Reviewer #2 (Public review):

In this manuscript, Ritter et al. propose a model of working memory (WM) that combines feedforward and rotational dynamics. The model is discovered by optimizing a linear RNN using a loss function that encourages maximization of signal-to-noise ratio (SNR) and minimization of activation magnitude. The authors argue that the optimized model outperforms other WM models in terms of SNR and energetic efficiency, while also better replicating key features of neural responses recorded in monkey pre-frontal cortex (PFC) during a WM task. The authors also draw connections to state space models (SSM) used for other machine learning applications.

My main issue with this manuscript is that it does not appear to convincingly demonstrate that rotational dynamics offer any advantage over purely feedforward dynamics. The authors adopt three criteria according to which they compare models:<br /> (1) SNR.<br /> (2) Energy efficiency.<br /> (3) Similarity to neural data.

In terms of SNR, purely feedforward models seem to perform similarly to the optimized models (Figure 1). Figure 1 does seem to show that the optimized network produces responses of smaller magnitude when the number of units is large, but the authors do not explain why adding rotational dynamics would produce such a relationship. In fact, the responses that are plotted for the feedforward network in Figures 1B, 2C, and 5E look similar, if not smaller in magnitude than those of the optimized model. Lastly, while the authors claim in the body of the text that the optimized model replicates key features of monkey PFC responses better than the purely feedforward model, this is not apparent to me from the comparisons plotted in Figure 5E-J. The authors thus do not show strong evidence that the model they propose beats what they claim is an established baseline on any of the three criteria.

Another weakness of the manuscript is that the comparison to attractor and feedforward models seems somewhat unfair. In Figure 1, the rotational model is optimized, while the parameters for the attractor and feedforward models seem to have been at least partially chosen by hand. Figure 5C again shows the three models side by side, but the fact that it compares the same network at different stages during training complicates the comparison. Instead, one should compare the rotational solution to the optimal attractor and feedforward models, respectively (obtained by constrained optimization). From looking at the flow-fields, it seems that a feedforward network with an optimized level of amplification may work just as well. On a mechanistic level, it is unclear what computational advantage rotations offer over feedforward dynamics in the WM context.

The choice of baseline models to compare against might be questionable. The simple line attractor model by Seung et al. (1996) was initially designed to explain oculomotor integration. It is true that a line attractor has been suggested as a mechanism for working memory, e.g., in the seminal work by Machens et al (2005). However, it seems fair to say that most studies employing non-linear networks have focused on point attractors as mechanisms of working memory (e.g., Wong & Wang, 2006; Driscoll, Shenoy, Sussillo, 2024). A point attractor arguably does not suffer the SNR issues of a line attractor, because it does not lead to integration of the noise over time. However, non-trivial point attractors cannot be implemented in linear networks of the kind studied by the authors of the present study.

The authors should expand their discussion to include other, potentially closely related work proposing rotation-like dynamics in artificial neural networks during working memory. In particular, the manuscript does not discuss Sharma, Proca, et al, ICML 2026, which describes a rotational solution to a similar WM task obtained by optimizing linear RNNs (Sharma et al., 2026, Fig. 6). Notably, Sharma et al. arrive at a similar rotational (and likely also non-normal) mechanism without using either noisy inputs or a constraint on energy efficiency. The authors of the present manuscript should discuss to what extent this finding contradicts their claim that "normative pressures on noise-robustness and energetic cost shape the complex dynamics of WM circuits." (present manuscript, Introduction). Given the obvious parallels between the two studies, a comparison between the present work and Sharma et al. (2026) would add necessary context to the Discussion.

The authors should also clarify the significance of the "novel method for optimization of continuous-time RNNs driven by noisy inputs" (see Discussion) that the authors propose. This method is mentioned in the first line of the Discussion section but is barely discussed, let alone sufficiently explained, in the previous Sections. The only time a comparison to BPTT with a simple MSE loss is mentioned, it is stated that the two procedures produce the same results. The novel method appears to consist of a loss with two terms, the second of which is a well-known L2-penalty on unit activations (Sussillo et al., 2015). It is not clear that the method is either novel or necessary to obtain the reported results.

Except for the fact that higher-dimensional networks also converge on rotational solutions, Figure 3 does not add much to the reader's understanding of the optimized model (except for panel F). I find the comparison to SSMs too superficial to provide real insight.

Figure 4 claims to show that the optimized model recapitulates "a range of properties observed in prefrontal cortex and other brain areas during WM tasks" (p. 7) but does not show neural data for comparison.

Reviewer #2 (Public review):

Summary:

Sarcomeres, the contractile units of skeletal and cardiac muscle, contract in a concerted fashion to power myofibril and thus muscle fiber contraction.

Muscle fiber contraction depends on the stiffness of the elastic substrate of the cell, yet it is not known how this dependence emerges from the collective dynamics of sarcomeres. Here, the authors analyze contraction time series of individual sarcomeres using live imaging of fluorescently labeled cardiomyocytes cultured on elastic substrates of different stiffness. They find that a reduced collective contractility of muscle fibers on unphysiologically stiff substrates is partially explained by a lack of synchronization in the contraction of individual sarcomeres.

This lack of synchronization is at least partially stochastic, consistent with the notion of a tug-of-war between sarcomeres on stiff sarcomeres. A particular irregularity of sarcomere contraction cycles is 'popping', the extension of sarcomers beyond their rest length. The statistics of 'popping' suggest that this is a purely random process.

Strengths:

This study thus marks an important shift of perspective from whole-cell analysis towards an understanding the collective dynamics of coupled stochastic sarcomeres.

Reviewer #2 (Public review):

Summary:

This study analyzed consequences of Agbl5 mutation on ependymal cells development and function. Authors first characterize their mutant mouse line reporting a reduced lifespan and severe hydrocephalus. Next, they report defect in ependymal cell cilia number and motility. They provide evidence for impaired basal bodies organisation, cilia glutamylation.

Strengths:

Description of a mutant mouse which implicate Cytosolic Carboxypeptidase 5 (the product of Agbl5 gene) for proper ependymal cells.

Weaknesses:

Description of phenotype are incomplete:

Previous comment: Microtubules are involved in the local organization of ciliary basal bodies (see Werner et al., Vladar et al.,2011; Boutin et al., 2014). It would be interesting that the author checks whether the subapical network of microtubule is glutamylated or not during ependymal cells differentiation and how this network is affected in their mutants.

Although authors now provide images of glutamylation in figure S8 their conclusion claiming that GT335 signal is increased in cilia of Agbl5M1/M1 mutant is not supported convincingly by those pictures. Quantification would be needed.

Reviewer #3 (Public review):

Strengths:

It's sort of novel to study the heritability of movie-watching fMRI data. The methodology the authors used in the paper is also supportive of their findings. Figures are nicely organized and plotted. They finally found that sensory processing in the human brain is under genetic control over stable aspects of brain function (here referring to neural timescale and resting state connectivity).

Weaknesses:

What I am worried about most is the sample size and interpretation of heritability.

(1) Figure 1. I assumed that the authors just calculated the ISC within each group (MZ, DZ, and UR). Of course, you can get different variations between each group. Therefore, there is heritability. Why not calculate ISC across the whole sample, then separate MZ, DZ, and UR?

(2) Heritability scores in the paper are sort of small. If the sample size is small, please consider p-values, which will tell more about the trustworthiness of your heritability.

(3) I don't understand the high-frequency signals in fMRI data. It's always regarded as noise, the band 1 here in particular.

(4) The statement "we show that the heritability of brain activity patterns can be partially explained by the heritability of the neural timescale" should come from Figure 5. However, after controlling for NT, the heritability decreased max. 0.025 in temporal areas. I am not sure this change supports the statement. If the visual cortex is outlined, and combining ISC changes in the visual cortex, I think this would somehow be answered. Instead of delta h2, adding a new model h2 would be obvious to the readers.

(5) Figures 7 and 8, when getting the difference of heritability, please also consider the standard errors of the heritability estimates. Then you can compare across networks/regions.

(6) I think movie VS resting state is a really important result in this paper. However, there is almost no discussion. Discussing this part would be more beneficial for understanding the genetic control over the neuron arousal and excitation circuits.

Comments on revised version.

The whole manuscript has been improved a lot, and the concerns have been clarified.

Reviewer #2 (Public review):

Summary:

Inactivating VHL mutations are common in clear cell renal cell carcinoma, and about half of those mutations unfold/destabilize the protein rather than directly interfering with critical protein-protein interactions. The authors identify a compound that can stabilize/refold mutant VHL and seemingly restore its ability to downregulate its major downstream targets.

Strengths:

The authors use a clever combination of virtual and cell-based screens, followed by suitable biophysical and cell-based validation assays, to arrive at a VHL refolder. This compound is suboptimal from an ADME point of view, but could be a starting point for further medicinal chemistry optimization. Success would have implications for other diseases linked to similar loss-of-function mutations.

Weaknesses:

In going from CP4 to CP4.29 the authors screened based on downregulation of HIF. This is logical but also introduces the danger of identifying chemicals that can downregulate HIF in an "off-target" manner i.e. non-specifically. It therefore essential to clearly show that CP4.29 downregulates steady-state levels of HIF and HIF target genes in cells with suitable (hydrophobic core) VHL mutants but not in isogenic cells lacking VHL.

Reviewer #2 (Public review):

Summary:

This study investigates coordinated beak-tongue movements in seed manipulation, biting, and dehusking in songbirds. A comparative analysis of the seed-eating process in two songbird species with different biting forces, the domestic canary and Java sparrow, was conducted using high-speed XROMM with anatomical marker tracking and quantitative behavioral analysis. The authors have done a great job analyzing upper and lower beak rotation and translation, seed orientation and movement speed, and tongue kinematics.

Strengths:

The methodological approach of using high-speed (500 fps) X-ray reconstruction for 3D kinematic tracking in small animals is novel and powerful. It enables high temporal resolution tracking of orofacial movements and could potentially inspire future orofacial research in mammals, including mice and marmosets. Moreover, this study encompasses a wide range of anatomical components involved in seed manipulation behavior, including the upper and lower beak, the tongue, and jaw muscles. The behavioral quantification of these components is solid. The findings that both the upper and lower beaks contribute to seed processing, that the lower beak exhibits greater up-and-down and left-to-right flexibility than the upper beak during seed processing, and that the tongue plays an important role in transporting seeds into the mouth are all solid conclusions consistent with observations of bird feeding behavior. Nevertheless, it is valuable to confirm and quantitatively characterize these observations experimentally. The videos are excellent and very informative.

Weaknesses:

(1) The paper often resorts to qualitative descriptions (e.g., "a high positive correlation of tongue velocity and seed velocity", "Compared to positioning, the measured velocities of both seed and tongue were much lower") instead of providing exact quantitative measurements or statistical results. The authors stated that temporal autocorrelation biases standard statistical analyses (lines 205-210), but this rationale does not justify the absence of statistical validation. Suggestion: use appropriate methods for time-series data, such as a permutation test, to test the significance of correlations between variables and avoid false positives.

(2) (Minor) The marker-tracking image shown in Figure 1B could benefit from the inclusion of a higher-contrast, zoomed-in frame of the head showing the metal markers without the red tracking points, alongside the same frame with the red tracking points overlaid, to provide readers with a clearer view of the X-ray image and the methodology and its precision.

(3) (Minor: possibly soften the mechanistic claim). The proposed mechanism of lingual papillae on the tongue surface may aid food manipulation and food movement towards the posterior region of the mouth is interesting, yet the evidence describing their morphology is not strong enough to support the claim about their functional roles. Furthermore, the claim that papillae orientation affects food transport in lines 294-296 lacks supporting experimental evidence. In addition, the roles of extrinsic and intrinsic tongue muscles in controlling dexterous tongue shape changes and movements are not discussed.

Reviewer #2 (Public review):

Summary:

Bonanno and colleagues combine unilateral pyramidotomy, continuous voluntary complex-wheel running, whole-brain intersectional CSN tracing, and c-Fos mapping to ask whether rehabilitation reorganizes the supraspinal collaterals of the intact corticospinal tract neurons. The study is technically ambitious and competent, the uPyX + complex-wheel + intersectional-tracing + BrainJ combination is smart and interesting, the behavioral effect is convincing, and the blinding and exclusion criteria are explicit. The central anatomical finding - a CSN-specific, whole-brain projectome comparison with subregional LPGi/GiA/MdV granularity - is a legitimate contribution that builds on Asboth 2018. However, the strength of evidence does not support the strongest causal wording in the current abstract, significance statement, and parts of the discussion: the results remain correlational, the MdV-behavior correlation is modest, and its significance is sensitive to the unit of analysis. A major revision is recommended, primarily of framing and quantitative robustness, rather than because the central dataset is unconvincing.

Strengths:

(1) Technically ambitious and technically competent study addressing a relevant gap: brain-wide mapping of intact-CSN reorganization under continuous voluntary rehabilitation.

(2) The combination of uPyX, complex-wheel running, intersectional tracing, and BrainJ whole-brain projection analysis is novel and well integrated.

(3) Behavioral effect is convincing, blinding, and exclusion criteria are explicit.

(4) The central anatomical finding (CSN-specific whole-brain projectome under rehab, with LPGi/GiA/MdV subregional resolution) is a legitimate contribution that builds on Asboth 2018. The closest recent works (Lemieux et al. 2024, Jeleva et al. 2026) study reticulospinal rather than CSN plasticity and are complementary rather than competing.

Weaknesses:

(1) Causal framing extends beyond what the current evidence supports.

The abstract and significance statement present MdV as a potential mediator, or even a central locus, through which rehabilitation re-establishes descending control of the impaired limb. This is stronger than the evidence. What the paper shows is that CSN collateral projection density in MdV has a mild-to-medium correlation with behavioral recovery, and that this region is already known from prior work (Esposito 2014) to be relevant for skilled forelimb function. That is an interesting anatomical correlation, not a demonstration of mediation. No manipulation of MdV or of MdV-projecting CST terminals is performed; there is no silencing, no pathway-specific perturbation during rehabilitation, and no test showing that the identified sprouting is necessary for recovery. The limitations section acknowledges this, but the prominent claims do not.

(2) The behavioral caveat on what is actually novel.

The cleanest way to state what is genuinely new, clearer than the abstract itself, is this: when a CSN population loses part of its spinal target domain (via contralateral uPyX denervating the opposite cord), some CSNs from the opposite cortex appear to redirect growth into brainstem collaterals (LPGi, GiA, MdV). The compensation is plausibly sufficient to restore gross descending drive to the impaired forelimb, but most probably inadequate for the fractionated, cortico-motoneuronal fine-grain control that the direct CST normally provides. That distinction - recovery of drive and even skilled locomotor control vs. recovery of fine precision - is consistent with the ladder-rung improvements the paper reports (footfall counts are an integrated gross-placement metric) and with the skilled-reaching literature (Esposito 2014 and similar), which suggests precision grip and digit individuation would not be fully recovered by an MdV-centered detour. This note is also translationally important when we ask what humans consider fine motor control, which is mostly object manipulation. Relatedly, the ladder task is "skilled" in the operational sense that it requires cortical control, but the motor output measured (gross paw placement, overreach) is not fine motor function in the sense of digit individuation, grip force modulation, or pellet manipulation. "Skilled" here does not even mean *acquired* skill: classical skilled reaching in rodents involves explicit training to acquire a novel motor program, whereas here mice are only habituated. The brainstem-compensation hypothesis is more comfortable with restoring cortex-dependent gross placement than with restoring acquired fine-motor skills.

(3) The anatomy sample is modest for the precision of the claims.

Projection analysis rests on n = 9 pooled controls, n = 5 uPyX−Rehab, and n = 5 uPyX+Rehab. For a whole-brain subregion analysis, this is not a large dataset, even with the sensible restriction to the Wang et al. spinally-projecting set. The three medullary hits are plausible, but some of the most specific conclusions rely on a relatively small number of animals for its most specific claims. This matters especially for the MdV-behavior correlation.

(4) Normalization enforces a zero-sum structure.

Projection density is normalized to the total CST tract signal. This is a reasonable way to control for tracing variability, but it imposes a relative structure on the data: an apparent increase in one region may partly force an apparent decrease elsewhere. This may matter and has to be looked into by the authors, because the manuscript interprets decreased density in some other targets as meaningful redistribution.

(5) The decision to merge PMn and MdV under a single "MdV" label needs more justification.

Since the discussion relies on prior literature assigning skilled forelimb function to MdV proper, the reader needs to know whether the signal truly localizes there or whether it may partly reflect a neighboring region grouped under the same atlas label. Related to this, laterality would be very informative: since the proposed compensatory route is anatomically directional, showing whether the increased signal is preferentially located on the expected side of the medulla would strengthen the interpretation.

(6) The c-Fos / Fig. 3 section goes beyond what the data directly support.

The section "Complex-wheel running recruits intact corticospinal neurons" and the figure title "Rehabilitation functionally recruits intact CSNs" go beyond the actual observation, which is that a higher fraction of CSNs in M1 and M2 are c-Fos+ in runners than in non-runners. "Functionally" is not supported: c-Fos is a transcriptional marker of recent activity, not a functional readout; it does not show that the CSN's output is used to drive behavior. "Rehabilitation" is not supported either: the contrast is runners vs non-runners, applied uniformly across Sham and uPyX groups - healthy Sham+Rehab animals are on wheels for leisure, and the c-Fos effect is present in them too. The finding is difficult to interpret without thinking of the simpler framing ("moving mice have more motor cortex activity than resting mice"), with no control for generic arousal or ambulation. This section is the softest link in the causal chain running - CSN activity - medullary sprouting - recovery.

(7) MdV-recovery correlation: unstated multiple-comparison correction and possible pseudoreplication.

The correlation (R² ≈ 0.33, p ≈ 0.01) is the backbone of the paper's "causal" claim. Panels L/M/N test three correlations (LPGi, GiA, MdV vs forelimb footfall recovery); only MdV is reported as significant. The Figure 5 legend applies Tukey adjustment to the t-tests in A-C but makes no analogous statement for the correlations in L-N. A 3-test Bonferroni (α = 0.017) would not flip the MdV result, but disclosure is warranted, and the three tested regions were pre-selected from the significant group contrasts in A-C, which, to a statistician, would further shrink effective α. More importantly, the figure legend states that closed and open circles represent CFA- and RFA-traced values, respectively, which suggests the correlation treats the two tracer channels per mouse as independent datapoints - doubling the apparent n (≈ 20 from 10 uPyX mice), with the result of a higher significance than one would have at the mouse level.

(8) Reporting issues.

The reader would benefit from judging statistical choices such as those above directly from a data table rather than interpreting the authors' choices. The SciScore rightfully flags multiple missing components of transparent reporting: missing RRIDs, no code availability, limited data availability, and no power calculation, among others.

Almost all these weaknesses can be addressed with a revision of the manuscript, especially in the framing of results.

Conclusion:

The core message - that rehabilitation is associated with a selective pattern of CSN collateral remodeling in the motor medulla, and that MdV projection density covaries with behavioral recovery - is defensible from the data and already a useful result. The current wording in parts of the abstract, significance statement, and discussion goes beyond this and implies a mechanistic conclusion (mediation, central locus, re-establishment of descending control) that the data do not yet establish. The manuscript would better match its evidence with "associated with", "correlates with", or "candidate locus" framing, unless a causal experiment is added.

Reviewer #2 (Public review):

Summary:

The manuscript by Kelman and coauthors investigates how viral communities differ in the genes they encode in healthy and degraded coral reef ecosystems. Across 19 viral metagenomes from Central Pacific reefs, the authors assess the frequency of integration/excision genes as a proxy for viral community temperateness and ask whether genes associated with central carbon metabolism covary with signatures of temperateness. The main finding is that viral communities with more temperate-related genes encode more genes from the Entner-Doudoroff pathway and other reactions interpreted as anaplerotic, whereas more lytic-associated viral communities show greater representation of some pentose phosphate pathway, TCA, and redox-associated genes interpreted as cataplerotic. The authors propose a model based on these patterns in which lytic viral metabolism helps suppress bacterial overgrowth on healthy reefs, while temperate viral metabolism may promote microbialization on degraded reefs. The study addresses an interesting and potentially important concept - that viral auxiliary metabolic genes are important components of microbial communities and can affect ecosystem functioning. Linking viral metabolism to coral reef microbialization is a creative conceptual advance. The manuscript is clearly written, and the reported enrichment of anaplerotic genes in temperate-associated viromes is an interesting pattern that could motivate future work on how viral metabolic potential varies across reef states.

Strengths:

(1) The study connects viral lifestyle, central carbon metabolism, bacterial overgrowth, and reef degradation in a framework that could be useful for future studies of coral reef ecosystems and viral ecology. This is an interesting synthesis that links viral auxiliary metabolism to broader questions about microbialization and reef state.

(2) The manuscript is generally clearly organized around a testable prediction: viral metabolic gene content should vary along a lytic-to-temperate viral community gradient. The reported enrichment of anaplerotic genes in viromes with a larger fraction of temperate viruses is a compelling result.

(3) The authors highlight several virus-encoded metabolic genes that may not have been previously reported in viral datasets or genomes. If supported by further validation, these observations could expand the known repertoire of viral metabolic potential.

(4) The modeling helps clarify the feedbacks the authors propose may connect viral lifestyle, bacterial metabolism, and coral reef degradation. It provides a foundation for generating hypotheses about how viral metabolic genes could influence reef microbial dynamics.

Weaknesses:

(1) The main limitation is that the evidence for several key claims remains indirect. The core analysis is based on correlations between metabolic gene frequencies and integration/excision-related genes. This does not demonstrate that the metabolic genes occur in temperate viral genomes, are physically linked to lysogeny genes, are expressed during infection, or alter host metabolism. Thus, the data support an association between VLP-associated metabolic annotations and a community-level temperateness proxy, but not a direct link between temperate phages and these metabolic functions.

(2) It is important not to equate community-level gene frequencies with genome-level or infection-level metabolic programs. A virome may contain more anaplerotic genes overall, but that does not demonstrate that individual viruses reprogram their hosts in an anaplerotic manner nor that infection produces a net anaplerotic effect. Individual viruses may encode both anaplerotic and cataplerotic genes, and a smaller number of cataplerotic genes could have stronger metabolic consequences depending on expression, enzyme efficiency, pathway position, and host context. This is an important limitation that should be acknowledged and, if possible, addressed with contig- or genome-level analyses.

(3) The ecological interpretation assumes that viral infection is strong enough to influence reef-scale bacterial population dynamics. However, the study does not directly measure infection frequency, lysis rates, viral production, burst size, lysogeny frequency, prophage induction, gene expression, or bacterial mortality. If viral mortality or lysogenic conversion were rare in these systems, the observed gene-frequency patterns could have limited ecosystem-level consequences. This makes claims about viral metabolism suppressing bacterial overgrowth, accelerating microbialization, or acting as a conservation lever more speculative than suggested.

(4) There are statistical limitations related to the use of relative gene frequencies. Because genes are normalized as percentages of known genes, the data are compositional. Apparent increases in some categories may partly reflect decreases in others. Bootstrapped Spearman correlations are useful for assessing the robustness of these associations, but they do not address compositionality or multiple testing.

(5) The anaplerotic/cataplerotic classification is central to the manuscript's conclusions and would benefit from more support. The framework is useful, but it depends on both annotation confidence and biochemical context. Sequence-similarity annotations alone may be vulnerable to misannotation, especially for central metabolic enzymes that share conserved domains across functionally distinct proteins. Stronger evidence that key genes contain key functional domains and/or are phylogenetically related to characterized enzymes would help support the proposed functions. In addition, many central carbon enzymes are reversible or context-dependent, so a clearer rationale for each classification would strengthen the interpretation.

Overall, the manuscript presents a valuable hypothesis and highlights new ecological patterns in coral reef viral metagenomes, but falls short of the evidence needed for the strongest claims. The work would be strengthened by analyses that directly link metabolic genes to viral genomes or lysogeny markers, address compositional effects, validate key annotations, and more clearly distinguish observed gene-frequency associations from hypothesized effects on infection, host metabolism, and reef state.

Reviewer #2 (Public review):

Summary:

This review by Dorrell and Whittington covers a number of aspects related to normative modeling of grid cells. They begin by discussing key experimental insights on grid cell phenomenology. Then, they discuss how grid cells can be used to perform path integration and how they size up as efficient codes of space. These two sections then lead the authors to discuss how combining path integration and efficient coding objectives leads to models of axis-aligned grid cells in a single module. Discussion on non-linear objectives leading to multi-modules is presented. The review ends with several outstanding questions and an optimistic outlook of how normative models (particularly, task-optimized RNNs) can be used as tools for advancing understanding in neuroscience.

Strengths:

(1) The review is timely and covers an area that has seen a lot of recent activity. This discussion around many of the different results (and kinds of models), I think, will be generally helpful for the field.

(2) Although I think the story could be a little more coherently made (see below), in general I enjoyed the author's flow from efficient coding -> efficient coding + path integration -> efficient coding + path integration + non-linear objective. This framing supports the specific conclusion the authors arrive at.

(3) I also really liked the message that the review made of how normative modeling, despite some of its challenges/limitations, can be used effectively in neuroscience. The discussion of cycling between "experimental" modeling (e.g., vanilla RNNs) and theoretically-grounded models was nice, and I think it helps demonstrate the value of this approach.

(4) Showing how the metric loss could be seen as a bandpass filter (Figure 3C) was nice and a contribution of the review.

(5) While the focus of P4 (conjunctive HD-grid cells) felt initially a little cast aside, the discussion around "brain and task-optimised RNNs with standard architectural choices use fundamentally different path-integration mechanism" was nice and I think helpful for steering the community to an interesting open problem.

(6) Identifying how "non-linear functionality" can lead to multi-modules was nice and not something that I have seen as clearly presented before.

Weaknesses:

(1) The authors view the experimental evidence for grid cells being linked to path integration as "specific and strong" and that the " key computational feature that defines entorhinal cortex [is] path-integration". I think experimentalists (at least the ones I work with) would push back on that. First, it's hard to isolate path integration in rodent experiments. So while Gil et al. (2018) did about as good a job as you could do, there are still other interpretations of the results that are not purely path integration dependent. And second, as the authors point out later in the review, there is experimental work finding that grid cells are disrupted in large environments and 3D. Path integration certainly happens (to some extent) in these spaces, which begs the question of how it is achieved with weakened grid coding. Thus, I think reducing the claims about how strongly grid cells are experimentally linked to path integration is called for.

(2) The authors introduce the idea of efficient coding of space and discuss how grid cells are not optimal. It is later clarified (Sec. 5.3) that multi-module codes can be efficient (even if not the most optimal). I was confused reading Section 3, because in Section 2 the multiple modules are discussed, but then in Section 3, they are dropped, and only a single module is being considered. Equation 2 was also a little confusing to me. Alpha is not defined, and I would have thought that it would be x^Tx' - g(x)^T g(x') and not x^Tx' g(x)^T g(x'). Given that there is no page limit here, I think a little more detail in Section 3 would be helpful.

(3) In Section 3, the authors make use of P2 (translation invariance within a module) to rule out (or, at least, question) certain models/approaches. While this is certainly a standard assumption made in theoretical work, it is not very well supported by experimental findings. In particular, Diehl et al. (2017), Ismakov et al. (2017), and Dunn et al. (2017) all found that individual grid fields systematically vary in their peak firing rate. In addition, Redman et al. (2025) found that, within a given module, there was a small but robust diversity of grid orientations and spacings. These suggest that grid cells within a single module may actually be able to encode properties of local space and give some support to normative models that find efficient space coding with grid cells by finding non-axis-aligned grid fields. I think this is all important to mention because: a) it provides more biological nuance to the question about spatial coding; b) it provides more ways in which to test models. For instance, in Redman et al. (2025), the Sorscher et al. (2022) model was shown to produce variability in grid properties that loosely matched what was found in real data. For tests like this (e.g., how much does a model reproduce variability in grid firing field peak rates), I think it is going to be important for continuing to evaluate models.

(4) The focus of the review, I know, is grid cells, but of course, grid cells are part of the MEC and the larger hippocampal network. I totally understand, at some level, you have to make a decision of what to model, but it seems that there are other functional classes of neurons (border cells, head direction cells) that all play an important role in path integration. And while the models the authors consider at the end of the review capture properties of grid cells really well, they do so at the cost of not modeling anything else. The authors mention this in the context of the models not capturing conjunctive grid-head direction cells, but I think the point is a deeper one, and more discussion of at what level it makes sense to consider grid cells only is important.

(5) As I mentioned in the Strengths section, I did enjoy the flow of the paper on how path integration + efficiency is needed to get grid single modules and path integration + efficiency + non-linearity is needed to get multiple grid modules. This creates the story that adding more of these theory-driven constraints helps lead to more "accurate" models of grid cells. But one alternative view is that, if path integration + efficiency is enough to get a single grid module (but only a single grid module), then maybe the utility (or need) of multiple grid modules comes from something else. That is, instead of saying "we need more constraints to get multiple modules", it could be evidence for "we need to re-think whether multiple modules might need a different theory to explain". While I understand this is a big picture question that maybe isn't entirely fair to ask of the authors, I think: 1) the authors do a nice job of positioning their review as a kind of discussion on what normative modeling can provide to neuroscience, so having this discussion on when the failure of a model to capture ALL aspects of the biological features motivates further constraints as opposed to a new approach, would be useful; 2) this question connects with the title of the paper, i.e. "what is the question?"

Reviewer #2 (Public review):

Summary:

This work presents a reproducible, scalable workflow for spike sorting that leverages parallelization to handle large neural recording datasets. The authors introduce both a processing pipeline and a benchmarking framework that can run across different computing environments (workstations, HPC clusters, cloud). Key findings include demonstrating that Kilosort4 outperforms Kilosort2.5 and that 7× lossy compression has minimal impact on spike sorting performance while substantially reducing storage costs.

Strengths:

(1)Extremely high-quality figures with clear captions that effectively communicate complex workflow information.

(2) Very detailed, well-written methods section providing thorough documentation.

(3) Strong focus on reproducibility, scalability, modularity, and portability using established technologies (Nextflow, SpikeInterface, Code Ocean)

(4) Pipeline publicly available on GitHub with documentation.

(5) Clear cost analysis showing ~$5/hour for AWS processing with transparent breakdown.

(6) Good overview of previous spike sorting benchmarking attempts in the introduction

(7) Practical value for the community by lowering barriers to processing large datasets.

Weaknesses

No significant weaknesses. The authors have responded to all my review critiques and suggestions.

Reviewer #2 (Public review):

Summary:

The study examines how Streptococcus pyogenes integrates bacterial and host-derived signals to regulate SpeB, proposing that Vfr acts as a protease-sensitive repressor whose degradation relieves repression of speB. The authors further suggest that neutrophil-derived serine proteases, including those associated with inflammatory conditions, may promote this transition, and thereby counterbalance LL-37/CovRS-associated suppression of speB. The conceptual framework is interesting and potentially important for understanding how host inflammation feeds into bacterial virulence regulation.

Strengths:

The work addresses a biologically significant question and does so using a broad and generally well-integrated experimental approach, including bacterial genetics, reporter assays, recombinant protein analyses, neutrophil-derived material, human blood infection, and mouse infection models. A particular strength is the effort to connect host inflammatory processes to bacterial regulatory behavior, which gives the study conceptual reach beyond a narrow mechanistic observation. The data support the view that Vfr is relevant to speB control and that neutrophil-associated protease activity may influence this pathway.

Weaknesses:

The main limitations are mechanistic. The physiological form, localization, and abundance of Vfr are not sufficiently defined to support the proposed model at full strength, and the evidence that Vfr functions as a SpeB-labile repressor under biologically relevant conditions remains incomplete. The relationship between Vfr and the broader RopB/SIP regulatory framework is also not yet firmly established. In addition, the reporter system is not yet benchmarked closely enough against endogenous SpeB protein output, and its growth-phase dependence is insufficiently resolved, which makes it difficult in some settings to distinguish promoter activity from mature protease production. The neutrophil protease component is likewise not defined beyond a general serine protease signal, and the potentially important LL-37/CovRS/Vfr connection is underdeveloped in the main text. Overall, the conceptual advance is promising, but several of the central mechanistic claims would benefit from more direct experimental support and more cautious framing.

Reviewer #2 (Public review):

Summary:

The authors have made a convincing argument that the current system of in vitro translation using E. coli extracts can be significantly optimized to work with much lesser components, while maintaining activity. They have showcased their improved activity using not only physical but also functional readouts.

Strengths:

The experiments are designed in a very logical and easy to understand manner, which makes it easier not only to follow the paper, but also reproduce the results. Functional assays with the synthesized proteins are a good way to demonstrate functionality and applicability of the system. They also benchmark their system against a commercial kit to show superior performance of their system.

Weaknesses:

The production of the lysate requires special instrumentation, limiting accessibility.

Comments on revised version:

Thank you to the authors for addressing the concerns both textually and experimentally. This work has significant value.

Reviewer #2 (Public review):

In their manuscript entitled 'ATP-driven conformational dynamics reveal hidden intermediates in a heterodimeric ABC transporter', Pečak et al. use elegant single-molecule FRET experiments in detergent to investigate the heterodimeric ABC transporter TmrAB. By combining simulations of the transporter's accessible volume with elegant trapping strategies, the authors identify an unresolved outward-facing open state and conclude that it is usually obscured by a rapidly interconverting ATP-bound ensemble. Overall, the study demonstrates that smFRET can resolve the short-lived intermediate states of TmrAB and potentially other ABC transporters that are obscured in ensemble measurements.

It is a very interesting study that highlights the power of combining high-resolution structural information with spectroscopic approaches. I had three major concerns with the original version, all of which have been addressed by the authors in this revised version.

Reviewer #3 (Public review):

Summary:

The manuscript of Mayer and colleagues analyzes the function of WIPI proteins in mammalian cells. The authors identified previously CROP as a complex consisting of WIPI1 and the retromer complex, primarily in yeast cells. In mammalian cells, both WIPI1 and WIPI2 exist, whereas retromer has a homologous complex termed retriever. The now find that WIPI2 can form a complex with retriever subunits. They name this complex CROP2. Their data further indicate that CROP2 and CROP1 have distinct substrate specificities as knock down of CROP2 subunits affect beta1 integrin sorting, whereas knock down of CROP1 affects EGFR and GLUT1. The further identify a similar sequence (FSSS) in both WIPI1 and WIPI2, which is required for their specific binding to retromer and retriever.

Strengths:

CROP1 and CROP2 seem to use similar features for their formation, and have different substrates, which is convincingly shown.

Weaknesses:

The analysis lacks information that this is a complex as claimed. It can be deduced from the immunoprecipitation analysis.

Comments on revised version.

The authors answered my questions and adjusted the text accordingly. Figure 10 was not part of the submitted version. It should be checked by the editor.

Reviewer #2 (Public review):

Summary:

The study aims to show that the early visual cortex is not merely a sensory-perceptual region that encodes stimuli while they are physically present, but also supports the formation and retrieval of long-term episodic memories. Instead, the authors demonstrate that spatially tuned reactivation of early visual cortex after a single encoding event supports memory-guided behavior, such as recalling an object's original location.

Strengths:

The study provides solid evidence that location information for single, trial-unique objects is reinstated in early visual cortex during both recognition and recall, even without explicit spatial demands, and the remembered vs. forgotten analyses link spatial tuning to behavior. The one-shot design and absence of explicit spatial instructions are important strengths that bring the paradigm closer to everyday, incidental episodic experiences and go beyond highly trained cue-target associations.

Weaknesses:

(1) Conceptually, the main findings would appear less surprising without a sharper theoretical contrast. Given basic retinotopic coding, it is natural that object identity and location are jointly encoded when an object is presented at a particular position, so spatially tuned reinstatement in V1-V3 can be interpreted as a reconfirmation of known properties unless more clearly contrasted with theories that emphasize more abstract, position-invariant cortical representations following hippocampal-cortical recoding. As currently framed, the introduction does not fully articulate what existing accounts might predict, or what pattern of results would have challenged those accounts, which somewhat weakens the perceived theoretical payoff.

(2) It also remains somewhat unclear why early visual cortex (V1-V3), specifically, is the critical locus for the spatial information of interest, as opposed to higher-level visual or parietal regions that could also provide a spatial scaffold; clearer rationale and, if possible, control analyses in additional regions would help here.

(3) Since gaze behavior is central to any spatial account, it would be helpful to report basic eye-tracking analyses comparing remembered versus forgotten trials, especially at encoding, to rule out systematic differences in fixation patterns that could contribute to the spatial tuning results.

Reviewer #2 (Public review):

Summary:

Adeno-associated viruses (AAVs) are popular gene therapy vectors, but AAVs can cause toxicity. This is particularly evident following expression of some transgenes, e.g., GFP, in the retinal pigment epithelium (RPE), which leads to loss of RPE cells and photoreceptors. Here, we sought to unravel the toxicity mechanism(s). Several transgenes, self and non-self, were tested for toxicity, with no clear correlation for this variable. RPE RNA-sequencing revealed upregulation of translational processes, cell stress, cytokine release, antiviral responses, and leukocyte infiltration pathways. Toxicity-inducing pathways were explored for causality by injecting toxic AAVs into mice deficient for intrinsic, innate, or adaptive immune pathways. The CHOP KO partially alleviated toxicity for RPE but not photoreceptors, whereas the type I interferon receptor KO partially alleviated toxicity for photoreceptors but not RPE. In situ hybridization of interferon pathway transcripts (IFNB1, IFNAR1) revealed that the RPE and retina can produce and potentially respond to interferon. These data suggest that transgene-induced cell stress responses in the RPE lead to RPE cell death, while interferon signaling contributes to the death of photoreceptors.

Strengths:

This manuscript used numerous KO mouse models to evaluate the interferon pathway, inflammatory cytokine pathways, the complement pathway, toll-like receptor signaling, cytosolic DNA sensing, double-stranded RNA sensing strain, intrinsic cellular stress pathways, as well as strains deficient for B cells and T cells or B cells, T cells, and natural killer cells. This is a robust piece of work with rigorous controls, groups, and timepoints tested. The RNA-sequencing data provided helpful guidance on the pathways that should be assessed when analyzing AAV toxicity to the retina.

Weaknesses:

The main weakness of the study is that it focuses on subretinal administration to neonatal mice, and the canonical TLR9-MyD88 was not found to have an impact on the AAV toxicity measured. More information could have been provided to understand the discrepancy.

Reviewer #2 (Public review):

Aims:

The authors sought to optimize SHARE-seq, a multimodal single-cell method, to improve the simultaneous profiling of gene expression and chromatin accessibility. Their goal was to enhance barcode design for better sequencing efficiency and cost savings, while improving overall data quality. They then applied their optimized method, easySHARE-seq, to study liver sinusoidal endothelial cells (LSECs) to demonstrate its utility in examining gene regulation and spatial zonation.

Strengths:

The improved barcode design is an advance, increasing the proportion of sequencing reads dedicated to biological information rather than barcode identification. This modification offers practical benefits in terms of sequencing costs and read length, potentially reducing alignment errors. The method also demonstrates improved RNA detection compared to the original SHARE-seq protocol. The biological applications showcase how simultaneous measurement of both modalities enables analyses that would be practically impossible with single-modality approaches, particularly in examining how chromatin states change along developmental or spatial trajectories.

Weaknesses:

There is a notable reduction in chromatin accessibility detection compared to the original SHARE-seq method, likely limiting the use of the method in certain situations.

Overall:

The authors achieve their aim of creating an optimized protocol with improved barcode design and enhanced RNA detection. The method represents a useful advance for specific experimental contexts where the trade-offs are appropriate.

Reviewer #2 (Public review):

Summary:

The manuscript by Payne and colleagues examines how cell-autonomous loss of the activity-dependent transcription factor NPAS4 reshapes spatial and temporal coding in CA1 pyramidal neurons of behaving mice. The work builds on the Bloodgood lab's established framework in which NPAS4 reorganizes inhibition along the somatodendritic axis of CA1 pyramidal cells, principally by regulating CCK+ basket cell synapses, and asks whether this transcriptionally driven reconfiguration of inhibition propagates into the spike-train statistics that underlie hippocampal function. The combination of sparse Cre delivery with channelrhodopsin-mediated optotagging in Npas4 fl/fl:Ai32 mice is technically elegant, as it permits within-animal comparisons of intermingled wild-type and knockout pyramidal neurons sharing a common LFP, which is a significant analytical advantage for spike-timing analyses and for controlling network-level confounds. The reported phenotype is internally consistent and converges on a coherent story: knockout neurons exhibit broader and less stable place fields, lower signal-to-noise within fields, increased out-of-field activity, weaker theta-phase coupling, and shallower phase precession slopes, with the temporal deficits at least partly explained by enlargement of the spatial receptive field.

Strengths:

Several aspects of the work deserve explicit recognition. The validation of the optotagging strategy is thorough, including the high-power stimulation control to corroborate WT classification and the post hoc histological alignment of GFP+ density with electrophysiologically identified KO fractions. The decision to test NPAS4 function in adult mice maintained in long-term enriched environments addresses an important gap, since most prior work has focused on juveniles or short-term induction paradigms. The acute slice recordings recapitulating the somatodendritic inhibition phenotype reassure the reader that the in vivo measurements are interpreted against a known synaptic substrate. The analytical framework, especially the difference maps across epochs and the linear regression decomposition of phase precession slope into genotype, field size, and theta modulation strength, is rigorous and goes beyond simple group-level comparisons. The conceptual contribution, namely the demonstration that an activity-dependent transcription factor can be tied to single-neuron coding properties in vivo, is meaningful, although it is fair to note that the direction of the effect, given that the CCK to place cell link and the NPAS4 to CCK link have each been established in prior independent studies, is largely along the lines one would predict.

Weaknesses:

The most consequential concern, in my view, is the experimental context in which the entire study is conducted. Every animal is housed in an enriched environment for two to three months, and Figure 1A itself shows that NPAS4 expression in CA1 is essentially undetectable in standard-environment conditions and only emerges with enrichment. This raises the question of whether the manuscript is in fact describing the function of NPAS4 in general, or the function of NPAS4 specifically as recruited by chronic enrichment. The paper, in its current framing, elides this distinction and presents the EE state as if it were the baseline, which it is not. EE is known to alter hippocampal connectivity, the dynamics of place cell ensembles, and the expression of many activity-dependent genes; the CCK to pyramidal cell connectivity that the authors invoke as the mechanistic anchor is also dense in standard housing, so the absence of detectable NPAS4 in SE conditions raises the further conceptual problem of how NPAS4-negative neurons would normally be innervated by CCK+ basket cells in the first place. A direct comparison of WT and KO neurons in standard-environment animals, even on a smaller scale, would discriminate between two very different interpretations, namely that NPAS4 has a constitutive role in tuning CA1 firing versus that it is specifically engaged by enrichment-driven activity and contributes to an EE-specific reorganization of coding. Recent work, including Chiaruttini and colleagues (2025), reports baseline NPAS4 expression in CA1, so the SE result in Figure 1A may itself underestimate normal expression and deserves further scrutiny. Without an SE comparison, the generality of the conclusions cannot be assessed, and the title and abstract risk overstating the scope of the findings, particularly when one considers that NPAS4 is also induced by contextual fear conditioning and other paradigms, which would predict context-specific effects rather than a uniform refinement function.

A closely related concern is the meaning of the knockout itself. Even under EE, only a few percent of CA1 pyramidal neurons express detectable NPAS4 at any given moment (Figure 1A), yet the AAV strategy deletes the gene in 30 to 60 percent of pyramidal neurons. In effect, the majority of cells classified as KO in this study would not have been expressing the protein under the relevant conditions, so the population that is statistically driving the WT versus KO differences must include a non-trivial fraction of neurons in which the deletion has no protein-level consequence. This dilutes the expected effect and raises a more interesting biological question: are the observed phenotypes carried by the few KO neurons that would have expressed NPAS4, or do they emerge from a constitutive function of the gene that is broader than the IHC signal suggests? An additional, related possibility is that NPAS4 expression segregates non-uniformly across functional classes, for example, concentrating in cells with particular firing-rate or spatial-tuning profiles, in which case the "KO" label is binary at the level of the manipulation but graded at the level of biological consequence. Stratifying the KO population by some proxy of activity history, or relating the magnitude of the phenotype to per-cell measures of recent firing, would help address this. As written, the manuscript treats the KO designation as homogeneous, while the underlying biology is almost certainly not.

A third concern, more conventionally statistical, is the treatment of cells as independent observations. The analyses rely almost uniformly on Kolmogorov-Smirnov tests applied to individual units pooled across animals, but cells recorded in the same animal share not only a common subject but a common network, since WT and KO neurons here are intermingled in the same CA1 microcircuit. Cell numbers per animal range widely, so a mixed-effects framework treating animal as a random factor, or a hierarchical bootstrap, would clarify which effects are robust against animal-level and session-level variability and protect against pseudo-replication. This concern is particularly acute for the smaller effects in Figure 2C-E, where the cumulative distributions overlap substantially, and the differences could plausibly be driven by a small number of mice or sessions. In several figures, the individual dots in supplementary panels are not labeled by animal or session, and that information would be useful for assessing how much of each effect is carried by which subset of the cohort.

The absence of a Cre/ChR2 expression control is a separate gap. The comparison throughout the manuscript pits Cre+ ChR2+ neurons (NPAS4 KO) against neighboring non-transduced neurons (WT). This is internally elegant, but leaves open the possibility that part of the phenotype arises from chronic ChR2 expression or constitutive Cre activity rather than from NPAS4 loss, especially given that most of the readouts are subtle. A small companion cohort of Ai32 mice without the floxed Npas4 allele, injected with the same AAV and processed through identical optotagging and electrophysiology pipelines, would address this definitively and is, in my view, a near-essential addition.

Several of the downstream phenotypes would benefit from stratified comparisons that hold first-order properties constant. Many of the downstream differences (stability across epochs, theta coupling, phase precession) could, in principle, be inherited from the upstream difference in firing rate, since the high-firing and high-spatial-information cells in the WT pool are likely contributing disproportionately to the group statistics. The authors do perform firing-rate-matched controls in Figure S4D-G, which is helpful, but the analysis should be extended in two ways: a parallel stratification by spatial information for the stability analyses in Figure 4, and matched comparisons of theta coupling (Figure 5) and phase precession (Figure 6) on neurons drawn from overlapping firing-rate and spatial-information distributions. The regression decomposition for phase precession is a step in this direction and shows that field size, not genotype, is the dominant predictor of slope; this finding, in my reading, deserves more prominent framing in the discussion than it currently receives, since it implies that the temporal precision phenotype is largely downstream of the spatial one rather than a parallel deficit.

The place field stability analysis is interesting but somewhat under-analyzed. The authors show that KO fields shift toward the field entrance more rapidly than WT fields and propose that this reflects an accelerated or dysregulated Mehta-effect-like dynamic. The framing is attractive, but the analysis does not establish that the shifts are systematic in the same way the classical Mehta effect is. An alternative reading is that the elevated out-of-field firing creates spurious local maxima that the peak-finding procedure occasionally classifies as field shifts, especially when in-field firing is reduced. A control analysis using a fixed reference window around the original peak, rather than re-identifying the peak each epoch, would help distinguish a genuine plasticity-like shift from instability driven by noise. The behavior of the WT population in epoch 4 also raises a question: would the drift intensify over longer recording windows, and to what extent is the apparent drift imposed by the repetitive structure of the task itself, in which animals are effectively running on a constrained linear /circular track that may impose drift-like dynamics across the population independently of genotype?

A final note on mechanism. The manuscript leans on prior work showing that NPAS4 regulates CCK+ basket cell synapses, and uses this as the mechanistic anchor for the coding deficits. The connection is reasonable but remains indirect within this study, since the authors do not measure CCK+ interneuron activity, perisomatic inhibition, or local circuit dynamics in the same animals. The discussion already acknowledges some of this, but the speculative framing of dendritic versus somatic inhibition contributions could be tightened, especially given that competing inhibitory sources (PV+ basket cells, axo-axonic cells, OLM interneurons) also shape the spatial and temporal features measured here. A more cautious mechanistic framing, distinguishing what is demonstrated from what is inferred from prior work, would be appropriate.

In summary, this is an ambitious and technically demanding study that makes a meaningful contribution by linking activity-dependent transcriptional regulation of inhibition to the spatial and temporal organization of CA1 spike trains in awake, behaving mice. The within-animal optotagging design is a real strength, the phenotype is internally consistent across multiple coding metrics, and the conceptual implications for how experience tunes single-neuron coding are significant. The principal concerns, namely the unaddressed enrichment confound that pervades the entire dataset, the conceptual ambiguity around what a KO designation actually means at the cell level when only a small fraction of CA1 neurons express the protein, the statistical treatment of nested observations from a shared microcircuit, the missing transgene control, the absence of stratified comparisons by firing rate and spatial information for the secondary phenotypes, and the somewhat overreaching mechanistic framing of the discussion, are all addressable, and if handled carefully would substantially strengthen the manuscript. With these revisions, the work would be a valuable contribution to the literature on how the molecular memory of activity shapes circuit-level coding.

Reviewer #2 (Public review):

This manuscript addresses an important and timely question in TDP-43 biology by systematically identifying regulators of TDP-43 anisosome formation, with a particular focus on nuclear export via XPO1. Using a combination of unbiased chemical screening, genetic perturbation, and advanced imaging approaches, the authors propose that inhibition of nuclear export modulates the abundance and biophysical properties of TDP-43 anisosomes. They further strengthen their findings by introducing an additional model system, a semi-permeabilized in vitro assay, which provides mechanistic evidence that XPO1 activity prevents anisosome dissolution by retaining nuclear RNAs. The study is conceptually innovative and has potential relevance for neurodegenerative diseases characterized by TDP-43 pathology. Some minor concerns remain, mostly about experimental design of the newly added data.

Strengths:

(1) The study employs an unbiased, hypothesis-free compound screen to identify regulators of TDP-43 anisosome formation, which is a major strength and reduces confirmation bias.

(2) The authors combine chemical and genetic screening approaches, providing orthogonal validation of key pathways and increasing confidence in the biological relevance of top hits.

(3) The focus on biophysical properties of TDP-43 assemblies, assessed through imaging and FRAP, moves beyond simple presence/absence of aggregates and provides mechanistic insight into the biophysical states of TDP-43.

(4) The use of multiple experimental modalities, including live-cell imaging, FRAP, pharmacological perturbation, and transcriptomic analysis, reflects a technically sophisticated and ambitious study design.

(5) The authors attempt to extend findings beyond immortalized cancer cell lines by incorporating organoid models, demonstrating awareness of disease relevance and translational importance.

(6) The authors extend their study by incorporating a semi-permeabilized in vitro system, which provides compelling evidence that inhibition of nuclear export promotes the retention of nuclear anisosomes, an effect driven by the accumulation of nuclear RNAs.

Overall, the manuscript is clearly written and logically structured, making complex experimental workflows accessible and the central hypotheses easy to follow.

Weaknesses:

(1) The manuscript has significantly improved with the revisions. Some experimental procedures and method details, as well has statements remain incompletely described:

a) What is the smear in Figure S1 after VLX treatment?

b) The authors state that "The reduction in TDP-43 signal was not due to protein elimination.", however no data is provided to support that statement.

c) The authors state that "TDP-43 shifts from phase-separated state to a soluble state ...", however no data is provided to support that statement.

d) Why did the authors choose cow lover cytosol for this study?

e) The experimental setup for supplementing with cytosol/ATP/GTP is unclear. A more detailed schematic would be helpful to understand at what stage in the experiment these factors were added. Which step of the protocol was performed at 37 {degree sign}C, which is indicated in the figure schematic but not described in the methods.

f) In the organoid model, the authors mention that they observe similar levels of total TDP-43, however they do not provide quantification. Instead, they provide a graph that shows highly significant changes in nuclear TDP-43, which was not addressed in the text.

Additionally, some questions remain unclear:

(1) The anisosomes induced by ATP/GTP or cytosol are insufficiently characterized. It remains unclear whether these structures correspond to canonical ring-shaped anisosomes, and whether they exhibit dynamic (liquid-like) or more static (gel-like) properties.

(2) The contribution of the cytosol and ATP/GTP supplementation experiments to the overall narrative is unclear. While the findings are intriguing, their interpretation within the context of the study is not well articulated. In particular, the rationale for including cytosol is not sufficiently justified, given that ATP/GTP alone induces a pronounced effect, whereas cytosol alone does not.

(3) The authors should address why endogenous XPO1 does not co-localize with anisosomes, whereas overexpressed XPO1 does. This raises the possibility that the observed co-localization may be an artifact of non-physiological protein levels, which should be discussed.

(4) The iPSC-based model remains insufficiently characterized. While the authors propose that this system recapitulates the accumulation of liquid and solid aggregates resembling anisosomes, it is unclear whether this phenotype is robustly observed and whether KPT treatment effectively modulates it.

(5) The rationale for the selected treatment durations is unclear, and the timing appears inconsistent across experiments (ranging from 3 to 16 hours), including within experiments involving the same compound. This variability should be justified or standardized.

(6) Several figure legends require clarification:

a) In the section stating "Collectively, our results suggest that the stability and dynamics of anisosomes are modulated by XPO1-mediated nuclear export ...", the cited figure appears to be incorrect. This should refer to Figure 5L rather than Figure 5J.

b) Figure 1B: Please specify the number of replicates per concentration, the number of cells analyzed, and the model used for regression analysis. Additionally, the legend indicates a treatment duration of 15 hours, whereas Figure 1A states 24 hours.

c) Figure 2G: The authors state "7 anisosomes per condition," but the graph displays only 4-6 data points. Please clarify what each data point represents.

d) Figures 3B and 3G: Please clarify whether a defined threshold was used to determine a "reduction in anisosome number."

e) Figure 4B: These do not represent biological replicates, as all samples derive from a single cell line; rather, they constitute independent experimental replicates.

f) Figures 5B and 5H: The legend states "n = 3 biological repeats," but the number of data points shown appears higher. Please clarify.<br /> g) Figures 5K, 6C, and 6E: "Mean Fluorescence Intensity (MPI)" should be corrected to "MFI."

h) Figure 6C: Please include the number of cells analyzed and provide relevant statistical measures (e.g., R², p-value).

i) Figure 6D: The experimental timeline is unclear. Please specify the duration of incubation and the timing of each step.

j) Figure 7B: Improved labeling is needed (e.g., clarification of "mean spot volume") to better align with the figure legend.

Reviewer #2 (Public review):

Summary:

The paper addresses an interesting problem. The authors develop a method to assess the probability of insertion of aromatic residues in intrinsically disordered regions of proteins, to insert in the interfacial regions of membranes.

Strengths:

(1) The idea of the article seems very interesting. The problem of membrane association mediated by aromatic residues is definitely worth studying. Aromatic residues, especially Tryptophan (W), but also, albeit to a lesser extent, Phenylalanine (F), and Tyrosine (Y), are well known to partition preferentially to the headgroup region of the lipid bilayer.

(2) The authors propose to decipher the sequence code for insertion of sequences containing aromatic residues in the membrane employing three types of calculation methods with decreasing order of detail and complexity, but increasing order of efficiency. First, all-atom MD simulations; second, the PPM method (protein positioning in membranes) from Lomize et al (2006), Protein Sci 15, 1318; and third, AroMIP, a mathematical model developed by the authors. The results obtained with the different simulations and mathematical methods are internally consistent.

Weaknesses:

(1) Aromatic residues have been shown to partition preferentially to the headgroup region of the lipid bilayer. Most of the papers on this problem were published in the mid 1990s to early 2000s. Some of the most important papers in this regard are the following: von Heijne, Annu. Rev. Biophys. Biomol. Struct. 1994, 23, 167-192; Doyle et al. Science 1998, 280, 69-77; Landolt-Marticorena, et al. J. Mol. Biol. 1993, 229, 602-608; Killian & von Heijne, TIBS 2000, 25, 429-434; Marx & Fleming J. Am. Chem. Soc. 2021, 143, 764-772. Strangely enough, none of these articles is cited.

(2) This is the most important point and the most serious weakness. The authors find that the PPM method is able to reproduce the results from MD simulations, and the AroMIP model is able to perform well in comparison with PPM and MD, after training AroMIP on a large set of IDR sequences (intrinsically disordered protein regions) of the human proteome. The defining feature of the AroMIP calculation is the recognition of the importance of flanking residues in the membrane-insertion propensity of a sequence containing a central aromatic residue. All this sounds good. However, this is all theoretical. There is no connection to experiment or to any method that draws from experiment. The entire approach relies on the assumption that the MD simulations produce the correct results. There is no proof of the correctness of anything. As one of the greatest physicists of our times, Richard Feynman, wrote, "The test of all knowledge is experiment. Experiment is the sole judge of scientific "truth"."

(3) The drawings in Figures 2 and 3 are incorrect and misleading. The size of the Tryptophan side chain is about 5.5 Å, whereas one-half of the bilayer ("a monolayer") thickness is about 15 Å. But in the figures, the lipid length and the Trp side chain seem about the same size. This is incorrect even in a qualitative sense.

Reviewer #2 (Public review):

The manuscript by Forbes, Skafida, Karapidaki et al. concerns the in silico identification of cis-regulatory elements (CREs) in large genomes using chromatin accessibility (ATAC-seq) and sequence conservation (genomic DNA sequencing) data. They exemplify this method by applying it to identify novel CREs in Parhyale hawaiensis, which they validated using reporter constructs.

The results are convincing and are well supported by the data and validations. Identified CREs are valuable for researchers interested in the regulation of the expression of genes they control.

The methodology on the whole is also valid, as suggested by the results and previous publications on various taxa. Sequence conservation, as stated by the authors, was long used as a method to identify regions of non-coding DNA with functional and evolutionary constraints. The same applies to ATAC-seq data, which has also been used as a proxy for functional regions in different animals such as sea urchins and amphioxus. The methodology proposed is likely to be successfully used by researchers working on a variety of experimental organisms.

The authors do not use existing genome assemblies and use short-read sequencing to identify conserved regions, and while it is not conceptually novel, such an approach is becoming more and more viable and useful considering the recent advances in next-generation sequencing technology and the decrease in price of short-read sequencing.

Two major weaknesses are:

(1) The novelty of the approach and its advantages should be more explicitly stated.

(2) The authors do not discuss in depth the strength of using a combination of two methods rather than either of the two, especially considering that previously known CREs do not overlap with conserved sequences.

Reviewer #2 (Public review):

Summary:

This article addresses a very pertinent question - what are the computational mechanisms underlying risky behaviour in patients having attempted suicide. In particular, it is impressive how the authors find a broad behavioral effect whose mechanisms they can then explain and refine through computational modeling. This work is important because currently, beyond previous suicide attempts, there has been a lack of predictive measures. This study is the first step towards that: understanding the cognition on a group level. Before then being able to include it in future predictive studies (based on the cross-sectional data, this study by itself cannot assess the predictive validity of the measure).

Strengths:

- Large sample size

- Replication of their own findings

- Well-controlled task with measures of behaviour and mood + precise and well-validated computational modeling

Questions, based on revised manuscript and replies to other reviewers:

(1) Replies to reviewers in general: Bayes Factors have been added, it would be good to also use common verbal terms to describe them (e.g. 'anecdotal', 'moderate' etc). For example, my reading of table S8 would be that for gambling rate there is only anecdotal evidence that it does not relate to PSWQ, BDI, and moderate evidence it does not relate to TAI.

(2) Reply to reviewer 1 Q2 (Predicting STB):

For the regression predicting suicidal ideation, it seems to me that what you did was a regression STB ~ gambling behaviour + approach + mood? Could you clarify? I had expected as a test of whether the task can predict STB risk something slightly different - a cross-validation (LOO or maybe 5-fold in the large sample): STB ~ gambling behaviour + approach [parameter from model] + mood [parameter from model]; and then computing in the left out participants: predicted STB. Then checking correlation between STB and predicted STB. This would allow testing whether the diverse task measures together predict STB (with the caveat, that it's cross-validated, rather than hold-out sample, unless you could train on one sample (in lab) and test on the other (online).

(3) Reply to reviewer 2 Q1 (parameter recovery): I'm looking at S3, it seems to still show only the scatter plots and not the correlation matrices, which are now added as text notes. Can you actually show these matrices? An off-diagonal correlation of 0.63 appears quite high. I think it needs to be discussed exactly which parameters those are, and whether that impacts the interpretation of the results.

(4) Reply to reviewer 3 Q3 (mood model): I would have imagined that the response would involve changing the mood equations (equation 8 main text) to include a term for whether the participant gambled or not, independent of the gamble value.

Reviewer #3 (Public review):

Summary:

This work use convolution neural network to optimize a voltage clamp protocol to identify features and parameters from human pluripotent stem cell-derived cardiomyocytes.

Strengths:

The major strength is the methodology used to bridge in silico prediction of cell behavior and mechanistic insights from experimental dataset.

Comments on revised version.

As highlighted by the authors, due to the variability of the hPSC-CM model, to increase the applicability of this method, additional experimental dataset from different hPSC-CM lines would increase the translation of this approach.

I personally found that the detailed description of the methods, including the rationale of including/excluding some parameters, is extremely helpful to whoever would like to use this approach in their research.

Reviewer #2 (Public review):

Summary:

Wenke et al. investigated the role of vlPAG somatostatin-expressing neurons in the mediation of analgesia during defensive states. A newly developed paradigm of cued fear-conditioned analgesia, which consists of a combination of an auditory fear retrieval session and a pain test, was used to evaluate this cell population's contribution to fear-mediated analgesia. Optogenetic manipulation of vlPAG SST+ neurons modulated the responses to a nociceptive cue (Hot Plate) presented concomitantly with an aversively conditioned tone. At the same time, alterations in the freezing levels could be observed during optogenetic activation of vlPAG SST+ neurons. In order to disentangle the impact of these cells on analgesia from their impact on the expression of defensive behaviors, the authors performed electrophysiological recordings from the dorsal horn in the spinal cord of anesthetized mice. A vlPAG-RVM-DH pathway was identified to trigger nociceptive C-fibers upon optic activation of the RVM. Finally, pathway-specific activation of SST+ vlPAG-RVM neurons could abolish CS-induced analgesia.

Strengths:

The study addresses a relevant topic, that is, brainstem circuits for pain-modulatory mechanisms as part of defensive states evoked by threat. This is important because the circuit mechanisms underlying pain are still not fully understood, and defining molecular markers of cellular circuit substrates may support the identification of potential pharmaceutical targets in treating pain. The authors confirm a previous study in that a somatostatin-positive cellular population presents a crucial vlPAG circuit element mediating anti-nociceptive effects. Key novelty aspects of the present study are the demonstration that these neurons seem to play a role specifically in threat-induced analgesia. This was possible by the elegant design and application of a novel fear analgesia paradigm, combined with cell- and pathway-specific optogenetics.

Reviewer #2 (Public Review):

Summary:

Overall, this study provides a thorough description of the formation of syncytia following wounding of the proliferation-competent diploid epithelium of the pupal notum. While this phenomenon has already been described briefly for this particular tissue by the Galko lab in Wang et al 2015, the authors provide a much more detailed description and characterisation of the process providing some novel insights (radial versus tangential border breakdown, cell shrinkage, timings, syncytia outcompeting mononucleated cells, etc.).

Strengths:

This paper provides an elegant, thorough, descriptive characterisation of syncytia-driven wound closure using state-of-the-art confocal live imaging of the pupal notum. The authors show that laser-induced wounding of this diploid, proliferation-competent epithelium results in the formation of syncytia of various sizes in the first few cell rows around the wound edge, which progressively become bigger as healing proceeds. This results in ~50% of cells becoming part of these syncytia. The cell fusion events were convincingly demonstrated by showing the disappearance of p120ctnRFP and E-Cadherin-GFP from cell-cell borders as well as cytoplasmic GFP mixing of GFP-positive cells with a GFP-negative cell.

Apart from cell-cell fusion by border breakdown that mostly happens in the first 2h following wounding, the authors also found that at later stages of wound healing cell shrinkage following cytoplasmic mixing contributed to syncytia formation.

Next, the authors provided some convincing evidence that syncytia outcompete mononuclear cells for being positioned in the first cell row around the wound.

The authors then show that radial border breakdown occurs much less frequently than tangential border breakdown. They suggest that radial border breakdown reduces the requirement for cell-cell intercalations. They also hypothesise that tangential border breakdown might allow fused cells to share resources and provide more resources to be used near the wound edge, e.g. for actomyosin cable formation. To test this, the authors generate single-cell clones that overexpress Actin-GFP. They then show convincingly how a single Actin-GFP-positive cell in the second cell row fuses with one GFP-negative cell in the first cell row. The Actin-GFP signal then spreads in the fused cell and labels some previously unlabelled actin-rich structure near the wound edge which most likely is the actomyosin cable. This provides some evidence for resource sharing by cytoplasmic mixing following fusion.

Comments on revised version:

The authors have extended their original manuscript by adding two key parts. First, they show a role of Atg1 in mediating cell fusion (Figure 4). Second, they provide additional evidence for a contribution of radial border fusions to wound closure through its effect on tissue fluidity and through computational modelling (Figure 7).

This new version of the manuscript is greatly improved and provides significant new insights into the role of syncytia in aiding wound repair. There are just a few minor, yet important, additions needed to back up Figure 4 which should not require new experiments.

Minor but important points:

The authors show a role of Atg1 in mediating syncytia formation in Figure 4. However, since the Pnr>+ side of the wound closes slower than the non-Pnr side (control side), a few additions to this figure would be important and should not require additional experiments.

(1) The authors should show, similar to the data shown in Figure 4D of the wound radius over time for control versus Pnr>Atg1RNAi, also the same type of data for control versus Pnr>+.

(2) Since Pnr>+ also slows down wound healing, albeit to a lesser extent than Pnr>Atg1, the authors should also show an extra graph that provides evidence that Pnr>Atg1RNAi reduces syncytia formation more than Pnr>+ does. E.g. Two graphs could be added that show individual cell size at 4 or 5h post wounding for control versus Pnr>Atg1RNAi as well as for control versus Pnr>+ and also another graph with the same data but comparing cell size between Pnr>+ and Pnr>Atg1RNAi. Otherwise, if the expected minimum cell size for a syncytium is easy to estimate, a graph could be added that shows the percentage of cells that are above this threshold (e.g. above 100 square micron) for control versus Pnr>Atg1RNAi and control versus Pnr>+ and Pnr>+ versus Pnr>Atg1RNAi.

Reviewer #2 (Public review):

Summary:

The paper tackles a very interesting question and provides a solid and systematic piece of data that may be useful for numerous NeuroAI works in the future. The question is how well can macaque monkeys with a "pretrained" visual system without human knowledge learn to categorize images based on different kinds of (sometimes arbitrary) category definitions. In general, I love the paper, and I think both the data and presentation of it are beautiful.

Strengths:

(1) The authors developed a scalable method for training and studying this behavior, and did an exhaustive evaluation of monkeys' behavior and learning process.

(2) Beyond the behavior result, they performed extensive analysis and control experiments to isolate the cue monkeys are using to perform the categorization.

(3) The extensive comparison of behavior with deep neural networks is also super interesting.

(4) The authors performed a very careful examination of generalization behavior in monkeys, similar to standard practise in machine learning.

(5) The presentation of the data is very beautiful and deliberately designed, kudos to the authors for their efforts!

(6) I really enjoyed the further categorization task based on human knowledge, and the arbitrary rule task; this really pushes our understanding of the visual categorization and learning capability of monkeys.

(7) The examination of *learning dynamics* in human vs monkey is also quite interesting, i.e., humans can "understand the rule" and learn much faster versus monkeys learning across a few days.

Weaknesses:

(1) Though all results are pretty cool, the organization of results, figures, and sections can be modified to flow even better.

(2) Maybe provide DNN categorization and generalization results for the non-main monkey experiments (Figures 2,3), those comparisons can be really interesting too!

Reviewer #2 (Public review):

Summary:

Sun and colleagues report the development of an engineered extracellular vesicle platform derived from NK-92 cells that display an anti-CD19 single-chain variable fragment (scFv) on their surface via fusion with LAMP-2B (V-CD19-Exo). In an MRL/lpr mouse model of SLE, the authors demonstrate that intraperitoneal administration of V-CD19-Exo reduces splenic CD19+CD20+ B cells, attenuates proteinuria and lupus nephritis pathology, downregulates pro-inflammatory cytokines (IL-17A, IFN-γ) and autoantibodies (anti-dsDNA, ANA), and improves survival from approximately 25% to 80%. The authors propose that this "cell-free" targeted extracellular vesicle strategy offers advantages over conventional cell therapies, including lower immunogenicity, scalable production, and no requirement for lymphodepletion.

The study addresses an important question in autoimmune disease therapeutics: how to achieve targeted B cell depletion while avoiding the complexities and safety risks associated with CAR-T/CAR-NK cell therapies. The concept is novel, and the initial in vivo efficacy data are encouraging. However, several significant limitations in experimental design, mechanistic depth, and evidence rigor temper the strength of the conclusions.

Strengths:

(1) Novel conceptual approach.

The adaptation of CAR targeting principles to extracellular vesicles represents a creative and potentially impactful strategy. By displaying CD19 scFv on NK-92-derived vesicles, the authors successfully confer B cell-targeting capability while retaining the cytotoxic effector functions of the parental NK cells. This "cell-free" concept addresses genuine limitations of live cell therapies, including the need for lymphodepletion, risks of cytokine release syndrome, and manufacturing complexity.

(2) Comprehensive in vivo efficacy readouts.

The study evaluates therapeutic effects across multiple clinically relevant endpoints: B cell depletion (flow cytometry), renal function (proteinuria, UPCR), renal histopathology (HE staining with semi-quantitative scoring), systemic inflammation (IgE, IL-17A, IFN-γ), autoantibody production (anti-dsDNA, ANA), and survival. This multi-dimensional characterization strengthens the phenotypic evidence for efficacy.

(3) Appropriate control groups.

The inclusion of non-targeted NK92-Exo as a control allows attribution of the observed effects to CD19-mediated targeting rather than non-specific vesicle-associated activities.

(4) Significant survival benefit.

The improvement in survival from 25% to approximately 80% in V-CD19-Exo-treated mice is substantial and represents arguably the most compelling evidence for therapeutic potential in this model.

Weaknesses:

(1) Mechanism of B-cell reduction remains unclear.

The manuscript reports a dramatic reduction in splenic CD19+CD20+ B cells (from 10.53% to 1.51%) following V-CD19-Exo treatment. However, the authors do not establish whether this results from direct cytotoxicity (e.g., perforin/granzyme-mediated killing, apoptosis induction) or from functional suppression/downregulation of CD19 expression. The authors speculate that the effect is likely mediated by cytotoxic proteins carried by NK-92-derived vesicles, but no data are provided to support this mechanism. Essential experiments would include the detection of apoptosis markers (Annexin V, activated caspase-3/7) in B cells, assessment of perforin/granzyme B content within V-CD19-Exo, or in vitro co-culture assays demonstrating direct B cell killing.

(2) Small sample sizes.

Most experimental endpoints were assessed with n=5 per group, which is marginal for detecting modest effect sizes and may amplify the influence of individual biological variation. While the survival study had n=10 per group, the main mechanistic and endpoint analyses would benefit from larger cohorts (n=8-10) to increase statistical power and robustness.

(3) No dose-response or dosing optimization studies.

All experiments used a single dose (10⁹ particles per injection) and a fixed schedule (twice weekly for three weeks). The absence of dose-response data leaves unclear whether the observed effects represent maximal efficacy or could be achieved with lower doses, and whether alternative dosing regimens could improve outcomes or reduce potential off-target effects.

(4) Lack of safety assessment.

The authors emphasize the theoretical safety advantages of extracellular vesicles over cell therapies, but no systematic safety evaluation is presented. Key missing data include: histopathological examination of non-target organs (liver, lung, heart, gastrointestinal tract), assessment of off-target immune activation (T cell responses, cytokine profiles beyond those measured), and evaluation of potential accumulation or toxicity with repeated dosing.

(5) Incomplete characterization of the engineered vesicles beyond targeting.

While the manuscript successfully demonstrates CD19scFv display and vesicle enrichment of exosomal markers, it does not characterize whether V-CD19-Exo retains the full spectrum of NK-92 effector molecules (perforin, granzymes, FasL, TRAIL, cytokines such as IFN-γ) at functional levels. Quantitative or semi-quantitative comparison of cargo between V-CD19-Exo and parental NK-92 cells or non-engineered NK92-Exo would help contextualize the observed in vivo effects.

(6) Sex as a biological variable is not systematically addressed.

The authors note in the Discussion that the same treatment showed more significant efficacy in male mice compared to females (data not shown), yet all main experiments were conducted exclusively in female mice. Given the strong sex bias in SLE epidemiology (approximately 9:1 female-to-male ratio) and potential differences in immune responses between sexes, this observation warrants systematic investigation rather than a footnote. Presenting the sex-differential data or alternatively, conducting adequately powered sex-stratified analyses would substantially strengthen the manuscript.

(7) Translational claims are premature.

The manuscript repeatedly emphasizes advantages over cell therapy (low immunogenicity, scalable production, no requirement for lymphodepletion) as if these are established properties of V-CD19-Exo. However, no experiments directly compare V-CD19-Exo to CAR-NK or CAR-T cells in terms of efficacy, immunogenicity, or safety. Similarly, claims of "scalable production" and "high batch-to-batch consistency" are not supported by any manufacturing or quality control data. These statements should be toned down or supported with empirical evidence.

Reviewer #2 (Public review):

Summary:

The LiFE protocol provides shortened light exposure, as well as timed food availability and exercise (running wheel) availability. It causes mice to sleep for the first half of the active phase and to be active during the second portion, thus consolidating activity. This has some positive effect on metabolic markers and some (but not other) behavioral markers. In two AD models, there is the suggestion of a protective effect, though most of the data is not significant.

Strengths:

The concept is important and builds on previous studies showing cognitive benefits and decreased brain pathology in mice with time-restricted feeding or shortened light exposure. The comparison to multiple different light, food, and exercise timing regimens in Figure 1 is quite interesting and informative. The use of 2 different mouse models (5xFAD and 5xFAD::PS19) is a strength, as this latter model is rarely used. The pathological endpoints are appropriate.

Weaknesses:

The LiFE protocol is strange in that it induces sleep during the first several hours of the active phase. The mice seem to show food anticipatory activity, then suddenly go to sleep for a few hours during what should be their most active time of day. Is this good? Would we want such a thing in humans? Why does this happen? What is the real-life implication? How do the mice eat if they are sleeping so much during their food period?

While many of the cognition and brain pathology experiments seem to trend in a positive direction, most are not significant, which calls into question the value of the intervention. There are a few that are significant, but the overall effect seems weak. The experiments with AD mouse models are generally underpowered and not controlled for sex, as female mice get pathology much faster in the 5xFAD model, and males have more severe pathology in the PS19 model. Combining them may mask effects.

In all, it is an interesting and thought-provoking study which shows striking effects of the LiFE intervention on activity patterns and sleep, with modest/inconclusive effects on cognition and brain pathology. While it feels very preliminary, the study does provide some valuable information for planning future studies of circadian interventions in neurodegenerative models, even if the protective effects here are not fully solidified.

Reviewer #3 (Public review):

Summary:

This study is significant as it established a protocol for the long-term culture of Schistosoma mansoni newly transformed cercariae which developed in vitro into sexually dimorphic forms. The impact of two different sera, Fetal Bovine Serum (FBS) and Human Serum (HS), added to the culture medium supplemented with human red blood cells was evaluated. The authors demonstrated that HS-cultured parasites were able to digest red blood cells, a critical step for long term parasite development. Furthermore, while most FBS-cultured parasites did not progress beyond an early liver stage, sexual dimorphism was clearly evident in the HS-cultured worms, albeit delayed compared to in vivo development.

Strengths:

This study could contribute to further in vitro studies for a better understanding of the unique sexual biology of Schistosoma mansoni and for screening novel schistosomicidal compounds. By increasing parasite development in in vitro studies this protocol could have a positive impact on the principles of the 3Rs (Replacement, Reduction and Refinement) for animal research.

Weaknesses:

As the authors mentioned "pairing between male and female parasites was rare. Pairing was rarely observed and only after day ~ 80 in culture. Egg production was also not achieved with this protocol.

Comments on revised version.

Some data presentation has been improved as suggested by other reviewers in the revised manuscript. The authors have also clarified the limitations of their long-term culture protocol for Schistosoma mansoni newly transformed cercariae which develop in vitro into sexually dimorphic forms with regards to male and female pairing. Additionally, they addressed my specific question regarding the culture conditions used for ex vivo/in vitro mating. The experimental conditions tested for in vitro developed parasites were the same as those for the pairing experiments. It remains to be investigated the factors that negatively influence pairing during the long-term in vitro culture of Schistosoma.

Reviewer #2 (Public Review):

This study introduces DUAL (Deep Unsupervised simultAneous denoising and simuLation), an unsupervised deep learning framework that jointly addresses denoising and realistic data simulation for cryo-electron tomography (cryo-ET). By leveraging a cyclic, unpaired learning strategy, DUAL avoids reliance on paired clean ground-truth tomograms, which represents a practical advantage over many existing supervised approaches.

Through extensive quantitative evaluations on benchmark datasets, together with qualitative and downstream analyses on diverse experimental tomograms, the authors show that DUAL performs robustly across both denoising and simulation tasks. For denoising, DUAL outperforms several widely used methods on the SHREC 2021 benchmark and achieves the highest particle-picking accuracy on the RELION benchmark, indicating strong downstream utility.

For tomogram simulation, the study presents an unsupervised framework that jointly denoises experimental tomograms and generates synthetic volumes that closely resemble experimental data. These simulated tomograms outperform existing approaches in downstream tasks such as particle picking and enable additional applications, including missing-wedge compensation and cross-domain adaptation, without requiring labeled training data.

Overall, this work represents a substantial contribution to the cryo-ET field by providing a versatile unsupervised tool that reduces dependence on labor-intensive manual annotation, enables realistic data augmentation for training downstream models, and facilitates artifact mitigation. As such, DUAL has the potential to accelerate methodological development and progress toward comprehensive in situ structural biology.

Reviewer #2 (Public review):

Summary:

In this work, Gupta & Murphy present several parallel efforts. On one side, they present the hardware and software they use to build a head-fixed mouse experimental setup that they use to track in "real-time" the calcium activity in one or two spots at the surface of the cortex. On the other side, they present another setup that they use to take advantage of the "real-time" version of DeepLabCut with their mice. The hardware and software that they used/develop is described at length, both in the article and in a companion GitHub repository. Next, they present experimental work that they have done with these two setups, training mice to max out a virtual cursor to obtain a reward, by taking advantage of auditory tone feedback that is provided to the mice as they modulate either (1) their local cortical calcium activity, or (2) their limb position.

Strengths:

This work illustrates the fact that thanks to readily available experimental building blocks, body movement and calcium imaging can be carried out using readily available components, including imaging the brain using an incredibly cheap consumer electronics RGB camera (RGB Raspberry Pi Camera). It is a useful source of information for researchers that may be interested in building a similar setup, given the highly detailed overview of the system. Finally, it further confirms previous findings regarding the operant conditioning of the calcium dynamics at the surface of the cortex (Clancy et al. 2020) and suggests an alternative based on deeplabcut to the motor tasks that aim to image the brain at the mesoscale during forelimb movements (Quarta et al. 2022).

Reviewer #2 (Public review):

Summary:

The study investigated the distinct roles of phasic and tonic pain in adaptive behavior. Phasic pain was proposed to function as a teaching signal, promoting avoidance of further injury, while tonic pain was hypothesized to support recuperative behavior by reducing motivational vigor. This hypothesis was tested using an immersive virtual reality (VR) EEG foraging task, in which participants harvested fruit in a forest environment. Some fruits triggered brief phasic pain to the grasping hand, which in turn reduced the likelihood of choosing those fruits. Concurrently, tonic pressure pain applied to the contralateral upper arm was associated with reduced action velocities. The authors employed a free-operant computational framework to quantify how phasic and tonic pain modulate motivational vigor and decision value. Importantly, model parameters were found to correlate with EEG responses, providing neurophysiological support for the hypothesized functional distinctions.

Comments on revised version.

All my comments have been well addressed.

Reviewer #2 (Public review):

This manuscript investigates the role of SOX17 in the formation and function of the Sertoli valve (SV) at the interface between seminiferous tubules and the rete testis (RT). Building on previous work showing that rete testis-specific deletion of Sox17 disrupts SV formation, leading to defective spermiogenesis and male infertility, the authors explore how SOX17 overexpression in Sertoli cells regulates the SV of rodent testes.

Using transgenic mouse models with ectopic Sox17 expression in Sertoli cells, the study demonstrates that SOX17 is not only required but can also modulate SV formation. Ectopic expression in Sertoli cells induces expansion of the SV structure and partially rescues SV defects and spermatogenesis in RT-specific Sox17 conditional knockout animals. The data support a model in which SOX17 acts through paracrine signaling to regulate SV formation, although the precise mechanisms remain to be clarified.

Overall, this is a well-executed study with novel and significant findings. The ability to experimentally manipulate SV size is particularly compelling and provides a valuable framework to study fluid dynamics and epithelial interactions in the testis. This work will be of broad interest to the reproductive biology and developmental biology communities.

Reviewer #2 (Public review):

Summary:

In this submission, Roy et al. examine the process of Drosophila PGC migration. Directed cell migration requires the concerted activities of chemoattractants and repellents to guide cells to the correct locale. In their submission, the authors describe a role for regulated Hedgehog (Hh) signaling to inform PGC migration. In prior work, the authors reported that Hmgcr potentiates Hh signaling, providing a permissive axis. A gap in the field, however, was the identification of the repulsive cues that guide PGCs out of the midgut and toward the future gonad. In the current work, the authors report that two wunen genes (wunen and wunen 2) inhibit Hh signaling, thereby repressing Hh activity. The model is that Hmgcr and wunen(s) balance the transmission of Hh signals to enable effective PGC migration.

Strengths:

A strength of this work is the comprehensive genetic analysis performed by the authors. The authors examine zygotic versus maternal contributions, autonomous versus non-autonomous requirements, and use a variety of RNAi and mutant allele combinations to examine genetic requirements and interactions. Another strength is that the data presented are generally clear and well quantified. Insets are provided to enhance visualization, and relevant data are quantified through replicated experiments.

Weaknesses:

Weaknesses of the work include a lack of biochemical data to validate some of the proposed interactions. Although the authors do report lipidomics data, little is done with these findings to validate or place the results in the context of a mechanistic model. Despite these issues, the conclusions stated are generally well supported by the results.

Reviewer #2 (Public review):

Summary:

The manuscript by Laverre et al. provides an interesting new test of selection on TF binding. Rather than focusing on sequence changes, this test is specifically for changes in predicted TF binding affinity. The authors report directional selection on 5.1% of tested regions in Drosophila, as well as a signal of selection on CTCF binding in the human CNS and male reproductive system.

Strengths:

Overall, I think this represents an important direction for the field of molecular evolution: now that TF binding can be predicted fairly well from sequence, it can be a very useful focus for tests of selection.

Weaknesses:

As mentioned several times in the manuscript, Jiang and Zhang (2024) pointed out some issues with a previous permutation-based version of this test. Foremost among these was the issue of ascertainment bias: when testing only experimentally supported TF binding sites from a focal species, and then asking what type of selection (or lack of selection) led to those sites, one is guaranteed to find more substitutions that increase affinity, simply because the sites were selected in the first place as those with maximum (empirically measured) affinity.

To address this issue, the authors simulated Drosophila CTCF peaks evolving neutrally and then tested different ascertainment cutoffs in Figure 4D. It was not entirely clear to me what is shown in Figure 4D: the text says the bins were stratified by derived delta-SVM, whereas the figure says SVM, and the legend says derived SVM (both without the delta). I was unable to find any clarification of this in the Methods section. In any case, I am not really convinced by this, for two main reasons. First, when analyzing empirical ChIP-seq data, I would guess that only a tiny fraction of the genome is bound (far less than 1%, especially in mammalian genomes). However, the most extreme bin in Figure 4D is taking the top 10% of (delta?) SVM values. What would Figure 4D look like at bins of the highest 0.1%, 0.001%, etc? My guess is there would be a strong uptick in the FPR. The second reason is actually more important and fundamental than the first. As long as this method is working as described, I cannot see any way that it would ‘not’ be impacted by ascertainment bias. As an extreme case, imagine that all TF binding sites tested had the maximum possible SVM scores; then none of them would have any chance of showing directional selection against binding, while even those that evolved neutrally would appear to have directional selection in favor of binding. Of course, real empirical data are not as extreme as this, but the same concept applies in less extreme scenarios.

This bias could explain patterns observed in the real data. For example: "We observe much more positive than negative directional selection, a pattern likely biological rather than methodological, since it is absent from simulations." This is exactly the pattern predicted under ascertainment bias (in the extreme-scenario thought experiment above). I suspect it is absent from simulations simply because the authors did not properly account for this bias in their simulations.

If the main result reported by the authors had been a lack of any directional selection in favor of binding, and instead only neutrality or directional selection against binding, then this ascertainment bias would not be an issue- it would only have made their results conservative. Unfortunately, this is not the case, and the directional selection in favor of binding, which is the main result emphasized from the empirical analysis, could be inflated by this bias.

Minor point:

The following statement: "In contrast, phastCons and phyloP scores lack such enrichment and have a lower dynamic range, suggesting that the conservation scores are less sensitive to fine-scale variation of TF occupancy and thus regulatory region function" is only true if one assumes that TF binding is the only function of this region. One could even turn this around and say the fact that the sites affecting TF binding are not the most conserved is actually evidence that TF binding is not a good indicator of these regions' entire function. I suggest the authors soften this claim that conservation scores are less sensitive to regulatory region function.

Reviewer #2 (Public review):

Strengths:

The paper's underlying question is interesting, extending the authors' prior work on RNA along similar conceptual lines. The paper combines both image analysis of leaves and a computational analysis of a simple model of leaf development.

Weaknesses:

The entire paper is based on the Runion model. More intuition about the Runion model would be useful for a broader readership that cares about the evolutionary aspect of this, but may not know the developmental model in question. Obviously, this is prior well-established work, but 2 - 3 sentences highlighting the key structural aspects of such a model would be great. Currently, that intuition is found implicitly in a sentence on page 2 ("complex leaf shapes need more specificity in their GRNs than their simpler unlobed leaf shape"), but the reader is left wondering - is the Runion model a detailed mechanistic one with multiple interacting genes/proteins? If so, how many? Or is it just 2 - 3 genes but with complexity entirely in how long they are each expressed/when they are turned off, etc.

The Runions model has nearly 100 free parameters. Random walks in 100-dimensional spaces have generic properties like a tendency to move toward regions of larger volume that have nothing to do with leaf biology. How do you disentangle the geometry of high-dimensional random walks from genuinely biological developmental bias? Would a toy model with 100 parameters and arbitrary phenotype categories also show "bias toward simplicity" if "simple" phenotypes occupy more volume?

The discussion of Figure 4 (PCA of parameter space) uses "area" loosely when what's actually being measured is bin count in a 2D projection of a high-dimensional space. I would think that, in general, PCA projections can be misleading about volume in the full parameter space, but I can't tell if that's an issue in this case. Some comments/thoughts here would be useful.

The classifier validation section is in the Methods section, but it seems critical to the whole story. The < 80% agreement with manual classification could propagate to the rest of the estimates in the paper. Again, some comments/thoughts here would be useful.

The authors should explain Mut2 and Mut5 in the main paper with a sentence or two, at least schematically, because how you mutate is obviously very relevant to interpreting a paper about biases in variation.

The two mutational schemes use additive perturbations to individual parameters. Real mutations presumably affect regulatory networks in more structured ways (e.g., changing binding affinities that affect multiple parameters simultaneously). How sensitive are the results to the assumption of independent single-parameter mutations?

The connectedness argument is made using a 2D PCA projection. Is there a way to check this statement in the full parameter space or perhaps in higher-dimensional projections to test the robustness of this result? Connected components can merge/split under different projections.

Reviewer #2 (Public review):

Summary:

In this study, Sato and Hamazaki have expanded upon previous work, describing quantitative genetic models for direct and indirect genetic effects and applied this to both simulated and real plant datasets of three different tree species. The methods are clearly described and accompanied by a number of R packages freely available to the wider community.

Strengths:

The main strength lies in the joint modelling of DGE, IGE and their covariance while also simultaneously modelling single-SNP fixed effects (including SNP interactions across neighbours) and a polygenic effect that goes beyond a simple kinship correction as found in many traditional GWAS models, to a compound kinship structure that accounts for DGE, IGE and their interaction.

Weaknesses:

There were some aspects that deserved more attention from the authors. For example, the authors found that a very large amount of phenotypic variation in citric acid content in grapes was explained by neighbour identity, along with over 1000 significant SNPs, yet there was little to no discussion of this result and how it could have arisen (apart from some mention of volatiles and ethylene - but without being explicit on the mechanism here). The simulation study also only considered the scenario of equal direct and indirect genetic variances, while previous studies, as well as the 3 real datasets presented in this study, show that DGE variance is almost always larger than IGE variance. A simulation study cannot be exhaustive, of course, but it seems more likely that in reality and for most traits, IGE will be more difficult to detect than DGE.

Reviewer #2 (Public review):

Summary:

The authors studied the resistance against octanoic acid, a compound of the noni fruit, in D. simulans, using experimental evolution and resistance/susceptibility in D. melanogaster cells. They identified novel candidate genes and performed functional tests.

Strengths:

The idea of using experimental evolution of a non-resistant species to develop resistance is interesting, and the idea of narrowing down a large list of candidate loci by CRISPR-based gene knockout in cell culture is innovative. The reviewer also liked the (easy) follow-up experiments to validate the results.

Weaknesses:

The reviewer is not convinced of the conceptual idea behind their approach: the intersection of the two approaches implicitly assumes that null alleles (or at least compromised alleles) should be selected during experimental evolution. The reviewer considers this unlikely, and the authors made no attempt to test this implicit hypothesis in their data. Along the same lines, it is not clear how to reconcile an upregulation of candidate genes in resistant flies with the knockout experiments.

The experiments to validate the effect of candidate genes did not match the experimental evolution conditions.

The statistical analysis suffers from some problems and an insufficient description of the analyses performed.

Although D. simulans GWAS data are available, the authors did not make an attempt to estimate the effect of selected variants in the candidate genes in the GWAS data set.

The reviewer would have liked to see more connection between the experimental evolution and the GWAS data. As some D. simulans genotypes have similar resistance to D. sechellia, it would have been interesting to test whether this genotype contributed to the observed resistance.

At several places, the authors discuss the challenge of studying a polygenic trait, but at the same time, they claim to have detected and validated candidate genes. It would be helpful if the authors could discuss why they consider that their assays could really detect the contribution of single loci to the polygenic trait. In particular, when GWAS did not detect their candidate genes.

It is not clear to the reviewer why the authors did not pay more attention to the highly significant peaks emerging from the experimental evolution study. Their functional validation would have been biologically more plausible.

Impact:

Given the obvious challenges of functional testing of polygenic traits and the clear limitations of the interpretation of the results, the study will be helpful for future studies aiming to characterize polygenic traits. Unfortunately, the results are just another piece of controversial results regarding resistance against octanoic acid, a trait that is rather easy to evaluate.

Reviewer #3 (Public review):

Pattern formation is responsible for generating the spatial organization of cells, tissues, and organs during embryogenesis. It operates within a multifactorial system including initial conditions, gene regulatory networks, extracellular signals, mechanical forces, stochastic noise and environmental inputs, and finally ensures the functional anatomy of an organism.

This study focuses on the one central aspect in pattern formation: how spatial heterogeneity arises from an initial condition and evolves into a more complex or distinct spatial pattern (non-trivial pattern formation as they termed). The authors made efforts to explore and characterize all possible ways to achieve the pattern formation by discussing how extracellular signals spread, how individual cells respond to those signals, and how those responses, in turn, modulate signal propagation.

Finally, their comprehensive analysis summarizes that there are three classes of interactions between extracellular signal and intracellular responses, corresponding to previously known mechanisms that can generate spatial patterns: Difference in morphogen concentrations in space, noise-amplification, and Turing pattern.

Reviewer #2 (Public review):

Summary:

In this study, the authors set out to understand how T cell leukemia cells enter and persist in the CNS, with a particular focus on the role of adhesion molecules known to regulate normal immune cell trafficking. Contrary to expectations, they find that loss of two key adhesion molecules does not impair CNS entry but instead leads to increased accumulation of leukemia cells, which is associated with enhanced cell proliferation in this environment. These findings challenge prevailing assumptions about how leukemia cells interact with tissue niches and suggest a potential therapeutic strategy combining adhesion blockade with chemotherapy.

Strengths:

The study addresses an important and longstanding question in leukemia biology using well-designed in vivo models and multiple complementary approaches. The key observation is robust and consistently supported across genetic models and experimental systems. The authors systematically test alternative explanations, including altered entry, exit, and immune evasion, which strengthens the interpretation that proliferation differences underlie the phenotype. The work has potential translational relevance, particularly in highlighting a possible strategy to enhance the efficacy of anti-proliferative therapies in the CNS.

Weaknesses:

While the central phenotype is clear, the mechanistic basis remains incompletely defined. Addressing the following points would strengthen the manuscript.

Major critiques:

(1) The central claim that integrin loss enhances CNS accumulation via increased proliferation is not mechanistically resolved; current data are correlative (EdU incorporation, distribution patterns) and do not establish that integrin-mediated signaling directly restrains cell cycle progression in the CNS niche. The authors should perform functional perturbation of candidate pathways identified (e.g., TGF-β) using pharmacologic inhibitors or genetic approaches (dominant-negative receptor or CRISPR knockdown) in vivo or in ex vivo CNS-derived T-ALL co-culture systems to test whether blocking this pathway rescues the WT proliferation phenotype; if not feasible, the mechanistic claims should be toned down and clearly presented as hypotheses.

(2) The relationship between altered spatial distribution and proliferation is suggestive but not directly demonstrated. The imaging data indicate differences in localization, but these observations are not quantitatively linked to cell cycle status. The authors could strengthen this point by incorporating spatially resolved proliferation analyses, such as combining EdU labeling with imaging or quantifying proximity to stromal or vascular niches, or alternatively by providing additional quantitative analysis of the existing imaging data.

(3) The conclusion that CNS accumulation is not due to altered trafficking (entry/exit) is suggestive but not definitive, as early seeding dynamics are not directly assessed. Authors should perform short-term homing or early time-point competitive trafficking assays (e.g., CNS quantification at 6-48h post-transfer) to rigorously exclude differences in entry kinetics; if such experiments are not feasible, this limitation should be explicitly acknowledged in the discussion.

(4) The therapeutic claim that integrin blockade synergizes with chemotherapy is promising but underdeveloped, as it lacks survival outcomes and a broader translational context. The authors should include survival analyses and, if possible, test combination treatment in a more clinically relevant setting (e.g., delayed intervention or alternative standard-of-care agents), or otherwise temper translational conclusions and discuss risks such as inducing proliferation in the absence of chemotherapy.

Reviewer #2 (Public review):

In this study, Sewell et al. use a novel approach to understand cell-specific BDNF signaling in the developing spinal cord. Using cultured E14 spinal cord, the authors used a mass cytometry approach to identify the levels of TrkB and p75NTR receptor expression, as well as 19 signaling markers and cell identification markers, to delineate activation of BDNF signaling in different cell types within a complex population. They identified that the level of receptor expression, while necessary, is not sufficient to determine the activation of signaling cascades. It has been known for some time that TrkB, indeed all RTKs, have the capacity to activate certain canonical signaling pathways; however, not all these pathways are always activated upon ligand treatment. This study begins to identify the conditions under which specific signaling pathways are activated by ligand. Specifically, the type of cell and maturation state are critical for determining signaling. The cytometry approach allows the clustering of cell types according to expression of specific markers, and overlaying those clusters onto the expression status of TrkB and p75 receptors, as well as specific activated signaling proteins. This study provides greater insight into when specific signaling events can be activated by BDNF than was previously known.

The comparison of levels of expression of TrkB and p75NTR is interesting to demonstrate which pathways may require one or both receptors for specific signaling responses.

It is very interesting that progenitors do not respond to BDNF despite abundant expression of TrkB, although they responded to the rescue treatment with phosphorylation of Erk and Akt. The development of competence to respond to BDNF is an interesting question for future analysis, and the authors suggest some possibilities in their Discussion.

The responses of glial cells in their culture preparation are also interesting. They see signaling responses to BDNF in astrocytes and "laden" microglia (presumably phagocytic). E14 spinal would not be expected to have a large population of glia at this stage of development, although the serum in their plating media would allow for the proliferation of the progenitors. Astrocytes are generally considered to have the truncated TrkB receptor, yet they see P-Erk, P-Akt, etc. in these cells in response to BDNF. This raises the question of which receptors are expressed in the glial populations and whether the responses in these cells are also maturation dependent, since the glia in their culture conditions are also likely to be immature.

Some specific comments:

(1) The authors should specify what is meant by "rescue" in the text. What is rescuing the cells from trophic deprivation when no BDNF is added? Is it the B27 and GlutaMax in the Maintenance media, and does this actually rescue the cells?

(2) Figure 3 - K252a blocked activation in most, but not all, lineages, especially in mature neurons. Is some component of the P-Erk activation in these cells TrkB independent?

(3) Figure 5 E, F - The correlation between receptor surface depletion and signaling is based on "surface-specific staining". Does the staining allow you to see internalized receptors to confirm that the receptors are internalized?

(4) The drawbacks to the study - particularly capturing snapshots in time to represent signaling cascades, are fully acknowledged in the Discussion. The interplay between TrkB-T1, TrkB-FL, and p75NTR cannot be elucidated from this study, but again, that is acknowledged and will require a different approach.

DOI: 10.1371/journal.pgen.1010346

Resource: RRID:WB-STRAIN:daf-2(e1370)

Curator: @olekpark

SciCrunch record: RRID:WB-STRAIN:daf-2(e1370)

What is this?

DOI: 10.1007/s11306-023-02025-7

Resource: RRID:WB-STRAIN:daf-2(e1370)

Curator: @olekpark

SciCrunch record: RRID:WB-STRAIN:daf-2(e1370)

What is this?

Reviewer #2 (Public review):

Summary:

In this work, the authors review the study of the neural correlates of consciousness (NCCs). They discuss several of the difficulties that researchers must face when studying NCCs, and argue that several of these difficulties can be alleviated by using intracranial recordings in humans.

They describe what constitutes an NCC, and the difficulties to distinguish between an NCC proper from the prerequisites and consequences of conscious processing.

They also describe the two main types of experimental designs used to study NCCs. These are the contrastive approach (with its report and non-report variants), and the supraliminal approach, each with their own merits and pitfalls.

They discuss the limitations of non-invasive methods, such as fMRI, EEG and MEG, as well as the limitations of the use of invasive recordings in non-human animals.

After setting the stage in this way, the authors provide an extensive review on the knowledge acquired by using invasive recordings in humans. This included population level measurements in vision and in other sensory modalities, as well as single neuron level studies. The authors also discuss studies of subcortical NCCs.

The second half of this work discusses the theoretical insights gained through the use of intracranial recordings, as well as their limitations, and a perspective for future work.

Strengths:

This work offers an impressive review, which will serve as a useful reference document, both for newcomers to the study of NCC as for experienced researchers. The inclusion of non-visual and subcortical NCCs is of particular merit, as these have been understudied.

Besides serving as a review, this work includes a perspective, exploring several directions to pursue for the progress of the field.

Weaknesses:

No major weaknesses.

Appraisal of whether the authors achieved their aims:

In this work, the authors have gathered an impressive review, and have discussed several important problems in the field of study of NCCs, as well as provided a perspective on how the field could move forward.

Discussion of the likely impact of the work on the field:

This work has the potential of becoming a must read for anyone working in the field of consciousness research.

Comment on previous version:

The authors have addressed all my concerns. Once again, my compliments for a nice piece of work.

Reviewer #2 (Public review):

Strengths:

The main strength of the work is to call attention to a new possibility of inverting the effect of TNS in humans by manipulating stimulation frequency, opening new indications for the therapy. This is highly relevant because of the recent popularity of TNS and its non-invasiveness, which lends itself to rapid testing and evaluation for new conditions and high willingness to adopt. The authors convincingly demonstrate a modest excitatory effect on bladder sensation with low-frequency TNS, which clearly warrants further investigation.

The high-level design of the hypotheses, concepts, and experiments are clearly articulated in both the methods and in particularly clear diagrams, letting the reader focus their attention on the most important findings.

It is rare to develop a new computational model of the lower urinary tract at a systems level, and even more so for it to incorporate circuits in the spinal cord and brainstem centers, and this work undoubtedly advances the field's ability to engineer such systems. Further, because the model is comprised of linked conductance-based point-neurons, it is an excellent tool to investigate how an arguably plausible wiring diagram for neural control of the LUT could result in stimulation frequency dependent effects on pelvic efferents. It is a proof of concept demonstrating how their mechanistic hypothesis of TNS could be implemented neurophysiologically by the nervous system. Further, the model is shared openly, which conforms to good modeling practices.

Weaknesses:

The main drawback of the work is the overinterpretation of the results. The human study and computational model are both proof-of-principle. The human study effect size is small and the sample size is modest; the computational model is poorly validated and does not generate physiologically typical urodynamic responses when simulating even healthy nominal LUT conditions. Thus, both the existence of a TNS 1Hz inhibitory effect (human study) and the mechanistic interpretation of its origin (simulations) remain provisional. For example, despite some caveats later in the work, the abstract stating there is a "frequency-dependent effect of TNS via the ability to alter urge perception and down-regulate bladder activity, corroborating model predictions," could easily be misleading, since a) the reduction in time of first urge with 1Hz stimulation was quite small relative to overall void time, b) reported intensity was essentially not impacted, and c) the model does not directly make predictions about these experiment outcome measures. Similar overreaching statements appear in the second to last paragraph of the introduction, the first paragraph of the discussion, and so on throughout the paper. Many of the analyses are bespoke to the idiosyncrasies of the dataset rather than field standards, making spurious results also more likely and the effects provisional. One example is the use of robust linear regression to identify significance in the experiment between the 1Hz and control groups AND removing outliers before the analysis, since the typical approach is to use robust regression when the outliers are left in the data. Taken together, the potential excitatory effect and mechanism are interesting, and perhaps worth further investigation, but are considerably more tentative than stated.

It remains ambiguous whether a TNS excitatory effect size shown (even if it ends up being repeatable) is clinically meaningful. The ROPE analysis is a reasonable start, but no attempt to connect the parameters chosen (e.g. 60s) to clinical outcomes were made. This is especially true given the washout results and lack of effect on perceived urgency.

There remain several reasons to treat the model results questionable. First, as the authors now note, the model under normal conditions does not generate normal function; a voiding efficiency of 15% is severely underactive. Second, the 1 Hz stimulation simulation appears to create normal voiding, suggesting that the implementation of the neural control circuits may not produce results that would generalize to other experiments. Third, analysis focuses on the model outcome of "time to void", but this outcome is not reported for the experiment, so direct comparison is not possible.

Reviewer #2 (Public review):

Summary:

Mutations in Leucine-Rich Repeat Kinase 2 (LRRK2) are a major cause of Parkinson's disease. LRRK2 PD-related mutations all result in increased kinase activity. Therefore, LRRK2 has been the focus of the development of kinase inhibitors. So far, two classes of kinase inhibitors have been identified: type 1 LRRK2-specific inhibitors that stabilize LRRK2 in a closed active-like conformation and broad-range type 2 inhibitors that stabilize LRRK2 in an open inactive-like conformation. Basiashvili et al. used here in cell structural biology to study the effect of both type 1 and type 2 inhibitors on the localization and structural conformation of LRRK2-I2020T.

Strengths:

They showed that Type 1 and not Type 2 inhibitors induce LRRK2 filament/ on microtubules. Furthermore, they were able to build a structural map of full-length LRRK2 I2020T bound to a Type 1 inhibitor in a closed kinase confirmation. Together, this work thus confirms the data of previous studies that showed that LRRK2 Type 1 and 2 inhibitors differently affect filament formation.

Previous Weaknesses:

All conclusions are fully supported by the provided data. However, as the authors indicated themselves, the physiological relevance of LRRK2 microtubule binding is questionable. Furthermore, although the authors used a full-length LRRK2 protein, like in previously published structures, the resolution of the N-terminal domains is rather poor. Therefore, it also remains unclear what we learn from this structure compared to the previously published structures.

Reviewer #2 (Public review):

Summary:

The role of the cerebellum in producing and modifying dystonic motor phenotypes has been of increasing recent interest to understand the pathophysiology of movement disorders, as well as to develop novel pharmacological and surgical interventions to treat these disorders. Previous rodent and human imaging studies have shown that in genetic, drug-induced, and injury-acquired dystonia, cerebellar dysfunction and output from the deep cerebellar nuclei have correlated with the development of dystonia symptoms. In some genetic dystonia patients, the strength of connections between the cerebellum, thalamus, and cortex could explain reduced penetrance or severity of symptoms in these genetically defined dystonia patients. Altogether, these studies have pointed to abnormal output from the cerebellum as a driver of abnormal motor output. Some studies have even gone as far as to suggest that no cerebellum is better than a cerebellum with abnormal output (see PMID 8491286). This indicates a critical need to understand the neural circuits underlying dystonia development, how the cerebellum drives symptom onset or severity, and if the cerebellum could be therapeutically targeted for the benefit of patients with dystonia.

Hipolito et al. use rigorous mouse genetics-based approaches to understand how a specific cell type, inhibitory projection neurons from the cerebellar nuclei, can drive dystonic phenotypes, especially severe dystonic phenotypes. The authors demonstrate a number of novel findings that further support a critical role for disturbed cerebellar output in driving dystonic phenotypes, and that disrupting this disturbed output may provide a novel therapeutic approach for dystonia. Specifically, the authors define a novel role for inhibitory neurons of the cerebellar nuclei in driving disease, and these neurons have not previously been observed to have monosynaptic connections into a specific nucleus of the thalamus. Disruption of these connections via deep-brain stimulation alleviated severe dystonic crisis with quick onset, and repeated stimulation sessions possibly had a long-term disease-modifying effect. Overall, these findings present novel insight into the circuits and mechanisms by which inhibitory neurons of the cerebellar nuclei influence dystonic states, and how these may be a viable therapeutic target for severe dystonia. My specific comments are below:

Strengths:

The manuscript uses rigorous mouse genetics techniques to provide fundamental insight into the role of inhibitory projection neurons of the cerebellar nuclei in influencing dystonic states. Solid experimental evidence is used to step-by-step illustrate circuit-level consequences of inhibitory projections of the cerebellar nuclei, and whether these can be manipulated for therapeutic benefit.

Weaknesses:

There are mild weaknesses in the approach around proving the specificity of the vGlut2 knockout, the long-term effects of silencing inhibitory projections, as well as the degree to which activation specifically drives dystonic crisis. These are addressed in my specific comments below.

Reviewer #2 (Public review):

Summary:

In this study, authors studied the synchrony between ripple events in Hippocampus, cortical spindles and Locus Coeruleus spiking. The results in this study together with the established literature on the relationship of hippocampal ripples with widespread thalamic and cortical waves, guided authors to propose a role for Locus Coeruleus spiking patterns in memory consolidation. The findings provided here, i.e. correlations between LC spiking activity and Hippocampal ripples, could provide basis for future studies probing the directional flow or the necessity of these correlations in the memory consolidation process. Hence, the paper provides enough scientific advance to highlight the elusive yet important role of Norepinephrine circuitry in the memory processes.

Strengths:

Authors were able to demonstrate correlations of Locus Coeruleus spikes with hippocampal ripples as well as with cortical spindles. Specific strength of the paper is in the demonstration that the spindles that activate with the ripples are comparatively different in their correlations with Locus Coeruleus than those which do not.

Reviewer #2 (Public review):

Summary:

In prior work, the authors developed an ML algorithm that computes spatial maps of "meaning": image regions that are likely to be given semantic labels by human observers. They also previously showed that "meaning" predicts fixations in humans and human infants. Here, these observations were extended to macaque monkeys, testing the hypothesis that meaning is a phylogenetically preserved driver of overt attention across primates.

Strengths:

The paper reports that fixated locations had higher values of meaning compared to nearby, non-fixated locations. Specifically, it shows that meaning values - as inferred from a neural network model - are useful in differentiating these two classes of locations, beyond the established effects of image salience and centrality on gaze. The reported results were consistent in both monkeys.

Weaknesses:

It is difficult to understand what, precisely, is meant by meaning from this paper, although the prior work from this group may offer some insight. Given that, it is not clear if "high-meaning" image locations tend to be objects, for example, or faces, or other such behaviorally relevant image features. Indeed, the utility of the meaning maps was not evaluated against other algorithms that consider more complex natural scene information. This is a particular concern as the paper does not demonstrate that meaning predicts where the viewer will look within the image; instead, it shows that meaning is one of the variables that differentiates fixated locations from nearby non-fixated locations. Because this is not a causal study by necessity, caution is also needed in interpreting the results. In our view, the most parsimonious interpretation may not be that meaning guides gaze in monkeys, but instead that people tend to name things that primate brains evolved to fixate on at the expense of neighboring locations.

Reviewer #2 (Public review):

Summary:

The authors find a strong trial-level relationship between tonic feedback responses and tonic learned responses.

Strengths:

The authors have performed several well-conducted experiments and thoughtful analyses to test the relationship between feedback responses and subsequent learned responses. The strength of the paper is the experimental control to probe this relationship and, eventually, oppugn the feedback error learning hypothesis.

Weaknesses:

In general, the processes studied in this manuscript and the past work have not explained the underlying mechanisms for the observed phenomena. Without knowing the mechanisms, the results are largely observational/correlational when linking feedback responses to learned responses, and there are no strong alternative hypotheses to explain the results. Most of the larger comments below stem from this theme, including:<br /> (i) what causes the phasic and tonic portions of the feedback response,<br /> (ii) justifying the phasic learned response,<br /> (iii) what are some alternative hypotheses that can explain the current results and past literature?

Suggestions to improve the paper are below.

(1) As mentioned above, it appears that there is limited mechanistic understanding of the underlying processes. For the feedback response, there is clearly a phasic and tonic component. It is not until one gets to the discussion that a potential mechanism is proposed, where presumably the phasic response may be velocity dependent, and the tonic response may be position dependent. On a somewhat related tangent, these responses somewhat mirror muscle spindles, which are known to have velocity and position-dependent responses, leading to the phasic and tonic firing during muscle stretch experiments.<br /> a) Can the authors provide more discussion on the work that they currently cite, which studied position and velocity dependent responses?<br /> b) Relatedly, did the authors put any thought into developing a model, using error inputs from the experimental trials, that can capture the feedback responses? For example, dF/dt * tau = a*pe + b*ve - cF + e, where F = force response, tau is a time constant to generate the force, a is a gain on position error (pe), b is a gain on velocity error (ve), c relates to the leak, and e is Gaussian noise. The leak would be needed to explain the equilibrium / steady state at the end of the trial. It could be very insightful if this, or some other similar flavour of model, could explain the phasic and tonic components of the feedback response. The advantage of a model in this form is that there are experimental inputs and the process evolves over time, rather than fitting static curves to the data.

(2) Aligned with past literature, the authors have characterized the early and late phases of both the feedback responses and learned responses as phasic and tonic. It is clear from the data that the feedback response data are composed of a phasic and tonic phase. However, it is less clear from the data in many of the figures that there is an actual phasic response in the learned response. Further, from a modelling perspective, it is conceivable that the fitting algorithm would partition the variance between the two components of equation 1, even though there may only be one true underlying process. This may also explain why there was no correlation between tonic feedback responses and phasic learned responses in Figure 7F.<br /> a) Can the authors provide more rationale on why the learned response would also have a phasic response? Is the assumption here that since the feedback response had a phasic response, the learned response should as well?<br /> b) Can the authors fit the learned response with only the tonic portion of the equation? Then, perform model comparison between the phasic+tonic learned response model and the tonic only learned response model using AIC/BIC, to justify whether or not a phasic portion of the model is needed to explain the data.<br /> c) Can the authors comment on the possibility that the learned response may just rise and then decay over time, without being the outcome of two distinct processes?

(3) The nicely controlled experiments do well to provide evidence against the feedback error learning hypothesis, which alone is a valuable contribution to the literature. However, the authors do not provide a strong alternative hypothesis. There is a proposal of alternative hypotheses. For example, on lines 494-498, referring to state estimation, which the authors then state could not explain all the results in the preceding paragraph. It would be beneficial to further bolster the possible explanations. Perhaps further discussion details on what the mechanisms are for the feedback responses (e.g., position or velocity dependence), and what states (position error, velocity error, motor commands, etc.) transfer into the learned response. Are they stored? Are they the outcome of a continuous process? This may be difficult given the current state of understanding in the literature, but it could substantially improve the paper.

Reviewer #2 (Public review):

Summary:

In this study, the authors tried to examine whether there are differences in the association between functional traits and extinction risk in adult and tadpole stages in Chinese anurans.

Strengths:

Overall, I think the basic idea of the study is interesting and important. It can be applied to other taxa with complex life cycles throughout the animal kingdom.

Original weaknesses:

I do not think the authors achieve their aims, as the results only partially support their conclusions. The study has several drawbacks that need to be clarified or revised, including the unclear threat categories for tadpoles, model selection and model averaging, the potential problem of AIC, and the omission of other important species traits.

Reviewer #2 (Public review):

Summary:

This work identifies a novel beta cell population primarily present in the islets from individuals with Type 1 Diabetes (T1D). This population is defined by increased expression of previously described transcription factors, including IRF1, BCL6, JUNB, and CEBPD. The authors postulate that the activation of these genes in beta cells during immune infiltration could be protective against beta cell destruction. This hypothesis aligns with experiments in NOD mice identifying a protected beta cell population. Overall, this work provides a hypothesis for how some beta cell populations survive immune infiltration in T1D.

Strengths:

This work uses a clever analysis approach, defining regulons using SCENIC and using these to recluster the data. This approach identified a novel beta cell population enriched in islets from individuals with Type 1 Diabetes that was very stable to different clustering resolutions. The authors also took many potentially confounding technical factors into account, removing ambient RNA and doublets, and often controlling for batch effects using pseudobulk approaches.

In addition to identifying a novel cluster in one published single-cell dataset, the authors also downloaded additional single-cell datasets that included cytokine treatment of human beta cells to validate the presence of this population in other datasets. In these datasets, the authors were able to identify a similar population of cells, labeled by similar transcription factors.

Weaknesses:

While the authors use a sophisticated approach to identify a novel beta cell subpopulation, more analysis needs to be done to ensure this cluster is biologically meaningful. First, the authors did not take the duration of diabetes into account in this analysis. The duration of diabetes is important because there are different levels of immune infiltration at different stages of diabetes. It would also be important to consider age at diagnosis, as the progression of disease is very different in early vs late onset populations.

Additionally, more exploration of potential confounding factors should be done when looking at the novel population vs other populations in the dataset. This would be further strengthened by adding analysis from datasets that more directly measure transcription factor activity, like single-nucleus ATAC-seq from the different disease states.

Finally, these data can't distinguish the response to the environment (i.e., cytokines) and protective programs. Especially given the similar program in alpha cells, the response to the environment seems likely. More analysis should be done, looking for a similar signature in other populations in the data.

reply to u/psitaxx at https://www.reddit.com/r/typewriters/comments/1ui2vvo/does_it_even_count_as_a_second_typewriter_if_its/

You're right that sometimes 1+1 is not equal to two.

For example 1 pile of laundry plus 1 pile of laundry is still 1 pile of laundry (just bigger), or 1 cloud merging with 1 cloud is still 1 cloud (albeit a different cloud). Sadly this is not the case with typewriters. 1 typewriter plus another 1 typewriter is 2 typewriters.

According to typewriter collector Keanu Reeves, you don't have a collection yet. That happens when you have 3, something you could never accomplish unless the math worked in the standard way.

Would that 1 typewriter + 1 typewriter = 1 typewriter! Then my collection of well over 30 wouldn't bother my wife, and I could say that I only have 1 typewriter...

Of course I can commiserate with your mint condition typewriter conundrum. Sometimes there's nothing better than getting something in mint condition, but often we all have hopes that it's not mint for one reason or another. I too was disappointed by a recent $21.95 purchase of an Olympia in perfect condition. Perhaps, as Frank Navasky said in You've Got Mail (Warner Bros., 1998), "I needed a backup."

Reviewer #2 (Public review):

Summary:

This study investigates how the human brain categorizes visual words from distinct writing systems (alphabetic vs. non-alphabetic). Using a repetition suppression paradigm combined with electroencephalography and magnetoencephalography, the authors conducted nine experiments with independent participants to identify the neural network underlying language-based categorization, characterize its temporal dynamics, and test whether this process operates independently of linguistic properties such as semantic meaning and pronunciation.

Strengths:

The study employs a well-validated design with clear control conditions and systematically manipulates key variables including writing system, language familiarity, and native language background. The use of nine experiments with independent participant samples strengthens the reliability and replicability of the results. The work combines EEG and MEG, cross-validating findings across imaging modalities to support the reported neural effects. A combination of univariate, multivariate, and connectivity analyses is used to characterize neural responses and network interactions. Results are consistent across multiple language groups and for both familiar and unfamiliar languages, supporting the generalizability of the identified neural mechanism beyond specific languages or prior experience.

Comments on revised version.

Earlier versions of the manuscript framed these findings as more directly reflecting the social-categorization function of language. In the revised manuscript, the authors now more carefully distinguish language-based word categorization from broader claims regarding social categorization and explicitly acknowledge that the current experiments do not directly test social evaluation or intergroup processes. These revisions improve the conceptual precision of the work and address my major concern from the previous review.

The additional methodological clarifications and supplementary analyses also strengthen the manuscript. Overall, I believe the revised version provides solid evidence for rapid language-based categorization of visual words across different writing systems.

Reviewer #2 (Public review):

Summary:

This article reports measurements of iEEG signals on the rat auditory cortex during cochlear implant or sound stimulation in separate groups of rats. The observations indicate some spatial organization of cochlear implant stimuli, but that is very different from cochlear implants.

Strengths:

The study includes interesting analyses of the sound and cochlear implant representation structure based on decoders.

Weaknesses:

The observation that responses to cochlear implant stimulation (stimulation) is spatially organized is not new (e.g. Adenis et al. 2024)

The claim that spatial and temporal dimensions contribute information about the sound is also not new there is a large literature on this topic.

The analyses supporting the claim that there is a mismatch between cochlear implant and sound representation are still unclear, particularly in Fig. 8.

Reviewer #2 (Public review):

Summary:

This study proposes a novel role for ensheathing glia (EG) in a Pink1-model of Parkinson's disease and shows that this cell population exhibits the highest number of DEG in a pre-symptomatic stage. In the olfactory system, there seems to be morphological changes in this cell-type that resembles an 'activated' state and the authors further show that the neuronal loss of Pink1 is responsible for this defect. The authors go on to show that manipulation of Pink1 in EG also leads to some defects in the visual system and in the dopaminergic neurons (DAN) that innervate the mushroom body (MB), and performed a screen based on the 'on-transient' defect of the ERG to identify potential genes that may modulate the function of EG in synaptic regulation. They focus on several genes related to vesicle trafficking including Vps13, and Vps35 and performed some additional experiments in the visual system and MB to propose the role of vesicle/lipid trafficking in EG as an important factor for PD pathogenesis.

Strengths:

The study proposes functional and mechanistic connections between several genes that have been linked to PD (PINK1, VPS35 and VPS13A/C). I feel that the data presented in Figure 1-Figure 3C are performed with rigor and are convincing/novel. The selection of Drosophila to study the questions is also a strength and the lab has extensive experiences in this field and model organism.

Weaknesses:

In this revised manuscript, a number of concerns raised by this and the other reviewer was addressed. The authors now admitted that some of the genetic reagents used in their screen and follow up assays were inappropriately utilized, and changed the latter half of the paper (Fig 3D-F4) quite significantly (e.g. now only 1 gene is considered as a hit in Fig3D, analysis of several genes in Fig4 have been removed and replaced by some experiments performed on Vps35). The transition between Figure 3D and Figure 4 is quite abrupt, and they don't seem to follow up on the CG17660 (the single hit from their screen, which is not further validated so it is not clear whether this genetic reagent is clean or not) and the effect of Vps35 RNAi in synaptic phenotype. Therefore, there is still a weakness in Figure 3D-Figure 4, which weakens the paper, especially since the new model diagram the authors provided in Figure 5 is not really investigated at the molecular level.

Reviewer #2 (Public review):

Summary:

Mochizuki and colleagues investigated how the germline (MIC) telomere was removed during programmed genome rearrangement in the developing somatic nucleus (MAC). Using an optimized oligo-FISH procedure, the authors demonstrated that MIC telomeres were co-eliminated with a large region of MIC-limited sequences (MLS) demarcated on the opposite side by a sub-telomeric chromosome breakage site (CBS). This conclusion was corroborated by the latest assembly of the Tetrahymena MIC genome. They further employed CRISPR-Cas9 mutagenesis to disrupt a specific sub-telomeric CBS (4R-CBS). In the uniparental progeny (mutant X WT), DNA elimination of the sub-telomeric MLS was not affected, but the adjacent MAC-destined sequence (MDS) may be co-eliminated. However, in the biparental progeny (mutant X mutant), global DNA elimination was arrested, revealing previously unrecognized connections between chromosome breakage and DNA elimination. It also paves the way for future studies into the underlying molecular mechanisms. The work is rigorous, well-controlled, and offers important insights into how eukaryotic genomes demarcate genic regions (retained DNA) and regions derived from transposable element (TE; eliminated DNA) during differentiation. The identification of chromosome breakage sequences as a critical architectural element of the genome separating TE-derived regions from functional genes is a key conceptual contribution.

Strengths:

New method development: Oligo-FISH in Tetrahymena. This allows high-resolution visualization of critical genome rearrangement events during MIC-to-MAC differentiation. This method will be a very powerful tool in this area of study.

The conclusion is strongly supported by integrated analyses of PCR-based assays, as well as cytological, genomic, and transcriptomic data.

Rigorous genetic analysis of the role played by 4R-CBS in separating the fate of sub-telomeric MLS (elimination) and MDS (retention).

Reviewer #2 (Public review):

Summary:

The authors discovered that HDAC1/2 are degraded in HSV-1 and PRV infections. They attempted to establish a new mechanism by which HDAC1/2 are translocated to the cytoplasm to be degraded in HSV-1 infection, and the degradation causes changes in histone acetylation to affect the DDR pathway.

Strengths:

(1) Interesting findings of HDAC1/2 degradation during HSV-1 and PRV infection, and it may impact more than the virology field.

(2) Significant work to identify the ubiquitin site in HDAC1/2 and K63 linkage.

Reviewer #2 (Public review):

Summary:

The manuscript by Do and colleagues aims to develop a workflow for isolating and identifying bacteriophages with potential applications in phage therapy against antibiotic-resistant pathogens. The workflow integrates geΦmapping as a strategy to identify potential phage sources, ΦHD as a device for phage concentration, and RΦ as a phage library constructed from the initial sampling, resulting in the discovery of 36 new phages. The paper is overall interesting, and the proposed method appears robust and effective.

Strengths:

The methods proposed combined state-of-the-art strategies to solve an ever-increasing problem of antibiotic resistance. The methods are robust, and the controls are appropriate. The integration of environmental sampling, concentration strategies, and downstream genomic characterization is a clear strength and provides a potentially scalable framework for identifying candidate therapeutic phages. The manuscript is clearly written overall, and the results support the main conclusions.

Comments on revised version:

The manuscript has been adequately improved and adjusted according to the comments. There are minor points such as Table S10 is labelled in the top of the page as Table S11. Also, is a little unconventional to cite result figures and tables in the introduction.

For the question 10, regarding why some of the most abundant vOTUs in the 5L sample were not detected in the concentrate. The answer does not satisfy, as it focuses on why very low abundant vOTUs will not be detected, but the question is why some of the most abundant vOTUs were not detected. This does not affect the results or interpretation made.

Reviewer #2 (Public review):

Summary:

The constraints on learning in the brain remain elusive. Using BCIs, Sadtler et al. demonstrated that the brain can rapidly learn new decoders that lie within the intrinsic neural manifold (short-term adaptation), while showing substantial difficulty learning decoders that lie outside the manifold. This finding suggests that neural manifolds impose constraints on learning. However, even among within-manifold decoders, there was considerable variability in learning rates that could not be explained solely by geometric factors.

Here, Gurnani et al propose that, in addition to manifold structure, neural dynamics (i.e., the flow field across states) impose critical constraints on learning. To test this idea, the authors trained RNNs that received real-time feedback (e.g., position error signals) during a BCI task in which the network controlled a cursor. The authors showed that short-term adaptation to a new decoder is facilitated by plasticity in sensory inputs, and that pre-existing dynamics influence the speed of adaptation across different decoders. These findings may explain previously unresolved constraints observed in BCI learning and suggest an important role for neural dynamics in constraining sensorimotor learning in the brain.

Strengths:

Overall, the work is highly impactful and is likely to motivate a new generation of BCI and learning experiments combining large-scale neural recordings with latent dynamical systems analyses. The paper is clearly written, and I only have minor comments, primarily for clarification.

Weaknesses:

There are no major weaknesses. Please see below for minor comments.

(1) If I understand correctly, most analyses do not distinguish between the preparatory phase and the movement phase. Given that the preparatory phase is largely controlled by feedforward input, I suspect that most of the dynamical constraints underlying learning variability arise during the movement phase. Is this correct? If so, could the authors clarify or directly test this distinction?

(2) P4: Position vs. velocity decoders: It would be helpful to describe whether and how the choice of velocity versus position decoders influences whether perturbations are learnable, and whether input-driven constraints arising in this task are similar.

(3) The variance criteria used to screen decoder perturbations may themselves covary with learning rate, behavioral asymmetry, and overlap with controllable subspaces. A quantification of this relationship would help contextualize the findings and inform the design of future BCI experiments.

(4) To support the comparison between Figures 3 and 7, and the conclusion that Figure 3 better matches the experimental data, which is an important point of the manuscript, could the authors provide quantitative values from the experimental data (e.g., how large is the change in variance within oPCs, etc)?

(5) Figure 8h: Is the variability in learning rates in models with different controller networks explained by the same dynamical constraints described in Figure 6? Demonstrating consistent dynamical constraints across model architectures would strengthen the paper's central conclusion.

(6) Figure 8f: Why does feedforward controllability differ between conditions? This is mentioned in the text, but no explanation is provided.

What stood out to me most is that college involves more than just academic adjustment. Students also have to adjust socially, emotionally, and financially, which can affect their success in school. I think the social and academic adjustments will probably be the biggest challenges for many first-year students including myself.

A lot of students feel like they have to figure everything out on their own but in reality, students use resources and ask for help when they need it to be succesful. This connects to the article about hidden curriculum because knowing where to get help is a big part of doing good in college.

Reviewer #2 (Public review):

Summary:

The manuscript by Sajid et al. describes a comprehensive behavioral, imaging and optogenetic dataset investigating the role of the mPFC in avoidance and escape behaviors. Although many movement- and task-related variables are encoded by mPFC GABAergic neurons, the main conclusion is that they are unlikely to control behavioral output.

Strengths:

The manuscript is generally well executed and plausible in its conclusions. It provides an alternative viewpoint to many articles describing the involvement of mPFC to behavior, based on a complex multi-stage behavioral paradigm acquired and analyzed in an unbiased way.

Weaknesses:

This reviewer sees two weaknesses.

(1) In some cases, the explained variance, marginal and conditional, is low, suggesting the models only modestly capture the complexity in the data.

(2) The manuscript is challenging to read due to the comprehensive and unbiased presentation style.

Comments on revised version.

The authors did a good job at addressing the reviewers' comments. One minor additional suggestion is to add references for the statement in the last paragraph of the discussion for the mPFC lesion studies.

Reviewer #2 (Public review):

Summary:

The authors investigated the effects of a low-protein diet (LPD) and a high sugar- and fat-rich diet (Western diet, WD) on paternal metabolic and reproductive parameters and feto-placental development and gene expression. They did not observe significant effects on fertility; however, they reported gut microbiota dysbiosis, alterations in testicular morphology, and severe detrimental effects on spermatogenesis. In addition, they examined whether the adverse effects of these diets could be prevented by supplementation with methyl donors. Although LPD and WD showed limited negative effects on paternal reproductive health (with no impairment of reproductive success), the consequences on fetal and placental development were evident and, as reported in many previous studies, were sex-dependent.

Strengths:

This study is of high quality and addresses a research question of great global relevance, particularly in light of the growing concern regarding the exponential increase in metabolic disorders, such as obesity and diabetes, worldwide. The work highlights the importance of a balanced paternal diet in regulating the expression of metabolic genes in the offspring at both fetal and placental levels. The identification of genes involved in metabolic pathways that may influence offspring health after birth is highly valuable, strengthening the manuscript and emphasizing the need to further investigate long-term outcomes in adult offspring.

The histological analyses performed on paternal testes clearly demonstrate diet-induced damage. Moreover, although placental morphometric analyses and detailed histological assessments of the different placental zones did not reveal significant differences between groups, their inclusion is important. These results indicate that even in the absence of overt placental phenotypic changes, placental function may still be altered, with potential consequences for fetal programming.

Comments on revised version:

The authors have adequately addressed all my previous comments.

Reviewer #2 (Public review):

Summary:

This study addresses the population genetic underpinnings of the extraordinary diversity of genes in the MHC, which is widespread among jawed vertebrates. This topic has been widely discussed and studied, and several hypotheses have been suggested to explain this diversity. One of them is based on the idea that heterozygote genotypes have an advantage over homozygotes. While this hypothesis lost early on support, a reason study claimed that there is good support for this idea. The current study highlights an important aspect that allows us to see results presented in the earlier published paper in a different light, changing strongly the conclusions of the earlier study, i.e., there is no support for a heterozygote advantage. This is a very important contribution to the field. Furthermore, this new study presents an alternative hypothesis to explain the maintenance of MHC diversity, which is based on the idea that gene duplications can create diversity without heterozygosity being important. This is an interesting idea, but not entirely new.

Strength:

(1) A careful re-evaluation of a published model, questioning a major assumption made by a previous study.

(2) A convincing reanalysis of a model that, in the light of the re-analysis-loses all support.

(3) A convincing suggestion for an alternative hypothesis.

Weakness:

(1) The title of the study is catchy, but it is explained only in the very end of the paper.

Reviewer #2 (Public review):

Summary:

Mutations in Leucine-Rich Repeat Kinase 2 (LRRK2) are a major cause of Parkinson's disease. LRRK2 PD-related mutations all result in increased kinase activity. Therefore, LRRK2 has been the focus of the development of kinase inhibitors. So far, two classes of kinase inhibitors have been identified: type 1 LRRK2-specific inhibitors that stabilize LRRK2 in a closed active-like conformation and broad-range type 2 inhibitors that stabilize LRRK2 in an open inactive-like conformation. Basiashvili et al. used here in cell structural biology to study the effect of both type 1 and type 2 inhibitors on the localization and structural conformation of LRRK2-I2020T.

Strengths:

They showed that Type 1 and not Type 2 inhibitors induce LRRK2 filament/ on microtubules. Furthermore, they were able to build a structural map of full-length LRRK2 I2020T bound to a Type 1 inhibitor in a closed kinase confirmation. Together, this work thus confirms the data of previous studies that showed that LRRK2 Type 1 and 2 inhibitors differently affect filament formation.

Weaknesses:

All conclusions are fully supported by the provided data. However, as the authors indicated themselves, the physiological relevance of LRRK2 microtubule binding is questionable. Furthermore, although the authors used a full-length LRRK2 protein, like in previously published structures, the resolution of the N-terminal domains is rather poor. Therefore, it also remains unclear what we learn from this structure compared to the previously published structures.

Reviewer #2 (Public review):

Summary:

This study develops a new artificial intelligence method for high-throughput analysis of skull bone marrow from MRI data, which may be useful for large-scale biological analyses. Using this method, the authors then attempt to estimate skull bone marrow adiposity (BMA) using T1-weighted signal intensity from MRI scans of ~33,000 people, followed by genome-wide association analysis; however, the approach is inadequate because T1-weighted signal intensity is not validated for measurement of bone marrow adiposity. If it could be validated, the study would be an important advance in understanding of bone marrow adiposity and skeletal biology.

Strengths:

This paper is well-written, and the figures are nicely presented. The neural network method used for analysing skull bone marrow is innovative, and the authors validate this through several approaches. Therefore, the authors have achieved the aim of developing a method for large-scale analysis of skull bone marrow from MRI data.

The GWAS is reasonably well-powered and addresses potential ethnicity differences, with one GWAS done across white males and females, and a separate GWAS in non-white participants. The methodology also conforms to common GWAS standards, including for mapping genetic variants to candidate genes. Moreover, the study further investigates the biological roles of these genes by analysing their expression in single-cell RNA sequencing data.

Weaknesses:

The fundamental weakness is that T1-weighted MRI signal intensity (T1W) is used as an estimate of BMA, but it has never been validated for this. The authors show that this T1W parameter measures something that is heritable and can be compared between subjects, but they don't show that it actually measures (or even estimates) calvarial BMA. There is an attempt to do so by comparing the T1W parameter with data from quantitative T1 images: the authors show a reasonable correlation with some of the quantitative T1 image data. However, this still does not show that the parameter is measuring BMA; it could be measuring some other biological characteristic, but this remains unclear. So, there is a need to validate the T1W parameter against an established measure of BMA, such as the bone marrow fat-fraction or proton density fat fraction measured from multi-echo MRI analysis.

Without validating this BMA measurement method, it is not possible to interpret the GWAS or other findings reported in the study.

A less critical weakness is that the GWAS has been done only on a single cohort, without replicating the findings in a follow-up cohort. For example, the authors could repeat their analysis on the remaining ~50,000 UK Biobank imaging participants for whom MRI data is now available. However, this would be pointless without knowing what biological characteristic(s) the T1W parameter is actually reflecting.

[UPDATE, June 2026: since writing this review in September 2024, the reviewer has changed their opinion and now has confidence in the reliability of the T1W method used to estimate BMA. The reviewer would like to explain that their original critiques were based largely on previous discussions with a colleague with expertise in magnetic resonance and medical physics, who was extremely negative about use of T1W signal intensity to estimate BMA; this colleague’s criticisms may not have been objective, and clouded the reviewer’s overall impression of the present study. The reviewer and others have since completed BMA analysis using dual-echo MRI data in the UK Biobank; the findings of these studies, both for genetic and pathophysiological associations, are largely consistent with the findings of the present study, underscoring the reliability of the T1W-based BMA estimates.]

Reviewer #2 (Public review):

Summary:

The authors show that deprivation of Arginine and Lysine induces a ~50% increase in the ratio of ubi-RPS27A to RPS27A, and this induction requires E3 ubiquitin ligase RNF25. The authors show ZAKalpha and EDF1 are not required for steady state or ribosome stalling-induced ubi-RPS27A, while GCN1 is required. The ratio of polysomes to monosomes is increased in RNF25 knockdown cells or when translation is activated by ISRIB in a RPS27A K113R mutant cell line. GCN2 KO cells indicate elevated levels of ubi-RPS27A, and overexpression of the GCN2 RWD domain reduces levels of ubi-RPS27A.

Strengths:

(1) The authors identified a novel pathway to sense amino acid deprivation, indicated by ubi-RPS27A, previously implicated in ribosome stalling.

(2) The authors find antagonism between two proteins known to act downstream of GCN1, giving insight into how signaling occurs from an upstream sensor of ribosome stalling to multiple downstream pathways.

Weaknesses:

(1) The authors suggest that, based on increased Polysome/Monosome ratios, there is more disome stalling in RNF25 KD cells and RPS27A K113R cells treated with ISRIB, but this readout is very indirect and could be driven by other changes in the cell other than ribosome stalling.

(2) While the authors propose that GCN2 and RNF25 compete for binding to GCN1, no evidence was shown that RNF25 binds to GCN1 in cells, nor that the interaction increases when GCN2 is absent.

(3) The use of USP16 to enhance the detection of ubi-RPS27A in many experiments brings the question of whether USP16 KO may alter the protein levels of any known regulators of ribosome collisions? (i.e. ZNF598, GCN1, EDF1, ZAKalpha, etc.) If USP16 KO causes changes in other important regulators of collisions, the authors could be identifying genetic interactions with USP16 in their experiments throughout the paper.

(4) In Figure 5E, the expression level of the GCN2 3K RWD domain looks to be lower than the WT RWD domain; perhaps this could be what is driving the smaller decrease of ubi-RPS27A seen with GCN2 3K vs WT.

Reviewer #2 (Public review):

Summary:

The manuscript by Benbow et al. identifies, through a genetic screen, key tubulin mutants that, with high confidence, rescue tau-mediated ND phenotypes. This manuscript is well written, and the experimental results strongly support the authors' claims that these tubulin mutants can rescue ND-linked phenotypes in C. elegans while having little to no direct effect on Tau aggregation.

Strengths:

Benbow et al. use a relatively unbiased forward genetic screen to identify mutations associated with phenotypes that suppress tauopathy-related defects. The authors then logically focus on the various α-tubulin missense mutations identified in H12, which are known to localize to the external face of microtubules. The authors also carefully compare their established tauopathy-associated phenotypes in the WT TauH model, with and without specific α-tubulin mutations, using appropriate controls throughout. Lastly, the authors provide partial mechanistic insight into the α-tubulin mutant-mediated rescue, showing that these effects are independent of tau aggregation and tau phosphorylation, and instead suggest that the α-tubulin mutations may confer altered microtubule assembly properties based on the sedimentation assays.

Weaknesses:

While the claims are largely supported by the experimental outcomes, the authors at times do not provide enough detail in the text for readers to interpret the data sets independently. In addition, some claims appear to be slightly overstated relative to the data or the degree of error associated with those data.

Reviewer #2 (Public review):

Summary:

Previous structural analyses of DELE1 by the authors revealed that the first α-helix within the TPR repeat domain provides the oligomeric interface of DELE1, and that DELE1 octamer formation is required for maximal ISR activation. Based on these findings, the authors designed peptides intended to bind this oligomeric interface and showed that these peptides interfere with DELE1 oligomerization in vitro and attenuate ISR activation in cultured cells.

Strengths:

The series of in-vitro data sets showing direct binding of the designed peptides to DELE1 and inhibitory effects on its oligomerization are convincing.

Weaknesses:

The physiological (or experimental) significance of inhibiting the DELE1-HRI-ISR pathway using these peptides has not been clearly demonstrated, particularly given that the very limited cell biological outcomes are tested in the current manuscript.

Reviewer #2 (Public review):

Summary:

Duan, Hua et al. used CUT&Tag and Micro-C to investigate that in primary trastuzumab-resistant HER2+ breast cancer cells, promoter H3K4me3 rather than H3K27me3 is strongly correlated with transcriptional activity. Resistant cells also exhibited more abundant promoter-enhancer loops and enriched cohesin at loop anchors, accompanied by shifts in A/B compartment status. Through multi-omics integration, the authors identified SGK1 as a key gene showing elevated promoter H3K4me3 levels, enhancer activation, strengthened chromatin loops, and upregulated transcription in resistant cells, and validated SGK1 as a potential therapeutic target. These findings reveal the coordinated interplay between three-dimensional chromatin architecture and epigenetic modifications, offering important insights into trastuzumab resistance in HER2+ breast cancer.

Strengths:

Previous investigations into trastuzumab resistance have largely focused on genetic mutations or individual epigenetic modifications. In contrast, this study moves beyond genetic or single epigenetic views by integrating histone modifications and 3D chromatin architecture into a unified framework, proposing a synergistic model of promoter H3K4me3, enhancer activation, and chromatin looping that underlies non-genetic resistance. It provides a new conceptual basis for understanding non-genetic resistance mechanisms. Secondly, using high-resolution epigenomic and conformational mapping together with bidirectional in vitro and in vivo functional validation, it establishes a solid link between epigenetic changes and phenotypes, and demonstrates that SGK1 inhibition suppresses tumor growth in a xenograft model, revealing clear translational potential.

Weaknesses:

(1) All findings are based on a single pair of cell lines, JIMT1 and SKBR3, which does not allow exclusion of cell line‑specific effects. The authors did not examine SGK1 expression levels, promoter H3K4me3 status, or relevant chromatin loops in tumor tissues from patients with clinical trastuzumab resistance. Consequently, whether the conclusions can be extrapolated to actual patient populations remains unclear, which limits the clinical relevance of the findings. It is recommended that the authors directly validate the key findings using tumor samples from patients with clinical trastuzumab resistance or analyze the correlation between SGK1 expression levels and disease-free survival or pathological complete response using data from public databases for HER2+ breast cancer patients, which would help address the current limitation of lacking clinical sample validation and the uncertainty regarding the association of SGK1 with patient prognosis and treatment response.

(2) In the Discussion, the authors propose that SGK1 may assume the role of AKT to sustain mTOR activation, thereby bypassing the dependence on HER2 signaling following trastuzumab inhibition. Although this hypothesis is supported by published literature, the present study provides no direct signaling evidence, such as examining phosphorylation changes of SGK1, AKT, mTOR, or their downstream effectors.

Reviewer #2 (Public review):

Summary:

The rebuttal aims to provide a statistical re-evaluation of Epp et al. to investigate the effects of CMRO2 uncertainty on concordance/discordance analysis between BOLD signal responses and CMRO2 change estimates based on an R2 framework. The authors observe markedly higher variance in CMRO2 compared to BOLD, which raises concerns about sign classification purely based on group means/medians.

Strengths:

The study is well motivated, and the analytical pipeline is rigorous and has been provided. Overall, the manuscript provides several thoughtful and rigorous analyses that contribute meaningfully to the ongoing discussion surrounding neurovascular coupling and CMRO₂ estimation.

Weaknesses:

Some aspects of the analytical framework could be improved, as well as the discussion of the caveats of the methods of this and the original paper.

(1) The binomial framework discussed on line 110 and described on line 321 reduces continuous ΔBOLD and ΔCMRO2 measurements to binary concordant/discordant labels, which may overemphasize unstable sign flips near zero effect sizes while discarding potentially meaningful magnitude information. The authors acknowledge that this overly strict approach yields very few meaningful voxels. A better justification or explanation of what we are meant to take away from this, other than the variability in the measurement, which is also explored elsewhere, would be helpful to the reader.

(2) In the methods, in the section entitled: Voxel Selection: BOLD Activation Mask, the authors describe their more traditional univariate statistical method as compared to the PLS approach used in the Epp paper. While I appreciate why the authors chose this approach, which simplifies interpretation, is it possible that this led to a lower number of discordant voxels? If yes, then I would suggest this be also added in the discussion of how the original Epp paper's methodological choices led to the very large percentage of discordant voxels.

(3) In the original paper, it looks to me like the discordant voxels have low CBF change and low rOEF. The gadolinium-based CBV measurement used to calculate OEF is a measure of total blood volume, while the blood volume that contributes to BOLD resides predominantly in veins and capillaries. Given the long PLD of the ASL acquisition and the total blood volume measurement, it seems to me that it is possible that discordant voxels may have high arterial blood volume, leading to overly large CBV measurement and an underestimation of CBF at this PLD (especially given their young age, for which I would expect ATT to be closer to 1-1.5s based on recent literature). While this is not currently discussed in this paper, it might be relevant to discuss how acquisition choices could bias some voxels towards erroneous CMRO2 estimates, which in turn would lead to these voxels being identified as discordant.

(4) In the methods, on line 267, the authors describe how they calculated ΔCMRO2 and how it differs from the original paper. A short discussion of how this choice is likely to affect the variance estimates would be warranted, given that the original paper seems to have chosen their method for the explicit purpose of decreasing error propagation. Especially, I wonder if this difference could account for the observation that "77.2% of voxels showed no statistically significant group-level ΔCMRO₂ effect".

Reviewer #2 (Public review):

Summary:

This study uses cortex-wide mesoscopic calcium imaging to investigate how adult vision loss induced by bilateral enucleation alters spontaneous cortical activity across behavioral states, including quiescence, locomotion, and anesthesia. The authors perform longitudinal imaging over two time scales, spanning days to weeks and weeks to months after enucleation, enabling them to track the changes of cortical reorganization.

The main findings are that oscillatory activity in V1 undergoes a strong reversal in its relationship to behavioral state. Before enucleation, V1 activity is positively correlated with locomotion and negatively correlated with quiescence, whereas after vision loss, this pattern reverses. State-transition dynamics are similarly altered: locomotion onset shows reduced V1 activation, while cessation of locomotion is associated with increased activity after enucleation, while it caused suppression during baseline. In addition, the authors report an increase in slow-wave (0.1-4 Hz) activity in V1 after enucleation, starting in the first week and lasting over many weeks. Although these effects show partial recovery over time, many abnormalities persist for weeks to months.

At the network level, the study reveals altered large-scale cortical organization, including reduced functional connectivity involving V1 that appears to remain impaired.

Strengths:

Overall, the work provides a thorough characterization of how adult vision loss reshapes cortical dynamics, particularly with respect to behavioral-state modulation.

Weaknesses:

However, there is also a lack of clarity due to the way the data are presented. Moreover, the study remains largely descriptive, as it does not address the mechanisms underlying these changes or their functional significance, making it difficult to interpret the broader implications of the observed cortical reorganization.

Reviewer #2 (Public review):

Summary:

This paper aims to test whether training models to play video games from visual inputs through reinforcement learning leads to better matches to human visual encoding during gameplay, compared to models with the same architecture and training images but with different training objectives. The authors find a slight advantage for the RL model, but encoding performance and generalization overall are weak and variable.

Strengths:

This was a reasonable hypothesis to test, and the model comparisons adequately represent other possibilities for training a model of the given architecture. The ResNet proxy is a particularly interesting way to benefit from a larger model's pre-training while still using the same constrained architecture and training set.

Weaknesses:

I always prefer to see learning curves for models on the tasks they were trained on, just to contextualize their performance on the brain encoding results, but they are not shown here.

The paper misses some of the relevant literature that has performed similar comparisons across learning objectives for visual encoding models, such as https://arxiv.org/abs/2112.02027 and https://pmc.ncbi.nlm.nih.gov/articles/PMC10569538/

The authors end up advocating for the idea that large-scale pre-training is needed in order to build good visual encoders for matching human data. In many ways, this was already known (given that brain encoding scores scale with imagenet performance, which requires at least a moderate amount of general-purpose image training to achieve). However, they also note that "the brain encoding performance of the ResNet model was not significantly different from that of the Untrained model." I would assume that an ImageNet-trained ResNet would be in the direction of the type of large-scale pre-trained model the authors advocate for (even when not trained for action generation), yet their results don't support this direction being the solution. Are their results about Resnet not surpassing an untrained model consistent with prior work, and if not, why not? How do they view this in light of their argument for the use of larger models?

https://www.instagram.com/reel/DZz9tE2B2Rd/

Dies Irae motif in:<br /> Stairway to Heaven by Led Zeppelin<br /> Frozen 2

Reviewer #2 (Public review):

Summary:

The goal of the work is to identify genes that are uniquely expressed in subsets of eye muscle-innervating motor neurons, as a way to identify candidate genes for strabismus, a congenital vision disorder in humans. The author's previous work identified birth-order differences that correlate with the positions of neurons in the oculomotor (cranial nerve III) motor nucleus. Here, they use Kaede photoconversion to distinguish early- from late-born neurons and identified transcriptional differences between them by bulk RNA sequencing of FACS-sorted cells. Separately, they used single-cell RNA-Seq to sequence the transcriptomes of 89 extraocular motor neurons. They find signatures of early-born mIII, late-born mIII, and mIV neurons. While there is some overlap in gene expression, some of the differentially expressed genes are confirmed by HCR as being unique to one of these three populations of extraocular motor neurons.

The authors test the functions of three differentially expressed genes in the vestibulo-ocular reflex by measuring the speed of rotation of the eye in response to the larval fish being tilted 15° from horizontal. One mutant, in the sim1a transcription factor, has markedly slowed responses. Although this is a global knock-out, the authors argue that this defect in the vestibulo-ocular reflex is due to a loss of sim1a function specifically in dorsal mIII neurons because sim1a is not expressed in the two upstream neurons in the vestibulo-ocular reflex circuit.

Strengths:

(1) This is the first time that transcriptional differences between and within extraocular muscle-innervating neurons have been described during development. In identifying differentially expressed genes that correspond with anatomical, functional, and temporal subdivisions of these neurons, they support the idea that gene expression programs established early in development underlie the functional differences amongst these neurons.

(2) The combination of bulk RNA-Seq and single-cell RNA-Seq strengthens the identification of sim1a-expressing early-born mIII neuron subtype.

(3) The work identifies candidate genes for strabismus.

Weaknesses:

(1) The authors show that sim1a is only expressed in mIII neurons and no other cells in the vestibulo-ocular reflex, as evidence that the phenotype in sim1a mutants is due to loss of its expression specifically in mIII neurons. However, as the authors note in the discussion, sim1a has other functions in zebrafish, including global calcium homeostasis via specification of the corpuscles of Stannius. The loss of this, or of some other sim1a function, could be indirectly responsible for the slow vestibulo-ocular response in sim1a mutants.

(2) The authors perform the vestibulo-ocular response test in sim1a mutants at 7 dpf, which is within a day of when the mutants die, raising the concern that the slowed response is due to a dire systemic condition. The argument that nav2 mutants also die at 7 dpf but have a normal response is weak, since death does not always take a single course.

(3) The evaluation of the sim1a mutant phenotype is limited to the vestibulo-ocular reflex. The authors do not explore whether the oculomotor neuron innervation of target extraocular muscles is affected in sim1a mutants.

Reviewer #2 (Public review):

Summary:

The manuscript concerns the mechanisms by which cells in a spheroid embedded in the extracellular matrix can escape, either as single or multiple cells.

Strengths:

Overall, the manuscript is well written and easy to follow. The claims are mostly justified by the data. Some data can be better analyzed and presented to strengthen the conclusion.

Weaknesses:

(1) The description around Figure 2c is not exactly well supported by their results. While values close to 0 for sigma3 dot g3 for solid-like spheroids indicate little correlation between the direction of maximum stress and maximum elongation, this analysis alone does not imply that highly stressed cells are necessarily less globular. The dot product combines the magnitudes of the two vectors and the angle between them. For the distribution graph, it would be useful to have the cumulative frequency equal 1.

(2) One of the central claims of the paper is that morphology alone is not a reliable indicator of mechanical state. Since the authors compute cellular stresses and cellular shape in their simulation (i.e., Figure 3a and b), can the authors directly plot these two quantities for individual cells in solid-like and fluid-like spheroids?

(3) There is experimental evidence showing the solid stress inside a spheroid is higher than at the periphery (e.g., https://www.nature.com/articles/ncomms14056). How does this cellular stress relate to these experimental measurements, since they are opposite to what is simulated here (i.e., the authors find max shear stress is lowest in the center and increases towards the boundary, which is opposite to what is measured?

(4) It's worth pointing out that stress fibers aren't really prominent in cells in 3D spheroids. Nonetheless, cells moving on collagen fibers would have stress fibers and utilize contractile actomyosin bundles to generate traction forces.

(5) In section 2D, it talks about the result that as the kcc associated with the boundary cell is decreased 10-fold for every 5 percent strain decrease in the fiber target spring length, can this result be shown? I have a hard time seeing where this came from.

(6) The results of single-cell vs. two-cell breakouts shown in Figure 5 b and c are very qualitative and should be accompanied by some quantitative comparison.

Reviewer #2 (Public review):

In their article, Peterson et al. wanted to show to what extent the classical "single hit" model of virion infection, where always the same quantity of virion is required to infect a cell, does not match with empirical observations based on human cytomegalovirus in vitro infection model, and how this would have practical impacts in experimental protocols.

Strengths:

- The use of a very simple and robust experimental assay, where they infected cells with serially diluted virions and measured the proportion of infected cells with flow cytometry. This convincingly showed how the proportion of infected cells differed from a "single hit" model which they simulated using a simple mathematical model ("power-law model"), and better fitted a model where virions need to cooperate to infect cells.

- The use of different cell types and virus strains, which allows to draw some generalizations.

- The exploration of the mechanisms that could explain this apparent cooperation, using biologically plausible simulations.

- The practical consequences that this phenomenon has for lab virologists as well as modelers.

Weaknesses:

- The impossibility to discriminate between biological mechanisms is an important limitation of this study and calls for developing experimental designs able to further understand this question.

- The outcome of the virion clumping remains highly sensitive to the choice of the clumps size distribution, which is itself very complicated to estimate, especially at high dilution.

- The impossibility to directly fit the mathematical models to the data limit them to a qualitative discussion.

Overall, this work is very valuable as it raises the general question of how the estimate of infectivity can be biased if extrapolated from a single virus titer assay. The observation that HCMV virions often cooperate and that this cooperation varies between context seems robust. The putative biological explanations would require further exploration.

This topic is very well known in the case of segmented viruses and the semi-infectious particles, leading to the idea of studying "sociovirology", but to my knowledge this is the first time that it was explored for a non-segmented virus, and in the context of MOI estimation.

Reviewer #2 (Public Review):

Strengths:

(1) Because the study compares genuinely infected cells with uninfected cells within the same infected cell population, it enables a clearer and more rigorous comparison.

(2) By using multiple Chlamydia species and cells from multiple host species (human and mouse), and obtaining consistent findings across these systems, the study demonstrates the generality of bacterium-induced epigenomic alterations.

(3) The study shows that the epigenomic changes are caused by reduced activity of JMJC domain-containing lysine demethylases, demonstrating through multiple complementary approaches-including the use of a demethylase inhibitor, overexpression of target-specific demethylases, and analysis from the perspective of cofactors required for JMJC domain-containing demethylases-that decreased lysine demethylase activity constitutes the molecular mechanism underlying the increased H3 methylation levels induced by Chlamydia infection.

(4) By performing ChIP-seq analyses of H3K4me3 and H3K9me3, the study clearly delineates, on a genome-wide scale, how infection leads to increased levels of these epigenomic marks.

Weakness:

(1) Reduction of cofactors such as Fe2+ or a-KG decreases the activity of JMJC-domain-containing lysine demethylases (thereby directly affecting histone H3 lysine methylation). However, these cofactors are also involved in the activities of other epigenetic regulators, such as TET enzymes that contribute to DNA demethylation and SIRT family proteins that mediate histone deacetylation. Therefore, it cannot be excluded that modulation of these factors indirectly leads to the changes in H3 lysine methylation dynamics targeted in this study.

(2) Related to point 1, although overexpression of JMJC-type demethylases has been shown to reduce the Chlamydia infection-induced increase in H3 lysine methylation, it is well known that over production of these enzymes, while target-specific, also leads to a genome-wide reduction of lysine methylation. Thus, a decrease in lysine methylation upon expression of these demethylases does not necessarily demonstrate that the infection-induced increase in H3 lysine methylation is caused by impaired JMJC-type demethylase activity.

Reviewer #2 (Public review):

Summary:

In this work, the authors propose a common value-estimation framework based on Bayesian inference and show that it can account for both participants' confidence in their value estimates ("value confidence") and for their confidence in their final choices ("decision confidence").

Strengths:

The study extends several established findings in the confidence and reinforcement-learning literature. In particular, the authors not only examine decision confidence but also directly model value confidence, and they replicate the idea that decision confidence reflects a combination of multiple computations, previously described for categorical decisions (Navajas et al., 2017), in the context of continuous value-based decisions. I therefore consider the work a useful contribution to the field.

Weaknesses:

However, I believe that the scope of the conclusions is overstated relative to the results that are actually presented.

(1) Interaction between value confidence and decision confidence

The abstract and introduction frame the study as addressing a major gap in the literature, namely, the lack of direct investigation of the interaction between value confidence and decision confidence. Yet the manuscript never directly tests the interaction between these two quantities. Instead, the authors show that the reported decision confidence depends not only on the probability of being correct, but also on the precision of the decision variable DV, which is related to the precision of the value estimates underlying value confidence. While this is related to the proposed research question, it is not a direct analysis of the interaction between value confidence and decision confidence themselves.

(2) Unified computational framework

Similarly, the claim that the study provides a "unified computational framework" appears somewhat overstated. The proposed models build on standard and well-established Bayesian frameworks and extend them specifically to account for decision confidence. While this demonstrates that both forms of confidence can be expressed within a common Bayesian formalism, the manuscript does not establish a direct computational interaction or shared mechanism between them beyond their dependence on the same underlying uncertainty estimates.

(3) "Phenotypes" interpretation

The interpretation of the observed individual differences as distinct "behavioural phenotypes" also appears overstated. The reported analyses primarily show continuous variability across participants in the relative weighting of different components contributing to confidence reports, rather than evidence for qualitatively distinct categories or computational subtypes of decision-makers.

(4) Decision confidence terminology

I also found some conceptual ambiguity in the terminology used throughout the manuscript. Early in the paper, decision confidence is defined normatively as the subjective probability of having made the correct choice, corresponding to P(DV>0). Later, however, the authors show that participants' confidence reports are better explained by a combination of this probability and the precision of the decision-variable distribution. Despite this distinction, the manuscript continues referring to the reported quantity simply as "decision confidence." Clarifying the distinction between the theoretical construct and the empirical reports (for example, by referring to "reported decision confidence") would improve conceptual clarity.

Reviewer #2 (Public review):

This study focuses on revealing the essential divergent function of the Acyl Carrier protein (ACP) in the deadliest human malaria parasite, Plasmodium falciparum. More precisely, using inducible KO, cellular and biochemical approaches, the authors determined that instead of a canonical role for ACP allowing the de novo synthesis of fatty acids in the apicoplast (essential relict plastid) of the parasite, the enzyme couples with pyruvate kinase II to generate nucleoside triphosphate to maintain parasite survival during blood stages. The study is novel, well-designed, providing interesting new data on Plasmodium and apicomplexa biology. The results convincingly support the major claim of the study. However, it is currently incomplete to support some claims on the essentiality of some apicoplast pathways.

In this study, Geher et al. focused on deciphering the role of the Acyl Carrier Protein (ACP) present in the relict non-photosynthetic plastid, i.e. the apicoplast of the most lethal human malaria parasite, Plasmodium falciparum. More particularly, they determined an essential function of ACP independent of its usual/typical function as the central protein for the normal function of the apicoplast Type II fatty acid synthesis (FASII) pathway. Rather, the protein seems to associate with the apicoplast Pyruvate Kinase II, together generating an essential nucleoside triphosphate (NTPs) source to fuel the apicoplast and parasite survival instead.

By generating a TetR-DOZY-based inducible KD line for ACP, they confirmed that the protein is indeed essential to maintain apicoplast integrity and parasite survival during asexual blood stages, as previously predicted and experimentally shown. They showed that ACP requires a biochemical modification, typically activating the protein for its function in the FASII pathway, i.e. binding of the 4-PP group by holoACP synthase. Then, they showed that the other enzymes of the FASII pathway are likely dispensable during the blood stage, as they were able to generate a KO line of the first enzyme of the pathway, FabD (which was predicted to be essential in P. falciparum). Based on a cell culture approach in a controlled culture medium, they further claimed that, unlike current evidence-based hypotheses, the FASII pathway (and thus a potentially FASII-linked ACP) has no role/activity during blood stages. Using a proximity biotinylation approach, they determined that ACP associates with the apicoplast pyruvate Kinase II (PKII), previously shown to generate NTPs in the apicoplast for energy and DNA/RNA maintenance (Xia et al. 2019), and not to fuel the FASII pathway as its main function in blood stages. Finally, they showed that the disruption of ACP induces the reduction of the presence/content in PKII in the parasite, as well as the drastic reduction of the apicoplast DNA and RNA content. Together, they concluded that the main function of ACP is indeed the NTP formation via its association with PKII, rather than its canonical role for the generation of fatty acids in the apicoplast.

This study is novel and focuses on a topic of particular interest in malaria biology, but also for most of the apicomplexa-related diseases, and beyond for plastid bearing orgnaisms and this unusual role for ACP. The study is well thought out with proper biochemical approaches that convincingly point to this association of ACP with PKII for NTP synthesis as a major function during P. falciparum blood stages. However, there are currently some important experimental issues/flaws, missing experiments that induced wrong interpretations and thus do not support some important claims of the study, notably for the role of FASII and the interaction between ACP and PKII.

Therefore, at this point, the study is only partial and would require major additions and/or important text edits/revisions before being considered for acceptance.

Major points:

From the graph of P. falciparum growth, we can see that in the lipid-rich condition, where both FabH KO and ACP KO can survive, the addition of mevalonate was essential for the growth of ACP KO. Along with the other evidence (PKII association, DNA levels...), we therefore agree that PfACP is involved in the mevalonate pathway. The authors claim that the FASII pathway is inactive/not essential in the P. falciparum blood stage. However, the authors have not shown any evidence on whether ACP is or not involved in the FASII pathway during the asexual blood stage. As currently designed, the experiments presented cannot conclude on that point for several reasons. Indeed, it was previously shown that (i) the expression of the protein from the FASII pathway are all present in blood stages and are significantly upregulated in patients that are under under "nutrient starvation" (Daily et al. Nature 2007), (ii) that, growing parasites under similar low lipid conditions in vitro induces an activation/upregulation of FASII, which can be measured by stable isotope precursor labelling and lipidomics (Botté et al. 2013), (iii) that growing the PfFabI KO line under deprived lipid conditions leads to parasite death (Amiar et al. 2020), indicating that the FASII pathway can become critical, if not essential, depending on the host nutritionnal content together correlating patients' data and metabolic adaptation for the same reasons in the related parastie Toxoplasma gondii (Amiar et al. 2020, Krishnan et al. 2020, Liang et al. 2020, Primo et al. 2021, Charital et al. 2024, Dass et al. 2024, Bitew et al. 2025).

Here, the authors are expecting to show that FabH (and thus the FASII pathway) is not essential in an experiment that is not designed to be in low lipid conditions but rather in lipid rich conditions: Such high lipid conditions of culture in this study is granted by daily feedings with high fatty acid supplement (30-90 uM palmitic acid and 30-60 uM oleic acid). These fatty acid concentrations were used previously by Mitamura et al. (2005) and Mi-ichi et al.(2007) to replace non-determined supplements such as Serum or Albumax supplement to grant similar growth by a completely controlled culture medium.

This means the concentrations above do not represent limited fatty acid concentrations, especially not with daily feeding (representing an excess supplied amount of lipids, unlike regular 48h feedings) that allowed the authors to easily reach very high non-physiological parasitaemia of more than 20%!! Amiar et al. previously showed essentiality of FabI in P. falciparum in the limited fatty acid culture at a lower concentration (<30uM 16:0, <45um 18:1), than the Mi-Ichi et al. controlled medium with regular 48 h culture feeding. Therefore, with the current experimental settings, the FAH KO is placed in high lipid conditions, thus preventing any conclusion on its essentiality under low lipid conditions.

Furthermore, it is too uncertain to conclude that ACP is only essential for the mevalonate pathway. This would be a similar discussion to the Yeh et al. 2011 and the Swift et al., where induced Apicoplast knockout caused parasites to require IPP to survive, but there were always remnant apicoplast vesicles and thus the putative presence of an active FASII in the parasite, where de novo fatty acid synthesis could be maintained. Amiar et al. (2020) and Krishnan et al. (2020) showed that disruption of FASII and absence of de novo FA synthesis in T. gondii could be compensated by the exogenous supplementation of myristic acid, C14:0. Here, high fatty acid supplementation using commercially available fatty acids may include unexpected fatty acid species such as myristic acid in palmitic acid or oleic acid, since all commercially available fatty acids guarantee only >99% but not 100%. If P. falciparum requires a very, very low amount of myristic acid to survive, the amount of possible contamination, like 1 nM, may be sufficient to maintain their survival. Thus, ACP and FabH might be very important to generate de novo fatty acids within parasites, but this was not shown by the authors.

Therefore, the manuscript currently contains incorrect conclusions on the potential essentiality/use of FASII, against current experimental evidence.

Reviewer #2 (Public review):

Summary:

The manuscript aims to determine the extent to which LC-mediated NA release in the CA1 region of the hippocampus (at both population and cellular levels) contributes to physiological arousal responses associated with innate behaviors (stress, locomotion). The manuscript is divided into two parts in which the authors compare time-locked responses in astrocytes, interneurons (pan-targeting), and pyramidal (CaMKIIa-driven targeting) cells.

In the first part of the manuscript, the authors perform bulk recordings of either NA release or calcium activity locked onto either 'natural arousal' events (tail lift, foot shock, force swim) or direct optogenetic activation of LC somas. A first aim is to identify an optogenetic stimulation frequency that would mimic NE release in the target area by low- and high-intensity stressors. In the second aim, they compared evoked responses across cell types and concluded that stressors and direct LC activation trigger similar responses in astrocytes but not in interneurons or pyramidal cells.

In the second - and most extended - part of the manuscript, the authors performed 2-photon cellular recordings of these different cell populations and compared responses evoked by the onset of locomotion vs. direct activation of the LC. Doing so, they observed a great degree of heterogeneity across these two conditions and across cell types. They conclude that NA effects on the hippocampus are primarily mediated by astrocytes and that LC-NA neuromodulation alone does not recapitulate the full breadth of 'natural arousal' modulations. They conclude that other neuromodulators likely contribute to how the hippocampus responds to high arousal levels.

Strengths:

Overall, the manuscript is well written and the figures are particularly clear.

Optogenetics is a very successful technique in contemporary neuroscience, yet one important identified limitation is that it operates largely in a non-physiological regime, driving spike rates in regions rarely visited under normal physiological operations. This has raised valid concerns about the physiological relevance of findings obtained from studies using this technique. Here, the authors aimed at calibrating optogenetic manipulations of the LC so as to match the physiological release of NA observed in specific behavioral contexts. This is a valuable endeavor that could bring the field towards more reproducible and broadly valid findings.

Another important open question is how different cell types coordinate to support global network activity and adaptive behavior. By recording distinct cell populations from the same region (CA1) and in response to the same category of endogenous versus exogenous events (locomotion or LC activation), it becomes possible to unravel important and specific operation modes, here also linked to a specific category of neuromodulation signaling.

Weaknesses:

This manuscript was difficult to review. There is clearly a lot of work and effort that went into it, and the multiple techniques seem well implemented, often with appropriate controls. Yet, the general framing, the links between experiments and interpretations, unfortunately, look questionable in my opinion. Below, I unpack what I think are the 4 main weakness points.

(1) Incomplete calibration of optogenetic manipulations to physiological regimes

While mapping optogenetic stimulation protocols to physiological variations is valuable, the proposed approach suffers from major limitations. First, the only parameter that is calibrated is the peak of NE release (as estimated from GRAB-NE fluorescence). Thus, it excludes other important aspects of the response, including trial-to-trial variability and the temporal dynamics of the response. Furthermore, stressor and LC activation conditions are simply non-comparable in terms of the duration of the stimulation (e.g., 3 min swim test versus 10s optogenetic stimulation), likely involving neuromodulation at different timescales (phasic vs. tonic). Albeit not explicitly mentioned, the number of trials and inter-trial interval between successive stimulations are also likely unmatched. On another note, the identification of the best stimulation frequency seems based on a grid of predefined values, while a more precise, continuous assessment could have easily been used. Finally, even though phasic NE release is known to depend on baseline tonic NE levels (especially with a sensor that reports a sublinear function of NE concentration), this dimension is ignored.

(2) Weak links between imposed stressors and spontaneous locomotion

The general approach is surprising: authors calibrated the optogenetic stimulation protocol on a range of stress-related behaviors and applied this to locomotion behavior. Indeed, while the first part of the manuscript uses different stressors in freely moving contexts to 'naturally' elevate arousal, the second part uses spontaneous locomotion bouts in a head-fixed situation as proxies for heightened 'natural' arousal. These two parts are very difficult to relate, and it is entirely unclear how NE regimes observed in the first context generalize to the second. Yet, on several occasions, the authors directly relate the first (fiber photometry, Fig.1) and second (2-photon, Fig. 2-6) parts of the manuscript. For instance, they conclude in favor of a "weak alignment between astrocytic responses to arousal and to LC stimulation on a cellular basis, despite the similarity of the bulk response." It remains unclear why closer preparations weren't used in the two parts, such as time-locked change in GRAB-NE2m fluorescence according to either locomotion onset or in a fear conditioning assay, both using fiber photometry in a head-fixed setting.

(3) LC optogenetics and spontaneous locomotion differ by more than the origin of the arousal drive

By directly comparing spontaneous locomotion and LC activation, the authors imply that the only difference between these two conditions is the origin of arousal: endogenous vs. exogenous, respectively. Furthermore, they interpret LC activation as triggering a pure NA effect while locomotion would reflect the conglomerate modulation from multiple neuromodulatory systems. On the one hand, LC activation likely results in the recruitment of other arousal centers (the raphe serotonin system, for instance, see 10.1101/2025.03.26.644382). On the other hand, differences between these conditions span well beyond specific arousal centers (see the massive motor-related activity in cortical dynamics: 10.1038/s41593-019-0502-4). Another, more methodological concern is the larger instability of the field of view during locomotion by comparison to optogenetic activation. While I am sure the authors corrected for movement-related translation in x and y directions, there might still be residual motion artefacts in the z direction that could account for some of the differences between the two conditions.

(4) Loose equivalence between locomotion and natural arousal

On many occasions, the authors draw a direct equivalence between spontaneous locomotion and 'natural arousal'. Arousal is a multifaceted concept that relates to far more behavioral readouts and network states than just locomotion. For instance, imagine a freezing mouse in response to a threat: locomotion would be absent, but the animal would still be quite aroused. It is ok to leave aside a particular readout and focus on other one(s) (especially thus in the case of arousal, which has many aspects). However, in that case, a single readout cannot be equated with 'natural arousal' as a whole. Instead, terms like 'locomotion' or 'locomotion-linked arousal' should be preferred. Indeed, in the particular case of locomotion, what is being readout is the upper part of the arousal continuum, whereas pupil size or whisker pad movements can also provide a more complete readout, including the lower and intermediate parts of that same continuum. While it is not necessary to include other arousal readouts (once claims are appropriately modified), the motivation for leaving out available readouts (lines 187-201) feels like a post-hoc rationalization.

In sum, these 4 points call in my opinion for a profound change in how results are presented and interpreted. If agreed, a solution could be to leave aside the first part of the manuscript, to provide a more accurate picture of the differences between optogenetic activation and spontaneous locomotion, and to better flag the limitations of the approach (a part that I believe is entirely missing in the current version).

Reviewer #2 (Public review):

Summary:

In this manuscript, de Vries and colleagues apply successful probabilistic inference and predictive coding frameworks to the question of biological motion perception. In contrast to most studies of predictive processing in humans, which rely on the presentation of discrete events, they instead aimed to track continuous predictions in the context of more naturalistic inputs such as biological motion. In these settings, the authors have previously demonstrated an inverted temporal hierarchy of prediction whereby high-level movement features (e.g., view-invariant body motion) are predicted earlier than lower-level ones (e.g., pixelwise motion). The specific question they set out to address in this manuscript is whether these predictions derive from prior beliefs about the biological and physical organization of biological movements versus the local extrapolation of motion from past observations.

The authors used anatomical MRI-driven source reconstruction of MEG activity recorded from human participants watching either normal, vertically-mirrored, or temporally scrambled movies. They then aimed to correlate activity in preselected ROIs with summary representations of these movies based on different visual features at 3 different hierarchical levels using RSA. Doing so, they could confirm that predictive processes could be identified prior to the change in the stimulus and organized anatomically along the visual cortical hierarchy. Critically, they report that mirrored movies selectively disrupted the highest processing level while the lowest level remained largely unaffected. Interestingly, the predictions at the intermediate level were boosted in mirrored movies, suggesting a possible channeling of predictions at this level when highest-level predictions are unavailable. Finally, disrupting all predictive aspects with the scrambled movies entirely abolished predictions at all levels, with signals mainly reflecting reactive bottom-up processing of inputs.

In sum, biological motion perception relies on a tight coordination of multi-level predictions based on both motion-related holistic and kinematics priors.

Strengths:

Overall, this is a very strong manuscript, with the text being clearly written. I liked the fact that the authors not only compared responses to normal videos against the same videos flipped upside-down, but also to temporal piecewise scrambling of that same video, allowing to identify the respective roles of holistic motion priors vs. temporal predictions. Of course, more work is needed to tease apart what key quantities are represented in these holistic priors. For now, the authors argue that they likely combine prior beliefs about the biological organization of bodies, such as the likely angle of joint movements, and about the physics of reality, such as gravity. Further work teasing apart these aspects would be interesting to read!

All analyses seem well executed and, while some aspects of the presentation of results could be slightly improved (see below), the manuscript is very clear and the conclusions are supported by the data. Finally, I liked the words of caution the authors added to the discussion. For instance, while they largely used negative vs. positive latency as a proxy for top-down vs. bottom-up processing respectively throughout the manuscript, they also accurately acknowledge that predictive computations could also modulate processes at positive lags, through, for instance, latency modulation.

Weaknesses:

The main aspect of the work I was left to struggle with is this idea that priors can be read out directly from large patterns of activity rates as measured with MEG. While some past experimental work does support this view, theoretical proposals also suggest that one benefit of predictive coding lies in its computational and energy-efficient properties, whereby only novel, unpredicted aspects are encoded in the rate of neural activity. Some other research lines, for instance, focusing on silent working memory, also report the brain's ability to store important computations in ways that are not reflected in costly increases in overall activity. The authors do not really unpack why they expect to see predictions to be encoded in such a way in the first place. They also do not discuss what that implies in terms of neural organization and whether other aspects of neural activity (e.g., oscillations, synaptic weights) could subtend predictive processing in this context. At the end of the day, this activity change is clearly there in the data, so that's totally fine to interpret that; it just would be helpful to unpack what such an implementation of prior beliefs would imply in terms of neural organization.

The other weakness point I see is the little consideration for behavior throughout the paper. Behavior is indeed mostly treated as a negative control, ensuring that differences between conditions at the neural level do not follow from different behavioral strategies or other peripheral factors. Critically, task design nicely incorporates two types of tasks: one that is related to motion (occlusion of movement) and one that's independent of it (color change of fixation cross). Yet, these conditions are not directly compared at the neural level. It would be useful to see whether the neural signatures of prediction are largely independent from the ongoing task or whether behavior gates the types of priors and prediction processes that are applied to incoming sensory inputs. Moreover, the text says that "neither in accuracy nor in reaction time was there a significant difference between conditions", yet significance stars in Figure 1d seem to suggest there is a difference in the fixation cross task. What am I missing? If there is indeed a difference in overall performance, can the results (esp. the reduced dRSA correlation strength in normal < inverted < scrambled movie) be interpreted in terms of a multi-tasking cognitive cost?

I also have some other minor questions and comments:

(1) In this task situation, prediction does not only come in the continuous domain but also relies on a mental simulation model, in particular in the occlusion task. However, corresponding literature, notably the work by Shepard & Metzler (1971) on mental rotation (as well as follow-ups), is not mentioned here, I believe. Could the authors perhaps mention this if they think that's relevant (if not, feel free to ignore).

(2) I'm concerned that the novelty of dynamic RSA as explained at lines 56-64 might appear slightly exaggerated. After all, isn't it just a generalization of matrix correlation in model and brain time domains? (Again, feel free to ignore if I misunderstood.)

(3) How do authors explain that high-level motion prediction is still significantly larger than zeros (correct?) in the inverted movie condition? Shouldn't it be entirely abolished?

Reviewer #2 (Public review):

Summary:

Dhillon and Lewis use the enhanced brightness of the new calcium indicator dye JF646-BAPTA attached to Orai1-bound HaloTag to identify single CRAC channel events detected as [Ca2+]i fluctuations rather than currents. This enables them to detect Orai1single channel kinetics of permeation, overcoming the currently unmeasurable single channel CRAC conductances (~ 20-40 fS). TIRF microscopy narrows the z-section and improves calcium event localization.

JF646-BAPTA reversibly blinks between fluorescent and non-fluorescent states, complicating single-channel detection. Blinking occurs both in permeabilized cells with saturating Ca2+ and in intact cells at physiological [Ca2+]i. Using voltage clamp and TIRF imaging, CRAC gating events were distinguished from blinking by analyzing fluorescence responses to voltage changes.

Hyperpolarization (-100 mV) increases fluorescence, indicating channel opening. Responses blocked by La3+ confirm specificity for Orai1, while minimum fluorescence at +30 mV corresponds to closed channels. Dynamic range and response kinetics help differentiate genuine gating from blinking artifacts. Long channel openings (seconds to tens of seconds) are observed, with most open times around 1.2 seconds. Longer openings (tens of seconds) are present but difficult to sample. Silent channels constitute 11% of puncta.

The paper carefully examines a new method to sample CRAC kinetics, which should enable further mechanistic studies of STIM control of ORAI and modulation by other signaling components such as calcineurin. Development of bright nonblinking dyes or dyes whose blink rates are directly correlated with a calcium-binding site will enhance this route of investigation.

Comments:

This is an excellent methodological study, rigorous and thorough. I wondered whether La3+ alone could alter JF646-BAPTA blinking, but the authors show that JF646-BAPTA exhibits reversible transitions to a non-fluorescent state (blinking) under both Ca2+-saturated and physiological conditions, independent of channel activity or the presence of La3+.

Strengths:

A novel method providing additional tools to study store-depletion induced Ca currents mediated by Stim-Orai family members.

Weaknesses:

Limited by blinking dyes, the only ones currently sensitive enough to measure the calcium fluxes through single channels.

Reviewer #2 (Public review):

This paper aimed to demonstrate that total spectral energy alone is sufficient to drive hardness perception and material identification. Through five user studies, they tested materials ranging in stiffness and with covered fingers to support their claim. Using a spectral energy compensation framework, they concluded that total spectral energy alone, regardless of frequency content, was sufficient to support material hardness percepts. However, it should be noted that all experiments used a tapping procedure, which is not the standard exploratory procedure when judging material hardness. A tapping method also selectively enhances vibratory feedback while limiting others. This fundamentally limits the scope of their work, and assessing their claim on generalizability would require further experimentation.

Some additional clarification and extension on the experiments are also suggested:

(1) According to Lederman and Klatzky (1987), pressure, and not tapping, is the exploratory procedure humans use to judge hardness. And during tapping instead (as used in all experiments), it is expected that the dominant cue available to the user comes from vibrations, as other mechanical cues, such as skin stretch, are limited. These vibrations could serve as a proxy for hardness, as claimed by the authors, but it is unclear if the participants are basing their evaluations on perceived hardness or vibration intensity. A more fundamental question that needs to be answered to support the paper's claim is whether a single tap is sufficient for conveying a material's hardness. To better support their claim, I recommend that the authors include an experiment using participants' bare fingers with materials of the same modulus but different damping coefficients. These materials would produce different vibration signals when tapped, but are equivalent in hardness.

(2) The setup text for experiment 4 does not match the results. Results suggest that a finger covered with a bubble and touching a soft material was used (i.e. dual compliance), but the setup describes otherwise. The authors should clarify this and confirm that this is different from experiment 2.

(3) As silicone, foam, and rubber can have very similar or different hardness depending on the specific material used, please report the hardness of each material tested (Shore or Young's modulus) to better understand the range of stiffness tested.

(4) In the "materials grouping and selection" section, it states that a pilot study suggested hard materials tended to be perceptually similar while softer materials were easily distinguishable. However, this contradicts the results in experiment 1. The authors should expand on the details of the pilot study and address the inconsistency between its findings and experiment 1.

(5) The methods section suggests that individual recordings for each material were performed before the experiment. Please clarify if this is correct, or if a single signal for each texture was used across all participants. Additionally, were the participants' tap pressure controlled during either the recordings or in the experiments? If not, how do the authors account for the difference in intensity that would be generated due to different tapping pressures across participants and trials?

Reviewer #2 (Public review):

Summary:

This paper offers a novel theoretical account of dopamine ramps. The key idea is that the reward prediction error (putatively signaled by dopamine) uses a partially model-based estimate for future value (the prediction target). Because the model-based value estimate emerges more rapidly than the model-free estimate, it inflates the RPE, and this inflation increases with reward proximity - hence ramps. The authors show that this account can explain many aspects of existing data on dopamine ramps across several different studies.

Strengths:

Overall, I liked this paper. The idea is interesting and plausible. The paper is well-written and clearly argued. The modeling has been done rigorously.

Weaknesses:

My major comments are: (1) it's not always clear which phenomena are uniquely well-explained by this new account vs. earlier accounts; and (2) the limitations of the account are not entirely transparent.

(1) The paper models some of the studies reported by Kim et al (2020). As was already shown in that paper, a standard TD error could explain the results (although a major limitation of that treatment was that it did not model the recursive effect of RPEs on learning, as discussed in the Mikhael paper). It's not clear if there's additional explanatory value provided by this new account, though, of course, it's good to know that those results are captured by the new account. Likewise, Mikhael et al (2022) already offered an account of their data (somewhat more complex than the standard TD model). Again, it's not clear if there's additional explanatory value provided by the new account (and again, it's nice to see that the model can capture these results). Finally, I found myself wondering whether the Guru et al (2020) result couldn't be explained by a more standard TD model (assuming the value function is sufficiently convex). I don't think it's essential that the new account provides additional explanatory value in every case, but I think it's important to convey to readers what's new and what's not, as well as what aspects of the data require particular kinds of mechanisms to explain. It would be really helpful to see the predictions of alternative TD models in order to make this clearer.

(2) The Mikhael model was motivated by the puzzle that ramping is observed in navigation tasks (with sensory cues) but typically not in classical conditioning tasks lacking sensory cues. The correction term, derived from normative considerations, explained this discrepancy. It's not clear to me if/how the new account can explain the discrepancy.

Reviewer #2 (Public review):

Summary:

This manuscript by Lorenzo and colleagues presents wide-field cortical imaging data obtained from experiments conducted with three triple-transgenic mouse lines that specifically express the calcium sensor GCaMP6f in neurons of layers 2/3, 5, and 6 of the neocortex, respectively.

It first includes a methodological contribution aimed at optimizing the analysis of the acquired signals, taking into account both the geometry of the neocortex and photon scattering in the cortical tissue, which affect fluorescence signals differentially depending upon their cortical depth of origin.

In particular, they built upon the work previously published in eLife by Waters in 2024, which, based on a simulation of photon scattering using a Monte Carlo random-walk model, provided an estimate of the tissue volumes contributing to the fluorescence signals measured from the surface in several mouse lines expressing Gcamp in a layer-specific manner.

The authors here additionally performed empirical measurements of the point spread function at different cortical depths to determine spatial kernels to be used to deconvolve wide-field imaging data acquired from their three-layer-specific GCaMP6f-expressing mouse lines. They assess the added value of this deconvolution approach based on recordings of the cortical responses evoked by whisker stimulation in the barrel cortex, using lightly anesthetized, layer 2/3 and layer 5 GCaMP6f-expressing mice.

Altogether, these proposed methods aim at optimizing the registration of recorded signals on a common reference frame, allowing to compare cortical spatiotemporal dynamics recorded from distinct layer-specific GCaMP-expressing mice.

The manuscript further contains a more neurophysiological contribution, directly utilizing the proposed methods to perform a comparative layer-specific functional connectivity analysis from data collected with the 3 different mouse lines, while the mice were head-fixed below the macroscope.

Strengths:

Wide-field 1-photon functional optical imaging, which allows recording cortical spatiotemporal dynamics over a large portion of the dorsal neocortex in mice, has become a tool of choice to study how activity over a wide range of cortical areas is orchestrated in various behavioral contexts. The ever-increasing availability of transgenic mice exhibiting pan-cortical calcium- or voltage-dependent sensors within specific neuronal populations is generating a growing interest in these approaches among the neuroscientific community.

Nowadays, it is possible to image specifically the activity of excitatory neurons whose cell bodies are located in given cortical layers. However, interpreting fluorescence signals recorded from the surface while originating from deep layers proves difficult due to photon scattering, which reduces image definition, as previously established by Waters et al. (2024).

The ability to correct for this blurring effect and to place the recorded signals within a common frame of reference is therefore essential not only for comparing activity across layers but also for integrating findings across studies, thereby advancing our collective understanding of neocortical physiology.

In this sense, this work by Lorenzo and colleagues is definitely both timely and valuable.

Overall, the manuscript is clearly structured and well-written, and the figures are of excellent graphic quality.

The proposed approach to correct the blurring of the fluorescent signals, which increases with depth, by means of empirical measurements of point spread functions and deconvolution, seems pertinent and efficient.

Finally, the authors have collected evoked and spontaneous dynamics of calcium signals from 3 different layer-specific GCaMP mice, which in itself represents a substantial experimental effort, not least because of the need to generate the animals. Out of these data, they provide a unique comparative analysis of layer-specific functional connectivity.

Weaknesses:

To fully benefit a large community, some aspects of the proposed methodological advances need to be more detailed in the manuscript and potentially refined. For instance, it is very difficult to evaluate, given the tiny confocal images provided in Figure 1, the potential contribution of GCaMP signal from apical dendrites of layer V neurons in Rbp4-GCaMP6f mice. It is also difficult for the reader to assess the added value of the layer-specific reference maps, given that functional image registration relies on nonlinear transformations and limited detail is provided regarding the procedure used to realign the functional data with these maps (lines 465-467). It is not really clear how the illustrated "composite maps" and the "five functional spots" used for the registration are computed. In addition, one could question the choice of the large time windows used to generate these composite maps/functional landmarks. Since the early component of the evoked responses is more likely to reflect the location of the initial thalamocortical inputs, restricting the analysis to the early phase of the responses might improve the accuracy of primary cortical area identification. This concern regarding the time window used to define specific cortical representation areas may also be relevant to Figure 4, which illustrates the results of the proposed deconvolution approach used to correct for photon scattering (although the time windows used for these analyses are not specified).

With regard to Figure 4, the reader might wonder why the results are not illustrated similarly for the layer 6 mice. It would therefore be useful to clearly indicate whether these data are not shown because they were not collected, or because it proved impossible to identify single whisker representations, despite the proposed deconvolution procedure.

Regarding the analysis of layer specificity in terms of functional connectivity, the authors extensively use the term "resting-state" to describe the behavioral context of data collection, given that the animals were not engaged in a goal-directed task. However, because the mice were experiencing head fixation beneath a functional epifluorescence macroscope for only the second time, it is questionable whether this state can truly be classified as "resting." As indicated by the global quantification of body movements, the animals most likely alternated between quiet wakefulness and more active phases.

To allow the reader to accurately interpret the reported functional connectivity differences, the authors should at least provide a quantification of the time animals spent in the quiet versus active states, and assess whether these proportions were comparable between the different mouse lines. Another way to address this issue would be to perform functional connectivity analyses after splitting the data according to these two states based on body movement quantification, although it is difficult to assess the feasibility of this approach without knowing the temporal distribution of these states within the dataset.

This seems particularly important since differences in neural cross-regional correlation patterns have been linked to arousal levels, with a comparable optical imaging approach, by Shahsavarani and colleagues (Cell Reports, 2023), who compared initial and prolonged resting periods. In addition, the authors report here that layer differences in functional connectivity are more pronounced in regions associated with the default mode network, whose activity is likely to differ between quiet and active wakefulness.

Finally, given the richness of the dataset, it would be very interesting to assess how the proposed deconvolution approach affects PCA-ICA-based functional parcellation of spontaneous cortical activity (Reidl et al., NeuroImage, 2007; Makino et al., Neuron, 2017) and whether it enables cross-layer comparisons of independent cortical modules. Such supplementary analyses would substantially increase the impact of this work.

Reviewer #2 (Public review):

This manuscript from Zuniga-Pflucker laboratory describes that thymic macrophages are heterogeneous in flow cytometric and transcriptomic profiles, containing two major populations characterized by TIMD4 and CX3CR1 expression. These macrophage populations are both parenchymal in the thymus but are unequal in developmental ontogeny, Flt3 expression history, and CCR2 dependency. The manuscript further reports the interesting findings that the depletion of thymic macrophages impairs thymocyte development at the DN3 beta-selection checkpoint. These results provide an important advance for further understanding of thymus biology, especially in view of the contribution of heterogenous thymic macrophage subpopulations.

However, Zhou et al. previously reported essentially similar heterogeneity in thymic macrophages. It was demonstrated that TIMD4+ macrophages and CX3CR1+ macrophages have distinct origins and are different in developmental characteristics (27). The authors should better clarify what was previously demonstrated and what is newly described in this study. Zhou, et al. also demonstrated that TIMD4+ macrophages are localized in the cortex whereas CX3CR1+ macrophages distribute in the medullary region. Whether or not these previous findings are reproduced and supported in the present study is important in view of the new finding that thymic macrophages are important for beta-selection, which is presumed to occur in the thymic cortex. The authors may be able to suggest more strongly that TIMD4+ macrophages regulate beta-selection in the thymic cortex through phagocytic efferocytosis. (Indeed, the Figure 1 legend states that frozen thymic sections were used for immunofluorescent staining to identify the localization of thymic macrophages, without showing the results.)

Reviewer #2 (Public review):

Summary:

In this manuscript, the authors probe the mechanisms by which Dyngo-4a, a dynamin inhibitor used to block endocytosis, impact caveolae dynamics. They provide compelling evidence that Dyngo-4a inhibits caveolae dynamics and endocytosis (as well as several other aspects of plasma membrane dynamics) by a dynamin-independent mechanism. They also provide strong computational and experimental data showing that Dyngo-4a inserts into membranes and decreases lipid packing in the outer leaflet of the plasma membrane. Finally, they demonstrate that the addition of excess cholesterol to cells reverses the effects of Dyngo-4a on caveolae dynamics, presumably by reversing lipid packing defects. Based on these findings they conclude that lipid packing regulates caveolae dynamics and endocytosis in a cholesterol-dependent manner.

This work should be of value to cell biologists interested in plasma membrane remodeling and membrane trafficking, biophysicists that study small molecule/membrane interactions and membrane remodeling processes, and chemists interested in designing drugs to target membrane trafficking machinery and pathways.

Strengths and weaknesses:

This work addresses the important topic of how a widely used endocytic inhibitor actually works. In the process of addressing this question, the authors uncover unexpected connections between how lipids are packed in cell membranes and membrane dynamics. The methods are appropriate and many of the claims made in this work are well supported by data.

The authors have also been responsive to comments raised during review by including additional experimental evidence that Dyngo-4a inhibits caveolae endocytosis as well as documenting the effects of Dyngo-4a on caveolae morphology.

The work also raises some interesting questions for the future. As one example, the authors note that in addition to inhibiting caveolar dynamics, Dyngo-4a inhibits generalized plasma membrane mobility, transferrin uptake, and fusion of fusogenic liposomes to the plasma membrane. More work will be required to determine whether these events are mediated by a common, lipid packing-dependent mechanism.

Reviewer #2 (Public review):

This study uses monkey single-unit recordings to examine the role of the STN in combining noisy sensory information with reward bias during decision-making between saccade directions. Using multiple linear regressions and clustering approaches, the authors overall show that a highly heterogeneous activity in the STN reflects almost all aspects of the task, including choice direction, stimulus coherence, reward context and expectation, choice evaluation, and their interactions. The authors report in particular how three classes of neurons map to different decision processes evaluated via the fitting of a drift-diffusion model. Overall, the study provides evidence for functionally diverse and anatomically intermingled populations of STN neurons, supporting multiple roles in perceptual and reward-based decision-making.

This study follows up on work conducted in previous years by the same team and complements it. Extracellular recordings in monkeys trained to perform a complex decision-making task remain a remarkable achievement, particularly in brain structures that are difficult to target, such as the sub-thalamic nucleus. The authors conducted numerous analyses of STN activities, using sophisticated statistical approaches and functional computational modeling.

Reviewer #3 (Public review):

Despite the abundance of RNA velocity tools, there are still major limitations, and there is strong skepticism about the results these methods lead to. In this paper, the authors try to address some limitations of current RNA velocity approaches by proposing a unified framework to jointly infer transcriptional and splicing dynamics. The method is then benchmarked on 6 real datasets against the most popular RNA velocity tools.

Comments on revised version.

The Authors addressed all my comments suitably. I'd like to thank them for the time they spent addressing them: the revised paper is much more convincing.

I have 2 very minor follow-up concerns:

(1) I appreciated the simulation study, however, no null simulation is present.<br /> We know RNA velocity tools are inclined to provide false positives: trajectories even when the data doesn't have any.<br /> I'd be helpful to add null simulations where the data has no trajectories and see if methods erroneously identify any.

(2) Several of the novel analyses are only reported in the Supplementary material and only references in the main text (e.g., "A validation of TSvelo on simulated data is provided in Fig. S1 and Fig. S2 in the Supplementary Information."). This is pity!

If allowed, I'd add some comments about the new analyses (simulations, computational benchmarks, etc...) also in the main text.

Reviewer #3 (Public review):

Summary:

The authors describe an interesting study of arm movements carried out in weightlessness after a prolonged exposure to the so-called microgravity conditions of orbital spaceflight. Subjects performed radial point-to-point motions of the fingertip on a touch pad. The authors note a reduction in movement speed in weightlessness, which they hypothesize could be due to either an overall strategy of lowering movement speed to better accommodate the instability of the body in weightlessness or an underestimation of body mass. They conclude for the latter, mainly based on two effects. One, slowing in weightlessness is greater for movement directions with higher effective mass at the end effector of the arm. Two, they present evidence for increased number of corrective sub movements in weightlessness. They contend that this provides conclusive evidence to accept the hypothesis of an underestimation of body mass.

Strengths:

In my opinion, the study provides a valuable contribution, the theoretical aspects are well presented through simulations, the statistical analyses are meticulous, the applicable literature is comprehensively considered and cited and the manuscript is well written.

Weaknesses:

I nevertheless am of the opinion that the interpretation of the observations leaves room for other possible explanations of the observed phenomenon, thus weakening the strength of the arguments.

I raised the following points in my original review, but I find that the authors have judiciously addressed these points through their various revisions.

I believe that the article constitutes a valuable contribution and that the results and conclusions are certainly worthy of consideration by the human motor control community.

(1) The authors model the movement control through equations that derive the input control variable in terms of the force acting on the hand and treating the arm as a second-order low pass filter (Eq. 13). Underestimation of the mass in the computation of a feedforward command would lead to a lower-than-expected displacement to that command. But it is not clear if and how the authors account for a potential modification of the time constants of the 2nd order system. The CNS does not effectuate movements with pure torque generators. Muscles have elastic properties that depend on their tonic excitation level, reflex feedback and other parameters. Indeed, Fisk et al.* showed variations of movement characteristics consistent with lower muscle tone, lower bandwidth and lower damping ratio in 0g compared to 1g. Could the variations in the response to the initial feedforward command be explained by a misrepresentation of the limbs damping and natural frequency, leading to greater uncertainty to the consequences of the initial command. This would still be an argument for un-adapted feedforward control of the movement, leading to the need for more corrective movements. But it would not necessarily reflect an underestimation of body mass.

*Fisk, J. O. H. N., Lackner, J. R., & DiZio, P. A. U. L. (1993). Gravitoinertial force level influences arm movement control. Journal of neurophysiology, 69(2), 504-511.

While the authors attempt to differentiate their study from previous studies where limb neuromechanical impedance was shown to be modified in weightlessness by emphasizing that in the current study the movements were rapid and the initial movement is "feedforward". But this incorrectly implies that the limb's mechanical response to the motor command is determined only by active feedback mechanisms. In fact:

(a) All commands to the muscle pass through the motor neurons. These neurons receive descending activations related not only to the volitional movement, but also to the dynamic state of the body and the influence of other sensory inputs, including the vestibular system. A decrease in descending influences from the vestibular organs will lower the background sensitivity to all other neural influences on the motor neuron. Thus, the motor neuron may be less sensitive to the other volitional and reflexive synaptic inputs that it may receive.

(b) Muscle tone plays a significant role in determining the force and the time course of the muscle contraction. In a weightless environment, where tonic muscle activity is likely to be reduced, there is the distinct possibility that muscles will react more slowly and with lower amplitude to an otherwise equivalent descending motor command, particularly in the initial moments before spinal reflexes come into play. These, and other neuronal mechanisms could lead to the "under-actuation" effect observed in the current study, without necessarily being reflective of an underestimation of mass per se.

(2) The subject's body in weightless is much more sensitive to reaction forces in interactions with the environment in the absence of the anchoring effect of gravity pushing the body into the floor and in the absence of anticipatory postural adjustments that typically accompany upper-limb motions in Earth gravity in order to maintain an upright posture. The authors dismiss this possibility because the taikonauts were asked to stabilize their bodies with the contralateral hand. But the authors present no evidence that this was sufficient to maintain the shoulder and trunk at a strictly constant position, as is supposed by the simplified biomechanical model used in their optimal control framework. Indeed, a small backward motion of the shoulder would result in a smaller acceleration of the fingertip and a smaller extent of the initial ballistic motion of the hand with respect to the measurement device (the tablet), consistent with the observations reported in the study. Note that stability of the base might explain why 45º movements were apparently less affected in weightlessness, according to many of the reported analyses, including those related to corrective movements (Fig. 5 B, C, F; Fig. 6D), than the other two directions. If the trunk is being stabilized by the left arm, the same reaction forces on the trunk due to the acceleration of the hand will result in less effective torque on the trunk, given that the reaction forces act with a much smaller moment arm with respect to the left shoulder (the hand movement axis passes approximately through the left shoulder for the 45º target) compared to either the forward or rightward motions of the hand.

(3) The above is exacerbated by potential changes in the frictional forces between the fingertip and the tablet. The movements were measured by having the subjects slide their finger on the surface of a touch screen. In weightlessness, the implications of this contact can be expected to be quite different than on the ground. While these forces may be low on Earth, the fact is that we do not know what forces the taikonauts used on orbit. In weightlessness, the taikonauts would need to actively press downward to maintain contact with the screen, while on Earth gravity will do the work. The tangential forces that resist movement due to friction might therefore be different in 0g. . Indeed, given the increased instability of the body and the increased uncertainty of movement direction of the hand, taikonauts may have been induced to apply greater forces against the tablet in order to maintain contact in weightlessness, which would in turn slow the motion of the finger on the table and increase the reaction forces acting on the trunk. This could be particularly relevant given that the effect of friction would interact with the limb in a direction-dependent fashion, given the anisotropy of the equivalent mass at the fingertip evoked by the authors.

I feel that the authors have done an admirable job of exploring the how to explain the modifications to movement kinematics that they observed on orbit within the constraints of the optimal control theory applied to a simplified model of the human motor system. While I fully appreciate the value of such models to provide insights into question of human sensorimotor behaviour, to draw firm conclusions on what humans are actually experiencing based only on manipulations of the computational model, without testing the model's implicit assumptions and without considering the actual neurophysiological and biomechanical mechanisms, can be misleading. One way to do this could be to examine these questions through extensions to the model used in the simulations (changing activation dynamics of the torque generators, allowing for potential motion backward motion of the shoulder and trunk, etc.). A better solution would be to emulate the physiological and biomechanical conditions on Earth (supporting the arm against gravity to reduce muscle tone, placing the subject on a moveable base that requires that the body be stabilized with the other hand) in order to distinguish the hypothesis of an underestimation of mass vs. other potential sources of under-actuation and other potential effects of weightlessness on the body.

In sum, my opinion is that the authors are relying too much on a theoretical model as a ground truth and thus overstate their conclusions. But to provide a convincing argument that humans truly underestimate mass in weightlessness, they should consider more judiciously the neurophysiology and biomechanics that fall outside the purview of the simplified model that they have chosen. If a more thorough assessment of this nature is not possible, then I would argue that a more measured conclusion of the paper should be 1) that the authors observed modifications to movement kinematics in weightlessness consistent with an under-actuation for the intended motion, 2) that a simplified model of human physiology and biomechanics that incorporates principles of optimal control suggest that the source of this under-actuation might be an underestimation of mass in the computation of an appropriate feedforward motor command, and 3) that other potential neurophysiological or biomechanical effects cannot be excluded due to limitations of the computational model.

Reviewer #2 (Public review):

Okabe and colleagues build on a super-resolution-based technique they have previously developed in cultured hippocampal neurons, improving the pipeline and using it to analyze spine nanostructure differences across 8 different mouse lines with mutations in autism or schizophrenia (Sz) risk genes/pathways. It is a worthy goal to try to use multiple models to examine potential convergent (or not) phenotypes, and the authors have made a good selection of models. They identify some key differences between the autism versus the Sz risk gene models, primarily that dendritic spines are smaller in Sz models and (mostly) larger in autism risk gene models. They then focus on three models (2 Sz - 22q11.2 deletion, Setd1a; 1 ASD - Nlgn3) for timelapse imaging of spine dynamics, and together with computational modelling provide a mechanistic rationale for the smaller spines in Sz risk models. Bulk RNA sequencing of all 8 model cultures identifies several differentially expressed genes which they go on to test in cultures, finding that ecgr4 is upregulated in several Sz models and its misexpression recapitulates spine dynamics changes seen in the Sz mutants, while knockdown rescues spine dynamics changes in the Sz mutants. Overall, these have the potential to be very interesting findings and useful for the field. My major concerns from the initial manuscript, especially regarding cherry picking and circularity have been addressed with revised analytical approaches. I have some remaining minor comments.

(1) The comparison between two wild-type samples versus wild-type-mutant samples is helpful - I think this could be added to the manuscript.

(2) For results of timelapse imaging - please spell out in the results section the direction of change (lines 270 - 277).

(3) Using linear mixed effect models for statistical analysis is a significant improvement. While a sample size (n) of mice = 3 is not ideal, I think given the multiple different mouse lines used and intensity of analysis, this is probably the best that can be done, although further validation in larger samples eventually is to be hoped for.

(4) The revised text is much improved, but I still think the authors should be upfront somewhere in the text that the schizophrenia-associated genes can only confer biased risk for schizophrenia (and that the clinical phenotype can also include autism). As I said before, I think this is the best we can do and I agree with their choices, but it is important not to overstate the link. The differences they see make it clear that these are still relevant distinctions.

Reviewer #2 (Public review):

Summary

This work explores the relationship between body structure and behavior by studying self-righting in Drosophila larvae, a conserved behavior that restores proper orientation when turned upside-down. The authors first introduce a novel "water unlocking" approach to induce self-righting behavior in a controlled manner. Then, they develop a method for region-specific inhibition of sensory neurons revealing that anterior, but not posterior, sensory neurons are essential for proper self-righting. Deep-learning-based behavioral analysis shows that anterior inhibition prolongs self-righting by shifting head movement patterns, indicating a behavioral switch rather than a mere delay. Additional genetic and molecular experiments demonstrate that specific Hox genes are necessary in sensory neurons, underscoring how developmental patterning genes shape region-specific sensory mechanisms that enable adaptive motor behaviors.

Strengths

The work by Roseby et al. is notable for its elegant experimental design, the development of innovative methods that are likely to benefit the fly behavior community, and the strong experimental support for its conclusions. The manuscript is clearly written, well structured, and presents thoughtfully designed experiments that have been further improved in the revised version. This updated manuscript includes a comprehensive set of behavioral experiments using an additional Gal4 line (ppk-Gal4), which yields confirmatory results and strengthens support for the original hypothesis. It also incorporates quantification of Gal4 line strength, improvements to existing figures, the addition of new figures, and overall refinement of the text.

Weakness:

A remaining limitation of this manuscript is the lack of a cellular and mechanistic analysis explaining how Hox genes give rise to the observed behavioral phenotypes. The authors note that this question is being addressed in an ongoing follow-up study, which will expand the project to examine the roles of all Hox genes across the sensory system and to characterize their expression patterns within each of its subcomponents, with the aim of providing mechanistic insight. I look forward to seeing this work in a future manuscript.

Comments on revised version.

I have no further recommendations for the authors; most of my comments and questions have been satisfactorily addressed.

Reviewer #3 (Public review):

Summary:

This revised study analyzes the somatic mutational profiles and transcriptomic expression of three zinc-finger genes (ZNF217, ZNF703, ZNF750) in 23 Kenyan women with breast cancer, using whole-exome sequencing and RNA-sequencing of paired tumor-normal tissues. A total of 358 somatic mutations were detected, and all three genes were significantly upregulated in tumors compared to normal tissues (ZNF217 showing the most prominent difference). The findings provide preliminary evidence for the idenfication of diagnostic/prognostic biomarkers or therapeutic targets in sub-Saharan African populations.

Strengths:

The study's key strengths lie in its focus on an underrepresented Kenyan cohort, addressing a critical gap in sub-Saharan African breast cancer genomic research. It integrates DNA-level mutation analysis with RNA-level expression data, leveraging standardized bioinformatics pipelines and rigorous quality control to deliver detailed insights into mutation types, functional impacts, and amino acid changes.

Comments on revised version:

After careful revision by the authors, the manuscript has become more rigorous. The limitations including small sample size and lack of functional validation are properly acknowledged, and conclusions are prudently presented as hypothesis‑generating rather than causal claims. Meanwhile, strengthened multi‑omics analyses, TCGA validation, logical reorganization of results and improved figure presentation further enhance the reliability of this work.

Reviewer #2 (Public review):

Summary:

In "Ecological diversification in rapidly evolving populations", the authors use a consumer-resource model with competition for 2 different resources to study diversification for cases in which ecology and evolution are separated (weak-mutation limit) and when they overlap. They find the potential for the timing of a mutation (and not just its associated fitness) to confer an advantage against fitter strains (which they call "priority effect"), and the aggregation of dominant trait values that lead to the definition of "ecotypes" that discretize and structure the community.

Strengths:

The authors introduce detailed analytical calculations in the limit of overlapping ecology and evolution, which is a case that typically eludes analysis. The work also pays particular attention to the timing of "invasion" by a mutation, whereas most approaches focus on the long-term outcome of evolution (e.g. fixation of a trait value).

Weaknesses:

The model makes important assumptions that limit its generality considerably. In particular, the two "evolving traits" defined in the model are very specific and by no means the simplest possible resource competition evolutionary model that the authors claim it to be. The manuscript is not clear enough to be reproducible, and the authors do not discuss in sufficient depth the huge amount of work that is presented in the manuscript. The bibliography omits important work focused on diversification emerging from eco-evolutionary interactions similar to the ones studied in the manuscript.

Reviewer #2 (Public review):

In this manuscript, the authors report a novel regulation of the outer mitochondrial membrane remodeling domains called mitochondria-derived compartments, MDCs. The team has previously established the main principles behind this recently identified quality control pathway, but the mechanisms that control MDCs formation remain incompletely understood. Using the baker's yeast model, the authors identify the conserved mitochondrial protease Yme1 as a crucial factor that regulates MDC formation. Mechanistically, Yme1's proteolytic function controls the levels of Ups1 and Ups2 lipid transfer proteins and the components of the membrane organizing complex called MICOS, thus providing a plausible model as to how Yme1-dependent proteolysis permits MDC formation through the removal of lipid and MICOS-dependent constraints. Finally, the authors show that this Yme1-mediated activity is also defined by metabolic conditions. In principle, this study is interesting and novel, and holds potential to provide new insights into the regulation of the MDC pathway that emerged as a new fundamental mitochondrial quality control mechanism. However, the following points should be carefully addressed.

Major points:

(1) Yme1 has been previously shown to regulate mitochondria-specific autophagy through Atg32 processing. Given the high similarity of the MDC pathway to piecemeal autophagy and the fact that both pathways share some of the core components, the authors should address the involvement of Atg32 in their model. It would also be important to include a brief discussion addressing the differences between piecemeal autophagy and the MDC pathway.

(2) The Rpt3 (P215L) expression experiment is interesting, but appears to be somewhat superficial due to the unclear mechanism by which the mitochondrial network morphology is restored in these cells. Could this result be replicated in the dnm1∆ mgm1∆ double deletion mutant, which is a well-established model for mitochondrial network restoration?

(3) Figure 3E. The changes in PE levels appear to be minor. While statistically significant, the observed differences may not be physiologically relevant. More in-depth lipidomic analysis data should be presented to substantiate the authors' argument and better address the questions at hand. Related to that, could PE or PA supplementation stimulate MDC formation?

(4) The connection between rapamycin treatment and Yme1-regulated MDC formation is unclear and puzzling and needs to be explained better.

(5) The MICOS complex is clearly involved in the regulation of MDC, but the manuscript misses the mark on providing compelling evidence and a clear explanation as to how MICOS contributes to said regulation.

Minor points:

(1) The authors should discuss potential reasons for the dramatically different rates of MDC formation in the S288C and W303 background cells. Does this have anything to do with generally more robust mitochondrial functions in the latter cells?

(2) Proper statistical analyses should be provided for all the graphs presented.

(3) The authors should include Yme1 immunoblots to confirm the identity of strains being studied and validate the presence or overexpression of Yme1 and its catalytic mutant in their experiments.

Reviewer #2 (Public review):

Summary:

The study presents an in-depth analysis of the peptide repertoire bound by a promiscuous chicken MHC molecule using mass spectrometry, x-ray crystallography and modelling. While the MHC can bind a very diverse set of peptides, the authors have found some new rules that govern peptide binding to this MHC that could help to build a predictive model to study the repertoire of pathogen-derived peptides.

Strengths:

The study uses a range of well performed experiment across multiple techniques and provides an in-depth analysis of the peptide repertoire, including peptide sequences, length, preferred residues, stability and MHC presentation.

Weaknesses:

The data overall support the analysis and conclusion well. The only caveat is linked to Figure 4, which does not describe the stability of the peptide-MHC complex, but instead shows refold yield, and the two are not always linked.

Reviewer #2 (Public review):

Summary:

Mione et al. aim to resolve a long-standing question in comparative neuroscience: whether the macaque brain contains a functional analogue to the distributed human multiple-demand (MD) network. To address this, the authors employ a direct cross-species fMRI comparison using a multi-step saccadic maze task in humans and a simplified two-step version in macaques. By contrasting goal-directed navigation against a control condition that requires similar motor responses but no strategic planning, the study isolates the neural signatures of cognitive control across species.

Strengths:

The most compelling aspect of this work is its methodological alignment. Previous attempts to compare these systems often relied on comparisons of human BOLD signals and macaque single-unit recordings. By running parallel fMRI protocols, the authors establish a shared measurement basis that allows for a more direct comparison. The resulting activation maps clearly demonstrate conserved network topology across dorsomedial frontal, lateral, and medial parietal, and insula cortices. Combining these results with recent research on functional and structural connectivity further supports the idea that these networks evolved across species and provides a helpful starting point for future comparative studies. The findings will be highly useful for researchers investigating the evolutionary origins of domain-general cognitive control, as well as for neuroimaging methodologists developing cross-species alignment pipelines.

Weaknesses:

However, there are several differences in how the two groups were studied that make it harder to compare the results precisely. The human task mixed 2-, 4-, and 6-step trials within the same experimental blocks, whereas macaques performed only 2-step trials. This design difference likely places human participants in a state of sustained proactive cognitive control (Braver, 2012), as they must remain prepared for highly demanding trials at any moment. This elevated baseline arousal may artificially inflate MD network activation during the simpler 2-step trials in humans, making direct magnitude comparisons with the macaque data difficult. Additionally, the general linear model combined correct and error trials into a single regressor. Given that macaques exhibited substantially higher error rates, this approach risks diluting task-specific planning signals with activity related to error monitoring and reward prediction errors. The preprocessing pipeline also applied a 4 mm full-width half-maximum smoothing kernel to macaque data acquired at 1.5 mm resolution. Relative to the smaller size of the macaque brain, this kernel is quite large and likely blurs fine-grained topographical distinctions. This may partly explain why the macaque lateral frontal cortex shows a single dorsal activation patch rather than multiple discrete patches seen in humans. Furthermore, there is concerning inter-individual variability in the macaque data. Normally, a functional network like the MD system is identified by consistent activation across all individuals. In this study, however, the two monkeys show substantially different activation maps and behavioral patterns. This lack of consistency renders the group-level results questionable, as it is unclear whether the group-level map represents a unified biological system or merely an average of disparate individual maps. Finally, the subcortical activations shown in Figure 7 require more precise anatomical localization to confidently distinguish cerebellar nodes from adjacent brainstem structures.

The authors demonstrate a broad functional correspondence between human and macaque cognitive control networks, moving the field beyond speculative homology. The data suggest that an extended, interconnected network is recruited by cognitive challenge in both species; however, the strength of this claim is limited by the inter-individual variability and methodological constraints noted above. Assertions of precise topological equivalence should therefore be tempered. The absence of ventrolateral prefrontal and strong dorsal parietal activations in the macaque group analysis may reflect genuine biological differences, but could also stem from limited statistical power, excessive smoothing, or task-design asymmetries. While the overall conclusions are plausible, they would be significantly strengthened by a more explicit discussion of these limitations and additional analytical clarifications regarding individual-level consistency.

Reviewer #2 (Public review):

Summary:

This is an extensive study using phylogenetic comparison across multiple plasmodium species to gain new insights in relation to their evolutionary pathways and the potential function of pir. In addition to establishing a framework to identify related orthologues across species as well as expanding paralogues families within a species, the work also focuses on understanding loss and gain of different PIRs and how this indicates a relative lack of functional constraints and essentiality for most members of the gene family.

The authors provide evidence that at least pirC has a conserved function and plays an important role in parasite growth in multiple species.

While this study represents a significant effort and does provide interesting new insights that would help our understanding of this complex gene family in the future, it has a number of limitations.

Strengths:

Extensive and thorough phylogenetic analysis that is supported by some biological validation. Provides an indication that the PIR gene family has limited biological constraints and evolved independently across different species, leading to rapid expansion and deletion of orthologous groups. Identified pirC as a functional and important member of the family that is conserved across the species.

Weaknesses:

The phylogenetic tree is based on a truncated sequence that focuses on the more conserved parts of the pir sequence. This could potentially lead to missing the key functional drivers of evolution. The biological validation of the role of pirC has some inconsistencies that need to be addressed.

Reviewer #2 (Public review):

Summary:

The authors set out to develop and validate the Crunchometer, a low-cost, open-source acoustic system designed to overcome the limitations of existing methods for studying feeding behavior in rodents. Their goal was to provide a tool that could precisely capture the microstructure of solid food intake, something often overlooked in favor of liquid-based assays, while being affordable, scalable, and compatible with neural recording techniques. By doing so, they aimed to enable detailed analysis of how physiological states, drugs, and specific neural circuits shape naturalistic feeding behaviors.

Strengths:

(1) Introduces a low-cost, open-source acoustic tool for measuring solid food intake, filling a critical gap left by expensive and proprietary systems.

(2) Makes the method easily adoptable across labs with detailed setup instructions and shared benchmark datasets.

(3) Provides high temporal precision for detecting bite events compared to human observers.

(4) Successfully distinguishes feeding microstructure (bites, bouts, IBIs, gnawing vs. consumption) with greater objectivity than manual annotation.

(5) Demonstrates compatibility with electrophysiology and calcium imaging, enabling fine-scale alignment of neural activity with feeding behavior.

(6) Effectively discriminates between fed vs. fasted states, validating physiological sensitivity.

(7) Captures pharmacological effects of semaglutide, although this is really just reduced feeding and associated readouts (bouts, latency, etc.)

(8) Has potential to distinguish consummatory vs. non-consummatory behaviors (e.g., food spillage, gnawing), however the current SVM model struggles to separate biting from gnawing due to similar acoustic profiles and manual validation is still required.

(9) Provides potential for closed-loop experiments

Weaknesses:

(1) Some neuroscience findings (calcium imaging of GABAergic vs. glutamatergic neurons) are based on small pilot samples (n=2 mice per condition), limiting generalizability.

(2) Chemogenetic and pharmacological experiments used small cohorts, raising statistical power concerns.

(3) Correlation with actual food intake is modest and sometimes less accurate than human observers

(4) Sensitive to hoarding behavior, which can reduce detection accuracy and requires manual correction for misclassifications (e.g., tail movements, non-food noises). However, these limitations are discussed and not ignored.

Comments on revised version.

The authors have addressed all my comments and have put forth a creative, accurate approach to assessing food intake in rodents.

Reviewer #2 (Public review):

Summary:

In this manuscript, Gazal et al., describe the presence of unique gaps and patches of BetaII-spectrin in medial sections of long human motor neuron axons. BII-spectrin, along with Alpha-spectrin forms horizontal linkers between 180nm spaced F-actin rings in axons. These F-actin rings along with the spectrin linkers form membrane periodic structures (MPS) which are critical for maintenance of the integrity, size and function of axons. The primary goal of the authors was to address if long motor axons, particularly those carrying familial mutations associated with the neurodegenerative disorder ALS, show defects in gaps and patches of BetaII-spectrin ultimately leading to degradation of these neurons.

Strengths:

The experiments are well designed and the authors have used the right methods and cutting-edge techniques to address the questions in this manuscript. The use of human motor neurons and the use of motor neurons with different familial ALS mutations is a strength. The use of isogenic controls is a positive. The induction of gaps and patches by the kinase inhibitor staurosporine and their rescue by Latrunculin A is novel and well executed. The use of biochemical assays to explore the role of calpains is appropriate and well designed. The use of STED imaging to define the periodicity of MPS in the gaps and patches of spectrin is a strength.

Weaknesses:

Primary weakness is the lack of rigorous evaluation to validate the proposed model of spectrin capture from the gaps into adjacent patches by the use of photobleaching and live-imaging. Another point is the lack of investigation into how gaps and patches change in axons carrying the familial ALS mutations as they age, since 2 weeks is not a timepoint when neurodegeneration is expected to start.

Comment on revised version.

The authors have given a point-by-point response to all the reviewer's concerns. They have also addressed concerns which I raised adequately. I have no further concerns.

Reviewer #2 (Public review):

Aims:

The authors sought to optimize SHARE-seq, a multimodal single-cell method, to improve the simultaneous profiling of gene expression and chromatin accessibility. Their goal was to enhance barcode design for better sequencing efficiency and cost savings, while improving overall data quality. They then applied their optimized method, easySHARE-seq, to study liver sinusoidal endothelial cells (LSECs) to demonstrate its utility in examining gene regulation and spatial zonation.

Strengths:

The improved barcode design is an advance, increasing the proportion of sequencing reads dedicated to biological information rather than barcode identification. This modification offers practical benefits in terms of sequencing costs and read length, potentially reducing alignment errors. The method also demonstrates improved RNA detection compared to the original SHARE-seq protocol. The biological applications showcase how simultaneous measurement of both modalities enables analyses that would be practically impossible with single-modality approaches, particularly in examining how chromatin states change along developmental or spatial trajectories.

Weaknesses:

There is a notable reduction in chromatin accessibility detection compared to the original SHARE-seq method, likely limiting the use of the method in certain situations.

Overall:

The authors achieve their aim of creating an optimized protocol with improved barcode design and enhanced RNA detection. The method represents a useful advance for specific experimental contexts where the trade-offs are appropriate.

Reviewer #2 (Public review):

Summary:

In this study, authors studied the synchrony between ripple events in Hippocampus, cortical spindles and Locus Coeruleus spiking. The results in this study together with the established literature on the relationship of hippocampal ripples with widespread thalamic and cortical waves, guided authors to propose a role for Locus Coeruleus spiking patterns in memory consolidation. The findings provided here, i.e. correlations between LC spiking activity and Hippocampal ripples, could provide basis for future studies probing the directional flow or the necessity of these correlations in the memory consolidation process. Hence, the paper provides enough scientific advance to highlight the elusive yet important role of Norepinephrine circuitry in the memory processes.

Strengths:

Authors were able to demonstrate correlations of Locus Coeruleus spikes with hippocampal ripples as well as with cortical spindles. Specific strength of the paper is in the demonstration that the spindles that activate with the ripples are comparatively different in their correlations with Locus Coeruleus than those which do not.

Weaknesses:

The claims regarding the roles of these specific interactions were mostly derived from the literature that these processes individually contribute to the memory process, without any evidence of these specific interactions being necessary for memory processes. There are also issues with the description of methods, validation of shuffling procedures and unclear presentation and the interpretation of the findings, which are described in points that follow. I believe addressing these weaknesses might improve and add to the strength of the findings.

Comments on revised version.

The authors addressed all of my major concerns during the revision. As a result, the study now provides convincing evidence as well as improved presentation of results, that makes this manuscript important to the broader field of neuroscience, beyond the specific sub-field.