).

4) Line 105-106. Referring to Figure 1E, it is not straightforward for the reader to understand what is meant by "free-running and entrained" conditions.

5) Figure 2C and 3G. There is a substantial decreased mortality in LysM-Cre+/+ versus WT mice. Any explanation?

6) Figure 5 depicts a protocol with LD and DD mice. Yet, it seems that only DD mice were analyzed. Is that correct? LD mice should be analyzed in parallel as controls.

7) Figure 5 and Sup Figure 5. There are huge differences in leukocytes counts between LysM-Cre+/+ and WT mice. Without being exhaustive, LysM-Cre+/+ display much more macrophages in bone marrow, spleen and lymph nodes, DCs in lymph nodes, NK cells in spleen and lymph nodes at both CT8 and CT20. This is very puzzling and questions about the pertinence of these "control" mice. Additionally, one might expect from these observations that LysM-Cre+/+ mice are more sensitive to endotoxemia, which is not the case (point 5).

8) Line 257. The effect of IL-18 is not totally surprising, since both detrimental and protective effects of the cytokine have been reported in the literature. This could be briefly mentioned.

9) Sup Figure 5A. The gating strategy has to be shown for each organ, separately.

10) Sup Figure 5D. The peritoneal cavity contains not only different macrophage populations with different inflammatory properties, but also different B cell populations including anti-inflammatory B-1a cells (plus NK cells, DCs...). Considering that LPS is injected i.p., more thorough analyses of the peritoneal cavity should be performed to properly interpret results of cytokine and mortality.

11) It is not clear whether endotoxemia was addressed with BMAL1 and CLOCK myeloid conditional knockout mice kept LD. Since time-of-day dependent differences in mortality were much less in DD mice (line 74), we probably expect only marginal differences in DD mice.

Reviewer #3:

This work provides a computational model to explain the change of grid cell firing field structure due to changes in environmental features. It starts from a framework in which self-motion information and those related to external sensory cues are integrated for position estimation. To implement this theoretical modeling framework, it examines grid cell firing as a position estimate, which is derived from place cell firing representing sensory inputs and noisy, self-motion inputs. Then, it adapts this model to explain experimental findings in which the environment partially changed. For example, the rescaling of an environment leads to a disruption of this estimation because the sensory cue and self-motion information misalign. Accordingly, the model describes mechanisms through which the grid cell position estimate is updated when self-motion and hippocampal sensory inputs misalign in this situation. The work also suggests that coordinated replay between hippocampal place cells and entorhinal grid cells provide means to realign the sensory and self-motion cues for accurate position prediction. Probably the strongest achievement of this work is that it developed a biology-based Bayesian inference approach to optimally use both sensory and self-motion information for accurate position estimation. Accordingly, these findings could be useful in related machine learning fields.

Major comment:

The work seems to provide a significant advance in computational neuroscience with possible implications to machine learning using brain-derived principles. The major weakness, however, is that it is not written in a way that the majority of neuroscientists (who do not work in this immediate computational field) could benefit from. It often does not explain why/how it came to some conclusions or what those conclusions actually mean - for example, right in the introduction, "This process can also be viewed as an embedding of sensory experience within a low-dimensional manifold (in this case, 2D space), as observed of place cells during sleep". It also does not provide a sufficiently detailed qualitative explanation of the mathematical formulations or what the model actually does at a given condition. So my recommendation would be to carefully rewrite the work to make it readable for a wider audience. I also fear that the work also assumes significant a priori neuroscience information, so people in machine learning fields would not benefit from this work in its current form either.

It is not clear why place cell input was chosen as sensory input. Place cells also alter their firing with geometry, sensory and contextual changes. Although grid cells require place cell input, place cell firing represents more than just sensory inputs. In fact, they may be more sensitive to non-sensory behavioral, contextual changes than grid cells. Moreover, like grid cells, they are sensitive to self-motion inputs, e.g., speed-sensitivity and, at least in virtual environments, head-direction sensitivity. This point would deserve a detailed discussion.

Reviewer #3:

The authors combine minimal and detailed models of hippocampal theta rhythm generation to understand the underlying mechanisms at the cellular-network level. In their 3 steps approach, they extend previous minimal models, they compare these minimal models with more detailed models and they use a piece (segment) of the detailed model to compare it to the minimal models.

I have a number of methodological issues with the paper. First, both models should be validated against experimental evidence given that the experimental results exist. The validation of a "minimal" model with data from another model is circumstantial and useful to link two models, but in no way is a scientific validation, in my opinion. Second, the model reduction arguments are simply taken as a piece of a large model. This is in now way a systematic reduction, which the authors should provide. In the absence of that, the two models are simply two different models. Third, it is not clear what aspects of the mechanisms cannot be investigated using the larger models that require the reduced models, given that the models do not necessarily match. Fourth, the concept of a minimal model should be clearly explained. They used caricature (toy) models of (2D quadratic models, aka Izhikevich models) combined with biophysically plausible descriptions of synapses. The model parameters in 2D quadratic models are not biophysical as the authors acknowledge, but they can be related to biophysical parameters through the specific equations provided in Rotstein (JCNS, 2015) and Turquist & Rotstein (Encyclopedia of Computational Neuroscience, 2018). In fact, they can represent either h-currents or M-currents. I suggest the authors determine this from these references. In this framework, the dynamics would result from a combination of these currents and persistent sodium or fast (transient) sodium activation. Fifth, from the original paper (Ferguson et al., 2017) their minimal model has 500 PV and 10000 PYR cells (I couldn't find the number of PV cells in this paper, but I assumed they were as in the original paper). This is not what I would call a minimal model. It is minimal only in comparison with the more detailed model. While this is a matter of semantics, it should be clarified since there are other minimal model approaches in the literature (e.g., Kopell group, Erdi group). Related to these models, it is typically assumed that the relationship between PYR to PV is 5/1. This is certainly not holy, but seems to have been validated. Here it is 20/1. Is there any reason for that? Sixth, the networks are so big that it is very difficult to gain some profound insight. What is it about the large networks and their contribution to the generation of theta activity that cannot be learned from "more minimal" networks?

Because of these concerns and the development of the paper, I believe the paper is about the comparison between two existing models that the authors have constructed in the past and the parameter exploration of these models.

I find the paper extremely difficult to read. It is not about the narrative, but about the organization of the results and the lack (or scarcity) of clear statements. I can't seem to be able to easily extract the principles that emerge from the analysis. There are a big number of cases and data, but what do we get out of that? Perhaps creating "telling titles" for each section/subsection would help, where the main result is the title of the section/subsection. I also find an issue with the acronyms. One has to keep track of numbers, cases, acronyms (N, B), etc. All that gets in the way of the understanding. I believe figures would help.

Another confusing issue in the paper is the use of the concept of "building blocks". I am not opposed to the use of these words, on the contrary. But building blocks are typically associated with the model structure (e.g., currents in a neuron, neurons in a network). PIR, SFA and Rheo are a different type of building blocks, which I would call "functional building blocks". They are building blocks in a functional world of model behavior, but not in the world of modeling components. For example, PIR can be instantiated by different combinations of ionic currents receiving inhibitory inputs. Also, the definitions of the building blocks and how they are quantified should be clearly stated in a separate section or subsection.

I disagree with the authors' statement in lines 214-216, related to Fig. 4. They claim that "From them, we can say that the PYR cell firing does not speci1cally occur because of their IPSCs, as spiking can occur before or just after its IPSCs." Figure 4 (top, left panel) suggests the opposite, but instead of being a PIR mechanism, it is a "building-up" of the "adaptation" current in the PYR cell. (By "adaptation" current I mean the current corresponding to the second variable in the model. If this variable were the gating variable of the h-current, it would be the same type of mechanism suggested in Rotstein et al. (2005) and in the models presented in Stark et al. (2013).) The mechanism operates as follow: the first PV-spike (not shown in the figure) causes a rebound, which is not strong enough to produce a PYR spike before a new PV spike occurs (the first in the figure), this second PV-spike causes a stronger rebound (it is super clear in the figure), which is still not strong enough to produce a PYR-spike before the new PV-spike arrives, this third PV spike produces a still stronger rebound, which now causes a PYR spike. The fact that this PYR spike occurs before the PV spike is not indicative of the authors' conclusions, but quite the opposite.

The authors should check whether the mechanistic hypothesis I just described, which is consistent with Fig. 4 (top, left panel), is also consist with the rest of the panels and, more generally, with their modeling results and the experimental data and whether it is general and, if not, what are the conditions under which it is. If my hypothesis ends up not being proven, then they should come up with an alternative hypothesis. The condition the authors' state about the parameter "b" and PIR is not necessarily general. PIR and other phenomena are typically controlled by the combined effect of more than one parameter. As it stands, their basic assumption behind the PRC is not necessarily valid.

The subsequent hypothesis (about PYR bursting) is called to question in view of the previous comments. The experimental data should be able to provide an answer.

The authors' should provide a more detailed explanation and justification for the presence of an inhibitory "bolus". What would the timescale be? Again, the data should provide evidence of that. In their discussion about the PRC, the authors essentially conclude what they hypothesis, but this conclusion is based on the "bolus" idea. The validity of this should be revised.

The discussion about degeneracy of the theta rhythm generation is interesting. However, because of the size and complexity of the models, this degeneracy is expected. Their minimal modeling approach does not help in shedding any additional light. In addition, the authors' do not discuss the intrinsic sources of degeneracy and how they interact with the intrinsic ones.

The last two sections were difficult to follow and I found them anecdotal. I was expecting a deeper mechanistic analysis. However, I have to acknowledge that because of my difficulty in following the paper, I might have missed important issues.

The discussion is extensive, exhaustive and interesting. But it is not clear how the paper results are integrated in this big picture, except for a number of generic statements.

The proposal that the hippocampus has the circuitry to produce theta oscillations without the need of medial septum input has been proposed before by Gillies et. (2003) and the models in Rotstein et al. (2005) and Orban et al. (2005). But the idea from this work is not that the hippocampus (CA1) is a pacemaker, but rather what we now call a "resonator". To claim that the MS is simply an amplificatory of an existing oscillator is against the existing evidence.

Reviewer #3:

The study by Jackson et al. characterizes the progression of the degeneration of axons and dendrites, including metrics on density and dynamics of dendritic spines and terminaux boutons (TBs), in the rTg4510 transgenic mouse model. The authors describe a decrease in the density of both structures, spines and TBs, as well as degeneration of neurites. Repression of the expression of the mutated version of tau was able to partially mitigate some of the negative effects observed in the non-repressed condition. When degeneration of the neuronal process was observed, the loss of a dendritic branch was preceded by a sharp increase in the loss of dendritic spines, while axonal loss was preceded by a long-lasting and progressive loss of TBs. While the findings are interesting, there are several concerns that dampened the enthusiasm on the study:

1) The data obtained with the rTg4510 mouse model must be very carefully interpreted given that the disruption of the endogenous gene Fgf14 that occurs in this mouse model contributes significantly to the neurodegenerative phenotype (Gamache et al., 2019). While the authors acknowledge the possibility that genetic factors other than tau hyperphosphorylation may contribute to the rTg4510 pathology, the results must be put into the perspective of the mouse model rather than into the perspective of the tauopathy exclusively. In this sense, it would be recommended that the caveats of the mouse model be included in the introduction.

2) The authors do not either mention the sex of the animals used in the study or how many mice from each sex were included in each experimental group. This is an important matter because it has been described that the rTg4510 mouse model presents with sex differences in the degree of accumulation of tau (Yue et al., 2009; Song et al., 2015).

3) A big concern is the identity of the neurons labeled. The strategy to label cells is very unspecific and no details are given on their identity. Different subtypes of pyramidal neurons with different densities of dendritic spines and axon boutons may be mixed up in different proportions in each group and batch. In fact, the resilience of different neuron subtypes to the pathology may be different too. If the authors cannot pinpoint the identity of the neuron imaged, an elaboration on this issue must be included in the manuscript. In addition, the manuscript must include representative images of the cortex of both genotypes showing the labeling pattern obtained with their approach. It is recommended to the authors to add more information about the vector.

4) How did the authors estimate the point of divergence between genotypes? The authors mentioned the 30-35 wk and 50 wk as points of divergence - which should be interpreted as the first time points where the differences between groups are significantly different - in lines 180-183. While the Wald test and the Akaike information criterion indicate that genotype is the factor with the most influence on the model estimates, it does not compute statistical differences between phenotypes at a given time point. Regarding the GAMMs, some fits suggest that data at earlier points may be very different between groups (i.e., Fig 2E, 5C, 6C). Is the decrease in density of TBs over time in WT mice significant? How do the authors interpret those fits?

5) Looking at the data in Figures 1E and 2E, one would expect more negative growth values in figs 5E and 6E, indicating a larger decrease in density. They are flat. Are these analyses well powered? Are the data in Figures 5E and 6E not representative?

Reviewer #3:

This paper describes a novel technique for measuring several distinct subcortical components, using naturalistic speech instead of the more typical clicks and tone-pips. The benefits of using extended speech (e.g., stories) include simultaneous measurement of middle- and late-latency components automatically.

The technique is of great interest with many potential use cases. The manipulation of the acoustics is reasonable (replacing voiced speech with click trains of the same pitch), does not degrade intelligibility, and reduces sound quality only in minor ways. The manipulation is also described clearly for others to implement.

The authors also investigate several variations and generalizations of the technique, and their tradeoffs, inducing responses from specific tonotopic bands and ear-specific responses.

The reliability of the ABR wave I and V responses is remarkable (especially given the previous results of the senior author using unprocessed speech); wave III is less so. Being able to simultaneously record P0, Na, Pa, Nb, P1, N1, and P2 simultaneously shows promise for future clinical applications (and basic science). The practical importance of using a lower fundamental frequency (i.e., typical of male speakers), is clearly established.

The technique has some overlap with the Chirp spEECh of Miller et al., but with enough tangible additional benefits that it should be considered novel.

The writing is very clear.

Major Concerns:

"wave III was clearly identifiable in 16 of the 22 subjects": Figure 1 indicates that the word "clearly" may be somewhat generous. It would be worthwhile to discuss wave III and its identifiability in more detail (perhaps its identifiability/non-universality could be compared with that of another less prominent peak in traditionally obtained ABRs?).

Reviewer #3:

In this study, Higgs et al. apply a systematic and hierarchical approach to testing the enrichment of imprinted gene expression in (mostly) adult tissues, culminating in a survey at the single-cell and neuronal sub-type level, which the authors achieve by exploitation of now extensive single-cell gene expression datasets. Arguably, there are no great surprises in this analysis: it reinforces previous studies showing/suggesting an enrichment for imprinted genes in the brain, with functions in feeding, parental behaviour, etc. But, it is conducted in a rigorous manner and makes highly informed inferences about the expression domains and neuronal subtypes identified. This level of detail is beyond any previous survey, therefore, the study will provide an excellent resource (although the fine details of the specific neuronal sub-populations in which imprinted gene expression is enriched are likely to be of interest to specialists only). Having, at all levels of their analysis, access to two or more single-cell datasets provides an important level of confidence in the analysis and findings, although there are some discrepancies between the enrichments found in comparing any two datasets. Moreover, the findings will give more prominence to neuronal domains that have received less emphasis in functional studies, for example, the enrichment of imprinted genes within the suprachiasmatic nucleus implicating roles in circadian processes.

Imprinted expression covers a range of allelic biases and we are still some way from really understanding what an allelic skew means in comparison to absolute monoallelic expression: biased expression in all cells in a tissue or a mosaic of mono- and biallelically expressing cells. So finding an imprinted gene expressed in a given cell type without knowing whether its expression is actually imprinted in that cell type is a problem. And certainly a significant proportion of more recently discovered brain-expressed imprinted genes seem to fall into a category or paternal bias rather than full monoallelic expression. The authors do acknowledge this caveat in their discussion (lines 491-499). Is it possible to stratify the analysis according to degree of allelic bias? Ultimately, scRNA-seq using hybrid tissues will be important to resolve such issues. In this context, the authors will need to discuss findings in the very recently published paper from Laukoter et al. (Neuron, 2020), although that study focussed on cortical neurons in which Higgs and colleagues do not find imprinted gene enrichments.

Another issue that could cloud the analysis, and particularly inference of how PEGs and MEGs could be involved in separate functions, is the issue of complex transcription units. The authors allude to Grb10 in which there are maternally and paternally expressed isoforms largely arising from separate promoters, which also applies to Gnas. There are also cases in which there are imprinted and non-imprinted isoforms. A problem with short-read RNA-seq libraries will be that much of the expression data for a given transcription unit cannot discriminate such differentially imprinted isoforms, as most of the reads mapping to the locus will map to shared exons. This caveat probably also needs to be mentioned in the text.

The authors give some prominence to Peg3 as an example of the role of imprinted genes in maternal behaviours (e.g., line 508) as reported in the original knock-out (Li et al. 1999). However, this particular Peg3-knock-out associated phenotype has been questioned by a more recent Peg3 knock-out in which it was not observed (Denizot et al. 2016 PMID: 27187722), suggesting that the initial phenotype could be a consequence of the nature of the targeting insertion rather than Peg3 ablation.

While a general picture that emerges is of imprinted genes acting in concert to influence shared functions (e.g., feeding), the authors also point out cases in which a single imprinted gene contributes to a neuronal function (Ube3a in the case of hippocampal-related learning and memory; line 511-512) but for which they did not find enrichment of imprinted genes in the relevant neuronal population. This poses some problems, but it could indicate that that particular function of the gene is not the function for which imprinting was selected if the gene is active in other domains, but is rather 'tolerated'. Of course, many imprinted genes will have multiple physiological functions, so the convergence on specific functions probably provides the best (but by no means perfect) basis for discerning the evolutionary imperatives.

DOI: 10.7554/eLife.59469

Resource: (ZFIN Cat# ZDB-ALT-181113-3,RRID:ZFIN_ZDB-ALT-181113-3)

Curator: @evieth

SciCrunch record: RRID:ZFIN_ZDB-ALT-181113-3

What is this?

La radio educativa busca poder recrear el proceso de aprendizaje, y generar diálogo entre los participantes de los programas para que interactúen entre sí y con la audiencia. Esta herramienta podría ser un apoyo para la planificación, preparación y desarrollo de las sesiones de los programas; los participantes colaborarían en la creación del guion, del cómo se abordarán las temáticas, y en general, del contenido del programa. De igual manera, se podría llegar a implementar durante las emisiones para interactuar de una manera más directa con los oyentes.

La creación de páginas web colectivas permitiría a los participantes de los programas compartir antes o después, según convenga, el contenido de cada programa, generar discusión entre los creadores y los oyentes, e infinidad de interacciones, o “actividades” dadas para apoyar o reforzar el contenido compartido.

Para muchos semiconductores amorfos y cristalinos, una dependencia exponencial del coeficiente de absorción puede tomar la formula empírica de Urbach [28]:

donde es una constante y (la energía de Urbach) es una energía caracterizada por el grado de desorden introducido de defectos y los limites de grano; también es interpretado como el amplio de la cos estados localizados de la cola asociados con los estados amorfos en la banda prohibida. La figura 12 representa el logaritmo del coeficiente absorción como una función del fotón de energía a diferentes tiempos de deposición (20,30,40 y 50 min). El valor de la energía de Urbach es calculado desde la pendiente inversa de la parte linear de las las curvas y también enlistado en la Tabla 3.

It show the actions and commitment of leaders for their follower. because whatever a leader says, his/her speech must be as rule for his/her followers.

How this chains of event can be related to the action of human being?

à 21.20 les écrans à tous les âges résultats de l'étude en 2027 https://youtu.be/ovbeMGfSO2M?t=1284

DOI: 10.7554/eLife.48964

Resource: (ZFIN Cat# ZDB-GENO-080110-3,RRID:ZFIN_ZDB-GENO-080110-3)

Curator: @evieth

SciCrunch record: RRID:ZFIN_ZDB-GENO-080110-3

What is this?

A short expression of time.

Strong message

DOI: 10.1016/j.devcel.2020.06.013

Resource: (ZFIN Cat# ZDB-GENO-110330-3,RRID:ZFIN_ZDB-GENO-110330-3)

Curator: @Naa003

SciCrunch record: RRID:ZFIN_ZDB-GENO-110330-3

What is this?

DOI: 10.1111/bph.15156

Resource: (ZFIN Cat# ZDB-ALT-151130-3,RRID:ZFIN_ZDB-ALT-151130-3)

Curator: @Naa003

SciCrunch record: RRID:ZFIN_ZDB-ALT-151130-3

What is this?

DOI: 10.1016/j.celrep.2020.03.072

Resource: (IMSR Cat# UNC_3,RRID:IMSR_UNC:3)

Curator: @ethanbadger

SciCrunch record: RRID:IMSR_UNC:3

What is this?

DOI: 10.7554/eLife.44431

Resource: (ZFIN Cat# ZDB-ALT-140521-3,RRID:ZFIN_ZDB-ALT-140521-3)

Curator: @evieth

SciCrunch record: RRID:ZFIN_ZDB-ALT-140521-3

What is this?

DOI: 10.7554/eLife.48914

Resource: (ZFIN Cat# ZDB-ALT-170113-3,RRID:ZFIN_ZDB-ALT-170113-3)

Curator: @evieth

SciCrunch record: RRID:ZFIN_ZDB-ALT-170113-3

What is this?

DOI: 10.1016/j.cub.2020.04.020

Resource: (ZFIN Cat# ZDB-ALT-151130-3,RRID:ZFIN_ZDB-ALT-151130-3)

Curator: @ethanbadger

SciCrunch record: RRID:ZFIN_ZDB-ALT-151130-3

What is this?

DOI: 10.7554/eLife.53995

Resource: (ZFIN Cat# ZDB-GENO-100414-3,RRID:ZFIN_ZDB-GENO-100414-3)

Curator: @evieth

SciCrunch record: RRID:ZFIN_ZDB-GENO-100414-3

Curator comments: ZFIN Cat# ZDB-GENO-100414-3

What is this?

James Wilson (Pennsylvania) offered the 3/5ths idea

Rutledge and Major Pierce Butler were apparently the architects of the 3/5th proposal in the Constitution.

Objectives: Motivations students have for college. Identify personal motivations as pathways to goals.

Le prisme du consumérisme est extrêmement réducteur, les sites de rencontres ouvrent leur porte à une population large et varié qu'il est abjecte de qualifier de consommateurs sans discernement. Les motivations des uns et des autres pour l'intérêt des sites de rencontres ne se réduit pas à la simple publicité d'accroche.

Peut-on dire que cette généralité s'applique à l'ensemble des utilisateurs ?

L'arrivée des sites de rencontres sur le web a provoqué une nouvelle tendance de rencontre version 2.0 : moderne, ils ont remporté un succès immédiat, loin des classiques agences matrimoniales, ils ont facilité le contact et la connaissance entre deux personnes à distance.

Justification de cette pratique pour les néophytes

Généralisation sur un journalisme de masse mis en évidence

L’enchaînement des idées a un effet persuasif au service de l'auteure. Fluide, "non, vous n'êtes pas seul. Oui, il existe d'autres manière de vivre." encourage le lecteur tout en défendant le point de vue de l'auteure.

Faits largement connu par une majorité au service de son point de vue et surtout du conseils. Raisonnement épistémique abductif

L'auteur introduit le terme digital signifiant en Francais Numérique. D'après l'Onisep, "le numérique englobe l’informatique, mais son périmètre est plus large car il recouvre aussi les télécommunications (téléphone, radio, télévision, ordinateur) et Internet.". On peut se questionner sur le titre de sa partie "Internet est addictif". C'est le digital ou Internet qui serait addictif ?

The benefit to archaeology, museum curation, and other areas presented by computer modeling and 3D printing cannot be overstated. These technologies allow us to explore artifacts, sites, and more, in ways that we never could before.

The Four-Component Instructional Model (4C/ID) immediately grabbed my attention when looking at the various models we had available. The process involves (a) learning tasks, (b) part-task practice, (c) support information, and (d) procedural information. It seems simple enough but design guidelines laid out in the tables within Merriênboer (2019) provide very thorough suggestions and a constructivist approach. The end product for a curricula or course designed in this approach is one with fidelity to professional activity.

encourages metacognition and challenges conceptual and structural models.

Encourages individualization, personalized learning tasks and self-directed learning. Shifts the responsibility over task selection from the teacher to the student (important!)

It's asking for thorough standards of acceptable performance to be set down, or made concrete by the instructor and ID, as a way to measure things effectively.

one of the things I like about this model is that it is asking you from the get go to think of profession-related activities that can be designed or simulated for students to understand and relate the material.

Find the activities in a college class that require routines and rote learning, or cognitive schemas that require practice and "strengthen them"

so break down a lecture theory session from a step by step session. Or a document that has theory information should have a clear divide when you get into the step by step process?

The idea of providing this supplemental information as a way to check against their current cognitive models by referring to theory or let theory guide their attempt at a learning task. It doesn't dictate a rigid structure.

I like that this model includes the idea of feedback and how to trigger thinking and reflection on their own behavior, cognitive models, etc.

this sounds a lot more involved unless you are working off a basic set of assumptions for mental operations and complex skills. Further understanding of psychological research and learning theories would be needed.

what the heck is this website, lmao.

4 components

Learning Tasks:

• aim at integration of (non-recurrent and recurrent) skills.
• provide authentic, whole-task experiences based on real-life tasks
• Are organized in simple-to-complex task classes and have diminishing support in each task class (scaffolding).
• Show high variability of practice.

the backbone of this model

4C/ID tag might be one of the models I want to look into as I might use it for the Art Modules Class

feedback is a constant loop

how to give feedback

learner guidance

first time i've heard of this one

reading check 3 material

Monarch lookup result: https://monarchinitiative.org/phenotype/HP:0001166

Monarch lookup result: https://monarchinitiative.org/phenotype/HP:0012385

Monarch lookup result: https://monarchinitiative.org/phenotype/HP:0001388

Monarch lookup result: https://monarchinitiative.org/phenotype/HP:0001533

Monarch lookup result: https://monarchinitiative.org/phenotype/HP:0002650

Monarch lookup result: https://monarchinitiative.org/phenotype/HP:0000098

Monarch lookup result: https://monarchinitiative.org/phenotype/HP:0001653

Monarch lookup result: https://monarchinitiative.org/phenotype/HP:0011800

Monarch lookup result: https://monarchinitiative.org/phenotype/HP:0000194

Monarch lookup result: https://monarchinitiative.org/phenotype/HP:0000322

Monarch lookup result: https://monarchinitiative.org/phenotype/HP:0000276

Monarch lookup result: https://monarchinitiative.org/phenotype/HP:0001290

Monarch lookup result: https://monarchinitiative.org/phenotype/HP:0001249

Monarch lookup result: https://monarchinitiative.org/phenotype/HP:0001263

allele id lookup result: https://reg.clinicalgenome.org/redmine/projects/registry/genboree_registry/by_caid?caid=CA414193300

allele id lookup result: https://reg.clinicalgenome.org/redmine/projects/registry/genboree_registry/by_caid?caid=CA915940579

DOI: 10.7554/eLife.46275

Resource: (ZFIN Cat# ZDB-ALT-130115-3,RRID:ZFIN_ZDB-ALT-130115-3)

Curator: @evieth

SciCrunch record: RRID:ZFIN_ZDB-ALT-130115-3

What is this?

DOI: 10.1016/j.celrep.2019.07.078

Resource: (IMSR Cat# OBS_3,RRID:IMSR_OBS:3)

Curator: @ethanbadger

SciCrunch record: RRID:IMSR_OBS:3

What is this?

DOI: 10.7554/eLife.42881

Resource: (ZFIN Cat# ZDB-ALT-120723-3,RRID:ZFIN_ZDB-ALT-120723-3)

Curator: @evieth

SciCrunch record: RRID:ZFIN_ZDB-ALT-120723-3

What is this?

DOI: 10.1016/j.cell.2019.01.019

Resource: (ZFIN Cat# ZDB-GENO-160122-3,RRID:ZFIN_ZDB-GENO-160122-3)

Curator: @ethanbadger

SciCrunch record: RRID:ZFIN_ZDB-GENO-160122-3

What is this?

allele id lookup result: http://reg.clinicalgenome.org/redmine/projects/registry/genboree_registry/by_caid?caid=CA398915284

Monarch lookup result: https://monarchinitiative.org/phenotype/HP:0001947

Monarch lookup result: https://monarchinitiative.org/phenotype/HP:0001942

Monarch lookup result: https://monarchinitiative.org/phenotype/HP:0002705

Monarch lookup result: https://monarchinitiative.org/phenotype/HP:0000369

Monarch lookup result: https://monarchinitiative.org/phenotype/HP:0000325

Monarch lookup result: https://monarchinitiative.org/phenotype/HP:0000322

Monarch lookup result: https://monarchinitiative.org/phenotype/HP:0000348

Monarch lookup result: https://monarchinitiative.org/phenotype/HP:0000256

Monarch lookup result: https://monarchinitiative.org/phenotype/HP:0001249

Monarch lookup result: https://monarchinitiative.org/phenotype/HP:0001263

variant id lookup result: https://www.ncbi.nlm.nih.gov/clinvar/variation/692266/

variant id lookup result: https://www.ncbi.nlm.nih.gov/clinvar/variation/692266/

Monarch lookup result: https://monarchinitiative.org/phenotype/HP:0001744

Monarch lookup result: https://monarchinitiative.org/phenotype/HP:0004719

Monarch lookup result: https://monarchinitiative.org/phenotype/HP:0001659

variant id lookup result: https://www.ncbi.nlm.nih.gov/clinvar/variation/547947/

variant id lookup result: https://www.ncbi.nlm.nih.gov/clinvar/variation/599396/

Monarch lookup result: https://monarchinitiative.org/phenotype/HP:0002487

Monarch lookup result: https://monarchinitiative.org/phenotype/HP:0000639

This has very good examples of what we can incorporate in our team's Code of Conduct. Respect and responsibilities are the two that stick out most for me.

First Factor encouraging professional environmentalists in their denial of social collapse in the near term

分類：3-2

1. M: no
2. wiki: no
3. special meaning from context (直覺): yes

3-2

1. M: no
2. wiki: no
3. special meaning from context (直覺): yes

3-2

1. M: no
2. wiki: no
3. special meaning from context (直覺): yes

Q：來自台語？ non-compositional？

Sentiment: Pos<br> Pragmatics: 很高級<br> (我的意思是 Made in China 就不會說是國外進口的)

分類：3-2

1. M: no
2. wiki: no
3. special meaning from context (直覺): yes

Sentiment: Pos<br> Pragmatics: 很棒<br> (Q: 默默)

分類：3-1

1. M: no
2. wiki: no
3. special meaning from context (直覺): no

分類：3-1

1. M: no
2. wiki: no
3. special meaning from context (直覺): no

分類：3-1

1. M: no
2. wiki: no
3. special meaning from context (直覺): no

分類：3-1

1. M: no
2. wiki: no
3. special meaning from context (直覺): no

分類：3-1

1. M: no
2. wiki: no
3. special meaning from context (直覺): no

分類：3-1

1. M: no
2. wiki: no
3. special meaning from context (直覺): no

分類：3-2

1. M: no
2. wiki: no
3. special meaning from context (直覺): yes

Sentiment: Pos<br> Pragmatics: 豐富多樣的味道

Sentiment: Neu<br> Pragmatics: 遇到喜歡的東西