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    1. On 2023-11-22 16:25:24, user Mark Houston Plitt wrote:

      After adding extensive new experiments and analyses this preprint was split into two manuscripts. <br /> We posted a new manuscript (https://doi.org/10.1101/202... "https://doi.org/10.1101/2023.11.20.567978)") focused on the in vivo two photon calcium imaging and virtual reality behavior.<br /> A revised manuscript focused on the in vitro physiology and and freely moving behavior will be posted at this DOI (https://doi.org/10.1101/202... "https://doi.org/10.1101/2022.01.04.474865)") in the coming months.

    1. On 2019-07-22 13:38:35, user Elias Grieninger wrote:

      If this works out the way I think it will, you can easily call it the next cognitive revolution of humankind. I am very excited to see what kinds of new things people will create, as soon as this technology is accessible.

    1. On 2018-09-05 08:55:11, user daniele marinazzo wrote:

      Dear Benedikt and Olaf

      I am posting this here since academickarma.org cannot find the preprint, but looks like the reviews are automatically ported there.

      thanks a lot for this really interesting work.

      Here some suggestions I collected:

      general impression: the paper is very well written, and clearly specifies why the toolbox is needed, and the theory behind it.<br /> The limitations are also properly described.

      general questions:<br /> 1. would it be feasible to design an experiment in which stimuli are isolated as much as possible, and use these shapes as prior for the estimation of overlapping responses?<br /> 2. This paper "A Statistical Framework for Neuroimaging Data Analysis Based on Mutual Information Estimated via a Gaussian Copula"(https://onlinelibrary.wiley... "https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.23471)") combines neuroelectrical data with stimuli (in the form of discrete data), and quantifies the interaction not only between different stimuli, but also, within the same ERP, between different ERP peaks (fig 13). Maybe you could use this info to estimate to which extent different stimuli overlap in the ERP, prior to your analysis?<br /> 3. would it be possible to extend the ANOVA to a MANOVA for multivariate analysis, as in the LIMO toolbox, within your deconvolution framework?<br /> 4. Do you consider effects of the combination of two responses, i.e. the fact that a "face" response could be different when combined to a color versus another? Here I don't talk about the overlap, which is the main motivation of your study, but to the fact that the separated responses could still be interacting? I refer to an interaction at the psychological/physiological level, not to the actual linear interaction, which you do model, so not sure whether this makes sense as a practical question.<br /> 5. How would the deconvolution look if we considered response-locked instead of stimulus-locked ERP?

      technical details:

      1. maybe you could describe the effect of the number of available events and the sampling frequency. I tried the extreme case of a single "car" event, and all the rest "faces", and of course the estimation is much noisier, but still present.
      2. how would the estimate change if using reconstructed sources instead of sensor space?
      3. This question comes from my experience with our blind deconvolution toolbox for fMRI (https://www.nitrc.org/proje... "https://www.nitrc.org/projects/rshrf)"), so maybe it's completely not relevant here: would it make sense to apply the toolbox when the timing of the stimuli are unknown, spotting a signature of a possible neural event in the data, and using the lag between the event and its signature as a parameter in the GLM? This would probably very short for EEG data.
      4. related question: qould it be possible to deconvolve also unknown (i.e. spontaneous) events?
      5. figure 6: why the data are padded with zeros before and after?
      6. You mention that truncating the spline has a filtering effect, it makes sense. Would this conflict with a filtering previously applied by the user?

      other small things:

      1. maybe you could add a sample of real EEG data to the code
      2. you could uniform the color scheme (sometimes you have parula, other times the cbrewer one).
      3. there are a few formatting issues in the figures.
      4. page 18, there a strange symbol replacing "pi" in "from 0 to 2X"

      code issues (more for github, but I put them here for consistency too):

      1. in the tutorial you use init_deconvolution, that does not seem to exist in the github version
      2. there is a typo "warning onW"
      3. the dependency on cbrewer could be made explicit, or you could check whether it's in the path, otherwise use a default color scheme
      4. simulate_data.m is called simulate_data2 in the function name

      thanks again, and looking forward to see more applications of this tool!

    1. On 2024-11-27 23:21:01, user Monica Berger wrote:

      Great preprint and I agree about the two basic types of hoaxes.

      I keenly followed the Conceptual Penis hoax (Boghossian and Lindsay) as it unfolded in real time as I was writing my book on predatory publishing. Although it initially correctly stated that the Conceptual Penis hoax as exposing gender studies, he later says they published in a predatory journal. This is incorrect. There were peer review problems but no predatory publishing.

      They submitted the article to a prestigious gender studies journal, NORMA: International Journal for Masculinity Studies, from Taylor and Francis. The journal rejected the article and transferred the article down to another T & F lower-tier open access journal, Cogent Social Sciences. This editorial process is called “cascading.” The less prestigious journal peer-reviewed it and, when it was accepted, requested an APC. After publication, the authors revealed the hoax, and the article was retracted; the journal explained that the peer reviewers for the lower tier publication lacked experience. See: https://www.skeptic.com/reading_room/conceptual-penis-social-contruct-sokal-style-hoax-on-gender-studies/

    1. On 2017-09-20 04:32:30, user Davidski wrote:

      Hello authors,

      I feel that this comment in the paper is somewhat misleading: "Unexpectedly, one Neolithic individual from Dereivka (I3719), which we directly date to 4949-4799 BCE, has entirely NW Anatolian Neolithic-related ancestry."

      This individual actually clusters with Balkan Chalcolithic and Neolithic samples, so in reality he's only distantly Anatolian Neolithic-related. His ancestors were probably in Europe for a couple thousand years.

      So I don't think that there's anything unexpected about the result, because apparently at the time Balkan farmers were pushing into the steppe looking for new farm land.

      Your comment almost suggests as if there was a direct migration from Anatolia to the Pontic steppe. But there's no evidence for such a thing.

    1. On 2021-01-10 10:20:45, user Stefano Campanaro wrote:

      Dear Francisco Zorrilla, Kiran R. Patil and Aleksej Zelezniak,<br /> I read your preprint and I really appreciated it. However, I would like to mention that we have recently demonstrated the feasibility of reconstructing the GEMS starting from Metagenome Assembled Genomes for hundreds of species and a series of microbial communities associated with the anaerobic digestion environment. Our paper was recently published in "Metabolic Engineering" with the title "Revealing metabolic mechanisms of interaction in the anaerobic digestion microbiome by flux balance analysis" (DOI: 10.1016/j.ymben.2020.08.013). The procedure used is similar to the one you reported and based on assembly with Megahit, binning with Metabat2, quality evaluation with checkM, GEMs reconstruction using CarveMe, evaluation of the GEMs using Memote. Additionally, we performed an additional series of analyses and verifications using other software. Without diminishing the importance of your study, on behalf of my co-authors, I think it could be interesting for you to compare the procedure reported in your preprint and the results obtained with our one.<br /> Thanks a lot.<br /> Sincerely<br /> Stefano Campanaro<br /> Associate Professor<br /> Department of Biology University of Padova

    1. On 2019-04-24 07:15:16, user ST Sebastian wrote:

      The topic “theranostic potential” is very misleading to reviewers and readers. Theranostics means therapeutics plus diagnostics. The studies did not show any diagnostic function. Using MRI to monitor drug release from cornea implant is not practical.

    1. On 2020-05-06 15:19:16, user Sinai Immunol Review Project wrote:

      Summary: Using publicly available scRNA-seq data of healthy human samples from 31 organs, the authors compare the gene expression levels of ACE2 and TMPRSS2 of each organ. The authors categorized each organ as susceptible to SARS-CoV infection based on its expression of ACE2 and further stratified 11 susceptible organs into three levels of risk for infection based on each organ’s TMPRSS2 expression (i.e. TMPRSS2 expression ratio >20% was defined as level 1). The authors claimed that for the first time, their scRNA-seq analysis showed the brain, gall bladder and fallopian tube as vulnerable to COVID-19 infection in addition to confirming previous molecular and clinical data implicating other organs susceptible to SARS-CoV2 (i.e. nose, heart, intestines, etc.).

      Limitations: The article advances its risk stratification strategy based on a couple of naïve assumptions that are being actively contested in literature: a) ACE2/TMPRSS2-mediated viral entry is the only route used by SARS-CoV2 to infect cells and b) ACE2/TMPRSS2 expression is stable in systemic inflammatory contexts such as COVID-19. Furthermore, their risk stratification does not consider the immune contexture of each organ. For instance, while several papers have suggested that reproductive organs (i.e. testes) are also susceptible to SARS-CoV2 based on their ACE2/TMPRSS2 expression, there are no reported clinical manifestations of COVID-19 in those organs. Lastly, even though they were using scRNA-seq data available from 13 different publications, their paper does not discuss how they accounted for variations in scRNA-seq protocols as well as how they normalized each data set for comparative analysis.

      Significance of the finding: Mostly confirmatory. While it is nice to see that there were clinical cases describing the adverse effect of COVID-19 for most of the eleven organs identified as SARS-CoV2 susceptible in this study, previous studies have already shown most of these organs to be susceptible to SARS-CoV2 using various approaches (including scRNA-seq analysis). Furthermore, their risk stratification strategy is simplistic as it doesn’t seem to take account of recent findings on SARS-CoV2’s alternative mechanisms of entry as well as reported clinical manifestations of COVID-19.

      Review by Chang Moon as part of a project by students, postdocs and faculty at the<br /> Immunology Institute of the Icahn school of medicine, Mount Sinai.

    1. On 2020-04-30 16:08:25, user Janet Smith wrote:

      Seems like there was a very short (2-4 weeks?) time between recovery from the Original infection and second challenge. Has there been follow up of serum antibody titre in both original (non-challenged) cohort and the challenged cohort? And/or has a later challenge been attempted?

    1. On 2021-07-12 01:32:59, user Robert George wrote:

      Great paper & new appraoch

      One minor issue regarding:<br /> ''The YHG H (H-L901) is thought to have formed in South Asia approximately ~48 kya (Sengupta et al. 2006).''

      As a modern aDNA paper, it should not rely on older, modern DNA based inferences. Instead, aDNA points to western Asia (Lazaridis; Nature 2016)

    1. On 2022-04-19 22:49:23, user Joseph Wade wrote:

      The following is a review compiled by graduate students participating in the Infectious Disease Journal Club, Department of Biomedical Sciences, University at Albany, SUNY:

      This paper addresses the mechanism by which SARS-CoV-2 infection causes inflammation. The authors argue that SARS-CoV-2 spike protein interaction with the ACE2 receptor results in a reduction in CFTR protein levels, which in turn leads to increased inflammation in the COVID-19 airway. This is impactful because the authors identify a mechanism of inflammation caused by SARS-CoV-2 infection that was not previously appreciated. Moreover, these findings link the pathophysiology of COVID-19 and cystic fibrosis.

      The paper is well written and easy to follow; the list of goals at the end of the Introduction, and the italicized conclusions at the end of each results section were particularly helpful and contributed to overall clarity. Overall, the data support the major conclusion that there is less cell-surface CFTR following Spike protein binding, leading to inflammation. Nonetheless, we felt that the title of the paper is overstated, and western blot experiments should be replicated, and quantified where appropriate.

      Major comments:<br /> The paper title is overstated. Specifically, the paper does not look directly in the “COVID-19 airway”, and the paper does not determine the extent to which CFTR-mediated inflammation contributes to inflammation during infection by SARS-CoV-2. We recommend rewording the title to something like “Inhibition of CFTR signaling by the SARS-CoV-2 Spike protein leads to inflammation”.<br /> Experiments involving western blots should be repeated. Where the differences in protein levels are modest, e.g. TRADD levels in Figure 1, the authors should quantify the band intensity normalized to the control. The authors could either include replicate blots as supplementary data, or quantify all western blot data from replicates, showing variability.

      Additional comments:<br /> It would be helpful if the authors could briefly describe the differences between the original Spike protein and the Beta variant in relation to why the Beta variant binds ACE2 more strongly.<br /> Figure 5 could be moved to the supplement since it reanalyzes data shown in other figures.<br /> The conclusion from Figure 6 is understated: “Thus the possibility of ACE2 being a physical bridge, direct or indirect, between Spike protein and CFTR cannot be excluded.” The data in Figure 6 make a more compelling case for an ACE2-CFTR interaction than the text suggests.<br /> The description of data for ENaC in Figure 8 should be expanded. First, it is not clear from panel A that ENaC gamma cleavage is higher in the presence of Spike protein, rather than overall higher ENaC protein levels with the same degree of cleavage. Second, there is no explanation for the lower band seen in panel B (first lane after the ladder).<br /> What is the significance of the pairs of lanes in Figure 6B? Are these replicates? Different elutions?<br /> What is the “lysate” lane shown in Figure 6B? Please also explain in the figure legend what “NRS” is.<br /> Figure 3 – what concentrations of Spike protein are used in glycoside-treated samples? The legend appears to have an error.<br /> The conclusion from Figure 7 is overstated. It does appear that the levels of CFTR at the cell surface are reduced more than total CFTR levels as a result of adding Spike protein, but the mechanism for this cannot be inferred from these data.<br /> Suggested future experiments:<br /> Test the Beta-1.315 Spike protein variant alongside the original S1S2 Spike protein for experiments shown in Figures 1, 3, and 7.<br /> Measure levels of additional cytokines/chemokines in Figure 1B.

    1. On 2017-11-13 09:13:20, user guillemaud wrote:

      PREPRINT PEER REVIEWED AND RECOMMENDED by PCI EVOL BIOL

      This preprint by Suffert et al. has been peer-reviewed by Benoit Moury and one anonymous referee and recommended by Benoit Moury for Peer Community in Evolutionary Biology. Peer-reviews, decisions, author's replies and the recommendation can be found here: https://evolbiol.peercommun...

    1. On 2022-11-23 12:54:23, user David Roe wrote:

      PING isn't the only algorithm for KIR genotyping. It would be a benefit to everyone if you could compare your results with that from this[1] and maybe other algorithms.

      1. Sakaue S, Hosomichi K, Hirata J, Nakaoka H, Yamazaki K, Yawata M, et al. Decoding the diversity of killer immunoglobulin-like receptors by deep sequencing and a high-resolution imputation method. Cell Genomics. 2022 Mar;2(3):100101.
    1. On 2019-07-26 09:35:13, user Carlos Lopez-Vazquez wrote:

      Interesting work... just wondering how far were the SRT applied in each plant from the minimum SRT required for nitrification? <br /> And how can they relate and influence the maximum growth rates of nitrifiers and their decay rates (e.g. due to predation and other endogenous processes)?

    1. On 2020-04-28 09:15:02, user Me Too wrote:

      Interesting study, lot of work done. Could the authors motivate why they only chose 1 distance, ie 10cm? It would've been great to see several different distances and the likely inverse relation with infection rate. <br /> Eg., would it not be interesting to see that infection would always occur no matter the distance as long as you're in the same room?

    1. On 2022-09-30 22:29:26, user MIT Microbiome Club wrote:

      Figure 2D displays nearly bimodal distribution of effect on focal by pairs of affecting species. Could be nice to explore if this difference is consistently (across RP, BI, CF) due to the same groups of affecting species.

    1. On 2013-11-15 03:16:18, user Animesh Ray wrote:

      Upon a reading of this paper, it remains unclear to me what the 2-state model of water accounts for in the context of protein folding/denaturation, which the continuous distribution (of kinetic energy) of water fails to do. Can the authors come up with a precise and quantitative test that would provide one answer for 2-state water model and another for continuous distribution model? I think that will be the only way to proceed, otherwise the 2-state model becomes just another way the same phenomenon can be explained. On a slightly different note, I seem to recall that the deuterium exchange studies with proteins in the 1960s and early 1970s (mostly by A Pullman and B Pullman in Sweden or their collaborators) had shown that the "cage" model of water around proteins is adequate to explain their exchange data.

    1. On 2018-12-22 17:39:08, user Ferrel Christensen wrote:

      "We assigned gender"--arbitrarily, regardless of the author's actual gender? How could this throw light on the question of whether quality of content rather than some sort of bias affects acceptance for publication? (And only one double-blind journal was included in the test?) The brief description above raises serious questions.

    1. On 2021-08-25 15:49:58, user Julie Hanson Ostrander wrote:

      An Open Access, read-only version of the peer-reviewed article published in Oncogene can be accessed through the following link: https://rdcu.be/cv1jC . The Open Access, published article can also be accessed through the J. Willard Marriott Digital Library at the University of Utah: https://collections.lib.uta...

      Additional disclosures found in the published manuscript.

      This work was supported by NIH grants R01 CA236948 (JHO, CAL), R01 CA229697 (CAL), F32 CA210340 (THT), T32 HL007741 (THT), U54 CA224076 (BEW), R01 CA248158-01 (CODS), and R01 AG069727-01 (CODS). ACS Institutional Research Grant #124166-IRG-58-001-52-IRG5 (JHO), University of Minnesota Masonic Cancer Center (CAL, JHO), the Tickle Family Land Grant Endowed Chair in Breast Cancer Research (CAL), National Center for Advancing Translational Sciences of the NIH Award UL1TR000114 (JHO), and Department of Defense W81XWH14-1-0417 (BEW). We thank Bruce Lindgren for biostatistics support, and the Masonic Cancer Center Biostatistics and Bioinformatics, Analytical Biochemistry, University Imaging Core (UIC), and Flow Cytometry cores. We also thank Zohar Sachs and Michael Franklin for critical reading of this manuscript.

      We disclose that CAL is a Scientific Advisory Board Member for Context Therapeutics, Inc. BEW, EC-S, KPG, and C-HY may receive financial compensation from intellectual property and tangible property licenses managed by the University of Utah. The remaining authors have no COI to disclose.

    1. On 2019-10-24 06:40:02, user Mark Rubin wrote:

      Interesting for us the confirmation that SPOP mutations are depleted in CRPC as compared to Primary Naive PCA. I am also interested in the potential pathology associations. Do you see Neuroendocrine/Small cell cancers in this population...hope RNAseq data comes soon

      Great work

      MarkI

    1. On 2021-09-27 18:29:26, user anna moroni wrote:

      Dear Authors, very interesting results. I noticed that in C-type inactivated Shaker channels, the selectivity filter is impressively similar to that of HCN4 channels in their non-conductive form (Saponaro et al, Mol Cell 2021,DOI:10.1016/j.molcel.2021.05.033). The comparison between WT and W434F mutant in Shaker highlights the large movement of Y445 and D447 sidechains, similar to those of Y482 and R484 observed by comparing conductive and non-conductive HCN4 SF. Further, C-type inactivated Shaker channels show two ion binding sites only and low conductance, two typical features of HCN, as well as reduced selectivity for K over Na (Kiss et al, 1999, DOI:10.1016/S0006-3495(99)77194-8). So, really striking similarities!

    1. On 2019-09-20 22:50:25, user Steve Rozen wrote:

      This is a very important paper. It shows for the first time that AA I causes liver cancer in mice. This is important because many East Asian liver cancers have been been exposed to AA (DOI: 10.1126/scitranslmed.aan6446). In light of Lu and colleagues' mouse study, then, there is very strong evidence that AA contributed to the development of many of these human liver cancers.

    1. On 2020-10-30 20:44:13, user Adrian Flierl ???????? wrote:

      Just came across this and having spent several years working on this, can confirm some of these data, but not all the conclusions.

      When one is talking about ANT4 (germ cells), one has to consider that the liver cells used for this study are transformed into a stem-cell like regenerative state. Hence one would expect to see ANT4 expression (similar to germ/stem cells).<br /> However, this may not be the case in adult, differentiated tissues, and it would be interesting to see if ANT4 is actually expressed in muscle cells aka myocytes or other finally differentiated cell types devoid of ANT1 and ANT2. <br /> Nevertheless, my data in myoblasts certainly support your findings in this study, as is the developmental aspect of ANT4 expression masking effects of ANT1/2 in stem cells.

      Regards,<br /> Adrian

    1. On 2023-09-25 16:32:21, user Leonardo Couto wrote:

      Dear Authors,

      I am Leonardo Couto, a master's student at the Federal University of Minas Gerais (UFMG), located in Belo Horizonte, Minas Gerais, Brazil. I am currently being supervised by Professor Juliane Karine Ishida. Throughout my academic career, I have been studying the interaction between plants and microorganisms, and your preprint titled "Disease Resistance correlates with Core Microbiome Diversity in Cotton" caught my attention. Recently, our research group received an invitation to evaluate preprints in order to gain experience and contribute to the advancement of science. Therefore, I have chosen your work for discussion in one of our meetings. In collaboration with other colleagues, we have brought forth some suggestions to assist in improving your article, and we hope they may prove useful to you.<br /> We noticed a lack of introductory content in his introduction, which would be essential to better understand his study.<br /> (Lines 95-100) - The explanation of cotton leaf roll disease (CLCuD) and its economic implications for the country is provided only at the conclusion of the preprint. We believe these topics could be explored in more depth in your introduction.<br /> We also note that the text is not structured into categories such as introduction, methodology, results, and conclusion. We understand that this may be in line with the journal's guidelines.<br /> In your methodology section (lines 62 - 68), we missed a more detailed description of your samples. For example, we are curious about the distribution of samples among susceptible, partially tolerant, and fully tolerant varieties. Furthermore, it would be useful to know how many samples are attributed to epiphytic leaves, endophytic leaves, rhizosphere, and endophytic roots. We suggest that compiling this data into a supplementary table could be beneficial.<br /> These are some small contributions we offer to help you further improve and refine your work. We hope these suggestions are valuable to you in advancing your research.

      Yours sincerely,

      Leonardo Couto<br /> Master's Student, Federal University of Minas Gerais (UFMG)

    1. On 2025-04-28 09:52:40, user Anne Hoffmann wrote:

      Review of “Feedback and feedforward control are differentially delayed in cerebellar ataxia” by Di Cao, Michael GT Wilkinson, Amy J Bastian, Noah J Cowan

      This review was done as part of the SfN Reviewer Mentor Program (Mentor: Dr. Andre Cravo, Mentee: Dr. Anne Hoffmann, https://www.jneurosci.org/rmp ).

      Summary:

      This study develops a computational model based on system identification to describe cerebellar contributions to feedforward and feedback control pathways as well as to the coordination between these two pathways. By comparing healthy controls and cerebellar ataxia patients in a visual tracking task with perturbations, the authors observed that both control pathways are preserved in patients, but that delays are higher for patients in both feedforward and feedback control loops. Additionally, patients exhibited a reduced feedback gain, which the authors interpret as a compensation strategy applied by patients to respond to the temporal incoordination between feedforward and feedback pathways rather than a consequence of the cerebellar damage itself. Finally, the authors demonstrate that the intact feedforward control mechanism can be leveraged in patients to improve tracking performance by presenting a 500ms preview of the future target trajectory. Although patients overall exhibited smaller performance improvements in the preview condition compared to controls, the authors suggest that the significant improvements seen in patients demonstrate that visual preview may serve as a direction for future therapeutic approaches to alleviate motor coordination problems in cerebellar ataxia patients.

      Major comments:

      • Page 4/Figure 2C: At the bottom of page 4 you cite that the mean (or is it the median?) feedforward time delay was 245ms in the patient group. This does not line up with the mean shown by the two error bars in figure 2C. Could you please check if this is a typo or if not mention which moments are described in the text and the figure (mean/median)? The same question applies to the value 144ms (patients) cited for the feedback pathway on page 6.
      • Page 10 (Section “Target preview improves …”): As shown in figure 5E you mention that there was no significant relationship between ataxia score and error reduction. Based on this non-significant result you suggest that path preview leads to performance improvements independent of ataxia severity. However, have you considered if the absence of significance here might be because of the relatively small sample size of 17 patients? You could consider performing a Bayesian analysis to provide evidence for the absence of an effect? Or maybe have you tried performing a median split of the patient data and compare the means for patients with a high vs. low score? I am asking because figure 5E gives the impression that there is at least a linear trend between error reduction and ataxia score.
      • Page 12 (end of paragraph): how did you conclude that the improvements in smoothness and phase lead where stronger for the control group compared to the patient group? Did you perform a quantitative analysis of the data presented in figure 6D?
      • Page 18, Table 1: please provide a more detailed description of the different diagnoses of your patient group. Do you have any information about the approximate onset of symptoms (age at onset) in these patients?
      • Page 21 & Figure 10: You mention that you compared “various” model candidates for the transfer functions of the feedback and feedforward pathways but in figure 10 you only mention the top two candidates for each pathway. Could you provide more details about the different models that were compared and how you selected which possible transfer functions could describe the feedforward and the feedback controller?
      • Page 24/section 5.6: Could you please provide more details about your statistical analyses. For example, did you test if the assumptions for ANOVA and t-test were satisfied? Which correction method did you apply to compute the degrees of freedom for your unpaired t-tests (DoF are decimal numbers here)? Could you provide F-values, p-values, and effect size measures for all the ANOVA results (for some analyses you only mention p-values)?

      Minor comments:

      • Figure 1D: how does the phase in the lower panel relate to average in figure 2B? Specifically, the range of phase shifts are very different in these two figures and I don’t understand why from the text. Why is there a phase lead shown in figure 1D, while there is a phase lag in figure 2B? Why does the gain increase in figure 1D and decrease in figure 2B? Please explain Figure 1C/D better in the text.
      • Page 4, 7th line from the bottom: You refer to figure 2B but it should be 2C
      • Figure 5 B/C: x-labels are missing
      • Figure 6A/B: For clarity maybe rename the y-axis labels to gain ratio and phase ratio?
      • Figure 8: Could you please specify somewhere in your methods what the total duration of one trial was? This was not clear to me from the various figures in the method and result sections.
      • Page 21/22 (section 5.4) and page 29 (section 6.1): Could you please improve the consistency in your notation of the transfer functions? Specifically, in the method section you use j? to denote the Fourier transform but in figure 10A you use s. Further, you use sometimes k, sometimes a, and sometimes b to refer to the gain and the pole of the filters.
      • This might be field/journal specific, but I would find it easier to follow as a reader if the statistical results would be added to the main text of the results instead of the figure legends
    1. On 2018-12-29 22:18:36, user Alex Zhavoronkov wrote:

      We are looking for collaborators with the WS microbiome sequencing data, annotated with age, sex and disease/lifestyle choice/drug, etc. to test the clock. Please forward this paper to your friends interested in the microbiome. We are interested in testing the clocks in a variety of applications.

      Constructive criticism, comments, and edits are very welcome. This paper needs to be polished.

    1. On 2017-04-12 17:34:48, user Ludmila Prokunina-Olsson wrote:

      Yep, the ref 4 was provided for the very generic statement "This mutational pattern is predominant pattern in bladder cancer and is also frequently found in breast, cervical, head and neck, and lung cancer". But not for the findings actually reported in the paper and now rediscovered by you :).

    1. On 2015-06-03 15:06:20, user Daniel S. Standage wrote:

      I caught a couple of minor typos in section IV: 2nd paragraph, "The rational" should be "The rationale"; 4th paragraph, "casual <br /> relationships" should be "causal <br /> relationships". I saw a couple of others, but couldn't find them again. :(

      Overall, excellent insight into current and impending challenges. I've discussed (or heard discussed) bits and pieces of what's in here before, but never have I seen this material been so completely, clearly, and concisely presented. Great work!

    1. On 2025-03-14 21:15:12, user Rajendra K C wrote:

      We discussed this paper in our RNA journal club. We found the discovery of this novel splicing mechanism quite interesting, which seems like a last-resort strategy for cells to remove transposons that have already integrated into exons while still retaining some level of functional protein. The mutagenesis experiment to identify SOS splicing factors was particularly interesting. One question that came up in our discussion: What happens to the excised transposon post-SOS splicing? Is there any evidence that it gets degraded or reintegrated elsewhere in the genome?

    1. On 2022-11-03 05:01:46, user Anubhav Prakash wrote:

      Dear Author, <br /> Congratulations for this very Interesting paper. I want to further understand two things<br /> 1. Does the down regulation of sox2 expression in the segregating patch, also triggers the expression of some different kind of adhesion molecule to facilitate the segregation? <br /> 2. Probably a little tangential to the paper, does the size of segregating sensory patches are similar in different individuals ? If it is similar, then how do u think that might be regulated. Can also throught as how the segregating patches being (Sox 2 down regulation/ lmx1 expression) positioned in the common sensory regions?

      This paper is very informative. Thank you very much.

      Anubhav Prakash <br /> Graduate student, NCBS (India)

    1. On 2023-06-30 09:31:40, user Chanel Thomas wrote:

      Dear authors<br /> We read your paper as part of our Genomes Journal Club at the Forestry and Agricultural Biotechnology Institute (FABI) at the University of Pretoria. We’d like to share a few of our thoughts and comments with you.

      We thought this was a really wonderful paper. One of the things that we found really exciting and novel was that you were not able to identify one characteristic that confers pathogenicity to banana (in contrast to e.g. tomato pathogenicity that can be traced back to chromosome 14 of Fol4287). This suggests either that a unifying characteristic does not exist for banana pathogens or that it is perhaps broken up into different components of the disease - e.g. a specific thing that causes browning, another that causes softening, etc which may be controlled by different genes, potentially harboured within different ARs. We thought that this was a message that you could state more clearly in the paper and in your abstract.

      Given the title of your manuscript, we interpreted that the message you chose as your selling point for the paper was the issue of segmental duplications and their role in evolution. We felt that we lacked the necessary background information to understand the significance of this. It would be really helpful to have some information on gene duplications in the introduction. From Fig. 6e, we could tell that segmental duplications were something unique to Fol4827, but it was unclear on how significant this is as opposed to having another type of duplication. For example: why are segmental duplications particularly important in evolution and/or why was it a surprise that they were involved. Fig. 6a gave a good explanation of the basic differences between the duplication types but (1) we felt that information would be useful earlier in the paper and (2) a written explanation would complement the figure nicely.

      Another query that came up was related to the RNA data from 8 days post inoculation. It is not clear in the methods which strains were used to infect the Cavendish bananas or which strains were inoculated onto the PDA medium prior to RNA extraction. Given that different races are distinguished by their pathogenicity on different subsets of banana varieties, we wondered if there are any implications for the RNA-seq results? I.e. if strains were used that are not usually found infecting the type of Cavendish used for inoculation in this study, would the RNA expression results possibly be impacted? <br /> The figures in the paper were informative and conveyed the data well. However some of the multi-panel figures were not always intuitive to read. For example, Figure 6 c,d and e are intended to be read together, which you can figure out by reading the legend, but a visual cue such as some background shading or a box would improve the readability. Similarly, the background shading in Fig. 1 that links panel d to the earlier panels wasn’t noticed by most of us (only 1 member picked it up) so it may be worth making that clearer.<br /> Overall we found this to be a really well executed study and we all thoroughly enjoyed reading and discussing it.

    1. On 2016-01-17 18:14:02, user Jesse Bloom wrote:

      This paper does a great job of putting together a dataset of curated ddG / dTm values for testing structure-based predictions.

      However, I don't think the conclusions about the distribution of stability changes are justified. The mutations here are pulled from the literature from a variety of studies, each of which only examined a small fraction of the many possible mutations that could be made to a protein. In general, these mutations were NOT chosen randomly, but were rather selected by the investigator because the mutation was of enough interest to make it worth careful characterization. Therefore, the distribution of stability effects (for instance, the dTm values shown in Figure 2 or Figure 4) reflects the aggregation of the personalized choice of many researchers about which mutations to study. These distributions are almost certainly different than the distribution of stability effects that would be observed if you instead systematically made all point mutations to any given protein.

      For instance, I would strongly suspect that if you made all mutations to any given protein, the distribution in Figure 2 would have many fewer stabilizing mutations. The reason that there are so many stabilizing mutations is almost certainly because there is an investigator bias towards characterizing stabilizing mutations.

    1. On 2021-07-22 08:24:50, user Alizée Malnoë wrote:

      Nawrocki et al. with this manuscript make an important step forward towards understanding the molecular origin of nonphotochemical quenching (NPQ) qI using the microalga Chlamydomonas as their study system. Indeed upon high light stress, chlorophyll fluorescence quenching is observed attributed to PSII photoinactivation, and this work demonstrates that it stems from PSII reaction center and that degradation of D1 by FtsH is required to relax to an unquenched state. The authors further show that qI formation is more rapid in presence of oxygen but independent of PSII activity (in DCMU) and propose that qI is due to oxidative modification to chlorophyll molecules of PSII reaction center (RC). Accordingly a minimal model was built with qI-ON RC and qI-OFF FtsH-processed/broken RC which successfully fits the experimental data indicating that photoinactivation mechanisms at donor and acceptor sides co-occur.

      Here are some suggestions/comments. Looking forward to discussion!

      The first point pertains to semantics, I would suggest using the word “photoinactivation” of PSII instead of “photoinhibition” as much as possible. I was convinced of this idea by Barbara Demmig-Adams when I worked on a review about qH, reserving the term photoinhibition for decrease in CO2 fixation. We proposed a possible new definition for qI that would be quenching due to photoinactivation of D1 rather than due to photoinhibition, as qZ and qH are also photoinhibitory (in that they decrease CO2 fixation). Take a look here, see intro, section 1.1.2. and 4.: https://doi.org/10.1016/j.envexpbot.2018.05.005.

      Title: I’d suggest a more descriptive title of findings stating where qI stems from, e.g. with oxygen sensitization of D1 as the origin of qI. As is, one could understand the title as qI doesn’t exist (dogma rose and fell kind of idea) but you mean instead molecular origin/mechanism of qI induction and relaxation, right?

      Throughout text, I’d suggest to use the word “relaxation” instead of “quenching loss”. The word loss is used elsewhere to describe fluorescence decrease i.e. quenching and it can be confusing to have the word “loss” used for both quenching induction and relaxation (e.g. line 94 loss= relaxation; line 96 loss= decrease).

      I’d also suggest for clarity to use “new synthesis” instead of “repair” (e.g. line 101), as repair encompasses both degradation and new synthesis, it seems confusing to read that qI relaxation is independent of repair but relies on degradation.<br /> e.g. Figure 2 title becomes: qI is transient and its relaxation is independent of new D1 synthesis<br /> and Figure 3 titles becomes: qI relaxation is due to PSII core proteolysis by FtsH

      Also for clarity, in title of Figure 1 add - is a quenching “due to energy dissipation”-<br /> And line 452 - Using quenching -add “of Fm” - might be beneficial for (f)uture studies

      Line 202 FtsH-mediated (name of protein uppercase here; not mutant italic lowercase)<br /> Line 211 side -> sites

      There’s a lot of crucially ;-)

      To go further in the discussion, here are some points that could be interesting to raise:<br /> - Addition of lincomycin blocks synthesis of all chloroplast-encoded proteins, impact on qI formation/relaxation.<br /> - qI relaxation in presence of nuclear gene synthesis inhibitor.<br /> - Slower relaxation of qI in the dark compared to low light (at least after 30min HL).<br /> - qI transient: explanation for differences between strains (e.g. 1009 vs. 124).<br /> - ftsh complemented line (in ftsh1-1), comment whether less qI compared to control because more repair enabled; due to higher level of FtsH in that complemented line compared to one in ftsh1-3?<br /> - formation of qI site precedes cleavage (line 242) would need deg mutant to definitely say that. Might be better to say that it precedes D1 degradation (at timepoint 0 there are some D1 fragments, so there has been cleavage already before HL starts).<br /> - damage at acceptor side triggers cleavage in the lumen? (1995 Plant Phys D1 qI https://www.jstor.org/stable/4276408. After HL stress, decrease in D1 but DCMU-binding sites 2x higher vs. D1 detection by antibody; propose preferential cleavage in the lumen).<br /> - if oxygen sensitization proceeds by PSII charge recombination (line 404), then should qI be enhanced in DCMU? Compare with hydroxylamine (HA)+DCMU to test it.

      • These publications about Arabidopsis mutants seem consistent with your findings, what do you think (although hard to say for sure without Fm quenching data):<br /> 2014 Plant J Kato & Sakamoto, https://onlinelibrary.wiley.com/doi/full/10.1111/tpj.12562<br /> Fig5b,c: similar levels of D1 as control (fug1) in fug1 var2 stn8 (~ equivalent Chlamy would be ftsh stl1) but with lower Fv/Fm so D1 is removed in fug1 var2 stn8 but would be quenching more than in fug1 due to more ROS in stn8 background; fug1 var2 has most damaged D1 so most quencher (OR alternative hypothesis phosphorylated RC are quenchers?)

      2011 Plant Cell LQY1, www.plantcell.org/cgi/doi/10.1105/tpc.111.085456 <br /> 2014 Plant Cell HHL1 LQY1, www.plantcell.org/cgi/doi/10.1105/tpc.113.122424 <br /> lqy1, hhl1 have faster rate of degradation and low Fv/Fm after HL due to higher Fo but Fm stays the same; that would be consistent with faster degradation, less quenching of Fm.

      2017 PNAS MPH2 www.pnas.org/cgi/doi/10.1073/pnas.1712206114 <br /> Impaired degradation of D1, low Fv/Fm, more quenching of Fm.

      Schematic model: the different shades of grey in the wheel do not represent light/dark, correct? maybe would be clearer to make it according to light treatment. The dash-line is not described: is it the alternative hypothesis to cleavage, that full degradation is required to relax qI? I like the purple scribble on Nter of D1 to signify ‘hey, degrade me!” ;-)

      Alizée Malnoë (Umeå University) – not prompted by a journal; I’m an assistant professor, my research group investigates the molecular mechanisms of plant photoprotection. Catherine de Vitry was my PhD studies advisor.

    1. On 2016-08-15 14:54:21, user Maikel Peppelenbosch wrote:

      I am Maikel Peppelenbosch, the apparent corresponding author. I posted this comment earlier, but it seems not to have been logged. This manuscript was not approved by me or most of the other authors and may be premature. Hence I would urge the scientific community to ignore this version.

    1. On 2016-07-31 15:32:39, user David Suter wrote:

      Here the description from the 2003 Pallier et al. study:

      "As early as 1 min after paraformaldehyde addition, HMGB1- and HMGB2-EGFP mostly diffused away from chromosomes of mitotic cells. This resulted in the absence of any detectable signal on the chromosomes after a 10-min incubation (Figure 8)."

      A mechanistic model was also proposed:

      "Rather, PFA or FA may alter the accessibility of HMGB to their target(s) by modifying the overall structure of the mitotic chromosomes, and/or inactivate the free form of HMGB1/2. HMGBs contain ?40 lysine residues, and a lot of these are expected to interact directly with the charged phosphate backbone of DNA. PFA reacts with the amino group of lysines, and the reaction product is a Schiff base. This is still charged, but both hydrogen bonding and van der Waals contacts of the lysine residue will be disrupted."

      Also as illustrated in Fig.4 of Kumar et al. 2008 cited above, neither formaldehyde nor methanol (both tested in the present study) seem to work for the mitotically-bound TF they looked at …but methanol/acetic acid does (not tested in the present study) - these earlier studies were not cited.

      Thus I am not totally clear about the extent of the novelty of the findings reported here - could the authors of the present study comment on this ? Is there any fundamental difference I missed between what was shown here as compared to earlier work ?

    1. On 2020-06-02 11:38:58, user Tobias Broger wrote:

      Dear Study team. Thanks for writing this up. The study is well-conducted and interesting. <br /> I have a question/comment: did you test antibody response against other antigens? I believe it is a major limitation of the assay you used that it only detects antibodies against S1 as there appear to be a number of patients including with mild disease that mount an immune-response against S2 or N proteins that the assay from this study would miss. I think this should be discussed (and I suggest you test your serum samples with 1-2 alternative assays that cover several virus proteins, which is quick). This could change your conclusion quite a bit. Happy to chat on the phone and provide some more background if there is interest. Tobias Broger.

    1. On 2023-02-24 02:32:18, user markyz wrote:

      Hello I like your article anything that makes data analysis more straightforward is going to be useful to many! In line with PMID:36750393 and the literature cited there, the tool should accept a background list, otherwise the enrichment test results could be invalid. Moreover the enrichment p-values should be corrected with FDR or similar, otherwise there could be many false positive results.

    1. On 2019-10-22 09:28:17, user Rebecca Gladstone wrote:

      Really nice, I had a brief stab at this a while back when exploring GPS, you've done a much better job, great to see GPS data in use! We were interested to see serotype 1 and 38 rank so highly as we didn't see this in GPS. Geek that I am I just mined pubMLST and saw serotypes 1 and 38 in four different CCs (SLVs), the minor serotype was always n=1 observation. I know this is a problem with bias in pubMLST and people only submitting the first occurrence, but it does make it difficult to rule out mistyping. I'd be interested to see which of the other serotype pairs were both observed multiple times within lineages as an extra layer of confidence in their co-occurrence?

    1. On 2017-01-03 19:41:50, user Graham Coop wrote:

      I note that I have not sent this paper to any journal, nor do I plan to. If you want to cite the paper, please cite the preprint. If you have comments leave them here, or shoot me an email. I will revise the preprint as needed.

    1. On 2025-11-10 16:12:23, user Ben Auxier wrote:

      The work presented in Tan et al. is provocative, suggesting that during asexual spore dispersal Neurospora crassa segregates its chromosomes across multiple nuclei, instead of the nuclei being mitotic copies of each other as previously assumed. While similar to the results that some of these authors have presented in Botrytis and Sclerotinia, showing this in a genetic model organism would provide the genetic tools to dissect this phenomenon.

      However, the results presented here lack definitive proof. The evidence presented can be summarised as follows:

      1) relatively low measured DNA per spore, based on DAPI fluorescence, compared to yeast

      2) chromosome specific probes show patchy distribution across nuclei, compared to probes that target all chromosomes.

      The first line of evidence suffers from an apples-to-oranges comparison, because the comparison is across species. The compound DAPI binds to the AT regions of DNA, and so differences between species in both AT% as well as those that affect general fluorescence, will influence this measurement. For instance, yeast has 62% AT genome, while Neurospora has 46%. Highlighting the futility of such comparisons, the data in Figure 1C shows clearly that while N. crassa has a haploid genome that is 4 times as large as yeast (compare 1st and 3rd columns), the fluorescence signal is equal (compare 6th and 4th column). Even assuming the authors’ hypothesis, there is still "too little" fluorescence given the genome size of N. crassa. Clearly, while within a species there is a strong correlation between genome size and fluorescence, when compared across species such correlation disappears.<br /> The second evidence is from fluorescent hybridisation. Here the authors show that a FISH probe specific to chromosome 1 is never found in more than one nuclei, while a telomere probe that targets all chromosomes is more often found in multiple nuclei. The main issue is that the claims rely on negative evidence, that is to say the absence of signal. However, the absence of signal is not a strong signal of absence. FISH is a very sensitive process, and differences in probe design and washing steps can greatly affect the process. Highlighting this, in 14% of spores, the authors' own chromosome 1 specific probe does not bind to either nucleus. If this data was to be taken at face value, this would indicate 14% of spores lack Chr1, which would be inviable. Extrapolating across all chromosomes, we would only expect 36% of spores to have all chromosomes and be viable ((1-0.14)^7). Such low viability is inconsistent with observed spore viability of N. crassa, which generally exceeds 95%. An alternative explanation is that the probe binding is not very efficient. This is supported by the telomere probe, where it remained undetected in 10% of conidia , despite this probe targeting 14 different chromosomal regions! Again, taken at face value this would indicate that a significant fraction of spore nuclei lack chromosomes entirely. Notably, only 64% of spores had all nuclei with signal from this telomere probe.

      Aside from inconclusive data, the claims here are also inconsistent with the basic empirical genetics of this fungus. It has been known for decades that when one wishes to isolate loss-of-function mutants in N. crassa, like the auxotrophic mutants used to demonstrate the "one gene - one enzyme" principle, regular macroconidia do not work. Instead, microconidia prove useful, allowing for the easy isolation of such mutants (Catcheside, 1954). This provides powerful evidence that macroconidia have redundant nuclei, which compensate for loss-of-function mutations (See Gross and Lester 1958 for further discussion). One only needs to read the experimental methods of Beadle and Tatum to see the effort in isolating auxotrophic mutants in this organism. Instead of simply mutagenizing conidia to obtain auxotrophs, they needed to use the extremely labour intensive purification process of individually crossing mutagenized conidia to a wild-type background, to be able to isolate homokaryotic ascospores (Beadle and Tatum; 1954). This is also true of genetic transformations, which are performed on macroconidia. Transformants always need to be purified, as the original colony that grows on selective medium generally contains a mix of transformed and untransformed nuclei. If the macroconidia would contain a single haploid genome, divided over multiple nuclei, as suggested by Tan et al, there would be no need to purify induced mutations.

      Further, the claims here are also inconsistent with the evolutionary dynamics of this fungus. This fungus is the foundation of the studies in heterokaryosis, the presence of multiple distinct genotypes within a single mycelia. Arising from either de novo mutations, or from fusion of two mycelial hyphae, such heterokaryons in Neurospora are quite stable. It is well-described that conidia can be heterokaryotic, meaning a single conidium inherits two distinct genotypes. This has been studied in detail for the soft mutation, where up to 40% of conidia are heterokaryotic, containing both the wild type and mutant alleles of the soft gene (Figure 2A; Grum-Grizmaylo et al. 2021). This is not due to specific dynamics of the soft mutation, as similar ratios of homokaryotic and heterokaryotic macroconidia have been observed in auxotrophs (Atwood and Mukai, 1955), which were used to form the current model of random segregation into multiple mitotic nuclei in Neurospora. A recent example of such evidence has been shown by Mela and Glass, who inserted either green or red fluorescence into the his3 locus in different genotypes, and using fluorescent microscopy they readily recover ±40% of conidia with both colors (Figure 1f; Mela and Glass, 2023).

      The claims made by Tan et al. are strong and in my opinion the evidence does not rise to the level needed. Measures like fluorescence intensity or hybridisation can never be definitive and therefore are at most a start of further experimentation. However, the predictions of incomplete chromosome sets per nuclei can be definitively tested through single nucleus sequencing. The technology has advanced to the level that single nuclei can be reliably processed for whole genome sequencing, which is the most reliable way to determine if these claims ultimately reflect reality.

      References:<br /> Beadle G.W. & Tatum E.L. 1945. American Journal of Botany. https://doi.org/10.2307/2437625 <br /> Mela A.P. & Glass N.L. 2023. Genetics. https://doi.org/10.1093/genetics/iyad112 <br /> Grum-Grzhimaylo, et al. 2021. Nature Communications. https://doi.org/10.1038/s41467-021-21050-5 <br /> Lester H.E. & Gross S.R. 1959. Science. https://doi.org/10.1126/science.129.3348.572 <br /> Catcheside D.G. 1954. Microbiology. https://doi.org/10.1099/00221287-11-1-34 <br /> Atwood K.C. & Mukai F. Genetics. https://doi.org/10.1093/genetics/40.4.438

    1. On 2017-02-03 19:45:16, user anon reviewer wrote:

      Thank you for your response. Unfortunately, size-exclusion chromatography is susceptible to contaminants of highly variable sizes (for example, non-specific protein-protein interactions can cause contaminants to co-elute with the protein of interest). This is clearly evidenced on the gel shown in Supplemental Figure 1b. There are visible bands toward the bottom of the gel, indicating that purified NgAgo contains smaller contaminants following SEC. These are most obvious for D704A and D863A, but can be seen in all lanes if the contrast is adjusted for the image. Therefore, I do not think you can rule out the possibility that this purification procedure eliminates RNase H contaminants.

      The reported activity differs from previously characterized catalytic Agos in several ways: 1. No 5'-end preference for guide strands; 2. Aspartate residues that align with catalytic DEDX tetrad of TtAgo are not required for observed activity; 3. RNA target is cleaved in multiple locations rather than at a single site. It is certainly possible that NgAgo activity is highly non-canonical, but there is an alternative explanation that has not been sufficiently ruled out. The manuscript would greatly benefit if you included more controls demonstrating that the activity is not due to a contaminant. The experiments that you mention (alternate purification procedure, side-by-side comparison with RNase H activity) are a good start, and I encourage you to add these data to the manuscript. The most convincing evidence would be to identify a catalytically dead mutant, although that will of course be challenging if NgAgo contains a non-canonical active site.

    1. On 2022-02-14 13:24:42, user Jessica Polka wrote:

      The following feedback has been provided by members of the ASAPbio Preprint Reviewer Recruitment Network

      Summary: Romero-Becerra et al. show in this work the consequences of the loss of the p38 kinase activator MKK6 in mice. Their data presented in this manuscript include a reduction in lifespan and a cardiac phenotype that starts in young mice and is characterized by cardiac hypertrophy that ends up in cardiac dilatation and fibrosis. The cardiac phenotype is also present in two cardiac-specific MKK6 KO models, which is consistent with an important role of MKK6 in the heart. Importantly, they present mechanistic data to propose a model in which MKK6 deficiency leads to hyperphosphorylation of MKK3-p38?/? and increased mTOR signaling, a well-known cause of cardiac hypertrophy. The paper is well structured, the data is clearly explained and the results are relevant in the sense that they identify a novel pathway in the development of cardiac hypertrophy. After reviewing this preprint, these are our questions and comments to the authors that we think may help to improve this manuscript.

      Major points:

      The image that is represented in Fig. 2D may not be the best to represent the size effect observed for cardiac fibrosis. Most of the mice seem to show a similar degree of fibrosis. Also these mice are 23-24 month-old, long after the first mice start to die due to cardiac dysfunction, so this specific population of Mkk6 KO mice may show some kind of resistance to develop cardiac dysfunction when compared to others that died before. If cardiac dysfunction is the cause of the death, it should be present earlier in life. Thus, measurement of cardiac fibrosis at an earlier time point would be important to sustain authors’ claims.

      The investigation of the two cardiac-specific KO is very relevant for the conclusions of this study. It would be important to know whether those cardiac-specific KO develop similar phenotype compared to that developed by the global KO, besides the cardiac hypertrophy already described.

      There is no mention of the n of animals used for the data shown in Fig. 5. For example, Fig. 1A has panels with n=2 and n=1, whereas Fig. 1B and Fig. 1C has n=4. This should be better described, and mentioned in figure legend.

      Minor points:

      Data shown on Fig.1 re MKK6 KO animals is shown at different ages. It would be beneficial for the message of the paper to clarify the progression of symptoms in these mice. For example, Fig. 1C shows phenotype at 20 months of age, but then kyphosis is shown on Fig.1D at 19 weeks of age. It could be a typo but anyway paper would improve by clarifying this aspect.

      Fig. 1D does not have a scale bar but taking into account that MKK6 KO animals are smaller, if images have been enlarged to show better the kyphosis, a scale bar is needed. Authors may discuss this possibility if they do not have the data available.

      Authors claim that MKK6 KO mice have decreased BW due to increased browning of the adipose tissue and increased energy expenditure. However, it seems they have not considered the possibility that they have a reduced food intake, which could be the case due to the ataxia present in these animals.

      On Fig. 2, the observed size effect on different cardiac parameters may be better visualized if authors include the baseline value 0 for each parameter measured.

      Scale bar units in Fig. 2D does not match with what is stated in Fig legends.

      After the mention of puromycin, the source of the remaining antibodies is not mentioned.

      Why did the authors use a global p38gamma but a MCK-driven p38delta-specific KO to study the role of these 2 kinases in the phenotype of MKK6 KO mice? We found no reasoning behind that explained in the manuscript and doing so would help the readership of this paper.

    1. On 2019-10-25 17:36:12, user Oh wrote:

      Hi, I thought your research was very interesting, so I would like to share some of my thoughts.

      In order to make a clearer connection between nuclear mechanotransduction, I think it is worth studying what other types of mechanical stress/forces (i.e. tension, compression, shear, static vs dynamic, ECM stiffness) are transduced through Vrkl/BAF pathway to regulate the downstream effectors. Also, is it just the disruption of myonucleus-sarcomere connections? To what extent do the myonuclei have to be detached to observe the changes in the BAF nuclear membrane accumulation? What happens when myonucleus-sarcomere connections are fully detached. When the nuclear membrane ruptures, does the BAF localization changes? The paper mentions that BAF is associated with repair of mechanically induced membrane rupture. When the nuclear membrane ruptures, does the BAF localization in the nuclear membrane changes? Also, the LINC complex plays a central role when cells migrate in 3D (2). Does the BAF expression change during migration? One study used a 3D collagen matrix and studied nesprin-3 activity in migrating fibroblast (2). Another study applied mechanical stress by using magnetic tweezer to analyze the consequence of nuclear strain on the LINC complex related gene expressions (1). Besides disrupting connection between myonuclei and sarcomeres through D-Titin knockdown to mimic mechanical stress, applying mechanical stress by magnetic tweezer or by exposing to different levels of ECM stiffness to induce changes in the BAF accumulation in the nucleus can possibly strengthen the data.

      Does this mechanotransduction pathway via Vrkl/BAF also play a central role in other cell types besides muscle cells? What about immune cells, metastasizing cancer cells, fibroblasts, and other migrating cell types that are regularly exposed to different mechanical forces?

      The Baf RNAi efficiency is confirmed with qPCR, but can the efficiency of the gene knock downs of the others (i.e., D-Titin/sls RNAi in Figure 3 or Vrik1/ball RNAi in Figure 6) be also verified with qPCR or western blot?

      For the experiment that showed that the Vrik1/Ball BAF kinase is required for the BAF localization at the nuclear membrane, is the Vrik1/Ball sufficient for BAF localization? Does adding Vrik1/Ball in muscles expressing Vrk1/ball RNAi recovers BAF localization near the nuclear membrane?

      The increase in the DNA content in the myonuclei in D-Titin/sls RNAi was quantified by DAPI fluorescence in IF, but can it possibly be verified with FACS to see changes in the cell cycle and to observe an increase in DNA content induced by elevated endoreplication with reduced BAF levels at the nuclear membrane. I think this will help confirm the observation that reduced BAF levels at the nuclear membrane is associated with endoreplication.

    1. On 2017-05-01 18:38:15, user Paul Carini wrote:

      The main broad conclusion of this work (“relic DNA contributes minimally to the characterization of microbial community structure”) is not well-supported by the data presented.

      First, the efficiency of relic DNA removal with the DNAse-based approach used in this manuscript does not appear to have been tested empirically. That is, there were no experiments reported to conclusively validate whether the DNase treatment effectively removed extracellular DNA or DNA from dead cells. The lack of such experimental controls makes it difficult to interpret their results, as there is extensive literature showing that DNA bound to soil/sediment components (minerals or organic matter) can be highly resistant to DNAse digestion (e.g. Romanowski, et al., 1991, Paget, et al.,1992, Cai, et al., 2006, Khanna and Stotzky, 1992, Lorenz and Wackernagel 1987, Nielsen et al., 2006). If DNAse treatment is not effectively removing relic DNA pools, it would reduce the apparent amounts of relic DNA and reduce the apparent influence of relic DNA on estimates of community structure.

      Second, the authors analyzed 6 unique samples from each ecosystem type (e.g. 6 unique soil samples and 6 unique lake water samples, etc.). They then assessed whether there were consistent increases or decreases in richness across all 6 samples from a given ecosystem. The problem with this approach is that this effectively obscures any effects that removal of relic DNA might have on estimated richness within individual samples. This is important to account for as we know from our work (Carini et al. 2016. Nature Micro.) that in 20% of our soil samples, removal of relic DNA had no significant effect on richness estimates. Thus, by running the analyses across all 6 samples combined, instead of quantifying relic DNA effects on a per-sample basis, it likely obscures any effects of relic DNA. This is apparent from their Figure 3, where there appears to be a subset of samples where removal of relic DNA reduced richness as the mean richness ratio is >1 for three of the sample types. Thus, instead of concluding that “relic DNA contributes minimally to estimates of microbial diversity”, it would be more accurate if the authors had concluded that the effects of relic DNA are variable and relic DNA does not always introduce biases.

      Just to be clear: we do not advocate that relic DNA is always important to consider when conducting DNA-based analyses of environmental samples. As we detailed previously (Carini et al. 2016), relic DNA is unlikely to obscure the ability to say that two distinct communities are indeed distinct with respect to both richness and community composition. Likewise, relic DNA effects are not going to be equally important across all samples from a given ecosystem type. Depending on the temporal variability, community turnover rates and the residence time of relic DNA, failure to remove relic DNA could have no effects or it could introduce significant biases. We still maintain that there are common situations where failure to remove relic DNA could obscure patterns in community structure (e.g. trying to detect temporal changes in communities or the short-term effects of environmental perturbations on microbial communities). While removal of relic DNA can complicate laboratory analyses and the presence of relic DNA can alter how we interpret our estimates of microbial community structure, the data presented here do not provide enough evidence to justify ignoring the potential importance of relic DNA.

    1. On 2022-04-02 16:22:59, user Aditya Singh wrote:

      Manuscript reviewed for Reviewer Mentor Program, SfN working with Mike X. Cohen as my mentor in Fall 2021:

      In this study, authors used high-density probes to perform large-scale neural recordings for identifying and characterizing the coordinated activity across MEC grid-cell modules in the presence and absence of visual cues. Authors aim to show that the inter-module activity in MEC is coordinated even in visual-sensory deprived conditions. The study is well-designed to test the hypothesis that local networks within MEC can calibrate the inter-module activity even in absence of visual feedback of animals' spatial location. With the cutting edge experimental and statistical tools, this study provides novel insights into the role of MEC activity in maintaining a coherent cognitive map.

      There are a few concerns about module classification, and application and validity of dimensionality reduction technique.

      Major concerns:

      The exact definition of the term "module" is unclear, and a more precise definition will help readers understand the impact of the findings. For example, is a module defined by a specific volume of tissue, or number of cells in a volume, or a number of cells within a network showing toroidal topology manifolds, or functional connectivity?

      Relatedly, what are the estimated physical dimensions of the modules in EC? Are different cell types part of each module across layers?

      How different are the probabilities that two cells with correlated activity could belong either to the same or different module (line 9-13 on page 5)?<br /> It is an interesting observation that grid cell modules retain the coordination even when mapping between grid cell activity and position deteriorates (line 23-24 on page 5). What does the coordination in such a case reflect, if not position? Are there any scenarios where position decoding is possible even when grid cell modules do not retain coordination?<br /> Are there any sessions when light recording is followed by dark session to compare the change in grid cell coordination across light-then-dark sessions? (line 37 on page 5)<br /> In line 37 on page 5, light recording sessions have a broader duration range of 30-60min. Is it possible that the difference in the duration of dark vs light sessions led to a systematic bias? It may be helpful for readers to compare the ratio of time in dark vs. light for all the individual animals or comparing the total time for each animal in dark (all dark session durations combined) and light (all light session durations combined).<br /> Similarly to prevent the doubts about systematic bias in spiking data (line 1 on page 6), it would be helpful to compare the ratio of spikes in light vs dark for each of the 842 grid cells identified in the experiments. Including the information about temporal distribution of spikes would also be helpful to address whether all the grid cells were active for full session or they varied their activity within different epochs of a session i.e were all 500+ spikes spread homogeneously over full session?<br /> McInnes et al mentions that UMAP is more reliable for large datasets and UMAP assumes there is manifold structure in the data. Considering this along with the fact that the number of datapoints processed through UMAP in this study are very close to the lower limit for UMAP, are there other alternate dimensionality reduction techniques (e.g. PHATE which may be more suitable for the size of datasets for this study) that lead to similar conclusions in this study? Could authors quantify the false positives that could occur due to limitations of UMAP in clustering of grid cells into different modules? <br /> Also, could authors demonstrate the reliability of module classification e.g. through cross-validation or split-half reliability?<br /> According to Roy et al 2000 (Neural Computation), low firing rates (<10spikes/s) often lead to spurious correlations. Hence, it may be helpful to compare the likelihood for actual, not down-sampled, mean-firing rate between light and dark. Also, considering the issue with correlations for low-rate spike trains, are there any alternative methods to best estimate inter-module coordination other than pairwise correlations? <br /> What are the approximate dimensions of the brain region around the tip of a tetrode vs a recording site on the neuropixel probes used in this study? If they are considerably different, should tetrode data have comparable statistical interdependence across modules as we expect for neuropixel probes? If not, it brings to question the validity of applying the statistical methods used for tetrode data to neuropixel data.<br /> Figure 2 and Supplementary Fig.2 - module classification and clustering issue: Supp Fig 2 - Grid M3 in ‘a’ and M2 in ‘c’ appear too close to the respective non-grid clusters, do the results hold if such grids are excluded from the data?<br /> Minor concerns:

      Introduction - Line 6-9: “the recurrent ...sensory inputs.” - a reference for this statement would be helpful.

      It may be useful to mention in discussion about which spatial encoding mechanism would render grid-cell coordination sustain in darkness? Odor cues and Path integration? (Fischler-Ruiz et al 2021 Cell), Step-count? Proprioception?

      Page 4 – line 1-2: “The grid cells within a module encode together a two dimensional quantity which, in some conditions – could be dissociated from the true position of the animal” It is not clear why authors mention this fact. It may be helpful to relate this to the findings.

      Page 4 - Line 14-16 – “Since population activity of an individual module lies on a two-dimensional manifold, the joint activity of M modules spans, at least in principle, a 2M dimensional space” Reference for Mathematical principle on which this statement is based.? References for this statement supporting whether the joint activity of M modules spans at least 2M dimensional space or different?

      Please add references for the statement in Line 8-9 on page 5.<br /> Line 16-18 – “However, during continuous motion…..the state of each module is faithfully mapped to the location of the animal in two dimensional space”. What is the definition of continuous motion (3cm/s?)? How consistent is it across the studies on spatial encoding of Ent cortex.? If there is tactile/olfactory feedback in the dark, would the modules represent increased accuracy of encoding spatial location?<br /> Line 26: For clarity, ‘rate adjusted likelihood’ could be referred to in the manuscript as ‘likelihoodRA’ instead of just ‘likelihood’.<br /> Page 15 – Methods: Open field foraging task in light: Were the odor-cues (excerements) removed from the arena during light-on recording session?<br /> Page 17 – Methods: Rate map analysis- What is the rationale behind using the bin size of (1/120)s for spike trains and tracking data? Isn’t this bin size too small?<br /> Page 18 – Methods: Markov decoder- How is the normalizing factor Z(t) defined?

    1. On 2023-06-09 21:42:09, user Lonki wrote:

      Could the authors please clarify the parameters by which they determined mice to have “pronounced pathology” and hence, inclusion in the data set? Could you please expand on the rational for selecting this subgroup for analysis?

      What happens when you test for correlation between changes in Iba+ and Neun+ counts with the weight loss observed at day 4-5 for individual mice?

    1. On 2019-06-27 20:29:47, user George Davey Smith wrote:

      This is a really interesting paper, with lots of good things, including a clear discussion of the latent causal variable approach and its relationship with that. A few comments:<br /> (1) A new term, "correlated pleiotropy" is introduced (see figure 1), which is covered by the widely used term in Mendelian randomization of "vertical pleiotropy", but it is vertical pleiotropy when you have mis-specified the primary phenotype (in this case, unknown). see https://www.ncbi.nlm.nih.go... for vertical pleiotropy with mis-specified primary phenotype <br /> (2) This issue is generally addressed in MR by bi-directional MR applying Steiger filtering between M and Y in fig 1 (see https://journals.plos.org/p... "https://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1007081)"). It would be very interesting to know how this approach and CAUSE compare<br /> (3) The approach to HDL cholesterol would be through multivariable MR (which gets the right answer) - currently this is not implementable in CAUSE. It would be interesting to know if in principle if MVMR could be implemented in CAUSE<br /> (4) The SBP to BMI association in figure 5 is interesting. It says conventional MR shows SBP lowers BMI . The SBP GWAS used is adjusted for BMI, so collider bias is introduced and you should get this effect. This is the right answer to the wrong question. CAUSE gets the wrong answer to the wrong question

    1. On 2021-05-17 20:59:10, user Virginia Bain wrote:

      Hello~ I have a question for the authors. It looks like there is nuclear DHFR by immunofluorescence in A549 cells in figure 2C but in 2F you nicely show that DHFR is only in the cytoplasm. What do you make of the nuclear DHFR in figure 2C? Thanks!

    1. On 2018-11-16 16:10:02, user Francel Lamprecht wrote:

      As a Bioinformatics student, I am currently doing research about the SBGN. Up to now I have only seen bits and pieces of the SBGN, without having the actual research results. This article made it possible for me to link the biological results with the SBGN. The article made me grasp the practical value of having such a graphical notation, as one can systematically work your way through the network and visualize the processes occurring within the body. Having the different processes and interactions that takes place in each organ/tissue, in different compartments clearly enhances understanding. The inclusion of screenshots of the various SBGN editors, as well as the XML file, gave me valuable insight into how visual map generation works.

    1. On 2019-11-11 16:31:57, user Li Ding wrote:

      that's interesting work, but I have a question to the authors about the result of reducing voltage at central electrode (5 to 1 kV). Yes, this caused ions flying slower so they can survive 4s acquisition time. Did you try to reduce the acquisition time say to 1.6 S and keep CE voltage -5kV unchanged? This in principle should give you same data quality as ion oscillated same number of cycles as if it is in -1kV and for 4s. Ion should not dye and yet you save the time! Do you have such result? If it is not as good, why?

    1. On 2020-06-01 23:31:17, user Rodrigo Vallejos wrote:

      This was a very interesting paper with a fascinating insight into the immunology aspect of treatment.

      A quick note, there seems to be an error in the text when stating the CD206:iNOS ratios. The HR-deficient genotype is mentioned twice and the unclassified is neglected.

      Figure 6D also seems contradictory to what is written in the text, since the triple compound treatment is succumbing rather quickly. Could this be a mislabelling of the treatments in the figures?

      A functional analysis of BRCA1 mutation would be great to see, such as RAD51 foci. I was initially confused by it since it seems that the wild type band was cut off from the gel on Figure 1C.

    1. On 2020-05-19 00:55:54, user Fraser Lab wrote:

      I am posting this review on behalf of a student from a class at UCSF on peer review: https://fraserlab.com/peer_... . The student wishes to remain anonymous. I will be happy to act as an intermediary for any correspondence.

      This manuscript by Wang et al., uses tagged PKD-2 extracellular vesicles (EVs) in C. Elegans to explore the potential role of EVs in directional transfer from one organism to another.

      Overall, they identify a mechanoresponsive nature of certain male sensory cilia to release EVs, which are then found to be specifically located on the vulva of his mating partner.

      The authors provide compelling evidence that the male tail sensory cilia can respond to global pressure to release EVs, in that the usage of agarose-coated coverslips and slides was a robust way to perturb the forces that a male nematode feels when mounted.

      Separately, they also provided evidence of directional transfer of EV cargo from male to hermaphrodite C. elegans during mating. Specifically, showing that in inseminated hermaphrodites, there was highly localized deposition of the male-specific PKD-2-carrying EVs along the hermaphrodite vulva. Though, this study was limited by the inability to perturb EV budding and determine causality between EVs and presence of PKD-2 on hermaphrodite vulvas.

      The major success of this paper was in their ability to tag and visualize EVs, and use this technique to identify a candidate mechanism of release for extracellular vesicles. All in all, this paper opens a door for determining potential biological functions for extracellular vesicles, which has been largely elusive in the field.

      Minor points:<br /> Figure 1B could benefit from having an inseminated control image, to visualize which signals are present as autofluorescence<br /> It was unclear how many worms were imaged in the directional transfer experiment, but having that number would be important in establishing reproducibility

    1. On 2020-11-09 15:43:05, user Robert wrote:

      How immunogenic is this peptide? Can you use it without developing an immune response to the peptide? How long after you start using the peptide, will your own immune response block the peptides from working?

    1. On 2020-08-18 08:55:53, user Guillaume van Niel wrote:

      A very interesting study that nicely completes former studies on distinct populations of EVs secreted by the epithelial cell lines HT29 and T84 (van Niel et al Gastroenterology 2001).

    1. On 2019-12-12 03:18:49, user Rad4Cap wrote:

      > "little is known about responses of old non-diabetic individuals to this drug. By in vitro and in vivo tests we found that metformin shortens life span and limits cell survival when provided in late life.... In sum, we uncovered an alarming metabolic decay triggered by metformin in late life"

      Is this true of the responses of "elderly" "diabetic individuals" to this drug? Or are they limited to "elderly" "non-diabetic individuals"?

    1. On 2020-04-27 18:04:28, user Alex Crits-Christoph wrote:

      I thank the authors for submitting this work. I was surprised that there was no citation of:

      https://academic.oup.com/ci...

      Which appears to be one of the first works on measuring intra-population variation in SARS-CoV-2. In particular, many of the conclusions from that work seem difficult to reconcile with the work of these authors:

      "However, very few intra-host variants were observed in the population as<br /> polymorphism, implying either a bottleneck or purifying selection <br /> involved in the transmission of the virus, or a consequence of the <br /> limited diversity represented in the current polymorphism data. Although<br /> current evidence did not support the transmission of intra-host <br /> variants in a person-to-person spread, the risk should not be overlooked"

    1. On 2020-07-03 07:22:26, user H. Etchevers wrote:

      Very interesting work! However, do not neglect other, earlier lineage tracing concerning the sources of pericytes in the developing embryo, that could enrich the interpretation of your study:

      1. ventral somite (sclerotome) for the aorta and body wall (https://www.sciencedirect.com/science/article/pii/S001216060800002X) wherein the authors also found that "vSMC and endothelial cells originate from two independent somitic compartments" and references therein concerning the next point:

      In the forebrain, face, neck and truncus arteriosus, vSMC derive from the cephalic neural crest (Etchevers et al., 2001, Jiang et al., 2000, Le Lievre and Le Douarin, 1975). vSMC of the heart septum (Waldo et al., 1998) and the proximal cardiac artery (Bergwerff et al., 1998, Etchevers et al., 2001) are also neural crest-derived, whereas vSMC of the coronary veins and arteries originate from the myocardium and epicardium respectively (Mikawa and Gourdie, 1996, Perez-Pomares et al., 2002, Vrancken Peeters et al., 1999).

      (also see Arima 2012 DOI: 10.1038/ncomms2258 and Maeda 2016 DOI:10.1016/j.ydbio.2015.10.026)

      1. neural crest-derived mesenchyme for much of the head, face and outflow portion of the heart and coronary arteries, which is relevant for your observation that at "At E9.5 we found Ng2- DsRed+ cells along the heart outflow tract and adjacent regions of the dorsal aorta" and would make it straightforward to cross with a Wnt1-Cre or other early neural crest driver Cre and floxed GFP reporter to confirm that these DsRed+ cells are indeed of neural crest origin.
    1. On 2019-06-28 23:38:55, user Constantino Dragicevic wrote:

      At Paul Delano's lab we are very happy that you contribute to this line of research. However, let me ask you caution when claiming to be the first to report cochlear oscillations related to cognition. In fact, my latest paper (Dragicevic et al., 2019, https://doi.org/10.1371/jou... is the first giving that kind of evidence, so we suggest you to respect our original finding please. I have sent an e-mail to the first author with this and other scientific comments.

    1. On 2019-11-05 16:43:34, user Cyrus Cheung wrote:

      BI598 Group 6 (Alondra, Cecar, Cyrus, Safiya, and Can)

      A review written by Boston University undergraduate students majoring in Neuroscience and Neurobiology as a requirement for the class, Neural Circuits (BI598).

      Summary:<br /> Multiple Sclerosis (MS) is an inflammatory, neurodegenerative disease of the CNS characterized by both grey and white matter injury. Hammond et al used the established experimental autoimmune encephalomyelitis (EAE) animal model to recapitulate features of the disease. Their goal was to evaluate whether complement dependent synapse loss contributes to grey matter degeneration in EAE, which they highlight as being understudied. The complement system is part of the innate immune response and it signals microglia to prune synapses by phagocytosis. Therefore, their experiments revolved around studying the effects of knocking out C1qa and C3, which are both immune signaling proteins that signal microglia to phagocytose cells and cause neurodegeneration.

      To determine if EAE hippocampus produces the complement proteins that could make synapses vulnerable to phagocytosis by glia, they analyzed C1q and C3 protein and mRNA expression by Western blot and qPCR. As shown in Figure 1A-C, they determined that the increase in protein expression of C1q and C3 in the EAE hippocampus was due to local gene expression, and not because of blood brain barrier breakdown. They also isolated CD11b+ microglia from the hippocampus and cortex, and found that CD11b+ cells in EAE mice overexpressed C3 28 days post immunization. However, they found no significant difference in the C1qa expression (Figure 1D). They concluded that microglia contribute to elevated C3 expression, and possibly C1q expression. They acknowledge, however, that the EAE elevated expression of complement proteins could be induced by other cells in the hippocampus.

      They performed IHC using anti-C1q and anti-C3d antibodies to investigate the locations of elevated C1q and C3. Through IHC, they measured an increase in C1q fluorescence across the hippocampus in EAE vs sham mice (Figure 2B). At high (60-100x) magnification they observed that C1q was diffusely localized throughout the neuropil but also was localized at higher density in small punctate regions, some of which co-localized with synapses or along the dendrites in both sham and EAE brains (Figure 2D-E).

      To answer whether the loss of C1q or C3 protects against EAE induced motor impairment caused by spinal cord damage, the authors compared the clinical score of WT EAE mice with the C1qa and C3 KO from 0 to 26 days post induction (Figure 3). The graph shows that only the C3 KO presented less severe motor deficits, C1qa KO did not alter the course of the EAE disease, and neither the C1qa KO nor the C3 KO changed the symptom onset.

      The researchers proceeded to study how knocking out C1qa or C3 in the CA1-stratum radiatum layer of the hippocampus could have a protective role for synapse loss. They used IHC to stain for two postsynaptic markers: Homer1 and PSD95 in mice 28-30 days post immunization. As seen in Figure 4C-D, the knockouts played a slight protective role in synapse loss, with C3 KO playing a greater effect.

      Hammond et al. took their results further by investigating whether C1qa or C3 KO alter the morphometric parameters of microglial activation induced by EAE. More specifically, they utilized IHC to stain for microglial proteins IBA1 in the CA1-SR of the hippocampus (Figure 5). They then tested for various parameters such as sum volume, sum intensity, surface area, and skeletal length as a way to test for change in cell morphology. The data from this figure suggests that the C3 KO prevented hippocampal microglial activation induced by EAE, however it also revealed that the C1qa KO had no effect on microglial morphology.

      Merits: <br /> The paper identifies an interesting gap in knowledge, the introduction highlights their goal of studying the role of grey matter degeneration, a result of complement-dependent synapse loss. The complement-dependent synapse loss was studied effectively by use of C1q and C3 knockout mice.

      Overall, they made a convincing argument that the C3 knockout has the most potential for any rescue of symptoms. This makes sense because C1q activates C3, which accumulates at synapses, hence microglia can detect it as a signal to phagocytize. It is intuitive to think that knocking out C3 would slow the engulfment by microglia and decrease the loss of synapses in EAE.

      Specific Critiques:<br /> Overall, the paper made a connection between multiple sclerosis and the complement pathway with a focus on the motor cortex. However, multiple sclerosis has many different symptoms beyond just motor movements, therefore the paper would benefit from an investigation into other symptoms and circuits of MS, such as vision and sensation. In the introduction, the paper also discusses the use of the induced EAE mouse model as a model of MS grey matter injury; however, the results section does not indicate the data refers to grey matter. An explanation of how the complement dependent synapse loss affects grey matter would tie the paper together.

      They reported that the inflammatory response was localized in the hippocampus through the qPCR analyses that showed local gene expression of C1q and C3. Overall in Figure 1 the error bars were large, the data would specifically benefit from a larger and more consistent sample size, since the N’s range from n= 5 to n=11. While Figure 1B-C quantifies levels of C1q and C3 expression in the hippocampus, Figure 1D shows data from CD11b+ cells isolated from the cortex and hippocampus, which was as stated, a way to “obtain sufficient cells”. In order to discern if the increased expression of C1qa occurred locally in the hippocampus or in both hippocampus and cortex, the author should include another set of graphs for the qPCR results of C1qa and C3 gene expression of just the hippocampal extracts separated from the cortex. Without this, it is inconclusive if microglia contribute to elevated C3 expression in EAE, as there is insufficient evidence of microglia directly elevating the levels of C1q and C3 in the hippocampus.

      The method of evaluating the protein levels of C1q and C3d in the hippocampus of EAE mice was well thought out. However, in order to make a stronger claim a few elements could be added to Figure 2. In addition to the treated hippocampal areas, a normalized signal should have been presented such as a DAPI stain in order to show the living cells in the localized area, allowing for a helpful comparison. An alternate would be to stain for blood vessel markers, allowing for the reader to understand the layout of the image presented. On the other hand, a simple addition of insets on top of the current images would give the reader a greater sense of what exactly they are looking at. This would allow for quantification of the data presented as the colocalization is not very clear. The C3 knockout would also benefit from an increased sample size, since this might make the large error bars smaller. A discussion of a discrepancy between a significant increase of C1q, but not C3, in the EAE mice and it’s relation to its synapse loss significance that is discussed in later figures would help bridge the paper together and explain something that might be unexpected.

      Figure 3 opens the possibility of targeting C1q and C3 to ameliorate the motor deficits caused by MS through investigation of the different complement pathways. However, the paper did not elaborate on why they concluded the alternative complement pathway as the most important in EAE. As stated in the introduction the complement system, classical, alternative, and lectin pathways all converge on the production of C3. Therefore, there is not enough evidence to conclude that the lectin and classical pathways are also not involved. An explanation on how they conceptualize these pathways would provide increased clarity to the conclusions made from the data.

      Through the pictures and graphs shown in Figure 4, the authors are trying to show that knocking out C1qa and C3 decreases the synapse loss seen in EAE. The data would benefit from an increased sample size, especially for the C3 KO model, as there are large error bars. They use Homer1 and PSD95 as postsynaptic markers but they should confirm the presence of synapses by including EM images or using a presynaptic marker such as Synapsin, in addition to Homer 1/PSD95. Authors could also run a western blot for synaptic proteins like DLG4, Synaptophysin or Neuroligin to confirm the presence or absence of synapse through the different conditions. Again, the data does not provide enough evidence to conclude that the alternative pathway is the most important in EAE. A more elaborate explanation of how the data concludes it is the alternative pathway would add clarity.

      Figure 5 displays the stained brain sections from the WT, C1qa KO, and C3 KO, sham and EAE immunized mice along with the quantification of various parameters to show that C3 KO mice with EAE have reduced microglial activation when compared to the WT EAE mice. To properly convey this, a few sentences on how C3 is specifically connected or related to microglia are needed. Considering the IBA1 stains can be expressed in other parts of the brain, it would be beneficial to check for other markers such as TMEM 119, a microglial cell surface protein that is not expressed in other neuronal macrophages or immune cell types. In addition to this, figure 5A, could potentially be combined with figure 1 considering they both look at morphology.

      Minor Concerns: <br /> Introduction<br /> The introduction includes a few grammatical errors:<br /> In the second paragraph, “triggers” should be “to trigger”<br /> In the fifth paragraph, the authors should have used present tense while writing about the previous findings.

      Methods<br /> They included background information about C1qa in the methods section. This belongs to the introduction section of the paper.

      The clinical score was mentioned in the methods and it would be beneficial to name the section appropriately or create an entirely new section.

      It was unclear why Homer1 and PSD95 was chosen to investigate synaptic loss in the CA1-stratum radiatum. Meanwhile, IHC with CamKII is more commonly used and could show synapses more clearly. More explanation could be provided.

      In the fourth line of the EAE section, instead of “each of two sites”, it should be “each of the two sites”.

      Figures<br /> In Figure 1D, the authors include data on the qPCR results of C1qa and C3 from CD11b+ microglia/myeloid cells and mention this in the results. However, they should clarify the role and importance of CD11b+ microglia/myeloid cells by adding background information in the introduction.

      Figure 2 and Figure 4 could be visualized better with a larger and higher resolution image.<br /> Figures 4 A through B are stained in different colors and the green is harder to see than the grey. It is recommended that they stick to one color so that they both images are easy to see.

      Results<br /> In the fifth line, “C1q and C3 protein” should be “C1q and C3 proteins”<br /> In the sixth line, the sentence should start as “By Western Blot, we found that...”<br /> Please review the paper for spelling, grammatical, and punctuation errors.

      Future Directions:<br /> The researchers investigated a motor impairment in a mouse model with C1q and C3 knockout, affecting the hippocampus as well. Different diseases, such as Alzheimer’s Disease, are heavily associated with hippocampal damage, indicating the complement pathway could be extrapolated as a therapeutic target for neurodegenerative diseases as a whole. Behavioral paradigms that test other symptoms of multiple sclerosis, such as vision, and tasks that test for Alzheimer’s Disease, such as working memory, could show the impact of the complement pathway on common diseases.

      As complement genes are important for neuronal development, confounding factors might have played a role in the experimental paradigm. Use of ASO to knock down genes and bypass development would help remove confounding variables in the experiment.

      Lastly, accounting for more time points prior to 28-30 days post immunization would allow for a clear visualization of the EAE progression. Including more time points for figures 4 and 5 would allow for a comparison of how synapse loss progresses through time.

    1. On 2020-07-03 14:12:43, user David Curtis wrote:

      So you're saying the variant might be protective against severe disease. In that case, we might expect its frequency to be lower in the severely affected cases I looked at than in the background population. That isn't what I see in UK Biobank.

    1. On 2019-01-23 16:42:36, user Marco Pessoa wrote:

      A quick look at the manuscript gives the impression that the title is misleading, to say the least, since the authors did not produce an assembly or any genomic sequence for P. edulis. They do state this in the introduction, but not the title nor the abstract.

    1. On 2022-01-12 21:43:15, user Clara B. Jones wrote:

      1 ... this article relies heavily on the "ecological constraints model" first proposed by S.T. Emlen in 1982 ... it seems appropriate to suggest that this classic Am Nat should be cited ...<br /> 2 ... throughout the publications on social mole rats derived from Clutton-Brock's lab, the presence of Castes is employed as a necessary diagnostic criterion for Eusocial classification ... as evident from even a cursory reading of EO Wilson's [1971] Insect Societies, as well as, Holldobler & Wilson's [1990] Ants, many social insect species are characterized by "totipotent" workers [like social mole rat "helpers"] who are not [more or less] sterile ... this observation is not disputed in the social insect literature ... indeed, numerous researchers divide the Eusocial category into "primitively Eusocial" [totipotent workers] & "advanced Eusocial" [more or less sterile Castes] ... this is an important distinction relative to the preprint by Thorley et al. because, as Gene Robinson [1992] & many others have pointed out, social insects exhibit a high degree of "phenotypic plasticity," a diagnostic criterion that might describe certain findings in the paper under discussion ...<br /> 3 ... as a related aside, these authors do not mention (a) "division of labor," a character trait universally employed as a diagnostic criterion for eusocial classification, in addition to the characters, (b) overlap of generations, (c) "cooperative breeding" and the presence of "helpers," & (d) "specialization" ... unless i am mistaken, the two eusocial mole rats generally classified, Eusocial--Damaraland & naked mole rats--exhibit "specialization" in the form of "temporal division-of-labor" ["age polyethism"], thus, meeting all of the generally-accepted criteria for Eusocial classification ...<br /> 4 ... this paper is not "tight" though, as indicated by its title, the authors' paper has a clear goal in mind ... instead, they veer off in numerous directions throughout their text, & it is not clear to me that the extrapolated, deviating commentary is useful rather than obfuscating ... perhaps the paper should have notes if the authors wish to add possibly relevant material for thought aside from allusions to material not related directly to "fitness" and "group size" ...<br /> 5 ... though one might question or request clarification about numerous definitions & other decisions made by Thorley & his colleagues, in the service of brevity, it is necessary to, finally, point out that Wilson '71 and Holldobler & Wilson '90 document the very wide range of architectures that are, diagnostically, "Eusocial" ... in his 2018? book, Genesis, EO Wilson suggests that numerous mammal species deserve Eusocial classification, &, in my 2014 Springer Brief on mammalian social evolution, i suggest that "cooperatively breeding" mammals should be classified with the Eusocial mole rats ...<br /> 6 ... all scientists welcome new ideas & clarifying, rather than obfuscating, empirical, as well as, experimental & quantitative, tests of the literature in their fields ... this preprint by Thorley et al. does not, in final analysis, modify the conclusions advanced in the work by Bennett, Faulkes, & others, going back to Jarvis' classic research in the 1980s, who have studied social mole rats and classified them, "Eusocial" ...

    1. On 2019-10-16 20:19:00, user Leighton Pritchard wrote:

      I think there may be a typo in equation 2:

      x_{SS} = \frac{(s - r_g- r_j) + \sqrt{(r_g + r_l -s)^2 + 4 s r_g}}{2 s}

      should be

      x_{SS} = \frac{(s - r_g - r_l) + \sqrt{(r_g + r_l -s)^2 + 4 s r_g}}{2 s}

    1. On 2022-09-13 22:06:06, user Hae Kyung Im wrote:

      In this paper, the authors attribute the wrong null hypothesis to the standard TWAS approach. The issue seems to stem from a confusion between the true parameter (a number) with its estimator (a continuous random variable). They state that the null hypothesis is that the estimator = 0, which is an event of probability 0.

      The better way to think about the error in the genetic predictors of gene expression is not to change the null hypothesis but in terms of an error in variables problem. Under reasonable assumptions of independence between reference and target sets, error in variables leads to attenuation and not inflation. Many papers have addressed this problem.

      More details

    1. On 2018-10-26 19:39:06, user Dorian Pustina wrote:

      Hi Kaori,

      I think you have done a great work here to achieve a comparison that is of high interest to the community. Congratulations. The most curious aspect was that you could run such a complex study on an i5 processor.

      About the results, I am not surprised that LINDA missed many subcortical/brainstem lesions. On the contrary, I was surprised that it got some of those lesions. The reason is simple: LINDA was designed and tested on large lesions, while ATLAS is composed of many cases with small lesions. In fact, I checked the ATLAS dataset a few months ago and the median lesion size was very low, around ~5ml.xt

      Brainstem lesions in particular would be very hard to detect with LINDA simply because LINDA is trained to expect some signal at its low resolution step, which probably is not there for small lesions. On top of this, I don't even think LINDA is considering the brainstem in the registration steps, it might mask it out completely.

      This said, I still think your work is very valuable. I have three minor suggestions:<br /> 1. Please describe the lesion properties in better detail, particularly lesion size,, and put this in perspective with the lesion sizes used in each of the studies that developed the respective methods.<br /> 2. In the conclusion paragraph, you state: "We observed that testing on multi-site data resulted in decreased segmentation accuracy." This sounds like the problem is the multi-site nature of the test dataset, which may discourage people from running multi-site studies. The drop in accuracy has more simply to do with the nature of lesion accepted in a dataset, their size and location. I don't see multisite studies to be a problem per se.<br /> 3. Looks like ASSD values in Table 4 do not match the values described in the manuscript.

      DISCLAIMER: I did not perform a thorough review of the paper. Any opinion expressed here is based on a quick superficial reading and should not be taken taken as proof of approval or disapproval.

    1. On 2022-01-21 22:03:41, user Debelouchina Lab wrote:

      Hello! This is the Debelouchina Lab at University of California, San Diego. We have begun doing preprint manuscript reviews during our “journal clubs” as a way to enhance our engagement with current literature and to hopefully assist with the manuscript if possible! Our lab also studies the behaviors of biomolecular liquid-solid transitions – with a focus on protein structure. We selected this manuscript out of curiosity for the spatial origins of solidification in liquid-liquid phase separated systems.<br /> Liquid-liquid phase separation (LLPS) is central to the spatiotemporal organization of biomolecules in the cell. Many of the proteins that are thought to mediate LLPS have also been found in pathological aggregates and fibrils that are associated with neurodegenerative disease. It has been demonstrated that liquid-like phase separated bodies can adopt gel-like or solid morphologies over time, which suggests that LLPS droplets may serve as nucleation points for pathological aggregates. This manuscript interrogates this process by characterizing the spatial characteristics of the liquid-to-solid transition within individual alpha-synuclein condensates using a set of fluorescence and infrared microscopy techniques. The authors found that droplets solidify form a central focal point that can be imaged through associated changes in fluorescence lifetime (via fluorescence lifetime imaging, FLIM) and protein secondary structure (via Fourier transform infra-red microscopy, FTIRM). To emphasize this significance in the text, we think it may be helpful if the authors added more background and discussion of previous literature on the spatial origin of solidification.<br /> These findings are exciting as they add new insight into biomolecular liquid-to-solid transitions, and relevant due to the potential role for liquid-to-solid transitions in neurodegenerative disease. We find that the combination of fluorescence microscopy techniques used here presents a strong model for studying spatiotemporal material properties of biomolecular condensates, which are challenging to characterize from a structural perspective due to their inherent heterogeneity and sensitivity to environmental factors. The power of these techniques is shown in their ability to complement the FLIM data into protein mobility (FRAP), structure (FTIRM), and interaction (FRET) components, providing a comprehensive look into the liquid-to-solid transition. We appreciated the use of small fluorophores rather than fluorescent proteins, as well as the confirmation by fluorophore-free techniques (TEM & cryo-SEM). Overall, we find that the data and the resulting model for the spatiotemporal dynamics of the liquid-solid transition are compelling.<br /> One area we are curious about is the sample handling, keeping a sample hydrated for 20 days is difficult. Would you be able to add a few words about the robustness of this moisture chamber in the main text? These aspects of the experimental design might not be obvious to a reader unfamiliar with the practical considerations of experiments like this, so more discussion would be helpful to anyone trying to reproduce the experiments. In a similar vein, a paragraph about the practical aspects of FLIM in the context of LLPS would be helpful. We also wondered about the necessity of the solidification timeline, how would the microscopy procedures described here work for a system that progresses to solid much faster than 20 days? What are the time limitations of these techniques? Would a faster system be expected to have the same center-growth effect as seen here?<br /> We were surprised that droplets appear to solidify from the exact center of the droplet in every case. If the model for solidification is that it begins from a (random) nucleation point, then why would droplet solidification always begin exactly in the center, as opposed to the inner or outer center regions that are mapped in Figure 1. We were left wanting more information about this, especially since FLIM is capable of resolving changes on these scales. It would be interesting to see if there are any cases where solidification does not begin from the exact center of the droplet. <br /> Some minor comments:<br /> -While the figures are clear and well-organized, a more colorblind-friendly palette could be used.<br /> -Infrared is occasionally hyphenated throughout the text.<br /> -The abstract figure may be clarified if the FLIM images were all of a single droplet, matching the cartoon.<br /> -The schematics describing the planes on the droplet are beautifully done and very helpful to understanding the figures.<br /> -Figure 1: formatting error with (e) placement.<br /> -Figure 2: (c) As we are unfamiliar with FTIRM, we thought it may be useful to have the corresponding secondary structure to each wavenumber (like the supplementary table 1 information) in the figure. Similarly, while supplementary figure 7 has a monomer and fibril control, we would have enjoyed that in the main figure.<br /> -Figure 4: (c) We wonder how consistent these recoveries are for several different droplets at the same time point.<br /> -For the TEM data (Fig 5), the results are a little bit different from other attempts to perform TEM on LLPS systems (for example, here: https://pubs.acs.org/doi/10... "https://pubs.acs.org/doi/10.1021/jacs.9b03083)"). A discussion of precedent would be appreciated in the main text. <br /> -Supplementary Fig. 11: We thought these EM images were fascinating and are curious if such images exist elsewhere for biomolecular condensates.


      We appreciated the chance to read and review this manuscript,<br /> The Debelouchina Lab

    1. On 2021-02-23 18:55:05, user Charles Warden wrote:

      Hi,

      Thank you very much for posting this pre-print.

      Am I correctly understanding is that the main goal is to help parse information from the existing database?

      For example, I am correct in understanding that functions to test/compare application of various scores to yourself is not included?

      This is essentially what I have done here, and I am curious if your package can help do something similar (for other scores):

      http://cdwscience.blogspot....

      Thank You,<br /> Charles

    1. On 2023-08-04 01:38:44, user Yun H. Jang wrote:

      Can you double check if the light intensity of the DLP printer is 0.08W/cm2 (80mW/cm2)? As far as I know, the maximum light intensity of the Lumen X printer is much less than that. Thanks.

    1. On 2025-05-22 15:29:14, user SM wrote:

      The manuscript has been revised and published:

      Mohammed S, Kalogeropoulos AP, Alvarado V, Weisfelner-Bloom M, Clarke CJ. Serum and plasma sphingolipids as biomarkers of chemotherapy-induced cardiotoxicity in female patients with breast cancer. J Lipid Res. 2025 Apr 5;66(5):100798. doi: 10.1016/j.jlr.2025.100798. Epub ahead of print. PMID: 40189207.

    1. On 2019-02-20 22:53:46, user GuyguyKabundi Tshima wrote:

      The link between malaria and climate in Kinshasa, Democratic Republic of the Congo in the bioRxiv preprint:<br /> What is the explanation for Plasmodium vivax malarial recurrence? Experience of Parasitology Unit of Kinshasa University Hospital of 1982-1983 and 2000-2009.<br /> From the preprint, I highlighted the link between malaria and climate:<br /> OBJECTIVE<br /> I wanted to highlight the link between the rainiest month and positive microscopy for malaria control purposes<br /> RESULTS<br /> November 2001 had the high number of positive samples.<br /> CONCLUSION<br /> Efforts for malaria control should be focus on the rain months.<br /> DISCUSSION<br /> The number of positive cases was recorded in 2001. 2001 was marked by the beginning of the resistance on antimalarials drugs involving a change towards the artemisinin derivatives, but it was in 2005 that the national malaria control programme PNLP introduced the combination of artesunate-amodiaquine to treat cases of uncomplicated malaria or simple malaria forms, also Artemether-Lumefantrine and Dihydroartemisinin-piperaquine for complicate malaria forms. The old combination was sulfadoxine and pyrimethamine for uncomplicate malaria and quinine for complicate malaria forms .<br /> It was also observed in the last quarter of the year with a pic or the highest number of confirmed samples at the month of November (Figure 5).<br /> In Kinshasa, the last three months of the year is the period of heavy rain with temperatures between 30 ° C and 38 ° C. These conditions are favorable to the proliferation of Anopheles that would promote the transmission of malaria during this time of the year without the use of mosquito preventive measures.<br /> RECOMMENDATION<br /> We promoted the use of the insecticide impregnated nets.

      William Seriki's recommended solution:

      Malaria parasite does thrive in areas that are habitable for it. Therefore, it’s not suitable enough to try and manage the prevailing effects of malaria parasite (such like buying and distributing mosquito nets and insecticides), but instead, a diagnostic approach should be taken to drastically reduce (more like eradicating) the prevailing effects of malaria parasite.<br /> With my factors; why Malaria is common among impoverished communities, I will recommend that the ‘Top-down & Bottom-up’ model of approach should be adopted. Whereby situations that have not really helped in the eradication of malaria parasite can be effectively approached.<br /> Now,<br /> taking a critical look at those factors one after the other. The ‘Top-Down’ approach helps your diagnosis to say if some of those factors are due to government negligence and therefore, appropriate steps can be taken by the government to ensure those factors do not exist anymore for malaria parasite to thrive.<br /> The ‘Bottom-Up‘ approach helps your diagnosis to say if some of those factors are due to community/Individual negligence and therefore, appropriate steps can be taken by the individuals (within the community) to ensure those factors are no longer in existence.<br /> And I think this solution in over all, will also help to close the social, health and economic gap impacting on community health.

    1. On 2020-03-15 14:33:24, user Annie Chai wrote:

      Wonderful piece of work! Thanks for the user-friendly Rshinyapps.<br /> I'm however bit confused with the discrepancies in the different output files though:<br /> I tried to built search space for LUSC, and downloaded the 3 output files as shown, I noticed that some signatures associated with the cell lines are not consistent.<br /> For example, LK-2 which is wildtype for PIK3CA, appeared to be representative model for "TP53mut, ~PIK3CAmut, and NFE2L2 mut" in the SubType map output. But in the "CELLect cell lines" output file, LK-2 was associated with "TP53mut, PIK3CAmut".<br /> Another cell line, KNS-62, is seen as representative model for "TP53mut,~PIK3CAmut,~NFE2L2mut, CDKN2Amut" in the SubType map output, but associated with "TP53mut, ~PIK3CAmut, NFE2L2mut" in the CELLect cell lines output...<br /> Could you please explain the discrepancies? Or did I interpret them wrongly?

    1. On 2019-04-01 06:21:39, user Veronica Hoad wrote:

      We read with interest your paper and would like to comment about ‘screening of blood for the presence of papillomavirus sequences until such time that it can be proven that<br /> the presence of HPV does not pose a risk.’ Given finite resources, blood services are increasingly using risk based decision making<br /> that balances safety, supply and affordability. Three key factors determine the<br /> blood safety risk for a particular infectious agent: the evidence of<br /> transfusion-transmission, the prevalence of infectious viremia among donors,<br /> and the severity of infection in transfusion recipients. For an infectious<br /> agent to be transfusion-transmissible, the agent must be present in the blood<br /> of donors who are asymptomatic/minimally symptomatic, retain viability after<br /> routine blood processing and storage, be in a state capable of causing<br /> infection via transfusion and present at a level higher than minimal infectious<br /> dose, and there needs to be a population of susceptible blood transfusion<br /> recipients (Ginzburg, Kessler et al. 2013).<br /> Many infectious agents have been found to be detectable in asymptomatic blood donors (Welch, Maclaran et al. 2003,Hudnall, Chen et al. 2008), but this finding is not synonymous with transfusion-transmissibility given that infectious virions must be present and<br /> the infectious agent must also survive modern blood storage techniques<br /> including leucodepletion of blood components. Like your study, there are other<br /> published animal models that have demonstrated transfusion-transmission of<br /> infectious agents in direct unprocessed blood (Brooks, Merks et al. 2007,<br /> Silva, Vieira-Damiani et al. 2016). <br /> However, this is not sufficient evidence to recommend blood donor screening in humans which must consider the three key factors as well as health economics and an operational assessment.<br /> Veronica C.Hoad, Claire E. Styles, Iain B. Gosbell, Australian Red Cross Blood Service.

      References<br /> Brooks, J. I., H. W. Merks, J. Fournier, R. S. Boneva and P. A. Sandstrom (2007). "Characterization of blood-borne transmission of simian foamy virus." Transfusion 47(1): 162-170.<br /> Ginzburg, Y., D. Kessler, S. Kang, B. Shaz and G. P. Wormser (2013). "Why has<br /> Borrelia burgdorferi not been transmitted by blood transfusion?" Transfusion 53(11): 2822-2826.<br /> Hudnall,S. D., T. Chen, P. Allison, S. K. Tyring and A. Heath (2008). "Herpesvirus<br /> prevalence and viral load in healthy blood donors by quantitative real-time<br /> polymerase chain reaction." Transfusion 48(6): 1180-1187.<br /> Silva, M. N., G. Vieira-Damiani, M. E. Ericson, K. Gupta, R. Gilioli, A. R. de<br /> Almeida, M. R. Drummond, B. G. Lania, K. de Almeida Lins, T. C. Soares and P.<br /> E. Velho (2016). "Bartonella henselae transmission by blood transfusion in<br /> mice." Transfusion 56 (6Pt 2): 1556-1559.<br /> Welch,J., K. Maclaran, T. Jordan and P. Simmonds (2003). "Frequency, viral<br /> loads, and serotype identification of enterovirus infections in Scottish blood<br /> donors." Transfusion 43(8):1060-1066.

    1. On 2019-01-30 18:43:10, user Tanai Cardona Londoño wrote:

      Quite interesting, thank you. I'm usually very skeptical of claims of HGT, but I think you do make a very convincing case.

      I think it is pretty well established that the rubisco from red algae is of proteobacterial origin (Delwich and Palmer 1996, doi:10.1093/oxfordjournals.molbev.a025647, for example). Are you aware of this? How is this even possible?

      I doubt that it could have been of endosymbiotic origin, unless one is willing to accept that, however unlikely, it came from the ancestor of mitochondria.

      Moreover, and for all we know, the original rubisco of the primary cyanobacterial endosimbiont was not of proteobacterial origin, unless one is willing to accept more than one source of cyanobacterial genes during the establishment of the primary plastid.

      So the only way that this can be explained is if HGT occurred from a proteobacterium into an ancestral red algae, and this rubisco gene somehow got into the red algal chloroplast genome. Am I wrong? Is the red algal rbcL not encoded in the plastid?

      What do you make of that within the perspective of your recent findings?

    1. On 2022-07-14 15:54:12, user Qian Zhu wrote:

      I am author of the smfishHMRF package (part of Giotto) that is used in one of your comparisons in Figure 6. I am highly doubtful about the results your presented of Giotto in Figure 6 and same of SpaGCN. I believe much of the results you are seeing is due to the selection of genes to find spatial domains than having to do with the underlying method. We also do not rule out improper usage of our package in this comparison. We will share our findings with you in a separate thread.

    1. On 2025-08-13 23:32:05, user Jeff Ellis wrote:

      “Our findings suggest an experimental framework for predicting evolutionary outcomes of pathogen effector-host target interactions with implications for plant disease resistance breeding.”

      This statement at the end of the abstract and end of discussion intrigued me. I asked the question what are these implications and how could these be used in disease resistance breeding? I think the statement begs at least some explanation and discussion. If not supplied I suggest that the statement should be deleted.

    1. On 2023-01-24 22:48:46, user Jackie wrote:

      I really wish they gave better info about their demographics. In particular, where the respondents were located. Is this truly an Australian issue? Or were the majority from say Brisbane or Sydney? Also, the happy folks are less likely to agree to do a survey...This is an important study, but the limitations could be better discussed

    1. On 2020-12-18 15:49:32, user AG wrote:

      Social selection can also produce consequence. The very fact of y-chromosome with high mortality rate during world war 2 can result a population which tend to produce more female offsprings since y-chromosome functions as unfit genes for survival in population went through major selection by wars. Russia has extraordinary high female offspring birth rate, which might be the historical selection against y carriers during ww2.

    1. On 2020-11-10 07:21:09, user Eddie wrote:

      I enjoyed reading this paper and finding out more about epithelial to mesenchymal transition in correlation with the membrane. I found figure 1 to show a strong and clear introduction towards your paper. The labeling of your figures also helped in understanding the structure and components. From what I gathered, figure 2b is supposed to show the reduction of ceramide expression leading to decreased migration. You add dots to specify the region where the ceramide expression is located and the migration, however it is unclear how you determine what to include within this dotted area. It would be nice to add, within your methods, your reasoning to incorporate certain regions within the dotted area as well as how you quantified your results. I enjoyed the side by side comparison for Figure 2c allowing for a direct comparison for your results. Connecting the points in the graph with the control and morpholino was a great addition to help understand and visualize your results. However, as mentioned before, you use dotted lines to assist in visualizing, and, for 3c, it is unclear how you determined these areas and what to include and not include. Additionally, it will help strengthen your argument if you clarify how you quantified your results for 3b and c. An explanation on your quantification for these figures will help better understand these figures and the reasoning. For the Wnt data, it is harder to visualize the change for Wnt signaling compared to the BMP signaling. Providing data that shows the normal expression of Wnt and BMP would help understand the change in expression for Wnt and BMP. Overall, this paper was a great read that presented nice evidence to support your findings. With a few adjustments clarifying your results, this will make the paper stronger.

    1. On 2017-10-09 16:15:46, user Ann Turner wrote:

      One major problem with this article is that the authors do not account for the high mutation rate in mitochondrial DNA, resulting in many parallel and reverse mutations. For instance, they show a clean division of 16519C vs 16519T, but this is a hotspot. In my database of <br /> full mitochondrial sequences from GenBank, 16519T is found in 1002 subclades, C is found in 1224 subclades, and 444 subclades have samples with both C and T.

    1. On 2020-03-31 22:15:39, user Mike Rayko wrote:

      Can you please double check? In our study (coming soon) we observe deletion in the samples from independent labs at 1605-1607, changing ND (AATGAC) to N (AAC).<br /> Also, Asp268 is 1604-1606 (at least in NC_045512.2)

    1. On 2017-05-17 21:44:19, user Willem van Schaik wrote:

      This is an interesting whole-genome sequencing based study to identify mechanisms that contribute to colistin resistance in K. pneumoniae. Mutations in mgrB, phoPQ and pmrAB are identified and complemented to confirm their role in colistin resistance. The major weakness of this study is that the authors are limited in their choice of isolates: they do not have the susceptible counterpart of each resistant strains, so it is impossible to identify all SNPs and indels that have accumulated in the resistant strain. This limits the scope of the study as the authors now only study the ‘known knowns’ outlined above. It would be good if the authors include this limitation of their study in the discussion.<br /> Some additional comments and suggestions are outlined below: <br /> The abstract lacks quantitative data. l. 30 Please provide an exact number, l. 31. ‘most common’: provide number of strains. <br /> The relevance of the ST2401 K. quasipneumoniae strain in the context of this study is unclear. It does not merit inclusion in the abstract, in my opinion. <br /> l. 49: better to write plasmid-encoded carbapenem resistance genes<br /> l. 54. The mortality associated with polymyxin-resistant Klebsiella infections seems awfully high. I believe the attributable mortality due to PMX-resistance is still not clear. See this interesting blog post: https://reflectionsipc.com/... for further insights on this topic.<br /> l. 58. I apologize for being a pedant, but the disturbance of the LPS leaflet of the outer membrane will not allow PMX to act on intracellular targets. For that to happen, the inner membrane needs to be disrupted as well.<br /> In the discussion on mgrB it may be good to refer to Kidd et al., 2017. EMBO Mol Med who were the first to systematically study the role of this gene in K. pneumoniae.<br /> l. 67. Specify that mcr-1 confers colistin resistance. It may also be relevant to note that mcr-1 appears to be relatively rare in Klebsiella.<br /> l. 97. ‘glycerol was added to 20% (v/v)’ may be a better way of phrasing this line<br /> l. 107. I assume cation-adjusted Muller-Hinton broth was used? Please specify.<br /> l. 130 – 132. I would really like to see a maximum-likelihood core genome tree here with additional reference isolates (downloadable from public databases), rather than a Neighbour-Joining tree of seven concatenated MLST alleles. It now is impossible to assess whether some of these strains (having the same ST) are truly clonally related.<br /> l. 166. Incision should probably be replaced by introduction<br /> l. 203. Provide exact number.<br /> l. 225. It is not immediately obvious what is meant by (65, 66% variant allele frequency)<br /> l. 235 – 237. Is it also not a possibility that in these strains mgrB has reverted to its wild-type state by excision of the IS element? <br /> l. 252 – 270. This section is difficult to follow. While some mutations are proposed to act as suppressors, it appears that experimental evidence cannot confirm this, so it may be better to rewrite this paragraph to reflect this key finding.<br /> l. 275 – 276. I am not entirely sure that it is correct to single out Brazil and Greece here.<br /> l. 292. I am not entirely sure whether this claim of primacy is relevant. Clearly, a truncation is a loss-of-function mutation and those have been complemented previously.

    1. On 2025-11-14 14:02:59, user Anonymous wrote:

      Dear authors,<br /> as a part of a group activity aimed to improved our skills and growth as scientists, we discussed recent BioRxiv preprints that we found to be of particular interest. This time, our choice fell on your manuscript: - Gamarra M. et al., Vesicular Rps6 released by astrocytes regulate local translation and enhance synaptic markers in neurons. The comments below are the results of this exercise and reflects thoughts and comments of several people. We hope our exercise may help you to finalize your efforts and publish your manuscript in a good journal.

      Comments

      The manuscript by Gamarra et al. investigates the regulation of local translation in axons and the role that astroglia plays in it. The authors described, for the first time, a novel mechanism of intracellular communication in the central nervous system (CNS) based on the release of extracellular vesicles (EVs). EVs are released from astrocytes and contain the ribosomal protein S6 (Rps6). The authors showed that these astrocyte-derived vesicular Rps6 are taken up by axons and are able to enhance synaptic markers and to fine-tune neuronal function. This work suggests that astrocyte EVs can actively influence neuronal local protein synthesis and synaptic plasticity through the transfer of specific non-neuronal ribosomal proteins, highlighting a novel mechanism for intercellular communication in the brain and opening new routes for therapeutic strategies based on EVs potential.

      Major comments<br /> 1. Figure2 - Bi and 2Bii<br /> The heatmap in Figure 2Bi shows an increase the levels of both Rpl26 and Rps6 that is rather due to the introduction of astrocyte in the co-culture (NA condition) of both DMSO and A?1-42-treated samples. The box and whisker graphs in Figure 2Bii report the normalized levels of Rpl26 and Rps6 from the same experiments (analyzed by proteomics from 4 independent cultures). The normalized level of Rpl26 in Figure 2Bii matches the increment in Rpl26 level displayed in the heatmap in Figure 2Bi while the normalized level of Rps6 shows a discrepancy in DMSO NA conditions.

      It will be helpful to clarify and provide further justification of these representations in particular since, in the text, the author stated that - Conversely, Rps6, a component of the S40 subunit was upregulated only in A?-treated co-cultures and not in basal conditions (Figure 2Bii, right graph). <br /> This statement doesn’t match what is depicted in Figure 2Bii, right graph). We agreed that the difference is not significant but there is definitely a trend toward an upregulation of Rps6 levels also in the DMSO NA.

      1. Figure 4 – A

      The authors analyzed puromycin labelling in axon as a readout for local translation upon incubation with EVs released from DMSO and A?-treated astrocytes. The latter treatment, as shown in Figure 3 – C, increases the number of EVs released by astrocytes.

      We agreed that might be helpful to clarify whether the number of EVs were normalized or not. We believe it is key to the paper message to clarify whether the effects of astrocytes EVs on axonal local translation is due to EVs content rather than too their numbers.

      1. Figure 5 - Biii

      These conditions seem to give a lot of variability, with 2 out of 3 experiments showing a similar trend as is observed for A?-EVs in Figure 5 – Ciii. We thought that, in order to better convince the audience and prove your point of the absence of any effect in axonal translation upon incubation with EVs generated from DMSO treated astrocytes, it may be worth to perform a 4th replicate.

      1. Figure 5 – D and E and Figure 7 - C

      It will be of help to clarify why the author chose to quantify the number of synaptophysin (Syn) and Homer puncta. Are Syn and Homer known to be regulated by local protein translation?

      The authors claimed an axonal effect of EVs derived from A?-treated astrocytes. Why is a post-synaptic marker included? In case Homer staining was included to be sure to look at synapses, why do the authors not quantify Syn only from synaptophysin and Homer positive puncta?

      Minor comments

      • A brief explanation on how well established the use of modified Boyden chamber is needed, especially in regard to possible direct contact between neurons and astrocytes due to the length of axons. <br /> • The authors should consider using a single nomenclature throughout the manuscript for better clarity and readability. For example, in Figures 1 and 2 a different notation is used for neuron monoculture (1 and N) and neuron-astrocyte co-cultures (2 and NA). <br /> • Figure 1 - Bii - O-propargyl-puromycin (OPP) is a tRNA analog that is incorporated in all newly synthesized proteins without a defined kinetics or a precise position in the aminoacidic sequence. From the immunoblot showed in Figure 1 – Bii, we would expect a smear in the lane cause newly synthesized protein should come in all different kDa. The same reasoning can be applied to the Amido Black staining.<br /> Why is it not the case?<br /> • Figure 1 - Biii – The data suggests that OPP-biotin conjugates can be consistently and unambiguously detected in neurites isolated from neuron-astrocyte co-cultures independently from their exposure to A? treatment. OPP-biotin signal looks comparable in both DMSO and A? treated co-culture.<br /> • Figure 1 – Ci – Out of curiosity, which is the common CC cluster found in both soma and neurite compartment?

    1. On 2017-07-21 13:59:33, user Nicolas Rode wrote:

      Nice manuscript! It would be nice to know the actual number of embryos that were initially injected to get an idea of the feasability of Medea gene drives. I was also wondering if the insertion location in the genome was known and if backcrosses were used to decrease the linkage between the Medea gene and the Corvallis (OR) genetic background (Fig. 2)?

    1. On 2025-02-15 02:39:09, user sa pa wrote:

      I read this paper with great interest.

      Is there a name given to the single-cell RNA-seq using the “Solution-phase indexing by kinetic confinement” technique that you are proposing?

      It would be easier to cite it as a single-cell RNA-seq method if it had a name like “xxx-seq”, but what should we call it officially?

      In the text, it says “Single cell RNAseq using Kinetic Confinement”, is this correct?

    1. On 2020-06-11 18:25:10, user Megan Hagenauer wrote:

      Useful analysis. We were also struck by how much the cell type signature database matters when performing deconvolution analyses. The question of whether to include cell type estimates as covariates when performing differential expression analyses is one that I still waffle over (our results definitely provided pretty lackluster guidance) - your simulations suggest a clear benefit. If you have the time, I would love to see whether you find a similarly clear benefit of including cell type estimates as covariates while performing differential expression (DE) in your simulations if you vary the signature database (currently you use Darmanis' data as the signature database for deconvoluting mixtures of Darmanis' data - what happens if you use a different signature database? e.g., the IP and CA datasets included in your Multibrain database?). That might provide a closer approximation to what we typically encounter when performing DE analyses on transcriptional profiling data from macro dissected data.

    1. On 2017-03-29 14:15:08, user Daniel Shanahan wrote:

      I think this is a very interesting proposal - the fundamental intent is to ensure that the scientific literature is correct and up-to-date. The current system of posting retractions/corrections as separate articles doesn't always work as intended even with the CrossMark system; your comments regarding external corrections (e.g., through comments) not being referenced correctly is also entirely accurate for formal, internal corrections - authors often do not recognise if an article has been retracted or corrected when citing in future articles (see http://researchintegrityjou... to emphasise this point). This system would ensure that all the information was available from the article itself, so long as the versioning system was robust. While the technology infrastructure described here should work, a key aspect would be the behavioural change of researchers citing articles - grey literature searches would need additional care, and referencing versions with the date of access would be very important (you often see web references lifted from citations with only the date accessed updated).

      It would also be interesting to know how evaluation would come into play for these amendments. In many cases, there is disagreement around what would require a correction (e.g., the recent COMPare Trials initiative), so there would need to be a workflow around when an amendment is published - you have mentioned stating who instigated it, but there would need to be a conflict resolution template for instances when certain parties disagree.

    1. On 2023-01-23 14:56:20, user Benjamin Himes wrote:

      Manuscript review<br /> dated January 23, 2023:

      by: Benjamin A. Himes

      “A robust normalized local filter to estimate occupancy directly from cryo-EM maps.”

      The version posted January 20, 2023, to biorxiv https://doi.org/10.1101/202....

      The problem being investigated:

      The interpretation and utility of cryo-EM reconstructions [maps hereafter] is often<br /> made challenging by spatially localized degradation that may arise from several<br /> sources. To this end, many tools exist to estimate and/or modulate cryo-EM maps<br /> non-uniformly. These tools are generally sensitive to artifacts in the cryo-EM<br /> maps, user-selected processing parameters like the local window size, and image<br /> intensity distributions that deviate from those generated by well-isolated<br /> globular proteins.

      The proposed solution:

      Forsberg, Shah, and Burt propose a non-linear filter based on the maximal value in a sliding<br /> window. While existing tools lean toward estimating local resolution or<br /> signal-to-noise ratios, the authors aim to avoid problems this may introduce by<br /> starting from the premise that the real-space image intensities should be<br /> relatively uniform at moderate-to-low resolution unless there is flexibility or<br /> compositional heterogeneity. By starting from this simple premise and selecting<br /> the max-value filter, the method aims to be robust as well as fast

      The results:

      The filter is implemented in Python. The authors have developed a clean and well-designed GUI that is<br /> easy to install, intuitive to use, well-documented, and interfaces beautifully<br /> with a USCF-Chimerx, a staple visualization tool in the field. The manuscript<br /> is well written, and the results clearly show they can measure non-uniformity<br /> in real-space cryo-EM maps. Beyond visualization, they demonstrate that this<br /> statistic can also be used to modify the cryo-EM map; however, the full utility<br /> of such modifications is somewhat less convincing. That is, of course, no<br /> concern, as the improved visualization should already be beneficial in cryo-EM<br /> map interpretation.

      Major concerns:<br /> None

      Minor concerns:

      1. Several minor phrasing issues result in<br /> statements that could be understood as factually incorrect if read out of<br /> context—these I’ve sent to the authors directly.

      2. “So-called ab-initio 3D reconstructions can<br /> now be made without user input bias.” It is worth<br /> noting that template-based particle picking or even blob-based picking combined<br /> with 2d classification can introduce model bias that persists even when<br /> ab-initio 3D reconstruction is used. Even the selection of 2D classes can<br /> introduce model bias from the users mental model of the target. See for<br /> example, Superstitious Perceptions Reveal Properties of Internal<br /> Representations, Gosselin and Schyns 2003 Psychological Science.

      3. Resolution in cryo-EM is not a contested term.<br /> It is the spatial frequency at which the reconstruction is no longer<br /> statistically reliable. The definition of where exactly that point is, however,<br /> has been contested in the past.

      4. A few relative qualifiers should be specific—Eg.<br /> “reasonably sized input”, “…reasonably set lower…” etc.

      5. In the methods section, the authors point out<br /> that the CDF in eq 6 is only valid for statistically independent voxels. While<br /> it is often the case that these conditions can be relaxed, I think it is<br /> reasonable to ask for an analysis of or justification for using this CDF, given<br /> that the core problem the authors address is one that, by definition, results<br /> from statistical dependence between pixels. As a simple example, consider a<br /> loop flipping back and forth between two positions, resulting in lower average<br /> occupancy. At any given time, the intensity measured in one of those positions<br /> is correlated with the intensity in the other.

      6. The value of 449’260 CPU seconds in table S1<br /> seems unreasonably high. Are these measurements the average of several repeated<br /> experiments? Please double-check.

      Final thoughts:

      The authors have presented an algorithm that is robust to common characteristics in cryo-EM maps<br /> and developed a tool to execute that algorithm that is easy to use and open to<br /> modify. Well done! I could install this and immediately use it in a project I<br /> was working on.

    1. On 2019-01-06 17:45:34, user Bruce Aronow wrote:

      this is really nice work! agree that specialized RAC1-to-actin coupling to modify cell projection behavior is incredibly important for different cell types to optimize. looking at a couple of single cell datasets that I'm analyzing.. 49a is hot for neutrophils, 49b is pan-myeloid

    1. On 2019-08-03 11:25:59, user Mick Watson wrote:

      Nice paper!

      Some papers of ours you might be interested in:

      We'd also recommend subsampling of the data which can help assembly

      Cheers<br /> Mick

    1. On 2019-11-14 01:06:59, user Guofeng Meng wrote:

      Dear kind readers to this manuscript,

      This is the first and corresponding author of this manuscript. In this system biology study, we reported that accumulated regulatory degeneration of brain contributes to the development of Alzheimer's disease (AD), which may be new causal mechanism of AD. This finding is also useful to drug target discovery of AD.

      This is my first project after joining a university. We applied new strategies and reported new findings. In our work, we took care of every steps without any misconduct. From my view, it should be an interesting story. However, it seems less attractive to editors and readers.

      The finding is meaningless?The method is not trustable? People don't like system biology? People don't trust early-career investigator? Or anything else?

      Can anyone leave me any comments to this manuscript? Your response will be valuable. Thanks in advance.

      With Best Regards,<br /> Guofeng,

    1. On 2020-03-15 16:08:18, user Irene Rodríguez Sánchez wrote:

      We are a group of biomedical students (3rd year) from Universidad Francisco de Vitoria (Madrid, Spain). As part of our assignments, we were asked to review this paper published as preprint. We would like to kindly share our thoughts and positive criticism in case that it would be of any help for the authors as well as the scientific community. Please see below our comments:

      Shabir et al. investigated the role of atherosclerosis and its relationship with haemodynamic changes, which can lead to neurovascular damage and dementia development.

      There are several major issues that require careful consideration:<br /> ? In the introduction, there are fundamental concepts that are not defined. For instance, the concept of ¨eNOS¨. <br /> ? It is not clear how many animals were used in some experiments. It seems that a maximum of five mice per group in total were used per experiment.<br /> ? Atherosclerosis is claimed to be the greatest risk of dementia but not many techniques are performed to determine this fact. Therefore, additional experiments should be conducted to substantiate these results.<br /> ? In the discussion, it is concluded that the reduction of the hemodynamic response is related to the functional decomposition of CNV. This contradicts what is mentioned in the results and figures. This should be clarified in the revised version.

      There are other minor issues which should be taken into account:<br /> 1. Materials and Methods:<br /> ? Viral vectors are used which can induce an inflammatory response. This process can impact the results generated. Was this aspect controlled for?<br /> ? The model used is quite aggressive (PCSK9mut and western diet) compared to other experimental models that could have been used (older mice treated with western diet).<br /> ? Primers and their annealing temperature are not well described. <br /> ? Oxygen conditions used to measure cerebral activity in the cortex are not mentioned.<br /> ? It is not taken into account that basal hemoglobin and oxygen levels may vary among healthy and atherosclerotic mice.<br /> ? The source of TNF? is not known.

      1. Results:<br /> ? Reduced Stimulus-Evoked Cerebral Haemodynamic Responses in ATH Mice:<br /> ? The n number used in the sibling cohort (n=2-4) is not clear. Initially is indicated that 5 mice per experiment will be used.<br /> ? Atherosclerosis is assessed by en face staining of aortae with Oil Red O but this method was not explained in the Materials and Methods´section.

      ? Reduced Stimulus-Evoked Neural Activity in ATH Mice:<br /> ? Figure 1: <br /> ? It is not clear if both experiments (neural activity and cerebral haemodynamics) were recorded simultaneously. Please clarify this.<br /> ? In the analysis of neuronal activity, the oxygen level was not described but it was noted in the cerebral haemodynamics experiments.<br /> ? Significant Neuroinflammation and eNOS Upregulation in ATH Mice:<br /> ? It was not demonstrated that IL1? and TNF? are coming from astrocytes.<br /> ? The meaning of the abbreviation NOS is not explained.<br /> ? The physiological basis for a significant increase in NOS3 were not specified.

      ? More viability or cell degeneration assay experiments with astrogliosis-associated proteins should be conducted to strengthen these results.

      Begoña Parrondo, Sofía Pérez, Mireya Robles, Irene Rodríguez, Teresa Vázquez y Elena Verdún

    1. On 2024-09-03 22:48:26, user Pooja Asthana wrote:

      Summary<br /> The study investigates the human protein DJ-1, which is known for its role in detoxifying the metabolic bioproduct methylglyoxal (MG). There has been an ongoing debate over whether DJ-1 acts directly on MG (direct substrate) or requires a protein intermediate acting as a protein/nucleic acid deglycase (glycated protein substrate). The authors used fixed-target micro-crystallography and mix-and-inject serial crystallography to structurally analyze covalent intermediates in the reaction catalyzed by DJ-1. One of the significant achievements of the study is the successful use of these advanced crystallography methods to determine the structure of key reaction intermediates: hemithioacetal and L-lactoylcysteine, providing new insights into DJ-1's glyoxalase mechanism. However, a major weakness is that the authors' claim refuting the alternative deglycase mechanism are not fully supported by the presented data. Despite this limitation, the study advances our understanding of DJ-1’s enzymatic function by leveraging MISC at synchrotron using the new flow cell injector.

      Major points<br /> Major point 1<br /> The claim made in the discussion that: “These results provide direct structural evidence supporting a growing number of enzymology studies also indicating that DJ-1 is not a deglycase…” is not supported by evidence presented in the manuscript. Although this work elegantly demonstrates that MG covalently modifies the catalytic cysteine of DJ-1 (Cys106), the crystallography experiments presented are unable to test whether the alternative mechanism (with a glycated substrate) occurs. More careful treatment of this logic in the discussion would strengthen the manuscript, and would help the manuscript to be more focused on the compelling X-ray crystallography results. We recognize it is difficult to “prove a negative” however these experiments affirm the primary activity without directly testing the alternative one.

      Major point 2<br /> The authors report compelling evidence that the DJ-1 catalytic cysteine (Cys106) is covalently modified by MG. However, the concentration of MG used was 50 mM, and non-catalytic cysteines might be covalently modified at this concentration. Indeed, it’s possible that one of the DJ-1 surface cysteines is covalently modified (Cys53), based on the large positive difference peak in the FO-FO difference density (Figure 5b, Figure S8) (although it is suggested that this is evidence of allosteric communication). Covalent modification of a surface cysteine leading to lattice disruption is consistent with the observation that MG is known to dissolve DJ-1 crystals. The manuscript could be strengthened by consideration of these points, as well as analysis of difference maps around Cys53 for the fixed target structure (e.g. add panel to Figure S1 showing FO(methylglyoxal)-FO(free) maps around Cys53). Discussion of the differences in modification rates for the catalytic and surface cysteines, and the impact of large versus small crystals, would be helpful.

      Major point 3<br /> Is it plausible that a second, synchronized turnover is captured by the mix-and-inject experiment? This claim might be developed by modeling the concentration profile of the intermediates along the 30 second time course (e.g. similar to Figure 4 in PMID 29848358). To this point, were the occupancies of the covalent adducts refined at each time point? Did the authors consider whether a mixture of species might be present? The evidence supporting the second turnover comes from the featureless difference map calculated between the 3 sec and 20 sec time points (FO(20s)-FO(3s) in Figure S6). Is there an alternative explanation for the decreased occupancy at this time point other than synchronized turnover? E.g. a problem with sample mixing resulting in lower substrate concentration at this time point.

      A related concern is whether the data as presented can discriminate between the two covalent intermediates (HTA or LC). Perhaps Figure S7 would be strengthened by adding the FO-FC difference maps for each of the intermediates modeled with the other species (e.g. the HTA dataset modeled with LC and vice versa). Can the authors comment on the lack of correlated negative (or positive) density in the FO-FO difference map matrix (Figure S5) in panels comparing sp2 and sp3 carbons (e.g. FO(15s)-FO(3s)). In this example, there is a large positive peak in the difference map for the sp2 to sp3 change, but no correlated negative peak.

      Minor points<br /> Minor point 1<br /> Was the covalent adduct observed in the MG-soaked DJ-1 crystals presented in Figure S1c modeled? Is the difference density consistent with the HTA or LC intermediates? Or a mixture of both?<br /> Minor point 2<br /> Is it possible that movement of the active site histidine (His126) away from covalent intermediate (Figure 4a) is consistent with histidine protonation? Or is the geometry such that protonation is unlikely?<br /> Minor point 3<br /> We find it helpful if the figure (or figure legend) includes PDB codes for their quick look up.<br /> Minor point 4<br /> The size of the scale bar in Figure S1a might be increased.

      Review by:<br /> Pooja Asthana, Galen J. Correy & James S. Fraser (UCSF)

    1. On 2023-10-31 15:03:11, user Scott C Thomas wrote:

      For table 1, it looks like citation 17 used an Illumina HiSeq platform. "Libraries Preparation and Sequencing<br /> Libraries were prepared using the Nextera DNA Library Preparation kit (Illumina) and sequenced on an Illumina HiSeq platform (leading to 40,552,111 ±9,650,536 reads/sample)."

      Also, Qiagen is a company, not an extraction kit. Qiagen manufactures many of the kits listed in table 1, so it is confusing to have "Qiagen" listed as a DNA-Exk.

    1. On 2025-12-01 02:54:02, user hibiscustea wrote:

      Hi, thank you for sharing this preprint, it’s really bold work, and I enjoyed reading it. A couple of things you might consider for the next version: the Introduction would really benefit from explicit hypotheses, just so readers know what the expected contrasts were between phenology and morphology. Some of the modeling assumptions (equal evolutionary variances, missing environmental forcing) could use a clearer justification too. And the transition to the empirical motifs comes a bit abruptly, the Doñana system is very seasonal, so V+ motifs might appear for several reasons besides coevolution.<br /> But overall, really interesting work. Looking forward to seeing where it goes next. I'm a PhD student and we are reviewing a preprint paper for a class, I chose yours, thank you for your work.

    1. On 2023-12-29 20:00:57, user Matthew Berg wrote:

      This manuscript has now been published in RNA Biology. https://doi.org/10.1080/154...

      Ecaterina Cozma, Megha Rao, Madison Dusick, Julie Genereaux, Ricard A. Rodriguez-Mias, Judit Villén, Christopher J. Brandl & Matthew D. Berg (2023) Anticodon sequence determines the impact of mistranslating tRNAAla variants, RNA Biology, 20:1, 791-804, DOI: 10.1080/15476286.2023.2257471

    1. On 2018-01-22 05:15:13, user Pavel Prosselkov wrote:

      No doubt you did a great job taking into an account individual player expertise as a gaming skill proficiency bias. But on the global level, gaming is more like an emergent property of our modern society with the access to it historically privileged to the high GDP countries (look at the variance distribution per country). It is that hidden but powerful dependable co-variate forcing you to conclude that Nation's GDP predicts Nation's IQ (as measured by a game).

    1. On 2023-10-21 12:39:11, user O. Elizabeth Plant-Sexton, PhD wrote:

      It would be interesting to know, among women who are incarcerated for murder, at what stage of the cycle they were in at the time of the murder. In the population among these women, are the ages a reflection of their menstrual ability? Higher population of menstrating women than post menopausal? If so, is their a correlation to when in the cycle most murders occurred?

    1. On 2025-03-06 02:33:06, user Charles Warden wrote:

      Thank you very much for posting this preprint!

      The "Code availability" section indicates "We provide supplementary files containing an R script with functions to run our CRF-based correction procedure as well as a tutorial notebook illustrating how to run it.".

      However, I think I see anything uploaded as supplemental files. Am I overlooking anything, or does the supplemental code need to be added in a revision?

      Thank you very much!

      Sincerely,<br /> Charles

    1. On 2018-01-08 13:43:18, user Mateusz Iskrzynski wrote:

      Dear Authors, <br /> to give feedback towards making the article more readable I would appreciate if all variables were properly defined, all symbols introduced. E.g. what is y, e, B_0, M_0? <br /> The basic symbols and concepts could be described before the equations and the more specific parameters e.g. around them.<br /> As a physicist I also want to advertise the numbered bibliography - it is much less distracting in the text.<br /> Best regards<br /> Mateusz Iskrzynski

    1. On 2020-04-06 19:53:41, user Valerie wrote:

      Thank you for sharing your research! Have you researched (or have plans to) how CQ/HCQ react with other diabetes drugs apart from Metformin (e.g., Glimepiride, Vildagliptin, etc.)?

      Diabetics are anyway at higher risk of complications from Covid-19, so it's concerning if CQ/HCQ which have shown some early signs of working cannot be used for that patient group.

    1. On 2022-04-25 09:38:07, user Cecilia Bang Jensen wrote:

      Thanks for an interesting read! Probably somebody has already pointed it out to you but if not, figure 3 b) in the paper seems to have an axis error (KSTAR and KSEA significance scores mislocated)

    1. On 2025-08-08 17:10:33, user Reviewer 6 wrote:

      A major flaw with this work, which none of 3 eLife reviewers point out, is that they only show results until the F2 generation and claim that this independently validates "transgenerational inheritance". However, in the original assay the learning is carried out on adult worms with F1 embryos (and germ cells) in utero which are exposed along with the parent (P0) to the pathogen. For maternal inheritance, effects at F2 are generally still considered intergenerational (ie maternal) effects - not "transgenerational" epigenetic inheritance. The effect would have to be shown at F3 (and also F4 as the original Murphy study showed) for them to really claim validation of the study.

      I refer the authors to the following review (PMID: 24679529), cited >2000 times, on transgenerational epigenetic inheritance which clarifies this point: "In the case of an exposed female... the fetus can be affected in utero (F1), as can the germ line of the fetus (the future F2). These are considered to be parental effects, leading to intergenerational epigenetic inheritance. Only F3 individuals can be considered as true **trans-generational ** inheritance (see Box 1), in the absence of exposure."

      Given the F2 effect in this eLife study is already quite small, its imperative that the authors show F3 and F4 data to actually test the original Murphy claim. Moreover, in some of the Murphy experiments, effects at F3 and F4 are even stronger than they are at F1. So, this should not be a problem if the authors have actually replicated the Murphy results.

    1. On 2021-10-02 21:14:31, user Travis Wheeler wrote:

      The results SEEM relevant and important ... but without code to test/review, there's really not much to say about the paper. The preprint has been posted for 2 months, but no RGN2 code is available; please share it.

    1. On 2021-04-25 09:57:27, user George Elias wrote:

      Some nice data here but I wonder if it is enough to have correlations to say that there is a guided coordination between vaccine-specific Th1 CD4 and CD8 T cell responses! I would keep this on the speculative part of the story.

    1. On 2022-10-24 00:05:14, user CDSL JHSPH wrote:

      This manuscript presents a wealth of supporting data for evidence of vocal learning and conformity among whale songs in the fin whale (Balaenoptera physalus). Romagosa and colleagues present a twenty-one yearlong observational study of three critical components of the songs produced by male fin whales. It is the first study to suggest a mechanism driving vocal learning and conformity in animal songs, specifically pertaining to the fin whale. Romagosa & colleagues’ comprehensive analysis includes a dearth of both temporal and spatial data. The assessment the inter-note interval (INI, i.e., rhythm), the 20-Hz note, and the High Frequency (HF) note of the fin whale song is used as a conduit by which the authors reveal patterns of change and adoption of different patterns over time. The authors use a wide geographical range, inclusive of 15 sampling locations grouped into 7 separate regions, with data collection spanning between 1999 and 2020. They provide thorough consideration of alternate interpretations of their data and use the existing literature to further bolster their proposed ideologies.

      This manuscript has immense potential to posit something novel to the field, based on the background the authors have provided. However, due to the seeming overreliance on existing literature in the discussion, limited exploration and elaboration on the data itself in the results section, and poor articulation of caveats in the sampling methodology, the significance of the findings presented are undermined. Based on the targeted journal, a re-organization of the manuscript’s structure may be suitable to address these more structural issues. Despite the incredible amount of data, there lacks thorough explanations of how the data directly supports the conclusions presented. The results section could be elaborated upon to increase the credibility of the stated conclusions (examples starting in line 93 through 106, 119 – 127, 136-144). The discussion section does not implicate the data presented in this paper in the conclusions being made by the authors as much as it should, and it seems to rely much more heavily on existing literature in the greater field (i.e., extending beyond marine mammals). Switching some of the description of the data from the discussion section into the results section will make both sections easier to read and understand. .

      As these studies are purely observational, the methodology should be highlighted more, and as stated previously, perhaps may merit a structural reorganization of the manuscript itself. Because of the several sampling differences such as those in instrumentation & manufacturer, including the supporting evidence for why these data are still usable and comparable is critical to the credibility of the work (see Supplementary Material, lines 30 – 50). This experiment should be included either in the main body of the text or highlighted more explicitly in the main body, so the reader knows to find it there. The inconsistencies between recording machinery need to be explained, as the authors have performed an additional study to verify these data. Using figure 1 to be referenced primarily by the methods section is a poor choice of ordering, and perhaps the visuals provided in figure 1 can be moved into the supplement since they are not showing any data. This would leave available a spot to move the experiment in the supplementary material into the main text.

      Additionally, including more detailed figure legends (i.e., explaining that each symbol represents an individual recording/represents one day, explaining the red circle in current figure 1A in the legend, etc.). The same descriptive wording used in the legend for Figure 3 (specifically the information provided in line 133 – 135) should be applied to all figures in both the main and supplemental data. The rationale for the groupings of regions in the histograms of INIs and HF note peaks in Figures 4A & B is unclear and not indicated. Figure 4B is not discussed in the text either. Having panels in figures that are not described in the text is confusing, as the reader cannot understand what the purpose is of what is being presented.

      Generally speaking, the manuscript was a delight to read. It was well-written, and I felt that the background and foundation for the work presented was laid out very well. This data that is being presented has exciting implications for the field and fills in a clear gap in knowledge. The amount of time and dedication that was given to these studies should not be understated. I felt that the authors framed their goals and provided comprehensive context for the material being shown. This research should be celebrated, and the authors should be pleased with the work that went into this manuscript!

    1. On 2021-10-14 16:31:13, user Colin Hawco wrote:

      Overall important work but I'd like to raise some issues.

      First the Destrieux atlas is not a functional atlas. People keep using these sorts of atlases in fMRI work and I have no idea why. The Superior temporal lobe is not a functional unit. That giant big mid-frontal region is not the DLPFC and not well overlapped with what may be reasonable activity patterns for tasks such as the NBack.

      Also, the analysis appears to use Beta values from various contrasts. IME the average t-value is more reliable as a metric because it is (de)weighted by the noise in the voxel/vertex/region. In any analysis of general patterns of activity, I have found more robust using individual t stats rather than betas.

      Also you included many contrasts, including several that have obviously lower ICCs, and in most of the paper appear to collapse across all regions and contrasts. For the Nback, I'd mainly focus on the 0 Bk, 2Bk, and most importantly, the commonly used 0vs2Bk contrast. Those look like they have relatively decent ICCs to me.

      Relatedly in the figures you average across all contrasts, but some of them are not very good contrasts and as a result, your reported regional ICCs are dragged down. Rather than a take all approach, I think it would be better to focus on the primary contrasts as the ones being used.

      I object to the use of ROI as regions which you found interesting when the entire analysis is based off an atlas; the more conventional use of ROI is parcels, etc, in the broader sense, rather than 'parcels I think are interesting versus those I think may be less interesting'. I'm being pedantic but it confused me.

      (everything after this is me pontificating on things I think are interesting in general). <br /> Interesting and important point that contrasts vs baseline have higher stability than two task contrasts, but I also think we forget this is a truism. If you have an imperfect measurement, and subtract another imperfect measurement from it, the reliability of the difference must, by definition, be lower than the reliability of the two separate things (of I see this is mentioned later in the discussion).

      Important that the SST and MID tasks had much poorer reliability. My opinion has between that reward tasks generally have very poor reliability, potentially because the signal is not strong enough, but also because people may vary quite a bit with themselves even how they respond to trials, and oscillate.

      One point of potential import is that a lot of these analyses being done across the field are assuming task activation should be stable, but the brain, and fMRI, is inherently dynamic. Averaging activity by model fit across these relativity short tasks may not provide a very stable metric. Considerations of dynamic processes may yield greater information, but a big challenge there is motion (its always motion...) which makes dynamic measures really hard.

    1. On 2018-07-09 03:50:57, user jvkohl wrote:

      Isoform-expression in all cell types of all individuals of all species is quantized energy-dependent and RNA-mediated. See: Dependence of RNA synthesis in isolated thymus nuclei on glycolysis, oxidative carbohydrate catabolism and a type of “oxidative phosphorylation” (1964) https://www.sciencedirect.c... Excerpt: "The synthesis of RNA in isolated thymus nuclei is ATP dependent." Without the ATP-dependent synthesis of RNA, you cannot get from isoform expression to cell type differentiation.

    1. On 2022-04-23 21:00:06, user JABS Editor wrote:

      This article was published in Journal of Applied Biological Sciences (JABS) 16(1): 89-101, 2022 with the title "GLYCOINFORMATICS APPROACH FOR IDENTIFYING TARGET POSITIONS TO INHIBIT INITIAL BINDING OF SARS-COV-2 S1 PROTEIN TO THE HOST CELL".

    1. On 2021-04-23 13:22:31, user Erik Gylfe wrote:

      Dear authors,<br /> I find your study provocative and interesting, but somewhat difficult to read due to erroneous reference to Figures. You point out that most previous studies have been performed by stimulating beta cells with unphysiologically high glucose and now demonstrate interesting Ca2+ responses at more reasonable concentrations of the sugar. From this point of view I think you provide important new data using an elegant experimental approach.

      However, I am concerned about the experimental design and some conclusions drawn. The data are taken as argument to return to very old and nowadays mostly abandoned ideas that Ca2+ influx has only a minor role during the first minutes of beta cell activation. Some of these old ideas are based on unfortunate experimental design, which I also find in the present study. The common denominator is simultaneously changing two parameters without considering that the timing of<br /> the effects may be different.

      I think that the isradipine experiments is a telling example. It is obvious that the effect of the used concentration of isradipine has a slow onset (Fig S3-1) and an even slower off effect (Fig 2) in your system. The timing definitely seems slower than that for the glucose response. Therefore, the most likely explanation of the results is that the Ca2+ channels are not initially blocked and glucose maintains some of its early effects on voltage-dependent Ca2+ influx. The same explanation likely applies to the diazoxide experiment (Fig. S3-2), particularly since the concentration is on the low side. In my experience glucose-induced intracellular Ca2+ release is unlikely when Ca2+ influx is blocked except after artificial elevation of cAMP (see ref 16, which by the way is not correctly cited in the reference list). Instead, the typical<br /> effect of glucose elevation under such conditions is a lowering of Ca2+<br /> due to ER sequestration. However, the latter effect would probably escape detection with the presently used low affinity indicator and is likely rather modest when raising glucose from 6-8 mM.

      It is stated in lines 322- 323 “The prominent role for intracellular<br /> Ca2+ release had strong early support from 45Ca2+ flux studies”. 45Ca2+ flux data are often difficult to interpret, and I think you may mix up efflux of 45Ca2+ from islets with intracellular Ca2+ release. Most 45Ca2+ studies are instead consistent with a [Ca2+]i lowering<br /> effect of glucose when voltage-dependent entry is prevented. See for example Bergsten et al. Am. J. Physiol. 255: E422-E427, 1988 for the effect of glucose on both 45Ca2+ and [Ca2+]i in the presence of diazoxide.

      The isradipine experiments reminds me about old data taken to indicate that first phase insulin release is independent of Ca2+ influx (Wollheim et al. J. Clin. Invest. 62: 451-458, 1978). In that study first phase insulin release was unaffected when glucose was elevated simultaneously with addition of 5 microM Verapamil. With more effective prevention of Ca2+ influx they might instead have discovered that glucose lowers basal insulin secretion under such conditions (Bergsten et al. 1988).

      Although I agree that Ca2+ release from (but also uptake into) the ER are important for shaping the slow [Ca2+]i oscillations, I get the impression that you implicate a more fundamental role of intracellular Ca2+ release in their generation. I think this must be discussed in relation to experiments indicating that the glucose-induced slow oscillations are maintained (with different kinetics) when intracellular Ca2+ uptake and release by the ER is prevented by SERCA inhibition (Liu et al., J Physiol 508: 471-481, 1998; Gilon et al JBC 274: 20197–20205, 1999).

      The effects of ryanodine and acetylcholineare interpreted only in terms of Ca2+ release from the ER. I think there is a striking difference in the responses. The effect of acetylcholine is rather similar to that of raising glucose from 6 to 8 mM. This is likely explained by the Na+-dependent depolarizing effect of acetylcholine being sufficient to trigger electrical activity in beta cells exposed to threshold glucose concentrations (see cited ref 22).

      The ryanodine data looks rather compelling but are inconsistent with other cited observations. As you acknowledge, the functional significance of ryanodine receptors in beta cells is controversial. I would like to see the effect of repeated exposures to 100 nM ryanodine which is expected to induce reproducible responses since the inhibitory effect of 100 microM ryanodine was reversible.

      In this context I lack information about how representative the<br /> observations are between experiments. As I understand it, the illustrations show results from individual experiments. It is stated in Methods “At least 3 slices/mice were used for each experimental condition” but nothing is stated about variation between experiments.

      Best regards,<br /> Erik Gylfe

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