Reviewer #1:

Using a simulation approach, the authors investigate the impact of removing group members likely to possess key social or ecological information on the topology of elephant social networks in order to better understand how poaching pressure may influence their resilience and functionality. Removals were based on three metrics thought to correlate with an individual's knowledge (age, degree, betweenness centrality) and compared to random removals for both an empirical network and virtual networks. Whereas targeted removals based on age had relatively limited impact on networks characteristics, removal of socially central individuals led to less integrated networks with potential consequences for the spread of adaptive information.

The manuscript was generally clear and well-written. The introduction nicely laid out the rationale for this study and the authors do a nice job walking the reader through the steps of the simulation (how the networks were constructed, how deletions were performed, etc.). I also appreciated the discussion given to the limitations of their approach, such as the lack of network restructuring in response to removals.

1) My main critique is that I believe the authors should be more cautious in attributing functional meaning to their network metrics, particularly given that data was unavailable to allow them to simulate a transmission process. For example, at L461-463, it is stated that targeted removal of individuals with high betweenness decreased the speed of information flow, but what was actually found was that values for weighted diameter increased. Put another way, weighted diameter provides an indication of how rapidly information could potentially flow, but not whether it in fact does so. The actual dynamics of information flow are going to depend on the nature of the information and how it is transmitted among individuals, as the authors note in the discussion (L627-640). I believe that the results should be reworded to focus more on what was actually found (i.e. changes in network metrics), with the potential functional relevance of those changes then examined in the Discussion.

2) In addition, I couldn't see if this was addressed anywhere, but is there empirical evidence to suggest that the mature elephants that possess high-quality information are those characterized by high degree or betweenness?

Thank you for the interesting read!

Reviewer #1:

The manuscript from Quiroga and colleagues reports a function for the mechanosensor Piezo 1 in myocyte fusion. The manuscript concludes via a series of in vitro experiments that Piezo 1 knockdown results in decreased myotube formation.

While overall the manuscript reports some potentially interesting observations, the main conclusion seems preliminary and the work would benefit from substantial additional validations in multiple models to strengthen the tie between myomaker and Piezo1 functions.

Major Comments:

1) siRNA reduces gene expression in a transient manner and it is unclear for how long there is significant silencing of Piezo1 RNA during differentiation. Therefore, a more consistent model that expresses consistent amounts of Piezo1 might be beneficial. Importantly, a more stable mutant form of Piezo 1 (generated with CRISPR/Cas9) was generated in a previous study (Tsuchiya et al, 2018, ref. 17). The long-term consequences of differentiation/fusion of myogenic cells following loss of Piezo 1 expression in the Tsuchiya study reached opposite conclusions to the current study. These findings raise concerns that are not clearly addressed in the present study. While the authors attempt to explain the opposite findings by the use of a different Piezo 1 silencing model, it is difficult to reconcile with the present data the very opposite findings.

2) Figure 3A and C have duplicated images showing siRNA of Piezo 1 in EDL and Soleus. The correct images need to be inserted.

3) Quantification of proteins levels downstream of Piezo silencing should be corroborated by western blot analyses. These include data presented in Figures 2 and 3.

4) In Figure 4, it would be helpful to include a graph illustrating the amount of Piezo1 silencing and the corresponding decrease in Myomaker expression.

5) In Figure 6, expression of myomaker and myomixer should be monitored following administration of Yoda1. If Yoda1 increases fusion at low concentrations, the fusion genes should be upregulated in expression.

6) In Figure 7 the myotube width should also be accompanied by quantifications of numbers of nuclei fused in the myotubes. This data will address whether cell fusion changes following Yoda1 treatment.

7) While the present work explores the function of Piezo 1 in myogenesis in vitro, no experiments address a potential parallel function of Piezo1 in vivo. Supporting data using injured/regenerating muscle should strengthen the overall message.

8) Figure 9 proposes an interesting hypothesis linking Piezo 1 to FSHD. However, the hypothesis is not supported by experimental data and remains rather exploratory in its current form.

Reviewer #1:

Taken collectively, the findings described in the manuscript provide a new perspective on how LAP2alpha influences the state of A-type lamins. By extension, one impact of the findings is that they provide a mechanism by which A-type lamin state is distinct within the nucleoplasm and at the nuclear lamina. The authors also arrive at some additional insights that are valuable. For example, the data supporting the initial peripheral localization of what is argued to be pre-lamin A during processing rather than filament assembly was interesting and, although indirect, largely convincing. I would encourage the authors to address the fact that this work drives a reinterpretation of their prior findings early in the paper. I also have some concern that the impact of the findings is somewhat narrow.

Major points:

1) Given that a major focus of the paper is to explain conflicting results with (the same group's) prior published data on the effect of LAP2alpha depletion, it would have helped to lay this out more clearly from the outset of the paper. As written, the reader is confused until arriving at Figure 3. I appreciate that resolving this conflict leads to a new perspective - namely that LAP2alpha influences the state of the lamin assembly in a way that disrupts its detection by the N18 antibody, but structuring the manuscript to get to this point as quickly as possible would improve its accessibility.

2) I found the plots in Fig. 1A and B confusing. Can the authors clarify how the measurements are achieved - through ROIs for the entire nucleoplasm/periphery? How do they capture the diffuse versus focal signal within the nucleoplasm? There is also some concern that the nucleoplasmic signal may simply be too low to detect robustly at early time points (leading to an increase at later time points as the protein accumulates). Line profiles (which are useful in Fig. 3) would be very helpful if used more broadly for assessing the data particularly for Figure 1.

3) Related to Figure 1 - the results for the deltaK32 mutant is essential for the interpretation and should be included in the primary figures.

4) The authors make no comment on the functionality of the mEos-tagged lamin A/C CRISPR lines. However, the comment suggesting that some clones could have altered nuclear morphology (line 225) raises some questions. How did the authors interpret this? Were these clones in which there were indels in some lmnA alleles affecting the levels? Or is this a consequence of the fusion? How do the authors explain the relatively low expression level of the mEos fusion relative to the untagged? If the MDFs are diploid, presumably we would expect this to be one allele tagged and one allele untagged. Given that the expression ratio is very different from this, could the tagged lamin A/C be targeted for degradation? As these cell lines are critical for the rest of the study, this information is important.

5) How does the deltaK32 mutation affect the ability to detect lamin A/C with the N18 antibody? Could this provide further insight into the impact of LAP2alpha by extension?

6) Greater explanation for the apparent paradox between the increase in immobile fraction by FRAP and the increased diffusion coefficient by FCS in the LAP2alpha-depleted condition is needed. The authors suggest that the latter is due to the loss of LAP2alpha binding (line 395), but some modeling would go a long way here. What form are the lamins thought to be in, and how does the bulk that LAP2 alpha would bring match the apparent changes in diffusivity?

7) One prediction that arises from the proposed model is that regulation of LAP2alpha levels will modulate the relative pool of A-type lamins at the nuclear interior versus the nucleoplasm. Beyond the knock-out cells, is there any other evidence of this relationship?

8) Much of the biochemical characterization seems confirmatory - e.g. the binding and gradients in Fig. 5A and B. Use of the assembly mutants of lamin here could be informative is essential to interpret the changes induced by addition of LAP2alpha.

9) With regards to the effects on chromatin mobility - over what time interval was the volume of movement observed? This is important because more fluctuations in nuclear position, for example, could influence this measure. In addition, telomeres are a confusing choice, given abundant evidence that there is crosstalk between the state of the nuclear lamina and telomere biology (e.g. lamin mutants affecting telomere homeostasis, etc.). At a minimum, acknowledging that telomeres may not reflect the effect on chromatin globally is important. Examples of the raw mean squared displacements would be more informative. Is the difference between lmna KO and lmna/Lap2alpha DKO (Fig. 6 right panel) significant?

10) How do the authors think the membrane integrated LAP2beta fits into the story?

Reviewer #1:

This is a rigorous and very interesting study on a timely topic: combining modeling traditions of (reinforcement) learning and decision-making. The central claim of the paper is that the often-used combination of reinforcement learning with the drift diffusion model does not provide an adequate model of instrumental learning, but that the recently proposed "advantage accumulation framework" does. This claim will likely be of interest for anyone studying learning and decision-making, ranging from mathematical psychologists to neuroscientists running animal labs. I have a number of concerns regarding this paper.

1) I think the basic behavior and model fit quality should be better described. The reinforcement-learning + evidence accumulation models (RL-EAM) are fitted to choices and reaction times (RTs). I find it therefore odd that we don't get to see any actual RT distributions, but only the 10th, 50th and 90th percentile thereof. What did the grand average RT distribution and model predictions look like (pooled across subjects and trials)? How much variability was there across subjects? I understand that that model was fit hierarchically, but it would be nice to (i) see a distribution of fit quality across subjects, to (ii) see RT distributions of a couple of good and bad fits, and to (iii) check whether the results hold after excluding the subjects with worst fits (if there are any outliers). Related, in the RT percentile plots (Figures 3 & 4), it would be nice to see some measure of variability across subjects.

2) The authors pit four competing RL-EAMs against one another. I have a number of issues with the way this is done:

-The qualitative model fits presented in Figure 3 are potentially misleading, as the competing models have different numbers of free parameters: DDM, 4; RL-RD, 5; RL-IARD, 5; RL-ARD: 6. RL-ARD has most free parameters, which might trivially lead to the best visual fit. For this reason, I find the BPIC results more compelling, and I think these should feature more prominently (perhaps even as bars in the main figure?).

-All three racing diffusion models implement an urgency signal. Why did the authors not consider a similar mechanism within the DDM framework? Here, urgency could be implemented either as (linearly or hyperbolically) collapsing bounds, or as self-excitation (inverse of leak); both require only one extra parameter.

3) I could imagine a scenario in which the decision-making process becomes progressively biased toward the more rewarding stimulus. In fact, this can be observed in Figure 7. Therefore, I wonder if the authors have considered RL-AEMs in which the choice boundaries do not correspond to correct vs. error, but instead to the actual choice alternatives (stimulus A vs. B). In such an implementation one can fit bias parameters like starting point and/or drift bias.

4) The authors write that RL-AEMs assume that "[...] a subject gradually accumulates evidence for each choice option by sampling from a distribution of memory representations of the subjective value (or expected reward) associated with each choice option (known as Q-values)." Sampling from a distribution of memory representations is a relatively new idea, and I think it would help if the authors would be more circumscribed in the interpretation of these results, and also provide more context and rationale both in the Introduction and Discussion. For example, an interesting Discussion paragraph would be on how such a memory-sampling process might actually be implemented in the brain.

Reviewer #1:

This article investigates how uncertainty about the value of alternatives affects the decision process through the lens of the drift diffusion model. The article proposes several models for how uncertainty might affect the drift rates or diffusion variance, and tests those models on four different food-choice datasets. The authors conclude that the best model is one in which the drift rate depends on the values of the options divided by their degree of uncertainty.

I think the article is pursuing an interesting question. The core set of results are perhaps not as surprising or as puzzling to a DDM audience as the introduction might have you believe, but from there the paper does a nice job of exploring different ways in which uncertainty might affect the choice process. This seems like a good set of models to consider, as they cover the obvious ways in which one might consider incorporating uncertainty into the DDM, and each one, except for the favored Model 4, has a clear inability to capture a facet of the data.

1) I could quibble about why the authors don't explore more variants of the favored Model 4, for example ones where the values are divided by non-linear functions of the uncertainty measure (e.g. squared or square root)? The results in Figure 4 are not a slam dunk for Model 4, as the effect of dC seems to outweigh C, while in the data it is the opposite. I don't think this is critical, but the authors might try an extra exponent parameter on uncertainty in Model 4. At minimum, the authors should discuss how they might modify Model 4 to better match the data.

2) As I alluded to above, I think the article somewhat mischaracterizes the DDM by saying that "the most straightforward way to include option-specific noise in the preferential DDM - by assuming that noise increases with value uncertainty - leads to the wrong qualitative predictions..." "Most straightforward" is subjective. The standard diffusion model sets the diffusion noise variance to a constant, and so no, adjusting the noise is not "straightforward"; in many DDM software packages it is not even an option. Instead the effect of uncertainty would show up in the drift rate (or boundaries), as it does here. So, I would urge the authors to temper their claims in the introduction and discussion about what the "straightforward" model would be. Many researchers who use the DDM think about the drift rate as a signal-to-noise ratio, and for them Model 4 would have been the straightforward model.

3) This isn't to say that what the article does isn't interesting or important. A standard DDM analysis would just fit different drift-rate and boundary parameters to high and low uncertainty conditions and then report the differences. This article takes a more elegant approach by explicitly modeling uncertainty in the DDM components. This is why I would urge the authors to do a bit more with that aspect of the paper, to try to better understand how uncertainty impacts the drift rates.

4) On Page 16 - the authors write "in line with the best fit parameters". What exactly do they mean here? Did they use the best-fitting parameters or not? Could the authors add a table to the supplements with the average best-fitting parameters for each model, for each dataset? That would greatly help in understanding the results.

5) Figure 4 - how were the experimental data and model simulations combined to generate these figures? For the data, was this one big mixed-effects regression including all datasets? How did the authors handle the random effects in this case, given the multiple datasets? The simulations are also vaguely described. How "similar" were the input values to the data; how exactly were these input values generated? Again, how were the simulations from different subjects/studies combined to generate a single plot per model? It would be useful, though not strictly necessary, to see the basic behavioral results broken down by study (in the supplements). It is unclear how consistent the patterns in Figure 2/4 are across the studies.

Reviewer #1:

This MEG study by Griffiths and colleagues used a sequence learning paradigm which separates information encoding and binding in time to investigate the role of two neural indexes - neocortical alpha/beta desynchronization and hippocampal theta/gamma oscillation - in human episodic memory formation. They employed a linear regression approach to examine the behavioral correlates of the two neural indexes in the two phases, respectively and demonstrated an interesting dissociation, i.e., decreased alpha/beta power only during the "sequence perception" epoch and increased hippocampal theta/gamma coupling only during the "mnemonic binding" phase. Based on the results, they propose that the two neural mechanisms separately mediate two processes - information representation and mnemonic binding. Overall, this is an interesting study using a state-of-art approach to address an important question. Meanwhile, I have several major concerns that need more analysis and clarifications.

Major comments:

1) The lack of theta-gamma coupling during the stimulus encoding period is possibly due to the presentation of figure stimulus, which would elicit strong sensory responses that mask the hippocampus activity. How could the author exclude the possibility? In other words, the dissociated results might derive from different sensory inputs during the two phases.

2) About the hippocampal theta/gamma phase-power coupling analysis. I understand that this hypothesis derives from previous research (e.g., Heusser et al., 2018) as well as the group itself (Griffiths et al., PNAS, 2019). Meanwhile, MEG recording, especially the gradiometer, is known to be relatively insensitive to deep sources. Therefore, the authors should provide more direct evidence to support this approach. For instance, the theta/gamma analysis relies on the presence of theta-band and gamma-band peak in each subject. Although the authors have provided two representative examples (Figure 3A), it remains unknown how stable the theta-band and gamma-band peak exist in individual subject.

3) Related to the above comment, the theta-gamma coupling is a brain-wide phenomenon including both cortical and subcortical areas and not limited to just hippocampus. Although the authors have performed a control analysis to assess the behavioral correlates of the coupling in other regions, the division of brain region is too coarse and I am not convinced that this is a fair comparison, since they differ from hippocampus at least in terms of area size in the source space. The authors could consider plotting the power-phase coupling distribution in the source space and then assessing their behavioral correlates, rather than just showing results from hippocampus. This result would be important to confirm the uniqueness of the hippocampus in this binding process.

4) About behavioral correlates. The current behavioral index confounds encoding and binding processes. Is there any way to seperate the encoding and binding performance from the overall behavioral measurements? It would be more convincing for me to find the two neural indexes at two phases predict the two behavioral indexes, respectively.

5) The author's previous works have elegantly shown the two neural indexes during fMRI and intracranial recording in episodic memory. The current work, although providing an interesting view about their possible dissociated functions, only focuses on the memory formation period (information encoding and binding). Given previous works showing an interesting relationship between encoding and retrieval (Griffith et al., PNAS, 2019), I would recommend the authors to also analyze the retrieval period and see whether the two indexes show consistent dissociated function as well.

Reviewer #1:

In this manuscript, the authors revisit DCC and NTN1 mutants in order to better define the basis for midline crossing defects. This group recently demonstrated that midline zipper glia (MZG) must migrate along the interhemispheric fissure (IHF) and intercalate across the midline while remodeling the meningeal basement membrane to provide a substrate for callosal axons to cross the midline. In this study, they show that DCC and its ligand NTN1 are required for proper midline zipper glia (MZG) distribution/morphology along the IHF, proper remodeling of the basement membrane, and subsequent corpus callosum (CC) formation. The data in figures 2 and 3 generally do a nice job of supporting the model that DCC and NTN1 are expressed in MZG and that the morphology and distribution of MZG are affected in DCC/NTN1 mutants. There appear to be some defects in MZG migration that may account for this (Figure 4). Due to technical limitations, the author's attempt to use a conditional knockout of DCC to genetically dissect whether CC formation defects are due to defects in MZG or callosal axons are a bit inconclusive (Figure 6). Finally, the paper ends with experiments showing that mutations in DCC identified in acallosal patients are loss-of-function using an in vitro cell morphology assay (Figure 7 and 8).

The authors are commended for the quality of their imaging data and for being as quantitative as possible when measuring their in vivo phenotypes, which is not often done with these types of studies. There are few issues that need to be addressed.

Major points:

1) In Figure 4, in addition to the migration defects of Sox9+ MZG, there seems to be a rather large increase in the total number of Sox9+ cells along the IHF by E16 (more than 2 fold, Figure 4G). The authors show there is no change in cell cycle or apoptosis of these cells in the supplemental data (Figure S4), so what accounts for this increase? Is this also seen with NFIA/B staining at E16?

2) Regarding the attempt to distinguish between DCC in MZG versus callosal axons (Figure 6), the incomplete deletion/loss of DCC protein (Figures 6C, I, J) is a bit concerning. It's not clear to me why this would happen, but it confounds the interpretation of the results. While the authors state "The severity of callosal agenesis was associated with the extent to which the IHF had been remodeled" (pg 15), they don't actually quantify this. It might be informative to generate scatterplots of IHF length vs. CC/HC length to determine if there is a significant correlation between the two. This might lend more evidence to a causal relationship between IHF remodeling and CC/HC formation.

3) At the end of the result section, the authors state: "mutations that affect the ability for DCC to regulate cell shape (Figure 8F), are likely to cause callosal agenesis through perturbed MZG migration and IHF remodelling." (pg. 19). While the authors nicely show that patient mutations in DCC affect the morphology of cells in cell lines (Figure 7-8), it is not clear why simply transfecting WT DCC into cell lines results in such a dramatic change in morphology, or why addition of NTN1 doesn't increase this. The authors mention that the cell lines could express NTN1 or that NTN1 is not required for the effect. This seems an important distinction. Did the authors check this? Could they use a function blocking antibody or a soluble fragment of the NTN1 binding domain of DCC to block NTN1:DCC interactions? DCC has been shown to function as a "dependence receptor" that can induce apoptosis in the absence of ligand; are the authors certain that the morphology changes they are seeing in DCC transfected cells aren't cytoskeletal changes resulting from caspase activation?

Minor points:

1) The authors should mention recent work showing Netrin localization to basement membranes during axon guidance (Varadarjan et al, Neuron 2017). The data in Figure 2 are very much in agreement with this previous work, and it should be mentioned in this context.

2) Figure S5A: Representative images from each genotype don't look comparable, even though there's no difference in quantification.

3) Did the authors check whether the cell lines they used in Figure 7-8 express DCC?

Reviewer #1:

This manuscript investigates TE diversity and variation across several clades of bdelloid rotifers, which are particularly interesting from an evolutionary perspective since they reproduce asexually. As stated by the authors, theory predicts that asexuality may lead to two opposite outcomes in terms of TEs content. In the absence of sex, TEs may not easily jump into new genomic backgrounds where they are not repressed, leading to a decline in TE content. On the other hand, there is no recombination without sex, which removes the selective pressure against TEs due to their involvement in ectopic recombination. The authors show that despite these extreme expectations, asexual rotifers do not seem to display any of these patterns, although recent insertions seem rare and possibly brought through horizontal transfers. They do not observe any clear effect of adaptation to desiccation on TEs content, which seems to exclude any effect of enhanced DNA repair mechanisms in controlling TEs. They observe less LINEs and more (recent) DNA transposons in bdelloid rotifers, which is consistent with the absence of sex (limiting LINEs spread) and horizontal transfers (more frequent for DNA transposons). The expansion of RNAi gene silencing pathways suggests that asexuality comes at a cost, such as the proliferation of TEs, the accumulation of genetic load, and the control of horizontal gene transfers that might be deleterious. I think this supports the hypothesis of strong TEs activity associated with the onset of asexuality, leading to a strong evolutionary response. This suggests that these clades survived the arms race with TEs. This work shows how intricate the coevolutionary dynamics between TEs and their hosts can be. The manuscript is well-written, analyses are sound and detailed. I have a few general comments/questions that I detail below: Horizontal gene transfer: given the abundance of recent DNA transposons in some clades (class I), it may be worth discussing a bit more this possibility (at this stage it is mostly discussed in the Conclusion).

If my understanding is correct, there is no assessment of TEs or SNPs heterozygosity for each individual. This might be interesting to explore. If TEs are deleterious recessive, one might observe more frequently at the heterozygous state. For intraspecific data, it may be interesting to look at how nucleotide diversity varies along the genome. Since variable recombination may be associated with diversity due to the effects of selection at linked sites, checking diversity along the genome may bring another layer of information about the frequency of sexual reproduction and its effects on TEs diversity. I acknowledge that this would be a rather exploratory analysis, and am not asking the authors to carry it, but I am curious to know how do methods designed to estimate effective recombination rates perform on these data (e.g. LDHat, or more recently iSMC for a single diploid genome).

Question related to demography and selection: would it be possible to obtain estimates of the effective population size for these clades? It would be interesting to have such an estimate to get an idea of the efficiency of purifying selection against TEs, and whether Muller's ratchet could explain the current abundance of TEs (in the case of moderate/small effective population sizes). I liked the idea of using the ABC to test for consistency with asexuality, but am wondering to what extent it is biased by non-constant transposition rates, which cannot be properly modeled by the coalescent simulation? I would also assume these simulations do not take into account past changes in demography (I believe this option has not been included in the software yet). This is not necessarily a major issue for me, as long as these limitations are mentioned. When presenting the ABC framework in the Methods section, you may want to give more details about the part carried with the abc package itself (e.g. which regression/rejection algorithms were used, etc.).

A few other comments linked to specific paragraphs/sentences:

• L419: why choosing LTR-Rs in particular (abundance and the fact they are not class I I guess).
• L450: Would it be possible to obtain a time in generations from, e.g., an approximate mutation rate?
• L455: Would it be possible to call heterozygote SNPs/elements?
• L550-656: do you examine the most recent elements only? It may be interesting to check these correlations for elements of different ages, since selection may have had the time to act on the most ancient TEs.
• L642: It might also be that longer elements display functional regulatory/promoter regions, and have a stronger impact on fitness.
• L725: I liked this part, but wondered if a slightly more detailed discussion was possible. As the authors state, the expansion of RNAi pathways is consistent with a control mechanism against TEs. It is important to detail alternative explanations since there is no functional evidence in this model that this expansion actually controls TEs proliferation (unless I missed something). Given the rather unique properties of these organisms, it may be worth discussing.

Reviewer #1:

This paper presents a very interesting set of techniques (monocular and binocular visuomotor tracking) to evaluate subtle differences in visual processing as a function of luminance.

Despite some technical caveats I'll explain below, the paper fairly convincing demonstrates that the monocular visuomotor tracking task can be used to identify millisecond-scale differences in visual processing lags, e.g. caused by different levels of luminance. The basic experimental analysis and comparison to traditional approaches were fairly thorough and convincing.

The binocular tracking component was less convincing, and the data were messy (which the authors acknowledge). Unfortunately, the very small sample size (N=5), lack of attention to trial order effects and learning of this new task, etc, reduce enthusiasm about this part of the paper.

While this seems like a solid paper in most respects, it seems it’s primary focus is to demonstrate that a 'new' technique visuomotor tracking (which is not new per se, but may be new in this field), gives results on delay estimation that are indistinguishable from traditional psychophysical techniques. This new approach requires fewer experiments and uses the richness of the full time series for analysis. The basic approach is near and dear to my heart in that it uses continuous-time system identification to really extract rich information.

However, while I think the technique (which I quite like) is promising, I do not know what the new finding is. The analysis also only scratches the surface. I think this is a solid, field specific paper that verifies a new method and, despite its technical contributions, may be suitable for a field-specific readership, with modest effort to address or at least acknowledge the technical limitations.

Technical Limitations:

1) The visuomotor behavior is not new; continuous tracking moving stimuli is an age-old process. What is potentially new here is the use of this behavior for identifying subtle differences in delay. For a fairly old review with several papers cited in this area, see:

Roth, S. Sponberg, and N. J. Cowan, "A Comparative Approach to Closed-Loop Computation," Curr Opin Neurobiol, vol. 25, pp. 54-62, 2014

But there are many (much older) papers dating back for example to McRuer on visuomotor tracking tasks for identifying control systems in human visumotor control, including careful analysis of visuomotor delay.

For a recent paper (in a non-human system) for detecting differences in delay, see:

Luminance-dependent visual processing enables moth flight in low light Sponberg et al, 2015, SCIENCE 12 JUN 2015 : 1245-1248

2) There are no error bars. With 40 trials per condition, a simple SEM may be sufficient.

3) The binocular data highlights a general problem which is that people need to learn this task, and if you are doing system identification during learning, you are doing system ID on a time varying system. This sounds like a confusing task and I agree with the authors that "higher level cognitive processes" are probably taking place but more importantly the learning system is not in steady state even after that many trials.

4) Very importantly, unlike the traditional psychophysics trials (which are based on perception not motor output), this data must be analyzed as a closed-loop system. There are now two pieces of visual information: exogenous reference and self-movement feedback. It is extremely likely that these are processed differently, via feedforward and feedback controllers. See these papers ... These are very new, so I wouldn't have expected the authors to know about them, but they will still be useful for understanding this concept and improving your analyses:

Yamagami, M., Howell, D., Roth, E., & Burden, S. A. (2019). Contributions of feedforward and feedback control in a manual trajectory-tracking task. IFAC-PapersOnLine, 51(34), 61-66.

Yamagami, Momona, et al. "Effect of Handedness on Learned Controllers and Sensorimotor Noise During Trajectory-Tracking." bioRxiv (2020). https://www.biorxiv.org/content/10.1101/2020.08.01.232454v1

That said, the highest-frequency responses - those picked up in the earliest moments of the impulse response function - are largely "open-loop", a fact that can be verified by noting that in the frequency domain, there is a very low gain (which is almost surely true with this data as it is in all other visuomotor tracking data across species that I am aware of, and that fundamentally must be true to ensure stable tracking!). So, the observations about short-time-scale (i.e, high frequency) differences being attributed to differences in the visual processing, are likely substantiated. But a more nuanced and accurate description of the theoretical basis for this is warranted.

5) One second is not steady state in human visuomotor tasks. Tracking bandwidth for visuomotor behavior is in the ballpark of around 0.5-2Hz, which means there is still significant phase lag at 1 Hz. So the 11 second trials, with the first second thrown away does not necessarily "erase" initial conditions. As one example, see a recent paper (again I wouldn't have expected you to know this, but it still shows 1 second is not long enough):

Zimmet, A. M., Cao, D., Bastian, A. J., & Cowan, N. J. (2020). Cerebellar patients have intact feedback control that can be leveraged to improve reaching. eLife, 9, e53246.

In Fig 4S2 in that paper you see that the phase lag at 1Hz is well over 90 degrees. Always wait 10 seconds to be certain, since at 0.1Hz, the phase lag is very low.

6) Perhaps most fundamentally, lag and delay are not the same thing. Delay induces a very specific time shift, but it should be noted that in a closed-loop system one can NOT just shift the closed-loop cross-correlation function (equivalent to the impulse response in this case due to the noise input). If the delay were only on the measured target signal, and not on the feedback of self-motion, then indeed a simple time shift would be adequate; but there is a complex and subtle "compounding" of the feedback delay in closed-loop that leads to a distortion, not a simple shift, of the impulse response function. These papers show different ways on how to estimate delay differences in closed loop correctly:

Luminance-dependent visual processing enables moth flight in low light Sponberg et al, 2015, SCIENCE 12 JUN 2015 : 1245-1248

Zimmet, A. M., Cao, D., Bastian, A. J., & Cowan, N. J. (2020). Cerebellar patients have intact feedback control that can be leveraged to improve reaching. eLife, 9, e53246.

I love the first paper's method, but it is not always applicable. I think it may be applicable in this case where one may be able to assume nothing changes but the delay.

Reviewer #1:

The manuscript describes analyses of genomic data to study the population structure and demographic history of Brachypodium distachyon - a selfing Mediterranean grass species. Major findings include the existence of large-scale population structure (3 lineages), discordance between geographical occurrence and genetic relatedness (clades within the lineages), and at shorter scales, signs of dispersal without interbreeding. These patterns are explained by a combination of near-complete selfing and seed dispersal. The methods are appropriate, results well reported, and writing is good. As such, the paper provides interesting insights into the evolutionary history of B. distachyon, but due to its descriptive nature, I somewhat question the paper's value for a wider audience (i.e. people not directly working with B. distachyon). At points, the authors also engage in speculation (not supported by data) where I feel that more simpler population genetic processes are ignored.

In my opinion, the biggest weakness is the descriptive nature of the paper: it describes the genetic structure and demographic history of B. distachyon, but potential processes giving rise to the structure are only speculated. In particular, the authors invoke pre- and post-zygotic reproductive isolation (lines 384 - 387) and pathogen-driven frequency-dependent selection (lines 431 - 435) as potential causes for the observed structure. However, as the paper provides no evidence for such processes, it's not clear to me why they need to be invoked in the first place? Evidence for seed dispersal over relatively short spatial scales is shown (within populations in Italy, Fig 4), but to my reading the results suggest little dispersal/gene flow over long distances (only few individuals with increased heterozygosity or signs of admixture). Therefore, I believe that the simplest explanation for the genetic structure is founder effects (perhaps human-induces, given the peculiar differences within the A and B lineages) combined with the near-complete selfing. This would explain the emergence of the genetic lineages and the lack of interbreeding. Furthermore, I would imagine that the genetic groups are locally adapted (e.g. there's extensive local adaptation among the selfing populations of A. thaliana), which would ensure that one lineage/accession doesn't take over when otherwise feasible (e.g. within the B lineage). If the authors argue otherwise, I would like to see more convincing evidence and/or discussion supporting the invoked processes.

Below I list a few more specific comments:

Lines 26 - 27: "[our study] identifies adaptive phenotypic plasticity and frequency-dependent selection as key themes to be addressed with this model system". While reading the abstract this sentence got me interested and I expected at least some analyses addressing these topics. However, the only place where they are mentioned again are two highly speculative sentences at the end of the discussion (lines 427 - 435). Although the authors write "themes to be addressed", I think that the complete lack of evidence for adaptive plasticity or pathogen-driven frequency-dependent selection in the current study makes this sentence too misleading to be left in the abstract.

Lines 51 - 53: "For plants, genome-wide coalescence approaches have therefore been largely restricted to domesticated species and Arabidopsis thaliana". This might have been true some years ago, but not anymore. Just to highlight a few wild plant species (and studies) where demographic history has been studied using whole-genome data: A. lyrata (Mattila et al. 2017 MBE), A. arenosa (Monnahan et al. 2019 Nat Ecol Evol), Capsella genus (Douglas et al. 2015 PNAS, Koenig et al. 2019 eLife), Boechera stricta (Wang et al. 2019 Genome Biol), Populus genus (Wang et al. 2016 MBE, Hou & Li 2020 Front Plant Sci), Coclearia genus (Bray et al. 2020 bioRxiv), and many more.

Lines 383 - 387: "Flowering time differences are at best part of an explanation for genetic structure. In the scenario of subsequent lineage expansions we propose here, reproductive isolation might have evolved when the lineages were geographically isolated; and it might include other pre- and post-zygotic barriers in addition to flowering time, namely niche differentiation or genomic incompatibilities". These sentences kind of come out of nowhere. First, I don't fully understand the distinction between genetic structure and lineage expansions. If the latter is a process beyond population structure (i.e. incipient speciation), the paper shows no evidence of that. In fact, as I outlined above, I would imagine that founder effects and near-complete selfing is enough to cause and maintain population differentiation without reproductive isolation?

Lines 389 - 390: "Furthermore, differences observed in the greenhouse are most likely exaggerated through artificially short vernalization times. As our outdoors experiment shows, all accessions produced flowers within two weeks when they went through prolonged vernalization during winter". How representative are these vernalization times of the natural growing conditions? Large differences were observed in the greenhouse experiment, but the authors argue that these are not meaningful because the outdoor experiment showed little differences. However, a single experiment conducted in Zurich certainly does not capture environmental variation existing across the Mediterranean, so I'm not convinced that the role of flowering time can be ruled out so strongly based on these results. That said, the near-complete selfing suggests to me that flowering time is likely not a major factor underlying the genetic structure, and founder effects are a better explanation for it.

Line 548: Only one species (B. stacei) was used to define ancestral alleles in the fastsimcoal2 analysis. There are multiple studies showing that the use of a single outgroup, especially based on parsimony, leads to unreliable inferences of ancestral and derived alleles (e.g. Keightley et al. 2016 Genetics, Keightley & Jackson 2018 Genetics). In particular, this leads to overestimation of high-frequency derived variants, distorting the shape of the unfolded SFS. As the observed SFS has more shared high-frequency variants than predicted by the demography model (Fig S5), I imagine that this is an issue. FSC2 also works with the folded SFS, so I wonder why the authors chose to use the unfolded SFS? Unless there is a compelling reason, I suggest to either add more outgroups or to simply fold the SFS.

Reviewer #1:

This is an excellent study of centrosome polarization in the process of establishing immunological synapse and the effect of kinesin-4 on this process. The authors use a variety of microscopy techniques and controlled perturbations of the cell to obtain beautiful images that clearly suggest that kinesin-4, by increasing frequency of pauses and subsequent MT catastrophes, limits MT length, which assists dynein pulling in polarizing the centrosome. They complement the experiments with modeling based on Cytosim; the model supports the conclusions from the data, and suggests some interesting ideas.

I am not an expert in experimental techniques, though I understand what's been done, and in my limited opinion, the results are first-rate. The paper is well written and accurate. Modeling, which I know intimately, is done very well.. I have just a few minor comments:

1) I was not quite clear what does the modeling say about the centrosome sometimes being in apical position, and sometimes half-way between apical and basal positions.

2) I understand that 2d modeling cannot address this issue explicitly, but can the authors speculate about the apparent ring of MTs along the periphery of the synapse in the non-polarized case?

3) My perhaps most significant comment: the model nicely integrates and explains the data, but is it predictive? A detailed model like that clearly can generate some nontrivial prediction that could be experimentally tested.

4) "Interestingly, in our simulations, a small number of KIF21B motors was sufficient to prevent the overgrowth of the MT network." - this is a bit counter-intuitive: if the motor number is less than MT number, how would this work? Or, by a "small number of KIF21B motors" you mean still greater than ~ 100?

Reviewer #1:

In this work the authors measure the activity of the octopaminergic VUM neurons that arborize throughout the somatic body wall muscles in the Drosophila larva. They use three different larval preparations: isolated CNS (no sensory afferents), semi-intact (CNS exposed while maintaining sensory input), and intact. They find that isolated CNS has rhythmic waves of activity in the VUM neurons, but that semi-intact preparations do not show rhythmic VUM activity. They also show that "harsh" or "gentle" touch elicits different responses in VUM neurons.

There are several interesting findings. The ability of VUM neurons to show rhythmic activity in the isolated CNS is a novel finding. It would be even more interesting to register these waves to that of the glutamatergic body wall motor neurons that drive locomotion. It is also interesting that touch applied to an anterior segment results in elevated VUM activity in a posterior segment, and conversely posterior touch leads to elevated VUM activity in an anterior segment, suggesting that sensory input dampens VUM activity.

There are also issues that need to be addressed, which are listed below.

1) The function of the VUM neurons in locomotion was not tested, e.g. by silencing or activating them. These experiments would greatly strengthen the paper.

2) The three larval preparations are poorly described. (a) The fictive preparation is clearest but still should have a citation to Pulver 2015 at first use, as that paper provides a detailed description of the isolated CNS prep. (b) The semi-intact prep is not well described: is the CNS pulled from the body? How can this be done without ripping the nerves? How can the intactness of the nerves be validated? (c) The intact prep sounds simple, but how is VUM GCaMP3 fluorescence measured in an intact larva as shown in Figure 4? Is the "intact" prep the same as the "in vivo" prep? One name should be used throughout for clarity.

3) The semi-intact prep showed Ca++ signals in only 5% of the preps. This makes me worried that the prep is unhealthy, and that the data from the 5% are not physiological.

4) Experiment 1 shows four individuals, but population data for all larvae were not shown. Selecting only a subset of the analyzed larvae is not appropriate; data from all should be shown.

5) Experiment 2 shows low resolution data (left) that is not interpretable. The data highlighted in the right panel is much better but again, only three examples are presented; no population data or statistics are shown.

6) It is also unclear how many larvae were analyzed in Experiment 2. Line 163 says "...~5% of the in vivo preparations (n=27)..." but is that 1/27 or 27/540? In addition, are the different stimulation patterns done sequentially on the same larva, or independently on different larvae?

7) The prep used for Experiment 3 is not mentioned. Not in the text, not in the figure legend.

8) The prep for Experiment 4 appears to be the intact larva, but if so, how were GCaMP signals measured? How were movement artifacts handled?

9) In Experiment 4, the term "crawling frequency" is not defined. Is it frequency that locomotion is initiated?

10) How do the authors standardize harsh and gentle touches?

11) It says "in very rare cases..." on line 246. Please give actual numbers.

12) The figures are cited out of order (1, 3, 2, 4).

13) Many references are missing in the first part of the Introduction, e.g. lines 64. 65, 73, 78, and 83.

Reviewer #1:

The authors have used hydrogen deuterium exchange mass spectrometry and molecular dynamics simulations to study the interaction between the sars-cov-2 spike protein and the ace2 protein. The results suggest that the protein-protein interaction induces extremely long-range allosteric effects on the spike protein, triggering the proteolysis of the spike protein. The results of this work have implications for the development of small molecule inhibitors.

In general, the manuscript is written extremely well. The work is timely, and the results will be of interest to many. The major conclusions of the work are generally supported by the results. However, there are several key - generally minor - details, enumerated below, the authors should provide in order to strengthen the manuscript and validity of the results.

1) The authors should provide more technical details of the molecular dynamics simulations in the supplementary materials. Could the authors provide more details on the equilibration protocol? Was there any analysis done or metric used to assess whether the system was properly equilibrated? How often were snapshots of the trajectory saved for analysis? How many Na+ and Cl- ions were added to achieve 0.15 M of salt concentration? Also, how many water molecules were added? These details are relevant to the non-casual readers.

2) The authors should probably include the techniques used to study the systems in the abstract section of the manuscript.

3) Also, the authors should probably also include the fact that they performed molecular dynamics simulations in the last paragraph of the introduction. This is not apparent until toward the end of the first paragraph of the results and discussion sections.

4) Page 7; line 147: Figure 4 is introduced before Figure 3. The authors should switch the order or modify accordingly.

5) Figure S1: Could the authors elaborate on Figure S1B in the figure legend? Is (i) measuring the binding of ace2 to the S protein? Is (ii) measuring the binding of RBD to the ace2 protein? The distinction between (i) and (ii) is not made in the figure legend.

In summary, the work is interesting and timely, and the manuscript will be of interest to many in the field. The authors should address the aforementioned points.

DOI: 10.1016/j.celrep.2020.108404

Resource: (ZFIN Cat# ZDB-ALT-071127-1,RRID:ZFIN_ZDB-ALT-071127-1)

Curator: @Naa003

SciCrunch record: RRID:ZFIN_ZDB-ALT-071127-1

Curator comments: allele name: li1Tg Danio rerio ZFIN Cat# ZDB-ALT-071127-1

What is this?

DOI: 10.1016/j.celrep.2020.108404

Resource: (ZFIN Cat# ZDB-ALT-070316-1,RRID:ZFIN_ZDB-ALT-070316-1)

Curator: @Naa003

SciCrunch record: RRID:ZFIN_ZDB-ALT-070316-1

Curator comments: allele name: c264Tg Danio rerio ZFIN Cat# ZDB-ALT-070316-1

What is this?

DOI: 10.1016/j.celrep.2020.108404

Resource: (ZFIN Cat# ZDB-ALT-071120-1,RRID:ZFIN_ZDB-ALT-071120-1)

Curator: @Naa003

SciCrunch record: RRID:ZFIN_ZDB-ALT-071120-1

Curator comments: allele name: zf52Tg Danio rerio ZFIN Cat# ZDB-ALT-071120-1

What is this?

DOI: 10.1016/j.celrep.2020.108404

Resource: (ZFIN Cat# ZDB-ALT-120320-1,RRID:ZFIN_ZDB-ALT-120320-1)

Curator: @Naa003

SciCrunch record: RRID:ZFIN_ZDB-ALT-120320-1

Curator comments: allele name: sd11Tg Danio rerio ZFIN Cat# ZDB-ALT-120320-1

What is this?

DOI: 10.1016/j.celrep.2020.108404

Resource: (ZFIN Cat# ZDB-ALT-110307-1,RRID:ZFIN_ZDB-ALT-110307-1)

Curator: @Naa003

SciCrunch record: RRID:ZFIN_ZDB-ALT-110307-1

Curator comments: allele name: pd12Tg Danio rerio ZFIN Cat# ZDB-ALT-110307-1

What is this?

Reviewer #1:

This study investigates asymmetry in functional gradients in human subcortical structures (thalamus, striatum and cerebellum). The authors found that the 1st principal gradient of thalamus and palladium are asymmetric, while that's not the case for caudate, putamen and the cerebellum. In the case of the caudate and cerebellum, their 2nd and 3rd gradients were asymmetric. Further analyses suggest that these differences arise based on connectivity between subcortical structures and the cerebral cortex. In the case of the thalamus and lenticular nuclei, asymmetry is stronger in regions with no direct or driver cerebral cortical afferent connections. In the case of the cerebellum and caudate, asymmetry is stronger in regions linked to cortical regions with higher inter-hemispheric asymmetry. The writing style of this paper is quite different from the usual papers. I actually quite enjoy this conversational/didactic style. Please see my major and minor concerns below.

1) The computation of the laterality index is not clear to me. In the methods section, it's defined as "(left_score - right_score) / (left_score + right_score), where left_score and right_score correspond to the sum of all functional connectivity values for each left and right structure (for example, in the case of thalamus, functional connectivity values in left and right thalamus)". This sounded like they were averaging across all voxels within for example across all thalamic voxels. But in Figure 2, I assume each dot represents a thalamic voxel. So what are the authors averaging over? Indeed, in the results section, the authors said "We then computed a laterality index that quantified the degree of asymmetry in each functional connectivity map from each seed (see methods), and plotted laterality index scores for each voxel in thalami and lenticular nuclei against their corresponding functional gradient value." So for each thalamic voxel, the authors computed the correlation of the voxel's time course to all brain voxels or something else? This was also not clear. After obtaining the correlation map for a thalamic voxel, how do the authors then compress the correlation map of the thalamic voxel into either "left_score" or "right_score". That was not really explained. Furthermore, in order to compute the laterality index, the authors need to define a homologous thalamic voxel on the other hemisphere. How was this done? Did the authors use a symmetric MNI template? Which one? This was also not explained.

2) "Projection of subcortical functional gradients to cerebral cortex" does not quite make sense to me. According to the authors, basically FC maps of voxels are weighted by the absolute gradient values of the voxels. Essentially this means that voxels with extreme gradient values are weighted more. In the case of the thalamus, lenticular nuclei and caudate, voxels with extreme gradient values are indeed voxels with high inter-hemispheric functional asymmetry (IHFaS), so this is ok. However, in the case of the cerebellum, motor regions in lobules I-IV have extreme gradient values as well. As such, these regions would also be weighted more. Thus the resulting projected subcortical gradients might not simply reflect gradient asymmetry. Perhaps it would make more sense to compute a laterality index based on the gradient scores (i.e., left score and right scores are gradient values), and then use the absolute value of the laterality index as the weight rather than the absolute gradient values.

3) The analysis level in Figure 5 is too coarse. By performing a weighted average of thalamic voxels' FC maps (or caudate or lenticular or cerebellum), the authors are ignoring variation in functional connectivity patterns across thalamic (or cerebellar or caudate or lenticular) voxels. A more direct test of the authors' hypothesis should be as follows. According to the authors' hypothesis, cerebellar/caudate voxels that exhibited greater gradient asymmetry should be more strongly correlated with cortical vertices with strong absolute laterality index. Then there should be strong positive correlations between the absolute laterality index of cerebellar/caudate voxels and the absolute laterality index of the cortical locations mostly strongly correlated with the corresponding cerebellar/caudate voxels. On the other hand, there should be weak correlations for thalamic and lenticular nuclei.

4) The authors suggest that no p value is necessary with a 1000-subject dataset. That might be true for certain things like functional connectivity maps, but a number of analyses, such as Figures 2, 4 and 5 do require supportive inferential statistics.

5) "IHFaS is more prominent in first order nuclei (compared to higher-order nuclei)" is not really quantified. The authors should specify in Figure S2, which nuclei are first order nuclei and which are non-first order nuclei. Perhaps the labels on the x-axis could be colored differently for first order and non-first order nuclei.

Reviewer #1:

The manuscript reports the results of a study examining the linear correlation between white matter tracts and AD- related pathology in the grey matter regions connected by the white matter tracts. The integrity of the tracts were measured using FA, MD, AD, RD (corrected for free water) and free water index (FW) and apparent fiber density (AFD). The white matter tracts examined were the cingulum (main and posterior branch), uncinate fasciculus, and fornix. The population studies were older healthy subjects at risk (based on family history) for developing AD. The AD related pathology were tau and amyloid measured using PET. The study was very well done and it addresses key questions in regards to the p-clinical phase of AD.

Questions:

a) It would be very helpful to the reader to understand the distribution of the global ABeta SUVR and temporal tau SUVR - given that studies dichotomise study participants based on high & low deposition, it would help readers better understand the context of the results. The mean and range given in table 1 is not enough.

b) Related to previous question, I would suggest that the same graphs be made for the ROIs at then end of the tracts - again it would help a reader understand the context of the study.

c) I am surprised that APOE e4 allele was not included as a covariate in the statistical model. Why not? Given that APOE increases risk of developing AD, it would seem to be a relevant parameter. Amyloid positivity has been shown to be associated with age, sex and APOE e4 status.

d) The negative results of the posterior cingulate and yet statistically significant results for the uncinate fasciculus are an interesting contrast. Both tracts connect regions with presumably high Beta and high tau deposition. Have there been studies that have compared the amyloid deposition in posterior cingulate cortex and anterior cingulate/anterior frontal regions? It might be supportive of the idea that posterior cingulate is further along the disease progression compared to the anterior frontal regions. Having the data plots as described in (a) and (b) could help in supporting the points made in the discussion.

Reviewer #1:

The manuscript by Carreño-Muñoz seeks to tackle an important problem in behavioral neuroscience, that is classifying behavior at fine resolution during free exploration in rodents. Though the goals of this study are lofty, this platform, in my opinion, isn't a substantive step forward in relation to other tools currently available.

Major concerns:

1) What is presented in this work is a piezoelectric based sensor to detect rodent movements. My main criticism with this work is that the behaviors were coded by hand. If the authors had developed a way to automatically measure spontaneous behaviors of interest, or even train a machine to detect behavioral signatures after some human input, this system would have broader appeal. As is, the experimenter uses standard whole animal tracking with ethovision, then observes what the animal is doing by hand, then quantitation is added to certain movements. This I believe, is not a major advance, as current weight bearing devices already have this capacity.

2) For the breathing and heartbeat studies in figure 2, I am not convinced that this approach is more beneficial than the standard EEG approaches.

3) Figure 3 is poorly developed and the biology is very questionable. "Shaking" after surgery as a read-out of pain is not a measurement currently used or seen in the pain field. Although the authors report that this measurement is reduced with BPN, there are other trivial or pure coincidental explanations for this unusual finding. This reviewer tends to believe that the anesthesia or some other surgical by-product, not with pain as a driver, is contributing to this phenotype. I don't believe the authors have discovered a new post-op pain behavior. If so, substantial data needs to be added to be convincing.

Reviewer #1:

The authors develop a Bayesian approach to modeling macroscopic signals arising from ensembles of individual units described by a Markov process, such as a collection of ion channels. Their approach utilizes a Kalman filter to account for temporal correlations in the bulk signal. For simulated data from a simple ion channel model where ligand binding drives pore opening, they show that their approach enhances parameter identifiability over an existing approach based on fitting average current responses. Furthermore, the approach can include simultaneous measurement of multiple signals (e.g. current and fluorescence) which further increases parameter identifiability. They also show how appropriate choice of priors can help model and parameter identification.

The application of Bayesian approaches to kinetic modeling has recently become popular in the ion channel community. The need for approaches that inform on parameter distributions and their identifiability, as well as allow model selection, is unquestioned. Also, it is ideal to use as much information in the experimental data as possible, including temporal correlations. As such, the authors’ addition is a valuable contribution.

Comments:

I note that my comments are restricted largely to the results rather than the mathematical derivation of the author's approach.

1) I understand that this is somewhat secondary to the paper's intellectual contribution. However, one thing that would be enormously useful is accompanying software usable by others. The supplied code is not well commented, and it is unclear whether it is applicable beyond the specific models examined in the paper. It was supplied as .txt files, but looks like C code. I did not spend the time to get it working, so an accompanying GitHub page or some such with detailed instructions for how to apply this approach for one's own model of interest would make this contribution infinitely better. Even better if there was a GUI, although easily adaptable code is of primary importance.

2) What are the temporal resolutions of the current and fluorescence simulations shown in Fig 1? I assume that they are the same. However, most current recordings are much higher temporal resolution than fluorescence recordings. If you were to reduce the sample rate of the binding fluorescence relative to current simulations to something experimentally reasonable, how would the resulting time averaging of the binding signal impact its enhancement of parameter identifiability?

3) For comparison, it would also be nice to see how addition of the binding signal in the data helps the RE approach. i.e. Is addition of the binding signal more important than choice of RE vs KF, or is optimization method still an important factor in terms of correctly identifying the model's rate constants or in selecting the true model?

4) Fig 7: For PC data, why is RE model BC appear to be better than KF model BC if the KF model does a better job at estimating the parameters and setting non true rates to zero? Doesn't this suggest that RE with cross validation is better than the proposed KF approach? In terms of parameter estimates (i.e. as shown in Fig. 3), how does RE + BC stack up?

Reviewer #1:

In this study Robert et al. describes the properties of long-range projections from the SuM to the CA2 area of the hippocampus. The authors identified direct excitatory and indirect inhibitory drive from SuM inputs on CA2 pyramidal neurons and showed that direct excitatory drive impinges on PV-positive basket cells. The overall effect of the input on CA2 activity was an increased precision of APs. The study also suggests that the input from the CA2 drives inhibition in the CA1 area. The study provides very interesting and new information about the cellular properties of SuM input in the CA2 area. This is an important question given the increasing importance of SuM inputs in social memory encoding. The study is timely, currently we have very limited data about the features and exact cellular profile of this input. The study is using elegant technical approaches to answer the central question of the study. While the study is addressing an important question and provides novel data, the author's central claim about the role of feed-forward inhibition would need to be strengthened by the addition of experiments addressing how E-I balance changes in trains in individual neurons and how this can be linked to changes in the temporal precision of synaptically evoked APs.

Action potentials are evoked with a current step. Since the study is focused on the network effects of feed-forward inhibition, it would be useful to see how the properties of synaptically evoked action potentials change. In the cortex and in the CA1 feed forward inhibition was shown to limit the temporal summation of excitatory inputs which lead to decrease in AP jitter (Gabernet et al., 2005, Pouille and Scanziani 2001). In order to map these dynamics APs should be evoked via synaptic stimulation and not through current injection.

The authors show recordings of monosynaptic EPSCs in pyramidal cells and interneurons. It would be important to know how inhibitory and excitatory PSCs change in a train. Recordings from single cells held at E-GLUT and E-GABA would allow the authors to monitor excitatory and inhibitory events in a train and map how their balance changes. Can the change in E-I balance explain the change in AP jitter?

What are the characteristics of the SuM-driven inhibitory currents? Does the latency and jitter of monosynaptic EPSCs and disynaptic IPSCs differ? If one is monosynaptic and the other is disynaptic one would expect significant differences in both of these parameters.

How do the authors exclude the contribution of feed-back inhibition? Feed-forward and feed-back inhibition both could have an impact on the temporal precision of APs.

Reviewer #1:

Expansins are mysterious cell wall proteins because they lack known hydrolytic activity but are somehow correlated with acid-induced cell wall loosening/extension and cell expansion. Here the authors catalog the tissue expression of several native promoter driven expansin-FP fusions (EXPA1, 10, 14, 15) and find partially overlapping expression patterns and evidence that some expansins are restricted to particular cell wall regions (e.g. tricellular junctions (Figs 1-4). Using Brillouin light scattering (BLS) microscopy they find that, contrary to several previous reports for EXPA1, EXPA1 overexpression induces tissue stiffening that is relatively independent of extracellular pH (Fig 5, 7). They corroborate these data using AFM of different cell walls in a similar tissue (Fig 8). Thus, EXPA1 overexpression results in shorter roots (Fig 9). While BLS seems like an interesting technique for studying cell walls, essential controls are missing making it difficult to interpret these results.

Major Comments:

1) Expansins have traditionally been identified with promoting cell wall extension by loosening the cell wall under acidic conditions. Recent reports have corroborated this: Ramakrishna et al., 2019 showed decreased lateral root initiation in mutants, implying EXPA1 plays a role in loosening, while Pacifici et al 2018 showed decreased cell elongation in expa1 mutants and increased cell elongation in EXPA overexpression lines, but only when grown on low pH (pH 4) media. All of these results are consistent with EXPAs playing a role in cell wall loosening. By contrast, the authors here find that EXPA1 overexpression causes cell wall stiffening and reduced root growth, that low pH (pH 4) media decreases this stiffening (Fig 5). Their discussion of these discrepancies is insufficient. For example, how do their levels of EXPA1 overexpression compare to Pacifici et al., 2018? How can they reconcile the results in these previous papers with their study?

2) Since the authors only really see changes in BLS of their EXPA1 line with over 10,000x overexpression (their inducible EXPA1-mCherry line with "only" >100x expression relative to wild type does not cause significant changes to cell wall "stiffness"), it is unclear how sensitive this technique is to cell wall changes. Controls are required to interpret these BLS experiments. For example, a known mutant or overexpression line with increased cell wall stiffness and another with decreased cell wall stiffness.

3) It will also be important to document whether the authors can replicate the lack of changes to cell wall stiffness in the expa1 mutant using AFM.

4) It would be helpful to see a detailed correlation analysis between the new technique (BLS) and an established cell wall analysis technique (AFM) across multiple data points (i.e. positive and negative controls for cell wall stiffness changes).

5) These AFM values are also presented on a scale that is almost 7x higher than previous data from the authors (e.g. Peaucelle 2014 JoVE). Please discuss.

6) The authors are comparing BLS data from the inner longitudinal cell wall versus AFM data from the outer longitudinal cell wall, which have very different properties. Please discuss.

7) EXPA1 gene overexpression is determined 7 days after Dex induction, but BLS experiments are conducted on plants that have been induced for a much shorter time (e.g. 3h). What is the expression of the EXPA1 gene over this timeframe of induction? Ideally, the authors would also use an EXPA1 antibody to monitor protein levels, since this is what is actually relevant.

8) It is difficult to see from the BLS shift maps provided (e.g. Fig 5A) where in the root the authors are imaging. Given that this is a relatively new technique to the cell wall field, it would be helpful to provide additional images to provide context to readers.

9) "Data not shown" (e.g. trans-zeatin treatments, line 149; EXPA1 protein levels, line 360) must be included as supplemental figures or the claims removed from the manuscript.

Reviewer #1:

In this manuscript, the authors report on two separate experiments designed to understand the relationship between lip-movement induced theta phase and auditory processing. In the first experiment, subjects detected tones embedded in noise while viewing silent videos. The results demonstrate that tone detection performance improved when tones are presented later relative to earlier in a trial. It was also demonstrated that correct detection, for tones that occurred later in the trial, was systematically linked with the phase of the theta oscillatory activity conveyed by the lip movements. In the second experiment EEG was recorded while participants viewed the silent videos and performed an emotion judgement task. Theta phase coupling was demonstrated between auditory and visual areas such that oscillations in the visual cortex preceded those in the auditory cortex.

The authors conclude that these results demonstrate that lip movements directly affect the excitability of the auditory cortex. However, due to the indirect nature of the reported effects, I do not believe this conclusion is justified. I elaborate on this concern below:

1) In experiment 1, the main finding that performance is better later in the trial could arise from many factors including non-specific attentional effects.

2) The analysis reported in the bottom of page 5 (comparing vector lengths for hits vs misses) is critical to the argument but the results are inconclusive (significant interaction, but subsequent comparisons not quite significant. Likely because the experiment is underpowered?).

3) In Experiment 2: the task performed by the listeners might have biased them towards speech imagery leading to the pattern of effects observed. Indeed, the observed involvement of the left hemisphere may be consistent with the involvement of speech imagery. This would render the observed link between visual and auditory cortices as somewhat trivial and not new (such links have been reported in many previous studies as acknowledged by the authors).

4) Most importantly, the authors do not provide any direct evidence that the auditory effects observed in Experiment 2 are related to those observed in experiment 1.

Other comments:

1) For the analyses in Figure 2A, were the number of trials over which the analysis is conducted adjusted for "first tone" vs "second tone"? Since the hit rate is higher for the second tone, there may be a concern that including more trials in the analysis would result in better SNR and hence a more robust effect.

2) In Experiment 2 the analysis is focused on phase effects. Can you report whether there are any power differences in the delta band in the "early" vs "later" time windows?

3) Line 176, the authors write "these results established that entrainment of theta lip activity increased in time". It is not clear to me which aspect of the results supports this statement.

4) Line 405: "any lag between visual and auditory stimuli onsets was later compensated...". I could not find mention of this elsewhere (i.e. how lags were compensated, how large they were). This is critical for interpreting the results and therefore should be described in detail.

5) Line 430-437 why did you choose to quantify the envelope in this way rather than just taking the wide band envelope?

6) Figure S3 is important and should be in the main text.

7) Line 473 "auditory pure tones"

8) The description in lines 478-481 doesn't make sense. It is unclear how loudness reported in line 480 (91dB SPL; incidentally this is very loud) relates to the later reported value of 72dB SPL.

9) Line 485 "embedded"

10) Please clarify whether in your loudness adjustment procedure you were adjusting the loudness of the tone, the noise or the SNR (and thus keeping the overall loudness of the stimulus fixed)

11) Line 537 "preceding"

Reviewer #1:

The manuscript 'Integron activity accelerates the evolution of antibiotic resistance' by Souque et al. investigates the genetic variations created by a class 1 integron during antibiotic exposure. In the study, the authors examine the evolution of an integron encoded on a R388 plasmid; they introduce three antibiotic gene cassettes into the integron and follow its evolution in the presence of one corresponding antibiotic - here gentamicin. They find that antibiotic exposure leads to a rapid re-shuffling of the integron cassette. The re-shuffling favors the aadB gene in the first position downstream of the integron promoter while mainly keeping the (original) last position in the integron. The study represents an interesting example of rapid adaptation to increasing concentrations of an antibiotic that is facilitated by mobile elements. While the experiments are overall interesting and very well designed, the study lacks a certain depth. In the sense that their results might be as well explained by random mutations (genetic diversity). In addition, the two parts of the experiments (integron analysis & chromosomal evolution) need to be connected as it is so far unclear what role the chromosomal mutations have in the integron-facilitated evolution.

Major Comments:

1) The authors don't mention whether they detected re-arrangements in the negative control that was evolved without antibiotics. Furthermore, re-arrangements might appear but at a very low frequency. What is the sequence coverage used in the study? How can the authors ensure they don't miss a low frequency of re-arrangements? It might be possible that random re-arrangements appear at a very low frequency that are only fixed under changing conditions (similar to mutations). The authors should clarify this point.

2) Did the authors measure the Integrase expression levels? This could ensure that there is no expression without stress to the cell.

3) Regarding the mutational analysis: Is there any sign of a cost to the integrase activity? The authors conduct an intensive analysis on chromosomal and plasmid mutations. Nonetheless, it is unclear how these mutations are generally connected to the integrase activity (and not only to the AB treatment).

4) The authors call the integrase activity 'adaptation on demand'. It would be interesting to know how fast a potential reversal would appear in the integron in the populations. Is there any evidence for a deletion of the duplication of the aadB gene after removal of the antibiotic? In the same line of thought, do the authors expect the other AB resistance genes to follow the same path when incubated in the corresponding antibiotic? It would be interesting to know how antibiotic 'type' dependent the experimental result might be.

Reviewer #1:

The importance of host associated microbiomes for health and disease of their hosts cannot be overstated. Fungi tend to feature more prominently in microbiome studies of soil or plants, but microbiome work in animals has mostly focused on bacteria, with fungi having received comparatively less attention. The current study addresses the question whether there is evidence for co-evolution or consistent ecological filtering of fungal communities in the animal gut, similar to what has been reported for bacteria. Such patterns have been termed "phylosymbiosis", even though the ecological interactions that underlie such patterns are largely unknown.

The strength of the study is the wide range of animals investigated, 49 species from eight different classes of vertebrates and invertebrates. However, this wide sampling also is a weakness, as few groups are well sampled. Members of the same species are found to have relatively similar bacterial and fungal microbiota, and fungal microbiota are found to be somewhat correlated with phylogenetic distance. There is also correlation between bacterial and fungal communities, but whether this is driven by independent effects of the host on both groups, or primarily by interactions between the two microbial groups remains unknown. Some of the other observations, such as the tendency of bacterial diversity to be higher than fungal diversity, are more difficult to parse, since it is not clear what the proper yardstick for diversity comparisons is (i.e., whether functional differences between fungal ASVs are comparable to functional differences between bacterial ASVs). This study provides interesting insight regarding the general characteristics of the fungal microbiome and its relationship to the bacterial communities and the host. It does not directly reveal how these communities might affect the host. As the authors themselves state, "The drivers of phylosymbiosis remain unclear".

Reviewer #1:

Major Comments:

The experimental design is inconsistent in at least three ways:

1) The genomes of 14 resistant clones were analyzed by whole exome sequencing (WES), whereas the genomes of the remaining 21 clones were analyzed using whole genome sequencing (WGS).

2) And the sequencing approach even differed among the six lines evolved in three separate drugs: doxorubicin, paclitaxel, and gemcitabine.

3) We feel the authors did not adequately explain how the different sequencing methodologies could affect their results and the inferences drawn from them. For example, one is likely to miss information with respect to copy number variants by only sequencing exomes. The authors highlight this fact in their discussion, but they do not explain by how much they could be off in their assessment.

In some cases the same parental clone was used to find replicate lines subjected to the same selective pressure, and in other cases, the same parental clone was used to find replicate lines subjected to different selective pressures.

Lines were evolved anywhere from seven to thirty weeks, and the length of the evolution experiments does not correlate with the selecting drug (e.g., three replicate lines were evolved in doxorubicin for 9 weeks and three other lines were evolved to this same drug for 12 weeks). Did the authors normalize by generations? Again, the authors do not address this issue in their manuscript.

Reviewer #1:

The manuscript by Kilroy and colleagues centers on demonstrating that inactivity is deleterious for DMD zebrafish and that electrical stimulation is highly beneficial in the model. The authors identify a subpopulation of inactive DMD (sapje) zebrafish that progress faster in dystrophic disease muscle breakdown. They use tricaine to restrict movement and show a faster myofiber breakdown in the severe DMD fish cohorts. The authors then use neuromuscular electrical stimulation (NMES) to improve muscle pathologies and overall DMD zebrafish outcomes. The authors go into extensive details in characterizing the consequences of NMES on normal and DMD zebrafish muscle growth, health, and overall function. Transcriptomic analysis reveals fibrotic and regenerative genes are modulated by NMES.

Overall, this is a strong manuscript on the effects of NMES/electrical stimulation on DMD muscle growth. It does lay several parameters for evaluation of NMES in the zebrafish model. The manuscript is fairly well-written and most of the experiments are presented in a straight-forward manner with clear interpretations. I do have some issues with one or two points that the authors try to extrapolate from their studies. I have significant issues with the description and use of tricaine as an inactivity paradigm in these studies as there are multiple interpretations of these findings. I have a few points about the NMES stimulation protocol and NMJ contribution that should be addressed. This is a good manuscript and can be an important addition to the field if these points are addressed.

1) The inactivity paradigm (e.g. figure 2) using tricaine as a means of inducing inactivity has pluses and minuses. There are issues with comparing it to rodent and human inactivity experiments (which usually involve hindlimb/limb immobilization), as the authors here are using chemical inhibition. Tricaine has systemic effects on multiple tissue types and organ systems including neurological and respiratory systems. I would be careful to call this model an inactivity model as a more appropriate model would be to physically restrain the zebrafish larvae to prevent movement. While technically challenging this experiment can be done and would likely be more reflective of the consequences of physical inactivity in the DMD fish than tricaine anesthesia. Mdx mice have respiratory consequences due to pulmonary muscle weakness, independent of an inactivity (Burns et al., J.Physiol., 2017).

The authors need to rule out if the consequences of tricaine administration is due to inactivity or pulmonary/secondary dystrophic pathology issues (e.g. swim bladder or respiration).

2) The NMES protocol is more extensively established by the authors and has a clearer interpretation. That being said, the main benefit of NMES is to stimulate muscle force/function in the absence of proper innervation by the NMJ, which is also disrupted in DMD. The authors do an excellent job in demonstrating that the NMJ does not change in morphology via immunofluorescence and anatomical observations. Can/have the authors evaluated the functional output of the NMJ in the NMES-treated DMD zebrafish? Were any electrophysiological measurements performed on the NMES treated DMD fish, independent of any therapeutic experimental protocol?

3) Hmox1 overexpression has been pursued as a strategy for DMD in mice by the Zoltan Arany and Joseph Dulak's groups, so the findings in figure 10 are supported. Have the authors evaluated whether or not the entire Hmox1 pathway was affected in the NMES-treated DMD fish?

Reviewer #1:

Obstructive sleep apnea is an important medical problem, with elevated cardiovascular risk as a common association. Intermittent hypoxic episodes are a good predictor of such risk so a connection is indeed plausible. Thus the manuscript starts with a good premise, but what limits my enthusiasm is the large number of loose ends in the story that make it likely that what we are seeing is a small amount of signal, with a large amount of noise, limiting potential mechanistic insights that are translatable.

Major comments:

1) OSA and intermittent hypoxia are clearly different things. Further the hypoxia of OSA is much less in the lung compared to the systemic organs. To illustrate this point, an upper estimate for alveolar CO2 is the venous CO2, or more commonly 10-15 mm Hg elevation over normal i.e. 55 mm Hg. At even 60 mm Hg CO2, local oxygen tension in lungs would be above 80 mm Hg. Systemic desaturation is because of widening A-a gaps and physiological/pathophysiological shunts. While severe OSA with prolonged apnea could indeed be worse, the clinical associations are seen even with milder disease. Thus a-priori it is very unlikely that the model reflects the disease accurately.

2) Given the limitations of the model, it is imperative that at least the pathways elicited by intermittent hypoxia be clearly defines so that even if we do not gain fully understanding of OSA, we may understand the consequence of intermittent hypoxia that may be relevant in another context. Here too the manuscript is lacking. The genomic analysis is interesting and indeed data rich. However, more attention could have been paid by exploring a hypothesis, ensuring multiple markers for target cell populations, and building a mechanistic model. In current form, the work is hypothesis generating, based on limited markers and analysis, and is extrapolated widely to other pulmonary disease without a solid rationale.

Reviewer #1:

In the manuscript entitled “ASIC1a is required for neuronal activation via low-intensity ultrasound stimulation in mouse brain", Lim et al. investigate the mechanism underlying the activation of brain neurons by transcranial low-intensity ultrasound stimulation. The authors propose that ultrasound stimuli-induced movements of the extracellular matrix and the cytoskeleton cause mechanical activation of ASIC1a in cortical neurons, which leads to Ca2+ influx and subsequent expression of pERK, which the authors used as a surrogate marker for neuronal activation.

While I agree that the finding that ultrasound activates neurons via activation of a mechanosensitive ion channel is per se very interesting, I have to say that in my opinion most of the conclusions and claims are not supported by the actual data.

1) The entire study is purely correlative. Thus, the authors made two independent experiments; on the one hand they show that in-vivo transcranial ultrasound stimulation induces pERK in various brain regions and on the other hand they shown that ultrasound-evoked Ca2+ influx in cultures of cortical neurons is probably mediated by ASIC1a. From this data they conclude that pERK activation is also mediated by ASIC1a activation. This is, however, pure speculation. The authors must provide additional evidence to support their claim. In my opinion the sole use of PcTx1 is not sufficient to prove that the Ca2+ signals are mediated by ASIC1a. Hence, firstly the authors should demonstrate that ASIC1a is indeed activated by ultrasound. This is a very simple experiment. All they would have to do is express ASIC1a in a cell line (e.g. HEK293, CHO, etc) and show that this expression renders the cells sensitive to ultrasound. Second, I would appreciate it if the authors would show that cortical neurons, especially those that show pERK activation, express ASIC1a in the first place. This would also be quite simple - just co-stain the brain sections with an anti-ASIC1a antibody. Third, if the authors want to keep up their claim (see title) that ASIC1a is required for ultrasound activation of brain neurons they should examine ultrasound-induced pERK activation in ASIC1a-knockout mice.

2) It is difficult to evaluate the Ca2+ imaging experiments, because the method - especially the ultrasound stimulation - is not very well described. Hence it is unclear to me how close to the cell the ultrasound stimulator was placed. Moreover, the N-numbers of the Ca2+ imaging experiments are rather small (by the way, it would make reading much easier if the N-numbers were indicated in the figure). Most importantly, it is unclear if the inhibitors (Gadolinium, GsMTx4 etc - Figure 2B-H) were applied to the control cells from the same panel or to different cells. In this context it would be important to know how many control cells actually responded to the ultrasound stimulation. Considering the low N-number, I was wondering if the authors may have had a hard time finding cells that responded and that this is the reason why the N-numbers are so small? I suggest examining many more control neurons and provide information about the proportion of cells that respond. If only for the controls as well as for the cells treated the various channel inhibitors.

Reviewer #1:

The study by Lenz et al. explores the acute action of retinoic acid (RA) in adult human cortical neurons. The main findings are:

1) Consistent with previous findings in mouse neurons, the authors reported enhanced excitatory synaptic transmission in RA-treated cortical layer 2/3 neurons.

2) Also consistent with previous findings, this enhancement is independent of gene transcription, but requires protein synthesis.

3) RA's effect on EPSC requires expression of an actin-modulating protein called synaptopodin. In the Synaptopodin deficient mouse mPFC neurons, RA's effect on EPSC is eliminated. Moreover, in synaptopodin deficient hippocampal dentate gyrus neurons, enhancement of LTP by RA is also reversed.

Overall, this study demonstrates RA-induced synaptic plasticity in acute human cortical neurons, thus expanding the previous findings from mouse neurons and immature human neurons induced from iPS cells to adult human cortical neurons.

Specific Comments:

1) Figure 3 shows that in synaptopodin deficient mouse neurons, RA no longer increases sEPSC amplitudes. The rescue experiments are very nice. However, in both WT neurons (stated in main text, not in figure) and rescue neurons (Fig. 3B), the baseline sEPSC amplitudes are significantly smaller than those of the KO neurons. Can the authors speculate why deletion of synaptopodin may lead to enhanced basal excitatory synaptic transmission?

2) The LTP experiments are a bit problematic. First of all, it was done in mouse hippocampal DG neurons, not cortical neurons. The effect of RA may be different in different neuronal types, as has been shown in previous mouse studies. It will be nice to examine whether RA changes basal synaptic transmission in these neurons in acute slices. Without knowing the effect on basal transmission, it is hard to interpret the LTP results. Second, why did WT DG show no LTP? Third, previous work by Arendt et al. (2015) showed that RA enhances hippocampal CA1 neuron basal EPSCs, and occludes further LTP. The observation here in the DG with RA treatment points the opposite direction. Can the authors offer some explanation (i.e. RA alters LTP threshold through some kind of priming)? Again, knowing the effect of RA on basal transmission specifically in the DG neurons would be informative toward understanding the effect on LTP.

3) The pharmacological treatments (ActD, anisomycin etc.) in this study are in general very long (6 hr) compared to conventional methods (less than 2 hr). To control for potential toxicity associated with prolonged treatment, vehicle control should be added in both Fig 5 and Fig 6.

Reviewer #1:

The manuscript by Sachella examines the role of the lateral habenula (LHb) in learning to associate a context and a cue with an aversive event. The methods use pharmacological and optogenetic modulation of LHb function. The data show that inactivation of the LHb impairs contextual fear conditioning (CFC) as well as cued fear conditioning (when testing occurs in a novel context). The disruption in context but not cued FC is also obtained when testing occurs in the context of conditioning (A) 7 days after training but the deficit in both is evident when testing occurs 21 days after training. Overall, similar results are obtained with cue-specific optogenetic inhibition using ArchT and more sustained optogenetic excitation across the entire training session with oChiEF. Finally, exposure to the context and tone 24hrs prior to the test rescued cued but not contextual fear.

The present paper provides an interesting set of studies looking at the role of the LHb in fear conditioning. There are many strengths to the paper. The variation in testing and training conditions is great. It allows to examine memory to the conditioning context when it is the only stimulus the animals learn about, as well as to examine the memory for the cue when tested in a novel context in the absence of influence from the conditioning context (i.e., cue test in context B), as well as in the context of conditioning (i.e., context A). This allows the authors to rule out overshadowing as an interpretation. For example, the LHb-inactivated animals do not present an augmented case of overshadowing in the cued and contextual fear training conditions. If that was the case in the CFC alone experiment, LHb inactivation would not have disrupted learning, but it did. Further, if the LHb had a specific role in summation of context and cued fear (this could account for the data in Fig 3 as ceiling levels could mask performance differences in 3B), then it would not modulate contextual and cued FC when examined independently (Fig 1 and 2). The authors allude to this briefly in line 226. Other strengths of the manuscript include excellent anatomical controls.

Despite the strengths, there are a number of weaknesses that need to be addressed. The major one, I believe, lies in the necessity for additional data to support the conclusions. Although there are a lot of data presented in the manuscript, together they are not a convincing set that speaks to one interpretation. Specifically, the idea that LHb inactivation/stimulation leads to weakening of the memory strength is interesting, but it also requires additional investigation to show that under conditions when the CFC is strengthened, LHb inactivation has a less devastating effect. Further, the authors concede on line 252-253 that more experiments are needed to determine whether LHb inactivation disrupts the associative or representation components of CFC. I agree but feel this should have been done in the present paper instead of the reconsolidation studies which are also incomplete. The authors argue 'under inactivation of the LHb, a cued FC memory is formed whose retrieval depends on the context in which the cue is presented'. However, the disruption of contextual fear makes this interpretation difficult to accept. If the correct context is needed for cued fear to be expressed then this suggests either a possible generalization decrement effect that is ameliorated by being placed in the same context or a context-gating effect. Both require some knowledge of the context where the cued fear learning occurred. Yet, this is difficult to reconcile with the consistent disruption in context fear.

The reconsolidation experiments, although interesting, lack clarity and the vehicle controls. A systematic investigation of exposure to the conditioned context or the conditioned cue (in context B) on fear to the conditioned context, the cue and both would help dissociate how retrieval-based reconsolidation acts in the current preparation. This may warrant an independent investigation/publication.

Some other arguments that I didn't find convincing: The equivalent reduction in exploration in the OF for the vehicle and muscimol animals is argued to suggest that similar contextual representation are formed between the groups and therefore the CFC differences are unlikely to be due to deficits in context encoding. The OF data are insufficient to argue this. Many aspects can modulate activity in the OF from the traditional anxiety argument (here similar reduction in anxiety) to a sense of familiarity. There is no evidence for similar contextual encoding.

Some additional comments:

The way the 24hr and 7d data are presented is a little odd. While the authors justify this, it seems strange from the reader's perspective to see the 7d test data before the 24hr test data. In addition, the 24hr tests data are referred to as long term memory, which can be perceived as odd relative to the longer 7d test. This section just needs to be revised for clarity in the presentation.

Does the difference in cued fear at the 24hr interval persist if conditioning differences are used as a covariate in the analysis and if a difference score is calculated from the baseline difference?

Reviewer #1:

The paper tackles an important aspect of neuroanatomical and language research concerning the lateralization differences related to functional lateralization of language. No clear cut results are currently available nowadays and methodological limitations of previous approaches are here addressed with a new type of analysis. Despite this new angle in the tractography analysis is of interest, the differences in the tasks that are used to address language lateralization are also as important. This may also explain possible differences in previous studies and also with the current one. This aspect seems to be missed in this work.

Although the Letter fluency task implies the use of language, this task is commonly considered in neuropsychological assessments as an executive function task. A more appropriate task would have been a Semantic Fluency task or as in previous work (Vernooij et al 2007) a verb generation task. There is a close relationship between executive function and many aspects of language production, there is not doubt about this. But this does not mean they are the same. Actually the Forceps minor has been found to be associated with individual differences in executive functions in language function (Mamiya et al 2018; Farah et al 2020). This is a limitation of the study and should be acknowledged since the results may differ with a more purely linguistic task, limiting the scope of the study and its conclusions in terms of language lateralization. I do believe the data are worth publishing and the methodological approach is novel but the reader should be clearly aware of the limits in terms of the conclusions the authors can draw from the selection of the sample that may correspond to lateralization of executive function for language more than language lateralization per se.

Reviewer #1:

The work by Pipitone et al. is a very carefully performed and technically sophisticated elucidation of the establishment of the thylakoid membrane system in Arabidopsis chloroplasts upon first illumination of cotyledons. Its charm is the three-dimensional resolution during a time course that allows it to follow the rapid changes occuring during the short time window in which the greening occurs. In addition, the authors included proteomics and lipidomics approaches complementing the morphological observations by sound molecular data. All together the study provides a very detailed catalogue of the processes that trigger chloroplast biogenesis that is highly useful for the community as it provides important numbers of size and development.

Improvements:

Actually the work has been performed very carefully and there is not much to improve.

The introduction could contain more references (e.g. lines 77, 83, 90, 93, 98,, 131, 132)

SBF-SEM should be spelled out at first mentioning (line 146) and maybe a bit more background about the technology would be helpful for the reader to understand it.

Line 244: The occurrence of starch granules is of course caused by the continuous illumination. It however may also have an impact on the final size of the plastid. It would be interesting to know whether chloroplasts at the end of a night phase are smaller than at the end of a light phase. This is not mandatory for the current manuscript but an interesting question to follow in future and maybe to be discussed.

Line 251: The surface area.... please define what is meant since membranes have two sides.

Lines 256-261: There is another study done in cell culture that has a similar design (Dubreuil et al ), are the two studies compatible with each other in their conclusion and if not, what are the differences?

Lines 549-551: This sentence is not perfectly clear to me. Maybe the authors can explain this a bit more in detail using examples.

Lines 564-573: I think it is worth noting that the interactions between PSII complexes located in neighbouring thylakoid membranes trigger the stacking of the grana. It is therefore tempting to speculate that stroma lamellae are established first and that these membranes are then stacked after PSII complexes are inserted into the membrane because they provide the adhesion points between them.

Reviewer #1:

The study by Lyengar et al describes age- and temperature-dependent changes in the neurophysiology of the giant fiber (GF) system in adult wild type and superoxide dismutase 1 mutant flies (SOD[1]). While the main GF circuit and downstream circuits exhibit little change when flies are reared at 25C, GF inputs and other circuits driving motoneuron activities show age-dependent alterations consistent with earlier studies. Rearing flies at 29C temperatures had no additional effects except that age-dependent progression of defects were accelerated, as it was expected from previous studies. In SOD[1] mutants, which are short lived, changes in the neurophysiology of the GF system were different from those induced by high temperature.

Overall this technically challenging, and well executed study provides a nice description of the effects of aging, high activity (induced by higher temperature), and loss of SOD function on the neurophysiology of the GF system. However, most of the described effects have been observed in other systems and are thus not entirely novel. Moreover, the study does not provide any insight into the mechanisms underlying the age-dependent alterations of the examined neurons. Thus, the overall significance of the described findings is limited.

Reviewer #1:

This manuscript compares the effects of a novel versus a classical augmented acoustic environment protocole on partial improvement of congenital hearing loss. The new protocol is based on the idea that temporal structure, and in particular auditory gaps in the augmented environment should improve perception of temporal features in sounds, in particular of auditory gaps.

Technically sound, the study describes how the encoding of gap in the auditory midbrain (inferior colliculus, IC) of a mouse hearing loss model is affected by the novel temporally enriched paradigm with respect to control mice and to the classical paradigm. The study clearly confirms that augmented acoustic environments improve spectral tuning, and detection of sound features with respect to control animals in IC. IC neurons also appear to show a more robust increase of sensitivity to amplitude changes (onsets and offsets) when the animals have gone through the temporal augmented sound environment, both in the presence and in the absence of background noise, as compared to the classical paradigm, at least if one considers the magnitude of the effects with respect to control. However, only few measures show a significant difference when directly testing between the classical and the temporally enriched paradigm. Thus, there is an overall impact of the temporal paradigm which is worth emphasizing as a small but likely useful increment of the auditory enrichment approach for improving hearing loss. This is a definitely interesting, even if somewhat expected result which could drive further studies on clinical practice. It seems however too specialized for broader readership. A few things in the presentation of the results could be improved, and behavioral data could eventually reinforce the message although it is not mandatory to make these results interesting :

1) A figure of the auditory enrichment setup would be nice, to better understand how this works. Are mice constantly submitted to the sounds? Are control mice in a more silent environment than normally housed mice?

2) The lack of behavioral data opens the question whether IC changes have actually an impact on perception. Although it is likely, it would be interesting to measure the magnitude of this impact.

3) What makes the study interesting is the tendential bias in favor of the temporal paradigm with respect to the classical one. This is however rarely significant in one to one comparisons for each sensitivity measure. To reinforce their point the authors could consider a multivariate statistical analysis (e.g. two way ANOVA) to show that over all their measures there is a significant improvement with temporal against classical.

Reviewer #1:

This report makes a logical connection between depressive-like behaviors induced in mice following LPS-injection to mimic bacterial infection and the down regulation of phospholipid transporting enzyme, ATP8A2, in the prefrontal cortex. The intermediary is IFN-gamma. The work is quite convincing that LPS down regulates ATP8A2 by upregulating IFN-gamma and that this has some limited effects on behavior. However, the impact of the findings is limited by several factors.

1) The use of FST and TST as measures of depression is increasingly falling out of favor as there is no face validity to humans. It is understood that these tests have been long in use and were in the past considered the best measures of "depressive-like" behaviors in mice but the field has moved on to much more relevant constructs such as social defeat, anhedonia etc. As it stands the behavioral analysis here is limited and the effects are modest at best.

2) The use of LPS as a model to induce depression also has limitations. The injection paradigm used is likely to have caused massive inflammation, as evidenced by the increase in cytokines, but what this is modeling is unclear and how the impact would be specific to depression later in life is equally unclear. Indeed, the references the authors cite for the LPS regime they use offer completely different mechanisms and impacts of the inflammation. This is not to say the current findings aren't important, they are, but rather this pathway may be one among many that is invoked following massive inflammation during early development which then has many non-specific effects.

3) There is no functional connection between down regulation of ATP8A2 developmentally and adult neural activity. Clearly a membrane phospholipid transporting enzyme is important, but exactly how it is important here, meaning what enduring impacts there are on neuronal function, is unknown.

4) The experiments were designed to test the relationship between IFN-gamma and ATP8A2 but then conclude that the behavioral effects are mediated by this connection. There could be many other effects of IFN-gamma that are not considered here but would be nonetheless blocked by the neutralizing antibody approach used. Thus the main conclusions of the manuscript are not supported in terms of the role of ATP8A2 in LPS-induced depression.

Reviewer #1:

Mackay et al. present a study on the phenotype of neurons from YAC128 mice, an HD model expressing mHTT with 128 CAG repeats. They show (i) that cultured cortical YAC128 neurons exhibit increased mEPSC rates transiently during development in vitro (i.e. between DIV14-18 but not at DIV7 or DIV21), (ii) that calcium release from ER by low-dose ryanodine increases mEPSC rates only in WT but not in YAC128 cells, and (iii) that blocking SERCA to deplete ER calcium stores reduces mEPSC rates in YAC128 neurons as compared to WT controls. These data are interpreted to indicate that a presynaptic ER calcium leak increases mEPSC rates in YAC128 neurons. Using rSyph-GCaMP imaging, the authors then show (i) an increase in longer-lasting AP-independent calcium signals in synaptic boutons of YAC128 neurons as compared to WT, (ii) less profound increases in calcium signals upon ionomycin-mediated equilibration to 2 mM extracellular calcium, (iii) less profound increases in calcium signals upon caffeine treatment in YAC128 boutons, and (iv) less AP-related calcium events in YAC128 boutons. A final dataset shows that evoked synaptic transmission in YAC128 striatum as assessed by iGluSnFR imaging is inhibited by ryanodine in WT but not in YAC128 mice. The authors conclude that the overexpression of mHTT with 128 CAG repeats in the YAC128 mutant causes aberrant calcium handling (i.e. calcium leak/release from the ER), which leads to increased cytosolic calcium concentrations, increased AP-independent release events, but reduced AP-evoked glutamate release.

Comments:

1) I think the authors show convincingly that (presynaptic) calcium handling is perturbed in YAC128 cortical presynaptic boutons. What is conceptually unclear to me at the outset is whether this specific phenomenon is related to HD pathology. The phenomenon is transient during the development of cortical neurons in culture and gone at DIV21. In contrast, the first subtle behavioural defects of YAC128 mice arise at about 3 months of age, overt behavioural defects at 6 months of age, and striatal and cortical degeneration still later.

2) The issue discussed above (1) could have been addressed in part with the slice experiments, which were conducted with tissue from 2-3 months old mice, but the corresponding data are too cursory at this point. They indicate a small defect in evoked glutamate release in the YAC128 model, but it is unclear whether mEPSC rates are altered. It seems important to test this as the increased mEPSC rates are proposed to be at the basis of the phenotype described in the present study. Indeed, the authors ultimately conclude that the YAC128 mutation causes increased mEPSC rates at the expense of evoked glutamate release. This is generally unlikely to be true as the mEPSC rates in question are very likely overcompensated by the vesicle priming rate.

3) The phenomenon of altered calcium handling in YAC128 neurons is shown convincingly. However, this finding is not unexpected given that previous studies indicated such increased calcium release from endoplasmic reticulum in HD models in other subcellular compartments, and it remains unclear how this defect is caused by the mutant HTT.

4) As already outlined above (2) it remains unexplained how the calcium handling defects increase mEPSC rates but decrease evoked transmission. The corresponding part of the discussion reflects this uncertainty. This is aggravated by the fact that several of the drugs used have complex dose-dependent effects that cannot easily be reduced to specific effects on calcium handling by the ER. For instance, it is unclear whether caffeine effects on adenosine receptors or PDEs have to be taken into consideration. In general, the sole reliance on partly 'multispecific' pharmacological tools is a bit worrisome.

5) There are several other aspects of the paper that are not immediately plausible. For instance, I have difficulties to understand why a calcium transient minutes before ionomycin treatment would affect the calcium signal triggered by ionomycin in the presence of 2 mM extracellular calcium (Figure 4); after all, the example trace shows that the calcium levels return to baseline within seconds. And more generally, in this context: Can differences in calcium buffers and the like be excluded? A direct assessment of absolute cytosolic calcium concentrations would be the ultimate solution.

Overall, the present paper describes a phenomenon in presynaptic boutons of an HD model, key aspects of which (e.g. increased ER calcium handling defects) have been described in other subcellular compartments of HD models. The connection of this phenomenon to HD is unclear as the developmental timelines of the appearance and disappearance of the cellular phenotype and the disease progression do not match. The opposite phenotypes caused at the level of presynaptic boutons on AP-independent and AP-dependent release remain disconnected. The mechanism by which mutant HTT causes these defects remains unexplored. The pharmacological tools used do not always allow unequivocal conclusions regarding the targets affected. I think some more work is needed to generate a clear picture of what exactly happens presynaptically in YAC128 neurons, and to show how this might relate to HD.

Reviewer #1:

Deng et al. studied the mechanisms underlying the wide propofol effect-site concentration range associated with loss of responsiveness. Data was acquired from two centers (MRI, Canada; Auditory, Ireland). This is a well conducted study. The results could also explain why older patients (with presumably smaller gray matter volume) are more sensitive to propofol. My major concerns relate to precision in language.

1) The authors studied mechanisms underlying why patients lose consciousness at a wide range of propofol effect-site concentration. This behavioral phenomenon is known and well described (Iwakiri H, Nishihara N, Nagata O, Matsukawa T, Ozaki M, Sessler DI. Individual effect-site concentrations of propofol are similar at loss of consciousness and at awakening. Anesth Analg. 2005;100:107-10). I would suggest that the. authors position their paper as such. They did not study general anesthesia per se, and the allusions to awareness under anesthesia may not be relevant.

2) Per comment 1 above. Please reword the intro and discussion section i.e., " Anaesthesia has been used for over 150 years to reversibly abolish consciousness in clinical medicine, but its effect can vary substantially between individuals." What type of anesthesia are you referring to? Anesthetic vapors? Please provide a reference for this statement or make it propofol specific. Awareness under general anesthesia is related to numerous factors, many of which are iatrogenic as detailed in the NAP 5 study "The incidence of awareness rose from 1 out of 135,000 general anaesthetics to 1 out of 8,200 general anaesthetics when neuromuscular blockers were used" (https://pubmed.ncbi.nlm.nih.gov/25204697/). Further, it is unclear when dreaming occurs (during induction which is reasonable to expect/during emergence which is also reasonable to expect versus during the anesthesia). My suggestion is to qualify your statements by stating that this should be further studied in the context of possible genetic predisposition to awareness (Increased risk of intraoperative awareness in patients with a history of awareness. Anesthesiology 2013;119:1275-83).

3) The term "moderate anaesthesia" is confusing to me, and would be to most clinicians. Please cite the description of what comprises moderate anesthesia. My interpretation is that the study was about sedation. Did you mean moderate sedation? (https://www.asahq.org/standards-and-guidelines/continuum-of-depth-of-sedation-definition-of-general-anesthesia-and-levels-of-sedationanalgesia).

4) "the antagonistic relationship between the DMN and the DAN/ECN #and# was reduced during moderate anaesthesia, with a stronger and significant result in the narrative condition relative to the resting state." Anticorrelation?

5) The suggestion that fMRI can be used to improve the accuracy of awareness monitoring is, in my opinion, not necessary and a stretch.

Reviewer #1:

This article proposes that the assembly of the Sars-CoV-2 capsid is mediated by liquid-liquid phase separation of the N protein and RNA. The strength of the manuscript is a series of in vitro experiments showing that N protein can undergo liquid-liquid phase separation (LLPS) in a manner enhanced by RNA. The authors also identify nilotinib as a compound that alters the morphology of assemblies consisting of RNA and the N protein. The primary weakness of the manuscript is that there is little data connecting the in vitro observations to intracellular events, or viral assembly. Taken together, I find the experiments interesting but, as detailed below, premature.

Major comments:

1) A key issue with any in vitro assembly process such as LLPS is a demonstration that same process occurs in the cell. This is an issue since many molecules can undergo LLPS in vitro in a manner unrelated to their biological function. In this work, the authors show that the N protein can undergo LLPS in vitro in a manner a) stimulated by RNA, b) enhanced by the R2 domain, and c) changed in morphology by nilotinib.

Their argument that this LLPS is relevant to the viral life cycle rests on: a) the observation that over-expressed N protein forms foci in the cytosol, and b) the number of these foci (but not necessarily their morphology as seen in vitro) is somewhat reduced by nilotinib. In my opinion, this is not a very convincing argument for two main reasons.

First, it is unclear why the N protein is forming foci in cells. Specifically:

a) Is it being recruited to P-bodies, or some other existing subcellular assembly? (Which could be examined by staining with other markers).

b) Is it forming a new assembly with RNA as they have proposed? (Which could be addressed by staining for either specific or generic RNAs, or purifying these assemblies and determining if they contain RNA)

Second, it is unclear that the foci seen in cells are related to the LLPS they observe in vitro or relevant to the viral life cycle. Specifically:

c) Is the assembly related to the LLPS they have observed in vitro beyond a poorly understood alteration with nilotinib ? (Which could be addressed by examining if the deletions they observe affect LLPS in vitro also affect the formation of N protein foci in cells).

d) Is the nature of this assembly relevant to the viral life cycle? (Given the difficulty of working with COVID, this is hard. My suggestion here is at a minimum to discuss the issue, and ideally do an experiment with a related coronavirus to test their hypothesis). Frankly, the idea that coronavirus would trigger a LLPS of multiple viral RNAs would seem to be inhibitory to efficient packaging of individual virions. A discussion of how the virus would benefit from such a mechanism, as opposed to a cooperative coating of a viral genome initiated at a high affinity N protein binding site would be important to put the work in context.

2) The manuscript would be improved by examining the presence of RNA in each LLPS, and the ability of RNA to undergo self-assembly under the conditions examined in the absence of the N protein. As it stands, in some cases, the authors could be studying RNA based self-assembly, that then recruits the N protein to the RNA LLPS by RNA binding (see Van Treeck et al., 2018, PNAS for specific example of this phenomenon). This may be particularly likely for some of the longer viral RNAs that can form more stable base-pairs and thereby promote more "tangled" assemblies (e.g. Tauber et al., 2020, Cell).

3) I found the CLMS to not fit well in this manuscript for two reasons:

a) As I understood the methods, the CLMS experiment is looking at cross linking in high and low salt, with some LLPS occurring under low salt. However, since the cross linking was not limited to the dense phase of the low salt condition, a significant fraction (perhaps majority?) of the N proteins will not be in the dense phase. Because of this, the cross linking is essentially mapping interactions that change between high and low salt. If the authors really want to do this experiment, they should separate the phases and examine the crosslinks forming in the dense and dilute phases under the same salt conditions.

b) A second issue with this cross-linking experiment is that the regions that dominate the changes in cross linking are not ones that appear to be important in driving LLPS in vitro based on their deletion analysis. If the authors want to include this data, it should be related to the deletion experiments and connected to the work in a manner to make it meaningful.

4) The work would be improved by comparing how alterations that impact LLPS affect specific biochemical interactions of the relevant molecules. In these experiments, the authors are examining assemblies that form through N-N, N-RNA, RNA-RNA interactions. In each case, biochemical assays could be used to examine which of these interactions are altered by deletions or compounds. By understanding the underlying alterations in molecular interactions, a greater understanding of the mechanism of the observed LLPS, and its relevance to the viral life cycle could be revealed.

Reviewer #1:

This study is based on previous work that exposure to valproic acid (VPA), which is used to model autism spectrum disorders, produces excess local synaptic connectivity, increased seizure susceptibility, abnormal social behavior, and increased MMP-9 mRNA expression in Xenopus tadpoles. VPA is an interesting compound that is also used as an antimanic and mood stabilizing agent in the treatment of bipolar disorder, although the therapeutic targets of VPA for its treatment of mania or as a model of neurodevelopmental disorders have remained elusive. The authors validate that VPA exposed tadpoles have increased MMP9 mRNA expression and then test whether the increased levels of MMP9 mediate the effects of VPA in the tadpole model. The authors report that overexpression of MMP-9 increases spontaneous synaptic activity and network connectivity, whereas pharmacological and genetic inhibition with antisense oligos rescues the VPA induced effects, and then tie the findings to experience dependent synaptic reorganization.

1) What is the exact nature of "increased connectivity"? Is there an increase in synapse numbers or solely an increase in dendritic complexity coupled with a functional plasticity? The authors should document properties of mEPSCs and mIPSCs recording in TTX to isolate synaptic properties. Coupling this "mini" analysis to quantification of synapse numbers will address whether the changes are solely due to structural plasticity or also due to a functional potentiation of transmission. These experiments should at least be conducted in MMP-9 overexpression, VPA treatment and VPA treatment+MMP-9 loss-of-function cases to validate the basic premise that there is an increased connectivity.

2) It is unclear why the authors focused on MMP-9 compared to other genes dysregulated by VPA. This point should be further discussed.

3) How does VPA alter MMP-9 levels? Is this through an HDAC dependent mechanism? Granted VPA has been proposed to work through a variety of mechanisms including HDAC inhibition.

4) Does SB-3CT rescue the expression levels of MMP-9?

5) How is increased MMP-9 produces the synaptic and behavioral effects? What is the downstream target (specific receptor?) that would produce the broad changes in synaptic and behavioral phenotypes? Or is this a rather non-specific effect of extracellular matrix? Based on years of data on MMP-9 function its impact on "structural plasticity" in general terms is not surprising but further mechanistic details and specific targets would help move this field forward.

Reviewer #1:

This manuscript has novelty in it’s approach. The authors use an animal model to abolish the circadian rhythm in mice to study the impact on susceptibility to challenge with LPS. The experimental approach they use involves both wild-type mice subject to sudden stop of the light-dark (LD) cycle and mice knocked-out for the Clock system (KO). I have some points of concern:

• The investigators show that mice shift from LD to DD become more lethal to LPS. If this is due to abolishment of the circadian rhythm, similar lethality should appear with the challenge of the KO mice. The opposite was found. Please explain.

• LPS is acting through TLR4 binding. Can the author provide evidence that TLR4 expression is down-regulated in transition from LD to DD? Does the same apply for the expression of SOCS3?

• TLR4 is a receptor for alarmins with IL-1alpha being one of them. Can the authors comment, based on their IL-1alpha findings, if this may be part of the mechanism?

Reviewer #1:

This manuscript uses simultaneous fMRI-EEG to understand the haemodynamic correlates of electrophysiological markers of brain network dynamics. The approach is both interesting and innovative. Many different analyses are conducted, but the manuscript is in general quite hard to follow. There are grammatical errors throughout, sentences/paragraphs are long and dense, and they often use vague/imprecise language or rely on (often) undefined jargon. For example, sentences such as the following example are very difficult to decipher and are found throughout the manuscript: "if replicated, an association between high positive BOLD responsiveness and a DAN electrophysiological state, characterized by low amplitude (i.e., desynchronized) activity deviating from energetically optimal spontaneous patterns, would be consistent with prior evidence that the DMN and DAN represent alternate regimes of intrinsic brain function". As a result, the reader has to work very hard to follow what has been done and to understand the key messages of the paper.

Much is made of a potential power-law scaling of lifetime/interval times as being indicative of critical dynamics. A power-law distribution does not guarantee criticality, and could arise through other properties. Moreover, to accurately determine whether the proposed power-law is indicative of a scale-free system, the empirical property must be assessed over several orders of magnitude. It appears that only the 25-250 ms range was considered here.

The KS statistic is used to quantify the distance between the empirical and power-law distributions, which is then used as a marker of the degree of criticality. It is unclear that this metric is appropriate, given that transitions in and out of criticality can be highly non-linear. Moreover, the physiological significance of having some networks in a critical state while others are not is unclear. Each network is part of a broader system (i.e., the brain). How should one interpret apparent gradations of criticality in different parts of the system?

The sample size is small. I appreciate the complexity of the experimental paradigm, but the correlations do not appear to be robust. The scatterplots mask this to some extent by overlaying results from different brain regions, but close inspection suggests that the correlations in Fig 6 are driven by 2-3 observations with negative BOLD responses, the correlations in Fig 7A-B are driven by two subjects with positive WMSA volume, and Fig 7B is driven by 3 or so subjects with negative power-law fit values (indeed, x~0 in this plot is associated with a wide range of recall scores). Some correction for multiple comparisons is also required given the number of tests performed.

Figure 1 - panel labels would make it much easier to understand what is shown in this figure relative to the caption.

Figure 2- the aDMN does not look like the DMN at all. It is just the frontal lobe. Similarly, the putative DAN is not the DAN, but the lateral and medial parietal cortex, and cuneus.

P6, Line 11 - please define "simulation testing"

Reviewer #1:

Using two behavioral experiments, the authors partially replicate known effects that rotated faces decrease the benefit of visual speech on auditory speech processing.

As reported by the authors, Experiment 1 suffers from a design flaw considering that a temporal drift occurred in the course of the experiment. This clearly invalidates the reliability of the results and this experiment should be properly calibrated and redone. There is otherwise well-known literature on the topic.

Experiment 2 should be discussed in the context of divided attention tasks previously reported by researchers so as to better emphasize how and whether this is a novel observation.

Additionally:

-The question being addressed is narrowly and ill-construed: numerous authoritative statements in the introduction should reference existing work. For instance, seminal models of Bayesian perception (audiovisual speech processing especially) should be attributed to Dominic Massaro. Such statements as "studies fail to distinguish between binding and late integration" are surprising considering that the fields of multisensory integration and audiovisual speech processing have essentially and traditionally consisted in discussing these specific issues. To name a few researchers in the audiovisual speech domain: the work of Ruth Campbell, Ken Grant, and Jean-Luc Schwartz have largely contributed to refine debates on the implication of attentional resources to audiovisual speech processing using behavioral, neuropsychology, and neuroimaging methods. In light of the additional statements of the kind "The importance of temporal coherence for binding has not previously been established for speech", I would highly recommend the authors to do a thorough literature search of their topic (below some possible references as a start).

-What the authors understand to be "linguistic cues" should be better defined. For instance, the inverted face experiment aimed at dissociating whether visemic processing depends on face recognition (i.e. on holistic processing) or whether it depends on featural processing (and it does constitute a test, as suggested by the authors, of whether viseme recognition is a linguistic process per se).

Some references:

-Alsius, A., Möttönen, R., Sams, M. E., Soto-Faraco, S., & Tiippana, K. (2014). Effect of attentional load on audiovisual speech perception: evidence from ERPs. Frontiers in psychology, 5, 727.

-Chandrasekaran, C., Trubanova, A., Stillittano, S., Caplier, A., & Ghazanfar, A. A. (2009). The natural statistics of audiovisual speech. PLoS Comput Biol, 5(7), e1000436.

-Jordan, T. R., & Bevan, K. (1997). Seeing and hearing rotated faces: Influences of facial orientation on visual and audiovisual speech recognition. Journal of Experimental Psychology: Human Perception and Performance, 23(2), 388.

-Grant, K. W., & Seitz, P. F. (2000). The use of visible speech cues for improving auditory detection of spoken sentences. The Journal of the Acoustical Society of America, 108(3), 1197-1208.

-Grant, K. W., Van Wassenhove, V., & Poeppel, D. (2004). Detection of auditory (cross-spectral) and auditory-visual (cross-modal) synchrony. Speech Communication, 44(1-4), 43-53.

-Schwartz, J. L., Berthommier, F., & Savariaux, C. (2002). Audio-visual scene analysis: evidence for a" very-early" integration process in audio-visual speech perception. In Seventh International Conference on Spoken Language Processing.

-Schwartz, J. L., Berthommier, F., & Savariaux, C. (2004). Seeing to hear better: evidence for early audio-visual interactions in speech identification. Cognition, 93(2), B69-B78.

-Tiippana, Kaisa, T. S. Andersen, and Mikko Sams. (2004) "Visual attention modulates audiovisual speech perception." European Journal of Cognitive Psychology 16.3: 457-472.

-van Wassenhove, V. (2013). Speech through ears and eyes: interfacing the senses with the supramodal brain. Frontiers in psychology, 4, 388.

-Van Wassenhove, V., Grant, K. W., & Poeppel, D. (2007). Temporal window of integration in auditory-visual speech perception. Neuropsychologia, 45(3), 598-607.

Reviewer #1:

This work claims to show that learning of word associations during sleep can impair learning of similar material during wakefulness. The effect of sleep on learning depended on whether slow-wave sleep peaks were present during the presentation of that material during sleep. This is an interesting finding, but I have a lot of questions about the methods that temper my enthusiasm.

1) The proposed mechanism doesn't make sense to me: "synaptic down-scaling of hippocampal and neocortical language-related neurons, which were then too saturated for further potentiation required for the wake-relearning of the same vocabulary". Also lines 105-122. What is 'synaptic down-scaling'? what are 'language related neurons'? ' How were they 'saturated'? What is 'deficient synaptic renormalization'? How can the authors be sure that there are 'neurons that generated the sleep- and ensuing wake-learning of ... semantic associations'? How can inferences about neuronal mechanisms (ie mechanisms within neurons) be drawn from what is a behavioural study?

2) On line 54 the authors say "Here, we present additional data from a subset of participants of our previous study in whom we investigated how sleep-formed memories interact with wake-learning." It isn't clear what criteria were used to choose this 'subset of participants'. Were they chosen randomly? Why were only a subset chosen, anyway?

3) The authors do not appear to have checked whether their nappers had explicit memory of the word pairs that had been presented. Why was this not checked, and couldn't explicit memory explain the implicit memory traces described in lines 66-70 (guessing would be above chance if the participants actually remembered the associations).

Reviewer #1:

In the present manuscript, Evans and Burgess present a computational model of the entorhinal-hippocampal network that enables self-localization by learning the correspondence between stimulus position in the environment and internal metric system generated by path integration. Their model is composed of two separate modules, observation and transition, which inform about the relationship between environmental features and the internal metric system, and update the internal metric system between two consecutive positions, respectively. The observation module would correspond to projection from hippocampal place cells (PCs) to entorhinal grid cells (GCs), while the transition module would just update the GCs based on animal's movement. The authors suggest that the system can achieve fast and reliable learning by combining online learning (during exploration) and offline learning (when the animal stops or rests). While online learning only updates the observation model, offline learning could update both modules. The authors then test their model on several environmental manipulations. Finally, they discuss how offline learning could correspond to spontaneous replay in the entorhinal-hippocampal network. While the work will certainly be of great interest to the community, the authors should improve the presentation of their manuscript, and make sure they clearly define the key concepts of their study.

Online learning is clearly explained in the manuscript (e.g. l.101). Both environment structure (PC-PC connections) and the observation models (PC->GC synapses) are learned online, and this leads to stable grid cells. Then, the authors suggest that prediction error between the observation and transition models triggers offline inference that can update both models simultaneously. However, it is hard to figure out what offline learning is exactly. The section "Offline inference: The hippocampus as a probabilistic graph" is quite impossible to follow. Before explicitly defining offline learning the authors introduce a spring model of mutual connection between feature locations, but it is not clearly explained if this network is optimized online or offline.

The end of this section is particularly difficult to follow (line 180): "In this context, learning the PC-GC weights (modifying the observation model) during online localization corresponds to forming spatial priors over feature locations which anchor the structure, which would otherwise be translation or rotation invariant (since measurements are relative), learned during offline inference to constant locations on the grid-map.".

What really triggers offline inference is only explained much further in the manuscript, l. 366. Interestingly, this section refers to Fig. 1G for the first time, and should naturally be moved at the beginning of the manuscript (where Fig.1 is described)

Along the same lines, the role of offline learning should be made much more explicit in Fig. 2.

The frequent references to the method section too often break the flow of paper and make it difficult to follow. The authors should start their manuscript with a clear and simple definition of the core idea and concepts, almost in lay terms and only introducing a few annotations, using Fig. 1 (perhaps with some modification and focusing especially on panels A and F) as a visual support, and to move mathematical equations such as Eq. 3 to the supplementary information.

The authors have tested their model on various manipulations that have been previously carried out in freely moving animals, such as change in visual gain and in environmental geometry. These sections are interesting but, again, would be much clearer if presented after a clear explanation of online and offline learning procedures, not in between.

Finally, the authors discuss the relationship between offline inference and neuronal replay, as observed experimentally in vivo (Figs 6&7). This is interesting but would perhaps benefit from some graphical explanation. It is not immediately obvious to understand the fundamental difference between message passing (Fig. 6A) and simple synaptic propagation of activity among connected PC in CA3. Fig. 7 is actually a nice illustration of the phenomenon and should perhaps be presented before Fig. 6.

Reviewer #1:

The authors present a workflow based on targeted Nanopore DNA sequencing, in which they amplify and sequence nearly full-length 16S rRNA genes, to analyze surface water samples from the Cam river in Cambridge. They first identify a taxonomic classification tool, out of twelve studied, that performs best with their data. They detect a core microbiome and temporal gradients in their samples and analyze the presence of potential pathogens, obtaining species level resolution and sewage signals. The manuscript is well written and contains sufficient information for others to carry out a similar analysis with a strategy that the authors claim will be more accessible to users around the world, and particularly useful for freshwater surveillance and tracing of potential pathogens.

The work is sufficiently well-documented and timely in its use of nanopore sequencing to profile environmental microbial communities. However, given that the authors claim to provide a simple, fast and optimized workflow it would be good to mention how this workflow differs or provides faster and better analysis than previous work using amplicon sequencing with a MinION sequencer.

Many of the June samples failed to provide sufficient sequence information. Could the authors comment on why these samples failed? While some samples did indeed have low yields, this was not the case for all (supp table 2 and supp figure 5) and it could be interesting to know if they think additional water parameters or extraction conditions could have affected yields and subsequent sequencing depth.

One of the advantages of nanopore sequencing is that you can obtain species-level information. It would therefore be helpful if the authors could include information on how many of their sequenced 16S amplicons provided species-level identification.

While the overall analysis of microbial communities is well done, it is not entirely clear how the authors define their core microbiome. Are they reporting mainly the most abundant taxa (dominant core microbiome), and would this change if you look at a taxonomic rank below the family level? How does the core compare, for example, with other studies of this same river?

This is a page note. I can write overall comments about the pre-print here.

Tags can also be added below.

Reviewer #1:

This study takes two existing models of hippocampal theta rhythm generation, a reduced one with two populations of Izhikevich neurons, and a detailed one with numerous biophysically detailed neuronal models. The authors do some parameter variation on 3 parameters in the reduced model and ask which are sensitive control parameters. They then examine control of theta frequency through a phase response curve and propose an inhibition-based tuning mechanism. They then map between the reduced and detailed model, and find that connectivity but not synaptic weights are consistent. They take a subset of the detailed model and do a 2 parameter exploration of rhythm generation. They compare phenomenological outcomes of the model with results from an optogenetic experiment to support their interpretation of an inhibition-based tuning mechanism for intrinsic generation of theta rhythm in the hippocampus.

General comments:

1) The paper shows the existence of potential rhythm mechanisms, but the approach is illustrative rather than definitive. For example, in a very lengthy section on parameter exploration in the reduced model, the authors find some domains which do and don't exhibit rhythms. Lacking further exploration or analytic results, it is hard to see if their interpretations are conclusive.

2) The authors present too much detail on too few dimensions of parameters. An exhaustive parameter search would normally go systematically through all parameters, and be digested in an automated manner. For reporting this, a condensed summary would be presented. Here the authors look at 3 parameters for the reduced model and 2 parameters in the detailed one - far fewer than the available parameter set. They discuss the properties of these parameter choices at length, but then pick out a couple of illustrative points in the parameter domain for further pursuit. This leaves the reader rather overwhelmed on the one hand, and is not a convincing thorough exploration of all parameters of the system on the other.

3) I wonder if the 'minimal' model is minimal enough. Clearly it is well- supplied with free parameters. Is there a simpler mapping to rate models or even dynamical systems that might provide more complete insights, albeit at the risk of further abstraction?

4) Around line 560 and Fig 12 the authors conclude that only case a) is consistent with experiment. While it is important to match data to experiment, here the match is phenomenological. It misses the opportunity to do a quantitative match which could be done by taking advantage of the biological detail in the model.

5) The paper is far too long and is a difficult read. Many items of discussion are interspersed in the results, for example around line 335 among many others.

Reviewer #1:

Studies in mouse models and humans show synapse loss and dysfunction that precede neurodegeneration, raising questions about timing and mechanisms. Using longitudinal in vivo 2-photon imaging, Jackson et al., investigate pre- and post-synaptic changes in rTg4510 mice, a widely used mouse model of tauopathy. Consistent with cross sectional studies, the authors observed a reduction in density of presynaptic axons and dendritic spines in layer 1 cortex that relate to degeneration of neurites and dendrites over time. Taking advantage of an inducible model to overexpress tau p301L, they show that reducing expression of tau by DOX early in disease progression, resulted in amelioration of synapse loss, also consistent with other studies. Interestingly, the authors observed a significant reduction of dendritic spines less than a week before dendrite degeneration. In contrast, they observed plasticity and turnover of presynaptic structures weeks before axonal degeneration, suggesting different mechanisms.

Overall the results are interesting and largely consistent with previous findings. The new findings shown in Figures 5 and 6 address the timing of pre and postsynaptic loss and structural plasticity and reveal interesting differences; however, the data are highly variable and there are several issues that diminish enthusiasm as outlined below. Moreover, this study does not include new biological or mechanistic insight into the differences in pre- and post-synaptic changes from previous work in the field.

The main weakness relates to the significance and relevance beyond this specific mouse model and brain region. I appreciate the strengths but also technical challenges of in vivo longitudinal imaging, including a small field of view. Thus, the rationale and choice of model and brain region, and validation of key findings is critical to support conclusions. In this case, the tau model, although used by others, has several caveats relevant to the investigation of synapse loss (see point 2 below) that weaken this study and its impact.

1) Most of the work in the model related to synapse loss and dysfunction have been carried out in hippocampus and other regions of cortex in this model and tau and amyloid models. Here the authors focused on layer 1 of (somatosensory) cortex and followed neurites of pyramidal cells labeled with AAV:GFP, an approach that does not enable one image and track axons and dendrites from large numbers of neurons. They observed divergent dynamics in spine and presynaptic TBS of individual dendrites and axons. Given the small number of neurons sampled, significant noise in their imaging data, these findings need more validation using other approaches. This is particularly important for the data and conclusion drawn from Figures 5 and 6 (see point 3).

To estimate the overall effect of genotype the authors fitted Generalized Additive Mixed Models (GAMMS) to their data given the variability in the data within animals and genotype. It would be helpful to those less familiar to provide more comparisons of data using additional statistical tests and analyses along with power analyses calculations.

2) Major caveat with inducible Tau mode Tg4510. While this inducible model has the advantage of controlling timing of tau overexpression in neurons, a recent study by Gamache et al (PMID: 31685653) demonstrated that there are issues with the transgene insertion site and factors other than tau expression are actually what is driving the phenotype. Thus, differences in synaptic and behavioral phenotypes are based on the mouse line used and this needs to be carefully controlled. This was not addressed or discussed. See https://pubmed.ncbi.nlm.nih.gov/31171783/ and https://pubmed.ncbi.nlm.nih.gov/30659012/

3) The interesting new findings presented in Figures 5 and 6 that address timing and differences in axonal and dendritic/spine plasticity and loss need to be validated with more neurons and animals. The sample size is small ( i.e. n= 18 axons from 7 animals and not clear how many neurons. Given the significant variability of the data even within animals, these experiments and data are considered preliminary.

4) How does anesthesia influence these changes in structural plasticity observed? This was not addressed or discussed.

Reviewer #1:

Xu and colleagues compared the intersubject correlation (ISC) and intersubject functional connectivity (ISFC) of participants listening to narrative and argumentative texts while undergoing fMRI. Replicating earlier findings, they show that ISC in the DMN was greater when participants listened to an intact narrative than when they listened to a sentence-scrambled version of the same narrative. Listening to a sentence-scrambled argument elicited ISC in language and control regions of the brain, though interestingly, there was no region in the brain where ISC was greater when participants listened to an intact version of the argument. Instead, there was greater ISFC between the IPS and language areas of the brain when participants listened to the intact argument than when they listened to the scrambled argument. The authors interpret their results as suggesting that listening to the intact argument did not recruit additional brain systems, but instead promoted the cooperation between regions that were already involved in processing the argument.

Most prior work using "naturalistic stimuli" has examined the neural responses to narratives. This manuscript extends this work in an important way by examining how the brain responds to arguments, which comprise a non-trivial proportion of the linguistic content people are exposed to on a daily basis. The ISFC results (Fig. 7) are particularly noteworthy and novel. My main concerns have to do with the possibility that ISC for the scrambled argument seems to be stronger and more extensive than that for the intact argument, and how this might affect the authors' interpretation of their results. Below are some suggestions and comments which I think the paper could benefit from considering further:

1) I think it would be helpful to run the Scrambled Argument > Intact Argument ISC contrast. Visual inspection of Figure 2 suggests that ISC for the scrambled argument might be stronger than that for the intact argument, especially in control regions. If this is truly the case, I think the authors should discuss what this might imply about what is happening during the scrambled condition and if this affects thinking of the scrambled condition as a control for low-level linguistic features. In particular, the 2.97 out of 5 comprehensibility rating of the scrambled arguments suggests that participants might have understood the scrambled arguments. If participants are actively trying to make sense of the scrambled argument text, it seems like this could then drive observed differences in ISFC between the intact and scrambled arguments as well (e.g., decreased connectivity between control and language regions when trying to make sense of scrambled text, rather than increased connectivity between control and language regions when processing an intact argument).

2) More broadly, I think the authors need to make sure their effects aren't driven by the scrambled conditions. For example, for Figure 2 - figure supplement 2, the (Intact Narrative - Scrambled Narrative) > (Intact Argument - Scrambled Argument) contrast can be driven by high ISC in the Scrambled Argument condition, which would suggest a different interpretation of the results. My suggestion would be to run the contrast as (Intact Narrative - Scrambled Narrative) > max((Intact Argument - Scrambled Argument),0) to make sure that the contrast isn't driven by a negative value on the right hand side of the inequality.

3) Point 2 also applies to Figures 6 and 7. Relatedly, the rightmost panel of Figure 6C suggests that the analysis is indeed capturing some edges where the SES of the Scrambled Argument is greater than that of the Intact Argument.

4) How well do the vertexes identified in Figure 7D overlap with the Intact Argument > Resting map? Given the authors interpretation that the ISFC results suggest cooperation between areas involved in processing the intact stimulus, I think this should be properly assessed.

5) Both ISC and ISFC capture only signal that is shared across participants. Most narratives are crafted such that all listeners have a similar interpretation. This is unlike arguments, where different listeners might agree with an argument to a different extent. If listeners had differing interpretations of the argument, ISC/ISFC would miss brain activity/connectivity involved in processing an argument. I think this possibility should be considered and discussed, especially given the null DMN finding for the argumentative texts.

6) For the t-tests on the behavioral ratings , it looks like the authors collapsed over the two texts within a category. This doesn't seem right, given that the ratings for each text are dependent. A mixed model approach would be more appropriate. I doubt this will change the results, but I think it would be good to follow best practices when possible.

Reviewer #1:

Major issues:

I have two major comments on the work.

1) The authors motivate the work from the use of naturalistic speech, and the application of the developed method to investigate, for instance, speech-in-noise deficits. But they do not discuss how comprehensible the peaky speech in fact is. I would therefore like to see behavioural experiments that quantitatively compare speech-in-noise comprehension, for example SRTs, for the unaltered speech and the peaky speech. Without such a quantification, it is impossible to fully judge the usefulness of the reported method for further research and clinical applications.

2) The neural responses to unaltered speech and to peaky speech are analysed by two different methods. For unaltered speech, the authors use the half-wave rectified waveform as the regressor. For peaky speech, however, the regressor is a series of spikes that are located at the timings of the glottal pulses. Due to this rather different analysis, it is impossible to know to which degree the differences in the neural responses to the two types of speech that the authors report are due to the different speech types, or due to the different analysis techniques. The authors should therefore use the same analysis technique for both types of speech. It might be most sensible to analyse the unaltered speech through a regressor with spikes at the glottal pulses a well. In addition, it would be good to see a comparison, say of a SNR, when the peaky speech is analysed through the half-wave rectified waveform and through the series of spikes. This would also further motivate the usage of the regressor with the series of spikes.

Reviewer #1:

The authors studied the over-representation of imprinted genes in the mouse brain by using fifteen single-cell RNA sequencing datasets. The analysis was performed at three levels 1) whole-tissue level, 2) brain-region level, and 3) region-specific cell subpopulation level. Based on the over-representation and gene-enrichment analyses, they interpreted hypothalamic neuroendocrine populations and monoaminergic hindbrain neurons as specific hotspots of imprinted gene expression in the brain.

Objective:

Though the study is potentially interesting, the expression of imprinted genes in the brain and hypothalamus is already known (Davies W et al., 20005, Shing O et al. 2019, Gregg et al, 2010 including many other studies cited in the paper). However, the authors put forth two objectives, the first being whether imprinted gene expression is actually enriched in the brain compared to other adult tissues, where they did find brain as one of the tissues with over-represented imprinted genes. Secondly, whether the imprinted genes are enriched in specific brain regions. The study objectives cannot qualify as completely novel as it is the validation of most of what is already known using scRNA-seq datasets.

Methods and Results

Pros:

-15 scRNA-seq datasets were analysed independently and they were processed as in the original publication.

-Two enrichment methods used to find tissue-specific enrichment of imprinted genes and appropriate statistics applied wherever necessary.

Concerns:

-It is not clear how the over-representation using fisher's exact test was calculated? It would be appropriate to include the name of the software or R package, if used, in the basic workflow section of Materials and methods.

-Why did authors particularly use Liger in R for GSEA analysis?

-GSEA plots generated using Liger and represented for each analysis in the paper by itself does not look informative. For eg. in figure 4 and other GSEA plots in the paper- i) Which 'score' does the Y-axis represent? Include x-axis label and mention corrected GSEA q value either in the legend or the figure. ii) Was the normalized enrichment score (NES) calculated? What genes in the cluster represent maximum enrichment? A heat map of the imprinted genes contributing to the cell cluster will add more clarity to the GSEA plots.

-Apart from the tissue-specific enrichment of gene sets, a functional GO/pathways enrichment of the group of imprinted genes will strengthen the connection of these genes with feeding, parental behavior and sleep.

-Are these imprinted genes coexpressed across the analyzed brain structures, as the authors repeatedly stress on the functioning of imprinted genes as a group?

-A basic workflow schematic might be necessary for an easy and quick understanding of the methods.

Overall, the study gives some insight into the brain regions, particularly cell clusters in the brain where imprinted genes could be enriched. However, the nature of the study is preliminary and validates most of previous studies. The authors have already highlighted some of the limitations of the study in the discussion.

Reviewer #1:

The manuscript “A computationally designed fluorescent biosensor for D-serine" by Vongsouthi et al. reports the engineering of a fluorescent biosensor for D-serine using the D-alanine-specific solute-binding protein from Salmonella enterica (DalS) as a template. The authors engineer a DalS construct that has the enhanced cyan fluorescent protein (ECFP) and the Venus fluorescent protein (Venus) as terminal fusions, which serve as donor and acceptor fluorophores in resonance energy transfer (FRET) experiments. The reporters should monitor a conformational change induced by solute binding through a change of the FRET signal. The authors combine homology-guided rational protein engineering, in-silico ligand docking and computationally guided, stabilizing mutagenesis to transform DalS into a D-serine-specific biosensor applying iterative mutagenesis experiments. Functionality and solute affinity of modified DalS is probed using FRET assays. Vongsouthi et al. assess the applicability of the finally generated D-serine selective biosensor (D-SerFS) in-situ and in-vivo using fluorescence microscopy.

Ionotropic glutamate receptors are ligand-gated ion channels that are importantly involved in brain development, learning, memory and disease. D-serine is a co-agonist of ionotropic glutamate receptors of the NMDA subtype. The modulation of NMDA signalling in the central nervous system through D-serine is hardly understood. Optical biosensors that can detect D-serine are lacking and the development of such sensors, as proposed in the present study, is an important target in biomedical research.

The manuscript is well written and the data are clearly presented and discussed. The authors appear to have succeeded in the development of D-serine-selective fluorescent biosensor. But some questions arose concerning experimental design. Moreover, not all conclusions are fully supported by the data presented. I have the following comments.

1) In the homology-guided design two residues in the binding site were mutated to the ones of the D-serine specific homologue NR1 (i.e. F117L and A147S), which lead to a significant increase of affinity to D-serine, as desired. The third residue, however, was mutated to glutamine (Y148Q) instead of the homologous valine (V), which resulted in a substantial loss of affinity to D-serine (Table 1). This "bad" mutation was carried through in consecutive optimization steps. Did the authors also try the homologous Y148V mutation? On page 5 the authors argue that Q instead of V would increase the size of the side chain pocket. But the opposite is true: the side chain of Q is more bulky than the one of V, which may explain the dramatic loss of affinity to D-serine. Mutation Y148V may be beneficial.

2) Stabilities of constructs were estimated from melting temperatures (Tm) measured using thermal denaturation probed using the FRET signal of ECFP/Venus fusions. I am not sure if this methodology is appropriate to determine thermal stabilities of DalS and mutants thereof. Thermal unfolding of the fluorescence labels ECFP and Venus and their intrinsic, supposedly strongly temperature-dependent fluorescence emission intensities will interfere. A deconvolution of signals will be difficult. It would be helpful to see raw data from these measurements. All stabilities are reported in terms of deltaTm. What is the absolute Tm of the reference protein DalS? How does the thermal stability of DalS compare to thermal stabilities of ECFP and Venus? A more reliable probe for thermal stability would be the far-UV circular dichroism (CD) spectroscopic signal of DalS without fusions. DalS is a largely helical domain and will show a strong CD signal.

3) The final construct D-SerFS has a dynamic range of only 7%, which is a low value. It seems that the FRET signal change caused by ligand binding to the construct is weak. Is it sufficient to reliably measure D-serine levels in-situ and in-vivo? In Figure 5H in-vivo signal changes show large errors and the signal of the positive sample is hardly above error compared to the signal of the control. Figure 5G is unclear. What does the fluorescence image show? Work presented in this manuscript that assesses functionality and applicability of the developed sensor in-situ and in-vivo is limited compared to the work showing its design. For example, control experiments showing FRET signal changes of the wild-type ECFP-DalS-Venus construct in comparison to the designed D-SerFS would be helpful to assess the outcome.

4) The FRET spectra shown in Supplementary Figure 2, which exemplify the measurement of fluorescence ratios of ECFP/Venus, are confusing. I cannot see a significant change of FRET upon application of ligand. The ratios of the peak fluorescence intensities of ECFP and Venus (scanned from the data shown in Supplementary Figure 2) are the same for apo states and the ligand-saturated states. Instead what happens is that fluorescence emission intensities of both the donor and the acceptor bands are reduced upon application of ligand.

much more positive/optimistic outlook than hobbes

If women corrupted the corruption will grow in society, because women shape the half of society, beside they playing an important role in families. so it shows that when a women corrupted then his son can also follow his path.

what does Krishna meant by three world? is it the three world that they believe to the life after death?

Arjuna stated that there are all my kinsman such as: teachers, fathers, sons i do not want to go the battle and kill them. is Krishna meant only his kinsman the reason to not fight or he was not able to do it?

Krishna state that if i want kingship, it is not because of victory or pleasure. then what Krishna meant by delight and life itself in the statement?

Arjuna stated in this part to Krishna about his weakness that he is able to fight, he do not have power to fight in the battle.

This helps to address the institutional type question

Dominance through Elites. This is a grievous danger. We've seen a constant perversion of Genesis 1:27-28 (fruitful, multiply, dominate earth) by those who see this charge as an exclusive office rather than a "law" to be governed through 3rd density complexes! Dominion was designed to be a service to 2nd density and the way toward 4th Density for those in 3rd: NOT SERVICE TO SELF!

DOI: 10.1111/bph.15156

Resource: ZFIN_ZDB-GENO-170316-1

Curator: @Naa003

SciCrunch record: RRID:ZFIN_ZDB-GENO-170316-1

What is this?

DOI: 10.1111/bph.15156

Resource: ZFIN_ZDB-GENO-130722-1

Curator: @Naa003

SciCrunch record: RRID:ZFIN_ZDB-GENO-130722-1

What is this?

DOI: 10.1111/bph.15156

Resource: ZFIN_ZDB-ALT-171010-1

Curator: @Naa003

SciCrunch record: RRID:ZFIN_ZDB-ALT-171010-1

What is this?

DOI: 10.1111/bph.15156

Resource: ZFIN_ZDB-ALT-130409-1

Curator: @Naa003

SciCrunch record: RRID:ZFIN_ZDB-ALT-130409-1

What is this?

DOI: 10.1016/j.celrep.2020.108054

Resource: (ZFIN Cat# ZDB-ALT-070316-1,RRID:ZFIN_ZDB-ALT-070316-1)

Curator: @Naa003

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Curator comments: allele name: c264Tg Danio rerio ZFIN Cat# ZDB-ALT-070316-1

What is this?

DOI: 10.1016/j.celrep.2020.108054

Resource: ZFIN_ZDB-ALT-141023-1

Curator: @Naa003

SciCrunch record: RRID:ZFIN_ZDB-ALT-141023-1

What is this?

DOI: 10.7554/eLife.52160

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Curator: @evieth

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What is this?

DOI: 10.1016/j.celrep.2020.03.024

Resource: (ZFIN Cat# ZDB-ALT-110310-1,RRID:ZFIN_ZDB-ALT-110310-1)

Curator: @ethanbadger

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What is this?

DOI: 10.1016/j.celrep.2020.03.024

Resource: (ZFIN Cat# ZDB-ALT-110308-1,RRID:ZFIN_ZDB-ALT-110308-1)

Curator: @ethanbadger

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What is this?

DOI: 10.1016/j.celrep.2020.03.024

Resource: (ZFIN Cat# ZDB-ALT-110308-1,RRID:ZFIN_ZDB-ALT-110308-1)

Curator: @ethanbadger

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What is this?

DOI: 10.1016/j.celrep.2020.03.024

Resource: ZDB-ALT-180625-1

Curator: @ethanbadger

SciCrunch record: RRID:ZDB-ALT-180625-1

What is this?

DOI: 10.7554/eLife.55913

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Curator: @Naa003

SciCrunch record: RRID:ZFIN_ZDB-GENO-060919-1

What is this?

(Gilgamesh) lets (no) girl go free to (her bridegroom) What the author meant by this sentence?

(he was the vanguard) Gilgamesh was brave.

DOI: 10.1016/j.cels.2020.02.007

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What is this?

variant id lookup result: http://localhost:8001/test.html

Monarch lookup result: http://localhost:8001/test.html

DOI: 10.7554/eLife.47699

Resource: (ZFIN Cat# ZDB-ALT-100721-1,RRID:ZFIN_ZDB-ALT-100721-1)

Curator: @evieth

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What is this?

DOI: 10.2337/db19-0508

Resource: (IMSR Cat# KOMP_VG15351-1-Vlcg,RRID:IMSR_KOMP:VG15351-1-Vlcg)

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What is this?

DOI: 10.7554/eLife.48914

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What is this?

DOI: 10.7554/eLife.48914

Resource: (ZFIN Cat# ZDB-ALT-141008-1,RRID:ZFIN_ZDB-ALT-141008-1)

Curator: @evieth

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What is this?

DOI: 10.7554/eLife.54935

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What is this?

DOI: 10.1016/j.devcel.2020.03.017

Resource: (ZFIN Cat# ZDB-ALT-110705-1,RRID:ZFIN_ZDB-ALT-110705-1)

Curator: @ethanbadger

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What is this?

DOI: 10.1016/j.devcel.2020.03.017

Resource: (ZFIN Cat# ZDB-ALT-150904-1,RRID:ZFIN_ZDB-ALT-150904-1)

Curator: @ethanbadger

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What is this?

DOI: 10.1016/j.devcel.2020.03.017

Resource: (ZFIN Cat# ZDB-ALT-110421-1,RRID:ZFIN_ZDB-ALT-110421-1)

Curator: @ethanbadger

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What is this?

DOI: 10.7554/eLife.54937

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Curator: @evieth

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What is this?

DOI: 10.7554/eLife.54937

Resource: (ZFIN Cat# ZDB-ALT-080528-1,RRID:ZFIN_ZDB-ALT-080528-1)

Curator: @evieth

SciCrunch record: RRID:ZFIN_ZDB-ALT-080528-1

What is this?

DOI: 10.7554/eLife.54937

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Curator: @evieth

SciCrunch record: RRID:ZFIN_ZDB-ALT-080528-1

What is this?

DOI: 10.7554/eLife.54937

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What is this?

DOI: 10.1016/j.cub.2020.04.020

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Curator: @ethanbadger

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What is this?

DOI: 10.1016/j.cub.2020.04.020

Resource: (ZFIN Cat# ZDB-ALT-120117-1,RRID:ZFIN_ZDB-ALT-120117-1)

Curator: @ethanbadger

SciCrunch record: RRID:ZFIN_ZDB-ALT-120117-1

What is this?

DOI: 10.7554/eLife.53995

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Curator: @evieth

SciCrunch record: RRID:ZFIN_ZDB-ALT-181113-1

Curator comments: ZFIN Cat# ZDB-ALT-181113-1

What is this?

DOI: 10.1016/j.celrep.2020.107790

Resource: (ZFIN Cat# ZDB-ALT-121009-1,RRID:ZFIN_ZDB-ALT-121009-1)

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Curator comments: Zebrafish: Tg(hcrt:nfsB-EGFP)

What is this?

This poem is a sonnet with 3 quatrains (lines of 4) and a couplet (set of lines), so in order to help your comprehension, you want to divide the stanzas by the structure.

Themes in poetry show up in the last lines or last stanza. A theme is a universal statement about what the author believes.

If we consider that poems are either a MEMORY or an EMOTION we can use word choice to figure out the theme. This poem is about emotion, particularly about not giving in to an enemy.

So universal theme would be...

symbolism - McKay calls his enemies "monsters", but he doesn't seem to be fighting fictional beasts in the poem, so not literal monsters.

Poetry 101:

Step 1 - Read this poem. Consider this just your first run through. Take notice of punctation (periods, comma, etc.). That's where you stop when reading a poem.

Step 2 - Read the poem again - But now you want to think in Costa's Level 2. So, notice rhetorical devices and what they are doing in the text. Divide your stanzas so you can chunk information and analyze in pieces.

Step 3 - Find the theme. Theme is a universal statement that tells us how the author feels about a topic. This is not a subject - like the beach - but what does the beach, or the setting, mean to the author. Themes are found in the last lines or the last stanza of a poem.

The world continues to change, warp, consume, ebb, and flow around us - good and bad directions.

So if we write a universal theme...

It's easy to ignore the word "but"; however, this is a conjunction that tells you the author is about to flip everything they've been saying.

It's a tiny word with a major impact. So this is a good place to make a division in a poem. To see what was being established, and how the author now wants to change it.

DOI: 10.1016/j.cub.2020.05.016

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What is this?

We mus examine life to be able to choose. We are born taught things that sometimes we don't understand but we just know because it was passed down to us, but it is up to us to experience these things t be able to decide for ourselves.

definition

• Point = That of which there is no part.
• Line = a length without breadth.

Ahuja, A. (2020, June 16). The two-metre rule and other imperfect Covid-19 risk calculations | Free to read. https://www.ft.com/content/30adbfa1-178f-47e2-97a8-bd41ca590999

the reason why the author wrote this article

Avis personnel de l'auteur et vision assez limitée du champ des possibles qu'offre une (des) rencontre(s) qu'elle soit virtuelle ou non.

L'auteur ne va t-il pas un peu vite dans sa déduction , le communautarisme comme il est évoqué ici appartient à un autre débat

Perception personnelle et réductrice de l'auteur qui ne représente pas la réalité. Les utilisateurs doivent-ils justifier d'une attente particulière pour motiver leur inscription ?

L'auteur se confère des qualités d'expert en évoquant une série d'hypothétiques troubles névrotiques liés à l'utilisation des sites de rencontres. Il vulgarise et banalise des pathologies sérieuses.

L'usage pathologique d'internet est réel, mais l'auteur invoque ici un problème de taille sans s'appuyer sur une source solide illustratrant la réalité des risques encourus par les utilisateurs des sites de rencontres.

Voici le premier argument. En effet, il nous est dit que ce phénomène de dispersion atteint son paroxysme avec les femmes actives qui doivent gérer vie professionnelle et vie de famille, mais il serait bon d'approfondir d'avantage. Nous pouvons nous demander pourquoi cela se produit chez les femmes actives. Nous pouvons donc réfléchir si cela n'est pas dû à ce qu'on appelle la charge mentale, puisque ce sont majoritairement les femmes qui s'occupent du foyer et des enfants. A l'inverse, nous pouvons nous demander si la dispersion n'atteint pas son paroxysme parce qu'elles sont d'avantage multitâches que les hommes. En effet, elles sont capables de faire plusieurs tâches simultanément, et cela peut aussi engendrer une dose supplémentaire de travail. Ce phénomène de multitâche peut donc entraîner d'avantage de dispersion de leur part.

I would hazard that US is far above optimal state size for wellbeing and wisdom.

But why does it rain money on them? And who else does that?

This is the scope of Test 2.

Figaro est sarcastique sur la censure

Il y a du mouvement. Il s'assoit et se lève

Il est dégoûté de lui-même et veut avoir une carrière honnête

Il parle de malhonnêteté et de ses avantages

Il parlait de ses différentes professions.

Et ainsi commence le plaidoyer de l'auteur et de son point de vue rhétorique, mettant bien en avant la puissance du numerique sur le biologique dans l'idée collective

optimal questions

YES I THOUGHT THE EXACT SAME FUCKING THING

Monarch lookup result: https://monarchinitiative.org/phenotype/HP:0000218