DOI: 10.21203/rs.3.rs-7072571

Resource: RRID:RRRC_00699

Curator: @bandrow

SciCrunch record: RRID:RRRC_00699

What is this?

DOI: 10.21203/rs.3.rs-7072571

Resource: RRID:RRRC_00699

Curator: @bandrow

SciCrunch record: RRID:RRRC_00699

What is this?

DOI: 10.21203/rs.3.rs-7072571

Resource: RRID:RRRC_00699

Curator: @bandrow

SciCrunch record: RRID:RRRC_00699

What is this?

DOI: 10.21203/rs.3.rs-7072571

Resource: RRID:RRRC_00699

Curator: @bandrow

SciCrunch record: RRID:RRRC_00699

What is this?

DOI: 10.21203/rs.3.rs-7072571

Resource: RRID:RRRC_00699

Curator: @bandrow

SciCrunch record: RRID:RRRC_00699

What is this?

DOI: 10.21203/rs.3.rs-7072571

Resource: RRID:RRRC_00699

Curator: @bandrow

SciCrunch record: RRID:RRRC_00699

What is this?

DOI: 10.21203/rs.3.rs-7072571

Resource: RRID:RRRC_00699

Curator: @bandrow

SciCrunch record: RRID:RRRC_00699

What is this?

Reviewer #3 (Public review):

Summary:

This manuscript investigates strategies to overcome resistance to PARP inhibitors (olaparib) in a mouse model of BRCA1-related triple-negative breast cancer. They test whether combining olaparib with a PI3K inhibitor (alpelisib) or with an immune-stimulating agent (Poly(I:C)) can improve outcomes in both drug-sensitive and drug-resistant tumors, and find that both combinations extend survival in sensitive tumors, while only the PI3K combination partially overcomes established resistance. To understand the biological basis for these differences, the authors apply a previously developed hydrogel-based assay that measures seven microRNAs directly within tumor tissue sections, preserving their spatial location. They then build a set of computational tools-based on a topic-modeling method called latent Dirichlet allocation (LDA), principal component analysis (PCA), and an image-similarity measure called SSIM-to interpret these spatial microRNA patterns and relate them to tumor drug sensitivity, treatment type, and the surrounding immune cell environment.

Strengths:

(1) The in vivo survival experiments are well powered and rigorously analyzed, with appropriate statistical testing (log-rank tests) clearly supporting the central efficacy claims: both combination therapies benefit sensitive tumors, and PI3K inhibition (but not Poly(I:C)) partially rescues resistant tumors.

(2) The spatial microRNA measurement technology, while previously published, is thoughtfully applied here to an earlier treatment timepoint (10 days) than prior work, which is a reasonable and useful extension aimed at capturing biology before advanced tumor changes complicate interpretation.

(3) The core finding that a microRNA topic dominated by miR-21 associates with drug sensitivity, while a topic dominated by let-7a associates with resistance, is supported by a formal statistical test (a Mann-Whitney U test comparing principal component scores between sensitive and resistant tumors), giving reasonable confidence in this specific result.

(4) The idea of layering an immune-infiltration similarity map (SSIM) onto the microRNA topic maps is a creative and potentially broadly useful way to connect molecular spatial patterns with tissue architecture, and could be adapted to other spatial biomarker studies beyond this one.

Weaknesses:

(1) Several of the paper's central spatial and immune-colocalization findings are supported only by visual inspection of small numbers of samples (commonly three tumors per treatment group) rather than by formal statistical comparison. This applies to the treatment-type separation shown in Figure 3B.iii and to the immune-microRNA co-localization patterns shown in Figure 4B.ii, both of which are described narratively without accompanying statistical tests.

(2) The claim that Poly(I:C) fails to alter the immune composition of resistant tumors, unlike PI3K inhibition, is stated qualitatively ("little difference") without a quantitative comparison, which weakens confidence in this specific interpretation.

(3) All spatial analyses derive from a single mouse model at a single treatment timepoint, with no independent tumor cohort used to test whether the identified microRNA topics and their treatment/sensitivity associations generalize. This is an appropriate scope for an initial proof-of-concept study but means the biomarker potential of the approach remains unproven outside this specific dataset.

Overall, the authors achieve their stated aims, and the evidence broadly supports the paper's central conclusions distinguishing drug-sensitive from drug-resistant tumors. The additional analyses linking treatment regimen and immune infiltration to spatial microRNA patterns are plausible and consistent with the survival data, though they rely on small sample sizes and, in places, qualitative rather than statistical comparisons, and would benefit from further validation. The analytical framework itself - particularly the SSIM-based integration of molecular and cellular spatial data - is a useful and creative contribution with potential utility for other groups working on spatial biomarker discovery.

Reviewer #3 (Public review):

Summary:

This study investigates how the activity of hypothalamic paraventricular oxytocin (PVNOT) neurons relates to physiological states in female mice, with a particular focus on behavioral states and thermogenic sympathetic activity. To address this question, the authors combined automated video-based behavioral classification with calcium imaging of PVNOT neuron activity. Sympathetic thermogenesis was inferred from surface temperature changes measured by infrared thermography, and the authors have made their custom analysis scripts available. The authors report that strong, pulsatile activation of PVNOT neurons was "occasionally" observed immediately before transitions from resting to active states. This observation suggests that PVNOT neuronal activity may facilitate the transition from rest to activity. This phenomenon was observed in both pair-housed and individually housed animals. Taken together, these findings raise the possibility that the oxytocinergic system contributes to naturalistic behavior transitions even in the absence of social interactions. However, concerns regarding the selectivity of GCaMP expression in oxytocin-expressing neurons call into question the validity of the recorded PVNOT neuronal activity. The revised manuscript improves the presentation and interpretation of the data. Nevertheless, because the authors have not provided additional experiments or analyses addressing the major methodological concerns, the evidence supporting the central conclusions remains essentially unchanged.

Strengths:

The oxytocinergic neural system is believed to subserve a wide range of physiological functions. Elucidating these roles requires monitoring PVNOT neuronal activity under diverse behavioral contexts, as well as manipulating this activity to establish causal relationships. In this study, the authors present a technically sound experimental framework that integrates behavioral tracking in both individually and group-housed mice with the monitoring and manipulation of PVNOT neuron activity. This setup represents a valuable methodological resource for researchers investigating the physiological functions of oxytocin.

Weaknesses:

(1) Immunohistochemical validation of selective GCaMP expression in oxytocin-expressing neurons showed that only 24-51% of GCaMP-positive neurons expressed oxytocin. As an alternative approach, the authors argue that the similarity between calcium dynamics recorded in virgin and lactating animals supports the identity of the recorded neurons as oxytocin neurons. While this physiological comparison is interesting, it does not constitute direct evidence for cell-type specificity of GCaMP expression. The revised manuscript now acknowledges that in situ hybridization targeting oxytocin mRNA would provide a more reliable validation, but such validation has not been performed. Therefore, uncertainty regarding the identity of the recorded neurons remains, limiting confidence in the interpretation of the calcium imaging data.

(2) Although the authors' interpretation is generally consistent with the data presented, their main conclusions rely heavily on observational findings. Moreover, optogenetic stimulation of PVNOT neurons failed to robustly recapitulate behavioral state transitions (Figs. 6D and S5B). Further interventional experiments remain necessary to rigorously test the authors' interpretation and establish a causal relationship between PVNOT activity and rest-to-active transitions. In particular, loss-of-function approaches targeting the PVNOT system, such as OXTR antagonism, inhibitory optogenetics, or cell-type-specific ablation, remain essential to determine whether perturbation of this system alters behavioral state transitions. Although the authors expanded the Discussion to acknowledge this limitation, the revised manuscript provides no additional experimental evidence addressing it.

Comments on revised version.

I appreciate the authors' efforts to clarify the manuscript and to discuss the limitations more explicitly. Nevertheless, because my major concerns have been addressed primarily through revised interpretation rather than new evidence, my overall assessment of the scientific support for the principal conclusions remains unchanged.

Reviewer #3 (Public review):

Summary:

This manuscript addresses a fundamental question in cognitive neuroscience: which event should serve as the temporal reference for neural processing during natural vision? While fixation onset has traditionally been treated as the analogue of stimulus onset in free-viewing experiments, the authors convincingly demonstrate that this assumption is incomplete.

The study utilizes a remarkable natural-viewing dataset consisting of simultaneous MEG and eye-tracking recordings collected during the exploration of thousands of natural scenes. The authors compare several candidate eye-movement events and evaluate which event best explains the timing of the early M100 response. Across several complementary analyses, saccade-related events consistently outperform fixation onset, with peak saccade curvature emerging as the event that best predicts neural response timing.

Strengths:

A particular strength of the work is that the conclusions do not rely on a single analytical approach. Instead, multiple independent analyses converge on the same interpretation, increasing confidence that the observed timing relationships are robust rather than analysis-specific. The comparison between natural-viewing responses and classical stimulus-onset responses is especially compelling and highlights qualitative differences in their spatiotemporal organization. Of particular conceptual importance, the findings support the broader perspective that perception is intrinsically linked to action and internally generated sensorimotor processes. This aligns well with growing evidence that oculomotor action and active sampling play central roles in perception. The work contributes to an important ongoing shift in how natural vision should be studied experimentally and interpreted theoretically.

Weaknesses:

I identified no major weaknesses in the study. The main limitation is the relatively small number of participants, despite the exceptionally rich dataset. Future work in larger cohorts and across complementary electrophysiological recording modalities will help establish the generalizability of the reported temporal relationships.

Reviewer #3 (Public review):

Summary:

This work aims to understand the mechanisms that platelets use to interact with and compact fibrin fibers during clot formation. This is an important process during wound healing and recent work has demonstrated that platelets play a critical role in generating the force required to drive accumulation of fibrin. The authors argue that current models are insufficient to account for the observed reduction in clot volume and propose that platelets actively 'wind-up' these fibers by undergoing myosin-dependent rotation. While interesting, the experiments performed by the authors do not directly test this mechanism and further evidence is required to support their claims.

Weaknesses:

(1) The motivation to switch from the system used in Figure 1 and 2 to the '2D fiber-retraction assay' is not clear. While the authors state that this system has 'reduced complexity' the differences between these assays appears to disrupt the 'cage-like' organization of fibrin around platelets shown in Figure 1 and 2 (compare images in Figure 2 with those in Figure 4). An in-depth comparison of two methods is needed to support the conclusions from the 2D system. Furthermore, the change in plasma volume (Figure 2 vs Figure 7) should also be tested - the authors state that this increases fibrin fiber formation, but this is not quantified or demonstrated in the figures. Notably, this appears to change the morphology of the fibrin fibers shown (comparing Figure 2 and Figure 7).

(2) It is unclear how the classification of platelets as 'fiber-winding' versus 'fiber compaction' differs in Figure 2. The criteria used for these classifications should be stated. Further, it seems premature to characterize fibers as wound without having established this earlier in the manuscript.

(3) Is the 'gearwheel' different from the 'cage' of fibrin fibers? They appear similar, but it is difficult to distinguish between these with only qualitative descriptions of these phenotypes.

(4) The quantification of platelet extensions in Figure 9 is confusing. While the those in 9A are clear, those in 9B are not. For instance, what is the difference between #7 and #8 in the middle panel of 9B? It does not seem like #8 is labeling an extension.

(5) It is unclear what the modeling accomplishes as there is no comparison between the results of these simulations and their experiments.

(6) The data presented in Figure 12 provides the most direct support for their mechanism, but falls short of directly testing their claims. These experiments should be repeated to include blebbistatin to test the contribution of myosin and include quantitative rather than qualitative comparisons of these experiments.

Comments on revised version:

The manuscript is substantially improved in clarity and organization. The authors have adequately addressed most of my concerns regarding presentation and interpretation through revisions to the text and figures. However, my primary mechanistic concerns remain unresolved. Although the proposed model is now presented more cautiously, the revised manuscript still does not directly test the central mechanism, and the conclusions therefore remain insufficiently supported.

Reviewer #3 (Public review):

Summary:

Normandin et al. explore the coding of stimuli predicting an aversive event in the prelimbic cortex. Stimuli could either be explicitly paired, explicitly unpaired, or novel but with an inferred association with the aversive event (generalization). Long-term tracking of GCaMP positive neurons allowed them to examine how coding evolves out to a month following training. In general, they found two types of ensemble codes. One was ensembles coding for each stimulus independently, but with enhanced responding to the one eliciting a freezing response. The other was ensembles that responded to all stimuli in proportion to their similarity to the stimulus paired with the aversive event, either increasing or decreasing their activation with the degree of freezing elicited by a stimulus. Importantly, this second set of ensembles was more stable across days, potentially providing a memory trace.

Strengths:

(1) The authors track ensembles in prelimbic cortex over long time scales, providing valuable information on the consolidation of neural codes.

(2) Neural coding of generalization is examined, which is under examined in the field.

Comments on revised version.

The authors have convincingly and thoroughly addressed my concerns. I have no further issues regarding this study.

Reviewer #3 (Public review):

Summary

This study aims to contest recent findings that prenatal exposure to natural sounds and anthropogenic noise before hatching affects development and fitness in an altricial songbird. To this aim, it attempts to estimate hearing capacities of zebra finch nestlings. It first uses responses to clicks in nestlings and adults to estimate differences in hearing thresholds but uses experimental parameters that systematically lower nestling responses. It then measures responses to tones but restrict nestling data to a protocol (long tones) that failed in adults; response to tones with a correct protocol (short tones) is repeated in adults only. Thirdly, it includes data on loud airborne sound making eggs vibrate, even though this has no relevance to embryonic vibration perception by direct contact with the incubating parent or in incubators. Lastly, it includes a lengthy discussion on how these results, even though inaccurate or incorrect, would show that zebra finch nestlings and embryos are "functionally deaf". It fails to note that even if the experiment had been performed correctly and still failed to detect an auditory response in 2 day hatchlings - which is unlikely given the above and findings in other songbirds - this would not somehow eliminate the developmental effects of prenatal sounds that have been empirically demonstrated.

Strength:

The study is not performed adequately to bring reliable answers, but it addresses the important topic of hearing development in altricial songbirds and the long-held (but untested) assumption that altricial avian embryos cannot hear. More broadly, there is a need to reassess avian auditory perception - and the methodological approaches to measure it - given the accumulating evidence that some bird species respond to high frequency biological sounds beyond their known hearing range.

Weaknesses:

The study presents many experimental flaws that specifically compromise response detection in immature animals in the first experiment, and data from the remaining two experiments are invalid. The revision did not fix any of these issues.

i) Response to clicks, Fig 1: Unlike what the revision claims, the deviations from validated protocols (too rapid stimulation, too few measurements, low temperature, reliance on clicks only) do lead to a potentially large underestimation of nestling hearing sensitivity. Calculation and new data trying to show that these deviations would not matter are wrong or insufficient. Even without strict conventions on ABR methodology, it is striking that every single experimental choice made here i) greatly differs from other avian studies and ii) reduces - rather than maximises - response detection, specifically for low amplitude signals (as in nestlings or near thresholds).

ii) Response to frequency tones, Fig 2: The experiment on frequency sensitivity (long tone burst) failed in adults (positive control) with 0 to only about half of the adults responding (Fig S1), and sensitivity underestimated by 30dB (Fig2). Measures with a failed positive control are invalid, even if the overall pattern of the few data points obtained in nestlings vaguely resemble (but does not match) expectations. That the experiment was repeated in adults with a correct protocol (short tone pips) - but not in nestlings - is highly misleading.

iii) Attempts to validate the protocols above by comparing to published adult values are meaningless (response A.3.1; Fig 5). The one experiment being compared (short tones) was only performed in adults in this study. This cannot validate results obtained by different methodologies in nestlings, for responses to clicks or long tones.

iv) Vibrations: No previous results on the effect of zebra finch heat calls on development rely on the hypothesis or assumption that airborne sound from heat calls makes eggs vibrate. This idea is solely attributable to the authors of the preprint, and is not biologically or experimentally realistic. All speculations from this experiment (Fig3) on vibration perception by embryos or heat call effects are meaningless.

v) Writing and presentation: The text throughout is highly misinformative for non-specialists or any reader not carefully inspecting figures (including in supplementary material) and methods. The confusion is greatly aggravated in the (6-page long) discussion which i) fails to recognise and account for the study shortcomings, and instead ii) greatly overstates the results and what they mean, and iii) misrepresents current knowledge by excluding highly relevant studies showing evidence of early sound perception in embryos and hatchlings, and introducing many errors in the presentation of published papers. This creates an illusion of a strong mismatch between heat-calls and zebra finch sensory capacities, for which the study actually does not provide any evidence for, and which is extremely unlikely to exist.

vi) The revision did not address any of the major flaws of the study outlined above (see detailed assessment below). In particular,<br /> ** For point i):<br /> - estimations for the effect of having done too few (400) measurements are wrong - the effect on hearing thresholds cannot be calculated, but would be much greater than 4dB with the expected 37% noise reduction with the standard 1000 sweeps;

- the new data provided does not measure the impact of high stimulus rate, and measures on adults largely underestimate effects on nestling response;

- body temperature, now provided in the revision, is at the lowest extreme for the species, which may increase hearing thresholds;

- tones do elicit a stronger and earlier response than clicks. Whether this is related to stimulus duration does not change the fact that clicks underestimate hearing thresholds, and delay hearing onset by several days.<br /> ** For points ii) and iv):<br /> No new data or any valid explanation was provided in the revision. It is still the case that nestling frequency responses were obtained with an experiment where the positive control failed; and the vibrating egg experiment is irrelevant to vibration perception in embryos and any observed effects of heat calls on development.<br /> ** The revision greatly lengthens the speculations about heat call perception, based on inaccurate or totally incorrect data.

Conclusion and impact:<br /> i) Overall, the study fails to provide any reliable estimate of zebra finch nestling hearing capacities: it underestimates hearing onsets and thresholds (clicks) and gives no information on nestling frequency sensitivity. It is extremely likely that a better designed ABR experiment, or a more sensitive methodology (e.g. electrophysiology), would have detected a response in 2 day-old nestlings, as in other songbirds. The conclusion of "deafness" in hatchlings (or embryos, which were not tested) is clearly unsupported.<br /> If zebra finch hatchlings were clearly deaf, a well-designed study would have shown this a lot more convincingly.

ii) The data on adults in not new (3 prior studies) - although this study generally underestimates zebra finch high frequency sensitivity. The presentation in relation to heat-calls is flawed: true values of heat-call frequency range and sound levels (>6kHz at 45dB) fall within the adult hearing range.

iii) Without any new evidence, this study does not progress our understanding of heat-call and noise impact on development. Exactly the same issue remains: that heat-call and noise effects on development contradict the general VIEW on hearing ontogeny in altricial birds. But we have learnt nothing from this study on hearing ontogeny or frequency sensitivity. The study provides no reliable neuroscience data to advance the debate.

iv) The largest section of the preprint is a highly speculative discussion, based on erroneous data and wrong interpretations, as well as a misrepresentation of what is known. That these issues would not be recognised is - in my view - of serious concern.

Detailed assessment:

The summary by the authors of my major concerns (R3.A0) is incorrect and misreport many of my statements, without giving any meaningful answers. I will not engage in such discussion.

My actual three major concerns do remain:

(1) Results on Fig 1 overestimate hearing onset and thresholds: All experimental parameters chosen to test responses to clicks are known to lower detection. From their cumulative impact, there is absolutely no doubt that the data in Fig 1 underestimate hearing capacities, and disproportionately so in nestlings compared to adults. Therefore, no quantitative estimate of nestling hearing threshold, absolute or relative (i.e. the estimated "54dB difference"), can be taken from this study. The age of hearing onset based on clicks (4 day old) is also wrong.

(2) All results on Fig 2 are false: 40 to 100% of adults (positive control) failed to respond within their normal hearing range (Fig S1). Data on nestling frequency sensitivity using this methodology (Fig 2) are clearly invalid.

(3) All results on Fig 3 are irrelevant: they assume zebra finch parents are hoovering in front of their nest while heat-calling rather than incubating their eggs, which is nonsense. Any speculation on the role of bone-conduction or vibrotactile stimulation for embryonic heat-call detection is simply unfounded.

The authors' responses to these comments are largely mistaken (see below), and do not change the 3 facts stated above. Overall, the data produced are unreliable, and so are the interpretations and conclusions.<br /> The conclusions that i) heat-calls fall outside of adult hearing range and ii) young nestlings (and embryos) are deaf, are both incorrect. The study provides no actual estimate of nestling hearing and how far off their hearing range heat-calls fall.

(1) Responses to clicks underestimate hearing abilities, specifically in nestlings.

(i) DISPROPORTIONATE EFFECT OF PROTOCOL ON NESTLINGS: As reported in other avian studies (e.g. Brittan Powell et al 2004), because of their immature neural system, nestlings show low amplitude waveforms compared to adults. This occurs even with sound much louder than their hearing threshold. For example here, 8 day old nestlings show waveforms of only low amplitude at 95dB, even though they respond to sound 40dB softer, at 55dB (Fig 1H).

This characteristic of nestling waveforms means that:<br /> - nestling responses are harder to detect,<br /> - nestling responses are more easily attenuated below the detection criteria (>2 S/N),<br /> - a low amplitude response in nestlings at a given sound level does not predict that softer sounds will not be perceived by the animal.

Any deviation in protocol that attenuates waveform amplitude will therefore disproportionately affect nestling thresholds (and artificially lead to the "54dB" estimate).<br /> Even without universal standards (resp R3.A1.1), knowing this, the protocol should be adjusted to maximise response detection. This study does exactly the opposite.

(ii) INSUFFICIENT MEASUREMENTS: using only 400 sweeps, rather than the typical 1000 sweeps, reduces response detection, especially in nestlings.

- The claim that using 1000 sweeps would only decrease the hearing threshold by 4dB (response A3.2; ms L530) is false:<br /> - Based on the square root relationship mentioned by the authors, using 1000 averages instead of 400 would improve the signal-to-noise ratio by 37%, which would allow detecting many small amplitude waves which are currently hidden in the abnormally high noise.<br /> - Lowering the noise floor level by 4dB does not mean that the hearing threshold would only decrease by 4dB. The improvement in hearing threshold would be much greater, especially in nestlings.

(iii) UNSUITABLY HIGH STIMULATION RATE: the new data on pairs of clicks greatly underestimate the attenuation by high stimulation rates, and so do adult measurements compared to nestlings'.<br /> - the click rate used in this study (25 clicks per second) is 5 to 25 times faster than most previous studies in young birds (e.g. Saunders et al 1973, 1974: 1 stim/sec or less; Katayama 1985: 3.3 clicks/sec; Brittan-Powell et al 2004: 4 stim/sec), and 6 times faster than zebra finch heat-calls.<br /> - responses to pairs of clicks (new data in Fig S3; L 543-552, response A3.3) does not measure the response dampening caused by high stimulation rates. Response attenuation (adaptation) after a single click is much weaker than after a train of 400 consecutive clicks (or even just 30). This paired-click measure ignores the cumulative attenuation observed with many consecutive stimuli. Paired-click paradigm is used to measure immediate refractoriness for other purposes (e.g. temporal resolution, diagnosis tool) but does not replicate high stimulation rates.<br /> It is unclear why the authors chose to use this weak approximation rather than simply replicating the measurements at the same slow rate as in other avian studies. This would have given a straightforward answer, comparable to previous studies that have demonstrated the detrimental impact of fast rate on response strength by directly comparing responses to different stimulation rates (e.g. Saunders et al 1973; Brittan-Powell et al 2004).<br /> - Adults are less sensitive to high stimulation rates than immature individuals (e.g. Saunders et al 1973; Khayutin 1985; Brittan-Powell et al 2004 and 6 references cited therein). Effects of fast rate in adults (new data in Fig S3) therefore largely underestimate effects on nestlings (not measured), and the high stimulation rate used in this study increases the relative difference between nestlings and adults.<br /> - Given the 2 points above, it is incorrect to conclude from this new data that the high stimulation rate used had "no effect on ABR amplitude or thresholds" (responses A3.3 L551). Instead, given that 40ms (i.e. interval for 25 clicks/ sec) is at the limit of what adults can handle after a single click, this new data does confirm that the stimulation rate is indeed too high and underestimates hearing in nestlings (and adults to a lesser extent).

(iv) LOW BODY TEMPERATURE can reduce ABR response.<br /> - The average body temperature (39.5C) now provided in the revised manuscript (response A2) is at the lowest extreme of the range for zebra finches. The normal average body temperature for zebra finches is 41C at low ambient temperature, rising to 43-44C at high ambient temperatures (when heat-calls would be produced). This value of 41C is consistent across many studies in wild and domestic zebra finches (e.g. Wojciechowski et al 2020 [avr 41C at 23C]; Udino and Mariette 2022 [avg 41, min=40C at 32C]; Bech and Midtgard 1981 [avr ~41C]; Pessato et al 2022 avg=41C at 27C]). The only study reporting a body temperature average as low as 39C (Cooper et al 2020) was indeed under hypothermic conditions, obtained in adult zebra finches under extreme fasting conditions (17hrs of food deprivation), at ambient temperature below thermoneutrality, during the night (i.e. during "nocturnal hypothermia", when bird body temperature is normally lower).<br /> Therefore 39.5C does corresponds to hypothermic conditions for most individuals. While this body temperature remains considerably higher than that used experimentally to supress hearing response, using a below-normal body temperature, added to other factors, may lower wave amplitude and therefore increase hearing threshold estimates.

(v) CLICKS UNDERESTIMATE HEARING ONSETS<br /> - My statement that, compared to tones, clicks elicit a smaller response, at a later age, is correct (Saunders et al 1973; Brittan-Powell et al 2004).<br /> - It does not matter whether this is due to differences in stimulation duration (response R3.A2.1), since clicks are always much shorter than tones. What matters is that hearing onset based on responses to clicks has been found to underestimate the earliest age at which an auditory response can be obtained by several days (Saunders et al 1973).<br /> - Without any valid nestling data for tones (see below), this study, based on clicks only, does not provide the true age of hearing onset.<br /> - The suggestion of using 170dB clicks (response R3.A2.1) is very odd. The correct approach would have been to use a correct protocol for tones in nestlings, rather than solely correcting it for adults (see below).

(vi) CONFUSING INTERPRETATION OF WAVE AMPLITUDE AND LATENCY<br /> - the text implies throughout that nestlings lacking an adult-like amplitude and latency is a sign of poor hearing (e.g. L122 "ABR wave I gradually reaches maturity 25 days after hatching").<br /> - It should be clarified that this is a characteristic of immature systems but does mean these nestlings do not hear. For example, this characteristic persists even in 10d old nestlings which have similar hearing thresholds to adults.

(2). Results on nestling frequency sensitivity are invalid. Using long (25ms) tones with subdermal electrodes is incorrect, and not used by anyone other than the authors. The failure to repeat the experiment with a correct protocol (5ms tones) in nestlings is misleading:

(i) When 60% of 4-day old chicks respond to clicks (Fig 1), they are described as "functionally deaf" (L78, L261). By contrast, when 0% to 60% of adults respond to tones in the core of their hearing range (Fig S1 for data shown in Fig 2), this is merely presented as a methodological limitation (text added L186-197), and the authors still proceed to presenting results on nestlings with a method that failed in adults (positive control).

(ii) When the positive control fails, the experiment is failed. This principle applies in Neuroscience as in any scientific field.

(iii) That the experiment repeated in adults with the standard and correct short tone protocol (5ms) gave normal results, is not a validation. Instead, it proves the point that 25ms is unsuitable (regardless of whether that is due to rising time, L399). This correction does NOTHING to fix results in nestlings, which were ONLY done with the unsuitable 25ms tones.

(iv) The mixture of adult data with 5 and 25ms tones, instead, creates confusion, because differences in protocols between nestlings and adults are blurred in the text. The abnormally small proportion of adults responding (0 to 60%) with 25ms tones is only shown in supplementary material, and attenuated in the main text (L186: "about half").

(v) The claim (response R3.A1.2) that long 25ms tones is commonly used with the ABR methodology used here is false:<br /> ** ALL (but 1) avian studies using tones of 20ms or longer (cited by myself or by the authors in their reply R3.A1.2) used a different ABR methodology, with electrodes implanted through the skull. The only exception, using 25ms tones with subdermal electrodes (as here), is a study by the authors themselves.<br /> ** All other avian studies with subdermal electrodes used short tones, of 5ms or less (e.g. Brittan-Powell et al 2002, 2004; Henry & Lucas 2008), as in the corrected adult experiment.<br /> ** My initial comment already specified this difference in electrode placement.<br /> ** The results here (failing in adults) unquestionably show that the method of long tones with subdermal electrodes does not work, and the literature shows that no one else uses it.

(vi) When the aim of the study is to contest effects of high frequency sounds (L39-45), it is odd to choose a protocol specifically directed at testing responses to very low frequencies (responses A3.1, R3A.0; L398-403) and that compromises all results.

(vii) That the shape of the few data points obtained in nestlings with long tones would broadly resemble expectations (responses A3.1, R3A.0) is not a validation: See ii.<br /> - The shape is not even correct:<br /> ** Why aren't 4 and 6 day old nestlings responding to any frequency when they were responding to clicks (in spite of poor detection conditions for clicks)?<br /> *** Why are they not responding, when 2 day old flycatchers respond to tones from 1 to 4kHz at 45dB, and 0.5 to 5kHz at 60dB (Aleksandrov and Dmitrieva 1992, Korneeva et al 2006)?

(viii) That only relative measures between nestlings and adults matter (resp R3.A4; L404) is incorrect. Measures that are qualitatively (see vii) and quantitatively (see i) wrong cannot be compared.<br /> If these data with long tones were indeed acceptable, why repeat the experiment in adults with a correct protocol, even before this flaw was pointed to during the review?

(ix) The unusually low number of measurements (400 instead of 1000; L530) will have reduced detectability of low amplitude responses, in nestlings and near thresholds, here as in the click experiment (see above). The claim that this would only increase threshold by 4dB (responses A3.2, L530) is wrong (see above).

(x) There is no explanation as to why only half of the 6-day old nestlings (n=5-6) were tested with tones. Would having 10 in this group shown a response at 6-day old?

(xi) Even the correct 5ms tone protocol in adults overestimated thresholds at higher frequencies (>3kHz) by up to 30dB compared to other published estimates (Fig 5). If inter-population differences among domestic zebra finches (L 393; Fig 5 legend; resp R3.A4) were enough to cause 30dB differences, results on domestic zebra finches here should not be extrapolated to those on wild-derived Australian zebra finches documenting developmental effects of heat calls.

The authors give no other elements than the above in their responses that could demonstrate the validity of their nestling frequency data.

Based on other studies, we can expect zebra finch hearing to develop sensitivity to high frequencies after that to middle frequencies. But pretending that this study provides any evidence towards this is wrong. There is no valid data.

(3) The experiment using loud sounds to make eggs vibrate is biologically and experimentally meaningless. The claim that these measurements would rule out vibration perception in embryos is totally unfounded. The preprint is creating the illusion of having ruled-out a mechanism that they have not tested.

(i) zebra finches are not hovering in front of their nest when heat-calling. They are in physical contact with the eggs while incubating, with vibrations expected to travel directly through solids from adults to eggs, without attenuation.

(ii) the claim that previous studies on heat-call developmental impact rely on the assumption of airborne sound making egg vibrate (response A3.5, R3.A8) is incorrect. This is conceptually wrong and there is no indication of this in any paper.

(iii) vibration perception in embryos in birds and other taxa (e.g. amphibian, reptiles and insects) rely on direct contact with the source, not on loud airborne sounds shaking eggs. Beyond any consideration on heat-calls, claiming that this experiment could give any indication of vibration perception in embryos is nonsense.

The authors give no information in their responses that could demonstrate the validity of this experiment.

(4) Interpretation and discussion

All other songbird studies, individually and collectively, show much greater hearing sensitivity in nestlings that what this study is trying to suggest (e.g Khayutin 1985; Korneeva et al 2006; Aleksandrov and Dmitrieva 1992; Rivera et al 2018; Platzen and Magrath 2004; Haff & Magrath 2012). The only way the authors can reach their conclusion is by:<br /> - presenting data that greatly underestimate hearing sensitivity in zebra finches (see sections 1 and 2) and overstating them, and;<br /> - misrepresenting current knowledge by excluding relevant papers and being unclear about what the literature shows.<br /> This study tries to impose the idea that zebra finch do not detect any sound before day 4-6 post-hatch, and have extremely rudimental hearing until day 8-10 post-hatch. By contrast, other studies show very young hatchlings respond to sound 1 to 3 days after hatching (first age tested) and show quite sophisticated, and totally functional, responses to relevant sounds at 5 days old.

This is not to say that hearing does not continue to improve post-hatch in birds, or that sensitivity to mid frequencies post-hatch would not precede that to high frequencies. But statements throughout this study are so exaggerated and/or wrong that nothing can be learnt. It is undeniable that this study fails to provide the useful and balanced assessment needed to establish were true heat-calls actually sit relative to zebra finch adult, nestling and embryonic hearing range.

It is literally impossible to correct every wrong statement in the discussion and responses to reviewers. I focus here on some examples related the claims of "nestling deafness" or of heat-calls being outside of adult zebra finch hearing range, as well as inaccuracies leading to an apparent match with current literature.

(i) MISALIGNMENT WITH OTHER STUDIES AND MISREPORTING

*** Neurological evidence

- Highly relevant evidence on response to sound in zebra finch embryos (Rivera et al<br /> 2018) is totally excluded.<br /> Excluding this study on the basis that it used a different methodology that does not directly quantifying auditory sensitivity (resp R3.A6a) is a poor justification. Evidence, even indirect or imperfect, should be brought to the attention of the readers.

- This applies to many other studies cited in my first review and arbitrarily excluded here. If one wants to conclude on "deafness", all evidence, even indirect, should be considered. Excluding non-ABR studies means the conclusion cannot be extended beyond flat ABR traces.

- Other studies show much greater sensitivity that what the text describes:

* Flycatcher hatchlings at 2-3d post hatch (first age tested), respond across a wide range of frequencies (0.3 to 5kHz), at low to moderate sound levels (45-65dB)<br /> (Aleksandrov and Dmitrieva 1992, Korneeva et al 2006).

* Stating that these studies in flycatchers "likely yield lower thresholds" (L364) is an astonishing understatement. Thresholds in Aleksandrov and Dmitrieva (1992) at 2-3 day old were 35 dB lower than those here at 4 day old with clicks, and 60 to 80dB lower than those with tones of 1-2kHz at 8 day old.

* Claims that "sensitivity improves rapidly postnatally, with ~40 dB threshold decreases (L365)" is also misreporting these studies' findings. Thresholds decreased by 25dB consistently at 9 out of 11 frequencies tested from 0.3 to 8kHz (Aleksandrov and Dmitrieva, 1992). A difference of 40dB was only found at 5-6kHz (Aleksandrov and Dmitrieva, 1992). Likewise, improvement also varied from 25 to 40dB in Korneeva 2006. These do not average to "~40 dB".

* Even birds developing 4 times slower than songbirds (budgerigars) show a response at 5 day old. My statement that Brittan-Powell 2004 shows an auditory response at 5d old is correct. Re-response R3.A2.2: Fig 1B shows one example for ONE individual. All other figures based on multiple individuals shows at least some individuals responded at 5-6 days old at frequencies less than 4Khz (Fig 1C, 4 and 5).

- Many inaccuracies on avian hearing remain uncorrected in the revision.<br /> * e.g. L 329: extrapolating high frequency hearing (>6khZ) from precocial species is incorrect because even adult chicken and ducks are not sensitive to high frequencies.<br /> The authors imply elsewhere that species of songbirds cannot be compared (resp A4, R3.A6), but make extrapolations from species that are far more remote, phylogenetically, developmentally and ecologically than other songbirds.

*** Behavioural evidence

- contradicts this study findings:

* When correctly cited and described, the literature, does not support the authors' statement that nestling behavioural response "typically emerges between ~5-10 days post-hatch" (L358). It emerges earlier, at an unknown age, including potentially from hatch (present at 1.5day) for innate responses (see below).

* Results in other songbirds are not consistent with this study finding that a "response to loud click stimuli is first detectable at 4-8 days post hatch" (L230). Instead they show nestling hearing capacities described here are abnormally poor.

* Therefore, the conclusion that "The timeline of behavioural studies closely matches the onset and maturation of ABR responses observed here in zebra finches" (L360) is wrong.

- shows a very early response (1-2day post hatch), with no known onset:

* songbird behavioural response to sound, with parental alarm call suppressing begging, has been demonstrated in nestlings as young as 1.5 or 3 days old (Khayutin 1985, Korneeva et al 2006, Aleksandrov and Dmitrieva 1992). None of these results are mentioned in the revision when discussing behavioural evidence (L357-360).

* Instead, they exclusively mention studies that started testing nestlings at 5-6 days old, or much later (e.g. 17 day old: Suzuki 2011; 14 day old: Barati & McDonald 2017).

* ALL of these studies (cited or not) demonstrated a significant response of nestlings to calls on the first age tested. NONE tested the onset of this response.

* the one study looking at progression across 3 ages, at 5, 8 and 11 day old shows parental alarm calls suppress nestling calling at day 5 as much as later on, with no effect of age (Platzen and Magrath 2004). The authors failed to acknowledge this in their response (resp A4) or revision (L359).

* the claim that Haff & Magrath (2012) showed nestlings did not respond at 5-6 day old but did at 10-11 days (resp A4) is wrong. At 5 day old, they responded to their own species alarm calls, as well as to another similar sounding species and to the sound of predators themselves (Table2 in Haff & Magrath 2012; as in Platzen and Magrath 2004).

- learning, not just hearing, improves nestling response with age:

* Haff & Magrath (2012) showed that by 10-11 day old, nestlings had learnt to also respond to heterospecifics, demonstrating that learning improves nestling response with age.

* the intensity of the response to low frequency sound improved more with age than that to high frequency calls. If improvement were related to hearing limitations, the opposite would be expected (Haff & Magrath (2012).

- nestlings discriminate complex calls, including at high frequency

* By 5-6 day old, nestlings can already discriminate several different sounds indicative of danger, among the complex natural acoustic background (Haff & Magrath 2012).

* nestling do not respond indiscriminately to any calls, but only to relevant sounds that specifically present a threat to them (Haff and Magrath 2012; Magrath et al 2006).

* the idea that nestlings only distinguish "low frequency broadband cues" (L362) is inaccurate (resp R3.A6). Nestlings respond to scrubwren chip calls and fairywren alarm calls that are narrowband calls with the fundamental at 8 and 10 kHz respectively (Platzen and Magrath 2004; Haff and Magrath 2012).

* These studies indeed "do not imply mature auditory sensitivity" (L362), they show that auditory maturity is not needed to show a perfectly functional response to biologically meaningful sounds in a natural context.

- Overall, every other songbird species tested shows greater hearing sensitivity than that proclaimed here for zebra finches. It is very unlikely that zebra finches would be such an outlier.

(ii) INCORRECT CONCLUSION ON DEAFNESS

Deafness of young zebra finch nestlings cannot be demonstrated because:

- the study has no valid response to tones in nestlings to establish the age of hearing onset.

- responses to clicks are greatly underestimated (see 1). Had correct, more sensitive, protocol parameters been used, it is very likely that:<br /> * Most 2 day old nestlings would have responded to clicks, instead of 60% of 4 day old nestlings.<br /> * Thresholds would be lower, especially in nestlings.<br /> The >54dB difference between nestlings and adults based on an assumed 95dB threshold in 2d old nestlings is wrong.

- based on data on other songbird nestlings, a difference of 25dB would be more realistic (at frequencies of 1-2 kHz, comparable to clicks, Aleksandrov and Dmitrieva 1992, Korneeva et al 2006). This greatly contrasts with the >54dB estimate here. While the authors qualify their estimate as "conservative" (legend Fig4, L293), it is actually greatly overestimated.

- Even assuming the data in this study were correct, the interpretation is erroneous. Concluding "deafness of young nestlings" is incorrect when 60% of 4 day old nestlings respond to clicks at 80dB. This is especially wrong given that the ABR method systematically overestimates thresholds by 20-40dB.

- That ABR is used in humans to diagnose deafness (L263) does not make ABR the most suitable method for birds, when evidence shows that other methods (e.g. electrophysiology or behaviour) are more accurate. Hearing screening in humans can only use non-invasive methods, and has additional criteria than accuracy (price, ease, etc).

- The discussion fails to acknowledge the implications of the limitations of the study (detailed above). For example, talking in broad terms of differences in protocols (L392-395), does not tell readers that this study, because of the parameters chosen, led to an underestimation of zebra finch hearing capacities.<br /> The discussion instead greatly overstates what the study shows (e.g. L229-232, 261, 277-278, 288, 332, 340, etc).

(iii) INCORRECT CONCLUSION THAT HEAT-CALL FALL OUTSIDE THE ADULT HEARING RANGE.

- heat-calls fall outside the adult hearing range solely because of the authors' decision to:<br /> * restrict heat-call frequency range to the authors' own estimation (7-10kHz) on 4 birds instead of using published values that caused developmental impact (e.g. Katsis et al: 6-10kHz), or were produced in vitro (5.9kHz; Anttonnen et al 2025);

* lowering heat call sound level (34dB at 10 cm) to measurements on 4 isolated birds under unknown temperature conditions for an unknown amount of time, when the same paper gives values of 43.4 dB at 1 m (range: 30.7-53.2) in standard in vitro conditions.

* As soon as EITHER of these two values is corrected, the statement that heat calls are outside zebra finch adult hearing range is false.

* In addition, assuming constant heat-call sound level across contexts is unreasonable. That zebra finch can produce inspiratory syllable very similar to heat calls and during inspiration at 65dB during song (Goller & Dalley 2001) argues against the assumption that heat-calls are always soft.

- Other species also show perception, not just production (resp R3.A10a), of calls above their known hearing range (10-20kHz; e.g. Duque et al 2020). The zebra finch is not the only case in birds of mismatch between signals perceived and known hearing range.

Reviewer #3 (Public review):

Summary:

The authors report and characterize the formation of aggregate microstructures by the motile filamentous cyanobacterium Fluctiforma draycotensis, which exhibits gliding motility accompanied by rotation along the long axis while excreting EPS. In experiments with motile F. draycotensis cultures, they observed the formation of granular structures composed of cyanobacteria and other material (iron, polystyrene beads, etc.), with macrostructures on the scale of 1mm within 24 hours. The structures were motile at speeds comparable to that of the cyanobacteria filaments, resulting in their growth through coalescence over time. Notably, such macrostructures were absent in nonmotile F. draycotensis, pointing to the role of filament motility in their formation. Through experiments examining the micro-scale dynamics, inert material such as small polystyrene beads was found to be transported by the gliding, buckling, and plectoneme dynamics of the filaments, pointing to the underlying mechanism by which particles are collected into larger-scale microgranule structures.

To interrogate the properties that drive the cyanobacteria filament buckling, plectoneme formation, and entanglement, the authors develop a mechanical model for filaments as nearly inextensible, slender bodies with resistance to twisting and bending under active gliding forces and torques and responding to fluid flows and surface adhesion. They derive expressions for the thresholds for buckling and twisting instabilities, which are additionally demonstrated and interrogated through simulation via the Immersed Boundary Method. Most importantly, bending and plectoneme formation only occur with sufficiently long filaments, and the threshold is shorter for bending than for plectoneme formation. Experimental observations with wild-type filaments agree with the model-predicted thresholds. The authors perform additional experiments with shorter filaments below both thresholds, including the filamentous bacterium Pseudanabaena, which fail to collect particles (though can in principle form macrostructures).

Strengths:

This work appears to be novel (notably, the discovery and characterization of the particle collection behavior of a filamentous cyanobacterium) and has interesting implications for both naturally observed cyanobacterial macrostructures as well as the controllable parameters in engineering them. The experimental and modeling work is well motivated, contributing to the broader understanding of macrostructure formation and material aggregation through active filament dynamics (not exclusive to cyanobacteria), as well as the underlying physical properties governing important filamentous cyanobacterium dynamics. As such, I would expect the results of this paper to be of broad interest to both biophysicists and microbiologists. Generally, the manuscript is well written with clear, compelling figures that illustrate the important conclusions of this study.

Weaknesses:

In the section on "Shorter gliding filaments cannot collect particles nor form granule macrostructures", the filamentous cyanobacteria considered *all* fall below the predicted thresholds for bending and twisting. The "long" F. draycotensis are 60 microns in length, notably less than the 120 and 320 micron thresholds derived in the previous section as well as the lengths of filaments considered in Figure 3D, yet these "long" 60 micron filaments form macrostructures. How can this be understood in the context of the model predictions? Is the nature of the macrostructures in Figure 4B, the microscale parameters, or the collection of particles somehow different than those with filaments an order of magnitude longer in earlier parts of the paper? The paper would be stronger if these sorts of questions were addressed in the text and/or with supplementary figures.

Reviewer #3 (Public review):

Most models of reinforcement learning in the brain treat the question of how the external world is represented as an afterthought. In "Error driven representation learning in the mesolimbic system", the authors begin by calling attention to this limitation of previous work, then proceed to show that neural activity in part of the ventral striatum evolves in a manner consistent with dopaminergic RPE-driven representation learning. Overall, the question is interesting, the modeling is well done, and the claims are bold. While I am not convinced that olfactory tubercle outputs _mainly_ reflect state features acquired through error-driven learning (see main points below), I am now more willing to believe that they might. I am confident that this work will spark discussion.

Strengths:

The latent weight trajectory inference approach is a nice application of Kalman filtering, the model validation and comparison steps are well executed and thorough, the discussion is nicely written and includes an appropriate caveat about the weight transport problem, and the general idea of striatal output being value-like but also having state-like aspects that evolve over time is thought-provoking.

Weaknesses:

In my view, the main limitation of this work is that the authors do not clearly rule out (1) non-representation learning and (2) non-error-driven learning. This fits with the narrow research question stated at the end of the introduction (L71-72), which is confirmatory in nature and does not claim to exclude alternatives, but other aspects of the framing are less consistent.

Main points of criticism

(1) Representation vs. value learning

The first two pages of the manuscript gave me the impression that the authors wish to draw a clear distinction between representation and value learning, and that they would squarely position this paper as a study of representation learning. The substance of the work does not seem consistent with this positioning, a mismatch that could be addressed either by incorporating new analysis and discussion or by changing the framing.

Examples of emphasis on representation:

- Abstract L14-17 defines value and representation learning and states why they are different.

- First three paragraphs of introduction explain the power of learned representations.

- Results L103-108 attribute state representations to OTu and value to downstream regions.

For this level of emphasis, it would be good to see a convincing argument that OTu MSN activity is better understood as a state representation than as the output of a value function.

Options for establishing OTu as state and not value:

- Explicitly claim in the introduction that previous work has established this, and explain why evidence of stimulus valence being encoded in this area (citations on L68-70) does not favour the value interpretation. Discussing how OTu differs from other parts of ventral striatum that are canonically seen as value coding would also help.

- Directly compare the extent of state vs. value encoding in the present data. The $w=1$ control is a good step in this direction, but its connection to value coding is mentioned only in passing.

(2) Error driven learning vs. other types of learning

Similar to my previous point, the authors seem to claim that the representational changes they study are specifically error driven. While the authors include a good number of controls and ablations, it was not obvious to me that any of them correspond to a form of non-error driven learning that could plausibly generate useful representations. Adding Hebbian learning or a sparsifying learning rule would strengthen this aspect of the work.

Reviewer #3 (Public review):

Summary:

In this study, the authors show that exposure of cancer cells to DSBs induced enzymatically or by ionizing radiation (IR) results in large-scale genomic amplifications (DIGA: DSB-induced genomic amplifications) that are detectable by FACS. This phenomenon was observed in diverse cancer cell lines and was shown to be limited by p53 in paired cell lines. In principle, DIGA could result from re-initiation of DNA synthesis within the same cell cycle, mitotic segregation errors, or repair-associated DNA synthesis. The authors conduct experiments to distinguish between these possibilities and conclude that DIGA is the result of extensive RAD51-dependent DNA synthesis.

Strengths:

The observation of genome amplification in response to DSBs specifically in cancer cells is striking, particularly at high IR doses or by AsiSI endonuclease induction. At 9 Gy, almost 50% of cells have a >4N DNA content. The correlation between high levels of DIGA and reduced clonogenic survival of melanoma cells suggests that DIGA contributes to IR-induced cytotoxicity. The authors convincingly show that DIGA occurs in a single cell cycle and is not due to chromosome mis-segregation at mitosis. Because DIGA is partially dependent on resection nucleases, POLD3, POLD4, RAD52 and RAD51, the authors conclude that DIGA results from repair synthesis by a BIR-like process.

There are significant weaknesses in the study:

(1) It is unclear to this reviewer how BIR-like synthesis could increase genomic DNA copy number by 50% or more, as indicated by the FACS plots. In the case of AsiSI, there are ~150 sites that are efficiently cleaved in U2OS cells. Each of these sites would need to prime extensive synthesis by an inefficient migrating D-loop mechanism. Studies of BIR-like synthesis at DSBs in U2OS cells have shown that repair tracts are fairly short, around 3-10 kb in length. Thus, it is hard to rationalize how BIR at a few hundred DSBs could initiate such large changes in genome size.

(2) The BrdU-FACS plot shown in Figure S4 shows that most of the cells have an 8N content 48 and 72 h after AsiSI induction and do not have much BrdU incorporated. This finding would suggest that genomic DNA doubling is mostly independent of de novo synthesis. Also, there does not seem to be a continuum from 4N up to 8N in these plots as one might expect from variable length DNA synthesis tracts.

(3) The requirement for XRCC4, XLF and LIG4 to promote DIGA is puzzling if synthesis occurs by a BIR-like mechanism. Although there is evidence that DNA synthesis at DSBs can be initiated by homology-directed strand invasion and terminated by NHEJ, this mechanism would limit the extent of synthesis to a few kb at each site. One might have expected an increase in DIGA in the absence of core NHEJ factors because of the increased number of DSBs available for BIR.

(4) Although the authors rule out re-replication as a contributor to IR-induced DIGA, the FACS profiles of MLN4924 and 9 Gy treatment look remarkably similar, and both would be inhibited by aphidicolin treatment. A previous study showed that DSBs induce endoreduplication in Arabidopsis (Adachi et al. PNAS 2011).

Reviewer #3 (Public review):

Summary:

The authors aimed at a comprehensive phenotypic characterization of the roles of all Rab proteins expressed in PN neurons in developing Drosophila olfactory system. Important data are shown for a number of these Rabs with small/no phenotypes (in the Supplements) as well as the main endosomal Rabs, Rab5, 7, and 11 in the main figures.

Strengths:

The mosaic analysis is a great strength allowing visualization of small clones or single neuron morphologies. This also allows some assessment of cell autonomy of the observed phenotypes. The impact of the work lies in the comprehensiveness of the experiments. The rescue experiments are a strength. The added developmental data strengthen the impact of the paper.

Weaknesses:

The main weakness is that the experiments do not address the mechanisms that are affected by the loss of these Rab proteins, especially in terms of the most significant cargos. The insights thus do not extend far beyond what is already known from other work in many systems.

Reviewer #3 (Public review):

Summary:

Cells and tissues are viscoelastic materials. However, metabolic processes that underly survival, growth and migration render the cell as an active matter at non-equilibrium. These two facts contribute to the difficulty of probing mechanical properties especially with sub-cellular resolution. However, the concept that the mechanical phenotype can be indicative of normal physiology necessitates approaches of defining the cellular phenotype. Here, Muenker et al evokes a powerful argument for mapping intracellular mechanics using optical tweezer- active microrheology. They present a suite of parameters towards a definition of a mechanical fingerprint. This is a compelling idea. There are some concerns as detailed below

Strengths:

These are technically challenging experiments and the authors provide systematic approaches to probe a system at non-equilibrium.

Weaknesses:

The importance of the mechanical fingerprint is diluted due to some missing controls needed for biological relevance. As it reads, sinusoidal waves are applied sequentially from 1- 1024Hz.<br /> Please clarify if amplitude is the same for each frequency, also how many frequencies are used?

On this point, due to perturbations due to alterations in pre-stress, are the orders of frequencies randomized?

How many beads are probed in a given cell.

Is the graph in 1 c G', G" per cell or average of many cells?

Figure 1e is quite nice, however is there an equivalent performed in a non-linear ECM such as collagen for comparison, in a similar vein can the equivalent be calculated for cells with/without treatment with low doses of cycloheximide to reduce protein synthesis? Yes, cytoskeletal elements are important for cell mechanics, but cytoplasm crowding is often an overlooked factor.

The biggest issue is the interpretation of the different factors as each of these cells have different energetic needs.<br /> The comparison between cancer cells with different aggressiveness, immune and epithelial cells.<br /> For example, some types of cancer cells will be dominated by glycolysis vs oxphos, which will influence both the cytoplasmic and nuclear mechanics?

It would be useful to carefully assess factors not restricted to<br /> a) Cytoskeleton<br /> b) Protein synthesis<br /> c) Metabolic state

For similar lines and/ or cells where there are lineages that are either more metastatic in cancer, normal counterpart or drug resistant in an effort to link the fingerprint to a biological output. Specifically, is migration, proliferation, survival correlated with the measurements.

The reviewer is sensitive to the technical difficulties of the experiments. However, the interpretation and importance of the mechanical fingerprinting requires additional work as mentioned above.

Reviewer #3 (Public Review):

Summary:

In this work, Styer et al. explore host selection as a means for recruiting microbes that may aid their host under stressful conditions, in this case under drought stress, as an alternative to target-SynCom design. They do so by subjecting rice plants to several generations of soil transplantation, and by using the most successful rice plants as donors for the next generation. By using several NGS approaches and very thorough bioinformatics analysis, the authors identify potential microbial taxa and the associated functions enriched in the conditions of interest.

Strengths:

In general, I think this approach was very much needed in the field as an alternative to SynComs, which are still not readily usable in croplands. This work sets the grounds for future similar approaches, using different stresses and different host plants.

In this work, the experimental setup is well thought-through and well-replicated. In addition, an exhaustive set of preliminary experiments was performed before deciding on the final panel of soils to use and scoring methodology. The figures are clear and well-explained.

Reviewer #3 (Public review):

Summary:

The study by Dwulet et al. explores how the development of spontaneous neural activity in primary sensory cortices influences the co-alignment of multiple sensory modalities in higher-order brain areas (HOAs). To address this question, they focus on connectivity between the primary visual (V1) and somatosensory (S1) cortices and an associative cortical area (RL) in mice. The authors combine experimental (wide-field and two-photon calcium imaging) and computational approaches to show that spontaneous activity matures at a different pace across these brain regions. Their data indicate that S1 develops more rapidly than V1, which is possibly beneficial for RL's integration of visual and somatosensory inputs through correlated spontaneous activity. Using a computational model, they demonstrate that a moderate correlation between V1 and S1 activity can optimally guide the formation of bimodal neurons in RL, which are crucial for maximizing the decodability of multisensory stimuli. This finding highlights the role of correlated spontaneous activity in primary sensory cortices in establishing co-aligned topographic multimodal sensory representations in downstream circuits.

Strengths:

The manuscript is well written and it provides strong enough evidence to support the main claim of the authors. The insights on the role of correlated activity on instructing co-aligned multisensory maps in HOAs are not trivial and are an important advancement for the field.

Weaknesses:

In the opinion of this reviewer, the study has no major weaknesses. A drawback of the work is that none of the predictions of the computational modeling have been corroborated through mechanistic experimental manipulations of early brain activity.

Comments on revised version:

The authors have addressed all my previous concerns. I have no further comments.

Reviewer #3 (Public review):

Summary:

The authors sought to define how GATA6-dependent programming of resident peritoneal macrophages regulates lipid metabolism and, in turn, eosinophil survival. By integrating a myeloid-restricted GATA6-deficiency model with cellular phenotyping, lipidomic analyses, and measurements of lipid mediators, the study attempts to connect macrophage transcriptional identity to sphingolipid and cysteinyl leukotriene pathways that may shape eosinophil persistence. The work also appears intended to provide a mechanistic bridge between prior observations from this group and others regarding GATA6-positive macrophages, lipid metabolism, and eosinophil homeostasis.

Strengths:

(1) The study addresses an important and understudied question: how tissue-resident macrophage identity controls the local lipid environment and thereby influences eosinophil survival.

(2) The use of a genetically defined GATA6-deficiency model provides a biologically relevant framework for testing the contribution of macrophage programming.

(3) The lipidomic data broaden the analysis beyond a single mediator and identify coordinated changes in sphingolipids and glycerophospholipids that may generate useful hypotheses for the field.

(4) The finding that GATA6 deficiency promotes eosinophil survival is clear, potentially important, and consistent with prior work cited by the authors.

(5) The study is performed by a knowledgeable team and brings together macrophage biology, eosinophil biology, and lipid metabolism in a way that is likely to interest several research communities.

Weaknesses:

(1) The central mechanistic chain-GATA6 deficiency leading to altered sphingolipid abundance, altered LTE4 production, and consequently increased eosinophil survival-is not fully demonstrated. The data support associations among these features, but the causal order remains uncertain.

(2) The cited literature linking sphingolipid and cysteinyl leukotriene biosynthesis does not substitute for direct testing in this model. Perturbation or rescue experiments targeting sphingolipid synthesis and cysteinyl leukotriene production would be needed to establish necessity and directionality.

(3) The broader lipidomic changes complicate the emphasis on sphingolipids. Because glycerophospholipids are also increased, the phenotype may reflect more extensive membrane-lipid remodeling, altered phospholipase activity, changes in the Lands cycle, or shifts in free fatty-acid availability.

(4) The manuscript would benefit from a clearer distinction between observations made directly in GATA6-deficient peritoneal macrophages and mechanistic inferences extrapolated from prior studies.

(5) The physiological and pathological relevance is not yet sufficiently established. It remains unclear whether enhanced eosinophil survival translates into altered eosinophil accumulation, activation, or tissue injury during inflammatory disease in the peritoneal cavity or lung.

Reviewer #3 (Public review):

In this work, Brown and colleagues report that the photosensor protein LITE-1 of the nematode C. elegans may also a chemosensor that can be activated by high concentrations of the compound diacetly. LITE-1 was described as a putative ion channel of the gustatory receptor family, which is mainly constituted by insect odorant receptors. These form tetrameric ion channels that can be activated by odorant. Specificity is achieved by forming heteromeric channels from three copies of the odorant receptor co-receptor (ORCO) and another subunit that resembles ORCO in the pore-forming C-terminus, but brings in a binding site for the respective odorant. LITE-1 has a very similar structure, according to Alphafold3 predictions, and also carries a binding pocket. In LITE-1, this was proposed to be occupied by a light-absorbing molecule that activates the channel when a photon is absorbed. Alternatively, compounds generated by absorption of high-energy photons may be formed in vivo and bound by the LITE-1 binding pocket. Koh et al. now demonstrate that another, non-light activated compound, diacetyl, at high concentrations, can activate cells expressing LITE-1. Such (chemosensory) cells are also responsible for the avoidance of high concentrations of diacetyl. For this aspect, the protein seems to act in neurons, which are not necessarily the same cells in which LITE-1 is evoking the photophobic response. LITE-1 activation in excitable cells, i.e muscles, causes strong body contraction and paralysis, and the authors show that this is also the case when diacetyl is present. This action is surprisingly rapid, i.e. within 10 seconds after adding diacetyl, raising the question of how the compound can enter the body so quickly. The authors further present molecular docking studies showing that diacetyl could occupy the binding pocket of LITE-1. Last, they show that another compound chemically resembling diacetyl, i.e. 2,3-pentanedione, can also induce avoidance in a LITE-1 dependent manner, though not as potently.

The data are intriguing and the demonstration of LITE-1 being a diacetyl chemosensor is interesting. Following the first submission and review, the authors addressed most of the questions that this reviewer had and improved the paper significantly. It will add to the further understanding of the still-mysterious multimodal sensory ion channel LITE-1.

The authors identified mutants lacking diacetyl responses. In their chemotaxis assay (Fig. 1A, B), they show that lite-1 mutants do not avoid high concentrations of diacetyl. However, the animals actually show attraction, as the chemotaxis index was positive. If the lite-1 animals were insensitive, they should be indifferent and the chemotaxis index should be close to zero. The authors now showed that other neurons contribute to the avoidance response that are not themselves bona fide chemosensory neurons, as the avoidance behavior remained in a tax-4 mutant, that is lacking most sensory neuron responses. The authors further showed that diacetyl responses of ADL can be rescued by expressing LITE-1 specifically in this neuron in a lite-1 mutant background, thus demonstrating that LITE-1 acts cell-autonomously in ADL to affect avoidance behavior.

The effect of diacetyl on muscle cells (Fig. 3C) is pretty rapid. As shown in the initial submission, already during 1 minute after application the animals are almost maximally contracted. The authors now provide a time course with data points every 10 seconds. This shows that contraction is maximal already after 10 seconds of exposure (provided the zero time point is the one where diacetyl is added). This is remarkable, as the compound would have to either pass the worm cuticle, or enter through the gut and diffuse through the body to reach the muscle cells. It would be of interest to compare this to time courses of other pharmacological agents that need to enter the worm's body for their action. As a comparison, often sodium azide is used to paralyze worms for imaging purposes. This molecule is even smaller than diacetyl, but azide action typically requires more time for maximal effects. Could there be active transport mechanisms involved? Maybe diacetyl can pass through some transporters for related molecules into / through intestinal cells quickly, to then reach the body fluid and muscle cells.

One alternative explanation could be that other mechanisms may be at play. E.g. diacetyl may be immediately sensed by ciliated chemosensory neurons that might release a signaling molecule that leads to activation of LITE-1 in muscles, or that sensitizes it somehow, responding to light used for filming animals. The authors addressed these concerns by repeating their assay in a lite-1 mutant background. The authors had tested unc-13 mutants to rule out indirect effects on the neurons recorded. Likewise, eliminating neuropeptide signaling via unc-31 mutants would have been informative, as neuropeptide signaling plays a role in LITE-1-mediated light avoidance behavior (PMID 40238937, 39489735).

Molecular docking studies are now described in more detail. The authors also provide a structural model of the diacetyl-docked LITE-1. In this model, only one of the four putative binding sites carries diacetyl. Maybe the authors could test how structure is affected if all four sites contain diacetyl? Mutations of LITE-1 that lack aminoacids shown to be contacted by diacetyl have been described (C300, R222). It would have been insightful to test if such mutants are still activated by diacetyl. This would also verify that there is not an unknown breakdown product of diacetyl that could affect LITE-1 function through oxidative pathways, as diacetyl has been implicated as a photosensitizer in the past.

Reviewer #3 (Public review):

Summary:

The authors aim to compare proposal models of perceptual decision making using a joint modeling approach, where they fit models to both behavioral outcomes as well as CPP. Most notably, they compare a standard evidence accumulation model with models that track the evidence without integrating it over time (extrema detection). The authors report that the joint CPP-behavioral data do not discriminate between two of their proposals.

Strengths:

This is an interesting finding that reinforces the idea that what we believe to see based on aggregation over trials may not be what happens on every single trial. The models are creative and the simulations are convincing, relating the models to multiple neural markers of decision formation. These include the CPP but also mu/beta power spectra.

Initial weaknesses:

Contrary to the original draft, the current version now clarifies the goals of the study as well as the role of internal vs external noise.

The authors clarify their stance on the optimality of extrema detection in the Public Review, where they make some good points. Specifically, they argue that models that have studied optimal decision making in the past have considered a quite narrow definition of optimality, for example, ignoring energetic costs.

The authors clarified the fixed value of the scaling parameter (which apparently was already mentioned in the initial draft, which I had missed). My comment about the discrepancy between the bounded integration model and the EZ diffusion model can thus be ignored. However, I do think that the similarity of the bounded integration model and the EZ diffusion model could be acknowledged in the manuscript somewhere.

I still think the choice of modeling the grand average accuracy and EEG signal may distort the results. As Figure 1 reveals, there is in fact substantial variation in the change in accuracy across conditions. I can imagine that one model may be preferred over another based on the aggregate data, while the other model is preferred for some participants. On the group level, this may lead to different conclusions, at least quantitatively (e.g., in terms of G2), but potentially even qualitatively. This is especially true for the neurally-informed models because of the covariation between behavioral and neural data.

Reviewer #3 (Public review):

Summary:

This study found that ADF serotonergic neurons have a significant role in extending lifespan mediated by HIF-1, as well as serotonin receptor SER-7 in the GABAergic RIS interneurons. The author focuses on the sufficiency and necessity of components from the central nervous system and how they contribute to aging upon hypoxia.

Previous work from the lab has identified that the stabilization of HIF-1 in neurons is sufficient to extend lifespan through the serotonin receptor, SER-7, which subsequently activates fmo-2 in the intestine and leads to lifespan extension. Building on this, the author sought to determine which serotonergic neurons are involved and found that serotonin signaling in ADF neurons is required for lifespan extension mediated by HIF-1.

The author next tested which subset of neurons requires Ser-7 expression to rescue hypoxic response. They found that ser-7 expression in multiple neurons is sufficient to induce fmo-2, with the top candidate being the RIS neuron. Ablation of the RIS neuron did not extend lifespan, suggesting that ser-7 expression in the RIS neuron is required for lifespan extension, positioning it as a key component in the longevity signaling pathway.

The author also investigated neurotransmitters and found that GABA and tyramine are important components in this circuit. They showed that the tyramine receptor called tyra-3 is required for vhl-1-mediated longevity. Given that tyra-3 is expressed in oxygen- and carbon dioxide-sensing neurons, the author demonstrated that these sensing neurons work downstream of serotonin signaling. Lastly, the author screened neuropeptide/receptor binding pairs and identified NLP-17 as playing a role in hypoxia-mediated longevity.

Originality and Significance:

This research is significant in that it uncovers components that are sufficient and necessary for lifespan extension via the hypoxic response. It provides comprehensive data supporting longevity induced by HIF-1-mediated hypoxic response, in conjunction with fmo-2, a longevity gene, as demonstrated in previous work from the lab. Moreover, it provides a number of new transgenic worm tools for C. elegans and aging communities.

Conclusions:

This study provides insights into how hypoxic response regulates aging in a cell non-autonomous manner, outlining a potential circuit involving neurons, neurotransmitters, and neuropeptides.

Reviewer #3 (Public review):

Summary:

Maigler & Lin et al present a convincing set of behavioral and electrophysiological experiments and analyses exploring how individual differences in taste preference map onto neural responses in the gustatory cortex (GC). They go on to examine how both preferences and neural responses shift following intervening taste experience. Their experiments are strengthened by examining tastes of distinct identities and palatability (sweet, sour, salty, bitter) and correspond each animal's individual preference to the palatability-related late phase of the neural response.

Strengths:

(1) They demonstrate a relationship between the behavioral expression of taste preference and palatability-related GC neural responses. The direct correlation of expression of taste preference with GC neural responses indicates that taste preference behavior may be less noisy than previously thought, reflecting actual neural activity.

(2) They address the stability of individual taste preference by comparing within and between session expression. This finding indicates that individual preference on any given trial or test session can differ from canonical palatability.

(3) The animal's preferences for various tastes are reflected in the neurophysiological recordings, despite that behavior and physiology sessions are separated by several weeks, with an intervening surgery, across multiple delivery methods (active licking vs passive delivery), and across homeostatic states (thirsty vs sated).

(4) They provide evidence that representational drift in palatability coding may arise from sensory experience rather than from the passive passage of time.<br /> The findings are novel and impactful, and the results are relatively complete.

Weaknesses:

(1) The authors state that "no differences in effects were observed between taste batteries" (Methods), but it is not clear which analyses were performed to determine this, especially considering that many of the analyses are within-animal. Without more clarity, it is difficult to evaluate whether the interaction of different tastes within the sets of stimuli bias the main conclusions.

(2) It is not clear how the analyses in Figure 3 emerge from the behavioral patterns across Figures 2A2 and 2B2. Along these lines, citric acid responses for R1 and R2 and saccharine responses for R7 and R8 are not shown in Figures 2A2 and 2B2, thus, we cannot determine if they change from the first to final BAT sessions. Salt, even at low concentrations, may not be palatable when rats are in a water-restricted state. It would be interesting to see Figure 3's analysis exclusively for sweet or for bitter tastes.

(3) A potential reason why a single taste experience session causes taste palatability responses in GC neurons to revert to a reflection of canonical palatability ranks, as is seemingly shown in Figure 6, is not discussed.

Reviewer #3 (Public review):

In this manuscript, the authors strived to develop a highly efficient drug survival assay for in vitro cultured human malaria parasites P. falciparum. This was done by generating a transgenic P. falciparum line using a creLox strategy that allows detection of (presumably) viable parasites by a β-lactamase assay. To estimate the Limit of quantification of the recombined P. falciparum NF54i-lacZ, the authors ultimately designed a protocol in which viable parasites are detected by the luminescence of β-D-galactoside generated by β-lactamase within the transgenic parasites. For this, the parasite must be incubated with rapamycin for 120 hours to induce CreLox recombinase, which places β-lactamase under an active promoter. Using this assay, termed MULTI-i2, the author shows interactions between two antimalarial drug pairs that were previously demonstrated by another assay. In the case of pyronaridine and piperaquine pair, the NULT-i2 assay generated some additional insights compared to the previous assay, presumably by virtue of including more concentration datapoints. In conclusion, the authors argue that the MULTI-i2 assay is much less resource-intensive and time-consuming and can be applied on a large scale at a much lower cost and with the highest efficiency.

Overall, the data generated in this manuscript are clear and well represented, and I am convinced that MULTI-i2 provides yet another of many drug assays for malaria parasites and could be put to good use. However, I struggle to fully appreciate the merit of his study, as the manuscript reads more like a technical document than a scientific study.

I particularly lack an understanding of the strengths and weaknesses/limitations of the MULTI-i2 methodology and, thus, its applicability. I also do not fully appreciate the need for such an elaborate luminescence-based experimental setup. It would be good if some of these issues were addressed.

Specifically:

(1) The whole assay is based on detecting parasites by luminescence after 120 hr (5 days) after drug exposure. During that time, presumably the parasites that survived the drug pressure regrow to a detectable level and, at the same time, perform efficacious CreLox-based recombination to produce β-D-galactoside for detection. Is this necessary? How superior is this detection method to other methods, such as Fluorescence-assisted Cell Sorting (FACS) e.t.c.? Moreover, the 5-day growth-CreLox-β-D-galactoside production could introduce a series of confounding effects. In my view, more studies (beyond comparisons with a single existing method) would be useful for understanding this entire process.

(2) Related to that above, how would MULTI-i2 perform in case of drugs that do not necessarily kill all parasites, such as artemisinin? In the case of artemisinin, it is becoming evident that at least a small fraction of the parasite revives after treatment via a temporary dormancy state. This has, in fact, also been shown for other drugs such as mefloquine, pyrimethamine, etc. Would such a situation produce a range of false readings? In general, in its current state, it is hard to see what the limitations of this method are, which makes it hard to decide whether to use it for a particular application.

(3) Given the stated cost and labor efficiency of MULTI-i2, it is disappointing to see only two applications for two drug pairs: atovaquone/proguanil and piperquine/pyronaridine, for both of which their interactions were already known. The manuscript would benefit greatly if the authors demonstrated more drug interactions and identified (and ultimately validated) new ones. This would certainly make MULT-i2 method more attractive. In particular, it would be nice to see if one could use MULTI-i2 for studies of triple combinations as enthusiastically suggested.

(4) Throughout the manuscript, the authors claim that MULTI-i2 is considerably less expensive and can be done much faster than previous methods. In my view, this is not exactly a scientific argument. The cost of an assay depends heavily on the cost of reagents and labor, which are subject to market price fluctuations. The efficiency and time consumption can very much depend on laboratory organization etc. Unless the author could specifically demonstrate where and how these assays are cheaper and faster, I suggest not discussing this.

Comments on revisions:

I have no more comments.

Reviewer #3 (Public review):

Summary:

The authors are trying to provide the molecular basis for the emerging role of VDAC2 in mitochondrial apoptosis. They use multiple approaches from biochemistry, cell biology, and structural biology.

Strengths:

Isolating the VDAC2-BAX complex and providing the molecular basis of this interaction in mitochondrial apoptosis is pretty innovative and significant.

The authors have tried to validate their results using multiple approaches, which corroborates the quality of the study.

Weaknesses:

The scientific data and its presentation could be improved.

Case#: Patient 9, female, Mexican, symptoms onset 6 yrs. ago, Mexico City

DiseaseAssertion: IRD

FamilyInfo: parents are non-sanguineous and asymptomatic, they also denied any history related to ocular diseases. Information disclosed that the mother had one stillbirth and three miscarriages, but denied any related diseases/health issues to this child.

CasePresentingHPOs: HP:00305, HP:00080, HP:0000493, HP:0025586, HP:0030329, HP:0012713

CaseHPOFreeText: Proband presented with light sensitivity as well as adaptation difficulties when going from dark-to-light. Right eye was 20/200 and left eye was 20/160 from the visual acuity test. Macular bull's eye appearance. Subnormal rod and cone responses. Peripapillary sparing retina.

CaseNotHPOs: HP:0007737, HP:0000750, HP:0000510

CaseNotHPOFreeText: No afferent pupillary defect. No anomalies in anterior segment.

Genotyping Method: QIAamp DNA Blood Kit was used to extract gDNA and quantification/purity of the sample was found using a NanoDrop 2000 spectrophotometer. 293 genes were sequenced. gDNA was sequenced via Illumina technology. Following, certain sequences were additionally analyzed against a reference genome in order to identify changes and interpret.

PreviouslyPublished: n/a

Variant: NM_000350.3(ABCA4):c.4926C>G (p.Ser1642Arg), NM_000350.3(ABCA4):c.5044_5058del (p.Val1682_Val1686del)

ClinVar: 99332, 99340

CAID: n/a

SupplementalData: Phenotype data in results section as well as figures 1, 2, and 3 showing phenotypic testing results.

Reviewer #3 (Public review):

Summary:

In their manuscript, the authors assess how TRPML1 influences lysosome trafficking and function in astrocytes using a neuron-astrocyte coculture system. Specifically, it was found that activation of TRPML1 reduces LEL motility in astrocyte branches while TRPML1 increases it, and a model was proposed in which TRPML1 coordinates lysosomal positioning along astrocyte branches, enabling LELs to locally regulate actin-membrane linkers that influence PAP (peripheral astrocyte processes) structure and plasticity. The authors primarily use pharmacology to buttress their findings.

Strengths:

TRPML1 is currently under investigation as a potential therapeutic target for lysosomal storage disorders and neurodegenerative diseases such as AD and PD. The manuscript is hence timely and important as the crosstalk between glia cells and neurons is likewise increasingly recognized as disease-relevant.

Weaknesses:

Unfortunately, experiments performed to investigate the role of TRPML1 are based purely on pharmacological tools. No bona fide KO data are available. At least for some experiments, this should be done. MLIV iPSC lines are available.

If no KO controls are being provided, at least the authors shall use ML1-SA1 (EVP-169) as an agonist instead of ML-SA1, because ML-SA1 activates all three TRPML channels, which would be a major problem for this study. Likewise, the ML-SI3 antagonist also has effects on other TRPMLs. EDME is a blocker with higher specificity for TRPML1.

When using pharmacological tools, the original papers relating to these tools may be cited. For example:

- For ML-SA1 https://www.nature.com/articles/ncomms1735 and for MK6-83 https://www.nature.com/articles/ncomms5681

- ML-SI3 as a blocker for TRPML1 seems problematic: https://pubmed.ncbi.nlm.nih.gov/33187805/ ; an alternative may be: https://www.nature.com/articles/s41598-021-87817-4

No reference is made to other important regulators of lysosomal cation homeostasis such as TPC1 or TPC2, two other Ca2+/Na+ permeable cation channels, or other TRPML channels. The authors should provide some data supporting or excluding a role of these channels.

Reviewer #3 (Public review):

Summary:

This manuscript describes a web-based tool that allows researchers to compare large numbers of representative ("plausible") conformations of proteins. It also includes energetic analysis from multiple widely used structure-prediction methods.

Strengths:

This tool will likely be useful for students who want to learn more about the ensemble properties of proteins. The resource is well organized and it represents a large amount of computing resources.

Weaknesses:

It is not entirely clear how the database may be utilized by other groups to advance research. It could be helpful if the authors add a short section that provides example use cases that illustrate how this database can support new strategies for studying protein dynamics.

Reviewer #3 (Public review):

Summary:

The authors were investigating the impact of introducing novel facultative bacterial endosymbionts into the pest aphid, Diuraphis noxia, to explore the possibility of using facultative symbionts as a crop protection tool. They successfully established the vertical transmission of both endosymbionts and performed a series of aphid performance and dispersal experiments together with measurement of aphid feeding on host plant health, growth, and metabolism. While most of the experiments revealed no effect of the endosymbionts, some significant treatment effects were found, showing that Rickettsiella reduced aphid dispersal, and Regiella reduced aphid population growth and feeding damage.

Strengths:

The team worked with two novel facultative symbionts (Rickettsiella viridis and Regiella insecticola) that they were able to successfully establish in D. noxia. The data were collected and analyzed using solid, well-described methodology.

Weaknesses:

While interpretation of the data is reasonable, the few experiments which revealed significant treatment effects rest on relatively small sample sizes.

Measuring symbiont density is difficult. The authors use quantitative PCR to measure the "density" of endosymbionts relative to a host gene. This is a standard approach in the field; however, recent work has shown that endosymbionts like the aphid primary endosymbiont, Buchnera, are variably polyploid [1]; further the aphid cells that house the symbionts are also highly polyploid and variable in their ploidy [2]. It is important to understand that what is being measured when using qPCR is DNA copy number and not quantification of the number of symbiont cells. Alternative approaches to measuring symbiont density include flow cytometry [3], and SymbiQuant [4], a machine vision tool that can quantitatively characterize symbiont populations from DAPI-stained confocal images. These alternate approaches also have their limitations. Currently, there is no perfect approach to measuring symbiont density, which remains an important measure in experiments such as these. Put simply, it is important for a reader to be aware of the limitations of each approach and interpret data accordingly.

[1] Komaki, K., and H. Ishikawa. 2000. Genomic copy number of intracellular bacterial symbionts of aphids varies in response to developmental stage and morph of their host. Insect Biochemistry and Molecular Biology 30:253-258.<br /> [2] Nozaki, T., and S. Shigenobu. 2022. Ploidy dynamics in aphid host cells harboring bacterial symbionts. Scientific Reports 12:9111.<br /> [3] Simonet, P., G. Duport, K. Gaget, M. Weiss-Gayet, S. Colella, G. Febvay, H. Charles, J. Viñuelas, A. Heddi, and F. Calevro. 2016. Direct flow cytometry measurements reveal a fine-tuning of symbiotic cell dynamics according to the host developmental needs in aphid symbiosis. Scientific Reports 6:19967.<br /> [4] James, E. B., X. Pan, O. Schwartz, and A. C. C. Wilson. 2022. SymbiQuant: A machine learning object detection tool for polyploid independent estimates of endosymbiont population size. Frontiers in Microbiology 13:816608.

Impact and Significance:

Food security and production, and pest control are major challenges facing the human population. This work contributes knowledge that will benefit the development of alternate pest control strategies in agriculture.

Reviewer #3 (Public review):

Summary:

Araveti et al. demonstrate that Type I Interferon (IFN-I) and Interferon-gamma (IFN-γ) play opposing roles in regulating lipid peroxidation and host resistance during Mycobacterium tuberculosis (Mtb) infection. While both the two pathways drive inflammation, they differ fundamentally in cell protection. IFN-I signaling triggers a destructive, self-amplifying cycle catalysing the generation of reactive oxygen species (ROS) and lipid peroxidation, which ultimately impairs the host's ability to clear Mtb. Conversely, the authors demonstrate that IFN-γ couples antimicrobial activation with cytoprotection. It primes macrophages to fight the mycobacteria while simultaneously shielding them from oxidative stress. It achieves this by sequestering iron, which successfully prevents ROS from converting into damaging lipid peroxidation products.

Strengths:

Ultimately, this study highlights a critical biological distinction: IFN-γ safely balances inflammatory activation with cellular defense, whereas IFN-I promotes uncontrolled pathological damage. This divergent coupling of inflammation and cytoprotection carries major consequences for disease progression and host survival.

Weaknesses:

This study demonstrates all the findings in specific mouse strains. How these translate in human macrophages is not well characterised, thus raising the issue of its overall impact in tuberculosis disease.

Reviewer #3 (Public review):

Summary:

This is an excellent piece of work which sheds greater light on the responses of cattle to both experimental and natural infection in cattle with Mycobacterium bovis infection, using data from different experimental and field groups.

Strengths:

The work is based on robust analysis of a range of highly relevant experimental and field sample sets, using transcriptomic approaches. It provides insight into pathogenesis and disease responses, as well as some evidence regarding potential future diagnostic advances.

Weaknesses:

I have some simple, but important comments on how the work is discussed. (Consequently, most comments focus on the discussion section). In particular, I suggest that the authors have confused or conflated the great progress that they have made in improving the understanding of the responses to M. bovis infection with an improved ability to practically improve the diagnosis of the infection in the field. Minor differences in specificity and sensitivity - and predictive values - of tests or assays being used can have profound effects in different prevalence settings on the farm, and I don't feel that this understanding is adequately reflected in the discussion in particular. In this respect, the authors really should, in my view, focus not on the outstanding results that come in or from their model fitting approaches, to focus on their model testing results in relation to extrapolated meaning / external validity. The text uses words like 'robust' and 'highly accurate' which are meaningless in the context of test interpretation in the field.

I get their enthusiasm, based on really interesting findings in relation to disease progression and immune and inflammatory responses, but these indistinct claims rather devalue the quality of the rest of their work in my view.

One great challenge in work of this nature, using natural cases from farms, is that there is no gold standard for identifying the cases that current diagnostic approaches miss and which they hope their new approaches can help with. This is not discussed or mentioned in their enthusiasm for what they have achieved. Their field datasets are based on the current, insensitively detected cases. It misses the 'occult' cases that are present but undiagnosed.

Reviewer #3 (Public review):

Summary:

In this submission Wang and colleagues jointly examine the association between depression and anxiety symptoms and individuals' affective reactivity to reward prediction errors in Ruttledge et al.'s gambling paradigm. Taking a bifactor approach to anxiety and depression in several non-clinical (and one clinical sample), the authors find that anxiety-specific symptoms relate to over-reactivity of mood to reward prediction errors (RPEs) as well as heightened mood variability , while depression-specific symptoms relate to blunted mood sensitivity to RPEs. These depression-, but not-anxiety specific relationships replicated in patient samples.

Strengths:

I was impressed that the data-driven, transdiagnostic approach employed by the authors uncovered specific relationships between anxiety and depression-specific factors and RPE reactivity in a well characterized task and computational model, especially in a non-clinical sample. This sheds new light on how these affective processes may be perturbed-and importantly, in different ways-by anxiety and depression symptoms. Likewise, the replication of the depression-specific finding (RPE hypo-reactivity) in a clinical sample was nice to see.

Weaknesses:

While the anxiety- and depression-specific factors had differential effects on mood variability (Fig 2A-D) and RPE reactivity (Fig 2E-G) in all samples, such that the correlations between the two factors and these mood parameters were significantly different, the anxiety factor was not consistently (significantly) associated with either mood-related parameter across samples. However, the authors resolve anxiety-specific predictive effects when they collapse across datasets. While it is intuitive that achieving a larger effective sample size would afford the power necessary to detect such individual differences, this struck me as a major caveat for this set of results.

The associations the authors observe between the 'common factor' of depression and anxiety and risk-aptitudes tendencies-presumably the alpha (exponent) parameter in a prospect theory-type subjective value model. But where is this analysis explained? (i.e. how was this model formulated and how were risk attitude parameters estimated?) And what is the interpretation of this finding-is there precedent for looking at risk attitudes in this task? And why would these predictive effects only be observed in relation to the common, but not unique factors of anxiety and depression?

Comments on revised version.

I believe the weaknesses identified in the previous round of review have been adequately addressed by the authors, and my suggestions concerning clarity of presentation have by and large been implemented by the authors.

DOI: 10.21203/rs.3.rs-7072571

Resource: RRID:RRRC_00699

Curator: @bandrow

SciCrunch record: RRID:RRRC_00699

What is this?

DOI: 10.21203/rs.3.rs-7072571

Resource: RRID:RRRC_00699

Curator: @bandrow

SciCrunch record: RRID:RRRC_00699

What is this?

DOI: 10.21203/rs.3.rs-7072571

Resource: RRID:RRRC_00699

Curator: @bandrow

SciCrunch record: RRID:RRRC_00699

What is this?

DOI: 10.21203/rs.3.rs-7072571

Resource: RRID:RRRC_00699

Curator: @bandrow

SciCrunch record: RRID:RRRC_00699

What is this?

DOI: 10.21203/rs.3.rs-7072571

Resource: RRID:RRRC_00699

Curator: @bandrow

SciCrunch record: RRID:RRRC_00699

What is this?

DOI: 10.21203/rs.3.rs-7072571

Resource: RRID:RRRC_00699

Curator: @bandrow

SciCrunch record: RRID:RRRC_00699

What is this?

DOI: 10.21203/rs.3.rs-7072571

Resource: RRID:RRRC_00699

Curator: @bandrow

SciCrunch record: RRID:RRRC_00699

What is this?

Reviewer #3 (Public review):

Summary:

In the revised manuscript, the authors provide additional details and evidence regarding the robustness and utility of their method.

Strengths:

(1) The authors provide a very precise automatic identification of marmosets in their home cage, to levels comparable to animal health professional.

(2) This method is robust across lightning, camera angles etc but importantly is able to identify marmosets in naturalistic conditions, which can be of tremendous value to neuroscientists and to ecological or behavioral studies.

(3) Easy to use and implement, requiring minimal settings. Phone videos can even be used.

Weaknesses:

While the manuscript improved tremendously from the previous version, given the nature of the paper, it is still a strenuous read.

Comments on revised version.

The authors did a good job of addressing my previous concerns and I don't have more comments.

Reviewer #3 (Public review):

Summary:

Interest was especially focused on how foreknowledge of the orientation of either the target or the distractor could be used to resolve the competition between these stimuli and properly report the target color. The target or distractor was pre-cued by a solid or dashed orientation cue. They were displayed with slightly different presentation frequencies, which allowed for examining their sensory processing with steady-state visual evoked responses (SSVERs). Furthermore, orientation decoding was performed, which revealed that orientation cues selectively affected processing after stimulus onset related to the dominant but not the non-dominant eye. EEG analyses additionally focused on parietal alpha activity and frontal theta, both during the anticipatory phase and the stimulus-processing phase. Cueing the distractor vs. the target induced increased right parietal alpha power during the anticipatory phase, but this did not result in direct inhibition of distractor features. During the stimulus-processing phase, cueing the distractor resulted in increased theta activity. Finally, a generalized linear model was employed wherein trial-by-trial behavior (precision in target color report) was predicted by target and distractor SSVERs, type of pre-cued stimulus (target/distractor), preparatory parietal alpha power, stimulus processing-related frontal theta power, eye dominance, and all their interactions. Performance in the case of reduced sensory processing of the target (based on SSVER) showed more deviations when sensory processing of the distractor was high, but no such effect was observed when sensory processing of the target was high. The latter effects were modulated by eye dominance and cue. Increased theta reduced distractor sensory processing but not target sensory processing. Increased alpha was only beneficial when sensory evidence for both target and distractor was low. Results were interpreted as favoring sensory gating before stimulus onset, reflected by increased parietal alpha (i.e., pro-active control), while theta activity especially seemed relevant to suppress distractor activity (i.e., reactive control) thereby favoring target-related performance.

Strengths:

The authors convincingly show that EEG can provide crucial information about how the human brain deals with the conflict between a target and distractor in a binocular rivalry paradigm with pre-cues signaling either the target or the distractor orientation. An important aspect of the study is the focus on precision of target color report, in combination with the possibility to assess SSVERs to the target and distractor. The strength of this study may actually also be its weakness; the question is whether the presented ideas on proactive and reactive mechanisms can be generalized to paradigms that do not employ binocular rivalry. Separation of target and distractor processing by selectively presenting them to the left/right eye increases the conflict when the target is presented at the non-dominant eye, but what happens in the absence of binocular rivalry concerning the target-distractor conflict?

Weaknesses:

An important aspect of the study relates to the cue manipulation. In many studies, cues are often informative but not mandatory. Couldn't one argue that in this study task performance crucially depends on cue processing, as without the cue, it becomes difficult to tell apart the target from the distractor. It could be argued that participants are able to do this based on the slight difference in flickering frequency, but I doubt whether this is possible at all. However, if this were the case, then they might use this as an alternative cue and ignore the orientation cue. What do participants experience while performing this task? As the cue can be considered to be mandatory, the question may be raised what strategy the participants actually employed. If the target was cued, they simply may have prepared for this orienting and could ignore the distractor. However, if the distractor was cued, they could use two strategies: search for the stimulus without the cued orientation, or first detect the distractor, and then orient towards the other stimulus. The ideas and results on parietal alpha and frontal theta in combination with the other findings are certainly very interesting, but recently, it has also been argued that frontal theta may be more related to action control (e.g., see Panek et al., https://doi.org/10.1093/cercor/bhaf276) and also pro-active control (Cooper et al., 2017). So, it might be that increased theta reflects suppression of the response related to the distractor, which feeds back on its sensory processing. This raises the question whether there is possibly also some evidence on functional connectivity between frontal and posterior regions that varies depending on the precise condition. Are the results also shining a new light on the relation between attentional orienting and eye dominance (e.g., see Schintu et al., 2020)?

https://www.thingiverse.com/thing:7402637/files

Reviewer #3 (Public review):

Zappit is an open-source implementation of arbitrary-access laser-scanning optogenetics for manipulation of neuronal activity in mice. As the method requires expertise ranging from optics, hardware control and programming, the authors make the point that this powerful strategy is underutilized in the field, and put forward a well-documented modular hardware and software platform aligned to the Allen Mouse Brain Atlas aimed at enabling the larger scientific community to use this approach (democratizing) for controlling cortical activity during behavior in mice.

The authors favor a galvanometric approach to laser targeting. The system is inexpensive, easy to build, well-documented and user friendly (Matlab based GUI and GitHub repository). The photo-stimulation laser is directed into an X-Y galvo scanner targeted to the specimen using a dichroic mirror and focused on the sample using a Plössl lens as scan lens which is also used as an objective. The scan lens/objective images the specimen onto a camera via tube lens (also a Plössl lens) in a 0.5X magnification ensuring to fit the extent of the mouse brain onto the camera sensor (USB-3 Basler acA120-40um).

The authors report short and reproducible onsite time (~ 0.5 ms) and block (mask) the stimulation source using the laser analog control (~0.5 ms). The system is reliable, aiming at up to 20 stimulation sites per sequence considered as quasi-simultaneous (10 ms). They minimize rebound by gentle ramping down of stimulation over 250 ms.

The system is fast to calibrate by mapping scanner positions to pixel space in the camera space and mapping stereotaxic coordinate onto the image of the exposed skull. The theoretical x-y PSF is 70 µm (measured ~90µm) while the authors make the point that due to scattering the photo-stimulation spot size (lateral extent) is about 1 mm in diameter. This is what they also observe in electrophysiological recordings using silicon probes. The effective radius of inactivation depends on laser power, but was about 1 mm for laser powers (1-2-4 mW) on which the authors observed significant behavioral perturbations - in several tasks: 1) a delayed response somatosensory discrimination, 2) a visual detection task assessing changes in temporal frequency of a drifting visual stimulus; and 3) a visual discrimination (International Brain Laboratory task) in which mice were tasked to report the location of visual stimuli by turning a wheel. As proof of principle, the authors used a photo-stimulation set composed of 52 bilateral sites positioned at 0.5 mm interval covering a large network of frontal, motor and somatosensory cortical areas. Indeed, photo-inhibition of frontal motor cortex sites produced robust increases in reaction time. In contrast, stimulation at other motor and somatosensory sites produced modest, but significant decreases in reaction times.

While the approach is not novel, it does serve the need of better disseminating this technique in the research community. Overall, the Zappit is well-documented and easy to build and use, and will have impact in increasing robust use of site directed photo-stimulation (exciting/inhibiting ensembles of neurons at particular ~1 mm size regions of interests across the dorsal surface of the brain). The authors also note that the axial resolution is ~1.5 mm.

Concerns & comments:

(1) While the authors argue that it offers the best utility to affordability trade-off - faster than motorized drivers and require much less power than DMDs (100X) and less expensive/easier to use compared to SLMs, in the current form, the manuscript does not clearly list the limitations of the approach. At such, in my opinion, the authors should include side by side comparisons (perhaps as a table). For example, clear statements should be included with respect to comparisons in lateral (x-y), axial (z) spatial resolution, as well as temporal sequential aspect of Zappit and other photo-stimulation techniques involving DMDs or SLMs.

(2) Is power really a limitation in terms of the laser sources? Or is this a disadvantage mainly because using less power has beneficial effects on the tissue health? It may be useful to provide metrics of comparisons along these lines between Zappit and DMD-based approaches.

(3) Arbitrary-scanning vs random scanning may be more appropriate to describe to strategy.

Reviewer #3 (Public review):

Summary:

An outstanding question in the field of high frequency oscillations (HFOs) in the context of epilepsy is how these oscillations emerge, considering that they occur at such high frequencies i.e., 250Hz well above the firing ability of single neurons. One hypothesis that has been suggested in the past is that neurons that fire in an out of phase fashion or rather at random intervals may contribute to a spectrum of HFOs ranging from 250-500Hz that observed in epilepsy. However, how possible it is that random action potentials could aggregate to the extent that they could give rise to HFOs in the so-called fast ripple (FRs) frequency range (>200 according to the authors) remains unclear. To test this hypothesis, they used computational modeling to randomly insert action potentials in a signal, and they found that this approach is sufficient to generate FRs. Some of the predictors of whether FRs could occur were neuronal count, firing rate and synchronization. Besides computational modeling, they used different model systems to test whether that would be possible to be observed in neuronal cultures, in epileptic rats (intrahippocampal kainic acid model), and human data. Neuronal cultures treated with picrotoxin did not show evidence that FRs could be generated more than chance aggregation of action potentials. They then asked whether synchronization and firing rate could play a role in the emergence of FRs. They found that changes in neural firing and synchronization, such as those occurring during differences phase of the sleep-wake cycle could affect the number of FRs occurring by chance aggregation, with more FRs seen during periods of wakefulness, a result that they replicated in human data.

The authors largely achieve their proposed aims of demonstrating that random neuronal firing can, in principle, generate FRs. Results from this study could influence current thinking around mechanisms generating FRs in epilepsy. The use of different computational approaches and model systems could offer new analytical methodologies for the study of FRs in the context of brain disease.

Strengths:

(1) The authors used a multi-level approach combining computational modeling with experimental datasets, including neuronal cultures, a rat model of temporal lobe epilepsy and human data.

(2) Identification of key parameters such as neuronal count, firing rate, synchronization and brain state in observed incidence of FRs generated through random aggregation of neural firing.

(3) Cross-species validation increases the likelihood of generalizability of the findings.

Minor weakness:

(1)The analyses conducted in human data lack direct comparison with sleep data due to no available data, but would encourage future investigations directly comparing HFOs during wakefulness and nocturnal sleep.

Comments on revised version.

The authors have addressed my comments and I have no further suggestions.

Reviewer #3 (Public review):

This revised manuscript by Fontana et al. aims to study how animals respond to fearful stimuli, with a specific focus on brain regions involved in predicting animals that passively freeze or those that actively evade the threat. I continue to be enthusiastic about the study. The study addresses an important question regarding individual variation in fear-related behavior and links these behavioral phenotypes to whole-brain activity patterns in adult zebrafish. The combination of a contextual fear conditioning paradigm, strain/sex comparisons, behavioral clustering, and AZBA-based c-Fos mapping makes this a valuable contribution to the field, not just in answering the question posed by the authors, but also in formulating a framework for using adult zebrafish for whole brain analysis of complex behaviors. Overall, I find the authors have responded to my concerns:

(1) I still think that separating memory acquisition and consolidation is an interesting question, and further use of the framework will need to eventually solve that; however, I also appreciate that this may be beyond the scope of the current study, and I appreciate the authors acknowledging this in the manuscript.

(2) Regarding Figure 3, I also agree that this is difficult to present differently, and I appreciate the authors adding text to the body to clarify things. My one request is that the sentence (lines 214-215) that reads: "This increase in evasion in the vehicle group likely represents a response to the water disturbance that occurs when solution is added to the tank." Be changed to: "This increase in evasion in the vehicle group may represent a response to the water disturbance that occurs when solution is added to the tank." While it is entirely possible, there are no concrete data to support that this is "likely."

(3) I appreciate the clarification regarding the PLS-derived contrasts in Figure 6A and in the body.

Overall, this is a really interesting paper that will have a wide-ranging impact. All of my concerns have been addressed.

Reviewer #3 (Public review):

Summary:

In this useful work, the authors characterize the auxin-based gene expression system (AGES) as a tool for studying ageing. They found that this system can be applied to ageing studies. In addition, they identified important drawbacks of the methods, including effects of insertion sites and sex on induction of the system, and that some auxin doses may have inadvertent effects on body mass and physiology. Overall, the study extends the AGES system for use in fly ageing studies and highlights some caveats. While the findings are solid pointers, more extensive characterization is needed to benchmark the extent of the caveats identified.

Strengths:

The study provides the first longitudinal evaluation of the AGES system's induction efficiency across the entire Drosophila lifespan. The authors also highlighted a number of caveats of the AGES system in ageing animals. These are all important points to be considered when using this system, and findings should be interpreted keeping these caveats in mind.

Weaknesses:

There were inconsistencies with auxin dosages between the figures.

(1) The authors used a higher dose of auxin (20mM) compared to the original AGES paper (McClure, 2022) in Fig 3. The auxin dose-dependent effects are not linear for TAG and protein levels, highlighting that the genotype-dependent effects may be highly variable and may yield quite different results in other studies.

(2) The results in Figure 4 showing the lack of induction in the brain are quite interesting; however, only 5mM auxin is tested. Characterizing dose-dependence for the variability of the induction in tissue types would be useful. At the very least, recapitulating prior results at 10mM should be done.

(3) Food intake and hydration status were not measured alongside body mass, TAG, and protein endpoints. The changes seen could be an effect of decreased feeding or fluid balance rather than metabolic reprogramming.

Reviewer #3 (Public review):

In the current work, Turrero Garcia et al. investigate the molecular, electrophysiological, and behavioral outcomes of conditionally knocking out cells from a unique developmentally-defined subpopulation in the lateral septum (LS). The authors focused on targeting cells from the Nkx2.1 developmental lineage that also expressed the transcriptional regulator Prdm16, which is uniquely upregulated in LS postmitotic neurons. They observed that this mutant line (Nkx2.1Cre;Prdm16fl/fl;Ai14; cKO) resulted in complete ablation of neurons from the Nkx2.1-lineage exclusively in the LS and not in the medial septum, making it an ideal model to study a lineage-defined subpopulation in the LS. They performed single-nucleus RNA-sequencing and uncovered four neuronal subtypes missing in the cKO. Furthermore, the authors validated this expression loss in the subtype expressing Crhr2 and observed a reduction of UNC-3 inputs (a neuropeptide with high affinity for Crhr2), highlighting additional disruptions in circuit connectivity. Loss of Prdm16 in Nkx2.1-lineage cells only resulted in mild electrophysiological changes in the LS. Finally, the authors performed a battery of behavioral assays to study anxiety-like behaviors and threat avoidance and observed increases in exploratory behaviors in some but not all assays. It should be noted that the cKO line also results in 30% loss of cortical interneurons, which could be contributing to the behavioral phenotype and not be exclusively due to LS loss. Overall, this manuscript takes a novel perspective by providing unique insights into how embryonic origin gives rise to mature molecular identity and distinct behaviors in adults. Therefore, it elegantly links developmental origin to mature molecular identity and function in the LS, an important question understudied in the field.

The conclusions of the work are overall supported by the data and limitations discussed, but some of the findings, in particular the histological and behavioral results, need to be extended.

Strengths:

(1) Utilizing developmental origin as a marker for mature neuronal identity and function is a valuable approach which remains under-utilized in the field and serves to provide a deeper understanding of how circuits are shaped to allow for appropriate behavioral responses.

(2) The authors perform a comprehensive analysis of the cKO mutant to determine the role of the developmentally defined Prdm16 in Nkx2.1-lineage cells at the molecular, cellular, electrophysiological, and behavioral level.

(3) The sn-RNAseq dataset in the LS of WT and cKO mice will be valuable to the neuroscience community.

(4) The authors perform an extensive array of behavioral paradigms investigating the balance between threat avoidance and exploratory behavior, performing all experiments in both male and female mice to determine whether the same developmental origin can lead to sex-specific differences.

Weaknesses:

(1) It remains unknown whether the reduction of UCN3 inputs to the LS is due to loss of the Nkx2.1-lineage in the LS itself or due to reductions in the number of UCN3 cells that provide innervation to the LS in the cKO (Figure 3). The authors speculate and include anecdotal observations that the perifornical region of the hypothalamus (PeFAH) provides inputs to the LS and could be the region driving the differences in UCN3 inputs in cKO. The authors should expand on this histological data and directly test whether the UCN3 inputs are indeed originating from PeFAH and whether the loss of Prdm16 in the Nkx2.1 lineage leads to a reduction in cell numbers in these inputs. These would disentangle the authors' claims on whether it is due to loss of Prdm16 in the Nkx2.1 lineage cells in the LS or whether it is due to loss of Nkx2.1 lineage neurons in upstream regions.

(2) The authors claim that there is an increase in exploratory drive in cKO mice, even though the dark-light test showed increases in time spent in the dark side for cKO mice in comparison to controls (Figure 5C). They discuss that this could be due to the mice being placed first in the light compartment of the chamber during the light hours, so they spend more time exploring the dark compartment of the chamber instead, which would be considered 'novel'. If this were the case, to make the results more solid, authors should place cKO mice in the dark compartment of the chamber during the dark hours and then record time spent in both chambers. If there was indeed an increase in exploratory drive in new environments, the authors should see increases in time spent in the light compartment.

(3) The result that cKO mice spend more time than controls exploring the inlet with TMT is of interest (Figure 5E, F). It needs to be highlighted that this is primarily the case in male mice and there is a trend in females. To confirm that the increases in exploration time of the inlet are not due to overall increases in general arousal, locomotion (e.g., velocity; pixels/frame) should be assessed in cKO vs controls.

(4) Statistical analysis correcting for repeated testing should be performed when running multiple t-tests in the same dataset, such as when analyzing histological results in Figure 1 and Figure 6 to increase confidence in the presented results.

(5) An important consideration that the authors address in the discussion is that the loss of Prdm16 in Nkx2.1-lineage cells is, for the most part, restricted to the LS, but other regions such as the cortex also show decreases in this population. Therefore, to strengthen the authors' conclusions that Nkx2.1-lineage neurons in the LS are indeed directly responsible for balancing threat avoidance and exploratory drive, targeted manipulation experiments, or at least additional c-Fos experiments assessing activity of Nkx2.1-derived cells in the LS will need to be performed in the future.

Reviewer #3 (Public review):

Summary:

Using a combination of Miniscope imaging and optogenetic manipulation, Amadei et al. reveal how oxytocin receptor neurons in the prefrontal cortex of mice control pyramidal subpopulations and socio-sexual behavior. This work was planned and executed carefully and provides a novel and important angle to study the oxytocin system in the cortex. According to their results, oxytocin receptor neurons help discriminate between sexual and non-sexual stimuli, most likely by controlling different pyramidal subpopulations that are either most active during trials that include a sexual stimulus or that include non-sexual stimuli. I highly appreciate this article; however, I have one major concern related to the modeling part.

Strengths:

(1) Well-designed experiments.

(2) Rigorous analysis.

(3) Generates a new avenue to study socio-sexual decision making and creates a hypothesis about the connectivity of oxytocin-sensitive circuits.

Weaknesses:

(1) Major

In their last figure (Figure 4), the authors generated a computational model that, according to the authors, reveals a potential network mechanism in which oxytocin receptor (OXTR) neurons are connected to both pyramidal populations with certain connectivity rules. Although the model seems to reproduce the experimental results, some assumptions of the model seem to be poorly supported. If I understood correctly, the authors simply assumed that the strength of the connections between OXTR neurons and MALE neurons is the same as the strength of the connections between OXTR neurons and OTHER neurons. The authors neither discuss literature supporting such connectivity nor provide experimental evidence for this. I also could not find information about the magnitude of the synaptic weights to each of these populations. I guess these parameters are critical for the outcome of the simulation, and it may be worth exploring the outcome of simulating the different combinations of connectivity and synaptic weights between OXTR neurons and pyramids, as well as the degree of recurrent connectivity within the pyramidal subpopulations. Further, the authors should at least discuss in depth how inhibitory OXTR neuronal subtypes (they have different properties that could potentially be implemented in the modeling) may match their computational model best. If the current model remains the most promising, the authors should clearly discuss which experimental trajectory should be taken next to actually provide proof for its correctness (e.g. whether and how it would be possible to determine the predicted connectivity experimentally).

(2) Minor

The authors state regarding counterbalancing in Figure 1 and Figure S4C: "The social presentation order (male or female first), as well as the locations of the social and milk options (left or right relative to start arm), were fixed over sessions within a given subject, but varied over subjects (Figure S4C)". In Figure S4C, it looks as if there are fewer animals in which the male was always presented first than animals in which the female was always presented first (~ 16 vs. 20). While the difference is not very big, it may be influential. To fully exclude a sequence effect, I would suggest adding male-first animals until both groups are the same size.

Reviewer #3 (Public review):

Kittur et al. ask whether human spatial memory is organized around topological relations (meaning coarse structural properties such as T-junctions, crosses, and holes) rather than around Euclidean properties such as angle and length. Across four experiments, adults and children studied letter-like figures and reproduced them from memory by drawing. The authors report two complementary patterns: metric features are systematically distorted, with angles pulled toward 90 degrees and line-length ratios compressed toward an average, while topologically critical features are comparatively well preserved. The central test contrasts T-junctions with L-junctions, which are visually similar but topologically distinct, since an L-junction reduces to a straight line, whereas a T-junction does not. A serial reproduction experiment amplifies both patterns across chains of participants, and a fourth experiment extends the findings to children aged five to eight.

Strengths:

The question is a good one and sits at a productive intersection of topics. It bears on debates about the representational format of cognitive maps, on proposals about the primitives of visual perception, and on a classic developmental claim from Piaget and Inhelder that has rarely been tested directly.

The drawing paradigm is well chosen and offers something that the group's earlier forced-choice work could not. Because participants produce an open-ended response, distortion of metric detail and preservation of structure can be observed within a single response, and the relationship between them can be examined directly. The serial reproduction experiment is a particularly effective use of this affordance. The choice of the T-junction versus L-junction contrast as the primary test is well-motivated, since it holds the number of junctions constant and varies only topological relevance. It is also worth noting for readers that the central claim of a representational privilege for topologically distinct features was previously established by this group using forced-choice paradigms in both adults and children. That a similar conclusion emerges from free generation is a genuine strength, since the two methods have very different sources of error.

The work is carefully executed. Sample sizes, dependent variables, and analyses were preregistered; stimuli were purpose-built for each question, including the deliberate exclusion of 90-degree angles so that no reference angle was available; drawings were double-coded; and the full set of raw drawings is being released publicly.

Weaknesses:

Drawing is treated as a transparent window onto representation, and motor limitations are not considered. Drawing is a motor act, drawing skill varies widely across individuals, and the manuscript does not discuss motor limitations at any point. As the study is designed, representational imprecision cannot be separated from difficulty of precise reproduction. The clearest way to resolve it might be asking adults to copy the figures exactly while the stimulus remains visible. If the biases persist under direct copying, then some portion of the effect is production rather than memory. Because the topological findings have already been demonstrated in keypress-only paradigms, this concern affects the metric distortion results most heavily, which are the novel contribution of the present paper.

Three distinct claims are treated as one, and the data speak mainly to the weakest of them. The paper moves between a claim about mnemonic robustness (topological features survive degradation better than metric features), one about representational architecture (topology is a base layer with metric detail superimposed on top), and a claim about priority (topology is encoded prior to metric detail). The experiments show evidence for robustness, which is a claim about what is lost first. Robustness does not entail architecture: an encoder with a single layer, whose loss happens to spare structure, produces the same pattern with no layered format and no claim about encoding order. Earlier work does address format, because false "same" judgments to topologically matched but metrically different items show that topology plays a role in what the system treats as equivalent. Preservation counting measures robustness, rather than equivalence.

Some alternative hypotheses to consider/address: (a) A capacity-limited memory that reconstructs from a prior produces the metric distortions with no commitment to topology. A literal absence of angle encoding, which the authors invoke, predicts noisy and unconstrained recall rather than recall pulled toward a particular value. The observed pattern reflects a structured prior. (b) The result that does discriminate might be confounded with local salience. A memory that adds uniform noise to all parts of a figure does not predict that T-junctions are preserved better than L-junctions; however, a three-way branch point is plausibly more locally distinctive than a corner, so a salience-weighted-but-topology-free account predicts the same ordering. (c) Motor simplification also predicts the same ordering, since omitting an L-junction converts a bend into a straight line, which is easier to draw, whereas omitting a T-junction requires dropping a stroke.

The better a feature works as a topological marker, the less variance it produces and the harder it is to test, so the method is best powered where the theoretical signal is weakest. Holes are the textbook case of a topological invariant and are reported to disappear from drawings less than one percent of the time, but they are excluded from formal analysis because they are at ceiling and have no matched comparison feature. It would be useful to see bidirectional rates for holes (both how often a hole disappears and how often participants spuriously close an open figure into a loop).

In Experiment 4, the conclusion of developmental stability rests on a nonsignificant effect of age, which is failure to detect a change rather than evidence of stability. An equivalence test or an estimate of the precision of the null is better support for developmental stability. Motor skill is confounded with age throughout. So this is an experiment that shows that the effect generalizes to childhood, but cannot adjudicate a developmental question.

In the serial reproduction experiment, chains were intermixed so that each participant contributed one drawing to each of ten chains. The final drawings are therefore linked through shared intermediate participants, and an individual with an idiosyncratic drawing style influences ten chains at once, so the reported degrees of freedom are somewhat generous. The analysis also focuses on the final drawings and sets aside the nine hundred intermediate ones, which are the data that would show where in a chain metric detail collapses and whether structure ever breaks.

To formalize the topology is to strengthen the argument: each figure is a one-dimensional complex (its underlying graph), treated intrinsically and up to homeomorphism. The homeomorphism type is what remains after suppressing all degree-2 vertices. Under this definition, every feature in this paper's taxonomy becomes one kind of object, namely a homeomorphism invariant of the graph: number of components, first Betti number, and the degree sequence of three or greater with its adjacency structure. Relatedly, the term "metric" needs to be unpacked. The 90-degree bias concerns angle, whereas the 4:2:1 result concerns length ratios, which are affine rather than strictly metric. The stronger statement available is that distortion appears at every level above topology in the transformation hierarchy while preservation occurs at the topological level, which connects directly to Chen's (2005) invariance hierarchy that is already cited.

Appraisal and impact:

The authors aimed to show that topological structure is preferentially retained in memory while metric detail is lost, and in the sense of relative preservation they succeed. The dissociation is real, replicates across two stimulus sets, amplifies under serial reproduction, and appears in young children. What the data do not establish is the stronger architectural claim that topology is a base representational layer, nor that the metric distortions specifically implicate topology rather than general properties of reconstructive memory. Separating these claims would communicate the well-supported result better. Conceptually, the work strengthens a growing case that coarse relational structure deserves a place alongside Euclidean properties in accounts of spatial representation. Practically, the public release of the full set of adult and child drawings, including excluded ones, is a resource that will support analyses well beyond those reported in this paper, and the serial reproduction design is a method that others will want to borrow.

Reviewer #3 (Public review):

Summary:

Normandin et al. explore the coding of stimuli predicting an aversive event in the prelimbic cortex. Stimuli could either be explicitly paired, explicitly unpaired, or novel but with an inferred association with the aversive event (generalization). Long-term tracking of GCaMP-positive neurons allowed them to examine how coding evolves out to a month following training. In general, they found two types of ensemble codes. One was ensembles coding for each stimulus independently, but with enhanced responding to the one eliciting a freezing response. The other was ensembles that responded to all stimuli in proportion to their similarity to the stimulus paired with the aversive event, either increasing or decreasing their activation with the degree of freezing elicited by a stimulus. Importantly, this second set of ensembles was more stable across days, potentially providing a memory trace.

Strengths:

(1) The authors track ensembles in prelimbic cortex over long time scales, providing valuable information on the consolidation of neural codes.

(2) Neural coding of generalization is examined, which is under-examined in the field.

Weaknesses:

(1) Difficult to determine if responses treated as encoding stimulus valence are driven instead by the behavior that the stimulus elicits, freezing.

(2) The study implies that the identified ensembles are causally related to valence memory, but no experimental interventions are performed to justify this.

Reviewer #3 (Public Review):

In this revised manuscript, White et al. aimed to understand the wound-induced syncytia formation behavior in wound repair of Drosophila melanogaster pupal notum. For this purpose, the authors characterized two different types of adherens junctions' outcomes during syncytia formation around the wound region - border breakdown versus apical shrinking which appear to happen in different time points and for different time durations. The authors characterized cell-cell fusion events using cytoplasmic, junctional and nuclear markers. They determined that about half of the cells within 70 um radii from the wound undergo cell-cell fusion. They studied wound induction on the border between control epithelia and pnr domain suggesting that Atg1 is required for post-wound syncytia formation and wound closure. They showed that during wound closure syncytia gradually invade the wound leading edge mostly by radial fusion events. The data suggests that intercalation of cells from the leading edge slows down the wound closure process. They propose that cell fluidity of syncytial cells plays a role in wound closure speed. Finally, the authors showed that actin is concentrated to the front edge of syncytia located in the wound leading edge. The authors described some aspects of syncytia formation during wound closure using different approaches.

Reviewer #3 (Public review):

Summary

Across species, dopamine release serves seemingly diverse functions, such as reinforcing memories and regulating locomotion and flight. However, whether distinct dopaminergic neurons (DANs) are allocated to each function is unclear. In this study, Toshima et al. have used the numerically simple organization of the Drosophila larval brain to answer this question. They use optogenetic activation to systematically stimulate a small set of DANs, individually and collectively, and study the effect on diverse functions such as memory formation, retrieval, and locomotion. The reproducibility of optogenetic activation is a strength of this approach. At the same time, this is a caveat, as optogenetic activation may not recapitulate natural modes of activation and may lead to outcomes not observed under natural conditions. They find that singly or collectively, DL1 DANs can induce punishment and/or safety memory formation and retrieval. DANs can even gate the expression of memory. Finally, the same DANs also modulate locomotion in the larvae. The authors speculate that dopaminergic neurons in other species may also share such overlapping functions. Their findings are nicely summarised in Figure 9.

Strengths

The study systematically activates neurons in the DL1 cluster. Individual and collective stimulation of the Dl1 DANs has been conducted to assess the induction and gating of aversive punishment memory, safety memory, and acute locomotion.

Specific adult Drosophila DANs are known to induce dual behaviors and functions. The same MP1/y1pedc DANs are recognized for gating appetitive memory expression and representing aversive teaching signals downstream of sensory stimuli such as electric shocks, bitter tastes, and heat. Neurons in the PPL1 cluster regulate adult flight and food-seeking behavior. The authors deserve credit for conducting an organized examination of dopaminergic neuronal functions in larvae, thereby making their findings more comparable and facilitating the proposal of a holistic model.

They have provided substantial evidence for their findings and have frequently presented replicated behavioral datasets. They have been transparent about the results that were difficult to explain. Additionally, they have provided an impressive body of supporting data to strengthen their main findings.

Weaknesses

As mentioned above, optogenetic activation may not recreate natural neuronal activation in response to external stimuli. This could have led to outcomes that will not occur under other natural circumstances.

Comments on revised version.

I appreciate the author's responses, and I do not have any comments or suggestions at this point.

Reviewer #3 (Public review):

Summary:

This manuscript examines functional plasticity in auditory cortices of people born deaf (or early deaf). Deaf participants (N=13, all native BSL signers) and hearing controls (N=18) performed a delay-match-to-sample working memory task in visual and somatosensory modalities, attending either to frequency (temporal task) or spatial pattern (spatial task). Using fMRI and univariate and representational similarity analysis (RSA), the authors test what type of information is represented in auditory areas of hearing controls and deaf participants. Different types of tasks and stimulus features are examined, including low-level sensory features (vibration/movement frequency and spatial position of stimuli on screen/hand) as well as task features (attending to space vs. frequency) and stimulus modality (somatosensory vs. visual).

There are a number of interesting findings. In early auditory cortices (right Heschl's gyrus), only Deaf participants show above baseline responses and only in the somatosensory task. In secondary auditory/multisensory STS, only deaf participants show above-rest responses to both visual and somatosensory stimuli. In RSA analysis, d/Deaf, but not hearing participants, show sensitivity to the frequency of somatosensory vibration when finger position is held constant (SFRm model). RSA in auditory cortices finds sensitivity to sensory modality (visual vs. somatosensory) information in both hearing and deaf participants, with an enhanced effect in the d/Deaf group. A similar d/Deafness enhancement effect was observed for task (frequency vs. spatial) but only within modality, not across, suggesting a less abstract representation.

Strengths:

The paper has a number of strengths. Running tasks in multiple modalities in the same study is a technical challenge and adds valuable information, since, as it turns out, early auditory areas of deaf people are sensitive to somatosensory but not visual frequency. Moreover, it makes it possible to look for modality coding.

The RSA analysis finds sensitivity to task and modality features not detectable with univariate analyses.

Overall, the findings of differences across modalities (visual and somatosensory) in auditory cortices are very interesting. Having parallel tasks in the two modalities makes it all the more important that only somatosensory stimuli activate HG. The discussion of this finding and ideas about alternative possible paths of somatosensory and visual information to the auditory cortices in d/Deaf individuals is also interesting.

The authors conclude that there is both evidence for shared and different function across hearing and deaf groups, and this makes sense. It's refreshing that the authors acknowledge that the simplicity dichotomy of preservation vs. change present in the literature and presented in the introduction turns out not to explain the findings.

Weaknesses:

The participant sample is not very large, but this is a difficult-to-recruit population, and it is within an acceptable range since the authors have taken care to run a robust study design and collect a large amount of data from each person.

Some weakness of the paper includes incomplete presentation of the results and conclusions that do not follow from the data.

The results are presented in a way that makes it hard to track what is significant and how the auditory ROIs are different or similar to the control regions. It is also difficult to connect the written text results with the figures. In some cases, the figures look like there is no significant effect, but the results report that there is one.

It is not clear that three-way interactions were tested for (e.g., group, by task, by modality). This complicates the interpretation of significant two-way interactions, e.g., task by group. For example, in STG a task by group interaction is reported, but the plot suggests that this is driven primarily by the somatosensory modality.

The paper suggests that frequency/temporal information is coded in auditory areas of d/Deaf participants but not location/spatial information. This is stated in the Results and in the Discussion as a major point. But this is not quite true in the somatosensory case and not true in the visual case at all, as far as I can tell. In the somatosensory case, since frequency is only coded in a finger-dependent manner, location is in fact coded in this regard. This pattern differs from what is observed in the somatosensory cortex, where frequency is coded in a finger-dependent and independent manner as well as location as such. In the visual case, it seems like temporal, i.e., frequency information, is not coded at all in the auditory ROIs. Although it is also puzzling that visual frequency is not coded in the visual 'control' ROI. An incomplete presentation of results motivates some conclusions that are not warranted, e.g., coding in the auditory cortex reflects a preservation of its function - i.e., frequency but not location coding.

A second related issue is that some dimensions (e.g., visual frequency, modality-independent task) show no neural response anywhere in the brain, including in the canonical visual, somatosensory, and amodal networks. For these dimensions, the current experiment does not offer a good test. This is fine but should be clearly stated in the results and the Discussion so there is no confusion about which hypotheses are really tested. Right now, the results say things like "the control ROI shows the expected pattern", but in some cases it's more complicated and failures to observe effects constrain what can ultimately be expected in auditory areas. This is okay, but needs to be made clear in the results and discussion, and auditory results need to be interpreted in this context.

Relatedly, the paper has no whole cortex searchlight analyses, and it is not clear why. If nothing comes out in the small sample of n=13, that is okay, but at least the collapsed hearing and d/Deaf sample data should be shown for each dimension. This will give the reader a sense of what is to be expected and contextualize the results.

Some claims are made which are not supported by the data: "However, it is likely that the representation of somatosensory frequency in deaf individuals relies on the same mechanisms used to represent auditory temporal frequency in hearing individuals." Likewise, the paper goes on to say, 'the underlying computations might be the same'. True, they might be, but they also might be different. No evidence is presented to support one or the other hypothesis, so both should be stated, and it should be stated that these cannot be distinguished based on the presented data.

Conclusions-wise, the paper sometimes makes sweeping claims that go well beyond what the evidence supports and fails to provide caveats. The first paragraph of the Discussion states: "Overall, these findings suggest that crossmodal plasticity relies on representational and functional configurations that are present across individuals and modulated by sensory experience." Such a sweeping conclusion about cross-modal plasticity in general is not supported or refuted by the present data. There is no evidence that 'representations' or 'functional configurations' are the same across groups. What are functional configurations? What representations are shared? Second, it is far too general to make claims about 'cross-modal plasticity' based on one study with one population.

Reviewer #3 (Public review):

The manuscript frames its work in fairness and disparities but does not show or directly test that its approach decreases differences between White and African Americans. While it is stated that the objective is not to equalize performance across groups, large parts of the paper repeatedly claim that the methods mitigate cross-ethnicity disparities and improve fairness. Improving prediction in African American participants relative to a non-adapted model is not necessarily the same as reducing the disparity between African American and White American participants. The adapted model should be evaluated in both groups, and the post-adaptation performance gap should be reported directly.

Additional prediction performance measures are needed. For example, in Li et al, different results and conclusions are made with MSE and the correlation between observed and predicted variables. In that paper particularly, aggression measures showed better correlation in African Americans but better MSE in White Americans. Such differences are important to note as they likely suggest different mechanisms.

Similarly, characteristics of the cognitive outcome need to be understood. For example, differences in MSE or MAE may reflect a difference in variance between the groups. The group with a larger variance will have a larger MSE. Correlation or other performance measures that are invariant to different mean or variance shifts can be helpful here.

While the authors note that for the paper they treat racial and ethnic backgrounds interchangeably, I do not think that is the best given the differences between them and the impact and history they have in American culture. Overall, the authors likely need to do a better job conceptualizing their results in the history of minoritized populations in the United States. It is immensely important not to treat them as biological domains without considerable qualification and to avoid language implying that observed domain differences are intrinsic properties of racial groups.

Changes in feature weight are not a proper way to identify the mechanisms of improved performance. At most, these analyses characterize how model coefficients change when target-group data are incorporated or upweighted.

The cross-validation strategy is suboptimal. First, the use of the matched splits of the ABCD data introduces data leakage. To match a validation set to the training set in such a manner requires that each split knows about the other split's characteristics. That is data leakage. Though the impact could be small. Second, African American breakdowns are not balanced across sites and scanners. Domain adaption methods may be learning a shortcut or proxy for African American like site, scanner, or something else. A likely better approach would be some sort of leave X sites out approach, where a model is trained on White Americans from a set of sites, adapted with African Americans from those sites, and applied (with and without adaptation) to the White and African Americans from the left-out sites.

Baseline models for comparisons to the domain adaptation are missing. Some simpler ones include a target-only model trained on the same 10-100 African American participants and a pooled model with a group indicator and group-by-feature interaction. Without these comparisons, it is difficult to know whether balanced weighting is learning target-specific neurobiological information or merely recalibrating the prediction distribution.

There are a few statistical issues:

(1) The repeated MAE estimates are therefore not independent observations. Paired t-tests cannot be applied across repetitions. Subject-level bootstrap or permutation procedures that repeat the complete training and testing process are needed

(2) The caption describes approximate 95% confidence intervals as {plus minus}1.96 × SD/n. Conventionally, the standard error would involve SD/sqrt(n). However, even if corrected, there would still be issues about the dependence among the overlapping resamples.

(3) Ten repetitions are likely insufficient, especially in the case of ten target participants. Results at n = 10 may be extremely sensitive to which children are selected. The authors should use substantially more repetitions and report the full distribution of results.

(4) The Friedman and Wilcoxon comparisons treat the 80 imaging phenotypes as the observational units. These phenotypes are highly dependent because they are derived from the same participants, many use overlapping images, and numerous task contrasts and structural measures are strongly correlated. This non-independence can make the comparison among adaptation methods look much more precise than it is. A hierarchical analysis by modality or a resampling strategy that preserves dependence among phenotypes would be more appropriate.

(5) The gap metric and AUIC are difficult to interpret. Gap is the absolute relative difference between target-group and source-group MAE, normalized by source-group MAE. It is sensitive to the denominator and may produce large values whenever source-group. MAE is relatively small. Reporting signed raw MAE differences and MAE ratios alongside this derived score would help. Similarly, the AUIC combines errors with an arbitrary sequence of target-sample sizes. IStatistically significant differences in AUIC do not necessarily indicate practically meaningful differences among methods.

(6) The correlation between baseline gap and adaptation gain is partly tautological. Those with the widest gaps have the most room for improvement and likely thus show the greatest improvement. While still of value, the authors may want to tone down their interpretation of the correlation and describe its limitation.

(7) Given that the sample sizes vary from approximately 4,000 to more than 11,000 depending on modality, the authors may want to consider a reduced sample matched in size across modalities. It is hard to fully know if the conclusion that connectivity is more robust given the wide-scale differences in sample size and feature dimensionality.

(8) PLS are sensitive to many factors like scaling and collinearity. Many recent papers have been written about their limitations when used for subtyping. Some of these hold for prediction too. I think showing the results are consistent with different prediction algorithms is needed. SVR and ridge regression are two common methods for regression prediction with neuroimaging data.

(9) The feature interpretation is partly circular. The method with the largest performance gain is selected, and its coefficient changes are then used to explain that gain. A method designed to give target observations greater influence will unsurprisingly change its coefficients more than naïve inclusion.

(10) The practical and ethical deployment scenario is underspecified. Supervised adaptation requires labelled cognitive outcomes from the target population and, as currently framed, may require choosing a model based on an individual's racial category. What are the implications of deploying race-specific models that need to be considered? It is not self-evident that this approach is preferable to developing a broadly representative model or directly modeling the social and technical sources of distribution shift.

(11) The paper is worded and interpreted much too strongly. The current study supports the conclusion that, within ABCD, giving a small labelled target-group sample greater influence can sometimes improve held-out target-group MAE relative to naïvely adding the same participants. It does not yet establish that the method improves fairness or identifies mechanisms of racial bias. Likewise, the abstract and conclusion overstate the results. The abstract states that all adaptation methods reduced target-group prediction error, while the Results show near-zero or negative benefits for several functional-connectivity phenotypes and instability of PRED and interpolation below 30 target participants. Similarly, "substantially reduce disparities," "improve equity," "consistently," and "practical path forward" are stronger than the analyses support. Finally, the limitations section is incomplete and omits the more consequential limitations.

(11) That only ten labelled participants are needed to change the results is troubling. This is a shockingly low number. Giving ten target observations disproportionate influence can move the fitted model, particularly when the balanced-weighting ratio is high. A measurable MAE change is therefore possible, but it may reflect a shift in intercept or slope rather than learning a stable target-group brain-cognition relationship. Further, the manuscript does not report the numerical improvement for the n = 10 condition in the text or a table. Visual inspection of Figure 4 suggests reductions of roughly 0.10-0.25 standardized MAE units for some high-gap structural phenotypes, approximately 10-20%, while low-gap connectivity phenotypes show little or no gain.

(12) The study lacks genuine external validation, which may be needed to fully convince readers that such a low number of subjects is needed to reduce biases.

Reviewer #3 (Public review):

Summary:

This paper addresses an important question (relationship between DN and dATN, and the role of retinotopic coding) and uses a set of novel analyses.

Strengths:

Important question, novel analytical approaches (pRF-informed functional connectivity analysis).

Weaknesses:

Some of the analyses are not described with sufficient clarity, especially the final analysis related to Fig. 3.

Comments on revised version.

Related to my previous comment 3), the removal of the labels "bottom-up" and "top-down" in the final analysis is a big improvement. However, I still don't fully understand how the 10 most aligned pRFs and the 10 most anti-matched pRFs are selected. The methods section on this has some ambiguity: "the 10 with the smallest Euclidean distance in RF center (x,y)". Does this mean that these are the pRFs closest to fovea? If not, what is the Euclidean distance referring to? Likewise, I don't understand how the anti-matched voxels are selected. This makes the interpretation of Fig 3 difficult.

My previous comment about baseline activation was to compare the matched voxels with randomly selected voxels, instead of with anti-matched voxels. The authors responded that there was a technical difficulty with this.

Reviewer #3 (Public review):

Summary:

Forbes et al. present a new approach for identifying cis-regulatory elements in large genomes. Using Parhyale hawaiensis, a crustacean with a large genome (~3.6 Gb, comparable in size to the human genome), the authors show that current methods for identifying cis-regulatory elements, effective in smaller genomes, are markedly inefficient in organisms with large genomes. To address this limitation, they combine bulk ATAC-seq and single-cell (sc) ATAC-seq to identify chromatin regions that are either ubiquitously accessible or specifically accessible in particular cell types. They further integrate comparative genomics across multiple Parhyale species (P. hawaiensis, P. aquilina, and P. darvishi), selected at appropriate phylogenetic distances (20-95 million years divergence), to pinpoint conserved open chromatin regions likely under functional constraint.

Using this strategy, the authors predict a set of ubiquitous and cell-type-specific cis-regulatory elements. Importantly, they validate these predictions using rigorous transgenic reporter assays, convincingly demonstrating that their approach can successfully identify functional regulatory elements where previous methods had failed.

Strengths:

The approach introduced by Forbes et al. is conceptually straightforward, efficient, and readily transferable to other organisms. The validation experiments show not only that a substantial proportion of the predicted elements are functional, but also that the method is capable of identifying both ubiquitous and cell-type-specific regulatory elements. Given that the identification of regulatory regions remains a major bottleneck in understanding the molecular mechanisms underlying processes of development and regeneration, this work has the potential to make a significant impact in developmental and regeneration biology, particularly for studies involving non-model organisms with large genomes.

An additional strength is the demonstration that only the genome of the focal species requires high-quality sequencing and assembly. In contrast, species used solely for comparative analysis can be sequenced at low coverage without assembly, substantially reducing costs and increasing the accessibility of the approach.

Weaknesses:

While the method is effective in identifying regulatory elements that are active ubiquitously or in differentiated cell types, it failed in detecting elements associated with developmentally regulated genes. This may be due to trivial reasons, such as very low level of expression of the selected genes. However, as acknowledged by the authors, it may also indicate inherent challenges in identifying regulatory elements associated with developmentally dynamic gene regulation, compared to those associated with genes expressed in differentiated cell types.

A second limitation, also acknowledged by the authors, is the absence of chromatin conformation capture data, which would help link distal regulatory elements to their target genes. This limitation may be particularly relevant for developmentally regulated genes, where long-range regulatory interactions may be critical.

Addressing these limitations will be an important direction for future work. Nonetheless, the approach as presented in this manuscript represents a key contribution that sets the stage for further methodological advances in the identification of cis-regulatory elements in large genomes.

Comments on revised version.

I am fully satisfied with the current version of the manuscript.

Reviewer #3 (Public review):

Summary:

The work describes an optofluidic automation setup to optically inhibit and enrich selected bacterial populations in confined microchannels through negative selection using light stimulation. The work is well described and the manuscript is well constructed.

Major comment:

The authors reported that methylene blue with 2uM incubation has superior performance than UV light. But it's also noted on line 152 there is an inhibition effect from the chemical affecting ~40% of the growth rate.

It will be noteworthy what is the growth curve or at least the MIC of methylene blue used on the MG1655 E. coli by the authors.

Significance:

The optics part of the work is well described, however the materials and methods details of the biological and microfluidic part can be extended.

Overall, the system demonstrated the practical use of combining microfluidics for enrichment of microbial population as a novel alternative method, despite that the efficiency is currently subpar to conventional methods.

But combining further with deep learning phenotype or growth rate monitoring, the technology represents a new path for phenotypic selection which is also novel that conventional methods cannot offer. The work will benefit readers in applied science seeking for new target enrichment based on optofluidics.

Reviewer #3 (Public review):

Summary:

This manuscript investigates the function of Su(Hw) binding sites found in two boundaries/insulators, homie and nhomie, in TAD formation that encompasses the eve gene. They tested the hypothesis that Su(Hw) binds homie and nhomie, thereby forming a stem-loop TAD. The authors used transgene reporters with various mutations, and the results support the hypothesis strongly.

Strengths:

They combine reporter assays (GFP and LacZ expression) with MicroC contact profiling to robustly support their conclusion. Overall, they propose how Su(Hw) mediates physical interaction between boundary elements (homie and nhomie).

Weaknesses:

The writing is quite dense and not easily accessible to outside readers.

Reviewer #3 (Public review):

The manuscript by Fuller et al describes a crosstalk between ARTG2A with components of the early secretory pathway, namely RAB1A and ARFGAP1. They show that ATG2A is recruited to membranes positive for RAB1A, which they also show to interact with ATG2A. In agreement with earlier findings by other groups, silencing RAB1A negatively affects autophagy. While ARFGAP1 was also found on ATG2A positive membranes, silencing ARFGAP1 had no impact autophagy. Notably, these ARFGAP1 positive membranes are not Golgi membranes.

The findings are interesting and the data are in general of good quality.

Comments on the previous version:

The revisions carried out by the authors are fine. The new data on ArfGAP1 and about the indirectness of the ATG2A and Rab1A interaction improve both clarity and strength of the manuscript. I have no further comments.

Reviewer #3 (Public review):

The authors have addressed most concerns from the initial review, significantly enhancing the manuscript. The expanded characterisation of the engineered THP1-CD1c system provides strong evidence that the observed T-cell responses are unlikely to result from residual conventional MHC recognition. The specificity of the CD1c-autoreactive T-cell lines is further confirmed by multimer staining, their inactivity against THP1-KO cells, and TCR-transfer experiments.

The revised data support the main conclusion that human CD1c-autoreactive T-cells recognise Mtb-infected cells that express Cd1c and exhibit cytotoxic effector functions. The difference between TCR-dependent recognition, shown by EM1 and EM2, and cytotoxicity, demonstrated by the original T-cell lines, is now more clearly presented.

Several limitations remain. The specific CD1c-associated lipid signalling molecule that enhances recognition of Mtb-infected cells has yet to be identified. Additionally, the bacterial luminescence assay lacks validation against CFU counts and cannot differentiate between intracellular and extracellular bacteria. The single-cell RNA sequencing was conducted with only two donors, and the lung immunohistochemistry remains qualitative without comparison to healthy or non-TB inflammatory tissues. These constraints limit detailed mechanistic insights and broader applicability, but they are appropriately reflected in the manuscript.

Overall, this is an important study that advances understanding of human CD1c-autoreactive T-cells in the context of mycobacterial infection. The evidence supporting the principal conclusions is solid, provided that altered CD1c-associated lipid presentation remains as a mechanistic hypothesis and the reduction in Mtb luminescence is not taken as direct evidence of selective intracellular bacterial killing.

Reviewer #3 (Public review):

Summary:

In this manuscript, Max Schwarze and colleagues examined the coupling distance between presynaptic Ca²⁺ channels and the vesicular release sensor at neocortical synapses in mouse. They propose that Ca²⁺ channel-release sensor coupling differs across cortical areas, with relatively loose (microdomain) coupling in prefrontal cortex (PFC) and tighter (nanodomain) coupling in primary somatosensory cortex (S1) for comparable pyramidal-neuron synapse types. To test this, they combine paired recordings and minimal stimulation with chelator manipulations (EGTA/BAPTA), mean-variance/MPFA-style analyses, presynaptic Ca²⁺ imaging, and computational modeling. They conclude that presynaptic coupling organization is area-specific in the mature cortex and contributes to regional differences in synaptic timing, reliability, and short-term plasticity.

Strengths:

This study tackles an important question and is strengthened by a cohesive body of evidence assembled from multiple complementary approaches. A major asset is the inclusion of high-value datasets, particularly the paired recordings between L5 pyramidal neurons and the systematic assessment of EGTA sensitivity, which provide a solid functional foundation for the authors' central claims. The work is further distinguished by its genuinely multimodal design: combining electrophysiology with presynaptic calcium imaging (and integrating these observations with quantitative analyses and modeling) offers a more mechanistic view of neurotransmitter release than any single method could provide. Overall, the direct, within-framework comparison of presynaptic release-control mechanisms across cortical areas for comparable synapse types is compelling and gives the conclusions a level of robustness and interpretability that is often difficult to achieve in studies of cortical synaptic diversity.

Weaknesses:

The principal limitation is incomplete cellular and synaptic specificity in parts of the study. The L2/3-L5PN experiments rely on minimal extracellular stimulation and therefore do not unambiguously identify the presynaptic neuron, its subtype or the number of recruited axons. Similarly, calcium imaging was performed at boutons on L5PN axon collaterals without identifying their postsynaptic targets. The imaging measurements could therefore combine boutons contacting pyramidal neurons and interneurons, potentially obscuring target-dependent differences in presynaptic calcium regulation. Recent connectomic studies demonstrate that local L5 pyramidal-cell axons can distribute substantial fractions of their output to inhibitory neurons, although the exact proportions depend strongly on pyramidal-cell subtype and distance along the axon.

The quantitative coupling-distance estimate is also model-dependent. The approximately 50-nm estimate for PFC synapses follows from a particular ring-like VGCC geometry and release-sensor model. The simulations demonstrate that this configuration is compatible with the data, but they do not uniquely identify the underlying molecular architecture.

Overall, the experiments support the narrower conclusion that the examined PFC and S1 synapses differ in functional Ca²⁺-channel-release-sensor coupling. The associated differences in synaptic timing, efficacy and plasticity are compelling, although coupling distance is not isolated causally from other regional differences in release-site number and quantal properties. The proposal that loose coupling is a general correlate of higher-order cortical function remains an interesting but currently speculative interpretation. Further comparisons across additional cortical regions and genetically or projection-defined synapse types will be particularly helpful in establishing the broader generality of this concept

Comments on revised version.

The authors have addressed most of my comments in the revised manuscript. I have only one remaining, relatively minor suggestion concerning point 5. I appreciate that the authors now acknowledge the possibility that the imaged boutons may contact different postsynaptic targets. However, the argument that interneuron-targeting boutons are likely to make only a minor contribution, based on the overall proportions of excitatory neurons or inhibitory synapses in the cortex, may not fully resolve this concern. Excitatory pyramidal neurons can distribute their outputs non-randomly across excitatory and inhibitory targets, and this distribution may depend on pyramidal-cell subtype and axonal distance. For example, a recent MICrONS/Allen Institute connectomic analysis of L5 extratelencephalic neurons in mouse visual cortex found that approximately two-thirds of their proximal synaptic outputs contacted inhibitory neurons. The proportion was close to 80% near the soma and decreased progressively with distance along the axon.

These findings concern a specific L5 pyramidal-cell subtype in visual cortex and therefore cannot be transferred directly to the PFC and S1 preparations examined here. Nevertheless, they illustrate that the postsynaptic target distribution of L5 pyramidal-neuron boutons cannot necessarily be inferred from the overall abundance of excitatory and inhibitory neurons or synapses.

Reviewer #3 (Public review):

Summary:

The authors explore the impact of a modest (<2-fold) reduction in the expression of Bcl11b on the differentiation of CD8+ T cells, with a special focus on the generation of memory-like cells (sometimes called "virtual" memory cells - or TVM). The manuscript covers a lot of ground, but highlights are using diverse models to show that reduced Bcl11b expression during thymic development (but not in naïve CD8+ T cells that have accessed the periphery) leads to enhanced generation of cells with phenotypic, transcriptional, epigenetic and functional characteristics of TVM; that this is a cell-intrinsic effect, but not apparently driven by enhanced responsiveness to cytokines (which promote TVM in some models); decreased Bcl11b improves T cell sensitivity at the mature (and likely in immature thymocytes). Myriad approaches and controls are used, providing a very thoroughly explored model.

This is a tour-de-force in applying the geneticists tool-box for investigating how a tantalizingly modest decrease in Bcl11b expression impacts the generation of TVM-like cells during thymic development. It is unreasonable to request additional data, but clarification of some key conclusions is needed.

Reviewer #3 (Public review):

Summary:

Tran et. al. investigate how inflammation in the skin influences the early stages of melanomagenesis. They use an autochthonous, tamoxifen-inducible mouse melanoma model (LSL-BrafV600E;Ptenfl/fl;Tyr::CreERT2, "BPT") to examine three inflammatory perturbations: transient depletion of regulatory T cells, acute ultraviolet-B irradiation, and contact hypersensitivity induced by 2,4-dinitrofluorobenzene (DNFB). They report that each perturbation promotes the recruitment of immune cells, especially inflammatory monocytes and macrophages, increased expression of inflammatory and tissue-remodeling factors, and enhanced vascular permeability, which ultimately increases the outgrowth of premalignant melanocytes measured by local pigmentation and expression of the melanocyte-associated gene Tyrp1. In the DNFB model, the authors showed that treatment with dexamethasone reduces the effects of contact hypersensitivity on pigmentation, inflammatory gene expression, and vascular leakage, potentially providing a translational angle.

Strengths:

An interesting observation is that transient Treg depletion promotes premalignant melanocyte outgrowth in the autochthonous BPT model while inhibiting the growth of transplantable B16 F10 tumors. This contrast is consistent with a role for Tregs in limiting inflammatory disruption of the skin during early tumorigenesis and highlights the value of autochthonous models. These findings may also have broader implications for understanding the stage- and context-dependent functions of Tregs in cancer.

Another strength of this manuscript is the comparison of three mechanistically distinct inflammatory perturbations. Treg depletion, UVB irradiation, and DNFB-induced contact hypersensitivity engage different inflammatory pathways but converge on myeloid-cell recruitment, inflammatory gene expression, and increased vascular permeability. This convergence strengthens the conclusion that an acute inflammatory microenvironment is associated with enhanced melanocyte outgrowth during the early premalignant phase.

Weaknesses:

The paper convincingly establishes a correlation between the inflammatory signature and melanocyte outgrowth across three distinct perturbations. However, the mechanistic claim that myeloid cells and/or vascular remodeling drive melanocyte expansion rests primarily on the dexamethasone experiments in the DNFB model. Because dexamethasone broadly affects immune, stromal, endothelial, and melanocytic compartments, these experiments do not establish that inflammatory monocytes/macrophages or vascular destabilization are specifically required for the melanocyte response.

A related limitation is that the proposed monocytic origin of the inflammatory macrophage population following perturbation remains inferred. Although the scRNA-seq data and pseudotime analysis in Figure 4 - Supplement 2 are consistent with a trajectory from monocytes to macrophages, they do not exclude local reprogramming of resident macrophages into an inflammatory state. This alternative is particularly relevant because resident macrophage populations have been implicated in vascular remodeling and tumor outgrowth (PMIDs: 36493773 and 40216154).

Finally, the assessment of melanocyte outgrowth is largely through increased pigmentation and whole-ear Tyrp1 expression. Although these measurements may reflect increased melanocyte abundance, they may also be influenced by melanogenic activity or increased Tyrp1 expression per cell. More direct evidence of melanocyte proliferation, such as Ki67 or EdU/BrdU staining specifically within TdTomato-positive melanocytes, would support the use of "expansion" and "proliferation" throughout the manuscript. Histopathological characterization of lesion architecture, atypia, proliferation, and invasion would also help establish the premalignant nature of the lesions.

Reviewer #3 (Public review):

Summary:

The authors tested the hypothesis that interactions among size- and age-matched rivals will lead to the emergence of social roles, accompanied by divergence in four aspects of individual phenotypes: growth, feeding behavior, fighting behaviors, and gene expression in clownfish.

Strengths:

The data of growth, feeding rate, and fighting behaviors support the authors claim.

Weaknesses:

The results obtained solely from the whole-body transcriptome are limited in supporting the authors' research question. However, the revised manuscript explicitly states this as a limitation.

Reviewer #3 (Public review):

Summary:

Zhu et al. present a proof-of-concept for targeting the lysosomal calcium channel MCOLN1/TRPML1endolysosomal ion channels to restore type 2 diabetes mellitus (T2DM). Using synthetic TRPML1 agonists (ML-SA8) and genetic manipulation, the authors demonstrate that TRPML1 stimulation triggers localized lysosomal calcium release. This calcium efflux sequentially activates CaMKKβ and phosphorylates AMPK at Thr172 in various cell models, including palmitic acid-induced insulin-resistant HepG2 cells. This signaling pathway promotes GLUT4 translocation to the plasma membrane and increases intracellular glucose uptake. When administered daily to diabetic db/db mice over six weeks, ML-SA8 lowers fasting and random blood glucose, improves oral glucose and insulin tolerance tests, reduces hepatic steatosis, and lowers serum ALT and AST levels.

Strengths:

Based on the TFEB-independent pathway activated by TRPML1 and the experimental approaches described by Medina's group (PMID: 31822666), the authors use a combination of pharmacological and genetic tools to dissect such an intracellular signaling pathway. Additionally, the animal experiments show consistent phenotypic improvements across independent metabolic parameters. The ability of ML-SA8 to restore glycogen deposition and clear hepatic lipid accumulation in db/db mice without causing weight loss or overt toxicity provides a strong rationale for exploring lysosomal targets in metabolic disease.

Weaknesses:

(1) The authors focus almost exclusively on hepatic GLUT4 to explain the observed glucose disposal. However, other glucose transporter isoforms such as GLUT2 dominate basal glucose transport. While the authors show increased AMPK phosphorylation in skeletal muscle and adipose tissue, they do not measure GLUT4 translocation or glucose uptake in these primary disposal organs. As a result, attributing systemic glycemic recovery primarily to hepatic GLUT4 translocation overlooks the major physiological roles of peripheral tissues.

(2) In both HepG2 cells and mouse liver tissues, ML-SA8 treatment increases total GLUT4 protein expression in addition to plasma membrane localization. Because total protein pools expand, the enrichment of GLUT4 in plasma membrane fractions cannot be cleanly attributed to acute vesicular translocation alone. The manuscript does not explain the timescale or mechanism behind this rapid total protein upregulation, leaving a mechanistic gap between acute ion channel gating and protein expression.

(3) While the in vitro specificity of ML-SA8 is well-controlled, the systemic animal experiments lack a specific rescue or knockout control. Small-molecule agonists administered intraperitoneally over six weeks can exert off-target effects. Without demonstrating that co-administering the TRPML1 inhibitor ML-SI5 blunts the therapeutic effect in vivo, or showing that ML-SA8 lacks efficacy in TRPML1-null mice, the definitive link between in vivo glycemic recovery and TRPML1 activation remains incomplete.

Reviewer #3 (Public review):

Summary:

In this manuscript, the authors describe two additions to an existing toolbox (SpikeInterface, Buccino et al., 2020, eLife). The first addition is an empirical simulator for extracellular recordings, in which spikes from predefined templates are added up with Gaussian noise. The second addition involves granting user-level access to intermediate processing steps along spike sorting algorithms. The authors demonstrate the toolbox by evaluating functions (e.g., event detection) or sets of functions (e.g., feature extraction + clustering) on their simulated data and suggest that a specific combination of function implementations provides performance improvement relative to kilosort4 (Pachitariu et al., 2024, Nature Methods).

The validity of the work is poor. In particular, the simulator is unrealistic and the ground truth dataset is too short. Several spike sorters are used as straw men, and most sorters compared have never been described in peer-reviewed literature or in sufficient detail. The purely feedforward architecture of the modules is very limiting and irrelevant for modern sorters. Finally, the reporting of results is sporadic and does not follow scientific reporting standards.

General comments:

(1) Abstract, lines 14-16: "We then leverage these results to create a modular component-based spike sorter that can outperform Kilosort 4 on dense and large simulated recordings and produce similar quantitative results on real data." However, the artificial data are not "large" - they are very short. And "similar quantitative results" cannot be assessed on real data because those data do not have any ground truth. Because this is a revision and the authors have already received similar feedback from this Reviewer, I am not sure what to recommend.

(2) In a previous comment, I indicated that the simulator itself is overly simplistic, and indicated that as far as I am concerned, the authors must improve it in one of two ways: (1) use a set of biophysical equations, with multi-compartmental modeling of currents and return currents; (2) use noised data from extracellular recordings; or (3) some combination thereof. The authors explained in their answer - but not in the MS - some of the shortcomings of biophysical simulators, but chose to do neither. My comment therefore remains unaddressed in the MS.

(3) In a previous comment, I indicated that the duration of 10 minutes is too short. The authors chose to extend the duration to 30 minutes, which is insufficient for units that have low firing rates. Units that fire e.g., 0.1 spikes/s would have fewer than 200 spikes. If this MS is to be taken seriously, the simulation should be done on durations that (1) are similar to the potential applications - which may be many hours or even days; (2) allow identification of low-firing neurons. Therefore, about 3 hours is the bare minimum. Therefore, at present, my comment remains unaddressed.

(4) In a previous comment, I indicated that some sorters have never been described in peer reviewed papers and therefore, they must either be removed from the present comparisons or be described in full. The authors chose to persist in relying on un-reviewed online documentation. Thus, my comment remains unaddressed, and the comparisons that involve those sorters (e.g., TDC, TDC2, SpyKING Circus 2) are simply invalid.

(5) In a previous comment, I indicated that some sorters (SpyKING Circus 2 and TDC2) are effectively straw men and suggested to reorganize the MS to demonstrate its main goal. Specifically, I suggested to reorganize the manuscript so that after every module is evaluated separately based on a limited ground truth dataset, a single "best" sorter would be constructed, and then tested extensively (and compared to the de facto state of the art). Such reorganization would both demonstrate the utility of a modular approach and clarify the general usefulness of the outcome. The authors agreed that these are straw men and gave a historical account of why these were included. However, they did not explain these considerations in the MS itself, and chose not to reorganize the MS. Therefore, my comment remains unaddressed.

(6) In a previous comment, I commented about the presentation, description, and interpretation of the results, and indicated that the choice to report point estimates makes any conclusions based on those results invalid. The authors did not make any changes to the reporting. Therefore, none of the results reported in the MS can be taken as an outcome of scientific inquiry. In other words, the MS does not deliver any solid findings - neither scientific nor methodological.

Reviewer #3 (Public review):

Summary:

The authors systematically compare six culture-media formulations using induced pluripotent stem cell-derived retinal pigment epithelium and fetal retinal pigment epithelium. They examine cell morphology, marker expression, barrier function, polarized secretion, lipid accumulation, ultrastructure, mitochondrial respiration, glycolytic function, and intracellular and extracellular metabolites. The results demonstrate that culture-medium composition and the choice of serum or B27 supplementation substantially influence retinal pigment epithelium phenotype and metabolism. Rather than identifying a single optimal medium, the study provides a comparative framework to guide medium selection according to the biological question being investigated.

Strengths:

The head-to-head comparison of six media under otherwise similar culture conditions addresses an important source of variability in retinal pigment epithelium research. The study uses a broad range of complementary approaches, including imaging, transepithelial resistance, electron microscopy, extracellular flux analysis, and targeted metabolomics. The inclusion of both induced pluripotent stem cell-derived and fetal retinal pigment epithelium increases the potential relevance of the findings across different cell sources. The matched comparisons of serum and B27 supplementation within MEMα and human plasma-like medium are particularly informative because they help distinguish supplement-associated effects from those caused by the basal medium. Overall, the dataset has the potential to serve as a valuable resource for selecting culture conditions and interpreting findings across retinal pigment epithelium studies.

Weaknesses:

The most important limitation is that the experimental unit and degree of biological replication are not clearly defined. It is unclear whether individual observations represent independent donors, clones, differentiated lines, culture preparations, wells, images, or sections. This makes it difficult to determine the independence, robustness, and generalizability of several comparisons.

The metabolic analyses also require additional methodological clarification. For intracellular metabolomics, the culture format, cellular biomass, extraction volume, pooling strategy, and normalization method are not reported sufficiently. Normalization of extracellular measurements to unspent medium accounts for differences in starting metabolite abundance but not for differences in cell number or biomass. Similarly, normalization of intracellular signals to medium 1 does not correct for differences in the amount of cellular material extracted.

For the Seahorse experiments, the main figures present unnormalized values even though the media produce differences in cell number, size, and protein content. These raw measurements represent total metabolic activity per well and may not reflect activity per cell. It is also unclear how normalization was performed because the Methods describe cell-count and protein measurements from two wells, whereas the stress tests included five to six wells per condition. In addition, measurements obtained after transfer into a common assay medium reflect metabolic adaptations retained from the preceding culture conditions rather than real-time metabolism within the original media.

Other limitations include insufficient information about the biological replication underlying the sub-RPE deposit analysis and the inability to fully interpret the effects of X-VIVO 10 because its composition is proprietary. Finally, public availability of the underlying metabolomics data would be important for a study intended to serve as a community resource.

Reviewer #3 (Public review):

Summary:

The primary objective of this study is to develop high-throughput screening assays utilizing homogeneous 3D cell cultures that more accurately replicate the intricate architecture and cellular communication found in tissues. The authors have chosen pancreatic islet β-cells as a model system to evaluate agents that modulate insulin release, which is particularly relevant given the increasing prevalence of diabetes mellitus-a significant global health concern. Moreover, the incorporation of human-based 3D spheroids, organoids, or organ-on-chip technologies into drug discovery protocols is essential for enhancing clinical translation, as candidate compounds identified using animal models have often demonstrated limited success in clinical settings.

Strengths:

This study was thoughtfully planned and skillfully carried out. The use of micropatterned hydrogels to observe 19 spheroids at once is an ingenious aspect, which has been effectively validated with Ca microfluorography. Overall, I found this investigation to be exceptionally well-executed and free from notable flaws, as the results clearly back up the conclusions. Additionally, the developed method achieved the proposed aims, providing a high-throughput format with 3D cultures. I believe this study deserves publication.

Reviewer #3 (Public review):

Short overview:

This study asks whether the perception of a volatile and attractive cue, diacetyl, leads to changes in metabolic nutrient-responsive programs in C. elegans. Using multi-omic and genetic evidence, they connect the transcriptional response to diacetyl to early activation of the DHAP-glycerol shunt, suggesting worms may activate this metabolic pathway in preparation for food intake. This work also identifies transcription factors involved, and while it does not test whether this response is diacetyl-specific, could identify a conserved pathway of food intake preparation.

Summary:

This study asks whether C. elegans can use a volatile food cue alone, in the absence of ingestion, to anticipate nutrient availability. Using the attractive odorant diacetyl, the authors show that fasting worms rapidly induce the DHAP-Glycerol shunt, a metabolic pathway normally associated with glucotoxicity and hyperosmotic stress, and that this response depends on the transcription factor MDT-15. This drives measurable metabolic rewiring (glycerol and phosphatidylglycerol accumulation) and confers protection against subsequent hyperosmotic stress. Prolonged diacetyl exposure further triggers a second, HLH-30-dependent wave of fmo-2 expression linked to depleted NTP levels, resulting in enhanced heat tolerance and food-seeking behavior upon refeeding. Together, the authors build a testable model for a pathway linking sensory cue detection to gene expression, metabolism, and physiological changes.

Strengths:

The central finding that smell alone without ingestion activates a metabolic-stress adaptation pathway is highly interesting and supported by a convergence of methods including RNA-seq, transcriptional reporters, metabolomics, and functional behavior assays. The transcriptomic time course distinguishes two temporally separable gene expression waves (the shunt at 30 min and fmo-2 at 90 min), and epistasis experiments comparing food status and osmotic stress (Fig. 1i-j) convincingly argue diacetyl acts as a food-predictive cue rather than mimicking osmotic stress. A particular strength is the test of the relationship between the two pathways identified in the study. RNAi knockdown of DHAP-Glycerol shunt enzymes block fmo-2 induction, and pretreatment with salt then switching to food deprivation shows that it is the shunt's activation state that drives fmo-2 expression. This is further reinforced by depletion of energetic mechanism (NTP/ATP). Figure 5 extends this upstream to MDT-15 validated through gene expression, metabolomics, and functional readouts. Throughout the study, transcriptional and metabolic findings are paired with functional outcomes such as hyperosmotic protection, heat stress resistance, or survival, strengthening the paper's model. Together, the authors largely achieve their aim of establishing that a food-related olfactory cue is sufficient to trigger an anticipatory metabolic and transcriptional program. The core claim that diacetyl sensing activates the shunt and subsequent fmo-2 induction and physiological protection is well supported by convergent genetic and biochemical experiments.

Weaknesses:

The authors show that diacetyl-induced fmo-2 induction does not require canonical olfactory sensing since neither diacetyl receptor mutants (odr-10, sri-14) nor a cilia-deficient strain (daf-19; daf-12) blocked the response. However, the pathway characterized here is defined almost entirely through diacetyl, and it remains unclear whether the anticipatory response reflects general food-predictive olfaction or a diacetyl-specific effect. This distinction is important because the authors' central hypothesis is whether volatile food cues in general can be used to anticipate nutrient availability. Testing a limited number of additional attractive odorants, ideally sensed through distinct chemosensory receptors, for a limited number of phenotypes, would establish whether this pathway is generalized to food-predictive smells or only diacetyl. This would increase the impact of the work. Identifying the mechanism(s) for diacetyl perception would as well, but is much more challenging and less likely to be feasible in this work.

The RNA-seq and reporter data disagree on when fmo-2 induction begins, and thus do not fully support that there are two temporally distinct waves. RNA-seq (sampled at 5, 15, 30, and 90 min) shows fmo-2 is not a significantly differentially expressed gene at 30 min and only reaches significance at 90 min, while the fmo-2 transcriptional reporter (Fig.S3a) shows detectable induction as early as 30 minutes. Other readouts in the paper use the reporter for fmo-2 but qPCR for shunt genes, further muddling the timing since mRNA should precede reporter visualization. Since reporter signal generally lags transcript level detection, this discrepancy could be further clarified through a time-course qPCR (like what was tested for the shunt genes) between 30 and 90 minutes. This would help establish whether the two waves are separated by time or whether this reflects a difference in detection method.

Minor weaknesses:

In Figure 1i, gpdh-1 induction is compared between control and 200 mM NaCl (3 hr exposure), with food present or absent. While this directly tests food status, a 3-hour exposure approaches the ~5-6-hour window previously shown to be sufficient for salt-food associative learning to form (worms move toward the high-salt side of a gradient plate if pretreated with high salt and food). This raises the possibility that, in the food-present condition, part of the measured gpdh-1 response could reflect an emerging learned association between salt and food forming during the assay itself, rather than purely reflecting an interaction between nutrient status and osmotic stress signaling. Testing a shorter exposure window (e.g., under 1 hour, as used for the diacetyl exposure in panel j) would help clarify this.

Thrashing is used throughout the paper as the primary readout for hyperosmotic stress resistance, including the central result that diacetyl protects against subsequent hyperosmotic stress (Fig. 2g). However, the authors don't explain why this was chosen as the main stress-resistance readout.

nhr-49 knockdown reduces gpdh-1 induction in diacetyl-mediated hyperosmotic protection but has no effect on development or survival on sustained hyperosmotic stress, raising the question of how the role of nhr-49 may be distinct from mdt-15 in this context.

Reviewer #3 (Public review):

Summary:

This is an interesting study investigating the relationship between transdiagnostic symptom profiles and computational parameters of abstraction and metacognition. The authors found that a transdiagnostic dimension they term compulsive hypersensitivity was negatively related to abstraction ability and metacognitive sensitivity, and a dimension termed social withdrawal was positively related to metacognitive sensitivity. Overall, the question of whether and how higher-level cognitive processes relate to transdiagnostic psychiatric factors is interesting and addresses a relevant gap in the literature.

Strengths:

This is an overall well-designed study, and the authors have clearly given considerable thought to data quality and validation during the study design. This is evident, for instance, in the use of multiple approaches to assess inattentive responding and acquiescence tendencies. In addition, the use of advanced modelling approaches for the task data represents a strength and allows the authors to capture individual differences in task behaviour in a more nuanced and mechanistic manner in comparison to what would have been possible with model-agnostic analyses.

Weaknesses:

Nevertheless, I would like to raise several concerns, detailed below.

(1) My most pressing concern regards the exclusion rate of roughly half the sample. Of the 512 participants who were tested, only 249 (48.6%) were included in the final analyses, meaning that more than half of the recruited participants were excluded. Although the authors state that these exclusions were based on preregistered criteria, this high exclusion rate still warrants further investigation. Pre-registering exclusion criteria does not eliminate the potential for selection bias or establish that the resulting sample is representative of the recruited sample. In fact, the authors even report that the excluded participants differed significantly on several psychiatric scores. I believe that a detailed account of the exclusion, including how included and excluded participants differed on relevant demographic or study variables, would be beneficial.

Additionally, the authors state that a sensitivity analysis including participants excluded from the primary analysis was preregistered but was not conducted because the excluded and included participants differed significantly. This does not seem a sufficient justification for omitting a preregistered sensitivity analysis; in fact, systematic differences between included and excluded participants warrant this kind of sensitivity analysis. I agree with the authors that this may lead to changes in task parameter estimates. However, I believe this change would be an informative result of the sensitivity analyses rather than a methodological problem to avoid.

Finally, I would like the authors to clarify some inconsistencies in the preregistered exclusion criteria. In the pre-registration, they first state that all participants who fail any infrequency question will be excluded. Later in the pre-registration, they state that participants with two or more failed questions will be excluded and that a sensitivity analysis will be done, in which participants with only one failed question are retained. Neither of these criteria matches what is reported in the manuscript ("Attention check + infrequency item mistake more than 2").

(2) The current sample size of n = 249 participants is not sufficient for a factor analysis with 176 questionnaire items, and I believe that the solution of using factor weights from a previous larger sample is generally sensible. I also commend the authors for wanting to assess the validity of this approach. However, both the methods and results reported for the comparison between the factor analysis based on the current, smaller sample and the large, previous sample are insufficient and warrant substantially more detail. Firstly, it is unclear to me whether the three-factor solution in the factor analysis with the current, smaller sample was empirically grounded or whether three factors were extracted to match the three-factor solution from the larger sample. For both factor analyses, I would recommend reporting the factor extraction methods, the criterion used to determine the number of factors, and whether alternative factor solutions were considered. Secondly, it is unclear what exactly was correlated across the two factor analyses (e.g., factor scores, item loadings, factor weights?). Finally, I do not believe that the result of significant correlation is sufficient to conclude that the dimensions replicate between samples, particularly given they indicate only moderate correspondence (r = 0.46, for instance, corresponds to only 21% shared variance), thereby providing very limited evidence for replication of the factor structure.

These concerns are particularly pressing given that the authors state the consistency of transdiagnostic symptom dimensions across samples and countries as a primary result in the discussion.

(3) It is also unclear to me whether any exclusion was applied based on the first part of the Deary-Liewald reaction time task. The supplement implies that it was ("This task result was used to [...] exclude participants who exhibit problematic behaviours"), but I could not find a corresponding report in the manuscript.

(4) The manuscript refers to an attentional check questionnaire used to identify and exclude inattentive participants, but does not provide a reference for this measure or describe the questions included. Given the substantial overall exclusion rate, it is particularly important that all exclusion procedures and criteria are described in sufficient detail to allow readers to assess their appropriateness and reproducibility. The authors should therefore provide the relevant reference and/or report the specific questions and criteria used to determine inattention.

Relatedly, when describing the control-neutral items in the Supplementary Materials, the authors state that further details are provided in the Supplementary Materials. However, I cannot find another, separate Supplementary Material that may contain this information

Reviewer #3 (Public review):

Summary:

The cell wall and plasma membrane are tightly associated in plant cells, but even after plasmolysis, sites of strong contact remain between the plasma membrane and cell wall. Several hypotheses have been introduced about the molecular makeup of these sites of PM-CW adhesion (e.g., Rui et al 2026 Cell; Qin et al 2026 Current Biol; Pérez-Sancho et al 2025 Cell). Here, the authors implicate two transmembrane lectin proteins in PM-CW adhesion via overexpression in Nicotiana benthamiana and via Arabidopsis knockout phenotypes for one of these candidates, the lectin receptor kinase LecRK-1.9. They further show that the PM-CW adhesion function of LecRK-1.9 requires the extracellular domain, suggesting that this lectin-like domain may interact with the cell wall. Interestingly, LecRK-1.9 has also been implicated in extracellular ATP binding in the context of biotic and abiotic stress responses (e.g., Choi et al 2014 Science).

Strengths:

Overall, the work is carefully conducted with high-quality imaging and quantitative image analysis. The results present an interesting candidate for future studies of plasma membrane-to-cell-wall attachment.

Weaknesses:

There are two major caveats to this work. First, all work was conducted with the kinase-dead version of LecRK-1.9, which eliminates a significant biological function of this protein, as evidenced by the major differences in expression pattern of wild-type vs kinase-dead LecRK-1.9. Second, controls are essential to document the expression levels of different protein variants and controls, since LecRK-1.9 expression is correlated with Hechtian strand density.

Reviewer #3 (Public review):

Summary:

In this manuscript, Flamholz et al. sought to determine whether consistent and significant interactions exist between the gut microbiome and disease pathology in sickle cell disease (SCD). By sequencing and analysing metagenomes from faecal samples collected from 98 SCD patients and 46 control subjects, they identified community-level shifts in both the bacterial and proviral gut microbiome of SCD patients. They further reported correlations between the proviral microbiome and multiple blood cytokines, whereas similar associations were not observed for the bacterial microbiome.

Strengths:

This work includes the largest SCD cohort analysed to date, enabling analysis with relatively strong statistical power. In addition to profiling the bacterial microbiome, the study also examines the gut proviral microbiome, thereby providing a more comprehensive investigation of the topic. The newly generated metagenomic dataset will also be valuable for further meta-analysis by the wider community. Overall, the authors have largely achieved their aims.

Weaknesses:

This study represents a single-centre cross-sectional investigation, and most findings remain correlative in nature. Additional mechanistic and/or longitudinal evidence would be required to unravel causality and the underlying mechanism.

Reviewer #3 (Public review):

Summary:

Using mice that overexpress S1PR1 in myeloid cells or specifically in neutrophils, the authors show that increased S1PR1 promotes neutrophil release from the bone marrow and accumulation in blood and peripheral tissues without causing baseline tissue injury. These cells acquire a CXCR4-high, CXCR2-low, CD101-low phenotype, survive longer, and display enhanced mitochondrial metabolism and mTOR signaling, together with reduced apoptotic, inflammatory, and ROS-related programs. Although phagocytosis is preserved, ROS production is markedly reduced. This is associated with impaired bacterial clearance in the lung but improved outcomes during influenza infection, including better survival, less weight loss, improved oxygenation, lower viral burden, and reduced lung inflammation. In contrast, myeloid S1PR1 deletion produces little detectable phenotype. The authors therefore propose that S1PR1 separates neutrophil persistence from inflammatory function, improving tolerance to viral lung injury at the expense of antibacterial defense.

Strengths:

This is a technically solid paper using novel mouse models to overexpress S1PR1 specifically in myeloid cells as well as neutrophils. The data are striking with respect to neutrophil expansion. The diverse roles of neutrophils and their population heterogeneity are an important scientific area that has led to many recent breakthroughs - PMC11785525; PMC12823425, thus this is a timely study.

Weaknesses:

The study mainly demonstrates what S1PR1 overexpression is sufficient to do, rather than establishing the physiological role of endogenous S1PR1. The conclusions should therefore be narrowed unless the authors provide stronger loss-of-function and physiological validation. As written, the abstract ("S1PR1 promotes mitochondrial fitness, enhances survival, and reduces inflammatory output") and the conclusion ("S1PR1 serves as a key regulatory axis") are sufficiency claims but should not be promoted as necessity claims. The honest sentence is: "Thus, a better conclusion would be that enforced S1PR1 expression is sufficient to reprogram neutrophils".

The authors do not confirm efficient S1pr1 deletion in neutrophils. Furthermore, the knockout is examined only under steady-state conditions and limited in vitro stimulation, but not in the bacterial or influenza models where the transgenic phenotype is observed. Without these experiments, the study cannot establish whether endogenous S1PR1 is necessary for the reported functions.

The degree of S1PR1 overexpression is not quantified relative to normal physiological levels. The authors should determine whether naturally occurring S1PR1-high neutrophils display the same survival, metabolic, trafficking, and inflammatory features observed in the transgenic cells.

Analysis of relevant human or mouse datasets, including sepsis, ARDS, viral infection, cancer, or aging, would also help establish whether this neutrophil state exists physiologically.

Surface S1PR1 expression appears similar between control and transgenic neutrophils, whereas total intracellular receptor is increased. This suggests that the phenotype may depend on receptor internalization or endosomal signaling. An internalization-deficient S1PR1 model, such as S1P1-S5A, would help distinguish sustained surface signaling from internalization-dependent signaling. The authors should also determine whether the phenotype requires ligand binding, Gi signaling, and mTOR activity.

The reduction in CXCR2 and decreased neutrophil accumulation in the airways could alone explain the protection from influenza-induced lung injury. The current experiments do not clearly distinguish neutrophil reprogramming from defective migration into the alveolar space.

Although this may be outside the scope of the current study, the authors should directly test whether CXCR2 inhibition reproduces the phenotype.

The reported reduction in viral load should also be confirmed using plaque assay or TCID50, and the possible contribution of NET formation should be examined.

Reviewer #3 (Public review):

Summary:

Most rod-shaped bacteria grow by one of two mechanisms: growth from the pole or growth from the midcell. It is rare for a single species to utilize both modes of growth, although a few examples do exist. Here, the authors have artificially induced E. coli cells to grow from the poles, either by expressing mreB from Myxoccocus xanthus in E. coli, leading to the mislocalization of MreB to the poles in large aggregates, or by forcing the localization of major cell wall synthesis proteins to the cell pole. The fact that cells switched modes of growth suggests an evolutionary pathway from midcell to polar growing cells as well as suggests that there might be unknown conditions in nature when cells may switch growth modes.

Strengths:

(1) The authors use a strain that has replaced mreB with a functional fluorescent version at the native site. This eliminates any effects of having two copies of mreB. Because MreB is fluorescently tagged, they can monitor its localization when mreB from M. xanthus is expressed in E. coli. They notice that MreBec now forms bright polar foci and that there appear to be changes to the cell wall at the pole.

(2) D-amino acids are specific to the cell wall, and fluorescent versions (FDAA) have been used to mark sites of new cell wall insertion. The authors use these FDAAs to determine how cell wall synthesis correlates to MreB and if that changes when MreBmx is expressed. Again, there is pretty clear evidence that cell wall synthesis follows MreB localization to the pole.

(3) MreB itself does not synthesize the cell wall, but localizes the proteins, such as PBP2, that do. Using a published method to force proteins to the pole, the authors show that when they target PBP2 to the pole, they can phenocopy the polar growth seen when MreB is polar. Interestingly, these cells become resistant to A22, a drug that targets MreB, suggesting that localized growth at the pole does not require MreB and is sufficient to maintain rod shape.

Weaknesses:

(1) The authors do not show what the poles of control cells look like, making it difficult to determine if there is a change when MreBmx is expressed. However, the localization of both MreBec and MreBmx clearly forms bright foci at the pole.

(2) While more quantification is needed, the authors show some evidence that RodZ, an MreB interaction partner, is needed for this polar growth, as cells lacking rodZ still form foci at pole-like regions when MreBmx is in the cell; however, these cells remain spherical and do not elongate from these foci.

When MreB is deleted, and cells become spherical, the authors were unable to cause the polar growth mode. They suggest that this is due to the lack of a preexisting pole; however, experimental evidence to test this is missing.

Conclusion:

Overall, the authors do a good job of showing that E. coli can grow with a polar method rather than a midcell method of cell wall insertion. It is unclear why MreBec forms at poles when MreBmx is present and even if this MreB is functional. The foci look similar to inclusion bodies, which are normally aggregates of misfolded proteins that migrate to the poles. Past work has shown that when MreB is more polarly localized, branches form, which is not seen here. Importantly, the authors also show that there is feedback between the localization of MreB and PBP2 as both appear to regulate the localization of the other.

Reviewer #3 (Public review):

Summary:

This study modeled vocal learning in zebra finches with a network of three components: a pathway with delayed/slow Hebbian learning that mimics the HVC-RA projection, a pathway with reinforcement learning that mimics the HVC-BG-RA projection, and a motor unit that mimics the syrinx and produces song output. The model convincingly reproduces key features of song learning and is a valuable step towards understanding how vocal learning is substantiated in the song system. Further examination of model assumptions and presentation of testable predictions would increase the impact of the study.

Strengths:

(1) The model reproduces several key features of song learning, including learning in a non-convex performance landscape, decreasing motor variability during learning, the relative importance of the HVC-RA and HVC-BG-RA pathways at different learning stages, and sleep-related deterioration.

(2) The model incorporates several key physiological properties of the song system, including performance-dependent dopamine signals to the BG, the neural variability in the system, and multiple global/local optima of the motor production landscape.

(3) The study convincingly demonstrates the advantages of a dual-pathway network over a single-pathway one.

(4) The model demonstrates the counterintuitive benefit of sleep-related performance deterioration for facilitating the escape from local optima to reach the global optimum.

(5) The model seems robust to some variations in model parameters and task structure.

Weaknesses:

(1) The study could be more impactful if the model can generate new, testable predictions. The predictions provided in the Discussion are not well justified. For example, with the HVC spine turnover, it seems unlikely that BG lesions would abolish overnight performance deterioration. Because the volatility term is inversely related to learning during the day, the changes in LMAN and RA during sleep are not necessarily larger than those during the day.

(2) The section "Neural activity patterns in the model parallels song system neurophysiology" seems fully anticipated because the model construction is based on the known neural activity patterns. Are there new testable predictions from the model?

(3) Certain model assumptions lack explanation or justification.<br /> a) The authors treat "the delayed maturation of the cortical pathway" as an important component. However, it is unclear if/how this component was implemented in the model. If it was not included in the model, the authors should remove statements related to the delayed maturation idea.<br /> b) How critical is the inverse relationship between learning and sleep-deterioration? If it is known that sleep-related deterioration is inversely related to learning during the previous day, a citation should be added. Similarly, it should be clarified if the spine turnover observed in HVC depends on previous plastic changes, like the assumed inverse relationship in the model.<br /> c) Spine turnover has been demonstrated in HVC and not yet in Area X, but the model implements volatility only in the HVC-BG pathway. It would be important to compare the effects of sleep-related volatility in the HVC-RA and HVC-BG-RA projections.<br /> d) In Table 1/Figure 8, the learning rate for HVC-BG is 10^4 times bigger than the learning rate for HVC-RA. What is the biological justification for this difference?

(4) Related to #3, it is unclear how changing those assumptions would affect model performance.

(5) It is not explained/shown why cross-day exploration (with sleep, sporadic) is better than continuous, non-sleep-related ones. Figure 7B presents results with different noise levels, which may approximate non-sleep-related, continuous volatility, but only for the single-pathway model. Comparable simulations by adding continuous volatility in the dual-pathway model would be helpful. For example, would just a bigger intrinsic noise in either pathway confer the same benefit in escaping local optima, e.g., epsilon-greedy exploration? If the authors can establish the advantages of sleep-specific volatility in its particular form (based on day learning) and relate it to other sensorimotor learning behaviors, it could increase the general impact of the study.

(6) The model description can be improved.<br /> a) What are mBG and mRA in Eqs 6/7?<br /> b) Where is the learning rate specified in Equations 1-8?<br /> c) It is unexplained why Equations 1-3 are not in the same form: Equations 1 and 2 normalize by activity, but Equation 3 normalizes by weight (Equation 8 also normalizes by weight). The authors should confirm that these equations are correct.<br /> d) How J_HVC is generated should be defined.<br /> e) How is R calculated in Equation 10? Does this model maintain a PPE for each syllable or for the overall performance? Would it make any difference in learning?<br /> f) Are the weight updates in Equations 9 and 13 added at different time points (e.g., immediately after each spike versus after a syllable). This should be clarified (perhaps a diagram would help).<br /> g) How are the optimal threshold and slope determined for Equation 14?<br /> h) Parameters in Equation 20 are not defined.

Reviewer #3 (Public review):

Summary:

This study examines how dominance hierarchy influences innate defensive behaviors in pair-housed male mice exposed to two types of naturalistic threats: a transient looming stimulus and a sustained live rat. The authors show that social presence reduces fear-related behaviors and promotes active defense, with dominant mice benefiting more prominently. They also demonstrate that threat exposure reinforces social roles and increases group cohesion. The work highlights the bidirectional interaction between social structure and defensive behavior.

Strengths:

This study makes a valuable contribution to behavioral neuroscience through its well-designed examination of socially modulated fear. A key strength is the use of two ethologically relevant threat paradigms - a transient looming stimulus and a sustained live predator, enabling a nuanced comparison of defensive behaviors. The experimental design is robust, systematically comparing animals tested alone versus with their cage mate to cleanly isolate social effects. The behavioral analysis is sophisticated, employing detailed transition maps that reveal how social context reshapes behavioral sequences, going beyond simple duration measurements. The finding that social modulation is rank-dependent adds significant depth, linking social hierarchy to adaptive defense strategies. Furthermore, the demonstration that threat exposure reciprocally enhances social cohesion provides a compelling systems-level perspective. Together, these elements establish a strong behavioral framework for future investigations into the neural circuits underlying socially modulated innate fear.

Comments on revised version.

The authors have addressed the initial major criticism regarding the lack of causal evidence for neural mechanisms, which has alleviated our concerns. This provides a more solid behavioral foundation for future investigations into neural circuit mechanisms.

Reviewer #3 (Public review):

Summary:

The manuscript builds on the observation that, at some synapses, low-frequency stimulation causes synaptic depression which can be reversed by subsequent high-frequency stimulation. Such low-frequency depression (LFD) cannot be easily explained by the depletion of a single vesicle pool. Here, Silva and colleagues propose a model of activity-dependent vesicle trafficking to explain LFD at synapses between cerebellar granule cells and molecular layer interneurons.

Strengths:

Overall, LFD is interesting and worthy of examination, and the authors provide new experimental results that are of the high quality expected from this group.

Weaknesses:

The study proposes a novel model of vesicle trafficking that is not explained by known biological mechanisms, and the manuscript does not adequately compare or discuss alternative models.

I have several concerns about how the authors interpret the data. First, the manuscript's primary conceptual advance is the idea that LFD involves vesicle undocking, rather than depletion. However, most experiments were performed under conditions that promote vesicle depletion (3 mM extracellular Ca2+). When experiments were repeated in physiological Ca2+, there appeared to be little or no LFD (stats are not provided). Second, the RS/DS/DU/undocking model, though not outside the realm of possibility, is not readily explained by known mechanisms and is only loosely supported by experimental findings. Third, when simulating LFD, the authors do not compare alternative models and use inappropriate language to imply that a model fit represents the truth (e.g. "the finding of identical experimental and simulated values confirms that the undocking mechanism accounts for LFD"). Finally, the model is presented in an overly complicated manner. The sheer amount of terms and nomenclature makes the manuscript confusing and difficult to read. Overall, the manuscript would benefit from added experiments and more statistics, a better justification and evaluation of the model, and more nuanced language.

Comments on revised version.

I appreciate the authors' detailed responses to my initial review of the manuscript. My suggestions reflect a sincere attempt to improve the clarity and accuracy of the paper. I disagree with some of the authors conclusions and their responses to my comments, but do not wish to make any further suggestions. The authors are entitled to their own views.

A final note: If the authors want to refrain from making definitive statements on underlying cellular mechanisms, they should consider amending the title of the manuscript.

Reviewer #3 (Public review):

Summary:

Through micro-electroencephalography, Hight and colleagues studied how the auditory cortex in its ensemble respond to cochlear implant stimulation compared to the classic pure tones. Taking advantage of a double implanted rat model (Micro-ECoG and Cochlear Implant), they tracked and analyzed changes happening in the temporal and spatial aspects of the cortical evoked responses in both normal hearing and cochlear-implanted animals. After establishing that single trial responses were sufficient to encode the stimuli properties, the authors then explored several decoder architectures to study the cortex ability to encode each stimuli modality in a similar or different manner. They conclude that a) intracranial EEG evoked responses can be accurately recorded and did not differed between normal hearing and cochlear-implanted rats; b) Although coarsely spatially organized, CI-evoked responses had higher trial-by-trial variability than pure tones; c) Stimulus identity is independently represented by temporal and spatial aspect of cortical representations and can be accurately decoded by various means from single trials; d) and that Pure tones trained decoder can't decode CI-stimulus identity accurately.

Strength:

The model combining micro-eCoG and cochlear implantation and the methodology to extract both the Event Related Potentials (ERPs) and High-Gammas (HGs) is well designed and appropriately analyzed. Likewise, the PCA-LDA and TCA-LDA are powerful tools that take full advantage of the information provided by the cortical ensembles.

The overall structure of the paper, with a paced and exhaustive progress through each step and evolution of the decoder is very appreciable and easy to follow. The exploration of single trial encoding and stimulus identity through temporal and spatial domains is providing new avenues to characterize the cortical responses to CI stimulations and their central representation. The fact that single trials suffice to decode the stimulus identity regardless of their modality is of great interest and noteworthy. Although the authors confirm that iEEG remains difficult to transpose in clinic, the insights provided by the study confirm the potential benefit of using central decoders to help in clinic settings.

Weakness:

The conclusion of the paper, especially the concept of distinct cortical encoding for each modality, is unfortunately only partially supported by the results. Although acknowledged by the authors, fundamental limitations related to CI stimulation might have weaken the results.

The authors stimulated in a Monopolar mode which, albeit being clinically relevant, notoriously generates a high current spread in rodent models. Thus, it seems possible that current spread ended stimulating indistinctly higher turns of the cochlea or even the modiolus in a non-specific manner, greatly reducing (or smearing) the place-coding/frequency resolution of each electrode, which in turn could explain the coarse topographic (or non-random) organization of the cortical responses.

Although the authors acknowledge that post-lingual CI users always have an adaptation period, their conclusion is based on measurements that are relatively "early" in the CI-use timeline so to speak since iEEG were collected a) acutely right after mono-aural implantation and stimulation, b) under anesthesia, c) using unmodulated pulse train fixed at 900pps regardless of the electrode used and thus lacking any temporal information shifts in relationship to electrode cochleotopic placement. Basically, all CI electrodes had the same rate whereas you would expect basal CI electrodes to be amplitude modulated at higher frequencies than apical electrodes.

Nevertheless, the reviewer wants to reiterate that the study proposed by Hight et al. is well constructed, relevant to the field and that the overall proposal of improving patient performances and help their adaptation in the first months of CI use by studying central responses should be pursued as it might help establish new guidelines or create new clinical tools.

Comments on revised version.

The reviewer would like to thank the Authors for their work on this new version. The reviewer is satisfied with the current state of manuscript and its associated public review and has no further comment.

Reviewer #3 (Public review):

I thank the authors for their extensive revision of this paper, and I found some elements greatly improved.

In particular, the authors do embrace a somewhat more speculative tone in the current version, which I think is fitting for this work, as the data seem (to me) to be not fully conclusive. The data set collected here is clearly valuable and unique (and I would encourage the authors to make it publicly available!), however, my overall impression is that the specific analyses reported here might not fully.

Despite the revised description of methods, results and figures, I still have trouble understanding many of the results and the authors conclusive interpretation of them. These are my main reservations:

(1) Regarding "individual prediction tendency" - thank you for adding clarifying methodological details and showing the data in a new Figure (#2). Honestly, however, I still can't say that I fully understand the result. For example, why is there also a significant response in the random condition as well? And how do you interpret the interesting time-course (with a peak ~200ms prior to the stimulus, and a reduction overtime from there?<br /> Also (I may have missed this, but...) what neural data was used to train the classifier and derive the "prediction tendency" index? Was it just the broadband neural response? Is there a way to know which sensors contributed to this metric (e.g., are they predominantly auditory? Frontal?)? And is there a way to establish the statistical significance of this metric (e.g., how good the decoder actually was in predicting behavioral sensitivity?). I don't see any statistics in the results section describing the individual prediction tendency.

(2) Regarding the TRF analysis - Thanks for clarifying the approach used to obtain 2-second long "segments" of speech tracking. This is an interesting approach, however I think quite new(?) , and for me it raises a whole new set of questions, as well as additional controls and data that I would have liked to see, to be convinced that results are significant. I will elaborate:

- Do I understand correctly that you segment the real and predicted neural response into 2-second-long segments and then calculate the Pearsons' correlation between them to assess the goodness of the model? This is very unclear, since in the methods section you state only that "the same" analysis was performed as for the full data - but what exactly? Clearly, values will be very different when using such short segments. I feel that additional details are still required (and perhaps data shown) to fully understand the "semantic violation" analysis of TRFs.

- I would like to reiterate my previous comment regarding the use of permutation tests to verify the validity of TRF-based measures derived. This would be especially important when using new approaches (such as the segmentation used here). The authors argue that this is not needed since this was not done in their previously published study. However, this sounds a bit like "two wrongs make a right" argument... why not just do it, and let us know that this 2-second segmentation approach allows estimating reliable speech tracking?

- Following up on my previous comment that defining "clusters" as at least two neighboring channels (Figure 3) - the fact that this is a default in Fieldtrip is by no means sufficient justification! This seems quite liberal to me, especially given the many comparisons performed. Here too, permutations can help to determine the necessary data-driven threshold for corrections. This is of course critical for interpreting the result shown in Figures 3E&G that are critical "take home messages" of the paper - i.e., that the prediction-index from the first part of the experiment is related to speech tracking in the second part of the experiment. To my eyes, this does not look extremely convincing, but perhaps the authors can show more conclusive data to support this (e.g., scatter plots of the betas across participant?).<br /> - A similar point can be made for the effect of semantic violations (though here the scalp-level result is somewhat more clustered). The authors point out that the semantic effect is a "replication" of their result reported in Schubert et al. 2023, but if I am not mistaken the results there were somewhat different (as was the manipulation). It would be nice to explicitly discuss the similarity/difference between these effects.

(3) Regarding the ocular-TRFs -

- Maybe this is just me, but I believe that effects that are robust should be clearly visible in the data, without the need for fancy "black-box" statistical models. In the case of the ocular TRFs, it is hard for me to see how these time-courses are not just noise (and, again, a permutation test would have helped to convince me...). The inconsistent results for horizontal and vertical eye-movements vis a vis the experimental conditions (single vs. multi-speaker conditions) don't help either, despite the authors argument that these are "independent" - but why should this be the case, especially if there is nothing really to look at in this task?<br /> - I remain with this scepticism for the mediation-portion of the analysis as well... But perhaps replications from other groups or making the data public will help shed further light on this in the future.

Minor<br /> - Thanks for adding information about the creation of semantic-violation stimuli. Since the violations and lexical-controls were taken from different audio recordings, it would have been nice to verify that differences between neural responses cannot be attributed to differences in articulations (e.g., by comparing their spectro-temporal properties).

Reviewer #3 (Public review):

Summary:

Dzhala and colleagues present findings from organotypic slice cultures suggesting that simultaneous modulation of the complementary cation-chloride cotransporters NKCC1 and KCC2 through inhibition of the WNK-SPAK/OSR1 pathway reduces seizure-like activity. The manuscript is generally well written, and the data support the conclusion that WNK463 exerts robust anti-ictal effects in this model. However, several issues should be addressed to strengthen the mechanistic interpretation and statistical rigor of the study, and improve confidence in the conclusions.

Strengths:

(1) The study addresses an important mechanistic question by investigating how inhibition of the WNK-SPAK/OSR1 pathway with WNK463 influences seizure activity and neuronal chloride homeostasis.

(2) The experimental design is logical and comprehensive, progressing from characterization of chloride dynamics to pharmacological and genetic interrogation of the underlying mechanism using multiple complementary approaches, including pharmacological inhibition, siRNA-mediated knockdown, electrophysiology, and chloride imaging.

(3) The combination of simultaneous extracellular electrophysiology and two-photon chloride imaging provides complementary functional and mechanistic information and represents a major technical strength of the study.

(4) The TTX experiments elegantly distinguish activity-dependent chloride accumulation from resting intracellular chloride concentration, substantially strengthening the central mechanistic conclusions.

Weaknesses:

(1) The mechanistic conclusions regarding KCC2 activation and NKCC1 inhibition are stronger than the data directly support. Throughout the manuscript, the authors conclude that WNK463 activates KCC2 and inhibits NKCC1. Although this interpretation is consistent with the established biology of the WNK-SPAK/OSR1 pathway, the evidence presented here is indirect. Specifically, the authors infer KCC2 activation and NKCC1 inhibition from the observation that pharmacological inhibition or siRNA-mediated knockdown of these transporters alters the effects of WNK463, together with measurements of chloride dynamics. While these findings are compatible with a KCC2- and NKCC1-dependent mechanism, they do not directly establish that WNK463 regulates either transporter. Direct evidence would require measurements of transporter activity, phosphorylation state, membrane expression, or other biochemical indices of transporter regulation. I therefore recommend that the authors both temper the mechanistic language throughout the manuscript and explicitly acknowledge in the Discussion that the proposed regulation of KCC2 and NKCC1 is inferred from indirect evidence rather than directly demonstrated in the present study.

(2) The conclusions drawn from the siRNA-mediated knockdown experiments should be interpreted more cautiously. First, it is unclear whether silencing NKCC1 or KCC2 induced compensatory changes in the expression or function of the complementary cotransporter. Given the well-established interplay between NKCC1 and KCC2 in regulating intracellular chloride homeostasis, compensatory adaptations could influence the interpretation of these experiments and should be addressed or acknowledged as a limitation. Second, the sample size for the siRNA experiments appears relatively small. It is unclear how many independent animals contributed slices to each experimental group, making it difficult to assess the degree of biological replication. In addition, effect sizes are not reported. Clarifying the number of biological replicates and reporting effect sizes would improve the rigor of the statistical analysis and increase confidence in these findings.

(3) The final pharmacological experiments require clarification, as the conclusions appear internally inconsistent. Earlier experiments suggest that the anticonvulsant effects of WNK463 depend on coordinated regulation of both NKCC1 and KCC2. However, in the final experiment, the authors state that simultaneous pharmacological inhibition of NKCC1 and KCC2 does not prevent the anticonvulsant effects of WNK463. In contrast, the accompanying statistical analysis indicates that combined transporter inhibition significantly reduces the effect of WNK463 relative to control conditions. These interpretations appear inconsistent and make it difficult to determine the extent to which the anticonvulsant action of WNK463 depends on NKCC1 and KCC2. The authors should clarify whether simultaneous inhibition of both transporters completely abolishes, partially attenuates, or merely reduces the magnitude of the WNK463 response, and revise the text accordingly. If the effect is only partially attenuated, alternative mechanisms contributing to the anticonvulsant actions of WNK463 should also be considered and discussed.

(4) The statistical analysis and reporting require further attention. First, median values should not be reported with standard deviations, as standard deviation describes variability around the mean rather than the median. For non-normally distributed data, the authors should report median values together with an appropriate measure of variability, such as the interquartile range (25th-75th percentile) or another suitable summary. Second, in several instances, ANOVA results are reported using only a single degree of freedom value (e.g., page 6, DF = 53). This is incomplete, as an F statistic is defined by two degrees of freedom: the numerator degrees of freedom (between-group variability) and the denominator degrees of freedom (within-group variability). Reporting statistical results using standard notation (F(df_between, df_within) = F statistic, p = value) would improve clarity and allow proper interpretation of the analyses.

Reviewer #3 (Public review):

Summary:

Ludlow et al. investigate the control strategy that flies use to stabilize flight during small roll perturbations. Using 3D kinematic analysis of freely flying Drosophila, Ludlow et al. ask how manipulating steering motor neurons alters the fast stabilization reflex, which spans only a few wingbeats. Bilaterally activating i1 or i2 wing steering motor neurons during free flight pitches the fly down via decreases in the wing stroke amplitude, whereas inhibiting the b3 wing steering motor neurons pitches the fly up via increases in the wing stroke amplitude. These results suggest that asymmetric recruitment of these steering muscles may be used to rotate the fly around the roll axis. Then, using quasi-steady aerodynamic modeling, they linearly interpolate changes in six kinematic features of wing strokes to describe which wing parameters produce the greatest corrective roll torque. They repeated this analysis with data from optogenetic activation of wing steering motor neurons. They argue that modeling of these optogenetic perturbations supports the hypothesis that i1, i2, and b3 muscles contribute to rotation around the roll axis by calculating roll torque changes from changing kinematics of a single wing, despite bilateral optogenetic activation. Finally, they support their claims about the redundancy of steering motor neurons by presenting connectomic analyses of the direct pathways from haltere sensory neurons to wing steering motor neurons. This analysis reveals two independent pathways from halteres to two wing muscle groups (b1 and b2 vs. i1, i2, and b3). Taken together, these data provide some evidence for redundant roll stabilization control strategies in Drosophila.

Strengths:

The central strength of this work is the high-quality free-flight kinematic dataset, which provides a detailed picture of how wing-stroke parameters change during natural roll perturbations and correction. The integration of quasi-steady aerodynamic modeling with these kinematic data offers a principled framework for linking muscle activity to torque generation. The use of split-Gal4 lines to target individual wing steering motor neurons with cell-type specificity provides a potentially precise approach to probe the contribution of specific muscles to roll torque. Together, these tools position this study to make a meaningful contribution to understanding the sensorimotor control strategies underlying flight stabilization in Drosophila.

Weaknesses:

The GtACR1 silencing experiments lack validation that the optogenetic manipulation actually suppresses motor neuron activity. Without a positive control to calibrate light intensity and duration, the absence of kinematic effects cannot be interpreted with confidence. The confocal images of the driver lines provided are insufficient to uniquely identify the targeted motor neurons and do not clearly show expression in the brain and nerve cord. The kinematic modeling relies on flight profiles derived from a single fly and a single trial, raising questions about whether they capture the full range of natural variation. Finally, the connectomics analysis largely recapitulates prior work and provides little new insight.

There is no evidence that optogenetic silencing of wing motor neurons with GtACR1 is actually suppressing their activity. There are many factors that could impact the efficacy of this manipulation: transgene expression, light intensity and duration, etc. While electrophysiology experiments would be ideal, this would be challenging. Another option would be to use a positive control with an obvious phenotype to calibrate the light intensity and duration. For example, silencing all motor neurons (e.g., using OK371-Gal4 or another driver labeling glutamatergic neurons) should essentially paralyze the fly. Without additional evidence, the lack of a kinematic effect in the GtACR experiments is not convincing.

The confocal images in Figure 1C are of poor quality and do not help identify the motor neurons. They only point to cell body locations, and the morphologies of the neurons are not clear. The glial sheath also appears to be labeled. The images as they currently exist are not sufficient to uniquely identify the wing motor neurons and should be updated, including brains, since the experiments manipulate activity across the nervous system. It should also be clarified that these split Gal4 lines were not generated in this work.

The connectomics analysis does not add much on top of what was already known. It doesn't necessarily need to be removed, and the authors acknowledge that it is basically repeating prior analyses in prior publications from another connectome dataset. But it could be reduced to a schematic summarizing prior work. The one thing that should be clarified is what the "haltere afferents" actually are. Campaniform sensilla only or other sensory neurons (e.g., haltere chordotonal neurons)?

In Figures 3 and 4, 50 kinematic profiles are created using data from a single fly and a single trial. Are these kinematic profiles representative of the range that flies use? Would the results generalize across different bouts? Figure 1i-m shows a much narrower distribution of kinematic features compared to Figure 3, which might reduce the resulting torque calculations. Why not sample from the distribution of data in 1i-m in Figure 3? Could there be some sort of bootstrapping for the modeling in Figures 3-4?

Reviewer #3 (Public review):

Summary:

This work studied the prion-like α-synuclein spreading hypothesis from the view of different host genotypes (M83 transgenic vs wild-type), fibril species (mouse vs human PFFs), and disease epicenter (striatum vs hippocampus). Major results include tracking neurodegeneration longitudinally with in vivo MRI, behavior, and survival in the same mice. Furthermore, this work sought to link atrophy patterns to structural connectivity and regional SNCA expression. Finally, the authors tested whether a connectome-based SIR spreading model could predict the atrophy in silico and generalize across seed sites.

Strengths:

(1) Same mice imaged repeatedly across four timepoints (−7, 30, 90, 120 dpi), giving true within-subject volumetric trajectories rather than cross-sectional snapshots.

(2) Investigate the atrophy pattern for striatal-vs-hippocampal seeding in PD.

Weaknesses:

(1) The hypothesis (regional vulnerability) is not novel, although the manuscript presents compelling and interesting results supporting it in Figures 2 and 3.

(2) The findings primarily establish statistical associations rather than causal mechanisms. This limitation appears inherent to the cross-cohort dataset utilized, which the authors should explicitly address in the discussion.

(3) The descriptions of the statistical analyses in Sections 2.5 and 2.6 lack sufficient detail. The authors should provide additional technical specifics to ensure reproducibility.

(4) Given that VBM was used to determine atrophy patterns, it is necessary to address how the multiple comparisons problem was handled in the statistical analysis to control for false positives.

Reviewer #3 (Public review):

Summary:

Statham and colleagues test an assumption underpinning a very large literature: that the stop-signal reaction time (SSRT) indexes the speed or efficacy of top-down inhibitory control. They argue instead, and support their claims with a total of eight datasets, that SSRT is substantially occupied by visuomotor deadtime (i.e., incompressible sensory and motor delays common to all visually guided responses), which varies across individuals, conditions and populations in ways that mimic effects usually attributed to inhibitory control. They propose two remedies: subtracting an independent estimate of visuomotor deadtime (T₀) from SSRT, and a new index, the selective stopping delay (ΔT), from the stimulus-selective stopping task.

Strengths:

The paper's principal strength is the combination of these components. That SSRT must contain peripheral delays is not itself new, as the authors point out (Boucher et al., 2007; Salinas and Stanford, 2013; Bompas et al., 2020). What is new is the quantification of the problem at scale, across seven archival datasets and a preregistered replication, together with the demonstration that T₀ can be recovered from existing stop-task data. That is important, as it provides a diagnostic that can be applied to data already collected. The authors' offer to assist others in doing so is exemplary. The supplementary analyses of trial numbers and participant pooling are very useful, and the paper provides important sanity checks, notably confirming that stop and ignore signals produce indistinguishable initial interference before pooling them.

Weaknesses:

The evidence for the central claim is strong but presented in a way that overstates it. Figure 2 reports 85% and 80% shared variance between SSRT and T₀, but these pool across datasets and, more critically, across response modality: manual and saccadic estimates from the same participants are plotted together with a single regression line through both. Because manual and saccadic deadtimes differ by roughly 130 ms, the resulting correlation largely reflects a between-condition difference rather than covariation among individuals. The numbers that speak to individual differences are more modest (40% for manual responses; 7% for saccades). The manual result is convincing and consequential; the saccadic result is not, and the explanation in terms of restricted range, while plausible, is offered after the fact and is directly testable by reporting the reliability of saccadic T₀ or correcting the correlation for attenuation. This limitation is arguably good news for the paper's practical message, since it implies saccadic measures are relatively protected, but the manuscript should make clear (including in the abstract) that the strong individual-differences case rests on the manual data, where motor execution delay is the main driver.

A related point concerns interpretation rather than analysis. Since SSRT is, on the authors' own account, approximately the sum of T₀ and a decision-related component, covariation between the two is expected on structural grounds; the preregistered correlation with reaction times from separate speeded blocks mitigates this, but the finding is less surprising than its current framing implies. What would determine whether past conclusions must be revised is not whether SSRT correlates with T₀ across individuals, but whether the decision-related component tracks the independent variable in any given study. The alcohol reanalysis could be a test case for this: the authors show that alcohol raises T₀ commensurately with SSRT and conclude the effects are "consistent with these effects being fully driven by visuomotor delays," yet (unless I missed something) they do not report the corrected measure for these data, while they do so for signal contrast and response modality. Running that analysis, and stating plainly what Campbell et al. (2017) would have concluded under the proposed treatment, would be an important demonstration.

The case for ΔT is the least developed part of the paper. ΔT is a difference between two independently estimated, individually noisy quantities, extracted by a non-trivial procedure (see also below), and no reliability estimates are reported for T₀, TS or ΔT. This would be possible based on the two-session design (and the group has prior work on the reliability of cognitive control measures). This matters because the argument that ΔT is superior rests, to some extent, on null findings: ΔT does not correlate with SSRT, with stopping accuracy, or with the differential response to stop and ignore trials. These null correlations are interpreted as freedom from confounds, but an unreliable measure would produce the same pattern, and the seven participants with implausible negative ΔT values indicate that noise is not negligible.

In addition, the subjective correction of dip onsets ("Departure points were visually inspected and adjusted if it was deemed that the algorithm had placed them in inappropriate places"), which is critical to the paper's central measurement, should be blinded to condition or show inter-rater agreement. Since T₀ and TS are compared across conditions and ΔT is their difference, this introduces researcher degrees of freedom.

This is particularly critical when a dip is not easy to extract. Figure 3 depicts an idealized ignore-trial distribution with a clean, deep dip. Real distributions are unlikely to look like this, and dip depth should depend on the behavioral relevance and salience of the ignored event; published work on rapid manual inhibition indicates that dips to behaviorally irrelevant events can be very shallow. Since ΔT is extractable only where the dip is resolvable, the generality of the method can be questioned. Ideally, the empirical distributions underlying every dataset analyzed should be shown to alleviate this concern.

One uncontrolled procedural difference also deserves comment. Corrective feedback about stopping too often or stopping too rarely was given after manual blocks only; saccadic blocks received none, and fixed rather than staircased delays were used throughout. Since the manual-saccadic contrast carries much of the argument, and saccadic blocks yielded both lower stopping accuracy (36% vs 52%) and far more exclusions (8 vs 1 of 37), this asymmetry offers an alternative to the interpretation that saccades are simply harder to inhibit.

A final point concerns the comparison between response modalities. Raw SSRT suggests that saccadic inhibition is faster than manual (174 vs 266 ms), while both proposed corrections reverse this, with SSRT−T₀ and ΔT each indicating that saccadic inhibition is slower (the latter consistently across nearly every participant). This is one of the clearest illustrations of the paper's thesis, but it is not taken up in the discussion, which returns to modality only to note that saccadic T₀ varies little (the one reference to variation across action modalities appears in the modeling section, without stating its direction). It would also benefit from a caveat. Both corrected measures subtract the same T₀, and manual and saccadic T₀ differ by roughly 130 ms, so the two do not corroborate one another independently (TS is itself longer for manual responses, and yields a shorter ΔT only once the larger manual T₀ is removed). The accuracy of the subtraction therefore matters here: if the manual regression slope of 0.75 reflects sub-additivity rather than attenuation, subtracting the full T₀ would overcorrect manual responses more than saccadic ones, and could produce the reversal on its own.

These concerns qualify rather than undermine the contribution. The core observation is robust, the diagnostic is practical and immediately applicable, and the case that a large body of work requires re-examination is well made. If the corrected analyses are carried through on the datasets already in hand, this will be an important paper for anyone who uses the stop-signal task.

Reviewer #4 (Public review):

Summary:

Short photoperiod is an important experimental manipulation in neurobiology, endocrinology, and metabolism studies. However, the molecular mechanisms by which short photoperiod gives rise to behavioral phenotypes that are seen in seasonal affective disorders remain unknown. Using the classic circadian model organism Drosophila, this study examines short photoperiod-induced hypersomnolence and identifies the circadian photoreceptor cryptochrome as a regulator of GABAergic tone within the clock neural circuit to promote wakefulness under short photoperiod conditions. The discovery has broad implications for understanding how short photoperiod modulates neural inhibition in circadian circuits in regulating sleep.

Strengths:

The Drosophila model provided a powerful platform to dissect the molecular mechanisms underlying short photoperiod-induced hypersomnolence. A battery of behavioral, imaging, circuit-manipulation approaches were employed to test the novel hypothesis that the circadian photoreceptor cryptochrome modulates GABAergic tone within the clock neural circuit to promote wakefulness under short photoperiod conditions.

Weaknesses:

The current model proposed by the authors suggests that the small ventral lateral neurons of the Drosophila clock circuit are GABAergic; however, this remains unclear. At present, the field lacks sufficient data and validated reagents to definitively establish the GABAergic identity of these neuropeptidergic neurons.

比 AVO 的满分更有说服力：只动“保留推理”和“上下文压缩”两个开关，基线同源、变量可数，这才是消融实验该长的样子。但同样只在公开集上，且起点 13.3% 很低——低分区的三倍不能线性外推到高分区，压缩带来的收益通常随基线上升而衰减。

把 30% 和 100% 相减当成 harness 的贡献，是这轮报道里最常见的读法错误。两个数字来自不同评测设置：一边是 ARC Prize 跑裸模型，一边是 NVIDIA 自建观测接口、记忆与监督器的完整系统。NVIDIA 原文明说过这不是受控消融，本文没有转述这句限定。

满分只在公开集上成立。ARC 设半私有/私有集，正是为了拦住针对公开题目的过拟合与反复调参，因此公开集 100 分不能推出私有集能力。任何把它转述成“达到人类水平”的说法，都已经丢掉了这句限定。

全文最该被引用的一句：NVIDIA 自己先声明这不是受控消融。也就是说 12% 的动作数优势里，后端模型、观测表示、记忆、上下文管理四个变量同时在变，无法归因给任何单一设计。读跑分新闻时，这类作者自认的免责声明信息量往往大于标题数字。

Reviewer #3 (Public review):

Summary:

In this manuscript by Ashworth and colleagues, the authors investigate the temporal dynamics of the rostral ventromedial medulla (RVM), a key output node in a major descending pain-modulation circuit. Using data from extrasellar single-unit recordings of RVM ON, OFF, and NEUTRAL cells in lightly anesthetized rats, the authors' computational modeling yielded two major findings: (1) heat-evoked ON burst and OFF pause, followed by exponential recovery components in10s of seconds; and (2) ON and OFF cells exhibit periodic fluctuations in ~5-minute cycles that are statistically predictable.

Strengths:

The manuscript's concept is innovative, offering the first quantitative analysis of multi-timescale dynamics in physiologically characterized RVM pain-modulating neurons. This advances a field that has mostly depended on qualitative or single-timescale descriptions. The authors use contemporary Gaussian process and probabilistic models to capture statistically predictable slow dynamics. The study is further strengthened by identifying ON-, OFF-, and NEUTRAL-type cells using well-established criteria grounded in decades of RVM research. The combination of rapid reflex-related responses and slower ongoing rhythms supports a dual-timescale framework, providing a more integrated understanding of how these neurons may regulate reflex activity and state-dependent processes.

Weaknesses:

Several limitations are noted. Incomplete characterization of light anesthesia during recording sessions, such as methohexital stability and clear criteria for identifying "lightly anesthetized" states. While the NEUTRAL cell control is helpful, it does not fully address concerns about circuit specificity or systemic confounds. The findings are male-dominant, which may limit their generalizability. The synchrony between ON and OFF cells was suggested but not directly tested. The heart rate coherence with ON, OFF, and NEUTRAL cell activity results is intriguing but does not fully clarify how these neurons influence heart rate, particularly within the "lightly anesthetized" model.

Reviewer #3 (Public review):

Summary:

The authors investigate how sleep loss and circadian disruption affect whole-organism metabolism in Drosophila melanogaster. They used chamber-based flow-through respirometry to measure oxygen consumption, carbon dioxide production, in wild-type flies and in mutants with impaired sleep or circadian function. These measurements were then integrated with a previously published metabolomics dataset to explore how respiratory dynamics align with metabolic pathways. The central claim is that wild-type flies display anticipatory coordination of metabolic processes with circadian time, while mutants exhibit reactive shifts in substrate use, redox imbalance, and signs of mitochondrial stress.

Strengths:

The study has several strengths. Continuous high-resolution respirometry in flies is challenging, and its application across multiple genotypes provides good comparative insight. The conceptual framework distinguishing anticipatory from reactive metabolic regulation is interesting. The translational framing helps place the work in a broader context of sleep, circadian biology, and metabolic health.

Weaknesses:

At the same time, the evidence supporting the conclusions is somewhat limited. The metabolomics data were not newly generated but repurposed from prior work, reducing novelty. The biological replication in the respirometry assays is low, with only a small number of chambers per genotype. Importantly, respiratory parameters in flies are strongly influenced by locomotor activity, yet no direct measurements of activity were included, making it difficult to separate intrinsic metabolic changes from behavioral differences in mutants. In addition, repeated claims of "mitochondrial stress" are not directly substantiated by assays of mitochondrial function. The study also excluded female flies entirely, despite well-documented sex differences in metabolism, which narrows the generality of the findings.

Reviewer #3 (Public review):

Summary:

In the manuscript titled "Dual regulation of chemical stress-induced DDI2/1 3 expression by a transcription factor Fzf1 and nucleosome in Saccharomyces cerevisiae" Du et al have discovered a dual role of Fzf1 in transcriptional control of DDI2/3 during cyanamide (CY) or MMS treatment. While previous literature established that Fzf1 regulates multiple targets (DDI2/3, SSU1, YHB1, and YNR064C) by binding the CS2 consensus sequence, it remained unclear why DDI2/3 uniquely undergoes a massive 1,000-fold induction under cyanamide (CY) stress, whereas the others show only a 20- to 40-fold induction. In this work, the authors showed that Fzf1 functions beyond standard transcriptional activation. Using MNase-seq and a series of promoter truncation mutants, the authors mapped Upstream Repressing Sequences (URS) in the DDI2/3 promoter that are heavily occupied by nucleosomes. The authors showed that Fzf1 is essential for chromatin remodelling and nucleosome eviction (specifically at the -2 nucleosome position) to de-repress the DDI2/DDI3 expression.

Strengths:

The two-tier mechanism of action of Fzf1 in controlling the DDI2/3 expression during CY/MMS stress is compelling and novel.

Weaknesses:

While the authors presented the in vivo MNase-seq data that show Fzf1 is necessary for nucleosome displacement, the current study lacks any in vitro mechanistic proof. As the authors acknowledge, it remains unknown whether Fzf1 directly displaces nucleosomes on its own (perhaps through unmapped post-translational modifications induced by chemical stress) or whether its ZF5 activation domain merely acts as a scaffold to recruit separate chromatin remodelling complexes.

To test nucleosome repression, the authors utilised extreme global interventions, such as deleting the SPT10 gene or halting de novo histone synthesis using a galactose-to-glucose medium shift in the RMY102 strain. While these methods effectively deplete histones and induce DDI2/3 up to 30- to 350-fold, completely depleting cellular histones causes massive, pleiotropic secondary effects across the entire genome, which can obscure specific regulatory relationships.

Reviewer #3 (Public review):

Summary:

The goal of this study was to test the hypothesis that prenatal alcohol exposure (PAE) can affect Alzheimer's disease (AD) pathogenesis using biochemical proxies, histology, and a behavioral paradigm sensitive to AD-related memory decline. Major strengths include the breadth of techniques used, consideration of different AD-related molecular markers, and the use of different ages as well as appropriate controls. The authors largely achieved their aims to show that PAE does affect amyloid precursor fragments (CTFs) and notch signaling very early on as well as long-term effects in adulthood, which may uncover a previously underappreciated mechanism that may contribute to AD-related neuropathology and behavioral outcomes during lifespan.

Strengths:

(1) Several techniques are used to address molecular, behavioral, and histological PAE-related changes.

(2) There is use of appropriate controls and an AD-relevant mouse model.

(3) Different ages are used to address age-related and long-term effects in AD and control mice.

(4) The novelty of results shows early changes in amyloid-related processes, affected by PAE.

Weaknesses:

(1) It is unclear as to whether there are sex differences, particularly in the adult cohort.

(2) More clarity is needed on sample size per cohort and whether mice that were used for anatomy and biochemical analyses were previously used for behavior. Including a table and noting any overlap would be useful.

(3) In many instances, two-way ANOVAs with treatment (PAE vs vehicle) and genotype as factors will be useful to report (e.g., Figure 1).

(4) In Figure 5 and line 253, it is stated that older mice have more severe deficits, but there are no direct statistical comparisons with younger AD mice.

(5 Lines 270-271 refer to mice as "presymptomatic", but these mice do have behavioral symptoms. Do the authors mean no neuropathology yet? Any data showing lack of robust neuropathology would be useful.

Reviewer #3 (Public review):

Vasilevskaya and Keller set out to experimentally distinguish between two variants of predictive processing: a hierarchical and a non-hierarchical variant. The hierarchical variant assumes a hierarchical organization in which internal representation neurons (believed to be a subset of layer 5 excitatory neurons) serve as a source of a teaching signal for local prediction error neurons as well as for the next higher level of the hierarchy, while simultaneously providing prediction signals to the preceding lower level. In contrast, the non-hierarchical variant posits that these layer 5 internal representation neurons provide local predictions to layer 2/3 prediction error neurons.

The interaction between internal representation neurons and prediction error neurons differs fundamentally between the two variants. In the hierarchical variant, internal representation neurons excite positive prediction error neurons and inhibit negative prediction error neurons, while at the same time being inhibited by positive prediction error neurons and excited by negative prediction error neurons. In the non-hierarchical variant, this pattern of connectivity is reversed.

This work is very exciting, timely, and carefully executed. The authors functionally, and later molecularly, identify layer 2/3 prediction error neurons in V1 and probe their interactions with genetically defined neuron types in cortical layers 5 and 6 using optogenetics. They demonstrate that the functional influence of putative prediction error neurons in layer 2/3 onto layer 5 is incompatible with the hierarchical variant, whereas the influence of layer 5 onto putative prediction error neurons in layer 2/3 is incompatible with the non-hierarchical variant. They then test an alternative hypothesis, in which layer 2/3 responses resemble prediction errors with respect to perturbations of artificial layer 5 activity patterns. To investigate this, they designed an experiment in which optogenetic activation of L5 IT neurons was closed-loop coupled to the mouse's locomotion speed in the absence of visual feedback, allowing them to probe the causal influence of L5 activity on layer 2/3 responses.

Finally, the authors hypothesize that their data are more consistent with a joint embedding predictive architecture (JEPA) and outline experimentally testable predictions arising from this framework.

While the work is overall convincing and provides important insights into the circuit-level implementation of predictive processing, I think the connection to JEPA networks would benefit from a more in-depth discussion of its relationship to recently proposed and implemented models. Below, I address the specific points raised by the authors (flanked by ' ... ' to make the author's statements stand out), in particular in relation to the model proposed by Nejad et al. (2025):

- 'The two proposals indeed share similarities in assuming that bottom-up input for both L2/3 and L5 arrives from thalamus, and that representations formed in L2/3 are used for predicting the activity of L5. However, there are a few important differences between the JEPA implementation proposal formulated here and the Nejad et al. model.

(1) There is no proposed mapping of computations in the Nejad et al. model onto different JEPA networks. We assume that the suggested mapping would be L4 and L5 as encoder networks, and L2/3 as a predictor network? In that case, it is different to our proposal, in which L2/3 is part of the encoder network.'

I think there is some confusion here. In Nejad et al., both L2/3 and L5 function as encoder networks. Each receives sensory input (with L2/3 receiving this input delayed via L4) and computes a latent representation of that input, denoted z_{L2/3} and z_{L5}, respectively. The prediction is obtained by comparing the output of L2/3 with L5 latent representations (via W_{L2/3->L5} * z_{L2/3}). In other words, L5 is the target in the learning objective.

Although, they did not explicitly state the mapping with JEPA, their model has the same fundamental property - predictive learning happens in the latent space. Also, this appears very similar to the roles assigned to L2/3 and L5 in your Figure 9B. The fact that you made this more explicit and the new data included, is in my view, a very interesting contribution. However, from an architectural perspective, it appears that your proposal and the model of Nejad et al. are conceptually very similar, and I do not see a substantial difference between the two. This should be made more clear in the Discussion.

- '2. Our proposal contains explicit prediction error neuron cell types within L2/3, while prediction errors in Nejad et al. are encoded in the gradients, and the layer origin of these signals is hypothesized to be L5 ('the learning-driving error signal originates in L5'). Hence, also the role of L5-L2/3 connection is distinct between the two proposals. In Nejad et al. this connection serves the role of error propagation and update for predictions in L2/3, while in our proposal this connection contains teaching signal (target representations) that are compared to predictions within L2/3. Similarly, the functional role of L2/3-L5 connection is also different, since in Nejad et al, it is supposed to carry predictions of L5 activity, whereas in our proposal we expect it to drive plasticity in L5 encoder.'

Indeed, in Nejad et al., the layer-dependent mismatch responses are modeled as gradients with respect to neuronal activity, and the model does not explicitly include prediction error neurons. However, this appears to be a modeling choice rather than a fundamental aspect of the proposal, and it does not preclude an implementation with explicit prediction error neurons. In fact, the authors explicitly acknowledge this possibility in the Discussion:

"The second approach would be to recast our model within a predictive coding framework... Predictive coding jointly optimizes both model parameters and neuronal activities, which could naturally lead to prediction errors observable in the activity of both L2/3 and L5 neurons. Note that these two views are not mutually exclusive."

While Nejad et al. hypothesize that the learning-driving error signals (that are distinct from their mismatch responses) originate in L5, the abstract loss function itself does not uniquely specify where the underlying comparison between the predicted representation (W_{L2/3 -> L5} z_{L2/3}) and the target representation (z_{L5}) must be implemented. The proposed biological implementation places this computation in L5, but from my understanding, the computational objective itself does not require this specific localization.

That said, I agree that your proposed model introduces a genuine difference. The functional roles assigned to the vertical projections are effectively reversed: in Nejad et al., the L2/3->L5 projection carries the prediction, whereas the L5->L2/3 projection conveys the error/gradient. In your architecture, by contrast, the L5->L2/3 projection carries the teaching signal (target). This is, in my view, a real and testable interpretational divergence that is worth stating clearly.

Therefore, I think the novelty lies less in the computational architecture itself and more in committing to a particular biological implementation-one that adds cell-type-specific detail to an implementation that Nejad et al. hypothesised as being compatible with their framework.

- '3. The difference outlined above also makes it evident that the two proposals should differ in how deep and superficial layers are expected to influence the activity of one another. Indeed, the proposal in Nejad et al. is based on the cortical column idea, and according to eq. 2 and 3 in the Methods, activity in L5 is a function of activity in L2/3, while activity in L2/3 is not a function of activity in L5. Our proposal is based on idea of layers forming parallel networks, where horizontal communication is the dominant mode of cortico-cortical interactions, and activity in deep layers serve as a teaching signal for L2/3. In our case, we expect the opposite - that activity in L2/3 depends on activity of L5, while activity of L5 is not immediately dependent on activity of L2/3 (only via plasticity route). This led us to propose one of direct tests for our framework - silencing L2/3 in a familiar setting should result in no immediate changes to L5 activity and behavior of the animal.'

My reading of Nejad et al. is consistent with your interpretation of the equations. Specifically, Eq. 3 makes L5 activity depend on L2/3 activity (albeit weakly, with a = 0.3), whereas Eq. 2 contains no L5 term, so L2/3 activity does not depend directly on L5 activity. In that model, the L5->L2/3 pathway carries the learning gradient rather than contributing to the activity dynamics. By contrast, in your proposed model, L2/3 activity depends on L5 activity, whereas L5 activity is not immediately dependent on L2/3 activity (except indirectly through learning/plasticity). So, you state that "silencing L2/3 in a familiar setting should result in no immediate changes in L5 activity.<br /> [...].

However, I am unsure how to reconcile this prediction with the results shown in Fig. 6. If I understand the figure correctly, optogenetic activation of Rrad-positive (positive prediction error) L2/3 neurons produces a small increase in L5 activity, whereas activation of Adamts2-positive (negative prediction error) L2/3 neurons produces a decrease in L5 activity. Although these experiments involve activation rather than silencing, they nevertheless suggest that perturbing L2/3 activity can have an immediate effect on L5 activity. Could you clarify how this is consistent with the proposed model? In other words, what aspect of the proposed circuitry makes activation effective while silencing is predicted to have no immediate consequence?

For comparison, Nejad et al. performed a related perturbation analysis in Fig. S15 by scaling the output of L2/3 neurons exhibiting positive mismatch signals (defined through the activity gradients), which increased L5 activity, whereas scaling neurons with negative mismatch signals produced the opposite effect. I am not entirely sure how directly these simulations map onto the experiments shown in your Fig. 6, since the Nejad simulations were performed during mismatch conditions, if I have understood them correctly.

- '4. The proposal in Nejad et al. relies on input reconstruction or variance maximization within the L5 autoencoder network to avoid collapse. Instead, our proposal has no reconstruction objective.'

Nejad et al. only require two encoders (like in JEPA), how these two are learnt can be done in several ways. While Nejad et al. focus on using a reconstruction loss to learn the L5 target, they also show that it works equally well with non-reconstruction objectives. Therefore, I do not think the presence or absence of a reconstruction objective constitutes a fundamental distinction between the two proposals.

As your current work presents a conceptual architecture rather than a fully implemented learning algorithm (in a model), the mechanism that would prevent representational collapse has not yet been defined. From my understanding, every joint-embedding approach must address this issue, whether through reconstruction, variance/covariance regularization, stop-gradient or EMA mechanisms, or other approaches. Thus, the absence of a reconstruction objective (or another anti-collapse mechanism) is not, in itself, a distinguishing feature of the proposed architecture, but rather an as-yet unspecified design choice within the learning objective.

- '5. Lastly, there is time-delay between inputs to L5 and L2/3 that is proposed in Nejad et al., while this is not something inherent to our proposal.'

I agree that the temporal delay introduced by L4 is a key component of the Nejad et al. model and is currently absent from your proposal. However, I would expect temporal delays to emerge naturally in your framework as well, given the multisynaptic and highly parallel organization of cortical circuits. More generally, implementing predictive learning over time (as in JEPA) requires comparing representations at times t and t+1, which seems to require some form of temporal delay. How else would you suggest this is done?

In general, I think the manuscript would benefit from a clearer discussion of its relationship to the model proposed by Nejad et al. (perhaps following the discussion above), including both the shared conceptual claims and the aspects that genuinely differ between the two frameworks. At present, some of the claims are presented as novel, although at least some of these core ideas have already been proposed in Nejad et al.

For example, the authors state: "Thus, we propose that layer 2/3 functions to predict layer 5 activity, not sensory input per se, hence making predictions in the internal representation space, not input space." This appears to be exactly what Nejad et al. proposed as discussed above - in their model L2/3 predicts L5 activity (purely in the latent space), as they state in the abstract.

That said, there are some interesting differences, and I think the community would greatly benefit from making these clear, including the roles assigned to interlaminar connections and the interpretation of the signals carried by these pathways. These differences are interesting and potentially testable, and I think the manuscript would be strengthened by explicitly distinguishing which aspects are in line with the ideas already present in Nejad et al. and which aspects represent new contributions.

Reviewer #3 (Public review):

Summary:

Knoerzer-Suckow et al. explore the mechanisms of organelle inheritance during endodyogeny in Toxoplasma gondii using an innovative dual-labeling approach to track the distribution of maternal organelles into daughter parasites. They can clearly distinguish between maternal and daughter-derived organelles using their dual-labeling Halo Tag approach. They reveal that different organelles are trafficked to daughter parasites in three broad patterns they have binned into groups. Their findings reveal a role for MyoF in the inheritance of micronemes and rhoptries, and notably, they observe that the inner membrane complex (IMC) is not recycled. Instead, the IMC undergoes a pronounced relocalization to the posterior of the maternal cell, where it is likely targeted for degradation.

Strength:

The data surrounding their MyoF knockdown experiments, IMC degradation, and trafficking of MIC2 after auxin washout are convincing. These data add to the knowledge of how organelle inheritance occurs in T. gondii, increasing the field's understanding of endodyogeny.

Weakness:

The inability to achieve higher temporal resolution due to phototoxicity precluded tracking of single micronemes, thus it remains possible that some micronemes follow a path similar to rhoptries and enter daughter cells before development of the residual body while others are recycled via the residual body.

Reviewer #3 (Public review):

The current paper addresses an important issue in evidence accumulation models: many modelers implement flat decision boundaries because the collapsing alternatives are hard to reliably estimate. Here, using simulations the authors demonstrate that parameter recovery can be drastically improved by providing the model with additional data (specifically, an EEG-informed estimate of non-decision time). Moreover, in two empirical datasets it is shown that those EEG-informed models provide a better fit to the data. The method seems sound and promising and might inform future work on the debate regarding flat vs collapsing choice boundaries. As an evidence-accumulation enthusiast, I am quite excited about this work, although for the more broader audience the immediate applicability of this approach seems limited because it does require EEG data (i.e. limiting widespread use of the method or e.g. answering questions about individual differences that require very large N).

Comments on revised version.

I thought the authors carefully addressed my concerns.

Reviewer #3 (Public review):

Using cryofixation-based serial block face electron microscopy of several subregions of the Drosophila antenna, the authors segment and assemble a high-resolution atlas of the anatomical structure, density, and spatial distribution of extracellular vesicles (EVs) and non-vesicular extracellular particles (NVEPs) in different Drosophila olfactory sensilla types. This systematic and thorough description is an important prerequisite to understanding the function of extracellular particles in intercellular signaling in the nervous system. Additionally, they describe examples of putative biogenesis events (budding/fusion), as well as neuronal and axonal cell degeneration events and measure the changes in particle accumulation in these altered microenvironments.

Overall, this is a significant and comprehensive analysis and represents an invaluable resource to this burgeoning field. The authors assemble an important dataset and their claims match the level of evidence provided.

Strengths:

(1) The authors use segmentations from four different patches of the antenna to provide a systematic ultrastructural survey of extracellular particles in native insect sensilla. The dataset captures the diversity of sensilla types and reconstructs ~7800 particles.

(2) We commend the authors for making the EM volumes available in the public Cell Image Library with accession numbers. It would be helpful to the community to also make the segmentations for this great resource easily accessible.

(3) The sample preparation technique appears to minimize typical artifacts associated with chemical fixation, as evidenced by the high reported sphericity of EVs.

Specific points:

(1) In Figure 3C, the authors should include a continuous measure of particle distribution in the sensilla. Currently, the authors define three categories of particle localization. In the five examples shown in Figure 3A, the spatial distribution of these particles appears quite distinct across classes. For example, EVs/NVEPs in large and small basoconic sensilla are largely restricted to the area proximal to the base, with a limited number located more distally. In contrast, intermediate sensilla show a marked concentration of particles more distally.

(2) The conclusion of different EV ratios across sensillum classes stems from a Kruskal-Wallis of p = 0.0476, with none surviving pairwise comparisons. This is not a strongly supported conclusion and is probably better characterized as a trend.

(3) The statement "selective enrichment of large, cargo-filled vesicles within the ab1 lumen suggests specialized EV-mediated communication adapted to the coordination demands of this neuronal population" seems speculative for a Results section without supporting functional evidence. It would seem better suited for the Discussion.

(4) Figure 4: Criteria for defining the classes of EVs.<br /> a) The authors should explain the rationale for classifying EVs using relative density rather than absolute density? We would expect EVs with similar contents to have similar electron density (similar darkness in the images). Would classifying them relative to the background, which itself might vary across sensilla or regions, create a possible confound, especially when comparing across sensillum classes?<br /> b) The two example images (in Figure 4A) of the cargo-filled EVs appear to have different densities themselves. Do the cargo-filled ones also display systematic differences in density and, if so, why is this another class instead of being a subcategory within the dense and lucent classes (i.e. dense with/without cargo, lucent with/without cargo)? The dense and lucent classes are defined by their density, whereas this is a more structural property.<br /> c) Regarding "Double" and "Ball-and-Socket" EVs, does the density vary between the two particles involved (e.g., does the inner structure consistently differ in density from the outer)?

(5) What was the rationale for the 200μm and 1000μm size cutoffs? A continuous distribution of maximum particle sizes would provide a clearer understanding of the data.

Reviewer #3 (Public review):

In this manuscript, the authors focused on Callorhinchus milii GSDMA/B (CmiGSDMA/B) and its upstream inflammatory caspase, CmiCASP1, and revealed that LPS directly engages the CARD domain of CmiCASP1, triggering its activation, which subsequently promotes the proteolytic cleavage of CmiGSDMA/B, yielding two N-terminal fragments with opposite functions. Moreover, consistent with GSDMD, the functional N241 of CmiGSDMA/B can mediate pyroptosis and exhibit bactericidal activity against Gram-negative bacteria in vitro. Based on these observations, the authors clarified that they uncovered an ancestral LPS-sensing CASP1-GSDMA/B axis in cartilaginous fish; however, several issues should be addressed.

(1) The evidence for direct and functional LPS sensing by CmiCASP1 remains insufficient. Although the authors propose that LPS directly binds the CARD domain of CmiCASP1 to trigger a non-canonical inflammasome-like pathway, the current support mainly comes from pull-down, competition, and in vitro cleavage/activity assays. These results are suggestive but do not yet establish a direct, specific, and physiologically relevant interaction. Additional quantitative binding and specificity analyses are needed to exclude indirect association, aggregation, or other assay artifacts. Therefore, the claim that CmiCASP1 functions as a bona fide direct LPS sensor appears overstated at this stage.

(2) The proposed antagonistic role of N288 is not yet convincingly supported. While the "dual-fragment antagonistic regulation" model is interesting, it currently relies mainly on overexpression/co-expression, co-IP, and localization analyses, which do not clearly distinguish a physiological inhibitory mechanism from a non-specific dosage or sequestration effect. Stronger support would require evidence for the relative generation and timing of N241 and N288, as well as quantitative data showing that N288 interferes with N241 membrane targeting, oligomerization, or pore formation. Testing the effect of selectively blocking D288 cleavage in the full-length protein would also strengthen this conclusion. At present, the antagonistic model remains premature.

(3) The physiological and evolutionary claims are stronger than the available evidence. Although the study shows that CmiCASP1 can cleave CmiGSDMA/B and that this module can be reconstituted in heterologous mammalian systems, these data do not demonstrate that such a pathway operates in elephant shark cells or tissues under physiological conditions. A similar concern applies to the antibacterial assays, which use HEK293T lysates rather than purified N241, making it difficult to exclude contributions from host-derived factors. The authors should either provide more direct evidence in a relevant chondrichthyan context or substantially tone down the evolutionary and physiological interpretations.

(4) The inhibitor data do not convincingly demonstrate suppression of CmiCASP1 activation. Although the authors state that Z-VAD-FMK blocks CmiGSDMA/B cleavage and pyroptotic phenotypes, Figure 1L and Figure 2H do not clearly show that CmiCASP1 activation or processing itself is inhibited. If CmiCASP1 remains processed in the presence of the inhibitor, it becomes unclear whether Z-VAD-FMK blocks CmiCASP1 activation, catalytic activity, or only downstream substrate cleavage. This point should be clarified with more direct biochemical evidence.

(5) The dosage control for GSDM-derived proteins in the antibacterial assays is unclear. In Figure 5, antibacterial activity is tested using HEK293T lysates or concentrated supernatants containing full-length CmiGSDMA/B, N241, or N288, but it is not clear how protein amounts were normalized across conditions. Differences in expression, stability, or recovery could substantially affect the apparent antibacterial activity. The authors should clarify how input was controlled and ideally provide quantitative normalization or matched-concentration assays to support the comparison.

Reviewer #3 (Public review):

Summary:

This study introduces a principled framework for optimizing multi-task batteries for individualized functional brain mapping. Through simulations and empirical validation, the authors show that selecting tasks to maximize differences in regional response profiles can substantially improve the identification of functional brain regions. The work represents a valuable methodological advance for precision functional mapping, although some assumptions underlying the broader applicability of the framework would benefit from further discussion.

Strengths:

The manuscript addresses an important methodological challenge in precision functional mapping using a rigorous combination of theoretical analyses, simulations, and empirical validation. The framework is practical and well supported by open-source software and a publicly available task library, making it readily accessible for adoption and further development by the research community. The manuscript is well written, logically structured, and clearly presents both the methodological framework and its practical implementation.

Weaknesses:

(1) The abstract and introduction emphasize the application of the framework to individualized brain parcellation. While the presented analyses convincingly demonstrate improved prediction of held-out task responses using atlas-guided parcel assignments, they do not directly validate whether the optimized task batteries improve the estimation of an individual's true functional boundaries. The empirical validation relies on atlas-defined parcel identities as the reference standard, yet substantial inter-individual variability in the location and extent of functional regions - particularly within association cortex - has been well documented. Consequently, improved recovery of atlas-defined parcel labels does not necessarily imply more accurate recovery of an individual's functional organization. It would therefore be valuable to clarify this distinction in the abstract and discussion and to discuss how inter-individual variability may influence the interpretation and generalizability of the parcellation analyses.

(2) The framework assumes that informative task batteries can be designed to distinguish neighboring functional regions. While this is compelling for well-characterized systems with distinct functional response profiles, it is less clear how the approach generalizes to finer-scale subdivisions within association cortex (e.g., subnetworks), where neighboring regions may exhibit highly similar task-response profiles and their functional roles remain incompletely understood. In these settings, the relevant functional dimensions may not yet be known, making it difficult to design optimized task batteries a priori. It would therefore be valuable for the authors to discuss how the framework could be extended to such cases.

(3) More generally, the framework assumes that the sampled task space adequately captures the functional dimensions that differentiate cortical regions. However, particularly within the association cortex, neighboring regions may exhibit similar task-response profiles while differing in the information they represent, their interactions with other regions, the computations they perform, or their cortical layer-specific response patterns. In such cases, the dimensions that best distinguish cortical organization may not be fully reflected in task-response profiles alone, but instead become apparent through complementary approaches such as representational analyses, task-evoked or resting-state connectivity, computational modelling, or laminar response profiles. It would therefore be valuable to discuss how the proposed framework relates to these complementary perspectives.

(4) Many task batteries inherently contain tasks that vary substantially in cognitive demand. Given that task difficulty is itself a major organizational axis in association cortex, it would be helpful for the authors to discuss how the optimization framework accounts for this. Specifically, could differences in task difficulty drive regional differentiation, even when tasks probe similar underlying cognitive processes? If so, how does the framework distinguish between organizational differences arising from a common demand axis and those reflecting more specific functional specializations?

(5) The Discussion places the proposed framework in the broader context of precision functional mapping and refers readers to a companion paper demonstrating advantages over resting-state ("inside-out") approaches. Given that these comparisons motivate several of the broader recommendations made in the Discussion, it would be helpful to provide a brief summary of the main findings of the companion paper here. This would allow readers to better understand the basis for these conclusions without relying on a separate manuscript.

Reviewer #3 (Public review):

Summary:

This manuscript reports a novel genetic model, Col20a1-CreERT2 knock-in mouse, to target terminal Schwann cells (tSCs) at mouse neuromuscular junctions in a cell-type-specific and temporal manner. The authors analyzed multiple publicly available single-cell transcriptome databases to identify Col20a1 as the tSC marker. The authors crossed Col20a1-CreERT2 and Rosa26-LSL-tdTomato to label tSCs successfully. In addition, the authors generated Col20a1-CreERT2; Rosa-tdT/ diphtheria toxin subunit A (DTA) to specifically ablate tSCs and analyze the role of tSCs in motor behavior, neuromuscular junction electrophysiological function, and the histology and ultrastructure of neuromuscular junctions.

Strengths:

The Col20a1-CreERT2 x Rosa26-LSL-tdTomato mice successfully labeled the tSCs at NMJs and reported the developmental distribution of the Col20a1-positive cell population. The Col20a1-CreERT2; Rosa-tdT/DTA mice successfully ablated tSCs, which did not cause changes in gross neuromuscular junction architecture, neuromuscular synapse physiology, or motor behavior.

Weaknesses:

The conclusion of this manuscript will be strengthened by additional analysis showing time-course data of tSC ablation and replacement by non-recombined Schwann Cells. Currently, it is not clear when and how long the tSCs are ablated, which makes it difficult to interpret the data and phenotype. Detailed review comments are provided to the authors in the "recommendations for the authors" section.

Reviewer #3 (Public review):

Summary:

The manuscript presents the development of a software tool and a computational workflow for the comparison and biological interpretation of GWAS results among three neurodegenerative diseases - AD, PD and FTD - using the data from the Human Protein Atlas. The multi-scale content analysis produces a representation of GWAS results highlighting enrichment at the level of brain regions, organ systems/tissues and cell types as well as in terms of molecular pathways. The manuscript argues that DLB, AD and FTD have differential 'modules' revealed by the procedure.

Strengths:

The system is leveraging vast knowledge sources including both the GWAS catalog and the HPA, and it is integrating these data. This synthesis of information is useful. It is making use of large investment in data generation and data warehouses in order to yield interpretation of epidemiologic results from GWAS in biological terms that may yield insight into disease processes and differences between diseases. The multiscale analysis recognizes the various biological lenses at which the implications of GWAS can be evaluated.

Weaknesses:

There is a lack of controls and/or disease comparators in the study. It is hard to assess that the workflow is performing 'as expected' without a set of positive or negative controls - or at least comparators - to gauge the performance of the tool. The statistical methods are simplistic and rely on Fisher's exact tests, UMAP analyses and clustering with little justification. There is a lack of power analysis and specification of the number of genes required for the procedure to 'work'. The mapping of GWAS hits to genes is simplistic and may in many cases be erroneous, and the implications of errors in these mappings are not considered. Many of the hits are not brain-specific - highlighting the complexity of gene function and the pleiotropic nature of gene activity. Moreover, the mapping between 'tissue enrichment' and 'tissue that is the functional driver of the GWAS signal' may be a logical flaw in the reasoning of the authors. Just because a tissue - such as liver enrichment in AD - is enriched in the GWAS gene-mapping analysis does not mean that that tissue found to be enriched is in fact the functional tissue that gave rise to the GWAS signal. Genes have different isoforms, functions, and regulatory mechanisms in different parts of the organism in different parts of development. In a phrase, the enrichment observed could be correlative and not causative, and in fact the enrichment could be driven by some hidden variable not considered. The etiologic tissue for neurodegenerative disease is the brain. The findings are not necessarily surprising or novel in the distinction between PD, AD, and FTD. Finally, the figures are perhaps not the best way to present results. There are many small pie charts that lack interesting results; the figures are in general hard to read and could use refinement in terms of fonts.

Reviewer #3 (Public review):

Summary:

This work describes how two chemosensory neurons in C. elegans drive opposite behaviors in response to a volatile cue. Because they have different concentration dependencies, this leads to different behavioral responses (attraction at low concentration and repulsion at high concentration). It has been known that many odorants that are attractive at low concentrations are aversive at high concentrations, and the implicated neurons (at least AWC for attraction and ASH for repulsion) have been well established. Nonetheless, by studying behavior and neural responses in a common context (odor pulses, as opposed to gradients) this provides a clear picture of how these sensory neurons may guide the dose dependent response by separately modulating odor entry and odor exit behaviors.

Strengths:

(1) This work provides good evidence that worms are attracted to low concentrations and repelled by high concentrations of 1-oct. Calcium imaging also makes it clear that dose-dependence of this response is stronger for ASH than AWC.

(2) This work presents calcium imaging and behavior with the same stimulus (sudden pulses in volatile odor concentration), while previous studies often focus on using neuronal responses to pulses to understand navigation of gentle gradients.

Weaknesses:

(1) As a whole it is not clear precisely how important AWC is (compared to other cells) for the attractive response (as the authors correctly acknowledge).

(2) The evidence that AIB minus AVA contains relevant information is weak. It appears the entrainment index in Fig. 6H for AIB-AVA could easily be explained by the negative entrainment between AVA and the stimulus (along with no effect or role for AIB). This is suggested by the similar p-values and similar distribution of random EIs (stretched and mirrored) between the first and last rows of this figure.

Reviewer #3 (Public review):

In this study, the authors aim to provide the most comprehensive and detailed topographic map to date of spinal projection neurons in the larval zebrafish brain. They achieve this by retrogradely photoactivating, in the rostral spinal cord, a photoconvertible GFP expressed pan-neuronally, and by constructing a template larval zebrafish brain atlas to regionalize the location of all labeled somata across the brain. The labeling strategy, together with the chosen animal model, provides strong support for the completeness of the dataset. The generation of a standardized anatomical atlas establishes a rigorous framework for analysis. Molecular and anatomical evidence suggesting evolutionary conservation of selected regions of interest appears solid.

Overall, the authors successfully achieve their aim. By generating this atlas of spinal projection neurons, they provide not only an anatomical framework of the regions involved, but also an important reference for improving the orientation and regionalization of the zebrafish brain, which has historically been difficult to define. This work may serve as a valuable resource for future evolutionary, developmental, and comparative studies of spinally projecting neuronal populations implicated in diverse functions.

Reviewer #3 (Public review):

Summary:

The central idea of the study is strong and potentially important: that the vulnerability of the cholinergic medial-septal population can account for a substantial fraction of prodromal-like AD phenotypes, thereby shifting part of the mechanistic focus from cortex-centered pathology to subcortical neuromodulatory circuit failure. The work has several notable strengths. The authors combine circuit mapping, calcium photometry, longitudinal EEG/EMG sleep phenotyping, histology, behavior, and a caspase-based lesion comparison to build a multi-level case for medial septal cholinergic involvement in REM Sleep and memory phenotypes. The inclusion of both a focal amyloid model and a partial cholinergic ablation model is especially valuable because it attempts to separate effects of Ch-neuronal loss from effects of amyloid itself.

However, the manuscript has several issues, from manuscript formatting to experimental design, overarching statements, insufficient exclusion of alternative explanations, incomplete quantification details for key histological results, a discussion that often moves beyond the actual data into speculative translational framing, and a discussion that completely ignores the early presence of p-tau in human AD patients and even lacks supplementary materials.

Strengths:

(1) The conceptual premise is compelling: cholinergic basal forebrain vulnerability is a real and important feature of AD, and testing whether selective medial septal cholinergic pathology can drive REM sleep and cognitive phenotypes is mechanistically interesting and clinically relevant.

(2) The experimental framework is broad and generally thoughtful, spanning anatomy, function, sleep architecture, EEG spectral parameterization, behavior, and histopathology.

(3) The projection mapping and photometry provide a useful systems-level introduction, establishing that MSChAT neurons are Wake/REM sleep-active and project strongly to hippocampal and cortical targets before the disease manipulations are introduced.

(4) The MSΔChAT comparison group is valuable because it allows the authors to argue that some phenotypes track with cholinergic loss rather than amyloid per se.

(5) The longitudinal sleep analysis is one of the strongest parts of the study, especially the emphasis on REM sleep quantity and bout architecture over time rather than relying only on an endpoint comparison.

Weaknesses:

(1) The title overreaches in its use of "prodromal phase." In the clinic, "prodromal AD" denotes a biomarker‑positive, pre‑dementia phase with subtle, progressive cognitive decline before widespread neurodegeneration, whereas here the authors demonstrate substantial cholinergic degeneration alongside cognitive impairment, which corresponds to advanced pathology within these models rather than a clinically prodromal stage. Moreover, APP knock‑in mice are amyloid‑centric, lack tau pathology, and don't recapitulate human disease staging; therefore, it would be better to avoid terms used for AD staging in the clinic. A more accurate framing of the title would be "Modeling the prodromal-like phase in an Alzheimer's disease mouse model".

(2) The opening statement in the abstract (line no 22) is overstated. Current evidence supports that changes in REM sleep, slow‑wave sleep disruption, and excessive daytime sleepiness are associated with a higher risk of AD and reflect early involvement of brain regions vulnerable to AD proteinopathy. No study indicates that REM sleep changes per se are a strong predictor on their own. For example, Jin et 2025 studied REM latency in AD and concluded that prolonged REM latency may be a marker of early neurodegeneration (PMID: 39868572). Thus, the opening statements need to be modified.

(3) Line 63: The current phrasing of neuromodulators being also essential for orchestrating sleep/wake states is very simplistic. Sleep/wake regulation is a highly complex process involving several interacting neurotransmitters and neuromodulatory systems. I recommend revising this sentence to reflect the broader, multi‑system nature of sleep/wake control.

(4) Line 64: "ACh is required for the generation of REMS" is incomplete. The sentence implies REM sleep generation depends exclusively on ACh. Instead, the sentence must emphasize that ACh is a crucial component of a broader REM sleep circuitry and explain why it is critical for REM sleep.

(5) Line 65: The sentence "Importantly, reductions and alterations in REMS have emerged as strong predictors of clinical AD onset" (Reference 37) is an overstatement of the evidence; Peas et al. 2017 analyzed a dementia cohort that included AD cases and concluded: "Despite contemporary interest in slow-wave sleep and dementia pathology, our findings implicate REM sleep mechanisms as predictors of clinical dementia." The authors should rephrase this to reflect that the study examined REM sleep changes in a mixed dementia population with AD, rather than to establish REM alterations as strong, standalone predictors of AD onset.

(6) Lines 73-75 address human Alzheimer's studies and state that basal BF-Ch neurons are vulnerable to Aβ but largely omit the well-established contribution of early tau pathology. In human AD patients, p-tau accumulation in BF is an early event (Braak I-II) and is closely associated with BF-Ch neuronal loss and BF atrophy and has been documented extensively. By relying almost exclusively on Aβ-centric framing, the current text risks implying that BF-Ch degeneration is solely amyloid-driven, which is not accurate. Even though the mouse model used here is "amyloid-heavy" and lacks tau pathology, the introduction should acknowledge the role of p-tau (especially when the paragraph contextualizes human studies) and clarify that in humans, BF-Ch vulnerability reflects converging amyloid and tau insults, so that readers do not infer a purely amyloid-dependent mechanism from the way the background is presented.

(7) Line 92: and elsewhere in the manuscript, I recommend avoiding the term "prodromal phase" and instead using the phrase "prodromal-like phase in an AD mouse model". The authors should be more precise in describing the disease stage in animal models that don't recapitulate human disease staging and ensure that clinical staging terminology is specific to human studies.

(8) Age and duration of pathology are major concerns. The different models are not adequately matched for amyloid exposure duration and age at testing. Age is the strongest risk factor for AD, and varying both chronological age and time under pathology across groups is a major design flaw. In MSChAT-AppNL-G-F/GFP mice, AAV injection was delivered at 11-13 weeks of age, and animals were sacrificed at 13-14 months post-injection (roughly 15-16 months old), whereas AppNL-G-F/NL-G-F knock-in mice and APPWT were 13-14 months old at the time of termination. Thereby, there is a difference in the duration of Aβ exposure across models. This mismatch directly weakens comparisons such as the lower epileptiform spike counts in MSChAT-AppNL-G-F versus AppNL-G-F/NL-G-F mice, because differences could simply reflect shorter cumulative pathology exposure rather than a genuinely weaker circuit-specific effect.

The same issue affects the internal control logic of the MSΔChAT model, which is intended to isolate cholinergic neuron loss from amyloid aggregation. For this comparison to be clean, ages and exposure durations should be aligned as closely as possible. Instead, MSΔChAT mice are tested earlier than the AppNL-G-F/NL-G-F and MSChAT-AppNL-G-F/MSChAT-GFP cohorts, introducing a 4 to 7-month age gap that complicates attribution of phenotypic differences solely to cholinergic loss versus amyloid pathology.

Finally, the absence of sham-operated controls is a concern, as it prevents separating the effects of the surgical procedure and AAV delivery from those of amyloid expression or cholinergic ablation.

(9) Line 115 through 117: The text cites Figure 2D, but does not refer to Figure 2C for the statement "their phenotypes were then compared in detail with MSChAT-AppNL-G-F and AppNL-G-F/NL-G-F global knock-in mice that were aged at the same time". Figure 2C depicts D54D2 amyloid staining in MSChAT-GFP vs MSChAT-AppNL-G-F mice. For clarity and consistency, I suggest adding a Figure 2C notation to this sentence (e.g., "Figures 2A, 2C").

(10) In Figure 1C-D, the authors map MSChAT projection targets across a wide range of brain areas, including hippocampal subfields, mPFC, primary cortices, entorhinal cortex, olfactory bulb, thalamus, anterior hypothalamus, amygdala, and medial habenula, and identify several of these as substrates through which MSChAT activity could influence REM sleep and cognition. However, the lateral hypothalamic area (LHA) is conspicuously absent from both the listed projection targets and the tracing panels shown in Figure 1D, despite the anterior hypothalamus being reported as an innervated region.

This omission is notable given that LHA-MCH neurons are among the best-established REM-sleep-promoting neurons, and the authors themselves cite prior work implicating LHA-MCH neurons in the AppNL-G-F REM sleep phenotype (ref. 49, 107; line 403) as an alternative cell-circuit candidate, a claim they explicitly try to weigh against their own MSChAT-centered model in the discussion.

a) The MSChAT neurons are reported to be REM sleep- and wake-active (Figure 1A-B), the same vigilance-state profile as LHA-MCH neurons,<br /> b) The Discussion directly engages with LHA-MCH neurons as a competing/complementary REM sleep-generating mechanism, and<br /> c) The reported anterior hypothalamus innervation (Figure 3C) raises the question of whether MSChAT axons specifically innervate LHA, and whether any projections specifically to LHA or LHA-specific amyloid deposition were examined. Clarifying this would help position the proposed MSChAT-hippocampal circuit mechanism relative to the well-established LHA-MCH REM sleep node.

(11) Line 125: "13- to 14-month-old MSChAT-AppNL-G-F mice immunohistochemical analyses employing the amyloid-specific antibodies....", in the methods section (Line 652) the authors mention MSChAT-AppNL-G-F and MSChAT-GFP mice were perfused 13-14 months after AAV injection (age at the time of injection was 11-13 weeks of age). This leaves the question of how they have 13- to 14-month-old MSChAT-AppNL-G-F mice available to study Amyloid-β load.

(12) Line 174-175: As currently written, the sentence could be read as both wild-type and homozygous AppNL-G-F/NL-G-F mice received AAV injections and were then aged 13-14 months post‑injection. In fact, the Methods clearly state that knock‑in mice are simply aged from birth without any AAV manipulation. The sentence should be rephrased to avoid suggesting that global APP knock‑in animals are part of the AAV‑injected cohorts.

(13) Lines 182-183, 196-197, and 209 refer to "Supplementary information" and imply that detailed behavioral data and analyses are provided in that section. However, in the current submission, the supplementary material consists only of Figures S1-S7 (Amyloid marker and cerebral vasculature, Aβ in hippocampus, GABA and glutamatergic neurotransmission, and sleep/wake parameters) and does not include supplementary figures or tables for the behavioral assays described in the main text. This discrepancy makes it impossible to verify the full behavioral dataset and the analyses referred to in the results section. The authors should carefully check the submission package and ensure that all referenced supplementary figures, tables, and detailed behavioral results are included and appropriately labeled.

(14) The lack of details for histological quantification is a major concern for a manuscript in which major conclusions hinge on Aβ load and MS-Ch neuronal counts. The histological quantification section is severely under-specified. The authors describe a 23% MSChAT loss, differences in regional Aβ burden, and a vascular association; however, the methods section is strangely silent about the quantification pipeline. For Aβ quantification, it is not clear whether "load" reflects percent positive area, plaque counts, or another metric; which Fiji thresholding algorithm(s) were used; how ROIs were defined; how staining batch effects were controlled; and how autofluorescence was normalized. For neuronal counts, the strategy for identifying and counting ChAT-positive neurons, normalization, and blinding are not described. There are no details on section spacing, axis of counting, the number of sections counted per animal, or whether both hemispheres were analyzed. Given that the reported differences are modest and central to the main claims, a more detailed and rigorous description of the image-analysis pipeline is essential.

(15) Statistical annotations in figures: There is inconsistency in how statistical significance is indicated across the figures. For example, in Figure 5C, the significance between MSΔChAT and AAV‑Aβ⁻ is indicated by a connecting bracket (**), whereas the comparison between AAV‑Aβ⁻ and AAV‑Aβ⁺ is marked by asterisks (***) placed above AAV‑Aβ⁺. In addition, the single asterisk above KI-Aβ⁺ does not clearly specify which pairwise comparison it refers to (e.g., AAV‑Aβ⁻ vs WT‑Aβ⁻ or another contrast). This heterogeneity makes it difficult to decipher exactly which group comparisons have been tested and found significant. The notation should be standardized and explicitly linked to the corresponding pairwise comparisons (for example, by using consistent brackets/lines and specifying all contrasts in the figure legend). Figures must be self-explanatory.

(16) Figure 5D statistical notation and group comparisons: The statistical markings in Figure 5D do not seem to match the results text and are difficult to interpret. The authors state that both MSΔChAT and AAV‑Aβ⁺ mice lack a preference for the novel object compared with AAV‑Aβ⁻ controls, yet the figure does not clearly indicate significance for MSΔChAT versus AAV‑Aβ⁻, and the notation over AAV‑Aβ⁺ is ambiguous. As a result, it is unclear which group differences are being tested and reported. It would be preferable to use the standard convention of placing significance annotations directly over the experimental groups (e.g., AAV‑Aβ⁺, KI-Aβ⁺⁺, MSΔChAT) or use notation above brackets to ensure that the figure labels are fully consistent with the statistical statements in the results.

(17) The discussion contains many compelling ideas, but it needs pruning and recalibration. The best discussion points are those linking the lesion comparison to REM sleep/cognitive outcomes and those situating MS cholinergic neurons within broader REM sleep circuitry. The least convincing sections are those implying disease-stage equivalence, prion-like spread, and direct therapeutic implications without sufficient evidentiary support.

(18) Line 448: The authors discussing reduced anxiety-like behavior in their model corroborates with the 3xTg mouse model (Ref: 116). Interestingly, they don't consider or include reports of anxiety-like disorders from human cohort studies that indicate the prevalence of higher anxiety and its association with preclinical and prodromal AD stages (SCD, MCI) and progression of AD. This apparent contradiction with the human literature is not discussed in the discussion section. The authors should explicitly address how their anxiolytic-like phenotype fits with clinical data (e.g., species differences, task specificity, disease stage, or model limitations) and clarify whether they view this as a limitation of the model or as evidence for a more complex relationship between amyloid, cholinergic dysfunction, and emotional behavior.

(19) Line 654 states, "Comparable durations of amyloid pathology," but this is not fully substantiated, as the onset and progression of amyloid in the AAV-driven MSChAT-AppNL-G-F model versus the global AppNL-G-F knock-in model are not described. The data support comparison at a similar late-stage amyloid burden, but not necessarily equal duration of pathology.

Reviewer #3 (Public review):

Summary:

The authors investigated the role of the nsP3 macrodomain catalytic activity in the replication and transmission of CHIKV in mosquito vectors. The conserved dual-host alphavirus catalytic site N24 has previously been shown to be essential for ADP-ribosylhydrolase activity. Despite this, mosquito-specific alphaviruses do not share this catalytic site. To assess whether the macrodomain catalytic activity of a dual-host virus was essential in insect hosts, the authors targeted the N24 site to abolish catalysis while maintaining binding capacity. The loss of ADP-ribosylation led to the emergence of compensatory mutations at site D31 that impact viral infectivity, dissemination, and transmission in Aedes sp. mosquitoes in vivo. The conclusions are well supported by the results and provide insight into the importance of nsP3 macrodomain activity in the mosquito vector, which hasn't been explored before.

Strengths:

The main strength of this study is the use of Aedes sp. mosquito models to investigate the selective pressure of macrodomain mutations in vivo. The functional characterization as well as the structural analysis of the mutants provide supporting evidence of a potential role of the compensatory mutations at site D31 in substrate recognition.

Weaknesses:

A considerable part of this study relies on the use of N24 mutant viral stocks generated in Vero cells, which yields an additional mutation at site 31 and consequently doesn't allow the authors to properly dissect the effect of mutation of N24 and D31 independently. It would be recommended to generate stocks with individual mutations in both A549 and U4.4 cells, pooling and concentrating them if needed. Replication of the N24A mutant in A549 cells does not lead to mutation at residue 31. Yet surprisingly, there is no reversion from N back to D at site 31 when the double mutant Vero stocks are passaged in A549. Since they are double mutants, it isn't possible to assess whether the defects in the growth of mutants N24A/T-D31N and N24D-D31H/N compared to WT are due to site 24 or 31, or both (Figure 2, panel c). Even though the authors emphasize that the compensatory mutation could have additional roles that impact viral infectivity and transmission in mosquito cells, it would strengthen the work to show that these mutations would spontaneously appear in stocks generated directly in mosquito cells. As a corollary, is it known whether insect-specific alphaviruses that lack macrodomain catalytic activity have corresponding mutations at site 31?

Additionally, there is a lack of consistency in the prevalence of WT virus at days 5 and 7 in in vivo experiments with Ae. albopictus and Ae. aegypti (Figure 3 and Supplementary Figure 2). This raises concern about the reproducibility of these experiments.

The inability to tease apart the roles of N24 and D31 in mosquito hosts partially prevented the authors from fully achieving their aims, but the work is nonetheless of interest to the field and suggests that more work is necessary to fully understand the role of the nsP3 macrodomain and its catalytic activity in the two disparate but obligate hosts for CHIKV and other dual-host alphaviruses.

Reviewer #3 (Public review):

The manuscript by Jiang and co-authors presents an analysis of experimental measurements (about 400k variants) from a deep mutational scan of the HIS3 enzyme. The authors assess the ability of a genotype to be "rescued" and show that this depends on mutation sites (in particular their solvent accessibility) and mutation effects (should be mild on folding stability or binding affinity). They further identify a set of super-compensatory mutations, and their results suggest that these mutations flatten the fitness landscape.

This finding is interesting and likely of interest to a broad community. The analysis seems sound.

However, I have a number of major concerns regarding the presentation and positioning of the work.

(1) It would improve the manuscript to clarify the present contribution with respect to a previous study by the same authors, namely Pokusaeva et al. 2019. Did the authors apply the same protocol to generate a new library of mutants, or did they re-analyse an already published library? If the library is not new, ambiguous sentences like "Nevertheless, to our knowledge, the His3p library remains one of the largest and most comprehensive resources that contains multi-site mutants" should be reformulated.

(2) Pokusaeva et al. 2019 is cited for the library and also for the deep neural network. It would be beneficial to briefly describe the architecture, the inputs and outputs, and the training procedure. Was the network trained on the current library? What is the purpose of this network? It looks more like an additive linear model (except for the global sigmoid) than a deep neural network. How does it relate to global epistasis models? The sigmoid function is designed to capture plateauing effects; doesn't that introduce some circularity issue in the reasoning?

(3) Are the super-compensatory mutations observed (conserved) across evolution? Beyond the fact that they are accompanied by mildly deleterious mutations in natural sequences. Can we predict them with variant effect predictors?

(4) The AAindex mention should be accompanied by a citation.

(5) Equations should be numbered. WCN formula seems to contain misformatting issues.

(6) A more explicit description of the structural data analysed (which PDB entry?) should be provided.

(7) I believe the citation Van Cleve and Weissman 2015 for the ProteinGym benchmark is incorrect. Additionally, is the Rosetta citation adequate?

(8) How is the definition of rescueability sensitive to the threshold choice?

Reviewer #3 (Public review):

Summary:

This manuscript asks whether two forms of explicit strategy use in visuomotor adaptation, i.e., algorithmic mental rotation and retrieval of a cached aiming solution, differentially influence implicit recalibration. The question is relevant because much prior work treats explicit strategy as a unitary process, whereas the algorithmic/retrieval distinction is theoretically meaningful and grounded in cognitive theory. Across three experiments, the authors report that algorithmic strategy conditions initially produced broader fitted implicit generalization functions than retrieval conditions, but that this difference was reduced or eliminated when reach variability and sensory prediction errors were more tightly controlled.

Strengths:

The paper is clearly written, theoretically well-motivated, and employs a commendably transparent and progressive experimental logic. The three-experiment structure, in which confounds are systematically identified and addressed, represents a strong model of cumulative experimental design (I will certainly use it in teaching courses on experimental methods):

Experiment 1 establishes an apparent difference in implicit generalization breadth. Experiment 2 attempts to reduce error spillover from Non-Critical targets by increasing angular separation and using delayed endpoint feedback. Experiment 3 uses an error-clamp design to decouple variable reaching from error feedback. This sequence is appropriate for testing whether the initial difference reflects a strategy-dependent change in implicit recalibration or instead follows from the distribution of movement plans and error exposure. The authors also provide reaction-time and performance data that are broadly consistent with the intended distinction between algorithmic and retrieval-like task performance.

Weaknesses:

The evidence does not support the strongest claims made in the manuscript, namely that algorithmic and retrieval strategies generally do not reshape implicit recalibration.

In general, I am skeptical of the authors' interpretation of null results. Several central conclusions depend on non-significant group differences, especially in Experiment 3. Non-significant tests are repeatedly treated as evidence that groups are equivalent or that confounds are absent (e.g., implicit recalibration magnitude (Algorithmic: 11.43 {plus minus} 6.43{degree sign}; Retrieval: 15.49 {plus minus} 8.99{degree sign}; t(38) = −1.65, p = .11), adaptation level before Exclusion probes (F(1,256) = 3.04, p = .08) and Exclusion RT differences (F(1,266) = 3.15, p = .08), whereas a modest model-dependent breadth effect (bootstrap p = .02) is treated as meaningful (for more on the model-dependent breadth effect, see below).

Without confidence intervals, equivalence tests, or Bayesian analyses, I think that the authors' interpretations comprise an inferential gap. A failure to find a significant difference is not equivalent to evidence of equivalence, particularly given that the implicit recalibration signal gets progressively attenuated across experiments (Experiment 1: ~16-17{degree sign}; Experiment 2: ~11-15{degree sign}; Experiment 3: ~7-8{degree sign}). With a substantially diminished signal in Experiment 3, the null result could partly reflect reduced statistical sensitivity rather than true equivalence.

My main technical concern is the analysis of generalization breadth already alluded to. The central claims rely on group-level Gaussian fits to only seven Exclusion probe locations spanning −45{degree sign} to +45{degree sign} around the Critical target. In several cases, the fitted centers and widths are poorly constrained by the sampled range. For example, in Experiment 2 the algorithmic group's fitted center is shifted to approximately 29{degree sign}, meaning that the probe range samples the function asymmetrically relative to its own peak. In Experiment 3, fitted centers are near or outside the sampled range, while estimated widths are very broad. Under these conditions, the width parameter may partly reflect extrapolation or parameter trade-offs between center, amplitude, and width rather than a genuine difference in generalization breadth.

Lastly, I think that the authors' use of an error-clamp paradigm is, from an experimental point of view, quite elegant. By controlling the sensory prediction error independently of reach direction, they can isolate implicit recalibration from the confounds identified in Experiments 1 and 2. However, I see a fundamental problem or question concerning construct validity here: In Experiments 1 and 2, the algorithmic strategy was operationalized as participants computing a counterrotated aiming direction in response to a visible cursor rotation. This is a naturalistic context where mental rotation is both required and meaningfully connected to task success. In Experiment 3, however, there is no visuomotor rotation to compensate for. The error-clamp renders the cursor feedback task-irrelevant. Instead, participants are instructed via text commands (e.g., "move towards 45{degree sign}") to reach invisible locations, rendering the "algorithmic strategy" in this context essentially an instructed spatial navigation toward arbitrary angular locations, not genuine visuomotor mental rotation driven by an error signal.

To put it differently, are we sure that the cognitive process engaged by the algorithmic group in Experiment 3 is the same as the algorithmic mental rotation strategy in Experiments 1 and 2? If not, then the null result in Experiment 3 may not speak to the original question about how algorithmic strategies interact with implicit recalibration after all. Instead, it may reflect the absence of a genuine strategy manipulation.

To their credit, the authors report a compelling RT dissociation that mirrors Experiments 1 and 2: The algorithmic group shows slower RT, which is decreasing over training (0.98s → 0.76s), whereas the retrieval group exhibits faster, stable RT (0.52s → 0.45s). While this pattern is consistent with genuine strategy differences persisting in Experiment 3, it could also reflect the greater spatial precision demands of reaching to invisible targets from text instructions, rather than genuine mental rotation per se. Reaching to an invisible location defined by a verbal angular label is inherently more demanding than reaching to a visible target, regardless of strategy type, and this demand is asymmetrically present in the two groups, since Non-Critical targets are invisible for the algorithmic group but visible for the retrieval group.

Thus, from my point of view, experiment 3 should not be used as definitive evidence that algorithmic and retrieval strategies during standard visuomotor adaptation cannot differentially influence implicit recalibration.