approved

approved

approved

Let's just remove the last sentence or replace something else because reach in a lot of things so it's just like these emails might be a band that have lower engagement because you get too much and we need to add the white matter

We need a better way to say this like some of these inboxes we can't actually tell like like a weird way but like if a yahoo email says it's except all and it works but then I tried to go sign up with it and I can then I'm about provider doesn't doesn't exist whereas if I try to create an account with just does exist don't exist so there are ways to check certain thingsbut is also a waste like do some deep checks but an acceptable inbox has been created to accept any variation of the username so instead of saying that we can't verify it let's just explain what it is it's possible it's some small cases to actually figure out if an inbox does or does not exist but except all is a domain that's been set up to meaning any username from a domain email address to go to a specific inbox and that's it and so how to and the white it's important we need to beat it up it has a higher stress it's more difficult to check if the email just doesn't engagement and which kind of accept all is important yahoo versus a main base one and making a decision on which wants to keep overtime or how long to keep them is important so they can create like a graph of all the six of these inboxes to create like a baseline and anybody who falls below the baseline get kicked off I thought amount of time the baseline says someone is less likely to return and pay or engage in why and the way it matters is that these emails will not bounce if they actually stop existing which is uneven worse thing than having the wrong email address or something and you not realizing this address is dead so

what are these merged into or with which other label can you add it here and then show me the label or add it I guess it should be one of the new labels or in use I guess

we can delete this role or whatever it is and a data a basic documentation is just like marketed as used or like you know deleted

For the disposable I wanted to be clear that in the database and Hetzner we have these labels in use but on the reports and for the client it's only disposable as a label and yes or whatever in the cell of the column disposable or whatever temporary email whatever we call it. what do you call the column of disposable email addresses where we say yes or – for now in the export what is that column called

意识不仅意识到对象，而且意识到自己正在意识，并把自己作为自己的对象，意识（Bewusstsein）是“我认识某个东西”；自我意识是“我认识到我是那个认识东西的‘我’”。

lets add everything missing from why it matters and how to that isn't already in report text and all the following Breached ompromised Address 149 emails This email address has appeared in a known data breach. The account may have been abandoned, taken over, or is now used as a spam trap by security researchers. Send with caution. If the address has been abandoned since the breach, it may bounce or generate complaints. Compromised Address 86 emails This email address has appeared in a known data breach. The account may have been abandoned, taken over, or is now used as a spam trap by security researchers. Send with caution. If the address has been abandoned since the breach, it may bounce or generate complaints. Compromised Address 14,695 emails This email address has appeared in a known data breach. The account may have been abandoned, taken over, or is now used as a spam trap by security researchers. Send with caution. If the address has been abandoned since the breach, it may bounce or generate complaints.

Compromised Address 757 emails This email address has appeared in a known data breach. The account may have been abandoned, taken over, or is now used as a spam trap by security researchers. Send with caution. If the address has been abandoned since the breach, it may bounce or generate complaints. ,885 addresses are real but need care (Monitor) Monitor is where the address is real but comes with a caveat, and the decision on each is yours. Some sit on accept-all servers: we confirmed the domain is live and its mail server responds, but the server says "yes" to every address at the connection level, so the one specific mailbox cannot be confirmed active by any tool. That is their server's setting, not a gap in our check. Another 1,334 verified as real but with lower delivery confidence. Many of these domains also sit behind security gateways like Proofpoint, Mimecast and Barracuda that only let mail through from senders they already trust, so even a real, active inbox can block a cold sender until you are recognised or allowlisted. A few carry other flags (breach history, a shared role inbox, heavy mail volume), each explained per label below.

Go label by label below and decide what fits your risk tolerance: some are worth sending to with care, others you may prefer to hold back. Either is a valid choice. Compromised Address 117 emails This address has appeared in a known data breach. It may still be a normal working inbox, or the person may have gone quiet on it since. We have placed it in Monitor, but the call is yours. The mailbox is deliverable, so it will not bounce; the realistic outcomes are that the person engages, ignores it, or clicks "report as spam". Decide by your own risk tolerance: send with care, or hold it back. 2,253 are real but carry a signal to watch (Monitor) Most of the Monitor bucket is normal for consumer email: addresses that have appeared in a data breach (Compromised), that sit on a lot of marketing lists (Saturated), or that have a higher historical complaint pattern. These are still your customers and still sendable, they are just the ones to watch for complaints and engagement. A separate 268 are flagged Possible Invalid (short or numeric localparts) for you to eyeball, and most will be real.

Compromised Address 911 emails This email address has appeared in a known data breach. The account may have been abandoned, taken over, or is now used as a spam trap by security researchers. Send with caution. If the address has been abandoned since the breach, it may bounce or generate complaints.

use this as final glossary description

lets add everything missing from why it matters and how to that isn't already in report text

Smarter DSA Follow Up: Boost commissions with lead management that optimizes your sales process! Proven strategies for loan agent conversion & business growth.

Re-engage lost leads with effective scripts and strategies. Learn how to restart conversations, rebuild interest, and turn missed sales opportunities into conversions.

Know More: https://callyzer.co/blog/re-engagement-scripts-for-lost-leads/

CC0 1.0 (Creative Commons) Zero Licence.

CC0 1.0 Public Domain Licence.

CC BY-SA 4.0

CC BY 4.0, as the article where the image has been published is licenced under this version

CC BY 2.0

CC BY-SA 2.5

see above comment

CC BY 4.0, as the article where the image has been published is licenced under CC BY 4.0

CC BY 4.0

see above comment

link of the original image

CC BY-SA 2.0

CC BY-SA 4.0

Is this image CC BY-SA or CC BY 4.0? I cant see the exact licensing of this reproduced image in the original openstax book by Molnar and Gair, so I'm assuming its CC BY 4.0 (as the original openstax book is licensed as CC BY 4.0).

are these images CC BY-SA or CC BY 4.0? I cant see the exact licensing of this reproduced image in the original openstax book by Molnar and Gair, so I'm assuming its CC BY 4.0 (as the original openstax book is licensed as CC BY 4.0)

Author? Link ? is this a reproduced or original image?

CC BY-SA 3.0

CC BY SA 4.0

accurate

CC BY-SA 3.0 AU

accurate

accurate

link of the original image

broken link of the original image.

CC BY 3.0 . Also, author is Blausen.com staff, not BruceBlaus , as mentioned in the attribution statement.

CC BY-SA 2.0

CC BY-SA 3.0

CC BY 4.0

CC BY-SA 3.0

CC BY-SA 2.5

CC BY 4.0

accurate

accurate

CC BY-SA 4.0

accurate

CC BY 3.0

CC BY 3.0

CC BY-SA 4.0

CC BY-SA 3.0

I'm assuming this is All rights reserved by the author

CC BY-SA 2.0 DE(Germany)

CC0 1.0 Universal Licence

incomplete? no links or CC licence

this is CC BY-NC-SA 4.0, not CC BY-SA

version number missing. it is CC BY-SA 3.0

CC BY 2.0

CC BY-SA 3.0

Creative Commons Attribution-Share Alike 2.0 France

version numbcer of CC licence missing, It is 4.0

accurate

CC BY 2.0

see above comment. Also "Figure" typo

version number of licence missing

CC BY ? or CC BY-SA?

version number of CC BY-SA missing

I only convert to PowerPoint at the very end, when the design is locked. That's a one-time conversion cost instead of a per-iteration cost. Bonus: you also get a live web version of the deck, basically for free on Netlify Drop.

eLife Assessment

This useful study employs an innovative chemoproteomic and multi-modal experimental approach to support VDAC2 as a key functional mediator of STX effects on mitochondrial bioenergetics. However, concerns remain regarding the physiological and disease relevance, including the reliance on cell lines, lack of loss-of-function validation, unresolved binding specificity, and limited justification for focusing on POMC neurons in the context of Alzheimer's disease. As a result, the evidence is incomplete for a link between VDAC2 and Alzheimer's pathogenesis, and aspects of the binding data raise alternative interpretations, including a potential role for other VDAC isoforms.

Reviewer #1 (Public review):

Summary:

In this study, Qiu et al. examine the effects of the estrogen mimic STX on mitochondrial function and its interaction with VDAC2 in PMOC neurons.

Strengths:

The authors employ a broad range of molecular, cellular, and chemoproteomic approaches with generally sound methodology.

Weaknesses:

The work suffers from major conceptual and experimental issues that substantially limit its scientific impact.

Major Concerns

(1) Lack of Rationale.<br /> The study provides no justification for investigating sex specific aspects of Alzheimer's disease by focusing on VDAC-mediated mitochondrial dysfunction in PMOC neurons. These hypothalamic neurons are not recognized as early or primary sites of AD vulnerability, making the biological premise unclear.

(2) Weak Link to AD Pathogenesis.<br /> Although mitochondrial dysfunction is well established in AD, the authors do not convincingly demonstrate a mechanistic or pathological connection between VDAC2 and AD. VDACs are not established contributors to AD etiology, and the manuscript does not strengthen this association.

(3) Unclear Relevance to AD Contexts.<br /> While the data support an interaction between STX and VDAC2 affecting mitochondrial parameters (ATP production, membrane potential, glycolysis, respiration) in PMOC neurons, the study does not show whether this mechanism is relevant to mitochondrial dysfunction in AD. No validation is provided in AD-related models or in contexts related to sex specific AD phenotypes.

(4) Interpretation of Competitive Binding Data.<br /> The competitive binding results in Figure S4B are not adequately interpreted. The dose-dependent competition observed for VDAC3 suggests it may be a stronger candidate than VDAC2, yet this possibility is not addressed.

Reviewer #2 (Public review):

Summary:

STX is a non-steroidal, CNS-selective estrogenic compound with neuroprotective effects in stroke and Alzheimer's disease models, but its molecular target has remained unknown for nearly 20 years. In this study, the authors identify VDAC proteins as the direct mitochondrial targets of STX using chemoproteomics, single-cell qPCR, electrophysiology, and metabolic flux analyses. They further show that VDAC2 is the primary functional target in female POMC neurons, linking STX-mediated VDAC modulation to enhanced mitochondrial bioenergetics and neuroprotection.

Strengths:

This study is strengthened by its innovative chemoproteomic approach, in which the authors developed a novel bifunctional STX probe (BF-STX) containing a photo-crosslinkable diazirine group and an alkyne handle to capture transient STX-protein interactions in living cells. The experimental design is further reinforced by rigorous controls, including no-UV negative controls and competition assays with excess unlabeled STX, which provide convincing evidence that VDAC1, VDAC2, and VDAC3 are genuine STX-binding targets rather than nonspecific artifacts. Finally, the authors validate the STX-VDAC interaction using multiple complementary approaches, including chemoproteomics, single-cell qPCR, planar lipid membrane electrophysiology, and Seahorse metabolic flux analyses, providing strong mechanistic support for their conclusions.

Weaknesses:

While the study provides convincing evidence that STX directly modulates VDAC function, several limitations remain. Most experiments were performed in immortalized cell lines rather than primary neurons or in vivo models, limiting their physiological relevance. In addition, the exact structural binding site of STX on VDAC remains unresolved, and no loss-of-function experiments (e.g., VDAC2 knockdown) were performed to establish a direct causal link between VDAC2 and STX's bioenergetic and neuroprotective effects. The non-linear dose-response at higher STX concentrations also requires further investigation.

eLife Assessment

This important study reports a novel phenomenon of maternal growth during pregnancy that is independent of growth hormone (GH), adding a new dimension to maternal biology of reproduction. The evidence is convincing and supported by state-of-the-art methodologies conducted in mice and persuasive observations in humans with hereditary isolated GH deficiency. Revised discussion should focus on possible mechanisms, including the role of IGF2, and on directions for future research.

Reviewer #1 (Public review):

This work evaluates the impact of reproductive history on growth, body weight and body composition in mammals. In mice, somatic growth is stimulated by the first pregnancy while the second pregnancy increases body weight mainly by increasing adiposity. To probe the role of pituitary growth hormone (GH), the key regulator of somatic growth in these processes, was addressed by comparing the impact of reproduction on growth in normal ("wild type") and genetically GH-deficient females and by detailed characterization of the profile of fluctuations in circulating GH levels in both types of animals. Additional studies addressed the possible role of other endocrine pathways (ghrelin and estrogen) in the pregnancy-related growth. Surprisingly, reproduction-related growth was independent of GH, ghrelin and estrogen. To determine whether these results may apply ("translate") to human physiology, data on various parameters of somatic growth were collected from women with hereditary GH deficiency. The findings indicate that GH-independent stimulation of growth by reproductive events also occurs in women.

Use of multiple animal models, rigorous characterization of GH levels in normal and GH-deficient females, and inclusion of data derived from a unique and well-characterised population of people with hereditary isolated GH deficiency and no GH replacement therapy are important strengths of these elegant and innovative studies. The results address a broader and clinically significant issue of permanent changes in body size, composition and function that result from pregnancy and lactation. This work also provides important background for further studies aimed at the identification of the mechanism involved and the role of specific reproductive events in the regulation of growth.

Reviewer #2 (Public review):

This manuscript describes the fascinating phenomenon of growth hormone (GH)-independent growth occurring in the mother during pregnancy. This growth was most pronounced in dwarf mice that are lacking the receptor for growth hormone-releasing hormone (GHRH) and therefore showing isolated GH deficiency. However, the pregnancy-induced growth could also be observed in wild-type mice, suggesting that it is a normal part of the maternal adaptation to pregnancy. The study falls short of identifying the mechanism(s) driving this pregnancy-induced growth response, but it certainly reveals a novel insight into maternal physiology. The authors have completed a range of experiments in mice to prove that, as well as being GH independent, the pregnancy-induced growth also did not require GH signaling in the liver (i.e. not another pregnancy-specific ligand operating through the GHR to promote IGF). They also provided complementary data from a population of humans with untreated isolated GH deficiency that are broadly consistent with the hypothesis. While it is important to consider the significant species differences between rodents and humans, both in terms of growth physiology and also in terms of evolution of placental somato-mammotrophic hormones, this unique population are a valuable resource and adds credence to the study. Overall, I find this a compelling research story, but disappointingly unfinished. There are some areas where additional information could improve the ability to interpret the data, and some additional concepts that could be considered in the discussion. There are also areas where additional experiments might provide important insights. However, I think that such suggestions can be considered as appropriate for future research, rather than delaying consideration of the current manuscript.

Main comments:

(1) Data in Figure 1 are remarkable - not so much the growth in pregnancy in the wildtype mice, because while elevated GH is well known in pregnancy, but growth in the dwarf mice is indicative of GH-independent growth. From these data, it seems that there is good evidence that growth in pregnancy is an adaptive function. However, it is possible that growth is achieved in dwarf mice and that in wildtype mice may have been mediated through different mechanisms. The dwarf mice showed an increase in liver and plasma IGF1, suggestive of an additional ligand driving IGF in pregnancy. One could hypothesize that such an effect could be mediated by an additional pregnancy-specific ligand activating the GH receptor. In humans, placental growth hormone could be such a ligand, but as far as we know, there is no placental GH in mice. In contrast, the wildtype animals showed suppression of liver and circulating IGF1, and low levels of pSTAT5 in the liver during pregnancy. These data (in Figure 5) are very surprising. Given the high circulating GH in pregnancy, as well as high placental lactogen (which would be expected to activate STAT5 in the liver through the Prlr), the low levels of pSTAT5 are unexpected and would seem to indicate some sort of acquired insensitivity to GH. Is this entirely driven by down-regulation of STAT5b protein, or could there be activation of other, negative regulators of STAT signalling, such as SOCS? What is causing such a profound suppression of STAT5? Regardless of the mechanism, this suggests that pregnancy-induced growth in wildtype mice is independent of circulating IGF1 (potentially a different mechanism or in addition to that seen in IGHD mice).

The data shown in Figure 6 are a major strength of the study, showing that the pregnancy-induced changes are not specific to one particular transgenic model, but still occur in a variety of models affecting GH through different approaches. Given the pregnancy-specific nature of the changes, however, it seems an oversight not to have evaluated the role of placental lactogens. Prlr is highly expressed in the liver, but the function of this hormone in the liver is not well established. Could the extremely high levels of PL be mediating this growth response? Given the low expression of STAT5 in the liver and the fact that plasma IGF1 is not markedly elevated, it seems more likely that this growth response may be mediated by locally produced IGF1 in target tissues.

I think these possibilities could be addressed by an expanded discussion of species variation in placental hormones, to highlight that humans have expansion of the GH locus, but rodents have expansion of the prolactin axis (see Soares, M. J. The prolactin and growth hormone families: pregnancy-specific hormones/cytokines at the maternal-fetal interface. Reprod Biol Endocrinol 2, 51, 2004). Importantly, placental GH and chorionic somatomammotropins (CSM) in humans are all variants of the GH gene, but CSM have preferential activity at Prlr. This seems to be a fundamental species difference in pregnancy biology, but has been interpreted as an example of convergent evolution, with conservation of prolactin and GH-like functions at the maternal-fetal interface, mediated by different mechanisms, likely contributing to the metabolic adaptations of the mother (see Newbern D, Freemark M. Placental hormones and the control of maternal metabolism and fetal growth. Curr Opin Endocrinol Diabetes Obes. 2011; 18: 409-416). While the preceding function has focused on explaining the evolution of placental lactogens (either prolactin or GH variants), the present data suggest that there are also mechanisms to maintain growth in pregnancy, independent of GH (even in the absence of a placental GH).

(2) The human data are very interesting, and my initial impression was that it seemed unlikely to be the same phenomenon. Was there any real evidence for "growth" in pregnancy? Pubertal maturation of long bone growth might be expected to prevent further growth in adulthood. However, these issues were appropriately discussed, and it seems well justified to evaluate this unique population of women with IGHD who underwent pregnancy. It would be very interesting to know if these women experienced elevated IGF1 during pregnancy, indicative of placental GH contributing to growth. Mechanistically, this might be more like the dwarf mouse situation of IGHD, that the situation in wildtype mice (associated with liver insensitivity to GH and low IGF1).

(3) It would be useful to include investigations that isolate the effects of pregnancy and the placental hormones. Such studies could include evaluating growth in pseudopregnant mice with IGHD (pregnancy-like changes in hormones but lacking the placental contribution) and in IGHD animals that experience pregnancy but not lactation (pups removed at birth). I accept that this might be too large an additional study to add for the present manuscript.

(4) It is an important and translationally relevant observation that pregnancy increased the risk of long-term weight gain, and that after the first pregnancy, the pregnancy-induced growth response was more directed to promoting fat deposition. Does this provide any mechanistic insight? Could a metabolic adaptation result in growth?

Reviewer #3 (Public review):

Summary:

The study describes an increase in body growth and body composition in both mice and women. In mice, the impact on growth is mainly seen during the first pregnancy, and the changes postpartum on body composition are also different during the first and second pregnancies. The study has used various knock-out models in the growth hormone axis to understand these changes as well as some gene expression analysis related to GH, IGF-1 and estrogen signalling pathways.

Strengths:

(1) The inclusion of various knock-out mouse models that allow for exploration of mechanisms related to the above-mentioned changes.

(2) The investigation of gene expression of GHR, IGF-1R and ER pathways.

Weaknesses:

The human findings are dependent on the patient's recollection of bodily changes after their pregnancies.

Conclusion:

The authors have partly achieved their aim of describing changes in growth and body composition that remain after pregnancy and the mechanisms behind these changes. This study may have importance for a wide variety of research areas as well as in the clinical setting. The study is also unique in its attempt to bridge findings in mice to a unique human model of congenital GH deficiency.

1.现代生产是社会化的大生产，不可能是独立的个体生产 2.无产阶级，作为除了出卖自己的生产力外别无他法生存，除了不摧残自己就无法发展自己的阶级，作为主要生产社会的阶级，只有消灭现代的生产关系，实现普遍的解放，才能实现自己的解放。 3.需要注意，不是先天就有一个完美的无产阶级，然后再革命。无产阶级在改变社会的过程中，也改变自身

公有制是这么来的

前一句话是如何跳到后一句结论的？

原始社会

能够自然发展自己个性的人和因历史偶然而承担被迫承担社会分工的人之间的差别，不是概念上的，更多是历史事实

AI：一种社会力量，看起来像一个独立于人的、反过来支配人的东西

AI：一个社会中，人们实际生活的方式，以及支配人的社会关系。

不是一个完整的人自由决定加入一个共同体，而是因为其社会关系导致他不得不加入某个共同体

劳动和生活的分离

eLife Assessment

In this valuable study, Zhang et al. investigated EEG neurofeedback as a method to modulate brain activity prior to painful stimulation and examined its effect on pain perception in a well-powered, double-blind study. Results showed that real, but not sham, feedback enabled learning-dependent enhancement of pre-stimulus α oscillations. However, the evidence for a neurofeedback-specific reduction in pain remains incomplete, as the current paradigm cannot distinguish between genuine neurofeedback effects and placebo effects. Nonetheless, this work is likely to be of interest to researchers in the fields of neurofeedback and pain.

Reviewer #1 (Public review):

Summary:

Zhang et al. investigated EEG neurofeedback as a method to modulate brain activity prior to painful stimulation and its effect on pain perception. Neurofeedback was designed to train participants to upregulate alpha power contralateral to the site of painful stimulation. Real or sham neurofeedback was administered to two independent groups. Each group performed two tasks: one in which participants were asked to modulate their brain signals (training task) and another in which they were asked to passively watch the feedback (non-training task). The authors reported an increase in alpha power during real neurofeedback training compared with sham training and non-training conditions. The authors also reported a decrease in pain perception during the training task, both in the real and sham neurofeedback groups. Additionally, in an offline analysis, the authors investigated brain dynamics with microstate analysis during the neurofeedback training. Also, they implemented a mediation analysis to infer which brain responses to neurofeedback training mediated changes in pain perception.

Strengths:

(1) The research question is licit and sound. EEG neurofeedback is a promising non-invasive technique with the potential to alleviate at least the sensory component of pain. The rationale for applying neurofeedback at the alpha band in the somatosensory cortex is well justified by the alpha-gating theory in pain modulation.

(2) The sample size is adequate to capture neurofeedback effects. The effort to conduct a double-blind study with a complex design paradigm and an adequate sample size is valuable and appreciated.

Weaknesses:

(1) Reported behavioral effects on pain reduction might be due to the placebo effect rather than neurofeedback, as pain ratings were reduced both in the real and sham neurofeedback groups during training. It is important that authors report this effect appropriately and disclose which information was given to the participants when they enrolled in the study, i.e., whether the paradigm was designed to reduce pain perception.

(2) The utility of training effects, especially in the sham group, is unclear. I understand that including the non-training condition allows the distinction between neurofeedback effects and arousal effects. However, interpreting training effects should not be the point of this study. What does it tell us that participants who received sham stimulation increased or decreased alpha power in the training session vs the non-training session?

(3) There might be hidden time effects (habituation/sensitization) on pain responses and/or on brain responses to neurofeedback. A within-session analysis comparing the first half of the training with the second half should be conducted to discard them.

(4) Connectivity analysis reflects spurious effects. In EEG, deriving phase-based functional connectivity at the sensor level is problematic due to volume conduction effects. EEG functional connectivity should be performed after source reconstruction, and measures discarding instantaneous phase lags should be preferred, which is not the case with magnitude-squared coherence. See (Bastos and Schoffelen, 2015).

Although neurofeedback is a promising technique for modulating pain perception, the current study adds limited novelty to the field, as its design could not disentangle whether behavioral effects (reductions in pain intensity and unpleasantness) were specific to neurofeedback training or due to non-specific effects (e.g., placebo). Nevertheless, the authors corroborated that brain states before painful stimuli could be modulated with neurofeedback (enhancement of alpha power).

Reviewer #2 (Public review):

Summary:

This study uses neurofeedback to modulate alpha-band activity and examines how this influences pain-related processing. The question is timely and methodologically elegant, because it addresses whether noninvasive modulation of ongoing oscillatory activity can causally shape pain perception and/or expectation-related processes.

Strengths:

The use of neurofeedback as a tool to modulate alpha activity is a major strength, because it provides a noninvasive and conceptually clean approach to probing the functional role of oscillatory brain activity. The design is also attractive because it links neurophysiological regulation to a psychologically meaningful outcome, namely pain processing. Further, the induced changes were also related to different EEG microstates and ERP components during the processing of the pain stimulus, and therefore the authors demonstrate a clear relation between preparatory prestimulus states and stimulus processing.

The manuscript appears to address an important and clinically relevant question, and the idea of testing whether alpha regulation can alter pain-related responses is of high interest for systems neuroscience and pain research.

Weaknesses:

Methodologically, it is unclear what alpha values were used in the analyses. It is stated that alpha was extracted within 2s windows of the 16s long feedback period. However, the values change across this period. Which value is used for the correlation with the pain ratings and all other analyses? Using the average across the 16s could reflect large values in the first half and low values in the final half, but for the relationship between alpha and pain, the last segments should be more relevant. If the initially elevated alpha activity subsides several seconds before the onset of the pain stimulus, it is difficult to see how it could influence subsequent pain processing.

Related, after the 16s feedback period, a fixation period is used with a 3-5s length. If alpha band activity is relevant for the consecutive pain processing, the amount of alpha in this period should be relevant. The authors should demonstrate that the induced alpha change during the feedback period remains stable during the fixation period and that the activity in this period is related to pain processing.

Further, it should be noted that the alpha band modulations related to alpha band training were accompanied by significant effects in other frequencies. Therefore, a clear relationship between alpha and behavioral pain ratings is not the only interpretation. Correlations with other frequencies or combinations of frequency band modulations should be incorporated to allow a more precise interpretation. Furthermore, in the sham feedback group, an increase in alpha band activity was observed (p=0.06), and the small difference in the pain intensity rating may be related to a clear outlier in the Sham group (Figure 4a).

In both groups, a main effect of training, regardless of sham or real feedback, was reported with a small difference between groups. But the main modulator seems to be related to the instruction to modulate the neural activity, and this large effect should be discussed in more detail regarding, for example, possible attentional processes.

A further central concern is that the visual feedback signal (the ball movement) may generate expectations that are not specific to alpha activity and that these expectation processes modulate the pain processing (ball down may indicate more pain). It is well known that intensity cues can generate expectations about upcoming perceptions, and the used feedback signal with an increasing or decreasing visual curve clearly signals what intensity should be expected. Therefore, it is important to show that the amount of positive (ball up) and negative visual displays is matched between the sham and real feedback group. Further, the authors should report whether the final ball position can predict the latter pain rating in both groups or differentially. Following this interpretation, alpha band activity is not directly related to pain processing but only serves as a signal that is transformed to a visual stimulus that then generates expectations.

Finally, the manuscript would benefit from a more explicit analysis of whether individual alpha changes are related to pain ratings within each subject. If higher alpha is truly linked to reduced pain perception, this should be visible at the participant level during learning of the neurofeedback procedure. Relatedly, there is no learning period incorporated, and usually participants are not able to regulate their alpha activity from the first trial on. The authors should include an analysis of the development of alpha band activity over learning and a relation of these individual alpha values and the corresponding pain ratings.

I cannot find a link to the preregistration in the current manuscript.

In summary, a "causal" relation of alpha activity with pain perception -that is mentioned several times in the manuscript- is not fully supported by the present results

• Antes de cada bloque de código, convendría señalar qué se hará y para qué sirve. Después, se podría indicar qué resultado deberían observar y cómo interpretarlo.

• Se introducen varios conceptos nuevos al mismo tiempo. Quizás sería mejor avanzar de manera más gradual

• Sería importante diferenciar con mayor claridad la desviación estándar del error estándar: la primera describe la dispersión de los datos, mientras que el segundo indica cuánto podría variar una media entre distintas muestras.

• También convendría distinguir la distribución de los datos de la distribución muestral de las medias, porque pueden confundirse fácilmente.

• Los puntajes z aparecen de manera un poco rápida. Podría explicarse primero, de forma intuitiva, que indican a cuántas desviaciones estándar se encuentra un valor respecto de la media.

• Al presentar pnorm(), sería útil aclarar que entrega la probabilidad acumulada a la izquierda de un valor y no la probabilidad de obtener exactamente ese valor.

• En este gráfico, plot() y abline() deben ejecutarse en líneas separadas. El signo + se utiliza para agregar capas en ggplot2, pero no funciona de esa forma con los gráficos de R base.

• En el eje vertical de la curva normal corresponde hablar de “densidad” y no de “probabilidad”, porque dnorm() calcula valores de densidad.

• Sería mejor evitar instalar y cargar varios paquetes si el práctico puede realizarse con R base. Esto reduce posibles errores y permite concentrarse en los contenidos estadísticos.

• Para calcular manualmente el intervalo de confianza, convendría utilizar el valor crítico de la distribución t. Si se usa 1,96, el resultado no coincidirá exactamente con el obtenido mediante t.test().

• Para construir un intervalo de confianza del 99 %, corresponde utilizar alpha = 0.01, no alpha = 0.05.

• La interpretación del intervalo de confianza podría ajustarse. No significa que exista un 95 % de probabilidad de que el parámetro esté dentro del intervalo ya calculado, sino que el procedimiento utilizado produce intervalos que contienen el parámetro en aproximadamente el 95 % de las muestras repetidas.

• Conviene utilizar de manera consistente el término “error estándar” y no emplear “error muestral” como si fueran equivalentes.

• Sería útil incluir primero un ejercicio completamente guiado y luego otro con una plantilla o algunas pistas, antes de pedir una actividad autónoma.

• Algunas preguntas, como “calcule”, “describa” o “analice”, podrían ser más específicas. Por ejemplo, se puede indicar qué medida calcular, qué resultado observar y qué aspectos debe mencionar la interpretación.

• el práctico parece bastante extenso. Sugiero concentrar la sesión en, la desviación estándar, el error estándar y el intervalo de confianza del 95 %. puntajes z

• pnorm(), el intervalo del 99 % y la comparación entre distintos tamaños muestrales podrían dejarse como profundización o trabajo autónomo.

Creo que este es el punto bisagra del práctico. Aquí comienza a verse contenido que no se ha enseñado en clases lectivas, por lo que sugiero hacer solo una introducción a IC, una sección más visual (una imagen incluso) y breve con tal de que la clase lectiva siguiente pueda ser retomado en profundidad

está buenísima esta nota para esclarecer y hacer interesante el vínculo ccss-estadística

temas de instalación y carga de paquetes lo vería al inicio de la sesión, me hace ruido que se presente casi al final

es la

es la

cálculo

rango de valores

explicaría brevemente qué hace la línea de código, así en caso de que alguien quisiera replicar el ejercicio facilite su reproducibilidad

pacman, y enseñar de qué se trata en este primer práctico

no aún

no se puede recordar, no se ha pasado aún

interpretación?

es una estimación de

no

no adelantar esto, confuso

ojo, no lo sabemos, no ha sido enseñado esto aún (fijarse en el contenido de las clases)

arriba de esto otro título 2.1 Cálculo de probabilidades a partir de Z

resultado

estandarizar los puntajes del vector

de manera directa

Cálculo de puntaje Z

un valor se aleja

+/-

no es más fácil simplemente mean(vector)? (en lugar del objeto, el cat ...)

recomiendo poner estos gráficos por separado, es decir, código de un gráfico, luego resultado

Case#: patient, 12, Somali, onset 5yo

DiseaseAssertion: STGD

FamilyInfo: parents and 5 siblings did not report visual issues

CasePresentingHPOs: HP:0000007, HP:0011504, HP:0000608

CaseHPOFreeText: BCVA 20/135 OD; 20/100 OS. Red-green color deficit. Bull's eye maculopathy, but no pallor of optic disc. Normal peripheral retina. Loss of macular photoreceptor layer; severely reduced cone function.

CaseNotHPOs: n/a

CaseNotHPOFreeText: n/a

Genotyping Method: whole exome sequencing

PreviouslyPublished: n/a

Variant: NM_000350.3:c.5882G>A p.(Gly1961Glu) ; NM_000350.3:c.634C>T p.(Arg212Cys)

ClinVar: 7888; 7898

CAID: n/a

SupplementalData: n/a

Per ClinVar entry, this variant was associated with this paper. however, after reading through the genotypes, this variant was not found. Likely this paper was mentioned to support the statement that "Loss-of-function variants in ABCA4 are known to be pathogenic"

PMID: 26780318

Gene: ABCA4

Disease: Stargardt Disease or Cone-Rod Dystrophy

PMID: 29765157

Gene: ABCA4

HGNC ID: 34

Case#: Braun Family 8 Proband (from left to right, top to bottom of available pedigrees), female

DiseaseAssertion: Stargardt

FamilyInfo: Unaffected carrier parents. c.5196+1137G>A maternally inherited; c.4577C>T (p.T1526M) paternally inherited

CasePresentingHPOs:

CaseHPOFreeText: "five or more of the following features of ABCA4-associated retinal disease: decreased visual acuity before age 20, decreased visual acuity as the first visual symptom, symmetrical fundus findings, pisciform flecks, beaten metal macular atrophy, bulls-eye maculopathy, peripapillary sparing, vermillion fundus, masked choroid on fluorescein angiography, nummular pigment overlying extensive macular atrophy, central outer retinal atrophy on optical coherence tomography and central scotomas on Goldmann perimetry."

CaseNotHPOs:

CaseNotHPOFreeText:

GenotypingMethod: one plausible disease-causing mutation detected in ABCA4 after assessing the entire coding sequence and canonical retinal splice junctions with automated bidirectional Sanger sequencing using an ABI 3730 sequencer

PreviouslyPublished: n/a

Variant: c.5196+1137G>A; c.4577C>T (p.T1526M)

ClinVar: 438100

CAID: CA26843511

SupplementalData: pedigree in fig s2

PMID: 29706639

Gene: ABCA4

HGNCID: HGNC:34

MonDO: MONDO:0019353

Bioinformatic analyses of an SQL-based database containing 12272 variants that appear in 178 IRD genes in 5706 individuals of Ashkenazi Jewish origin based on the gnomAD database (version 2) and variants that were published in the scientific literature that was extracted from HGMD. Authors extracted information regarding IRD variants from various sources (including data of 5706 Ashkenazi Jewish (AJ) samples and a large cohort of Israeli patients with IRDs) to estimate carrier frequency of IRD mutations in different subpopulations in Israel. Two major databases aiming to estimate carrier frequency of IRD mutations in the Israeli population (Fig. 1): “gnomAD-AJ-IRD DB” containing data of 5706 AJ controls extracted from gnomAD and “HW-IRD DB” containing data extracted from our cohort of Israeli patients with IRDs.

See Fig 2 for breakdown of variants analyzed.

The final DB (IRDB) (Fig. 1 and Table S7) includes all 399 variants from “gnomAD-AJ-IRD DB” and “HW-IRD DB” that were considered here as pathogenic mutations in 111 known IRD genes.

To establish the “HW-IRD DB” (Fig. 1), we collected data on Israeli IRD patients with a known cause of disease (a cohort of >2000 IRD families). The HW-IRD DB includes 289 pathogenic mutations (Fig. 1) that were identified in IRD patients who have biallelic variants.

SupplementalData: S7, carrier frequency data for each mutation in all nine studied subpopulations. Carrier frequency was calculated as 2pq where p = 1 − q and q was calculated as the root square of the number of homozygous patients plus half the number of compound heterozygous patients divided by the population size

Variant: NM_000350.2:c.4895dup,p.Asn1632fs

CAID: CA915941330

Case: Ashkenazi Jewish patient with inherited retinal disease, STGD, CRD

CasePresentingHPOs: HP:0000548 (Cone/cone-rod dystrophy, CRD)

CaseHPOFreeText: Stargardt disease (STGD)

Case#: Family MD-0242 Proband, 18yo at onset, Spanish

DiseaseAssertion: AR Stargardt

FamilyInfo: n/a

CasePresentingHPOs:

CaseHPOFreeText: STGD diagnosed based on "bilateral central vision loss; fundus presenting with a beaten-bronze appearance and/or the presence of orange-yellow flecks in the retina from the posterior pole to the mid-periphery; fluorescein angiography showing typical dark choroid; and normal to subnormal electroretinogram (ERGs)."

CaseNotHPOs:

CaseNotHPOFreeText:

GenotypingMethod: Haplotype analysis, ABCR400 microarray, direct sequencing for confirmation

PreviouslyPublished: n/a

Variant: c.1715G>C p.Arg572Pro

ClinVar: 99073

CAID: CA226919

SupplementalData:

This paper is not publicly available. Requested from library since it is said to contain this variant.

This variant is in table 3 as a "potentially neutral polymorphism or change of unknown significance" but it is unclear which patient this is associated with, what their phenotype is, what their genotype is

Case#: Patient #15, Moorfields Eye Hospital, London, UK, female, 39yo at onset, 51yo at report,

DiseaseAssertion: ABCA4-Associated Retinopathy, clinical diagnosis of STG

FamilyInfo: no additional family-member WGS data available

CasePresentingHPOs:

CaseHPOFreeText: BCVA= OD: 6/9, OS: 6/9, Fishmann Classification=2 (fleck-like lesions anterior to the vascular arcades and/or nasal to the optic disc), early changes to foveal photoreceptors, Extent of FAF abnormalities with Regard to Vascular Arcades: beyond. ffERG Group: 1(normal). PERG: Abnormal. FAF in Fig. 1B. characteristic yellow-white pisciform flecks in the RPE of the posterior pole that were hyperautofluorescent on FAF imaging or progressive atrophy of the macular RPE

CaseNotHPOs:

CaseNotHPOFreeText:

GenotypingMethod: Haplotype analysis, ABCA4 mutation screening was performed by next-generation sequencing, sequenced as part of the retinal panel at the Molecular Vision Lab

PreviouslyPublished:

Variant: c.5196+1137G>A p.[=,M1733Efs∗78] c.[1715G>A;2588G>C] p.[(R572Q;G863A,G863del]

ClinVar: 7900

CAID: CA226918

SupplementalData:

PMID: 33841504

Gene: ABCA4

Disease: Stargardt

This paper is listed under the ClinVar citations for this variant, but only as a paper that references other variants in the initiator codon that have been observed in individuals with ABCA4-related conditions

Case#: Case 3, male, onset at 24yo, Italy

DiseaseAssertion: STGD1

FamilyInfo: third-degree consanguineous parents; both parents healthy heterozygous carriers of the ABCA4 variant

CasePresentingHPOs: HP:0000529, HP:0007754, HP:0000518

CaseHPOFreeText: progressive deterioration of vision at age 24. Bilateral macular dystrophy and diffuse lens opacities on ophthalmologic examination. OCT, ERG, and VEP consistent with Stargardt maculopathy.

CaseNotHPOs: n/a

CaseNotHPOFreeText: n/a

Genotyping Method: NGS (custom enrichment panel), Illumina NextSeq550; variant confirmed by Sanger sequencing.

PreviouslyPublished: n/a

Variant: NM_000350.3(ABCA4):c.2828G>A (p.Arg943Gln), homozygous; rs1801581

ClinVar: Variation ID: 7913

CAID: n/a

SupplementalData: n/a

PMID: 34008801

Gene: ABCA4

HGNC ID: 34

Case#: DNAID 072884/Pat255, female

DiseaseAssertion: STGD1

FamilyInfo: n/a

CasePresentingHPOs:

CaseHPOFreeText: "In classic STGD1, loss of central vision starts around the second decade of life, but both early- and late-onset subtypes have been extensively described." No patient-specific details provided

CaseNotHPOs:

CaseNotHPOFreeText:

GenotypingMethod: smMIPs sequencing of the complete ABCA4 locus

PreviouslyPublished: n/a

Variant: c.1519G>T (p.Asp507Tyr); c.5312+3A>T (p.Asn1734Glyfs*14) phase not confirmed

CAID: CA958508

SupplementalData: tables s9 and s11

PMID: 22334370

Gene: ABCA4

HGNC ID: 34

Disease: Retinitis Pigmentosa

Case#: a 29-year-old man

DiseaseAssertion: Stargardt Disease

FamilyInfo: NR

CasePresentingHPOs: HP:0007663

CaseHPOFreeText: 20/70 visual acuity in both eyes

CaseNotHPOs: NR

CaseNotHPOFreeText: NR

Genotyping Method: ABCA4 microarray (ABCR5000 chip)

PreviouslyPublished: NR

Variant: NM_000350.3:c.5882G>A, p.G1961E and c.5018+2C>T (rare splice variant)

ClinVar: 7888, NR

CAID: CA119132, NR

SupplementalData: No CAID or ClinVar ID were found for the rare splice variant c.5018+2C>T

This variant is listed in supplementary tables S2 and S3. Proband TI-50 is a "solved" patient, meaning the phenotype matches the genotype. Homozygous female with MD/CORD- all that is provided.

ABCA4 expression in HEK293 cells showed equivalent expression of wild type and this variant, indicating that this variant does not impact protein function (BS3_Supporting; PMIDs).

Case#: Patient#010382, Chinese, male, 29yo at onset

DiseaseAssertion: stargardt

FamilyInfo: n/a

CasePresentingHPOs: STGD1 diagnosis based on the following criteria: "a bilateral central vision defect; fundus displaying a beaten-bronze appearance and/or orange-yellow flecks in the retina from the macula to the midperiphery; fluorescein angiography presenting with a typical dark choroid; and normal to subnormal ERG results." BCVA=0.4/ 0.6

CaseHPOFreeText:

CaseNotHPOs:

CaseNotHPOFreeText:

PreviouslyPublished: n/a

Variant: p.P2097S; c.6050G>A p.(Cys2017Tyr) phase unknown

ClinVar: 2202780;

CAID: CA341277622;

SupplementalData: supplementary table S4 has phenotype information

Paste this into Claude / ChatGPT / Gemini:

"Look at our last 20 conversations and find the 2-3 tasks I come back to most often. Pick the strongest candidate for a Project and design it for me: • Project name • 5-sentence custom instructions (voice, behavior, what to avoid • 3-5 reference files I should upload • One sample opening prompt to kick off a new chat inside it Be specific to me. No generic productivity assistants. (If you don't have access to my chat history, ask me to paste 5-10 recent prompts and work from those.)"

eLife Assessment

This important study investigates whether perceived gender is represented in the brain in a category-invariant manner across faces, bodies, and objects, identifying the right middle temporal gyrus (rMTG) as a potential locus. The evidence is incomplete due to major conceptual concerns, weak statistical methods, and unaddressed low-level confounds like stimulus size and motion. This work will be of interest to psychology and social neuroscience researchers in face and person perception literature, provided the authors temper their claims regarding abstract representation.

Reviewer #1 (Public review):

Summary:

This manuscript investigates whether the human brain contains a shared category-general representation of gender across faces, bodies, and gender-associated objects. The authors acquired fMRI data while participants viewed male and female stimuli from three categories in a one-back task. They then used searchlight MVPA, cross-category decoding, regression-based RSA, CNN vs. brain representational comparisons, and PPI analyses. Their main finding is that gender information could be decoded from distributed occipitotemporal regions within each category, whereas a cluster in the rMTG showed convergence across cross-category decoding and RSA. The authors concluded that this rMTG representation resembles intermediate layers of fine-tuned CNNs and that face and body gender processing share similar functional connectivity patterns.

Strengths:

The question is potentially important, particularly for social cognition, object recognition, and the use of neural network models to interpret high-level visual representations. Previous behavioral studies have shown cross-category adaptation between bodies and faces, and even between gender-associated objects and faces, so the attempt to test for a neural counterpart using fMRI is well motivated. The use of multiple complementary analyses including within-category decoding, cross-category decoding, regression RSA, CNN comparisons, and effective connectivity analyses is also a strength. The convergence of cross-category MVPA and RSA in a right MTG cluster is potentially interesting and deserves attention.

Weaknesses:

The largest problem is conceptual. The term gender is used as if it refers to the same construct across faces, bodies, and objects. This is not self-evident. In faces and bodies, the stimuli seem to contain visual cues from which observers infer binary gender categories. In objects, however, the relevant information is almost gender stereotype, cultural association, or learned semantic association. These are not equivalent constructs. The manuscript therefore needs to distinguish much more carefully between perceived gender, biological sex cues, gender-associated visual features, and gender stereotypes. Without this distinction, the title and main conclusion are too broad. The object condition is particularly problematic. Javadi & Wee (2012) showed that gender-associated objects can bias subsequent judgments of ambiguous face gender, and they discussed two possible mechanisms, including shared neural substrates or top-down modulation induced by the gender concept. However, their behavioral adaptation study does not directly demonstrate that objects, faces, and bodies are encoded in the same neural representational format. The present manuscript treats these object stimuli as if they provide evidence about the same kind of gender representation as faces and bodies, but that step requires additional empirical support. Independent ratings of object gender association, cultural familiarity, visual similarity, and semantic category are essential here.

A second major concern is stimulus control. The face images were taken from Chinese male and female actors, the body images were headless bodies in underwear, and the object images were selected because of prior gender associations. This design introduces many possible confounds: hairstyle, makeup, skin texture, body shape, clothing, color, luminance, object category, object function, curvature, spatial frequency, and cultural familiarity. Cross-category decoding can be significant even when a classifier relies on shared visual statistics rather than an abstract gender code. For example, female-associated stimuli may differ from male-associated stimuli in color, shape, brightness, texture, or semantic category in ways that are consistent across faces, bodies, and objects. The present analyses do not adequately rule out these alternatives. Foster et al. (2019) are especially relevant in this respect. They reported that body sex could be decoded from both body- and face-responsive regions. However, the sex of well-controlled faces, for example faces excluding hairstyle cues, could not be decoded from face- or body-responsive regions. This finding should make the authors more cautious. The fact that the present study used more ecological face stimuli may increase sensitivity to gender-related cues, but it also increases the possibilities that decoding is driven by uncontrolled external features rather than by an abstract gender representation. Accordingly, because no additional visual, semantic, or stereotype-based model RDMs were included in the RSA analysis, this result alone cannot establish an abstract, category-independent gender representation. Any systematic difference between male- and female-associated images will load onto the gender RDM. At least, the authors should include additional model RDMs for low-level visual features. In addition, the current RSA analysis has another limitation. The neural RDMs are based on only six condition-level patterns, producing a 6 × 6 matrix. The theoretical model includes only binary gender and category RDMs. This is too coarse to support the claim of category-independent gender representation. Ideally, all the RSA analysis should be performed at the item level rather than at the condition level.

The cross-category decoding result in rMTG is promising but not yet conclusive. The authors identify a right MTG cluster by overlapping thresholded maps from three cross-category decoding analyses. This is useful descriptively, but it does not by itself establish a common representational code. The overlap of thresholded maps depends on the chosen threshold. If the authors want to make a formal conjunction claim, they should use a valid conjunction-null approach such as a minimum-statistic conjunction evaluated under the appropriate conjunction null, rather than simply displaying the intersection of thresholded maps. Even if this approach cannot be adopted in this study, the issue should be included as a limitation.

In the PPI analysis, the reported similarity between face and body connectivity matrices is a little bit small (r = 0.08). The claim of a shared functional network should therefore be softened unless the authors test whether this correlation is significantly larger than the face-object and body-object correlations, correct for multiple comparisons, account for the non-independence of matrix elements, and report participant-level distributions and confidence intervals.

Reviewer #2 (Public review):

Summary:

The study tests whether male/female-related information is represented in a form that generalizes across faces, bodies, and gender-associated objects. Using within- and cross-category MVPA, regression RSA, comparisons with fine-tuned CNNs, and connectivity analyses, the authors identify a right middle temporal gyrus region whose patterns generalize across the three stimulus classes. They conclude that this region provides a category-general, mid-level representation of gender and acts as a neural hub.

Strengths:

The question is novel and important, while the logic of the study is straightforward. Examining faces, bodies, and objects within the same participants provides a useful extension beyond the predominantly face-based literature. Cross-category decoding is also a stronger test of shared information than simple anatomical overlap between within-category maps. The combination of MVPA, RSA, computational modelling, and connectivity analysis is ambitious, and the replication of the CNN layer profile with both AlexNet and VGG16 is a useful characterization of relevant information.

Weaknesses:

(1) The construct labelled "gender" is not equivalent across stimulus classes. For faces and bodies, the male/female label is intended to track a property of the depicted person, albeit one inferred imperfectly from appearance; for objects, masculinity or femininity is not an intrinsic property of the object but a culturally contingent association that may vary across observers and contexts. Treating both as levels of a single binary factor risks conflating person-category information with gender-stereotypic object associations and interpreting their common neural discriminability as evidence for one abstract concept of gender. The term "object gender" could also be confused with grammatical gender in some languages (e.g., French or German).

(2) The CNN analysis does not isolate the shared male/female component. The authors correlate the complete six-condition neural RDM with the complete CNN RDM. However, rMTG also carries substantial information about whether an image is a face, body, or object. Consequently, the peak correspondence with Conv4 may reflect category structure rather than the representation that supports cross-category male/female decoding. The current analysis does not establish that shared gender-related information specifically depends on mid-level features.

(3) The connectivity interpretation is overstated. PPI measures task-dependent covariance; it does not establish information transmission, directionality, or an upstream-to-downstream processing sequence. The reported face-body connectivity similarity is also small (r=.08). Also, describing rMTG as a "hub" is not justified without network-centrality measures, lesion evidence, or causal perturbation.

The authors partly achieve their aims. The results provide credible evidence that patterns in rMTG contain information that generalizes across binary male/female-labelled faces and bodies and masculine/feminine-associated objects. They do not yet establish a genuinely abstract representation of gender, a specifically gender-related correspondence with intermediate CNN layers, or a neural hub that transmits information through a directed network. With more precise framing and targeted reanalysis, the study could make a useful contribution to research on social vision and cross-category representation.

Reviewer #3 (Public review):

Summary:

In this work, the authors investigate whether gender information is encoded in the brain in a way that is invariant to the object being perceived. They design an fMRI experiment in which 22 participants perform a one-back repetition detection task in a block design. Images shown are of three types (faces, objects, and bodies) and of two perceived genders, male and female. They perform MVPA, RSA, and functional connectivity analyses to determine whether gender information is invariant to the type of image being perceived. They report an area in the posterior right middle temporal gyrus (rMTG) that is found in their gender decoding analysis across categories. To confirm that this area encodes gender information, they perform a regression-based RSA with category and gender model RDMs, and report that the gender model RDM is significantly correlated with brain representations in that area. Finally, to further investigate the representations in this area, they perform a model-based RSA in which they first fine-tune a deep neural network for gender classification, and then study the correlation between model RDMs and brain RDMs. Consistent with a previous report in face processing (Jiahui et al., 2023), they find that gender information is more consistent with representations in middle-to-late layers of the networks. Additional functional connectivity and PPI analyses are reported to reveal differences in co-fluctuation of brain activity within occipital and parietal nodes when perceiving different types of male/female images. Based on these results, the authors conclude that rMTG represents gender information invariant of the category perceived, although rMTG also afforded decoding of category information.

Strengths:

Whether perceived gender is represented in a manner invariant to the category of the stimulus is a legitimate and interesting question, and one of relevance particularly to the face and person perception literature.

The model-based RSA, in which RDMs from networks fine-tuned for gender classification are compared against brain RDMs, is an interesting approach, and the layer-wise profile the authors obtain converges with a previous report in the face processing literature (Jiahui et al., 2023).

Weaknesses:

A substantial number of inferences are drawn on the basis of weak statistical methods and a suboptimal design. My concerns are set out below, ordered by severity.

(1) The statistical tests are not appropriate for classification and RSA, and are prone to false positives. Classification accuracies and RSA correlations may be positively biased, and the true null distribution may therefore be centered above the nominal chance level, or above zero in the case of RSA. Testing against a theoretical value with a one-sample t-test under these conditions inflates the false positive rate, especially with few test samples per classification, and does not afford valid population inference for information-like measures (Combrisson & Jerbi, 2015; Allefeld et al., 2016). The concern applies to every inferential claim in the manuscript, including the identification of the rMTG cluster on which the paper's central conclusion rests. The established remedy is permutation testing, in which the labels are randomly permuted and the full analysis, including cross-validation, is re-computed so that any bias is captured in the empirical null distribution (Stelzer et al., 2013; Etzel & Braver, 2013). This approach has been applied in comparable face-decoding studies using both classification and RSA (Guntupalli et al., 2017). I raise this methodological concern here because it is the clearest way to convey why the reported statistics cannot be safely interpreted at face value.

(2) The decoding analyses do not appear to test generalization to left-out stimuli. From my reading of the design, each run contained all six conditions presented three times in random order, with each block containing 12 images (10 unique plus two repetitions serving as catch trials). If all images were presented in every run, the same images would be present in both the training and test sets of the cross-validation. Under these conditions, the interpretation of a general "gender" code is difficult to justify: the classifier may be exploiting low-level image features specific to the particular exemplars rather than gender per se. This bears directly on the paper's central claim, which concerns an abstract, category-invariant representation of gender, a claim that requires decoding to generalize to stimuli the classifier has not encountered.

(3) There is no evidence that participants perceived the stimuli's gender as the authors assumed. Perceived gender may be subject-specific, yet no norming is reported establishing that participants actually rated or processed the stimuli according to the gender the authors assigned to each image. Some images are likely to be more ambiguous than others. This is a construct validity issue rather than an analysis issue: the class labels used throughout the decoding analyses, and the gender model RDM used in the RSA, both rest on an assumption about the participants' percepts that is never tested against the participants themselves.

(4) The rMTG ROI reported in Figure 2c appears to overlap almost perfectly with the motion-sensitive area hMT+. The reported effects may therefore be driven, at least in part, by low-level motion signals arising from the rapid on/off changes of the stimuli and the associated optic flow. I am not claiming that the results are fully driven by this, but no control reported in the manuscript rules it out, and this region is the centerpiece of the paper's conclusion.

(5) Stimulus size is confounded with category in the functional connectivity analyses. The authors report that functional connectivity differed between faces and objects, and between bodies and objects. However, faces and bodies were shown with the same visual extent, while objects were larger. Given that the nodes being investigated are in visual areas, it is unclear how these differences can be attributed to category rather than to the low-level difference in stimulus size. The same confound bears on the behavioral task performed within the scanner: participants can perform the one-back task more easily, simply by detecting size differences, since two images of different sizes are clearly not the same image, rather than by processing the image content. This affects what can be assumed about participants' attention to the stimulus category or gender.

(6) No motion quality control is reported for the functional connectivity analyses. Functional connectivity is well known to be highly susceptible to head motion, yet the manuscript reports no summary of how much subject motion there was, no indication of whether volumes with excessive motion were removed or censored, and no account of quality control on the measured data more generally.

(7) The use of famous faces introduces an avoidable confound. The face stimuli were famous faces. Famous and familiar faces are known to recruit substantially more widespread activity than unfamiliar faces, extending well beyond the core visual system (Gobbini & Haxby, 2007; Natu & O'Toole, 2011; Visconti di Oleggio Castello et al., 2017; Kovacs, 2020). For a study focused specifically on gender, this introduces a source of variance that unfamiliar faces would have avoided, and it complicates the comparison of the face conditions against the body and object conditions.

(8) The rationale and benefit of fine-tuning the deep neural networks are not established. The manuscript does not report the original, non-fine-tuned accuracy of the models that required fine-tuning, so the benefit of the procedure cannot be assessed; given that the final validation accuracy is low, it is unclear that fine-tuning actually helped. AlexNet and VGG are trained for object classification on large datasets, and fine-tuning with 2,000 training images may not be sufficient to genuinely shift the objective. Whether the activation patterns and RDMs changed in any significant manner after fine-tuning is not reported, and the rationale for selecting the specific layers used is not stated.

(9) Taken together, the analyses as presented do not establish the paper's central claim. My concern is not that the reported effects are necessarily absent, but that the combination of statistical tests that do not account for possible positive bias, a cross-validation scheme that may not guarantee generalization across stimuli, a key region that coincides with a motion-sensitive area, and gender labels that were never validated against participants' own perception leaves too many open questions for the results to be evaluated as they stand.

(10) I would add one broader consideration. Perceived gender is likely to depend on culture and to vary across individuals. A binary male/female contrast in 22 participants, without evidence that those participants perceived the stimuli as the authors intended, is a narrow operationalization of a construct that is unlikely to be so simple. Even if the analyses were fully sound, caution would be warranted in generalizing from this design to claims about how the brain universally represents gender.

eLife Assessment

This important study extends a model of cortical normalization (ORGaNICs) to interacting cortical areas and shows that communication through coherence and communication subspaces can arise from a single set of dynamics. The evidence is solid, showing analytically that contrast-dependent gamma dynamics and a low-dimensional inter-areal communication subspace arise from one parameter set, though the comparisons to data remain qualitative and the analytics rest on a linearization that is not checked against numerical simulation. The work will interest neuroscientists and theorists concerned with inter-areal communication, cortical oscillations, and divisive normalization.

Reviewer #1 (Public review):

In this paper, Pal and colleagues propose a mechanistic unification of two influential accounts of inter-areal communication: communication through coherence and communication subspaces. A major strength of the paper is that it does not treat coherence and communication subspaces as independent phenomena, as typically done, but instead derives both from the same circuit with divisive normalization. In this framework, noise-driven fluctuations around the normalized fixed point determine covariance and cross-power structure (which, in retrospect, makes so much sense to be related). Then, they show how these determine linear prediction performance and the effective dimensionality of the communication subspace. They also show (however not very visually, see recommendation below for a figure) how divisive normalization is crucial to shape inter-areal coherence and the dimensionality of communication.

I found this conceptual contribution potentially very influential, but somewhat obscured by the technical complexity of the model. The central intuition (I think) is that recurrent normalization can organize cross-area fluctuations, both frequency-specific correlations and cross-covariances. Took me a while to grasp this insight, mostly because I was stuck with the model details. Note that I have some experience with network dynamics, but not with this particular model.

Reviewer #2 (Public review):

Summary:

The authors extend the ORGaNICs framework (a recurrent circuit that dynamically implements divisive normalization) to connected cortical areas with explicit top-down feedback. Because the network has a known analytical fixed point that coincides with (or closely approximates) the normalization equation, the authors can linearize about that fixed point and derive closed-form expressions for the power spectral density, inter-areal coherence, and communication subspaces. Using a two-area instantiation (V1 & V2) with a single fixed parameter set and no data fitting, they show the model reproduces: (i) contrast-response functions with steeper slope V2; (ii) gamma-band power and coherence peaks that shift to higher frequency with contrast; and (iii) a low-dimensional inter-areal communication subspace that is lower-dimensional than the within-area subspace. They derive parallel predictions of what happens by changing model parameters: feedback gain enhances inter-areal and suppresses within-area communication, and normalization is necessary for both the oscillatory dynamics and the reduced subspace dimensionality. A three-area extension (V1&V4, V1&V5/MT) is used to argue that differential top-down feedback can dynamically route functional connectivity.

Strengths:

(1) Analytical tractability: Deriving power spectra, coherence, and communication-subspace structure in closed form from a known fixed point is genuinely valuable.

(2) Conceptual unification: Framing coherence and communication subspaces as arising from the same normalization-driven dynamics is an elegant and useful contribution.

(3) Breadth from few assumptions: A large range of phenomena (contrast gain, gamma dynamics) emerges from normalization-based model assumptions.

(4) Biological grounding: The mapping of model variables onto identified cell types connects the abstract computation to known cortical microcircuitry.

(5) The prediction that input-gain versus feedback-gain modulation produce distinct spectral signatures gives experimentalists a clear way to test the framework.

Weaknesses:

(1) Comparisons are qualitative, not quantitative: The theory/experiment panels are visual side-by-side comparisons. There is no quantitative goodness-of-fit for any predictions.

(2) The simulations use τ ≈ 1 ms for all cell types, which the authors acknowledge is unrealistically short; realistic values would shift the gamma peaks to lower frequencies.

(3) Divisive normalization is a special case and is recovered exactly only for the identity recurrent matrix (self-normalization). Some statements that the circuit implements divisive normalization exactly need softening.

(4) The element-wise (multiplicative) interaction in the modulator dynamics is not tied to a specific cellular mechanism.

Reviewer #3 (Public review):

Summary

The work of Pal and colleagues considers a hierarchical and multi-population version of the "oscillatory recurrent gated neural integrator circuits" (ORGaNICs) model, showing through analytics that the model captures multiple relevant experimental results: first of all, its oscillatory dynamics produce a profile with high resemblance to experimental results, both in terms of decay of power at high frequency and in terms of shifting peak as a function of stimulus contrast. Second, inter-areal communication subspace dimensionality is lower than within-area dimensionality. The authors then proceed to further characterize the model's response properties as a function of input and feedback gain. In particular, they find that frequencies transmitted with higher strength also carry more information, that changing gain modifies the dimensionality of communication subspaces, and that these properties can be used in a three-layer model, where an upstream area can select which downstream area to communicate to, based on the strength of feedback gain.

Strengths

This work demonstrates that a single-circuit model with normalization properties can capture both the oscillatory dynamics and the inter-areal communication properties measured in cortical circuits, matching multiple experimental results. The full analytical tractability of the model is highly advantageous, allowing for easier exploration of parameters, replicability, and effective interpretations of results compared to purely numerical approaches.

The work also makes a useful conceptual link between normalization, coherence-based communication, and subspace-based communication. In particular, it shows how both phenomena can emerge from the same circuit dynamics, where normalization is a key factor.

Interestingly, the model is also extended to multiple areas, showing how attention (in the form of changes in feedback gain) can synchronize the activity of a downstream area with one of two upstream areas, thus effectively selecting which area to communicate with.

In general, this is an interesting computational framework and a useful starting point for future modeling work. A particular strength is that it connects normalization, oscillatory dynamics, coherence, and communication subspaces within one analytically tractable model, making it possible to generate mechanistic hypotheses about when inter-areal communication should be stronger, lower-dimensional, or preferentially routed through feedback.

Weaknesses

Although I see the analytic approach as a strength, at the same time I regard the lack of any numerical comparison as a big weakness. Circuit simulations would not only confirm the correctness of the analytics, but also offer further insights on the error margins and on the regimes where the analytics are valid. This is because, to my understanding, the analytics are based on a linear approximation around the operating regime, which means deviations might be expected, especially for high gain levels in the input, or in the feedforward and feedback pathways.

Another problem is that the analytically tractable model seems to rely on effective connectivity weights that break Dale's law. Numerical simulations with explicitly modeled excitatory and inhibitory units might give insights into effects due, e.g., to the additional transmission delays mentioned in the Discussion.

Another weakness is the use of the term "predictions" to indicate features of the model dynamics that are purely described in the context of the model parameters. Although the model's response properties may certainly lead to predictions, I think the term requires a better contextualization in terms of neurophysiology and experimental neuroscience. The Discussion draws very interesting and valuable bridges between neuron morphology, interneuron types, and model parameters. But it seems it's left to the reader to backtrack and figure out which biological mechanisms or experimental manipulations should correspond to changes in input or feedback gain, and how these should be distinguished from possible changes in feedforward gain.

Relatedly, the manuscript places substantial emphasis on modulation of feedback gain, but does not comparably explore modulation of the feedforward gain, β2, which regulates the V1-to-V2 drive. This seems important because changes in feedforward gain could also influence communication subspace dimensionality and oscillatory dynamics. Therefore, predictions related to top-down feedback modulations should be taken with a grain of salt.

Last but not least, the model dynamics are split among multiple elements and nonlinear interactions, reaching a level of complexity far higher than the other ORGaNICs formulations present in the literature. The authors derive these dynamics in the supplementary material, as a dynamical system that converges to a fixed-point solution that includes "exact divisive normalization". I wonder, however, if there could be simpler solutions that also produce normalization, either approximate or in a different form than the one proposed by the authors. Note also that the designation of "excitatory neurons" is misleading: despite the presence of two explicitly inhibitory populations, the "excitatory" units also interact with negative effective weights both recurrently and in the inter-areal interactions, thus breaking Dale's law.

On the dignity of work and the trap of efficiency.

Standards for discernment

from Greek roots meaning "walking together". It means church members listen to one another, share ideas, and pray to find what God wants.

0x: biểu thị giá trị hexa (thập phân) 8100: mỗi 1 số là tương đương 4 bit tổng 16 bit

How much variance in the 70-dimensions do the first 2 PCs account for?

The discrete structure of the PC space is interesting. I wonder how the space would change if you were to train the model on non-instantaneous windows. I would hypothesize a smoother space but, maybe paradoxically, the possibility of greater classification accuracy given the autocorrelation of the velocity components.

Also, the 70-dimensional latent space post-optimization is intriguing. Any idea why that size is optimal with respect to the input features?

-a -> (-a)^2 = a^2 and a -> a^2. For the onto part, there is no integer which squared gives 2. There is also no integer which squared gives a negative integer.

Let a be an integer. a -> |a| = a and -a -> |-a| = a.

Each negative integer is mapped to the nonnegative counterpart, and each positive is mapped to itself. Thus, the entire range of the the nonnegative integers is assigned to an element of the domain twice except 0.

A search for this company leads here: https://therouge.org/the-lower-rouge-rivers-bones/

Which reports that after bison bones were scarce people began raiding native cemeteries until finally human bones were no longer accepted for making glue charcoal and even (?) gelatin.

为啥CI 是否含零= p-valur for β = 0 β =0 意味着某个变量是无关的（比如说beta1=0，说明sexmale是无关的，无法拒绝H0） 而Sexmale通过Estmate +- 2.5 Std Error 的的到CI不含0，说明Sexmale 的系数很淡是零（beta=0）-》拒绝H0

• Overview & System Requirements:
  • Allows independent Claude Code sessions to communicate across local terminals, distinct machines, or web instances.
  • Requires Claude Code v2.1.224 or later running on macOS or Linux (active by default).
  • Driven automatically by Claude using two core tools: ListAgents (for discovery) and SendMessage (for delivery).
• How to Use Cross-Session Messaging:
  • Listing Active Sessions: Use the /list-agents command to inspect reachable local sessions, subagents, and Remote Control peers along with their assigned names.
  • Naming Sessions: Use /rename or launch with --name <name> to give sessions distinct identifiers (otherwise auto-named based on the working directory, e.g., myapp-3f).
  • Sending Messages via Natural Language: Prompt Claude directly—you do not run SendMessage yourself.
    • Example: "Ask the session running in my other terminal whether the migration finished"
    • Example: "Explain what we just did to the session working on the payments API"
  • Interacting Across Machines: Connect sessions using Remote Control to reply across machines or to web sessions (remote instances can receive replies, but cannot initiate new outbound exchanges).
• Inbound Message Controls & Governance:
  • Manage incoming messages using the crossSessionInbound configuration:
    • accept: Automatically delivers inbound messages to Claude.
    • hold: Displays a approval prompt before message delivery.
    • refuse: Rejects and drops incoming messages automatically.
  • Safety Boundaries: Inbound messages arrive as plain text; they cannot execute slash commands, grant permissions, or alter system configurations (CLAUDE.md).

Publication date: 2026/7/29, Journal: iScience, Volume: 29, Issue: 8, Article number: 116849, Publisher: Cell Press.

Interesting

Black Social Workers' pushback

intersectionality

Voire même adopter un comportement plus dangereux pour satisfaire l'algo du score !

Dans la police d'auto connectée que je "suivais", la majorité des assurés qui étaient éligibles au cashback (ils étaient pas nombreux) ne faisaient pas la démarche de le demander. S'il n'était pas automatique, il n'était simplement pas réclamé. On parle ici de centaines d'euros pourtant !

J'ai l'impression qu'à part le nombre de kilomètre et l'horaire, les contrats télématiques n'ont jamais trouvé d'autres pistes sur le "bon" ou le "mauvais" comportement, sinon l'avantage compétitif aurait été très élevé. La transition du pay as you drive, qui a vite pris la suite, n'a pas eu le succès escompté en Frnace

Un objectif complémentaire affiché, mais sans doute pas réalisable, c'est le nudge. Mais je ne sais pas si c'est quelque chose qui a été effectivement observé dans les système de télématique. Pas dans celui que j'ai pu traité en tout cas. Si je rends plus prudent un conducteur via ce dispositif, l'assuré gagne et les autres aussi. C'est une forme de prévention par modification d'habitude.

The librarian makes a well thought out and descriptive way to tell the reader of the yearn for knowledge the boy has (In reference to the boy in the red hoodie) . I picked this example specifically because the author uses many descriptive words not only here but throughout the book. It allows for the reader to feel the emotion coming from the passage and at least for me made for a way more enjoyable and colourful reading experience.

I find this interesting solely because some Fantasy books, while entertaining, are these exact same "practical" books, but packaged to be enticing and easier to swallow for some people. For some, they find it easier to read technical books on how to fix their problems, while other people find it easier to relate to a character's problems and use the character as a model example on how to fix their own problems. Maybe I'm over explaining what theme is but I found the comparison very neat as a person who doesn't read fantasy a lot because I struggle seeing the books as more than a story

eLife Assessment

This valuable descriptive study describes the expression of a developmentally relevant transcription factor in the adult Tribolium brain. The evidence supporting the claims is convincing and based on a very detailed and rigorous analysis of light microscopy data, which, however, lacks single-cell resolution. This neuroanatomical study is of interest to the field of insect neural development and neuroscience.

Reviewer #1 (Public review):

Summary:

Pang et al. investigated the expression pattern of the transcription factor foxQ2II in an adult beetle brain. They find nine distinct clusters, with many neurons expressing Glut/ChaT and dopamine. Some of the dopamine neurons resemble cell types described in Drosophila. Several neurons seem to project to prominent higher brain regions such as the MB and CX, and might even connect to both.

Strengths:

The authors use state-of-the-art labeling techniques for the analysis of individual cell types, such as beetle brainbow, to investigate the until now unknown expression of the transcription factor in the adult beetle brain.

Rigorous cell reconstruction and image analysis revealed a better understanding of the anatomy of the labeled cells.

Weaknesses:

The brainbow labeling seems to include all cells labeled by the enhancer trap line, as well as the ones not expressing foxQ2II. Thus, it is unclear how useful this data is to compare individual cells to other insects.

The functional relevance of this transcription factor in the adult brain cell is still unknown. It is therefore unclear if the described neurons have any specific function and if they require this transcription factor for normal function.

Overall, the neural reconstructions are missing single-neuron details; it is difficult to compare the shown cell types to specific cell types in Drosophila based on the presented data, and this finding remains speculative.

Reviewer #2 (Public review):

Summary:

The authors provide the first thorough profiling of neurons in Tribolium characterized by the expression of the transcription factor foxQ2, which will be useful for developmental neurobiology. They use state-of-the-art methods convincingly to not only identify the neurons, but also to further characterize them anatomically and neurochemically.

Strengths:

Thorough and meticulous application of state-of-the-art anatomical methods in a non-standard laboratory organism.

Weaknesses:

No weaknesses were identified by this reviewer.

Comments:

I don't really have any major suggestions at all. Loved the work.

There is only one tiny nitpicking aspect:

P21: "Biogenic amines are involved in learning and memory and setting arousal threshholds (Davis, 2023), which are functions performed by the mushroom bodies and related to the function of the central complex in goal directed navigation, respectively."

MBs mainly process olfactory memory. At least in Drosophila, most other kinds of memories are being supported elsewhere.

https://pubmed.ncbi.nlm.nih.gov/10454381/

such as, e.g., visual pattern learning in the CX

https://pubmed.ncbi.nlm.nih.gov/16452971/

or motor learning in motor neurons

https://pubmed.ncbi.nlm.nih.gov/38779314/

or ventral ganglion, antennal lobes, and median bundle for place learning:

https://pubmed.ncbi.nlm.nih.gov/10706599/

If the authors focus on MBs, this sentence ought to reflect the fact that the function of the MBs is much narrower than the current sentence appears to suggest.

Author response:

We are very happy that our work was positively received by the reviewers and editors and we are looking forward sharing our results via eLife. 

We have added more details on the generation of the Tribolium brainbow-lines and we have submitted the respective plasmids to Addgene and give the respective IDs. Some additional minor changes were done to make the text more clear. 

Public Reviews: 

Reviewer #1 (Public review): 

Summary:

Pang et al. investigated the expression pattern of the transcription factor foxQ2II in an adult beetle brain. They find nine distinct clusters, with many neurons expressing Glut/ChaT and dopamine. Some of the dopamine neurons resemble cell types described in Drosophila. Several neurons seem to project to prominent higher brain regions such as the MB and CX, and might even connect to both. 

Strengths: 

The authors use state-of-the-art labeling techniques for the analysis of individual cell types, such as beetle brainbow, to investigate the until now unknown expression of the transcription factor in the adult beetle brain. 

We would want to add that this work establishes and introduces the brainbow system for the first time in an arthropod outside Drosophila melanogaster and that we are the first (outside flies) to relate the expression of a neural transcription factor with neural projection and neurotransmitter content.

Rigorous cell reconstruction and image analysis revealed a better understanding of the anatomy of the labeled cells. 

Weaknesses: 

The brainbow labeling seems to include all cells labeled by the enhancer trap line, as well as the ones not expressing foxQ2II. Thus, it is unclear how useful this data is to compare individual cells to other insects. 

We kindly disagree with the first statement: not all cells of the enhancer trap are labelled but a subset. Therefore, we call it “sparse labelling” in our manuscript while we do not reach “single cell labelling”, which admittedly limits both precision and use.

The functional relevance of this transcription factor in the adult brain cell is still unknown. It is therefore unclear if the described neurons have any specific function and if they require this transcription factor for normal function. 

Previously, we published that this gene has an important function in neural development during embryogenesis. Actually, we have done extensive RNAi experiments to test for an e ect during postembryonic development. We found surprisingly small defects when looking at alterations in several imaging lines. However, we found some changes in behavior. Given the extensive data presented in the current paper, we decided to publish these functional data (another 12 figures/suppl. figures) separately. 

We also note that the identity/function of neurons is determined by a mix of transcription factors. Disentangling the individual role of each of those transcription factors indeed is an exciting question and a major endeavor beyond the scope of this paper.

Overall, the neural reconstructions are missing single-neuron details; it is difficult to compare the shown cell types to specific cell types in Drosophila based on the presented data, and this finding remains speculative. 

Indeed, we do not reach single cell resolution, which is below the standards of fly neurobiology. However, compared with all other arthropods we reach a unique level of precision. Specifically, we are the only ones outside fly research that relate the expression of a developmental transcription factor to neural projection and neurotransmitter content. 

We also think that combining our transgenic line with dopamine-expression was su icient to compare the labelled cells to fly neurons. From what we saw in that analysis, we feel that most homology assessments of single neurons across such large evolutionary distances will remain hypothetical to some degree.

Reviewer #2 (Public review): 

Summary:

The authors provide the first thorough profiling of neurons in Tribolium characterized by the expression of the transcription factor foxQ2, which will be useful for developmental neurobiology. They use state-of-the-art methods convincingly to not only identify the neurons, but also to further characterize them anatomically and neurochemically. 

Strengths: 

Thorough and meticulous application of state-of-the-art anatomical methods in a nonstandard laboratory organism. 

Thank you for this encouraging comment. 

Weaknesses: 

No weaknesses were identified by this reviewer. 

Comments: 

I don't really have any major suggestions at all. Loved the work. There is only one tiny nitpicking aspect: 

P21: "Biogenic amines are involved in learning and memory and setting arousal threshholds (Davis, 2023), which are functions performed by the mushroom bodies and related to the function of the central complex in goal directed navigation, respectively." 

MBs mainly process olfactory memory. At least in Drosophila, most other kinds of memories are being supported elsewhere. https://pubmed.ncbi.nlm.nih.gov/10454381/ 

such as, e.g., visual pattern learning in the CX https://pubmed.ncbi.nlm.nih.gov/16452971/

or motor learning in motor neurons  https://pubmed.ncbi.nlm.nih.gov/38779314/

or ventral ganglion, antennal lobes, and median bundle for place learning: https://pubmed.ncbi.nlm.nih.gov/10706599/ 

If the authors focus on MBs, this sentence ought to reflect the fact that the function of the MBs is much narrower than the current sentence appears to suggest. 

Thanks for this clarification – we have rephrased:

"Biogenic amines are involved in learning and memory and setting arousal threshholds (Davis, 2023). This relates to the mushroom bodies’ function in olfactory memory, and the function of the central complex in visual pattern learning and goal directed navigation, respectively."

eLife Assessment

This paper describes a valuable tool for the detection and analysis of dentate spikes, network events in the dentate gyrus that are common yet understudied. This tool could help standardize dentate spike detection and analysis across labs and is therefore likely to be of interest to hippocampal neurophysiologists. However, the strength of evidence for its broad usefulness was viewed as incomplete, due to several identified bugs in the program and insufficient explanations of parameter selection and methods.

Reviewer #1 (Public review):

Summary:

Esfahany et al. describe a new platform (Toothy) to identify and analyze dentate spikes and sharp wave ripples from silicon probe electrophysiology data. The goal is to facilitate and standardize the extraction of DS1 and DS2 events, which have highly variable properties across recordings from different labs. The manuscript describes the basic workflow of the Toothy pipeline, including loading data, assigning channels along a linear probe, customizing parameters, selecting ideal channels for analysis, and classifying DS1 and DS2 events.

Strengths:

The manuscript is clear and easy to follow and does a good job of describing the platform. Overall, this will be a useful analysis pipeline that can help to standardize DS analysis across labs and datasets.

Weaknesses:

The current version has several bugs that prevent analysis, and the documentation of analysis parameters needs to be improved.

(1) In limited testing, the pipeline had several bugs, and I was not able to complete the full analysis of a dataset. Loading data from .mat or .npy files gave errors (it seemed that the metadata was not loaded correctly from the pop-up window). I was able to load a .nwb file, which worked well. The probe configuration tool was a bit difficult to understand, and there was not much documentation to help, although it worked when simply entering the x-y coordinates of the channels. It also crashed several times while trying to make a probe configuration due to it trying to save when a small typo was briefly entered. The initial analysis worked well, and the auto-selected channels matched our recording notes and seemed appropriate. DSs and ripples were extracted. An error came when trying to classify DSs, and the program repeatedly crashed across a variety of parameters. Overall, parts of the pipeline worked well, but others had significant bugs that need to be addressed.

(2) The authors should provide test data that can be run through the pipeline. Ideally, this could use a variety of data types, probes, and conditions so that it is clear how they differ.

(3) There are a lot of parameters that can be adjusted, but very little information about how they are chosen and what goes into parameter selection for a dataset. Additional documentation with more information on adjustable parameters, channel selection, and best practices would help improve the utility of the tool. Ideally, this could also integrate citations (either in the manuscript or documentation) to support some of the choices made during parameter selection.

(4) There is no validation presented against other analysis methods or datasets. While there is no ground truth of when DSs occur, this may limit the ability of this tool to become the standard for DS analysis. A section comparing the analysis used in the pipeline to other published analyses would be helpful.

(5) In the manuscript, it would be helpful to further describe the rationale for initially detecting DSs and SPW-Rs on all channels, when they are network events that occur across channels.

(6) A section on what hardware and software are necessary to run the pipeline should be added.

Reviewer #2 (Public review):

Summary:

This work provides an open-source, Python-based, graphical user interface for curating the detection and classification of dentate spikes (DSs) from hippocampal local field potential (LFP) recordings. The tool may also be used to detect, but not classify, sharp wave-ripples (SPW-Rs). The tool utilizes previously published Python packages for loading LFP files and creating experiment-specific probe objects. Detection and classification parameters are clearly defined and logged in a parameter file before starting processing. Once LFP data has been mapped to the probe object, event detection occurs across all channels. DSs are detected as qualifying peaks in the filtered DS band LFP, while SPW-Rs are detected as qualifying peaks in the filtered ripple band amplitude envelope. An initial curation step allows visualization of the LFP, instantaneous current source density (CSD), and depth-by-frequency band power plots for determining the approximate channel locations of key anatomical regions (i.e., CA1, the hippocampal fissure, and the hilus of the dentate gyrus). The optimal channel for detection is further refined in the next step by comparing event waveforms and quality metrics across channels. Artifacts and noisy waveforms can also be manually excluded during this step. Finally, DSs detected from the optimal channel are classified by computing the CSD profile around events and then clustering the first two principal components of all CSDs. The authors claim that this customizable tool will standardize DS detection and classification.

Strengths:

Toothy's detection and classification algorithms are appropriate and well-validated in the literature. The ability to change many parameters, the CSD calculation method, and clustering algorithm is helpful for precise replication of methodology that has varied previously. Default parameters optimized for mouse recordings provide a standardized starting point for rodent researchers.

The authors' commitments to transparency and user-friendliness are to be commended (e.g., clear instructions, defined and logged parameters, multiple visualization options, etc.) and are likely to be appreciated by new users. Researchers with little-to-no coding experience should find this tool especially powerful for jumpstarting their own DS analyses.

While not the focus of the paper, the capability to detect SPW-Rs provides an additional use case for Toothy and streamlines simultaneous analysis of SPW-Rs and DSs.

Weaknesses:

I encountered unexpected errors while trying to load LFP data into Toothy for testing, indicating that the "data ingestion" stage of Toothy requires minor code revision.

Toothy's utility for recordings that do not produce an LFP depth profile is unclear. According to the authors, Toothy allows probe designs with irregular spatial sampling (e.g., tetrodes) to be used. However, recording from a linear probe with electrodes spanning from approximately the hippocampal fissure to the hilus of the dentate gyrus is required for Toothy's full functionality. For example, Toothy uses a DS type classification algorithm that relies on sufficiently sampled CSD depth profiles that tetrode recordings cannot provide. As such, usage is currently restricted to detection only for certain recording setups.

The documentation on Toothy's output could be improved. Specifically, the work does not state which files different data are saved to or list the properties saved per detected event. Furthermore, the work does not discuss the potential importance of DS properties that are saved besides those related to the timing of the DS and its type.

Reviewer #3 (Public review):

Summary:

Esfahany et al present a novel, UI-based tool to detect dentate spikes from hippocampal local field potential recordings, called Toothy. Toothy is easily accessible, compatible with many popular recording formats, and guides users entirely via UI through the dentate spike curation and analysis process. The functional and interactive visualizations enable users to gain a detailed understanding of their data and rigorously analyze dentate spike phenomena. This tool will be broadly useful for anyone who studies hippocampal electrophysiology. Furthermore, by expanding access to dentate spike analysis, it may encourage more scientists to explore this understudied but critical phenomenon.

Strengths:

(1) Toothy provides several ways for users to interact directly with parameters, revealing the ramifications of these choices. Most parameters are adjustable and made obvious via a UI panel. Their effects are then visualized across channels and individual events. This will help users think critically when selecting parameters.

(2) Toothy is fully UI-based and pip-installable, lowering the barrier to entry far below what most electrophysiology analysis tools offer.

(3) The channel selection tool is broadly useful for identifying DG hilus and CA1 pyramidal locations. Since subregional and laminar localization of electrode sites is critical to correctly interpret hippocampal recordings, this tool could be more generally used to identify site locations across the hippocampus.

Weaknesses:

(1) The rationale behind parameter choices is not explained. In order to function "not as a black-box detector", as the authors state, all initial parameter choices should be explained with citations. If possible, these citations would also be available from Toothy directly, alongside citations describing alternative parameter choices. This will help users make informed choices. For instance, a user analyzing data from rats would need to adjust the default ripple frequency band upwards (150-250Hz), and would benefit from guidance to adjust this properly.

(2) The Results describe the functions of Toothy from the perspective of the user, but there is no Methods section describing what Toothy does between UI displays. This would allow readers to compare the tool directly to analysis pipelines as described in the Methods sections from other papers. Particular attention should be paid to justifying the analysis decisions that cannot be changed by the user, such as detecting events off of a single representative channel instead of across a consensus of multiple channels.

(3) It's unclear whether or how Toothy evaluates data quality to confirm that its analyses return interpretable results. At a minimum, the tool should confirm adequate sampling rate (e.g. <=1kHz) and inter-site spacing for CSD (e.g. <=50um).

(4) The paper does not put Toothy into context among the other common open-source electrophysiology analysis toolboxes. Consider Rippl-AI (Navas-Olive & Rubio et al, 2024) or pynapple (Viejo et al, 2023), to give a few examples. The paper would be strengthened by addressing how Toothy extends beyond the capacities of these other tools and how Toothy can be integrated into a workflow that also uses these other tools.

Author response:

We thank the editors and reviewers for their thoughtful and constructive assessment of Toothy, and for recognizing it as a potentially valuable resource for standardizing dentate spike (DS) analysis across labs. We are especially glad that the reviewers found the manuscript clear and easy to follow, judged the detection and classification algorithms to be appropriate and well-validated, and appreciated the tool's graphical user interface (GUI) based, pip-installable design for lowering the barrier to entry for DS analysis.

We also understand the concerns raised. Most importantly, we will resolve the data-ingestion and classification errors that reviewers encountered and release an updated version of Toothy that we have verified end-to-end across input formats and datasets. Alongside this, we will provide downloadable demo dataset(s) spanning multiple file formats, probe types, and recording conditions, so that users can confirm a correct installation and see how these cases differ.

To make the pipeline more transparent, we will add a section describing what Toothy does between user steps, including the rationale for decisions users cannot change, such as detection from a single representative channel. We will also expand the documentation of parameter choices with supporting citations and alternatives, and surface this guidance within Toothy where feasible, consistent with our aim that the tool not function as a black box.

We will clarify Toothy's scope and current limitations. Recordings with irregular spatial sampling (e.g., tetrodes) are supported for detection but not for CSD-based DS-type classification, which requires a laminar probe spanning approximately the hippocampal fissure to the hilus; we will state this explicitly and evaluate adding an optional waveform-based classification mode (Santiago et al., 2024) to extend type classification to such recordings. We will also add data-quality checks (including sampling rate and inter-electrode spacing) that warn users when a recording may not support reliable results.

Finally, we will situate Toothy among existing open-source toolboxes, describing how it differs, extends beyond, and interoperates with them, and we will add a comparison of Toothy's outputs to previously published analyses while being explicit about the limits of such comparisons. We will of course also address the remaining technical clarifications and figure edits raised by the reviewers.

We are confident that addressing these points will make Toothy clearer and more useful to the hippocampal community.

Dennis McCarthy: When the best way into the 21st Century is through a Selectric typewriter<br /> by [[Dennis McCarthy]] in Daily News accessed on 2026-08-10T12:06:13

Columbo S1, E1 "Murder by the Book"<br /> Episode aired Sep 15, 1971<br /> https://www.imdb.com/title/tt0066932/<br /> Written by Steven Bochco and directed by Steven Spielberg

"Benefits for Classrooms" instead of Benefits for Instructors and a transformative experience

By recognising COP and CMA decisions as relevant to treaty interpretation, the Court gives institutional decisions a role in shaping the operative content of the climate treaties without formal amendment. This becomes particularly important at paragraph 224, where decisions 1/CMA.3 and 1/CMA.5 are treated as confirming 1.5°C as the parties’ primary temperature goal, even though the Paris Agreement itself refers to keeping warming “well below 2°C” while pursuing efforts towards 1.5°C. Falk’s account of quasi-legislative institutional power is useful here: formally non-binding decisions can acquire normative force through consensus, repetition and judicial recognition. The route through Article 31(3)(a) VCLT is orthodox as stated by Orakhelashvili, but that does not resolve the larger question of how far subsequent agreement can reshape treaty obligations. The concern is sharper for dualist systems such as India, where the legislature consents to treaty obligations on the basis of the text ratified. If that content can later be expanded through conference decisions without renewed legislative approval, the link between international obligation and domestic democratic consent becomes considerably weaker.

The Court’s rejection of lex specialis is convincing at one level, but paragraph 170 raises a real problem about treaty autonomy. The Paris Agreement reflects a carefully negotiated balance, so applying a separate customary duty of prevention at a “stringent” standard risks adding an obligation that States did not expressly bargain for. The Court’s reliance on silence is also uneasy in light of Lotus: it effectively assumes that general international law continues to apply unless States have excluded it, whereas the opposite presumption could also be made. Simma’s criticism in Kosovo points to the same difficulty. At the same time, rejecting lex specialis preserves the customary duty and prevents the Paris Agreement’s “facilitative” and “non-punitive” compliance mechanism from becoming the only meaningful response to breach. Keohane’s emphasis on stable institutional expectations, however, shows why the regime-integrity concern remains important: if external customary rules can supplement a carefully negotiated treaty regime, its boundaries become less predictable.

The Court’s treatment of these principles suggests a distinction between binding rules and interpretive principles: the latter guide the application of law without creating independent obligations. This is defensible, but the Court does not clearly explain the legal status of such principles. Its treatment of equity is particularly unclear: after acknowledging in Continental Shelf (Tunisia/Libya) that equity is a general principle “directly applicable as law,” it nevertheless confines equity to interpretation. The exclusion of polluter pays is also revealing. Although the Court relies on soft-law instruments to support other principles, it rejects polluter pays largely because it lacks specific treaty anchoring. That unstated distinction is significant because polluter pays is the only principle among those considered that directly allocates the costs of environmental harm to its contributors.

The Court’s extension of the customary no-harm principle to climate change is persuasive but leaves an important methodological gap. Corfu Channel and Pulp Mills concerned identifiable, bilateral harm, whereas climate change involves cumulative contributions by virtually all States. Nuclear Weapons establishes that the duty can extend beyond national territory, but that addresses where harm occurs, not whether a State’s conduct can be causally connected to it. The Court therefore moves too quickly from establishing the customary rule to extending its application to a fundamentally different factual context. Barcelona Traction offered a stronger basis for addressing this difficulty through the concept of obligations owed to the international community as a whole. The Court’s reference to climate change as a “quintessentially universal risk” points in this direction, but it does not fully develop the connection.

The Court adopts a functional rather than source-based approach to applicable law, asking what rules are most relevant to the questions before it rather than working through Article 38 source by source. As Higgins suggests, this reflects international law as a process of authoritative decision-making rather than a fixed catalogue of sources. The inclusion of five guiding principles also supports Panezi’s observation that general principles often do more work than the formal taxonomy suggests. The trade-off, however, is predictability: if relevance determines inclusion, the scope of applicable law ultimately depends on judicial assessment. Still, given the integrated nature of climate change, the Court’s approach is arguably preferable to artificially separating treaty, customary and principled obligations.

The Court gives the General Assembly's questions a deliberately broad interpretation. It understands relevant conduct to include all State acts and omissions that adversely affect the climate system through greenhouse-gas emissions, and it rejects any territorial limitation on its inquiry. The Court nevertheless states that it is identifying existing obligations rather than creating new ones. By extending scope to licensing, subsidising and production, the Court relocates the legally relevant conduct from the atmosphere to the domestic regulatory decision. The Assembly asked about emissions; the Court answers about the chain of State decisions that make emissions possible. That is a defensible reading of an ambiguous phrase, but it is a choice, and it determines the reach of every obligation that follows.

The fact-to-norm move in para 137 is unexamined and substantial. Because the risk is "indisputably established" by science, the Court identifies a general standard of conduct in the abstract- two sentences after quoting Bosnia Genocide for the proposition that due diligence "calls for an assessment in concreto" (ICJ Reports 2007 (I), p. 221, para. 430). Scientific certainty about risk is thus the premise licensing a doctrinal generalisation the Court's own precedent resisted. The Court expressly treats the IPCC reports as the "best available science" on the causes, nature and consequences of climate change. The duty to prevent environmental harm cannot be assessed without considering what States knew, or reasonably should have known, about the risks created by their conduct. The precautionary approach reinforces this point: uncertainty cannot be used as a reason for postponing measures where there is a threat of serious or irreversible harm.The difficulty is that the Court does not fully explain the legal status of the scientific material on which it relies. The IPCC is not a law-making institution, and its reports are not themselves sources of international law. They are evidence capable of informing the interpretation and application of existing legal rules. Kelly's argument that custom is increasingly demonstrated identifying the real dange- an obligation of due diligence with no external referent is one whose standard the adjudicator supplies.

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TODO How exactly does this work?

It leads naturally on.

The Problem

How attackers are registering their own MFA methods: 1. If your MFA method isn't fully enforced, then the attacker may be prompted to set it up. 2. Even if MFA is enforced, and the attacker tries to get into the account, they can try to register their own MFA method.

From my understanding, it seems like the Risk-based policies address issues with problem #2. Regular CA policies with P1 can protect against problem #1.

By default, there is nothing stopping a risky sign-in from registering a new MFA method... - A Risky Sign-in is separate from a sign-in from an untrusted source. The "risk" here is a metric attributed by Microsoft.

eLife Assessment

This study reports important and invaluable findings that advance understanding of how attention is distributed between what we look at directly and what lies outside the center of gaze during active visual search. The evidence supporting the main claims is solid, with a large and rich dataset spanning multiple brain areas, although some aspects of the interpretation would benefit from additional controls and clearer separation of attention from eye-movement planning. The work will be of particular interest to researchers studying attention, visual perception, and eye movements behavior.

Reviewer #2 (Public review):

Summary:

In natural visual behavior, such as when one is looking for a face in the crowd, the eyes are moved from site to site, seeking possible matching targets. This involves attention both to the current view at center of vision (the foveal location) as well as to upcoming views via attention to targets in the periphery. While it has been established that attention generally enhances neuronal response (compared to simple visual activation) at the attended spatial location, this study provides solid evidence that attention during active visual search leads to neuronal response enhancement only when the eye moves towards targets that exhibit the desired feature and category. This study thus moves the field towards understanding the neural encoding of active vision.

This study examines the neuronal basis of feature selective attention during active, freely behaving visual search. Traditional electrophysiological studies on visual attention in monkeys commonly used an eye fixation with covert attention paradigm, but have not sufficiently addressed the roles of both foveal and peripheral attention in play during natural looking behavior. Here, the authors present a novel paradigm in which, during eye movement mediated search neuronal receptive fields are recorded in multiple cortical areas (sensory V4, temporal and prefrontal areas). In this manner, as the eye foveates, items in the array fall into foveal or non-foveal recorded sites. Thus, the experimental paradigm is elegant, offering the opportunity to make multiple types of comparisons: target/distractor, towards/away from fovea, areal. Specifically, following a category cue (face, house, hand, flower), freely initiated saccades are made to locate a categorically matching 'target' in an array of distractors. Feature attention is assessed by comparing eye saccades made to targets vs to distractors. Spatial attention is assessed by comparing saccades made 'towards' vs 'away' from targets. Statistics are rigorous and nicely designed. Detailed association of simultaneously obtained eye movement sequences and neural parameters are well done. These are valuable data which will contribute to our understanding of attentional modulation in visual search.

The significance of these findings is fundamental. Decades of attention research in vision have been based on the paradigm of visual fixation and covert peripheral attention. However, increasingly the field has moved towards understanding how the visual system works during active vision. Here, the authors use an active visual search paradigm and record from key mid-tier (V4) and higher order (IT, PFC) areas. They find enhancement of attention both in the foveal and peripheral locations, and, furthermore, marked by a high degree of feature and categorical specificity. That is, while attention generally enhances neuronal response (compared to simple visual activation) at the attended spatial location, this study provides solid evidence that attention during active visual search leads to neuronal response enhancement only when the eye moves towards targets that exhibit the desired feature and category. This provides valuable data for the concept of a foveal-peripheral spatiotemporal attentional window in natural vision. The controls (comparisons of neuronal response during looks to targets vs distractors and looks towards and away from the target) and statistical rigor make these findings compelling. There will likely be additional future impacts of this study. For example, the eye movement patterns collected in this study may also provide a valuable dataset for future study of understanding search strategies. Goal-directed vs non-goal-directed task comparisons could be designed to test possible circuit models. Although much remains unknown regarding how and where frontal and temporal signals are integrated during active search, these data contribute important guideposts for future models of active visual search.

Reviewer #3 (Public review):

In this manuscript, the authors investigate the role of attention in foveal processing during a naturalistic task. They record neural activity from extrastriate visual areas V4 and inferotemporal cortex, as well as from the lateral prefrontal cortex, in macaques performing a free-gaze visual search task. In this task, animals searched for a face or house target among multiple complex stimuli, with no constraints on eye movements. Unlike classic studies of visual attention, which often rely on controlled fixation, this work examines neural activity in both foveal and peripheral receptive fields during naturalistic eye movements.

The main question addressed by the authors is how feature-based attention is distributed and coordinated across foveal and peripheral visual fields during active search, and how this attentional processing influences saccade behavior. The authors show that foveal units in visual areas exhibit feature-based attentional enhancement, with stronger responses when a fixated stimulus is a target compared to when the same stimulus serves as a distractor. Peripheral units in visual and prefrontal areas show both feature-based and spatial attentional modulation, consistent with prior work. Finally, the authors show that attentional modulation depends primarily on stimulus category rather than response magnitude, with neurons showing similar enhancement for all images within the target category regardless of how strongly individual images drive the cell.

There are several notable strengths of this paper including:

(1) Disentangling feature-based and spatial attention during naturalistic vision remains a central challenge. This paper tackles both simultaneously, parsing neural populations by object selectivity (face-selective, house-selective, non-selective) and RF position (foveal vs. peripheral).

(2) The unconstrained search task (Fig. 1A) moves beyond the dominant fixed-gaze, cued-attention designs (Zhou & Desimone, 2011) to study attention as it operates during natural behavior, with sequential fixations and voluntary saccades.

(3) The scale of the multi-area recordings is a major strength and is well aligned with current trends in primate and human neuroscience toward large-scale, multi-area recordings. Simultaneous recordings from visual and prefrontal areas, comprising over 4,900 foveal units and more than 1,500 peripheral units, enable meaningful cross-area latency comparisons and area-specific analyses of attentional modulation. This study builds on the authors' previous analyses of this dataset by expanding the scope to show that feature-based attention generalizes across neuronal classes and operates on categorical identity rather than response magnitude.

(4) The combination of simultaneous multi-area recordings and a rich behavioral paradigm provide a dataset that is well suited for population decoding, cross-area interaction analyses, and trial-by-trial prediction of saccade choices, which could substantially deepen mechanistic understanding beyond the largely univariate comparisons presented here.

While the data broadly support the paper's main conclusions, several issues limit the strength of the mechanistic interpretation and should be taken into consideration:

(1) Receptive field size is not explicitly quantified and may confound foveal-peripheral comparisons. Units are classified as foveal or peripheral based on responsiveness to the cue versus the search array (Methods, p. 17), but the manuscript lacks essential information about receptive field sizes, eccentricities, and the number of search stimuli falling within each receptive field and related proper controls. This is critical because receptive fields in visual area V4 at foveal eccentricities are relatively small (Gattass et al., 1988; Desimone & Schein, 1987), whereas receptive fields in inferotemporal cortex can span several degrees to tens of degrees and often include the fovea (Op de Beeck & Vogels, 2000; DiCarlo & Maunsell, 2003; Zoccolan et al., 2007). Given the 2{degree sign} × 2{degree sign} stimulus size, multiple search items could potentially fall simultaneously within peripheral receptive fields. This introduces a potential confound, as attentional modulation is known to be strongest when multiple stimuli appear within a single receptive field (Reynolds et al., 1999). Although the authors acknowledge this issue for visual area V4 (p. 17), it is neither quantified nor controlled for. Without explicit receptive field mapping relative to the search array, comparisons between foveal and peripheral units, as well as between visual areas, are difficult to interpret cleanly.

(2) Attentional modulation is difficult to dissociate from saccade planning and decision-related signals. The free-gaze paradigm enhances ecological validity but introduces a temporal confound: mean distractor fixation durations are approximately 156 ms (p. 9), while attentional effects emerge between 137 and 170 ms after fixation onset (Fig. 2). As a result, the reported attentional modulation coincides with preparation of the subsequent saccade. Neural activity measured in the primary analysis window (150-225 ms; p. 19) therefore likely reflects a mixture of visual, attentional, motor planning, target recognition, and behavioral relevance signals, all of which are known to modulate responses in visual areas at similar latencies (e.g., Chelazzi et al., 1998). Moreover, target fixations (~257 ms) and distractor fixations (~156 ms) occur on fundamentally different behavioral timescales, which may inflate apparent foveal attentional effects. While the authors suggest that these timing differences support the idea that foveal feature-based attention facilitates prolonged fixation on target stimuli, this interpretation is not fully supported by the current analyses. That said, the saccade-aligned analyses of peripheral units (Fig. S3) partially mitigate this concern by demonstrating that feature-based modulation persists through saccade execution.

(3) The "attention-out" condition for spatial attention lacks directional control. In the spatial attention analyses (Fig. 4D-F), the "attention-out" condition appears to include all fixations followed by saccades directed away from the receptive field, regardless of saccade direction. This differs from classic spatial attention designs, which typically use controlled anti-saccades or saccades to fixed locations opposite the receptive field (e.g., Moore & Armstrong, 2003; Gregoriou et al., 2009). Saccades directed toward locations adjacent to, but outside, the receptive field may still partially engage spatial attention mechanisms near the receptive field via broad attentional fields or motor preparation gradients (Bisley & Goldberg, 2010). In addition, the "attention-out" condition likely contains a heterogeneous mixture of trials in which the stimulus in the receptive field is either a target or a distractor, since feature-based attention effects are derived from this same pool of trials. As a result, spatial and feature attention effects are not fully orthogonal, and variance related to feature attention may already be embedded in the spatial attention baseline.

[Editors' note: the authors have provided responses to each of these points.]

Author response:

The following is the authors’ response to the original reviews.

Public Reviews:

Reviewer #1 (Public review):

Summary:

This manuscript aims to differentiate between foveal and peripheral attentional mechanisms in visual and frontal brain regions in monkeys engaged in a free-gaze visual search task.

Strengths:

The manuscript is clearly written, the question is important, and the behavioral task is interesting.

Weaknesses:

I have two major concerns.

(1) The authors interpret divergence in neural responses to target vs nontarget as attention. But it is not. The subject has to attend to both target and nontarget stimuli to determine the stimulus category and thereby decide on the next action. Thus, divergence between target and nontarget responses could reflect categorical discrimination, but I am not sure this can be interpreted as attentional modulation. While it may be tempting to suggest that finding a stimulus of a specific category is "feature attention", analogous to, e.g., attending to the red stimulus, I don't believe this is correct. For the former, the animals have to attend to a stimulus, and examine the stimulus to determine the stimulus category, unlike a simpler discrimination, which may pop out. Given this, I am unconvinced that the interpretations in this manuscript are valid.

We thank the reviewer for raising this concern. Selective attention is a process of focusing on goal-relevant stimuli (targets) while ignoring irrelevant distractions. Importantly, attentional selection is not limited to simple visual features (e.g., color, shape, or motion); it can also operate over more complex features. For example, objects themselves can serve as units of attentional selection [1, 2], and feature-based attentional effects have been observed when searching for images that match the cued images or image patches [3, 4]. In this context, attention can be directed either to overall features of an object or to objects as configurations of multiple non-spatial features. Furthermore, attention to the category of stimuli has been extensively investigated in fMRI experiments in humans [5-8], and it has been shown that attention can warp the representations of semantically related categories when participants search for different categories [7].

Similarly, in our study, monkeys were trained to search for images that matched the category of the cue. The neural responses to targets versus distractors were compared while constrained to the same stimuli across different trials, ensuring that the observed response divergence was not due to the physical category of the targets and distractors. We also included only neural responses occurring prior to fixations associated with target selection, that is, before the monkeys made a behavioral choice, thereby controlling for potential contributions of target detection or decision-related signals to the observed effects.

We have clarified and addressed this point in the Discussion as follows:

“Feature-based attention to simple visual features such as color, shape, or motion has been extensively studied [1, 3, 5, 7-9, 11, 12, 64]. Attention can also operate over more complex features. For example, objects themselves can serve as units of attentional selection [65, 66], and feature-based attentional effects have been observed when searching for images that match the cued images or image patches [6, 67]. In this context, attention can be directed either to overall features of an object or to objects as configurations of multiple non-spatial features. Furthermore, attention to the category of stimuli has been extensively investigated in fMRI experiments in humans [68-71], and it has been shown that attention can warp the representations of semantically related categories when participants search for different categories [70]. In this study, the neural responses to targets versus distractors were compared while constrained to the same stimuli across different trials, ensuring that the observed response divergence was not due to the physical category of the targets and distractors.”

(2) Regarding the RF classification of foveal and peripheral RFs for IT and PFC, prior work suggests that neurons in IT cortex (especially AIT) and PFC have RFs that largely include the foveal visual field. So, it would be important to include figures that show the RFs of neurons classified as foveal versus peripheral for all three areas.

We thank the reviewer for raising this important point. We agree with the reviewer that neurons in IT cortex and PFC often have RFs that include the foveal visual field. We did record foveal units with both focal and broad foveal RFs; however, in our analysis we only included neurons with focal foveal RFs to exclude the influence of peripheral stimuli. We defined focal foveal-RF units as those that responded solely to the cue in the foveal region and not to items in the search array presented at least 5° away from the central fixation point, ensuring that their RFs did not extend to these peripheral locations. The items were also separated by at least 5° from each other, excluding the possibility that peripheral stimuli fell within their RFs during fixations. By definition, their RFs were restricted to the central point. This is further supported by Fig. S1A-H, which shows no responses to items in the search array at peripheral locations. We have made modifications in the Results and Methods as follows:

“Notably, the items in the search array were presented at least 5° from the central fixation point and were also separated by at least 5° from each other, excluding the possibility that peripheral stimuli fell within their foveal RFs during fixations.”

And:

“In this study, our focus was on units with focal foveal RFs and units with localized peripheral RFs. All further analyses were conducted on these units.”

We modified Fig. 1 to illustrate the RFs of neurons classified as peripheral, which were also characterized in our previous study using the same dataset [9]. The peripheral population exhibits no responses to the central cue (Fig. S1I–T).

Reviewer #2 (Public review):

Summary:

In natural visual behavior, such as when one is looking for a face in the crowd, the eyes are moved from site to site, seeking possible matching targets. This involves attention both to the current view at the center of vision (the foveal location) as well as to upcoming views via attention to targets in the periphery. While it has been established that attention generally enhances neuronal response (compared to simple visual activation) at the attended spatial location, this study provides solid evidence that attention during active visual search leads to neuronal response enhancement only when the eye moves towards targets that exhibit the desired feature and category. This study thus moves the field towards understanding the neural encoding of active vision.

This study examines the neuronal basis of feature-selective attention during active, freely behaving visual search. Traditional electrophysiological studies on visual attention in monkeys commonly used an eye fixation with a covert attention paradigm, but have not sufficiently addressed the roles of both foveal and peripheral attention in play during natural looking behavior. Here, the authors present a novel paradigm in which, during eye-movement mediated search, neuronal receptive fields are recorded in multiple cortical areas (sensory V4, temporal, and prefrontal areas). In this manner, as the eye foveates, items in the array fall into foveal or non-foveal recorded sites. Thus, the experimental paradigm is elegant, offering the opportunity to make multiple types of comparisons: target/distractor, towards/away from fovea, and areal. Specifically, following a category cue (face, house, hand, flower), freely initiated saccades are made to locate a categorically matching 'target' in an array of distractors. Feature attention is assessed by comparing eye saccades made to targets vs to distractors. Spatial attention is assessed by comparing saccades made 'towards' vs 'away' from targets. Statistics are rigorous and nicely designed. The detailed association of simultaneously obtained eye movement sequences and neural parameters is well done. These are valuable data that will contribute to our understanding of attentional modulation in visual search.

Strengths:

The significance of these findings is fundamental. Decades of attention research in vision have been based on the paradigm of visual fixation and covert peripheral attention. However, increasingly, the field has moved towards understanding how the visual system works during active vision. Here, the authors use an active visual search paradigm and record from multiple areas (V4, IT, PFC). They find enhancement of attention both in the foveal and peripheral locations, and, furthermore, a high degree of feature and categorical specificity. This provides valuable data for the concept of a foveal-peripheral attentional window in natural vision. The controls (comparisons of neuronal response during looks to targets vs distractors, and looks towards and away from the target) and statistical rigor make these findings quite compelling.

Weaknesses:

While the study is generally quite strong, there are a few weaknesses to be addressed.

(1) Little rationale is provided for recording in the selected areas, V4, IT, and PFC. Given the respective roles in sensory, object recognition, and goal-directed behavior, some rationale for this design should be offered, and commonalities/distinctions between these areas should be discussed.

We thank the reviewer for the suggestion and we modified and added the rationale to the Introduction as follows:

“V4 and inferotemporal cortex (IT), as the middle and high-level areas of the ventral visual stream, are important for object recognition and categorization [27-34], and their roles have been extensively studied in central vision. At the neuronal level, however, most investigations have largely neglected their functions during active, free-gaze visual search. The prefrontal cortex, including LPFC, has long been implicated as a source of top-down signals that bias the selection of attended features and modulate visual cortical responses [6, 9, 11, 35-40]. Although target-related visual responses have been reported in IT during visual exploration [41], and target-selective responses have been observed in the human medial temporal lobe (MTL) [42] and medial frontal cortex (MFC) [43] during visual search, these studies did not map the receptive fields (RFs) of recorded neurons.”

We also added a discussion as follows:

“Some studies have provided evidence for integration between peripheral and foveal feature information across saccades, including features such as stimulus color [58, 59] and object orientation [60, 61], and visual features have been shown to be predictively remapped prior to saccades [62]. Our finding provides a potential neuronal mechanism that may support this integration process [63]. We found that LPFC’s extensive representation of the visual periphery provides a neural substrate for monitoring the broader search array. Crucially, our finding that LPFC activity temporally precedes attentional effects in the visual area—consistent with previous studies [6, 9, 11, 35-40] suggests that it does not merely reflect peripheral sensory input. Instead, LPFC likely acts as a top-down orchestrator, projecting task-relevant templates derived from current foveal goals onto peripheral candidate locations, a possibility that warrants further investigation.”

(2) Given the reliance of all analyses on saccadic behavior (towards target/distractor, towards/away from target), additional description and summaries of eye movement behavior during single trials and across trials should be provided.

We thank the reviewer for this helpful suggestion. We have added a description of saccade behavior to the Results as follows:

“The mean number of saccades monkeys made to find the target after the onset of the search array was 2.25 ± 1.35 (mean ± SD across trials; Table 1) of correct trials, and the mean saccade amplitude was 7.99° ± 3.58° (mean ± SD across saccades; Table 1). Monkeys could fixate on each distractor or the target freely, provided they did not maintain fixation on the target for longer than 800 ms. Across sessions, 42.44% ± 3.6% of saccades were directed to distractors, 57.56% ± 3.6% to targets, and 12.59% ± 3.46% were saccades away from targets (see our previous studies [44-46] for detailed behavioral analyses).”

We have modified Fig. 1A and its legend to illustrate the saccadic patterns of monkeys during the search task.

We have also included Table 1, which summarizes eye movement behaviors.

(3) The dependency of findings on top-down (categorical & feature-specific) task design should be discussed.

We thank the reviewer for the suggestion and added a discussion as follows:

“In this task, attention is strongly guided by top-down goals, which bias processing toward behaviorally relevant features and object categories [2, 50, 51]. Top-down attention, including categorical and feature-specific components, has been shown to modulate neural processing across the visual pathway based on task demands and to originate from distributed frontoparietal control networks [11, 35-38, 40]. Our study provides further insight into the mechanisms of goal-directed visual attention, as it is among the first to demonstrate foveal feature attention effects during free-gaze visual search, as well as the distribution of feature and spatial attention across the entire visual field.”

Reviewer #3 (Public review):

In this manuscript, the authors investigate the role of attention in foveal processing during a naturalistic task. They record neural activity from extrastriate visual areas V4 and inferotemporal cortex, as well as from the lateral prefrontal cortex, in macaques performing a free-gaze visual search task. In this task, animals searched for a face or house target among multiple complex stimuli, with no constraints on eye movements. Unlike classic studies of visual attention, which often rely on controlled fixation, this work examines neural activity in both foveal and peripheral receptive fields during naturalistic eye movements.

The main question addressed by the authors is how feature-based attention is distributed and coordinated across foveal and peripheral visual fields during active search, and how this attentional processing influences saccade behavior. The authors show that foveal units in visual areas exhibit feature-based attentional enhancement, with stronger responses when a fixated stimulus is a target compared to when the same stimulus serves as a distractor. Peripheral units in visual and prefrontal areas show both feature-based and spatial attentional modulation, consistent with prior work. Finally, the authors show that attentional modulation depends primarily on stimulus category rather than response magnitude, with neurons showing similar enhancement for all images within the target category regardless of how strongly individual images drive the cell.

There are several notable strengths of this paper, including:

(1) Disentangling feature-based and spatial attention during naturalistic vision remains a central challenge. This paper tackles both simultaneously, parsing neural populations by object selectivity (face-selective, house-selective, non-selective) and RF position (foveal vs. peripheral).

(2) The unconstrained search task (Figure 1A) moves beyond the dominant fixed-gaze, cued-attention designs (Zhou & Desimone, 2011) to study attention as it operates during natural behavior, with sequential fixations and voluntary saccades.

(3) The scale of the multi-area recordings is a major strength and is well aligned with current trends in primate and human neuroscience toward large-scale, multi-area recordings. Simultaneous recordings from visual and prefrontal areas, comprising over 4,900 foveal units and more than 1,500 peripheral units, enable meaningful cross-area latency comparisons and area-specific analyses of attentional modulation. This study builds on the authors' previous analyses of this dataset by expanding the scope to show that feature-based attention generalizes across neuronal classes and operates on categorical identity rather than response magnitude.

(4) The combination of simultaneous multi-area recordings and a rich behavioral paradigm provides a dataset that is well-suited for population decoding, cross-area interaction analyses, and trial-by-trial prediction of saccade choices, which could substantially deepen mechanistic understanding beyond the largely univariate comparisons presented here.

While the data broadly support the paper's main conclusions, several issues limit the strength of the mechanistic interpretation and should be taken into consideration:

(1) Receptive field size is not explicitly quantified and may confound foveal-peripheral comparisons. Units are classified as foveal or peripheral based on responsiveness to the cue versus the search array (Methods, p. 17), but the manuscript lacks essential information about receptive field sizes, eccentricities, and the number of search stimuli falling within each receptive field and related proper controls. This is critical because receptive fields in visual area V4 at foveal eccentricities are relatively small (Gattass et al., 1988; Desimone & Schein, 1987), whereas receptive fields in inferotemporal cortex can span several degrees to tens of degrees and often include the fovea (Op de Beeck & Vogels, 2000; DiCarlo & Maunsell, 2003; Zoccolan et al., 2007). Given the 2{degree sign} × 2{degree sign} stimulus size, multiple search items could potentially fall simultaneously within peripheral receptive fields. This introduces a potential confound, as attentional modulation is known to be strongest when multiple stimuli appear within a single receptive field (Reynolds et al., 1999). Although the authors acknowledge this issue for visual area V4 (p. 17), it is neither quantified nor controlled for. Without explicit receptive field mapping relative to the search array, comparisons between foveal and peripheral units, as well as between visual areas, are difficult to interpret cleanly.

We thank the reviewer for the helpful suggestion and apologize for not explicitly providing essential information about the RFs of the units. We added a detailed description of RF properties to the Results as follows:

“The RFs of these peripheral units were further mapped using a visually guided saccade task and quantified by the number of stimuli that activated each unit (Fig. 1F-K). The eccentricities of the peripheral RFs were 6.22° ± 1.31° (mean ± SD) in V4, 7.04° ± 1.52° in IT, and 6.68° ± 1.56° in LPFC. The sizes of the peripheral RFs were 3.67° ± 1.87° in V4, 6.86° ± 3.11° in IT, and 8.65° ± 3.02° in LPFC. The numbers of items from the search array falling within peripheral RFs were 1.49 ± 0.55 in V4, 2.2 ± 0.72 in IT, and 2.56 ± 0.74 in LPFC (also see our previous study [44]).”

The reviewer is correct that multiple items from the search array did fall within the RFs of peripheral-RF units. However, for focal foveal units, only the fixated stimulus fell within the RF, due to the design of the search array and the definition of these units used in our analyses (see our reply to Reviewer 1, Public Review, Question 2 for details). We agree with the reviewer that attentional modulation is typically stronger when multiple stimuli fall within RFs. In our design, peripheral RFs, on average, contained more stimuli than foveal RFs. Therefore, this difference in RF size would, if anything, be expected to bias toward stronger attentional modulation in peripheral units. This would make our observation conservative, thereby further supporting rather than undermines our main finding of robust feature-based attentional enhancement in foveal units, challenging the prevailing view that such modulation is predominantly peripheral. However, we agree that, when comparing the latency of attentional effects across brain regions in Fig. 3, we cannot rule out the influence of the number of stimuli arising from differences in RF size.

(2) Attentional modulation is difficult to dissociate from saccade planning and decision-related signals. The free-gaze paradigm enhances ecological validity but introduces a temporal confound: mean distractor fixation durations are approximately 156 ms (p. 9), while attentional effects emerge between 137 and 170 ms after fixation onset (Figure 2). As a result, the reported attentional modulation coincides with the preparation of the subsequent saccade. Neural activity measured in the primary analysis window (150-225 ms; p. 19), therefore, likely reflects a mixture of visual, attentional, motor planning, target recognition, and behavioral relevance signals, all of which are known to modulate responses in visual areas at similar latencies (e.g., Chelazzi et al., 1998). Moreover, target fixations (~257 ms) and distractor fixations (~156 ms) occur on fundamentally different behavioral timescales, which may inflate apparent foveal attentional effects. While the authors suggest that these timing differences support the idea that foveal feature-based attention facilitates prolonged fixation on target stimuli, this interpretation is not fully supported by the current analyses. That said, the saccade-aligned analyses of peripheral units (Figure S3) partially mitigate this concern by demonstrating that featurebased modulation persists through saccade execution.

We thank the reviewer for raising this important question. We agree that the temporal overlap of visual, motor planning, target recognition, and behavioral relevance signals with attention can result in mixed activity, which needs to be dissociated. Therefore, when calculating feature-based attention, we did implement a series of controls. We added a discussion as follows:

“A major challenge in interpreting neural activity related to attentional modulation is the inherent temporal overlap of visual processing, motor planning, and target recognition signals in the free-gaze visual search task [73]. To isolate genuine feature-based attention from potential confounds, we applied several stringent analytical constraints, consistent with prior studies [3, 5, 6]. Specifically, by restricting our analysis to fixations where the subsequent saccade was directed away from the RFs, we dissociated attentional modulation from the preparatory motor activity associated with saccade execution. Furthermore, by comparing responses to the same physical stimulus alternating its role as a target or distractor across trials we eliminated any potential bias introduced by stimulus identity or physical category. We restricted our analysis to fixations preceding target selection that is, before the monkeys made a behavioral choice to minimize contributions from target detection or decision-related signals.”

We thank the reviewer for pointing out the issue of different timescales for target versus distractor fixations. To address this, we conducted a control analysis by computing foveal feature-based attentional modulation using fixations on targets and distractors with matched fixation durations. We obtained similar results. We have updated Fig. S2 to include this control analysis.

We also clarified this point in the Results as follows:

“We also obtained similar results when controlling for fixation durations on targets and distractors (i.e., there was no significant difference between fixation durations on targets and distractors; Wilcoxon signed-rank test, P > 0.05; Fig. S2K–P).”

Lastly, as the reviewer correctly pointed out, the interpretation that foveal feature-based attention facilitates prolonged fixation on the target was not supported. We have revised the Results as follows:

“On average, target fixations (256.69 ± 197.44 ms [mean ± SD]) were significantly longer than distractor fixations (156.26 ± 45.94 ms; Wilcoxon rank-sum test, P < 0.0001), and during these prolonged target fixation, foveal feature-based attention modulation was consistently observed.”

(3) The "attention-out" condition for spatial attention lacks directional control. In the spatial attention analyses (Figures 4D-F), the "attention-out" condition appears to include all fixations followed by saccades directed away from the receptive field, regardless of saccade direction. This differs from classic spatial attention designs, which typically use controlled anti-saccades or saccades to fixed locations opposite the receptive field (e.g., Moore & Armstrong, 2003; Gregoriou et al., 2009). Saccades directed toward locations adjacent to, but outside, the receptive field may still partially engage spatial attention mechanisms near the receptive field via broad attentional fields or motor preparation gradients (Bisley & Goldberg, 2010). In addition, the "attention-out" condition likely contains a heterogeneous mixture of trials in which the stimulus in the receptive field is either a target or a distractor, since feature-based attention effects are derived from this same pool of trials. As a result, spatial and feature attention effects are not fully orthogonal, and variance related to feature attention may already be embedded in the spatial attention baseline.

We thank the reviewer for this important question. We performed a directional control analysis by computing spatial attentional modulation using paired fixations from the attention-in and attention-out conditions. Only saccades directed in nearly opposite directions—defined as having a saccade direction angle ≥ 170° within the 0–180° range—were included. We obtained similar results (Author response image 1). 

Author response image 1.

Peripheral spatial attentional modulation in V4, IT, and LPFC. Population response to stimuli followed by saccades directed into their RFs (attention in) versus directed approximately opposite and outside their RFs (attention out), shown for V4 (A), IT (B), and LPFC (C). Shaded area denotes ±SEM across units.

We did control for feature-based attention when calculating spatial attentional modulation. We apologize for the lack of clarity and have added a description of this control to the Methods as follows:

“The saccade-target stimulus in the RF during attention-in fixations was matched to a stimulus in the same location during attention-out fixations; in both conditions, this stimulus always served as a distractor for that trial, except in the “Distractor fixations to T” condition (Fig. 5 and Fig. S4), in which it instead served as the target. This design eliminates differences due to feature-based attention between the attention-in and attention-out conditions.”

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

(1) Figure 3C: Unclear how to compare LPFC vs V4 for foveal units since only data from peripheral LPFC is shown?

We thank the reviewer for pointing out this mistake. In Fig. 3C, we only compared LPFC peripheral units, V4 peripheral units, and V4 foveal units. We have corrected this in the legend of Fig. 3 as follows:

“Shown are cumulative distributions of feature-attention effect latencies, computed from individual foveal face-, house-, and non-selective units in V4 and IT, and from peripheral non-selective units in V4,

IT, and LPFC.”

(2) On page 8, last para: For units with peripheral RFs ... Is this controlled for whether the saccade is to targets or to distractors?

We thank the reviewer for the question. We indeed addressed this concern by separating fixations based on whether the subsequent saccade was directed to a target or a distractor, and by analyzing attention modulation within each condition. Therefore, attention effects were evaluated while holding the saccade destination constant, effectively controlling for potential confounds related to saccade target selection.

(3) Page 9: The authors find that target fixations were longer than distractor fixations and conclude that this supports the idea that foveal feature-based attention increases fixation duration, but this interpretation is pure conjecture, and there is no experimental manipulation presented in this paper that helps to establish this interpretation.

We thank the reviewer for this important comment. We agree that this observation does not, by itself, support our original interpretation, and we have modified it in the Results. Please refer to the last paragraph of our Reply to Question 2 from Reviewer 3 (Public Review).

(4) Data analysis: receptive field. The authors state that visual response to a cue and the stimulus array was assessed during the 0-200 ms window after stimulus onset. However, after the array onset, the animal could saccade within the 200 ms window. How do the authors ensure uniform stimulation during the 0-200 ms window?

We thank the reviewer for this question. The activity of units in V4, IT, and LPFC within the 200 ms window after array onset primarily reflected visual stimulation prior to saccades, because typical saccade latencies were approximately 150–200 ms, and the response onset latencies of these units were around 50 ms.

(5) On page 19, the authors state that to assess feature attention in peripheral RFs, they divided trials into target and distractor fixations. In the former, there was a target in the neuron's RF. This is confusing. I assume target fixations imply fixating on a target, but the authors may mean fixations where a target is in the RF. Please clarify.

We thank the reviewer for pointing out this confusion. In the original manuscript, we intended to sort fixations by whether a target stimulus was located within the unit’s peripheral RF. To avoid further confusion, we have revised the description in the Methods as follows:

“we sorted fixations during the search period, following a procedure similar to that in our previous study [5], into two types: “target” – a target stimulus was located within the unit’s peripheral RF; and “distractor” – the same stimulus appeared in the same peripheral RF location but served as a distractor.”

(6) Figure S1: Are these example units? How many trials? SEM? The sharp rise and no noise are inconsistent; the former suggests minimal smoothing, while the latter suggests lots of smoothing.

We thank the reviewer for these questions. We showed average responses across all units in Fig. S1. On average, there were 941.79 ± 182.56 trials (mean ± SD across sessions). Shaded areas indicate ±SEM across units. The sharp rise reflects the synchronous response of neurons to the stimulus, while the smooth appearance and low noise result from averaging across a very large number of units and trials.

Reviewer #2 (Recommendations for the authors):

Major comments:

(1) One weakness of this manuscript is the lack of a rationale for choosing V4, IT, and PFC. Specifically, what are the predictions of the roles of these respective areas in the integration of current and peripheral (future foveal) views? There is a significant literature linking the pre-saccadic peripheral stimulus and the post-saccadic foveal stimulus, suggesting that both spatial and temporal integration occur. However, whether such integration occurs at high or low cortical levels is unknown. By recording from mid-tier (V4) and high-order areas (IT, PFC), the authors have an opportunity to address this question. However, there is no mention of this topic, either in the introduction, results, or discussion. I find this omission surprising. At the very least, it should contribute to experimental design rationale and some discussion.

We thank the reviewer for the suggestion and we modified and added the rationale to the Introduction and a discussion about this integration. Please refer to our Reply to Question 1 from Reviewer 2 (Public Review).

(2) As both behavior and neural recordings are collected, a figure on saccadic patterns would enhance the reader's understanding. Questions that come to mind are: What does a single search trial look like? How many saccades are there per trial? How often is the target identified after 1, 2, 3, etc saccades? What is the average size of a saccade? Although this is not a study of search strategy per se, a modicum of description of the search sequences would provide context on the behavior. I suggest an illustration of one or more sample trials; a summary of saccade behavior would also be helpful for understanding the data in relation to behavioral performance.

We thank the reviewer for this helpful suggestion. We have modified Fig. 1A and its legend to illustrate the saccadic patterns of monkeys during the search, providing an example of a single search trial. Additionally, we have added a description of saccade behavior to the Results and included Table 1, which summarizes eye movement behavior. Please refer to our Reply to Question 2 from Reviewer 2 (Public Review) for further details.

(3) "Consistently, the probability of making a saccade to a peripheral target was higher following distractor fixations (75.22%) than following target fixations (48.44%, or 63.49% after probability calibration; see Methods), indicating the important role of peripheral feature-based attention in guiding eye movements" It should be noted that this target-oriented visual search is fundamentally a top down task. Once the target is found, the reward is obtained; saccades to distractors are not rewarded, so saccades are more likely. So certainly this task design would increase the post-distractor saccades and decrease the number of post-target saccades. Please clarify the behavioral paradigm: once a reward is obtained, does the task continue, or is a new trial initiated?

We apologize for the confusion regarding the behavioral paradigm. We would like to clarify that when the target was found and fixated for 800 ms, the reward was delivered and no further saccades occurred. However, if the target was not fixated for 800 ms, the search could continue. It is worth noting that the target fixations in our analyses were restricted to those occurring during ongoing search behavior, excluding target fixations associated with trial termination and reward delivery. Moreover, we compared the probability of making a saccade to the target, rather than the absolute number of saccades, following these fixations. We have modified the Results for clarification, as follows:

“Two monkeys performed a category-based visual search task, where their objective was to fixate on one of the two search targets that matched the category of the cue (Fig. 1A, B). Specifically, the monkeys were presented with a central fixation point for 400 ms, followed by a cue lasting 500-1300 ms. After a 500 ms delay, a search array appeared with 11 items, including two targets, randomly chosen from 20 possible locations (Fig. 1E). The monkeys had 4000 ms to find one target and maintain fixation on it for 800 ms to earn a juice reward. Fixating on either target completed the trial, and the monkeys did not search for the second target. A new trial began after the reward. It is worth noting that the two target stimuli matched the category of the cue but were different images. The monkeys were required to maintain fixation throughout the cue and delay periods. During search, however, eye movements were unconstrained, and monkeys could revisit each search distractor or target as long as they did not fixate on a target for 800 ms.”

(4) The fact that there are many more peripheral units in LPFC suggests that this is a region of foveal/periph integration. Combined with the finding that the LPFC leads the attentional effects, this should be a discussion point.

We thank the reviewer for the suggestion and we added a discussion as follows:

“Some studies have provided evidence for integration between peripheral and foveal feature information across saccades, including features such as stimulus color [58, 59] and object orientation [60, 61], and visual features have been shown to be predictively remapped prior to saccades [62]. Our finding provides a potential neuronal mechanism that may support this integration process [63]. We found that LPFC’s extensive representation of the visual periphery provides a neural substrate for monitoring the broader search array. Crucially, our finding that LPFC activity temporally precedes attentional effects in the visual area consistent with previous studies [6, 9, 11, 35-40] suggests that it does not merely reflect peripheral sensory input. Instead, LPFC likely acts as a top-down orchestrator, projecting task-relevant templates derived from current foveal goals onto peripheral candidate locations, a possibility that warrants further investigation.”

Minor comments:

(1) Figures 2A-D. "These face-selective units also showed slightly enhanced responses to house targets in IT (P < 0.05), but not in V4 (P = 0.89)." It does not appear enhanced.

We agree with the reviewer that the effect is modest and does not appear strongly enhanced. However, the average response in the 150–225 ms time window to the house target was significantly higher than that to the house distractor in IT face-selective units (Wilcoxon signed-rank test, P = 0.042). We modified the description in the Results as follows: 

“These face-selective units also showed weakly but significantly enhanced responses to house targets in IT (P < 0.05)”

(2) Figure 3. For population comparison, a bootstrapped null distribution was used, and a 2-sided permutation test was used to determine the latency difference between the target and distractor; please show these results (described in text) in a figure. Figures 3A-C are described as the latency of individual units. So each of these graphs is the mean of multiple units? So this is also a population analysis? What is the difference between these two comparisons? This is somewhat confusing.

We apologize for the confusion and thank the reviewer for pointing this out. Each panel in Fig. 3 shows the cumulative distribution of latencies across individual units within each brain region, reflecting the variability of response timing across single neurons. For this analysis, we first calculate the latency of each unit separately. In contrast, population-level latency is measured from the averaged responses of all units within each region (Fig. 2), which captures the overall timing of the population response rather than individual variability. Statistical comparisons at the population level are performed using a two-sided permutation test. We modified Fig. 2 to better illustrate the population-level latency results.

(3) Did peripheral RFs span more than a single stimulus in the array? If so, how does this impact the interpretation of Figure 5?

We thank the reviewer for pointing this out. The reviewer is correct that, in peripheral RFs, more than one stimulus from the search array could fall within the receptive field (1.49 ± 0.55 in V4, 2.2 ± 0.72 in IT, and 2.56 ± 0.74 in LPFC). We controlled for this in our analysis of both feature-based and spatial attention effects for peripheral units in Fig. 5. For feature-based attention, we performed the analysis in a stimulus-by-stimulus manner within each category (house and face), such that when a given stimulus served as the target, it was the only target within the RF, and when it served as a distractor, it was the only distractor of its category within the RF. Although additional distractor could still fall within the RF, their identities were random across conditions and thus would be averaged out. A similar approach was applied to spatial attention, where the stimulus-by-stimulus comparison was extended across all four categories, and attention-out stimuli were paired with the corresponding saccade-target stimuli in the attention-in condition, with the effects of other randomly present distractors averaged out. Therefore, the effects shown in Fig. 5 reflect comparisons at the level of individual stimulus, minimizing confounds from other stimuli within the RF.

(4) Figure 5G: "during "Target fixations to D", there was no significant feature attentional enhancement in response to the peripheral target (Wilcoxon signed-rank test, P > 0.05; Figure 5G-I left panels). It appears that there is some effect of spatial attention during Target Fix to D trials.

We thank the reviewer for pointing this out and have revised the Results as follows:

“We further found that spatial attentional enhancements to the saccade target were reduced during target fixations compared to distractor fixations in V4 and IT when activity was aligned to fixation onset (Wilcoxon rank-sum test, P < 0.05; Fig. 5G, H versus Fig. 5A, B), although this effect was not completely abolished.”

(5) The specific areas of IT and LPFC that were recorded should, as much as possible, be mentioned.

We thank the reviewer for the helpful suggestions and have added a description of the specific IT and LPFC recording sites to the Methods as follows:

“Recordings in IT spanned the central IT cortex, encompassing the area between the anterior middle temporal sulcus (AMTS) and the posterior middle temporal sulcus (PMTS), including TE and TEO. Recordings in LPFC were located anterior to the arcuate sulcus (AS) and lateral to the principal sulcus (PS), mainly covering areas 45 and 44.”

(6) It is often difficult to distinguish the different lines, e.g., red solid vs red dotted, due to their overlap. Would the removal of the error band make this clearer? If so, could put full figure with error bands in the Supplementary Figure.

We thank the reviewer for this helpful suggestion. To improve visual clarity, we adjusted Fig. 6, Fig. 7, Fig. S2, Fig. S3, Fig. S4, and Fig. S6 by changing the line styles and placing the shaded error bands beneath the traces, allowing the lines to remain clearly visible despite overlap.

(7) For easy access, the number of saccades to/from targets/distractors should be put into a table.

We thank the reviewer for the suggestion. We calculated the probability of saccades to and from targets and distractors for each session and report the mean ± SD across sessions in Table 1, as the mean number of saccades per trial was only 2.3. Please refer to our Reply to Question 2 from Reviewer 2 (Public Review) for Table 1.

Reference

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Reply to the reviewers

POINT-BY-POINT response letter

We thank both reviewers for useful suggestions. Our responses are indicated in blue in the text below.

Reviewer #1 (Evidence, reproducibility and clarity (Required)):

Alves et al investigate the mechanisms of anterograde IFT in Euglenozoa that lack the canonical heterotrimeric kinesin-2 motor and the kinesin-associated protein KAP. Through a combination of comparative genomics, in vitro analyses, live-cell imaging, and genetic analyses in Trypanosoma brucei and Leishmania mexicana, the authors show that the kinesin-2 proteins KIN2A and KIN2B form homodimeric motors in vitro with distinct in vivo ciliary localization and functions. The authors report that KIN2B is essential for flagellum assembly despite contributing to only a minority of long-range anterograde transport events that colocalize with IFT trains. By contrast, KIN2A is dispensable for flagellum assembly despite being proposed to mediate most anterograde IFT. Based on these observations, the authors propose a division-of-labour model in which KIN2B mediates entry of IFT proteins into the flagellum while KIN2A performs the majority of anterograde transport.

The work addresses an important evolutionary and mechanistic question. The phylogenetic analysis, in vitro motor characterization, and comparative analyses in two trypanosomatid species are major strengths. However, I believe that several of the key mechanistic conclusions are not yet directly supported by the available data and would benefit from either additional experimentation or a more cautious interpretation.

We agree that there are limitations to our study and have discussed them below and in the revised manuscript. We make it clearer that it is a working model, which we believe is currently the best one to explain the available data. We also note that Reviewer 2 considers the model “compelling”.

Major comments:

1- The authors should revise the statement in the Introduction that in bloodstream-form T. brucei "only knockdown of both KIN2A and KIN2B impacted flagellum length". However, Douglas et al. (2020) reported that individual depletion of either kinesin reduced flagellum length, with a stronger additive phenotype upon combined depletion. The authors should correct this point and clarify more explicitly how the present study extends the earlier work, both conceptually and technically.

Thanks for the comment, the statement has been corrected (see below). Compared to the Douglas study, there are major advances both conceptually and technically. Below, we are talking only about function, the studies on kinesin motility (Figs 2,3,5,6) being entirely new since this was not looked at in the 2020 publication where localisation studies were limited to fixed cells.

In technical terms, our study relies on gene deletion or inactivation using potent Cas9 approaches in both T. brucei (procyclic stage) and L. mexicana (Beneke et al. 2017, ref [58]; Asencio et al., 2024 [72]) while the Douglas study used RNAi, with strong efficiency against KIN2A (~10% mRNA still detected) and more limited impact on KIN2B (~30% mRNA left). This could explain the stronger phenotype reported for KIN2A depletion. Moreover, the published study was done with bloodstream stage trypanosomes, which are much more sensitive to flagellar pertubation than procyclic ones (Broadhead et al., 2006; Ralston & Hill, 2006 [34, 35]) used here. A reminder has been added in the text for non-trypanosome readers. The introduction has been edited as follows (p. 5):

“In the trypanosome bloodstream stage (which develops in mammals and can also be manipulated), knockdown of KIN2A or KIN2B individually reduced flagellum length by 4-5 µm, and an additional reduction was observed with combined silencing [30]. These results suggest that the two kinesins may be redundant. Nevertheless, silencing of KIN2A alone, but not KIN2B, severely impacted the growth rate of bloodstream trypanosomes and led to spectacular cytokinesis defects [30], hinting at potentially some distinct functions. However, knockdown efficiency was less potent for KIN2B (~30% residual mRNA vs ~10% for KIN2A), hindering firm conclusions. It should also be reminded that bloodstream trypanosomes are more sensitive to flagellum perturbation compared to procyclic cells [34, 35].”

In conceptual terms, there are major advances. The 2020 study demonstrated the importance of KIN2A and KIN2B for proper flagellum assembly but did not provide information about the contribution of each kinesin, neither on their trafficking. First, we formally demonstrate that KIN2A and KIN2B are homodimeric and that heterodimers cannot be formed. Second, this study is revealing not only distinct localisation and motility profiles, but also different contributions to flagellum construction: KIN2B is mostly found in the proximal portion of the flagellum and governs access of IFTs to the flagellar compartment while ensuring limited protein transport while KIN2A is found all along the flagellum and is responsible for the majority of IFT transport, although it cannot import IFT proteins. Third, KIN2B can substitute to KIN2A (albeit a bit less efficiently), while KIN2A cannot substitute for KIN2B. These results are the basis of the original division-of-labour model presented at Figure 9.

2- The localization of KIN2A in the current manuscript appears somewhat different from that reported previously, where KIN2A was described as more enriched near the basal body region. This discrepancy should be discussed. In particular, the relatively strong cytoplasmic signal in the current study may obscure a weak basal enrichment, and this possibility should be addressed

The Douglas et al. study used a rabbit antiserum raised against aa 391-696 of KIN2A. Following aldehyde fixation, the signal was found in the cytoplasm, in the basal body area and along the flagellum. The region selected contains two coiled-coil domains (aa 406-464 and aa 604-636), and antibodies recognising coiled-coil domains are well known in the community for producing false positive on centrosomes and basal bodies, due to over-representation of these domains in centrosomal proteins (see for example Nido et al. Mol. Biosyst. 2012). In the 2020 study, western blot analysis detected a reduction in the amount of KIN2A upon triggering RNAi confirming specificity by this assay. However, no immunofluorescence (IFA) images of the knockdown cells were presented. Many years ago (actually well before the 2020 paper was published), Dr Welch kindly shared with us an aliquot of their anti-KIN2A that we used in both western blot and IFA in a cell line expressing double-stranded RNA of KIN2A in a tetracycline-inducible manner. Western blot demonstrated an at least 8-fold reduction of the signal (see below, so reproducing data in the Douglas publication) but the signal obtained by IFA was not much modified, especially the spot at the flagellar base (see images below). It is therefore likely that this signal is not KIN2A-specific.

Figure response 1. Cells from the KIN2ARNAi cell line were grown without (non-induced) or with tetracycline for 3 days to trigger RNAi against KIN2A. A. Western blot with the published anti-KIN2A antibody demonstrates RNAi efficiency and antibody specificity by this assay (Bertiaux et al. Curr. Biol. 2018 [33]). B-C. IFA with the published anti-KIN2A antiserum produces signal at the flagellar base, along the flagellum and in the cytoplasm in KIN2ARNAi cells in both non-induced (NI) and induced conditions (3 days)(B. Morga, unpublished data). Since the IFA signal is not reduced upon induction, it is likely unspecific.

Therefore, the discussion has been updated as follows (p. 20):

“The localisation of KIN2A had been previously reported in the T. brucei bloodstream stage using a rabbit antiserum raised against aa 391-696 of KIN2A, which labeled the flagellum, the basal body area and the cytoplasm [30]. Using the same antiserum provided by Dr. Welch, we confiremd this localisation in procyclic trypanosomes. However, while expression of KIN2B double-stranded RNA produced an 8-fold reduction of the signal obtained with this antibody by western blot, it did not impact the IFA signal at the flagellar base (our unpublished data), questioning antibody specificity in this assay.”

Here, we used endogenous N-terminal tagging with mNG to ensure expression via the 3’UTR of each kinesin, which is the major element controlling expression level (Clayton MBP2014). This allowed direct live imaging, avoiding potential biases due to fixation or antibody specificity. The mNG::KIN2A signal was detected as moving particles along the flagellum (in both anterograde and retrograde directions) without visible enrichment in the basal body area. As requested below (point 3), quantification has been performed on individual images from videos where the full flagellum appears in focus. Again, no concentration at the base could be detected, in contrast to IFT81::mNG or mNG::KIN2B.

Similar results were obtained upon N-terminal tagging by the TrypTag consortium who detected signal in the flagellum and the cytoplasm, but no specific enrichment at the flagellar base (Billington et al. 2023 [49], see image below).

Tagging KIN2A at its N-terminal end with mNG labels the flagellum without visible enrichment at its base and with some cytoplasmic signal (image from TrypTag.org)

Finally, tagging KIN2A and KIN2B in L. mexicana produced the same location as observed in T. brucei (Fig. S4, with improvements as requested below, see point 5).

3- Temporal projections are useful for interpreting particle movement, but they can be misleading when used to assess protein distribution along the flagellum. Representative single-frame images would be more appropriate for localization analyses, and quantitative fluorescence intensity profiles would strengthen the conclusions, particularly for Figures 4B-D and 7D/F.

Individual images are actually visible in the videos showing the full image series (Video S3 for IFT81::mNG, S4 for mNG::KIN2A, S5 for mNG::KIN2B, S6 for mNG::KIN2B with tdT::IFT140, S7 for mNG::IFT81 in KIN2A KO and S8 for mNG::KIN2B in the KIN2A KO). Kymograph analyses monitoring the movement of individual particles (Fig 5B,D,F) provide a global representation along the length of the flagellum. As requested, we extracted temporal projections from five cells where the full flagellum length is in focus and made graphs with their fluorescence intensity profile. This is redundant with the videos but these images can be presented as supplementary material if considered useful by the editor. This further confirms the conclusions: KIN2A is found along the length of the flagellum without obvious concentration at its base while KIN2B is present at the base and mostly at the proximal portion of the flagellum.

Fluorescence intensity profiles of 5 cells where the flagellum base is in focus. A clear enrichment is detected at the base for mNG::KIN2B (right) but not for mNG::KIN2A (left). (“series” correspond to cells)

4- The authors compare the localization and dynamics of KIN2A and KIN2B with those of IFT81. However, only one allele of IFT81 is tagged, meaning that a proportion of IFT81 molecules within trains are presumably unlabelled. This could influence measurements of train frequency, intensity, and colocalization, and should be discussed when interpreting the data. This limitation should be explicitly discussed.

We haven’t done double tagging for IFT genes since the signal is very bright for all IFT-B proteins that were looked at by us or others (Absalon et al. MBoC2008; Adhiambo et al. JCS2009; Franklin et al., MolMic2010; Bhogaraju et al., Science2013; Huet et al., eLife2014, JCS2019; Edwards et al., PNAS2018). Nevertheless, we have compared IFT trafficking in a cell line where one allele of IFT172 (gene encoding another IFT-B protein present with same stoechiometry, Subota et al. 2014, [46]) was tagged with tdTomato and the other one was not (like here with mNG::IFT81) with a derived cell line where the wild-type IFT172 allele had been deleted. Frequency of tdTomato::IFT172 trafficking was similar in both conditions, showing that tagging one allele is sufficient to detect all IFT trains (Jung et al. unpublished data).

This was expected knowing the size of IFT trains and the relatively large number of copies of the IFT-B complex. Briefly, volumetric electron microscopy data show that train length varies from 200 to 900 nm (Bertiaux et al., 2018 [38]). CryoEM data revealed a periodicity of 8 nm for each complex B present in trypanosome IFT trains (Staggers et al. 2025 [61]), so each train should contain at least 25 copies. Therefore, tagging one allele out of two should provide at least 12 copies of the fluorescent protein per train. Therefore, it is reasonably likely that virtually every train contains the fusion protein.

We have added this sentence in the text (p.11):

“Since only one allele of IFT81 was labelled, the fusion protein is competing with products of the untagged allele. However, IFT trains are composed of multiple copies of IFT-B complexes with an 8 nm periodicity [61] and their average length of ~200 nm [38] means that each train should contain at least 25 copies, making it highly likely that the vast majority of trains is labelled.”

5- The localization analysis of KIN2A and KIN2B in L. mexicana provides important validation of the observations made in T. brucei. However, no marker of the basal body or transition zone is included. Therefore, the statement that "KIN2A was found throughout the flagellum without clear enrichment at the base, whereas KIN2B is highly concentrated at the flagellum base" is not fully supported. Co-labelling with a basal body or transition zone marker would strengthen this conclusion.

When talking about L. mexicana, we are not making a statement about a specific area (basal body, transition zone or transition fibres) but are always using the term “flagellum base”. This is easily visible thanks to its proximity with the mitochondrial genome (kinetoplast), that is tightly connected to the proximal part of the basal body (Robinson & Gull, 1991 [113]). This proximity is obvious on the transmission electron microscopy image shown at Fig. 8A or on the double staining of live cells with DAPI at Fig. 8B-C. For more clarity, we have added images of cells expressing mNG::KIN2A or mNG::KIN2B costained with DAPI, showing the close proximity of the kinetoplast signal with both KIN2A and KIN2B (Fig. S4A-B); further confirming the absence of enrichment for KIN2A (Fig. S4A) and a clear concentration for KIN2B (Fig. S4B).

6- The distinction between proximal and full-length KIN2B particles is central to the proposed model. However, alternative explanations should be considered and discussed. For example, differences in particle intensity, signal-to-noise ratio, focal plane position, train convergence near the base, photobleaching, or effects of fluorescent tagging on train stability could potentially contribute to the observed behaviour. The authors should also to clarify whether tdT::IFT140 is expressed from the endogenous locus (whether all cellular IFT140 is tagged). If untagged IFT140 remains present, this could influence the interpretation of the colocalization analyses.

IFT140 is endogenously tagged, here with the plasmid tagging strategy that has been validated previously [54]. This is now clearly stated at page 13:

“However, the combination of mNG::KIN2B with endogenously tagged tdT::IFT140 was the only one to result in a viable cell line where both signals were positive and exhibited the same profile as in single tagging.”

as well as in Material and Methods (page 29)

For the double-tagged cell line, cells expressing mNG::KIN2B were nucleofected with the plasmid p2845TdTomatoIFT140 [54] linearised with MfeI, allowing the expression of IFT140 (Tb927.10.14470) fused to tdTomato (tdT) at the N-terminus from its endogenous locus.”

For the proposed alternative explanations:

-particle intensity/SNR: it is correct that fluorescent particles display variable intensities, something that we previously reported (Buisson et al. 2013 [39]). Nevertheless, kymograph analyses did not detect particular correlations. For example, brighter traces do not display faster (or slower) movements. The signal-to-noise ratio is indeed more complex in the proximal part of the flagellum for mNG::KIN2B due to the higher abundance of anterograde and retrograde particles in this area. Nevertheless, full-length particles can usually be monitored from the base on kymographs (see Fig. 5F). These differences therefore cannot explain the results.

-focal plane position: analyses are exclusively performed with cells where the flagellum is in full focus or with a segment of the flagellum that is in full focus.

-train convergence near the base: what the reviewer means by “convergence” is not clear to us. Anterograde trains are assembled in this area while retrograde trains complete their trip at the base of the flagellum. Previous quantification performed on cells expressing GFP::IFT52 showed that the frequency of both anterograde and retrograde was not higher at the base compared to the tip (Buisson et al. 2013 [39]). Therefore, they do not “converge” (like trains coming from different lines and ending at the same train station for example).

-photobleaching: kymograph observations show that the vast majority of anterograde traces convert to several retrograde ones (this was quantified in details in Buisson et al. 2013 [39]), including KIN2B proximal particles, so bleaching could not explain the results.

-effects of fluorescent tagging on train stability: adding a fluorescent reporter could indeed impact train behaviour, as observed by our difficulty to achieve double tagging while single tagging worked for almost all IFT proteins and motors tested. Since flagella are essential for trypanosomes, disruption of IFT train assembly would mimick IFT knockdowns (Kohl et al. 2003 [71]) and explain why cells either did not grow or retained only one tagged IFT/motor. In terms of stability, once a fluorescent particle is detected, kymographs show that it usually runs throughout flagellum length and is converted to retrograde trains (see above). Fig 7J shows that less than 5% of trains labelled with IFT81::mNG arrest during their trip in control cells. The only exception is of course mNG::KIN2B where the majority of fluorescent particles arrest and convert to retrograde ones towards the end of the proximal portion of the flagellum. This could have reflected an impact on motor trafficking. However, IFA performed with an anti-KIN2B antibody on a wild-type cell line demonstrates that most KIN2B signal is found in this portion (Fig. S3B), showing that untagged protein displays a similar location, ruling out the possibility of an artefact due to mNG tagging of KIN2B. In contrast to the published anti-KIN2A antibody, IFA performed with anti-KIN2B antibody on knockdown cells showed a drastic signal reduction, confirming signal specificity (Fig. S3D-E).

Only one copy of IFT140 was tagged, but as discussed above (point 4), it is unlikely that many trains would be missed given the high number of IFT complexes present per train.

In summary, none of these alternative explanations are likely. Describing each of them individually would take quite a lot of space, therefore we do not wish to increase excessively the length of the manuscript to avoid confusing the reader.

7- The velocity comparison between proximal and full-length KIN2B particles may be confounded by positional effects along the flagellum. Since transport is expected to be slower near the transition zone, it would be more appropriate to compare proximal particles with the proximal segment of full-length particle trajectories only.

IFT rates have not been quantified in the trypanosome transition zone since this portion is rather challenging for live imaging both because of its short length (350 nm; Trépout et al, JSB 2018) and of its positioning in the flagellar pocket. Since acquisition time is 100 ms and that trains run at a speed of around 2 µm/s, this means that only 2 time points would be available, preventing reliable measurements. Assuming the IFT velocity was slower in this area (as shown in C. elegans), the impact would be minimal since the proximal portion of the flagellum where most KIN2B particles are detected is 10-15 µm. 8- The authors state that IFT81 distribution in KIN2A knockout flagella resembles that observed in wild-type cells. However, comparison of the images and movies suggests potential differences, including reduced proximal signal and increased distal accumulation in the shorter mutant flagella. Representative single-frame images, together with fluorescence intensity profiles along multiple flagella, would facilitate a more rigorous comparison between genotypes.

As explained above, the full sequence of individual images is available in the videos. Nevertheless, we indeed noticed some variabilities in the IFT distribution profile, but these are also encountered in control cells (see figure below). For technical reasons, the deletion was done in pSMOX cells (Beneke et al. 2017, ref [58]) that turn out to be more difficult to immobilise for image acquisition, increasing variability from cell to cell compared to our usual 427 cells. We are showing below temporal projections of several control and KO cells. If the editor finds these useful, these panels can be provided as supplementary material. Beyond this distribution aspect, quantifications presented at Fig. 8H-I-J revealed parameters that are unchanged (frequency, 8H) and those that are moderately (speed, 8I) or drastically (frequency of arrested trains, 8J) modified.

Temporal projections of cells expressing mNG::IFT81 where most of the flagellum is in focus. (A) pSMOX (control) cells, (B) kin2a-/- cells. Although the flagellum is shorter, the flagellar distribution profile looks fairly similar.

9- The current data do not fully exclude a handover mechanism between KIN2B and KIN2A within the proximal flagellum. While the proposed model is attractive, alternative cooperation-based models remain plausible and should be acknowledged more explicitly.

We indeed considered a handover mechanism between KIN2B and KIN2A at the exit of the transition zone and have now further expanded this section (p. 21):

“At this stage, it is not clear if KIN2B progressively hands over IFT trains to KIN2A, in a situation equivalent to the transition that takes place in the intermediate portion of cilia between the heterotrimeric kinesin-2 and OSM-3 in C. elegans [21], or whether IFT trains are released once the transition zone is crossed and then associate again with any of the two kinesins. In the first situation, single molecule imaging revealed that the heterotrimeric kinesin is responsible for progression of IFT trains through the transition zone before being progressively replaced by the homodimeric kinesin OSM-3 in the proximal segment of the axoneme, OSM3 ensuring transport to the tip of the cilium [21]. A similar case could be considered here, but in a shorter portion of the axoneme and between two homodimeric kinesins. In the second situation, trains might “hang around” after they have crossed the transition zone and then be picked up by KIN2A for efficient transport.”

10- The presented data do not yet fully support that KIN2A contributes to most anterograde transport and that KIN2B mainly regulates IFT entry into the cilium while mediating only a minority of long-range anterograde transport events. This interpretation is also difficult to reconcile with the genetic data, given that KIN2B is essential for flagellum assembly whereas KIN2A is not. More generally, the proposed division-of-labour model remains largely inferential and should be presented more cautiously.

We agree that a model has always limitations and is bound to evolve. The model actually explains the genetic data since KIN2B can substitute to KIN2A, while the reverse is not possible, something observed in two different organisms (T. brucei and L. mexicana). Definitive evidence would have been the coexpression of KIN2A and IFT proteins with two different fluorescent reporters, but unfortunately, this turned out to be impossible. A third kinesin able to transport IFT particles was not identified, neither by genome mining (Wickstead et al. 2006; 2010 [36, 83], this study), nor during the TrypTag project (Billington et al. 2023 [49]). Therefore, these two kinesins must be responsible for all the transport of IFT proteins (in the anterograde direction).

We have added these sentences to the discussion (p.22):

“However, formal evidence that KIN2A transports IFT particles could not be obtained since co-expression of KIN2A and an IFT protein with two different fluorescent reporters turned out to be impossible. One therefore cannot rule out the possible contribution of other kinesins as observed in Tetrahymena [14, 82]. Nevertheless, a third kinesin able to transport IFT particles was not identified, neither by genome mining ([36, 83], this study), nor during the TrypTag project [49]. Therefore, KIN2A and KIN2B must be responsible for all the transport of IFT proteins (in the anterograde direction).”

Other comments:

1- For consistency, the authors should consider using the same plotting style for similar datasets (e.g. Figures 2C and 7C).

Maybe there is a confusion in figure number but Figure 2C quantifies the in vitro movement of truncated KIN2B on brain microtubules while Figure 7C reports flagellum length in trypanosomes without KIN2A, so there are very different datasets. Fig. 2C relates to 2A and 2B, which are all in the same format and Fig. 7C is the only graph reporting flagellum length.

2- Figures S2A and S2D are difficult to interpret due to the low signal-to-noise ratio of the staining. The authors should consider whether these data provide sufficient additional information to justify inclusion.

IFT172 staining looks indeed “cleaner” on methanol-fixed cells where signals is present mostly on the flagellum and at its base. However, most cytoplasmic IFT material is lost in these conditions (Absalon et al. MBoC2008 [52]). Here, we wanted to show that there was not impact on the global distribution of IFT proteins in the various cell lines used for the study, hence the PFA fixation followed by methanol extraction, which looks perhaps less nice but shows all the IFT material present in the cell (Bertiaux et al. 2018 [38]). As a reminder, biochemical fractionation have shown that a lot of IFT proteins are found in the cytoplasm, not only in trypanosomes, but also in other organisms (see for example Ahmed et al. JCB2008).

The same argument is valid for Figure S2D to probe for a possible pool of KIN2A at the base of the flagellum. We therefore consider important to maintain these two series of figures.

3- Figures 4 and S4 would benefit from schematics of T. brucei and L. mexicana highlighting cell morphology, flagellum, and the position of the basal body/transition zone. Such schematics would greatly aid readers less familiar with these systems. Larger panels and higher-magnification insets at the flagellar base would also improve readability. Given the overlap in content, Figures 4 and 5 could potentially be combined.

Such cartoons have been published in multiple articles but if the editor finds them useful, we can add them to the figures. Higher magnification panels reach the resoltion limit and do not add more information to the manuscript.

4- The statement that KIN2A and KIN2B exhibit velocities "compatible with IFT" and are "a bit slower than IFT81" should be corrected, since Figure 5G indicates significant differences among populations.

The sentence has been rewritten as :

“This showed that KIN2A and KIN2B particles have a speed compatible with IFT, although they are both a bit slower than IFT81, a difference that is statistically significant (Fig. ____5G).”

5- Figure 6A would benefit from higher-magnification insets highlighting representative proximal and full-length KIN2B particles.

As said above, increasing the magnification hits the resolution limit and is not very useful. Video S7 provides annotations highlighting individual examples of KIN2B associated to IFT140 navigating till the tip of the flagellum and of KIN2B particles trafficking without IFT140 and limited to the proximal portion of the flagellum.

6- The manuscript is somewhat descriptive in places and would benefit from tigher editing. A more focused presentation of the key findings and their implications would improve readability and sharpen the paper's central message.

We have rewritten some parts of the text and added sub-headings in the discussion as requested by Reviewer 2.

Reviewer #1 (Significance (Required)):

The work addresses an important evolutionary and mechanistic question. The phylogenetic analysis, in vitro motor characterization, and comparative analyses in two trypanosomatid species are major strengths. However, I believe that several of the key mechanistic conclusions are not yet directly supported by the available data and would benefit from either additional experimentation or a more cautious interpretation.

Reviewer #2 (Evidence, reproducibility and clarity (Required)):

SUMMARY: In this manuscript, the authors characterize kinesin-2, which is responsible for flagellum formation and function in Trypanosoma brucei and Leishmania mexicana. They show that T. brucei kinesin-2 comprises KIN2A and KIN2B proteins, each of which forms a homodimer and moves processively along microtubules in vitro. In cells, KIN2A and KIN2B exhibit distinct behaviors: KIN2A moves faster and traverses the full length of the flagellum, whereas KIN2B is slower and enriched near the flagella base. KIN2A KO mutants are cilia assembly competent and display only mild effects on IFT transport and flagellum assembly, while KIN2B KO mutants fail to assemble a normal flagellum. KIN2B-depleted cells are largely non-flagellated, with a clearly shortened flagellum remnant. Notably, KIN2A trafficking appears largely normal within these short flagella, indicating that KIN2A can still access the flagellum in the absence of KIN2B. Based on these findings, the authors propose a division-of-labor model in which KIN2B is primarily responsible for importing IFT components, whereas KIN2A performs most anterograde transport within the flagellum.

COMMENTS: Overall, the authors use a broad range of biochemical and cell-biological approaches to define the properties of Trypanosome kinesin-2 and conclude with a compelling working model. While the experiments appear carefully executed, the results are generally convincing, several points should be addressed before publication: • Figure 2: The in vitro reconstitution assays show two velocity populations for KIN2A (slow and fast), and a slow-moving population is also observed for GCN4-fused KIN2B. The authors interpret the slow-moving populations as "autoinhibited" conformations and the fast populations as "active". Consistently, in live-imaging (Figure 6E), proximal KIN2B particles move slowly when not co-localizing with IFT140 but move faster when associated with IFT trains. This suggests multiple motile states may reflect regulation by tail conformation and/or cargo loading rather than canonical autoinhibition. By definition, many autoinhibited kinesins are characterized by reduced microtubule engagement (or failure to bind) rather than simply reduced velocity. Therefore, it is not yet clear that the slow populations observed here should be described as autoinhibited. It rather seems more akin to a 'gear shifting' mechanism described for kinesin-1, -2, and -3 (Coppin et al, 1997; Gicking et al., 2022). Otherwise, if the authors retain this terminology, they should provide additional evidence, such as microtubule-binding affinity measurements for the slow vs. fast populations. It would also be informative to test whether kinesin-2 velocities shift in the presence of defined cargo/IFT components in the in-vitro assay.

We thank the referee for this important comment and apologize for our overly specific interpretation of the two velocity populations. We agree that a reduced velocity alone is insufficient to identify an autoinhibited state. The principal purpose of the experiments in Figure 2 was to determine the maximum in vitro velocities attainable by the individual motor proteins and to assess whether these were compatible with the transport velocities measured in vivo. For this reason, we removed the distal C-terminal stalk and tail regions and replaced the native dimerization regions with a GCN4 leucine zipper, thereby minimizing potential regulatory effects arising from the native stalk and tail.

We have therefore revised the manuscript to remove the terms “autoinhibited” and “active” when referring to these populations. We now describe them operationally as “slow-moving” and “fast-moving” populations.

We agree that measurements of microtubule-binding or landing rates, together with reconstitution using defined IFT components, would be valuable for resolving the mechanism underlying the different motile states. However, such experiments would require a systematic analysis of motor–IFT interactions and stoichiometrically defined complexes and are beyond the principal scope of the present study, which is focused on the composition, function and evolutionary diversification of kinesin-2 complexes.

• Figure 3A: In the SDS-PAGE, the apparent sizes of KIN2A and KIN2B proteins appear larger than expected. The authors should clarify whether this is due to tags, unusual amino acid composition, gel conditions, or known anomalous migration of these constructs. Both proteins migrate at the expected position (124 kDa for KIN2A and 126 kDa for KIN2B). To make this clearer, we have added the position of the 130kDa molecular marker on Figure 3A-D. Images of the whole gels (Fig. 3A-D) are shown below.

• Figure 3D: The co-immunoprecipitation experiments require additional controls to support the conclusions. Specifically, the authors should include input (total lysates) lanes prior to immunoprecipitation and compare His-tagged KIN2B levels between co-immunoprecipitated and flow-through fractions. Reciprocal co-immunoprecipitation (e.g., pull-down via His-tag followed by anti-Flag Western blotting) would further strengthen the evidence. The experiment has been repeated to include input (total lysates, new Fig. 3D, lanes e,f) and with reciprocal co-immunoprecipitation, either with Flag-tagged KIN2A (new Fig. 3D, lanes a-b) or 6xHis tagged KIN2B (new Fig. 3D, lane c-d). It further confirms that KIN2A cannot pull down KIN2B and vice-versa.

• Figure 5H: The observation that KIN2A (0.88 {plus minus} 0.19 trains/s) and KIN2B (1.18 {plus minus} 0.18 trains/s) are lower than IFT81 (1.31 {plus minus} 0.21 trains/s) does not by itself prove that "neither motor alone can perform with the whole anterograde transport". These frequency differences could arise if KIN2A and KIN2B are not independent transport populations. The key missing test is whether KIN2A and KIN2B bind the same IFT trains. If such an experiment is technically infeasible, the language should be toned down. We agree and have modified the text accordingly:

“This suggests that neither KIN2A nor KIN2B alone could perform the whole anterograde transport of IFT complexes. If KIN2A and KIN2B are binding independently to IFT trains, the sum would be too high to explain the frequency of IFT81 trafficking. However, two other options could be considered: either some kinesins do not associate to IFTs or some KIN2A and KIN2B associate together to the same IFT train.”

The ideal experiment would be to follow KIN2A and KIN2B simultaneously with reporters of different colours. We tried tagging KIN2A and KIN2B with various reporters (GFP, YFP, mCherry, tdTomato, mNG, mScarlet), but so far only mNG worked, so it’s not been possible to do two-colour imaging.

Another option was to monitor mNG::KIN2A simultaneously with a fluorescent IFT marker (as for KIN2B and IFT140, Figure 6 & Video S7), but unfortunately all the attempted combinations failed, either because cell lines did not grow or because the signal for one of the two markers was lost.

• Figure 7B: Douglas et al. (JCS, 2020) reported suppressed cell proliferation, cytokinesis, and motility in KIN2A-depleted T. brucei cells, but not in KIN2B-depleted cells. The authors should discuss how their findings differ from the previous report. As discussed above and now modified in the text (see response to point 1 of reviewer 1), this is explained by two reasons. First, there is a clear difference in RNAi efficiency in the published study with only 10% mRNA left for KIN2A but still 30% for KIN2B. Second, the 2020 work was performed in the bloodstream stage of T. brucei, which is more sensitive to flagellar pertubations than the procyclic stage (Broadhead et al. 2006; Ralston & Hill, 2006 [34, 35]). Here, we used complete gene deletion in L. mexicana or Cas9-guide disruption with interruption of all three reading frames in T. brucei. In these conditions, the KIN2B gene product is absent, leading to the strong phenotype observed for both organisms. This is now mentioned in the discussion (p. 21):

“These results differ from the RNAi knockdown results published on the bloodstream stage of the parasite where knockdown of KIN2A, but not KIN2B, turned out to be lethal. This could be explained by the less potent efficiency of RNAi against KIN2B [30]. Since bloodstream cells are more sensitive to flagellar perturbations [34, 35], the reduced flagellar length observed here upon deletion of KIN2A might be sufficient to interfere with cell division.”

• Formatting: Some paragraphs are very long and would benefit from division into shorter paragraphs. Similarly, substructuring the long discussions with subheadings aligned with the results would improve readability. We have improved the presentation of the manuscript by splitting some paragraphs and have added subheadings in the discussion.

Reviewer #2 (Significance (Required)):

While the existence of KIN2A and KIN2B has been reported previously, their ability to form a homodimer and their distinct contributions to flagella assembly and IFT have not been clearly established. The manuscript further discusses the evolutionary origin of IFT-transporting motors, expands on the separability of import into cilia and of transport along the axoneme, and, for the first time, demonstrates that homodimeric motors can build cilia. Thus, it is expected that the manuscript will appeal to a broad readership.

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

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Referee #2

Evidence, reproducibility and clarity

Summary: In this manuscript, the authors characterize kinesin-2, which is responsible for flagellum formation and function in Trypanosoma brucei and Leishmania mexicana. They show that T. brucei kinesin-2 comprises KIN2A and KIN2B proteins, each of which forms a homodimer and moves processively along microtubules in vitro. In cells, KIN2A and KIN2B exhibit distinct behaviors: KIN2A moves faster and traverses the full length of the flagellum, whereas KIN2B is slower and enriched near the flagella base. KIN2A KO mutants are cilia assembly competent and display only mild effects on IFT transport and flagellum assembly, while KIN2B KO mutants fail to assemble a normal flagellum. KIN2B-depleted cells are largely non-flagellated, with a clearly shortened flagellum remnant. Notably, KIN2A trafficking appears largely normal within these short flagella, indicating that KIN2A can still access the flagellum in the absence of KIN2B. Based on these findings, the authors propose a division-of-labor model in which KIN2B is primarily responsible for importing IFT components, whereas KIN2A performs most anterograde transport within the flagellum.

Comments: Overall, the authors use a broad range of biochemical and cell-biological approaches to define the properties of Trypanosome kinesin-2 and conclude with a compelling working model. While the experiments appear carefully executed, the results are generally convincing, several points should be addressed before publication:

• Figure 2: The in vitro reconstitution assays show two velocity populations for KIN2A (slow and fast), and a slow-moving population is also observed for GCN4-fused KIN2B. The authors interpret the slow-moving populations as "autoinhibited" conformations and the fast populations as "active". Consistently, in live-imaging (Figure 6E), proximal KIN2B particles move slowly when not co-localizing with IFT140 but move faster when associated with IFT trains. This suggests multiple motile states may reflect regulation by tail conformation and/or cargo loading rather than canonical autoinhibition. By definition, many autoinhibited kinesins are characterized by reduced microtubule engagement (or failure to bind) rather than simply reduced velocity. Therefore, it is not yet clear that the slow populations observed here should be described as autoinhibited. It rather seems more akin to a 'gear shifting' mechanism described for kinesin-1, -2, and -3 (Coppin et al, 1997; Gicking et al., 2022). Otherwise, if the authors retain this terminology, they should provide additional evidence, such as microtubule-binding affinity measurements for the slow vs. fast populations. It would also be informative to test whether kinesin-2 velocities shift in the presence of defined cargo/IFT components in the in-vitro assay.
• Figure 3A: In the SDS-PAGE, the apparent sizes of KIN2A and KIN2B proteins appear larger than expected. The authors should clarify whether this is due to tags, unusual amino acid composition, gel conditions, or known anomalous migration of these constructs.
• Figure 3D: The co-immunoprecipitation experiments require additional controls to support the conclusions. Specifically, the authors should include input (total lysates) lanes prior to immunoprecipitation and compare His-tagged KIN2B levels between co-immunoprecipitated and flow-through fractions. Reciprocal co-immunoprecipitation (e.g., pull-down via His-tag followed by anti-Flag Western blotting) would further strengthen the evidence.
• Figure 5H: The observation that KIN2A (0.88 {plus minus} 0.19 trains/s) and KIN2B (1.18 {plus minus} 0.18 trains/s) are lower than IFT81 (1.31 {plus minus} 0.21 trains/s) does not by itself prove that "neither motor alone can perform with the whole anterograde transport". These frequency differences could arise if KIN2A and KIN2B are not independent transport populations. The key missing test is whether KIN2A and KIN2B bind the same IFT trains. If such an experiment is technically infeasible, the language should be toned down.
• Figure 7B: Douglas et al. (JCS, 2020) reported suppressed cell proliferation, cytokinesis, and motility in KIN2A-depleted T. brucei cells, but not in KIN2B-depleted cells. The authors should discuss how their findings differ from the previous report.
• Formatting: Some paragraphs are very long and would benefit from division into shorter paragraphs. Similarly, substructuring the long discussions with subheadings aligned with the results would improve readability.

Significance

While the existence of KIN2A and KIN2B has been reported previously, their ability to form a homodimer and their distinct contributions to flagella assembly and IFT have not been clearly established. The manuscript further discusses the evolutionary origin of IFT-transporting motors, expands on the separability of import into cilia and of transport along the axoneme, and, for the first time, demonstrates that homodimeric motors can build cilia. Thus, it is expected that the manuscript will appeal to a broad readership.

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Referee #1

Evidence, reproducibility and clarity

Alves et al investigate the mechanisms of anterograde IFT in Euglenozoa that lack the canonical heterotrimeric kinesin-2 motor and the kinesin-associated protein KAP. Through a combination of comparative genomics, in vitro analyses, live-cell imaging, and genetic analyses in Trypanosoma brucei and Leishmania mexicana, the authors show that the kinesin-2 proteins KIN2A and KIN2B form homodimeric motors in vitro with distinct in vivo ciliary localization and functions. The authors report that KIN2B is essential for flagellum assembly despite contributing to only a minority of long-range anterograde transport events that colocalize with IFT trains. By contrast, KIN2A is dispensable for flagellum assembly despite being proposed to mediate most anterograde IFT. Based on these observations, the authors propose a division-of-labour model in which KIN2B mediates entry of IFT proteins into the flagellum while KIN2A performs the majority of anterograde transport.

The work addresses an important evolutionary and mechanistic question. The phylogenetic analysis, in vitro motor characterization, and comparative analyses in two trypanosomatid species are major strengths. However, I believe that several of the key mechanistic conclusions are not yet directly supported by the available data and would benefit from either additional experimentation or a more cautious interpretation.

Major comments:

1. The authors should revise the statement in the Introduction that in bloodstream-form T. brucei "only knockdown of both KIN2A and KIN2B impacted flagellum length". However, Douglas et al. (2020) reported that individual depletion of either kinesin reduced flagellum length, with a stronger additive phenotype upon combined depletion. The authors should correct this point and clarify more explicitly how the present study extends the earlier work, both conceptually and technically.
2. The localization of KIN2A in the current manuscript appears somewhat different from that reported previously, where KIN2A was described as more enriched near the basal body region. This discrepancy should be discussed. In particular, the relatively strong cytoplasmic signal in the current study may obscure a weak basal enrichment, and this possibility should be addressed
3. Temporal projections are useful for interpreting particle movement, but they can be misleading when used to assess protein distribution along the flagellum. Representative single-frame images would be more appropriate for localization analyses, and quantitative fluorescence intensity profiles would strengthen the conclusions, particularly for Figures 4B-D and 7D/F.
4. The authors compare the localization and dynamics of KIN2A and KIN2B with those of IFT81. However, only one allele of IFT81 is tagged, meaning that a proportion of IFT81 molecules within trains are presumably unlabelled. This could influence measurements of train frequency, intensity, and colocalization, and should be discussed when interpreting the data. This limitation should be explicitly discussed.
5. The localization analysis of KIN2A and KIN2B in L. mexicana provides important validation of the observations made in T. brucei. However, no marker of the basal body or transition zone is included. Therefore, the statement that "KIN2A was found throughout the flagellum without clear enrichment at the base, whereas KIN2B is highly concentrated at the flagellum base" is not fully supported. Co-labelling with a basal body or transition zone marker would strengthen this conclusion.
6. The distinction between proximal and full-length KIN2B particles is central to the proposed model. However, alternative explanations should be considered and discussed. For example, differences in particle intensity, signal-to-noise ratio, focal plane position, train convergence near the base, photobleaching, or effects of fluorescent tagging on train stability could potentially contribute to the observed behaviour. The authors should also to clarify whether tdT::IFT140 is expressed from the endogenous locus (whether all cellular IFT140 is tagged). If untagged IFT140 remains present, this could influence the interpretation of the colocalization analyses.
7. The velocity comparison between proximal and full-length KIN2B particles may be confounded by positional effects along the flagellum. Since transport is expected to be slower near the transition zone, it would be more appropriate to compare proximal particles with the proximal segment of full-length particle trajectories only.
8. The authors state that IFT81 distribution in KIN2A knockout flagella resembles that observed in wild-type cells. However, comparison of the images and movies suggests potential differences, including reduced proximal signal and increased distal accumulation in the shorter mutant flagella. Representative single-frame images, together with fluorescence intensity profiles along multiple flagella, would facilitate a more rigorous comparison between genotypes.
9. The current data do not fully exclude a handover mechanism between KIN2B and KIN2A within the proximal flagellum. While the proposed model is attractive, alternative cooperation-based models remain plausible and should be acknowledged more explicitly.
10. The presented data do not yet fully support that KIN2A contributes to most anterograde transport and that KIN2B mainly regulates IFT entry into the cilium while mediating only a minority of long-range anterograde transport events. This interpretation is also difficult to reconcile with the genetic data, given that KIN2B is essential for flagellum assembly whereas KIN2A is not. More generally, the proposed division-of-labour model remains largely inferential and should be presented more cautiously.

Other comments:

1. For consistency, the authors should consider using the same plotting style for similar datasets (e.g. Figures 2C and 7C).
2. Figures S2A and S2D are difficult to interpret due to the low signal-to-noise ratio of the staining. The authors should consider whether these data provide sufficient additional information to justify inclusion.
3. Figures 4 and S4 would benefit from schematics of T. brucei and L. mexicana highlighting cell morphology, flagellum, and the position of the basal body/transition zone. Such schematics would greatly aid readers less familiar with these systems. Larger panels and higher-magnification insets at the flagellar base would also improve readability. Given the overlap in content, Figures 4 and 5 could potentially be combined.
4. The statement that KIN2A and KIN2B exhibit velocities "compatible with IFT" and are "a bit slower than IFT81" should be corrected, since Figure 5G indicates significant differences among populations.
5. Figure 6A would benefit from higher-magnification insets highlighting representative proximal and full-length KIN2B particles.
6. The manuscript is somewhat descriptive in places and would benefit from tigher editing. A more focused presentation of the key findings and their implications would improve readability and sharpen the paper's central message.

Significance

The work addresses an important evolutionary and mechanistic question. The phylogenetic analysis, in vitro motor characterization, and comparative analyses in two trypanosomatid species are major strengths. However, I believe that several of the key mechanistic conclusions are not yet directly supported by the available data and would benefit from either additional experimentation or a more cautious interpretation.