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    1. On 2019-07-01 05:17:30, user Gal Haimovich wrote:

      This is very interesting, and a cool method, but the authors should verify that CYP3A4 protein is not secreted as well in their mice, e.g. as suggested here: https://bpspubs.onlinelibra...<br /> Also, in fig 1B, there are very faint bands of CYP3A4 protein in ScA and VsA which the authors ignore. This could explain the 5EU-RNA in adipose tissue.<br /> There is also clear HD5EU staining in adipose tissue at level similar to kidney(fig 2C). Perhaps a quantitative analysis of these images (instead of showing a single image example) will show clear differences.<br /> I am not convinced that the 5EU-RNA from adipose tissue originated from Liver, or that the differences seen are a result of the ccfRNA and not the change in diet that changes transcriptional or RNA decay programs.

    1. On 2025-11-20 15:34:33, user Evolutionary Health Group wrote:

      We at the Evolutionary Health Group ( https://evoheal.github.io/) "https://evoheal.github.io/)") really enjoyed this paper. Here are our highlights:

      This work demonstrates that metagenomes contains a vast layer of “not yet genome-resolved” biodiversity. According to the results, up to ~80% of putative species-level clusters are not represented by genomes of cultivated organisms or by MAGs.

      The study quantitatively shows that species discovery is far from saturation and strongly habitat-dependent. Human gut samples and anthropogenic environments are already densely sampled and contribute few new lineages per additional metagenome, whereas soils, aquatic ecosystems, the rhizosphere, and non-mammalian hosts remain true hotspots of unexplored diversity.

      A separate and fundamentally important result - confirmed numerically- is the observation that the structure of prokaryotic diversity follows the same universal statistical laws (the power-law Willis-Yule / Yule-Simon distributions) as that of eukaryotes. In other words, the authors demonstrate that the same simple macroevolutionary regularities operate across the entire Tree of Life.

    1. On 2020-02-09 15:49:43, user ResearchGuy wrote:

      I see several commenters have asked about the COMPOUND probability of ALL FOUR sequences occurring naturally in what would seem to be a section of nCoV that affects what kind of cells that can infect. I have seen no answers to that question. Someone, preferably several someone's, please answer it. If I had the skills I would do so myself, but I don't think any of the posts have even stated the exact individual probabilities so I can't multiply them together for myself.

    2. On 2020-02-01 07:05:39, user Anon wrote:

      I noticed that several people have pointed out that QHR63300.1 has all of the same insertions and is from Bat. Can anyone explain why this is the only Bat CoV with these insertions?

      If you search for matches to QHR63300.1 the best hit by far is the Wuhan Seafood Market CoV, which infects humans.

      It's also hard to understand why QHR63300.1 was uploaded 4 days ago (Jan 27, 2020) from Wuhan Institute of Virology.

    1. On 2021-05-15 20:43:02, user Vicente Velasquez wrote:

      This was overall a really good read, and great evidence is presented to demonstrate a connection between leptin and the canoncial WNT pathway.

      Strengths:<br /> - Methodologies on zebrafish and mice feeding and measuring are well described and easily replicable. <br /> -Staining images are high resolution and can be clearly read and analyzed by the reader.

      Some critiques I have with the paper include:

      -Figure coloring choices are not color-blind friendly. The use of bright reds and yellows are not comfortable to the above listed. I suggest choosing colors that would not be this way ( such as less bright/more subdued colors).

      -Figure 3B's grey boxes look less like data and more like formatting errors. Please use another method to demonstrate what the grey boxes are saying, so it can stand out more.

      -For your immunohistochemistry, please cite your antibodies/techniques. While you do cite the papers that you used for the protocol, this is incredibly inconvenient as the cited papers are not clear and specific as well. It would be fine to just list what antibodies you used, and just cite the protocol from the papers.

      -Figure 5A has an odd break in weight recordings, and this break is not explicity stated. Is there a reason for this? Please state it in the figure or in your results section.

      -Zebrafish pictures in 3A should be moved to a supplemental figure. <br /> -Figure1A-D zebrafish pictures should be moved to better allow room for E and F elaboration.<br /> -Consider getting more data points or using more for E and F to establish a stronger significance.

      -Figure 2A-D also has data from female fish, even though you state that males were only used due to variances in female weight due to eggs. If this is the case, the female data should not be present in your main figures; either move female data to a supplemental figure or do not include the female data.

      • A potential direction I recommend ( especially for mice/mammalian systems) is to analyze fat levels via radiolabeling. This can also be done in the zebrafish, as you did see a weight change in circumstances of extreme nutrient surplus.
    1. On 2023-02-02 09:25:55, user Sebastian Van Blerk wrote:

      Dear authors,

      There seems to be a mistake with the primers described in the methods section.<br /> The 515F-806R primer pair only amplify the V4 region. They do not amplify V3-V4.

      If these primers were indeed the ones used for sequencing then this is a comparison of V1-V3 vs V4.

      Kind regards,<br /> Sebastian Van Blerk

    1. On 2023-09-21 08:42:03, user Diego del Alamo wrote:

      This is a comment about version 5 of the manuscript.

      These results are thorough, compelling and persuasive. They also stand in contrast with other papers, published in the aftermath of alphafold's release, that argue the opposite.

      The main concern for me is the absence of any testing or discussion surrounding the relax step of the pipeline - the manuscript never uses the words "relax", "minimization", and "openmm" (the package used by alphafold for all-atom minimization), and I did not find details in the accompanying github repo. It is therefore unclear how much of the results should be attributed to the neural network itself and how much should be attributed to the minimization step following structure prediction. We can't rule out the possibility that the strain being measured results from this second step. Were that the case, it would cast doubt on the role of the alignment and templates as the authors suggest in the discussion.

    1. On 2019-04-23 12:47:59, user Brian Levine wrote:

      In this study, the researchers assessed concurrent validity of questionnaires against established measures in a sample of 217 participants. There is a strong motivation for this kind of study, which provides useful information for researchers assessing memory, imagery/scene construction, navigation, and future thinking. The researchers are commended for a comprehensive study reflecting many hours of effort in order to execute these measures. My comments will be largely focused on the measures of autobiographical memory (AM), some of which were developed by my group. This comment grew out of a discussion with my trainees who also read the article, including Nick Diamond, Carina Fan, Raluca Petrican, Stephanie Simpson, and Lynn Zhu. I thank the authors for posting this preprint, open to community commentary.

      A major contribution of this paper is an emphasis on subjective experience, which, although impossible to assess directly, is important to the consideration of episodic memory. This paper supports the view that subjective and objective instruments do not assess the same thing. As stated by the authors, the use of these instruments depends on the goals of the study. Where we disagree is the premise that seems to be implied in the title, which is that questionnaires (and to some extent, the objective tests) are measuring something different than what they purport to measure.

      My main critique of the approach is that it lacks nuance in terms of levels of analysis within AM, which is itself a multifaceted construct. The authors took a strictly univariate approach in which each criterion measure is treated as a unitary measure of a latent construct. Normally, multiple measures would be deployed in a latent construct approach because no single measure is process-pure.

      A main finding of the present study is that overall, subjective ratings (either on questionnaires or self-/other ratings of laboratory test performance) correlate with each other to a greater degree than the subjective/objective comparison. This is interesting though not surprising given that subjective measures do not measure the same thing as objective measures, and that they share measurement error bias. This is also the case for the scene construction measure which is held as objective, but in fact takes subjective ratings into consideration in the scoring.

      In the Autobiographical Interview (AI), internal details are treated as a measure of a person’s capacity to recover contextual information from past events; external details reflect content not specifically related to the defined event and are therefore considered to be inversely related to cognitive control over memory retrieval. A recovered detail is neutral with respect to subjective/conscious experience. Patient M.L., who had a specific impairment in conscious re-experiencing of the past due to frontotemporal disconnection, showed only marginal reductions in internal detail production, even though his “remember” ratings for the same events suggested a profoundly reduced conscious experience (Levine, Svoboda, Turner, Mandic, & Mackey, 2009). He also showed reduced activation of the AM network when presented with rich retrieval cues for these events. Even more to the point, patients with severe medial temporal lobe amnesia, including H.M. (Steinvorth, Levine, & Corkin, 2005) have produced events with substantial internal details (see also Cermak & O'Connor, 1983).

      The SAM episodic subscale, on the other hand, was developed specifically to probe the subjective experience of recollection at the trait level. As noted by the authors, we found that these were unrelated in our original SAM paper in healthy young adults (Palombo, Williams, Abdi, & Levine, 2013; see also Hebscher, Levine, & Gilboa, 2018 for a similar finding), nor were people with Severely Deficient Autobiographical Memory (SDAM) impaired on AM for recent events using the AI. Considering these findings, the above-described patient findings, and the more general findings of dissociation between subjective recollection and recognition performance, as illustrated in the Remember/Know technique, a strong relationship between these two measures should not be expected.

      Nonetheless, some relationship between recovered details and self-reported episodic autobiographical re-experiencing at the trait level could be expected. I believe the lack of relationship is owing to the fact that the AI was designed to elicit the richest possible event descriptions from participants. As the authors note, internal details are scored liberally for the sake of reliability (i.e. the “benefit of the doubt” rule where any detail that could reasonable be considered internal was classified as such). However, there was another purpose in eliciting rich episodic autobiographical memories, which was to avoid a false positive classification of memory impairment based on incidental factors, such as misunderstanding instructions, which is of particular importance in studies of aging and clinical samples. Accordingly, under the most commonly used administration method, the subject selects an event for each time period that is highly accessible and likely well-rehearsed. The resulting score therefore reflects the participant’s best possible narrative production. This is why M.L. and H.M. could produce seemingly normal autobiographical narratives.

      The SAM, on the other hand, is explicitly designed as a measure of trait mnemonics, not cognitive function as assessed by performance on a given test. The instructions for the episodic questions are “When answering, don’t think about just one event; rather, think about your general ability to remember specific events.” Even assuming that the SAM and the AI are designed to assess the same construct (which as I argue above is not the case) there is a difference between asking how one performs in general versus assessing how they perform when asked to give their best possible narrative by the examiner. By analogy, an introverted person may appear extroverted if required in certain social situations. There is no requirement to cue 5 lifetime period events as originally specified in our 2002 aging study. The AI scoring system has been applied to memories cued in different ways. Harvesting unrehearsed events from significant others may be a more effective way to estimate one’s typical retrieval abilities as opposed to their best possible performance.

      The present paper used a sample of young adults. The AI as implemented in our 2002 study was developed for use in older adults and in patients. The internal detail measure is very sensitive to medial temporal lobe integrity. While this has been demonstrated in neuroimaging studies of healthy young adult samples (Hebscher et al., 2018; Palombo et al., 2018), its sensitivity to individual differences in a homogeneous sample of young adults is limited relative to individuals with compromised medial temporal lobe function, especially at the behavioral level. Nonetheless, the proportion of internal/total details or internal details/word count should be examined rather than the raw count of internal details, as the latter is confounded with verbosity. A comprehensive test of this relationship should also examine detail subcategories and time period effects. Given the foregoing I do not expect that this would change the results substantially, but it should be done for completeness.

      It is intriguing that the parallel analysis on subjective vs. objective measures of spatial memory yielded significant relationships. This speaks to the complexity of AM relative to spatial memory. In navigation, the criteria for success are clearer than for AM. If someone arrives at the correct location (or gets lost), their subjective and objective experience are consonant. But if someone recalls an episode, it is unclear if the correct criterion is subjective experience or imagery or quantity of detail. As noted above, I agree with the authors that there is a distinction between subjective and objective measures, and that one’s selection of measures should be governed by the goals of the study. I would not agree that the present findings call into question whether or not internal details “is actually a good measure of recall ability” given that this measure (or its variants) has been used in over 170 studies (for table of studies, see AutobiographicalInterview.com), with good evidence for the validity of the internal/external distinction, including associations to brain structure and function. I also disagree that the findings of this study justify the use of vividness ratings alone as proxies for memory recall ability, especially in patients, who may show greater variability and less reliability in their introspective ratings than healthy adults. In any case, generalization to aging or clinical samples from a homogenous sample of younger adults is not justified.

      There is great richness to these data that could be exploited in a multivariate data-driven approach. I recognize that this was not the goal of this study, but a multivariate approach such as Canonical Correlation Analysis (CCA) would allow the researchers to detect latent variables and patterns of association across these measures opaque to a series of bivariate correlations and linear regressions. This feels like a lost opportunity in favor of an assumption-laden approach that results in a flat, protracted series of individual analyses that is difficult to follow. In fact, much of the analyses here are already exploratory in that they assess the ability of questionnaires to predict performance on constructs other than the one they were hypothesized to measure. Data driven multivariate approaches are well-suited for such goals.

      Finally, I had difficulty understanding the justification for proposing a single sentence test of any psychological construct. Classical test theory dictates that the reliability of a composite is better than the reliability of a single item. While single items may be useful as a screening technique, for pathognomonic signs, or when doing mass testing, they should not be used for assessment of complex traits, where interpretations of individual items may vary across individuals. A brief questionnaire for each construct would be more stable and does not pose an undue burden on participants. There are no psychometric data presented here to support the use of a single item measure aside from the fact that they showed sensitivity in this sample of healthy adults. These overfitted coefficients will shrink if tested in a separate sample. The composite test of all 15 single items could be subjected to psychometric analysis, but it is unclear if this is of interest.

      Cermak, L. S., & O'Connor, M. (1983). The anterograde and retrograde retrieval ability of a patient with amnesia due to encephalitis. Neuropsychologia, 21(3), 213-234.

      Hebscher, M., Levine, B., & Gilboa, A. (2018). The precuneus and hippocampus contribute to individual differences in the unfolding of spatial representations during episodic autobiographical memory. Neuropsychologia, 110, 123-133. doi:10.1016/j.neuropsychologia.2017.03.029

      Levine, B., Svoboda, E., Turner, G. R., Mandic, M., & Mackey, A. (2009). Behavioral and functional neuroanatomical correlates of anterograde autobiographical memory in isolated retrograde amnesic patient M.L. Neuropsychologia, 47(11), 2188-2196.

      Palombo, D. J., Bacopulos, A., Amaral, R. S. C., Olsen, R. K., Todd, R. M., Anderson, A. K., & Levine, B. (2018). Episodic autobiographical memory is associated with variation in the size of hippocampal subregions. Hippocampus, 28(2), 69-75. doi:10.1002/hipo.22818

      Palombo, D. J., Williams, L. J., Abdi, H., & Levine, B. (2013). The survey of autobiographical memory (SAM): a novel measure of trait mnemonics in everyday life. Cortex, 49(6), 1526-1540. doi:10.1016/j.cortex.2012.08.023

      Steinvorth, S., Levine, B., & Corkin, S. (2005). Medial temporal lobe structures are needed to re-experience remote autobiographical memories: evidence from H.M. and W.R. Neuropsychologia, 43(4), 479-496.

    1. On 2020-12-18 10:07:47, user disqus_32giU6NoIA wrote:

      Really nicely written and helpful paper. It would be good if you stated in the paper what PacBio technology you used. Is it correct in assuming you used CLR data, rather than CCS/HiFi?

    1. On 2017-11-21 21:40:08, user Timo van Kerkoerle wrote:

      Congratulations on the great paper! Very impressive work.

      There are some inaccuracies which I thought would be good to point out. I can imagine that some people thought this represents the first 'solid evidence' about the brain mechanisms of alpha because it is stated in the manuscript that previous papers use a global reference? This is not the case for our 2014 PNAS paper. For the laminar recordings we referenced to the metal shaft of the probe, which is right next to the contact sites, giving something like a bipolar LFP. We compared this with a silver/silver chloride wire in the recording chamber (as also noted in the paper), which didn't change the results. Furthermore, the crucial finding that implicated the involvement of layer 5 in the alpha rhythm (figure 4 of the PNAS paper) are based on CSD and MUA signals, for which the reference is not relevant. The V1-V4 data (not V2 as mentioned in the discussion) used bipolar LFP signals as well.

      Also, it might be good to note that only in early visual areas alpha has been shown to be strongest in the deep layers. In particular, Bollimunta, Chen, Schroeder and Ding J. Neuroscience 2008 showed that alpha was only strongest in the deep layers for V1 and V4, but in the superficial layers in IT.

    1. On 2024-01-20 00:06:45, user Pamela Bjorkman wrote:

      This paper was published as: Cohen, AA, Gnanapragasam, PNP, Lee, YE, Hoffman, PR, Ou, S, Kakutani, LM, Keeffe, JR, Wu, H-J, Howarth, M, West, AP, Barnes, CO, Nussenzweig, MC, Bjorkman, PJ (2021) Mosaic nanoparticles elicit cross-reactive immune responses to zoonotic coronaviruses in mice. Science 371: 735-741. PMCID: PMC7928838 doi:10.1126/science.abf6840

    1. On 2018-01-22 03:30:20, user BenjaminSchwessinger wrote:

      Thanks for posting this preprint. The detail of analysis and the availability of all code is great. it is excellent to see more plant pathogenic obligate biotrophic fungi sequenced. My 'feel' is that these genomes may well look pretty different to some of the better studied non-obligate oomycetes and fungi e.g. 'two speed' genome with effectors clustering to TEs. I could conceived that at least a subset of effectors may well be required in obligate biotrophs as they have to infect the host to complete the life-cycle.

      Some thoughts and questions:

      • Would be great to see some read length statistics on your PacBio sequencing to get a better understanding why the genome is still in a good number of contigs.

      l. 146 Instead of beginning and end of contig I would use 5' and 3' prime of the sequence.

      l. 185 ff. I got confused here as the numbers didn't add up for me 6039 single-copy groups give rise to 6,844 one-to-one mappings? I think I get it after reading it several times, yet some rephrasing may well help. Else proteinortho with the synteny flag may have also been an option for doing this analysis.

      l. 223ff: The observation of smaller parts of the genome being reshuffled in DH14 vs. RACE1 is pretty interesting. We saw something similar comparing the two haploid genomes in wheat stripe rust fungus (see Figure 2, https://www.biorxiv.org/con... "https://www.biorxiv.org/content/early/2017/12/07/192435)"). Wonder how this all happens. Else http://assemblytics.com/ may also be a useful too in future to compare two genomes with each other in regards to structural variations.

      l. 265ff: Great analysis on paralogous. We still need to do this for our candidate effectors, yet we saw an overall 'clustering' of candidate effectors. I liked the part of looking if SPs are enriched on certain contigs. Does this also hold true if you consider gene content and not only contig length?

      Figure 4A would be easier to interpret if it were normalized to the number of genes analyzed and n given within the figure.

      l. 353 ff: Mirrors what we found in wheat stripe rust and others in P. coronata, where candidate effectors do not reside close to TEs in general and not in gene sparse regions. We also see that candidate effectors such as CEPS in Figure S2 C have no really close neighbours. This is pretty intriguing to me. Any thoughts on this? Have you tested if CSEPs are somewhat linked to BUSCOs following the idea that some effectors are necessary in obligate biotrophs. If that is the case for you guys as well, i would be happy to look into if the BUSCOs or effectors tor which this is true are conserved.

      l. 380 ff: The analysis of a TE burst in Bgh is very interesting indeed. I think it would profit from a bit more detail on what kinds of TEs were found and how much each family covered. Figure 5 also lacks some details about the usage of all these acronyms used in the figure eg. BOTR? Increasing font size and including a key in the legend would be great.

      What I wonder with BGH is where did all the old TEs go? Wouldn't you expect to have some of the older TEs still present around the same age/%id as in the other Blumeria? Within the Blumeria how many TE families were specific to each species? Could it be that your database does not include the most recent TEs from other fungi?

      Supplemental figure[:-3]: Not sure that joyplots are the best representation here. A circos plot maybe a better visualization.

      Great work. Gave me some good pointers for my own work.

    1. On 2015-11-19 22:01:43, user Peter Frost wrote:

      I agree that Europeans became light-skinned relatively late in time. Beleza et al. (2013) estimate that the derived alleles at SLC45A2 and SLC24A5 originated between 19,000 and 11,000 years ago. Canfield et al. (2014) suggest a time range of 19,200 to 7,600 years ago for the derived allele at SLC24A5. These are estimates, and the exact dates will remain unknown until we can retrieve ancient DNA from the late Upper Paleolithic / early Holocene. Most likely this change took place during the second half of the last ice age.

      I disagree with the conclusion that these derived alleles originated among early European farmers. Yes, these alleles are absent from late hunter-gatherers in Spain, Luxembourg, and Hungary, but they are present in late hunter-gatherers from Sweden (Motala), Karelia, and Russia (Samara) (see discussion at: http://www.eupedia.com/foru... "http://www.eupedia.com/forum/archive/index.php/t-30957.html)")

      The authors acknowledge this point towards the end of their text:

      "We find a surprise in six Scandinavian hunter-gatherers (SHG) from the Motala site in southern Sweden. […] A second surprise is that, unlike closely related western hunter-gatherers, the Motala samples have predominantly derived pigmentation alleles at SLC45A2 and SLC24A5."

      This seems to undermine the argument that light European skin originated in Neolithic farmers from Anatolia, and then spread into Europe through migration. Such an argument fails to account<br /> for the presence of the same alleles in northern and eastern Europeans at the same time, if not earlier. Again, we won’t be able to resolve this problem until we can retrieve earlier ancient DNA, particularly from the hunter-gatherers of northern and eastern Europe.

      References

      Beleza, S., Murias dos Santos, A., McEvoy, B., Alves, I., Martinho, C., Cameron, E., Shriver, M.D., Parra E.J., & Rocha, J. (2013). The timing of pigmentation lightening in Europeans. Molecular Biology and Evolution, 30, 24-35.

      Canfield, V.A., Berg, A., Peckins, S., Wentzel, S.M., Ang, K.C., Oppenheimer, S., & Cheng, K.C. (2014). Molecular phylogeography of a human autosomal skin color locus under natural selection, G3, 3, 2059-2067.

    1. On 2015-11-17 16:59:33, user Ian Derrington wrote:

      Dear authors. This work is stellar! I must comment, as I have been doing recently on Minion related publications. It seems in nearly all ONT-related publications, there is an apparent blatant neglect of citing any work from Prof. Jens Gundlach's group, who has peer reviewed publications on the *initial* successes of nanopore sequencing (doi:10.1038/nbt.2171) as well as more extended publications showing species identification etc with nanopores (doi:10.1038/nbt.2950). Given that every one of ONT's patent they are using the nanopore MspA, which Gundlach et al (Including myself, Ian Derrington) pioneered the use of, it is essential that this work be cited in academic fairness. Please consider this for future publications.

    1. On 2016-06-15 12:52:58, user Jean Manco wrote:

      Unfortunately, this study, by limiting itself to mtDNA, was incapable of picking up the genetic signals of a third major migration - that from the European steppe in the Copper Age, with deeper origins in Siberia. MtDNA has proved very effective in distinguishing between Mesolithic hunter-gatherers in Europe and incoming farmers from the Near East over most of Europe. However by the late Neolithic, all the mtDNA haplogroups common in Europe today had already arrived. The most obvious differences between the Late Neolithic and subsequent populations lie in the arrval of new Y-DNA haplogroups and a genome-wide component, both found earlier on the European steppe. See Allentoft et al. 2015, Haak et al. 2015, Jones et al., 2015, Lazarides et al. 2014, Mathieson et al 2015, full citations for which can be found in the recent review Montgomery Slatkin and Fernando Racimo, Ancient DNA and human history, PNAS, June 7, 2016, vol. 113, no. 23, pp. 6380–6387.

    1. On 2020-05-11 07:48:24, user Wiep Klaas Smits wrote:

      Great idea to benchmark the different tools. I don't know though how generalisable the results are when using only E.coli as a testcase. I can imagine that other phylogenetic groups (e.g. gram positives) will show significant differences? Would it be possible to run this on for instance B. subtilis as well to see if this conforms to the E.coli results?

    1. On 2019-10-25 22:44:48, user Bryan Ivan Ruiz wrote:

      The manuscript by Xu et.al proposes a post-transcriptional mechanism required to maintain mitochondrial dynamics modulated by Clock. Using both in-vitro and in-vivo experiments, the authors clearly show a novel role of the Clock in regulating mitochondrial morphology, specifically in the post-transcriptional regulation of Drp1. This post-transcriptional model is novel and has not been previously characterized in hepatocytes. The investigators have conducted a significant amount of biologically relevant experiments to test and validate to validate their conclusions. Through a combination of assays, immunofluorescence, rt-PCR and western blots, the data shows that Clock modulates Drp1 activity via mRNA degradation thus controlling mitochondrial fission. Further the authors provide a potential new roll for mitochondrial fission repressor Mdivi-1 through the evident mitigation of ROS production, the reduction of NAFLD and the recovery of the membrane potential of Clock?19 mitochondria. This is a novel and significant mechanism in regulating mitochondrial homeostasis in a circadian context. However, despite the amount of work that has been done in this manuscript, there are important issues that must be addressed. <br /> Minor Issues<br /> • In line 135-137 the authors state that the mitochondrial matrix of Clock?19 mice hepatocytes was deeply stained implying a pH change may observed in the mitochondria (Figure 1B). This is inaccurate observation as Figure 1A displays less stained mitochondria in the Clock?19 hepatocytes vs Wt hepatocytes. In order to remediate this pH levels should be quantified and more representative images should be utilized. <br /> • In Figure 3 the authors state that in Clock?19 mice display a smaller mitochondrial surface ranging approximately 0.1-0.3 um2 However the bar plot displays the majority of Clock?19 mice have a slightly larger mitochondrial surface ranging from 0.3-0.5 um2. The overall conclusion is still sound however changes in the manuscript must be made to report finding this accurately.<br /> Major Issues<br /> • In Figure 1E The authors use ATP6 as a marker to observe mitochondrial morphology. The use of ATP6 is supported since the authors show the relative protein concentrations of ATP6 are equivalent in both Wt and Clock?19 mice mitochondria (Fig 3C). However due to the differences in fluorescence intensity a change in mitochondrial morphology is difficult to support. In order to remedy this the authors could report two images of similar fluorescence intensity.

      • Line 214 the authors claim mitochondrial fusion genes (Opa1, Mfn1 and Mfn2) display decreased mRNA expression in Clock?19 vs Wt. This is untrue as Mfn1 was shown to have higher mRNA expression in the Clock?19 sample (Figure 3D). The authors can consider in situ hybridization to verify their claims. <br /> • The authors further claim fission protein DRP1 expression was increased in Clock?19 mice (Figure 3E). However in the results reported this is untrue, when observing the blot both DRP1 and phospho-DRP1’s do not appear to show increased band density. This claim must be redacted or band density must be calculated and show significant difference. However, the claim that FIS1 is elevated in expression is supported.<br /> • In Figure 5E mtDNA proteins are used to demonstrate the inhibition of mitochondrial fission by Mdivi-1. This is an indirect way to demonstrate inhibition of mitochondrial fission. In order to further support this point the investigators can consider blotting for Fis1 in tandem with the mitochondrial proteins reported in Figure 5E.

    1. On 2019-08-02 13:33:37, user Michael Jeltsch wrote:

      Last time when I checked - a few years back -, the extracellular domain of human VEGFR2 consisted of 7 IgG-like domains (and not of 8). This part of the Results section are not results but the results of previous research, correct? When I started to read it, it was not clear to me that you are talking about VEGFR-2 in general and not about the results of your work (that becomes clear only later when you switch to the real results with the sentence "In case of HyVEGFR-2..."). It is difficult to evaluate your analysis about the domain structure since you do not show the complete alignment of the whole EC domain of hydra VEGFR2 with the other VEGFR2 sequences, but only a partial alignment. I think you need to show the complete alignment and (to show which domains are equivalent and what the intervening sequences are).

    1. On 2020-04-27 06:28:50, user Noah Dolev wrote:

      Hi all,

      Thank you for having a look at Segment2P. Our AWS credits are expiring this month, so we are shutting down the website www.segment2p.co.il. However, the Github repository is still available to anyone who wants to use the model on Amazon's platform. In the coming weeks, I will release an additional repository with a model that can run locally.

      All the best,<br /> Noah Dolev

    1. On 2019-03-28 19:21:25, user Sónia Melo wrote:

      One of the best discussions I've ever seen in a paper. An unbiased approach that questions a paradigm with data. "...one cannot help but wonder whether the prevailing paradigm is based on anything more than a circular argument in which exosomes are believed to arise by endosomal budding for the sole reason that exosomes have been defined in that manner."<br /> Hope to see this out there soon!

    1. On 2021-04-29 17:51:43, user Jay wrote:

      It seems like the difference in MPRA results may be due to the ATRA differentiating the progenitors to neurons--> the difference of MPRA results (ATRA MPRA vs DMSO MPRA) may be due to different cell types and not the added ATRA itself. How did yall account for this? It would be really great if yall could do this MPRA in differentiated neurons without adding ATRA to compare the two (ATRA progenitors vs neurons).

    1. On 2020-05-22 07:46:35, user Lien Decruy wrote:

      The current title and pdf for this paper was uploaded by mistake. The correct title is: "Evidence for enhanced neural tracking of the speech envelope underlying age-related speech-in-noise difficulties" and the correct preprint is 489237v1 is still accessible through the Info/History tab. Please look at the final version of this paper via the doi link in the Journal of Neurophysiology.

      My apologies,

      Kind regards,<br /> Lien

    1. On 2016-04-13 15:46:19, user Maria Kalyna wrote:

      This is very interesting work. It definitely changes our perception of frequency and landscape of alternative splicing, and consequently of its importance, in unicellular organisms.

      You introduce “intron in exon” category to analyse alternative splicing events. Recently, we used a similar term, “exonic introns” (abbreviated to “exitrons”), to describe an alternative splicing event that either includes or removes internal regions of annotated protein-coding exons: http://genome.cshlp.org/con...

      Our definition is narrowed to exonic introns within the protein-coding exons that allowed us to analyse functional significance of such events at the protein level and their evolution. By analysing orthologs, we could show that almost all tested exitrons originate from protein-coding sequences. We also obtained evidence that intron loss/retroposition played a role in exitron evolution as at least some exitrons correspond to a single or multiple exons in orthologs and/or paralogs.

      Though there are obvious differences in definitions of “intron in exon” and “exitron”, it appears that at least some Sch. pombe introns in exons are exitrons. Since Sch. pombe underwent extensive intron loss, we are wondering whether you see any evidence of intron loss at the borders of such introns in exons (aka exitrons); meaning that such introns correspond to protein-coding exons in orthologous genes bordered by canonical/regular introns?

    1. On 2019-10-18 16:28:23, user °christoph wrote:

      The cumulative GC skew is a valuable tool to verify correct assembly of bacterial genomes but there are caveats. Cyanobacteria frequently lack clear GC skews (not the Synechococcus/Prochlorococcus group which have clean GC skews). Also, while most GC skews show a "V" shaped minimum region, others have rather a "U" shape, which make pinpointing the minimum difficult. Also "W" shapes are not uncommon, and it is difficult to pinpoint the minimum among several "secondary minima" although the "minimum region" seems clear. Lastly, I would suggest that you rather talk of "origin region" because *the origin* should be reserved for oriC (origin for DnaA-dependent replication from oriC). In my experience, the GC skew minimum locates close to oriC but can be up to 20 kb away.<br /> Thanks for considering!

    1. On 2021-11-22 14:56:37, user William Matchin wrote:

      This manuscript does not cite Goucha & Friederici (2015 - NeuroImage) or Matchin et al. (2017 - Cortex), papers that replicated core aspects of Pallier et al. (2011). Namely, these two papers both found selective jabberwocky structure effects in posterior STS and anterior inferior frontal regions extremely similar to Pallier et al. (2011), not finding these effects in anterior temporal cortex or inferior angular gyrus/temporal-parietal junction. These papers also used a within-subjects design, remedying one of the deficits cited by this manuscript that Pallier et al. (2011) only used a between-subjects design. Given that the results of Goucha & Friederici and Matchin et al. are congruent with Pallier et al. and bolster the same claims, these papers must be cited by the present work.

    1. On 2021-04-16 09:55:19, user Nathalie Balakina-Vikulova wrote:

      The part "3.1.2 Simulation of the physiological mode of contraction-relaxation cycles" of the 1st Version of the article "Mechano-calcium and mechano-electric feedbacks in the human cardiomyocyte analyzed in a mathematical model" was not included in the final version of the article published in the The Journal of Physiological Sciences (doi: 10.1186/s12576-020-00741-6). This results were sent as a conference paper for «IEEE Ural-Siberian Conference on Computational Technologies in Cognitive Science, Genomics and Biomedicine (CSGB)» csgb.ieeesiberia.org in 2021.

    1. On 2021-01-16 19:09:46, user Nicholas Markham wrote:

      McAllister et al. have generously posted their excellent C. difficile physiology manuscript on bioRxiv. This careful investigation of how selenophosphate synthetase governs metabolism exemplifies the power of CRISPR-Cas9-mediated bacterial gene deletion and restoration. Thank you to the authors for sharing their work. It has introduced me to Strickland metabolism, and I expect the reviews will be positive. I wonder if referees will ask for more discussion on what molecular mechanisms are responsible for the difference in phenotype between plasmid complementation and gene editing. They might wish to see how protein levels are similar or different. It’s understandable one wouldn’t speculate on how atmospheric hydrogen makes a striking difference in phenotype, but I’m very curious to think about how this variable affects the whole field!

    1. On 2025-01-07 19:02:17, user Thomas Munro wrote:

      This is an ingenious idea. The name azo-morphine will likely cause confusion, however, given that the scaffold used is naltrexamine. The name is already in use for azo-substituted morphine derivatives. A full semi-systematic name would be unwieldy, but could be used to derive a distinctive acronym like IBNtxA, which would make literature searches much easier.

    1. On 2018-07-16 10:32:49, user John Thompson wrote:

      No conflict of interest. I do not work for nor have any financial interest in any DTC genetic testing company. No affiliation listed because work was not done in association with any current or past employer.

    1. On 2020-08-07 17:18:38, user E. Laurel wrote:

      This is really interesting work. Have you subtyped the cell lines and PDX models you're using? I'd be interested in seeing if classical and basal lines responded differently or had different base levels of DDR1 or NETs. Perhaps this is one targetable difference between the subtypes or maybe it would work on all patients.

    1. On 2021-02-01 17:20:59, user Cornelis Grimmelikhuijzen wrote:

      35 years ago, I published this paper in Cell and Tissue Research:

      Organization of the nervous system of physonectid siphonophores<br /> C. J. P. Grimmelikhuijzen, A. N. Spencer & D. Carré <br /> Cell and Tissue Research volume 246, pages463–479 (1986)

      We investigated several siphonophores, including Nanomia biuga, stained their nervous systems (with peptide antibodies) as well as the muscle fibers (with rhodamine-labeled phalloidin). Did the authors ever read it?

    1. On 2016-12-05 17:15:04, user Geraint Duck wrote:

      This is an interesting idea. However, I wonder about some other "hidden costs" of review that may also need to be considered. For example, the cost of access to both data, software, and *other papers*. Would a "full-time reviewer" have access to the array of non-OA journal subscriptions needed for a complete review? Some publishers will provide access to their own journal collections should you agree to review, but how often is (just) this sufficient? And related, access to software and/or equipment (which you do allude to in your article already) to properly assess and/or run supplied code (especially code that uses proprietary programs, e.g. Matlab and the like).

    1. On 2024-11-20 14:47:46, user MD Devignes wrote:

      This is a preliminary version of our study on HLA epitope recognition. A more advanced and peer-reviewed version is about to get published in Bioinformatics Advances (DOI: 10.1093/bioadv/vbae186).

    1. On 2025-10-08 15:32:30, user M.A. wrote:

      Great work. It would seem that the "baseline setting" (Figure 2) is unfairly favoring the semi-supervised methods. The same labels are used as input to guide integration AND for performance evaluation; this allows ss-methods to overfit the data, especially scGen and scDREAMER, which have many parameters. Wouldn't it make sense to report the rankings based on a more realistic scenario, such as one with partial annotation or partially incorrect labels?<br /> On another note, silhouette coefficients have been reported to be suboptimal for this kind of benchmarks, and more appropriate metrics have been proposed, see e.g. https://www.nature.com/articles/s41587-025-02743-4

    1. On 2023-05-09 07:22:54, user Cedric Maurange wrote:

      Beautiful work really, congrats! Would be nice to acknowledge the previous work on Chinmo and Broad in the neuroepthelium:<br /> - Dillard C, Narbonne-Reveau K, Foppolo S, Lanet E, Maurange C. Two distinct mechanisms silence chinmo in Drosophila neuroblasts and neuroepithelial cells to limit their self-renewal. Development. 2018 Jan 25;145(2):dev154534. doi: 10.1242/dev.154534. PMID: 29361557.<br /> - Zhou Y, Yang Y, Huang Y, Wang H, Wang S, Luo H. Broad Promotes Neuroepithelial Stem Cell Differentiation in the Drosophila Optic Lobe.<br /> Genetics. 2019 Nov;213(3):941-951. doi: 10.1534/genetics.119.302421. Epub 2019 Sep 17. PMID: 31530575; PMCID: PMC6827381.

    1. On 2022-10-23 03:08:24, user Alex Crits-Christoph wrote:

      Genomic and phylogenetic evidence proves this preprint false for a very simple reason: the 'endonuclease fingerprint' observed in SARS-CoV-2 is also present in the bat coronaviruses most closely related to SARS-CoV-2. Thus, any hypothetical engineer of the RE sites would have to go to enormous lengths to purposefully mimick natural bat coronaviruses that have only been discovered in the past 2 years: a very dubious proposition. The far simpler alternative is that the sites evolved via natural recombination from natural bat coronaviruses.

      Further, if one examines the genomic regions around each restriction enzyme sites, we find that SARS-CoV-2 shares general genetic similarity with the virus(es) it shares the RE site (or lack therefore) with. This would further indicate that they were inherited via recombination. For example, two BsaI sites missing in SARS-CoV-2 are also missing in the RpYN06 batCoV, which follows naturally from the phylogenetic prediction that RpYN06 is the nearest neighbor in that region. Correspondingly, SARS-CoV-2 shares not just the lack of the BsaI sites in this region, but several other mutations as well: a signal entirely inconsistent with engineering and entirely consistent with natural recombination. The same is true with other natural batCoVs if you examine any of the RE sites described in this work.

      For the engineering hypothesis, this would have to imply that someone not only modified the RE sites to match natural viruses, but also unrelated nearby sites as well - an even more ludicrous proposition that I do not think even these authors can defend.

      Finally, this sort of analysis can be be done systematically by reconstructing a recombinant ancestor of SARS-CoV-2, as the two papers below did:<br /> https://www.nature.com/arti...<br /> (See Fig 2)<br /> https://www.science.org/doi...<br /> (See Fig 6)

      The recombinant ancestor is a reconstruction of the common ancestor of SARS-CoV-2 and other known bat viruses in each region of the genome. The recombinant ancestor of SARS-CoV-2 indeed shares the exact BsaI/BsmBI RE pattern of SARS-CoV-2, minus a signal synonymous mutation: thus further proving that these sites were naturally acquired via recombination. This follows intuitively from the observation that different bat viruses each have some of the RE sites described in this work, and that each bat virus that shares an RE or lack therefore with SARS-CoV-2 is the most recent common ancestor of that genomic region.

      For more, please read:<br /> https://twitter.com/flodeba...<br /> https://twitter.com/acritsc...<br /> https://twitter.com/K_G_And...<br /> https://twitter.com/zhihuac...

      And the data described in my comment is fully available at:<br /> https://github.com/alexcrit...<br /> In particular, the file with 'alignment-with-RpYN06.fasta' which includes a comparison with several batCoVs ignored in this preprint.

      Let us be clear, this is firm phylogenetic proof that the RE pattern in this work is natural. I would not use the word 'proof' lightly in science, but if we cannot use it in such a clear circumstance, we cannot use it at all. If the authors have any integrity they should gracefully retract their work here.

    1. On 2017-01-28 12:37:12, user Ratnesh Tripathi wrote:

      Good and interesting findings.<br /> Canu is a haploid assembler, so it did fairly good assembly for a homozygous clone in nile tilapia. <br /> Please include the stats of alternate assembly (a_ctg.fa) obtained in FALCON assemblies to have insights for persistence of heterozygosity in homozygous clones, if any.

    1. On 2020-10-07 09:42:53, user David Peters wrote:

      Unfortunately, genomic studies too often recover false positives in deep time studies when compared to phenomic studies, the only studies that include a wide array of fossil taxa. In an online phenomic study Vulpavus, Protictis and Nandinia are basalmost Placentalia, the outgroups to the Carnivora, the basal-most of the placental clades. Talpa is an overlooked extant member of the Carnivora. Ursus arises apart from dogs and cats, which find last common ancestors in Tremarctos, Speothos and Borophagus. Arctodus, the short-faced bear, is a giant wolverine (Gulo). Seals and sea lions have separate terrestrial ancestors and became aquatic by convergence. Online cladogram here: http://reptileevolution.com...

    1. On 2019-09-12 07:30:00, user Prof. Calum Semple wrote:

      Effective shunt fraction - eGFR for the lung will be included as a secondary outcome measure in the @BESStudy where we will trial endotracheal surfactant in infants with life threatening #Bronchiolitis

    1. On 2020-01-18 17:34:02, user Sirius wrote:

      Very great insight into what's in these dangerous vaporizers. One question though, is it not possible that all those siloxane compounds come from column bleed? Also, it would be useful to see a table with the match qualities, and an example chromatogram. It's also useful to highlight that making an identification of a compound based on comparison with a mass spectrum from the NIST library qualifies as a tentative identification, not a full identification.

    1. On 2019-09-20 10:33:38, user Julie Tucker wrote:

      A great read - modelling as it should be done; informed by and informing biological insight and mutational studies. Would be good to see some statistical significance statistics on Figure 5. And are the suppressor mutants still responsive to cytokine stimulation? Perhaps this information is in the supplemental, which I have yet to find on biorxiv.

    1. On 2022-03-22 18:03:56, user Emily wrote:

      Hello authors,

      Thank you for posting this excellent article on the gut microbiome in adult gars. I do, however, have a few comments and questions on your study.

      First, you mention that you are studying the GMB of the fish. You describe this acronym as the gut emicrobiome; however, when I researched this acronym, I was unable to find anything. Is this a typo? Where could I find this information?

      Second, I was confused about the sample collection of the gar. How did the fishermen catch the fish, specifically what bait was used? Would this affect the gut microbiome of the gar? What food was fed to the fish grown in the lab? Were there significant differences in the GMB based on the collection method? Following that, you state that you squeeze the GMB; I am confused about how you obtained the feces. How did you squeeze the gut microbiome? Would this affect a change in the location? Would you be able to elaborate further on this?

      Next, while investigating your methods sections, I found some missing information in your PCR step. To improve your study and help end the replication crisis, I would add the PCR cycle number and temperature for this amplification. Thank you for including the variable region and specific primers you used.

      Finally, I read your sections on bioinformatics and phylogenetic analysis and had a suggestion. When I first read the paper, I could not find your Good’s coverage and how you clustered the OTUs. I would move the information on how many clustered sequences and the similarity percentage right after removing the chimeras. Moreover, I was curious about an internal standard for sequencing and clustering analysis. I suggest adding a known strain you grew in the lab to the sample as to confirm that your sequences and binning are correct. Another suggestion would be to elaborate on why you adjusted the identity percentage to 99% with a coverage of 70%. Did this help with the phylogenetic analysis? Is this a quality control for the phylogenetic analysis similar to chimera removal?

      Overall, this study highlighted the gut microbiome of tropical gar and allowed for further research questions to be asked. I appreciate the amount of information on the method section and implore you to add my suggested feedback. Thank you for your time.

      SHSU5394

    1. On 2019-12-16 15:00:23, user Thomas Munro wrote:

      I think it would be of interest to give the proportion of mutants that fall into a given category (e.g. constitutively active, loss of function, etc). Also, for readers from other disciplines, a brief description of how to interpret the activity values in Supplementary Table 1 might be helpful, i.e. "a value greater than 1 represents ..."

    1. On 2020-04-16 12:20:55, user Mukesh Mahajan wrote:

      A recent article on "Paratope prediction and its application to ab-ag docking" is really very nice work by Bonvin group. This article will significantly guide researchers about structural understanding of HV regions in the antibodies. However, I was unable to understand how to select the probable residues involved in the ab-ag interaction from the probability plot (output)?

    1. On 2020-08-10 14:43:33, user Renzo Huber wrote:

      The manuscript entitled “Estimation of laminar BOLD activation profiles using deconvolution with physiological point spread functions” promotes the usage of layer-dependent GE-BOLD models to account for venous drainage effects in laminar fMRI signals. <br /> The described study uses previously acquired data and a previously developed laminar vascular model to show that the model can be inverted for the removal of unwanted vein effects ini GE-BOLD data.

      The research field of layer-fMRI is gaining a lot of interest these days. And the effect of draining veins is one of its major limitations. Thus, the proposed solution in this manuscript will be very relevant for a wide readership.

      There are more than a handful of layer-dependent vascular models published out there. The one described here stands out in its elegant simplicity, user-friendly applicability, and careful validation with experimental results.

      I enthusiastically support the publication of the study in it’s present form. <br /> Though, if time constraints permit, I believe the manuscript could be further improved by considering the following specific point.

      1.) <br /> There are quite a bit of parallel efforts to model (and ultimately remove) venous drainage in layer-fMRI. While the authors (rightfully?) ignore most of these efforts that are not from their own group or the Uludag group, I would find it appropriate to acknowledge all of them to some degree somewhere in the manuscript:

      Heinzle J, Koopmans PJ, den Ouden HEM, Raman S, Stephan KE. A hemodynamic model for layered BOLD signals. Neuroimage. 2016;125:556-570. doi:10.1016/j.neuroimage.2015.10.025

      Puckett AM, Aquino KM, Robinson PA, Breakspear M, Schira MM. The spatiotemporal hemodynamic response function for depth-dependent functional imaging of human cortex. Neuroimage. 2016. doi:10.1016/j.neuroimage.2016.06.019

      Lacy TC, Robinson PA, Aquino KM, Pang JC. Cortical Depth-Dependent Modeling of Visual Hemodynamic Responses. bioRxiv. 2020. doi:10.1101/2020.03.16.993154

      Corbitt PT, Ulloa A, Horwitz B. Simulating laminar neuroimaging data for a visual delayed match-to-sample task. Neuroimage. 2018;173(February):199-222. doi:10.1016/j.neuroimage.2018.02.037

      Merola A, Weiskopf N. Modelling the laminar GRE-BOLD signal: integrating anatomical, physiological and methodological determinants. In: Proc Intl Soc Mag Reson Med. ; 2018:2299. https://cds.ismrm.org/prote....

      If the authors find it appropriate, they could even refer to a recent layer-fMRI analysis suite that uses the author’s P2T-PSF model to deconvolve the GE-BOLD data one a column-by-column basis with user-defined P2T values as described here: https://layerfmri.com/devein/

      Huber L, Poser BA, Bandettini PA, et al. LAYNII: A software suite for layer-fMRI. bioRxiv. 2020:1-20. doi:10.1101/2020.06.12.148080

      2.) <br /> If I understand the model correctly, the appropriate P2T-values depend on the number of layers that the data are binned into. Maybe the manuscript would benefit from a discussion about this and maybe the authors could add an extra Table of how the reported values of P2T values in the range of 5-9 for 16 layers refers to any other commonly used number of layers.

      3.) <br /> I am not sure if I fully agree with the simplified statement of lines 63-67 that tries to disregard all sequences (but GE-BOLD) as “low-sensitivity”. I would recommend the authors to expand a bit on what they mean here. Maybe it would even be appropriate to add an entire paragraph in the discussion section about the “sensitivity” of their proposed model too.

      I think it would be appropriate to discuss whether the “sensitivity” of GE-BOLD and its alternatives is referring to the desired laminar signal or instead if it refers to the sensitivity of unwanted artifacts. In fact, I believe most of the large GE-BOLD sensitivity is coming from low spatial frequencies. Whereas the detection power at high-spatial frequencies (at the laminar scales), however, is rather low: even lower than some non-BOLD methods. Thus, I would like to encourage the authors to rephrase the above referred statement.

      In this context, I find that it would be appropriate to also discuss the “sensitivity” of the laminar signals upon the deconvolution. E.g. I happen to know that the strong GE-BOLD sensitivity with 8-10% signal change is reduced to weaker 4%, when going from GE-BOLD to VASO. This is a dramatic sensitivity loss of approx a factor of two.

      With the proposed deconvolution model, however, the original BOLD sensitivity of 8-10% at the surface is reduced to 3% at the surface (compared Fig. 2a and 2b). This is an even stronger sensitivity loss; more than a factor of two.

      I would recommend to the authors to comment on their view of the sensitivity in the manuscript.

      4.) <br /> The two datasets that were chosen here have been previously somewhat criticised in the field about the risk of double-dipping. I think, the manuscript would benefit from a brief discussion about this:

      The underlying assumption behind the “leakage free” profiles from Fracasso et al., should be explicitly mentioned. I would believe that there is a class-5 principle diving vein every 400-500 micrometer (Duvernoy). Thus, it is surprising that the flat profiles extracted from 0.5mm data are considered to be leakage-free. In my opinion, as long as there is noise present in a collection of several thousand profile samples, one can always find and pick a subsample of profiles that match the expectation. One could even do so with experimental data that are acquired without excitation pulses :-) Selecting the profiles based on the same features that are of interest might be considered double-dipping by some people.

      The underlying assumptions behind the vertical alignment (lines 353-354) of layer-fMRI profiles in the Koopmans et al. data has been criticised in the field. Aligning many noisy profiles based on laminar features that are later used for the analysis might be considered double dipping by some people.

      Maybe the authors can comment, why the current study is not at the risk of double dipping.

      5.) <br /> I believe the statement about the baseline hemodynamic condition (line 309) could be extended. In the neuroscience application community (e.g. recent de Hollander paper) there might have been a bit of a confusion about the sensitivity to baseline physiology in the combination of layer-dependent vascular deconvolution. Maybe the authors can extend the discussion with the following points.

      Does the deconvolution still allow applications that assume linearity? <br /> -> E.g. is the deconvolved signal difference of task A and task B the same as the difference between the deconvolved signal of task A and the deconvolved signal of task B? <br /> -> E.g. is the deconvolution applicable to task modulations that happen on top of a non-baseline background activity.<br /> -> E.g. what happens if my inter-stimulus intervals are too short for the signal to go back to baseline? Can I still apply the deconvolution signal, then? <br /> -> E.g. If I just add a 1% offset on the y-axis of Fig 2a, the neuroscientific conclusion about the relative difference of the layers would be unchanged. However, would the shape of the devonvolved signal in Fig. 2b still look the same? Would the neuroscientific interpretation still be the same for the devonvolved layer profile with such an additive offset?

      6.) <br /> Maybe the authors can briefly mention the existence of blooming effects of large pial veins and whether they are accounted for in the model.

      7.) <br /> I found it a bit hard to interpret the similarity of the multiple TEs in Fig. 3b. However, normalizing the profiles on the y-axis would help. Maybe the authors might consider including an additional panel like they showed it in their 2016 ISMRM presentation (minute 1:50): https://youtu.be/7s2o2I0QrW... I found that figure very informative.

      The above comments are submitted to BioRxiv and the journal in the same form.

    1. On 2025-07-07 07:47:13, user Pietro Roversi wrote:

      Pioneering work that pushes the boundaries of human PPI hypothesis making and fully realises the promise of many earlier pieces of work such as https://doi.org/10.1073/pnas.0805923106 and https://doi.org/10.1126/science.abm4805 . As the signal underpinning the hypothesis on novel PPIs is harvested from MSAs - this tool also enables novel hypothesis making on the interactomes of most Eukaryotic proteomes!

      One detail: as the authors have already acknowledged in full, some of the complexes in Figure 5 can be easily improved if tools to detect self-association are brought to bear on stoichiometry, and models are built that allow for multiple copies of certain subunits that are oligomeric or present in more than one copy in the complex.

      In particular, the TZC complex in Figure panel 5I likely misses additional copies of B9D1, B9D2, TMEM216, TMEM107, TMEM218 and TMEM231. TMEM67 and TMEM237 are also likely dimeric across the interface to neighbouring complexes - giving the TZC the ability to polimerise.

      I am looking forward to the final published version of the paper.

    1. On 2022-08-27 15:52:20, user Mark A. Hanson wrote:

      The first version of this article was accidentally missing its Acknowledgements section. This has been rectified in v2. To ensure this information is present regardless of manuscript version, we would like to additionally post this information here:

      We would like to thank Samuel Rommelaere, Jean-Philippe Boquete, Emi Nagoshi, Lukas Neukomm, Kausik Si, and Anzer Khan for helpful discussion. We would also like to thank Brian McCabe, Mariann Bienz, Barry Ganetzky, Steven Wasserman and Lianne Cohen, the Vienna Drosophila Resource centre, and the Bloomington Drosophila Stock Centre for fly stocks requested over the course of this research. This research was supported by Sinergia grant CRSII5_186397 and Novartis Foundation 532114 awarded to Bruno Lemaitre.

    1. On 2020-02-14 18:42:43, user Arthur Jenkins wrote:

      Inadmissible evidence in obesity genetics

      Background<br /> My initial interest in this area came out of an interest in adiposity and the various well-recognized failings of existing phenotypes as proxies of the underlying pathophysiology and genetics of obesity. Our initial report of familial segregation was an unexpected result of testing a new rationally constructed phenotype against diabetes family history in a small convenience sample [1]. We saw that result as generating an hypothesis requiring further testing and identified the NHANES data set as the most powerful available to us. We conclude that we have replicated and extended our original finding in the NHANES data [2].

      I provide this history, which is implicit in our preprint [2], to emphasise that we did not arrive at our current position through any pre-conceived model of the genetics of obesity. At the time of publication of our initial study (2013) the claims for polygenes with small effect sizes were modest (1-2% of variance) and did not conflict with our results. Since that time the claims for polygenes have grown to the extent that the strongest of those claims are now in conflict with our analyses and interpretations.

      Feedback<br /> Journals<br /> I expected that in attempting to publish our findings I would engage with reviewers familiar with obesity genetics and at least come away with a better scientific understanding of the apparent discrepancies between our findings and those coming out of genomics. Far from it. A first attempt in a specialist obesity journal made clear in a very helpful way that our audience was elsewhere and we decided that we must approach geneticists.

      The results were at first just disappointing – rapid editorial rejection by the first two genetics journals tried ("not a good fit", "unlikely to receive favourable reviews") – but then quite startling - the third genetics editor rejected without review on the basis of a mis-statement of the hypothesis tested ("that BMI has a strong, single gene effect detectable in a segregation analysis"). It requires more than ignorance to describe our hypothesis of individually rare, but collectively common, variants as a single gene and we finally got the message that evidence against polygenic small-effects explanations for obesity is inadmissible in the genetics literature.

      Preprint<br /> Early tweets visible on the preprint site along the lines of "what do you think?" produced few responses. More recently one directed explicitly at geneticists produced an offsite response from an eminent obesity geneticist "Inconsistent with the direct empirical evidence" (unidentified but presumably GWAS) which led me to an offsite discussion. In that discussion the eminent obesity geneticist defended the strongest claims of the small-effects polygene model using, among other things, an intuition that our results are more consistent with a single gene model, which could then be definitively excluded on lack of genomic evidence: our result must therefore have other unspecified, and presumably artifactual, explanations. My attempts to engage with this discussion have so far (14/02/20) not been successful. Other partly overlapping discussions focus on our lack of genomic data in the context that only genomic evidence is relevant to this area. I have questioned this faith and hope for some response.

      Conclusions<br /> The geneticists' responses to our work support the proposition that a polygenic small-effects explanation for obesity is one of those entrenched under-performing big ideas that currently permeate the biomedical literature [3]. It is certainly under-performing in terms of both mechanistic insights into the problem and effective applications, but perhaps not so much in terms of interests vested in the genomics industry, broadly defined. It appears to be entrenched behind a strategy of oxygen-denial to conflicting evidence and the faith of some genomicists in the omnipotence of their methods. It is time to ignore the antagonism of the vested interests and faith-based dismissals and assess our work on other more objective criteria. Perhaps by the epidemiologists?

      References<br /> 1. Jenkins AB, Batterham M, Samocha-Bonet D, Tonks K, Greenfield JR, Campbell LV. Segregation of a latent high adiposity phenotype in families with a history of type 2 diabetes mellitus implicates rare obesity-susceptibility genetic variants with large effects in diabetes-related obesity. PLoS One. 2013;8:e70435.<br /> 2. Jenkins AB, Batterham M, Campbell LV. Segregation of Familial Risk of Obesity in NHANES Cohort Supports a Major Role for Large Genetic Effects in the Current Obesity Epidemic. Preprint https://www.biorxiv.org/con...<br /> 3. Joyner MJ, Paneth N, Ioannidis JP. What Happens When Underperforming Big Ideas in Research Become Entrenched? JAMA. 2016;316:1355-1356.

      14/02/20

    1. On 2021-02-08 20:11:04, user Nicholas Markham wrote:

      Outstanding manuscript by Pruss and Sonnenburg! Thanks to the authors for posting on bioRxiv. These elegant experiments show how C. difficile toxin-mediated host inflammation alters the metabolic phenotype of C. difficile itself. In particular, the authors identified an inflammation-associated upregulation of the sorbitol utilization locus responsible for permitting increased C. difficile growth in the presence of sorbitol. By altering the host's ability to produce sorbitol, including the use of an aldose reductase knockout, they show differential severity of C. difficile infection. This novel work is exciting because it reveals multiple potential therapeutic targets. I imagine the reviews will be kind. I wonder if looking more at the infected ARKO mice would be helpful: fecal CFUs, fecal toxin amounts, and histopathology.

    1. On 2015-04-08 19:27:33, user Mostly Greek islands ancestry wrote:

      Perhaps the EDGAR gene prevalent in East Asian may have contributed to the high cheek bones and straight hair of Scandinavians and many East Europeans compared to my relatively flat cheekbones and curly brown hair from my mostly Mediterranean origins?

    1. On 2022-10-20 21:34:50, user Moshe Tsvi Gordon wrote:

      In the third and fourth panel of Figure 2F it looks like the low FRET <br /> states might be a result of photobleaching. In those traces did you see <br /> recovery to the higher FRET state or was the transition to a low FRET <br /> state permanent?

    1. On 2017-04-26 17:31:58, user Tanai Cardona Londoño wrote:

      I have read this paper with great interest. I think this approach can be also applied to other proteins that are also shared between oxygenic photosynthetic organisms with extensive fossil record that are also found in methanogens. One suggestion is protochlorophyllide and chlorophyllide reductases, which have homology to nitrogenase and to the nickel-tetrapyrrole biosynthesis enzyme required for the synthesis of cofactor F430 of methyl coenzyme M reductase (see, DOI: 10.1126/science.aag2947), a key enzyme of methanogenesis.

      A tree of these enzymes could be calibrated on protochlorophyllide reductase using cyanobacteria fossils and fossils from photosynthetic eukaryotes. And It could also be cross calibrated on the nitrogenase homologs using cyanobacteria fossils.

      Similarly, you could use the same approach with rubisco and phosphoribulokinase, which have homologs in methanogens (see, DOI: 10.1038/ncomms14007).

      I have done something similar to try to time the origin of water oxidation in Photosystem II. It could perhaps give you some ideas on what other calibrations points you could use for an improved clock (see, DOI: doi.org/10.1101/109447) "doi.org/10.1101/109447)").

    1. On 2020-04-08 19:18:30, user Sinai Immunol Review Project wrote:

      Summary of Findings:<br /> -The authors utilize homology modeling to identify peptides from the SARS-CoV-2 proteome that potentially bind HLA-A*02:01.<br /> -They utilize high-resolution X-ray structures of peptide/MHC complexes on Protein<br /> Data Bank, substitute homologous peptides with SARS-CoV-2 peptides, and calculate MHC/SARS-CoV-2 peptide Rosetta binding energy.<br /> -They select MHC/SARS-CoV-2 complex models with highest binding energy for further study and publish models in an online database (https://rosettamhc.chemistr...).

      Limitations:<br /> -The authors only utilize computational methods and predicted SARS-CoV-2 peptides must be validated experimentally for immunogenicity and clinical response.<br /> -Due to computational burden and limited availability of high resolution X-ray structures on PDB, authors only simulate 9-mer and 10-mer peptide binding to HLA-A*02:01.<br /> -Since the authors compare select existing X-ray structures<br /> as a starting point, backbone conformations that deviate significantly between test and template peptides are not captured. Furthermore, Rosetta modeling protocols do not capture all possible structures and binding energy scoring does not fully recapitulate<br /> fundamental forces.(1,2)

      Importance/Relevance:<br /> -The authors identify and publish high-scoring SARS-CoV-2 peptides that may direct<br /> a targeted, experimental validation approach toward a COVID-19 vaccine.<br /> -The authors utilize Rosetta simulation to further filter results from NetMHCpan 4.0,<br /> supporting machine learning prediction with structural analysis.<br /> -The authors develop RosettaMHC, a computationally efficient method of leveraging<br /> existing X-ray structures for identification of immunogenic peptides.

      References:<br /> 1.Bender, B. J., Cisneros, A., 3rd, Duran, A. M., Finn, J. A., Fu, D., Lokits, A. D., . . . Moretti, R. (2016). Protocols for Molecular Modeling with Rosetta3 and RosettaScripts. Biochemistry, 55(34), 4748-4763. doi:10.1021/acs.biochem.6b00444<br /> 2.Alford, R. F., Leaver-Fay, A., Jeliazkov, J. R., O'Meara, M. J., DiMaio, F. P., Park, H., . . . Gray, J. J. (2017). The Rosetta All-Atom Energy Function for Macromolecular Modeling and Design. J Chem Theory Comput, 13(6), 3031-3048. doi:10.1021/acs.jctc.7b00125

      Review by Jonathan Chung as part of a project by students, postdocs and faculty at the Immunology Institute of the Icahn school of medicine, Mount Sinai.

    1. On 2018-12-27 14:39:13, user Clement Kent wrote:

      The authors present an interesting investigation of biased GC in primate genomes. However, their selection model is purely additive, while the results of Glémin 2010 and subsequent papers show that some W->S mutations are equivalent in effects to an overdominant S allele which is favored in heterozygous (W,S) individuals but disfavored in homozygous (S,S) individuals. Glémin solved the stochastic equations for this case and showed that an excess of polymorphisms and a deficit of fixations could result. Suggest authors read this: Glemin S. Surprising fitness consequences of GC-biased gene conversion: I. Mutation load and inbreeding depression. Genetics. 2010;185(3):939-59.

    1. On 2016-12-04 22:59:22, user Sriganesh Srihari wrote:

      This is an interesting concept. One could look at different combinations -- mutations/upregulation/amplification of oncogenes, mutations/downregulation/deletion of tumour suppressors and different combinations of the two to study SL. Please see this:<br /> http://biologydirect.biomed...<br /> which provides predictions for DNA-damage response-related SL based on mutual exclusivity from these combinations.

    1. On 2020-04-02 17:27:46, user Sinai Immunol Review Project wrote:

      Potent neutralization of 2019 novel coronavirus by recombinant ACE2-Ig

      Keywords:<br /> ACE2, Ig-like protein, SARS-CoV neutralization

      Summary:<br /> Angiotensin-converting enzyme 2 ACE2 is a negative regulator of the renin-angiotensin<br /> system. In the lung tissues, ACE2 is expressed on lung epithelial cells (AT2 cells) and has been identified as a receptor for SARS-CoV-1 and SARS-CoV-2[1].<br /> Administration of recombinant human ACE2 has been shown to protect mice from severe acute lung injury induced by acid aspiration or sepsis and lethal avian influenza H5N1[2,3].<br /> Human recombinant ACE has already been shown in animal models and humans to<br /> have a fast clearance rate with a half-life of only a few hours[4], thereby limiting its therapeutic potential.<br /> To address this protein stability limitation, the authors generated a fusion protein that links the extracellular domain of human ACE2 to the Fc domain of human IgG1. This fusion protein was shown to have a prolonged half-life and neutralize viruses pseudotyped with the S glycoprotein of both of SARS-CoV and 2019-nCoV in vitro, thus providing a potential therapeutic for COVID-19.

      Critical analysis:<br /> ACE2-Ig fusion proteins are promising but their ability to neutralize the virus and reduce viral load remains be tested with intact Coronaviruses in vitro and in animals before being tested in clinical trials.

      Implications for current epidemic:<br /> If neutralizing capacity can be validated with 2019-nCov viruses, this novel drug target provides a promising therapeutic strategy for the treatment of COVID-19 patients. Nonetheless, the authors mention potential cardiovascular side-effects stemming from the role of ACE2 in the renin-engiotensin system. These will need to be examined prior to the initiation of a phase I clinical trial.

      References:<br /> 1. Kuba K, Imai Y, Rao S, Jiang C, Penninger JM: Lessons from SARS: Control of acute lung failure by the SARS receptor ACE2. J Mol Med 2006, 84:814–820.

      1. Imai Y, Kuba K, Rao S, Huan Y, Guo F, Guan B, Yang P, Sarao R, Wada T, Leong-Poi H, et al.: Angiotensin-converting enzyme 2 protects from severe acute lung failure. Nature<br /> 2005, 436:112–116.

      2. Zou Z, Yan Y, Shu Y, Gao R, Sun Y, Li X, Ju X, Liang Z, Liu Q, Zhao Y, et al.: Angiotensin-converting enzyme 2 protects from lethal avian influenza A H5N1 infections. Nat<br /> Commun 2014, 5:3594.

      3. Haschke M, Schuster M, Poglitsch M, Loibner H, Salzberg M, Bruggisser M, Penninger J, Krähenbühl S: Pharmacokinetics and pharmacodynamics of recombinant human angiotensin-converting enzyme 2 in healthy human subjects. Clin Pharmacokinet<br /> 2013, 52:783–792

      By Maria Kuksin

    1. On 2021-07-07 15:55:33, user Jonasz Weber wrote:

      Dear authors,

      Thank you very much for your scientific work on assessing the reliability of molecular weight (MW) markers in SDS-PAGE. The findings of your study are highly relevant for researchers using this methodology for analyzing proteins. Also, in our lab, where we are using different MW markers, we have experienced variations and discrepancies. In your work, you have tested all MW markers on TGX pre-cast gels. We preferentially use Bis-Tris and Tris-acetate gels, and we see differences between the MW prediction precision divergent from your results. Did you consider expanding your dataset using more gel types as the earlier mentioned BT and TA gels?

      I look forward to your reply.

      Best regards,<br /> Jonasz Weber

    1. On 2018-04-01 05:09:26, user Shi Huang wrote:

      Why no discussion at all on Y and mtDNA data? May be something inconvenient? Why were Y chr haplotype A and BT so commonly found in ancient Turkmenistan (in supplementary table), when only CT are thought to have left Africa in the OOA model?

    2. On 2018-06-06 10:27:19, user Balaji wrote:

      I am posting the following jointly with Jaydeepsinh Rathod.

      It is clear that the people of the Turan were a mix of people from Iran and India. From Iran came Anatolian agriculturalist ancestry and from India AASI as well as ANE. You have found that BMAC people had 60% Iranian agriculturalist-related ancestry, 20% Anatolian<br /> agriculturalist-related ancestry and 13% ANE-related ancestry and 6% AASI. Suppose from Iran came people with 60% Iranian agriculturalist-related ancestry and 40% Anatolian agriculturalist-related ancestry and from India came people with 62% Iranian agriculturalist-related ancestry, 26% ANE-related ancestry and 12% AASI and suppose they mixed in equal proportions, the resulting population would have the ancestry that was found in BMAC.

      There is additional evidence that ANE found in Turan may have come from India and not from Kelteminar hunter-gathers or from West Siberian hunter-gathers. Figure S3.10 shows that other than the three Indus_Periphery outliers (Gonur2_BA, Shahr_I_Sokhta_BA2 and Shahr_I_Sokhta_BA3), it is Sarazm_EN and Gonur1_BA_o which share most alleles with Birhor. The high allele sharing of Indus_Periphery with Birhor is explainable by their high AASI. But Sarazm and Gonur1_BA_o have no AASI. Their high allele sharing with Birhor must be due to the West Eurasian alleles of Birhor being similar to those in Sarazm_EN and Gonur1_BA_o. Sarazm_EN has been modeled as 75% Ganj_Dareh_N, 2% Anatolia_N and 23% West_Siberia_N and Gonur1_BA_o as 61% Ganj_Dareh_N and 39% West_Siberia_N. In spite of the mainstream BMAC people having about 6% AASI and Sarazm_EN and Gonur1_BA_o having 0% AASI, the latter share more alleles with Birhor. This must be because the mainstream BMAC people have half their Iranian agriculturalist-related ancestry from Iran and only half their Iranian agriculturalist-related ancestry from India. The “Iranian agriculturalist-related” ancestry in Iran and India must be different as suggested by the Metspalu et al. 2004 finding that “mtDNA haplogroups which are shared between Indian and Iranian populations exhibit coalescence ages corresponding to the early upper paleolithic” and that they are present in India "largely as Indian-specific sub-lineages". Therefore Sarazm_EN and Gonur1_BA_o are likely to have been emigrants from the Indus civilization – they could not have come from Iran since they have almost no Anatolian agriculturalist-related ancestry.

      If the Indus population already had 26% or more ANE instead of the 10% ANE based on your assumption that Indus_Periphery was typical of the Indus population, then there is no<br /> room for Steppe_MLBA to bring in additional ANE and WHG ancestry.

    1. On 2020-04-10 03:06:23, user Sinai Immunol Review Project wrote:

      Summary/Main findings: <br /> Lon et al. used a bioinformatic analysis of the published SARS-CoV-2 genomes in order to identify conserved linear and conformational B cell epitopes found on the spike (S), envelope (E), and membrane (M) proteins. The characterization of the surface proteins in this study began with an assessment of the peptide sequences in order to identify hydrophilicity indices and protein instability indices using the Port-Param tool in ExPASy. All three surface proteins were calculated to have an instability score under 40 indicating that they were stable. Linear epitopes were identified on the basis of surface probability and antigenicity, excluding regions of glycosylation. Using BepiPred 2.0 (with a cutoff value of 0.35) and ABCpred (with a cutoff value of 0.51), 4 linear B cell epitopes were predicted for the S protein, 1 epitope for the E protein, and 1 epitope for the M protein. For structural analysis, SARS-CoV assemblies published in the Protein Data Bank (PDB) acting as scaffolds for the SARS-CoV-2 S and E amino acid sequences were used for input into the SWISS-MODEL server in order to generate three-dimensional structural models for the assessment of conformational epitopes. Using Ellipro (cutoff value of 0.063) and SEPPA (cutoff value of 0.5), 1 conformational epitope was identified for the S protein and 1 epitope was identified for the E protein, both of which are accessible on the surface of the virus. Finally, the Consurf Server was used to assess the conservation of these epitopes. All epitopes were conserved across the published SARS-CoV-2 genomes and one epitope of the spike protein was predicted to be the most stable across coronavirus phylogeny.

      Critical Analysis/Limitations:<br /> While this study provides a preliminary identification of potential linear and conformational B cell epitopes, the translational value of the epitopes described still needs extensive experimental validation to ascertain whether these elicit a humoral immune response. The conformational epitope analyses are also limited by the fact that they are based off of predicted 3D structure from homology comparisons and not direct crystal structures of the proteins themselves. Additionally, since there was not a published M protein with a high homology to SARS-CoV-2, no conformational epitopes were assessed for this protein. Finally, while evolutionary conservation is an important consideration in understanding the biology of the virus, conservation does not necessarily imply that these sites neutralize the virus or aid in non-neutralizing in vivo protection.

      Relevance/Implications:<br /> With further experimental validation that confirms that these epitopes induce effective antibody responses to the virus, the epitopes described can be used for the development of treatments and vaccines as well as better characterize the viral structure to more deeply understand pathogenesis.

    1. On 2021-10-13 11:33:20, user Martin Humphries wrote:

      An interesting paper. G3BP1, G3BP2, DDX3X, and RBM3 are all found in the meta adhesome defined in "Definition of a consensus integrin adhesome and its dynamics during adhesion complex assembly and disassembly" (PMID: 26479319).

    1. On 2020-02-12 04:16:32, user Dave wrote:

      What an exciting time to be alive to witness such technologies emerge. I can only hope it becomes reality and easily accessible to all people as quickly as possible. The possibilities can be endless. Perhaps even people who have trouble sleeping will be able to use the app to induce sleep at will. Who knows where this could go.

    1. On 2020-02-16 22:45:05, user Stas Rybtsov wrote:

      Thanks a lot, wonderful manuscript striking functional data excellent bioinformatics. Hope it will be published in high profile journal. <br /> I have a few questions and comments; <br /> What is the difference between Pro-HSCs and HECs? Both appear at day 9.5 and disappear at day 11.5. Both have the same phenotype. <br /> Note, according to our data CD41-FITC antibody does not sort out all CD41+ cells (fluorochrome is weak) it is better to use CD41-PE abs they sort out all HSC precursors including pro-HSCs. :) ...

    1. On 2015-05-07 19:53:39, user Ake Lu wrote:

      Hi

      This is a great approach. However, in my study I have multiple correlated traits measured in same subjects such that the proportion of overlap is "1". Could I still apply this approach to assess an overall effect of a SNP on multiple traits.

      Thanks!

      Ake

    1. On 2018-09-27 20:52:27, user Michael Hoffman wrote:

      This manuscript and the associated standard it describes seem motivated mainly for compressing sequence data. It is odd that previous efforts at genomic compression seem mostly ignored here. It is particularly odd that there is no comparison with CRAM, which is already in production use by the European Nucleotide Archive.

      The authors write that MPEG-G is not just compression, and that there is "a lack of efficient, perennial and reliable solutions offering a complete framework—beyond compression—for the representation of the genomic information." It is rather unclear what precisely this means and where, exactly, existing solutions fall down in the authors' eyes.

      This manuscript briefly describes a limited access "MPEG-G Genomic Information Database" for which you can request access by sending an email. It does not explain why the database is immediately available to the public or what conditions the authors will impose for granting access to the database. Given that the database was likely created using data made public by others unconditionally, I find it rather unfortunate that the authors do not feel the need to follow this norm themselves. Neither do they cite the resources they used in creating this database.

      I also immediately noticed several typographical errors and the figures do not appear to have been created with much care. This appears to be a draft that requires further proofreading and editing before wider distribution.

    1. On 2021-03-14 23:13:50, user Alfonso Martinez Arias wrote:

      This is a very good attempt to recapitulate the early stages of human development from human Embryonic Stem Cells (hESCs). However, the manuscript is not clear in certain places and raises a number of questions that I summarize below by way of helping the authors and contribute to the discussion of this important research topic.

      On page 3, it would be good to know if they use an agonist or an antagonist of Wnt signalling; CHIR is an agonist and not an antagonist as stated.

      On the same page the authors state that they ‘consistently observed the emergence of cavitated cystic structures” and yet, in the methods section they state “Following completion of any given aggregation experiment (from day 4 to 6), all cystic structures those clearly displaying a cavity were included in further analyses. Non-cavitated structures were excluded from downstream analyses”. What is the<br /> frequency of the occurrence of cavitation? How is ‘clearly displaying a cavity’ decided?

      It is not at all clear whether the structures resulting from the unsupervised aggregation, in particular those selected for<br /> further study, have any Primitive endoderm/hypoblast. Along the same lines, it would be good to show a comparison of their blastocyst-like structures with ‘natural’ blastocysts to ascertain how similar they are. A comparison with images from published studies (see e.g PMID: 20123909 and PMID: 22079695) suggests that there are substantial, maybe significant, differences,

      It would also be helpful to clarify whether the structures express Sox17 or not, as there seem to be contradictory statements: “we found that<br /> the expression of the core Hypo lineage determinant genes, PDGFRA and GATA6, was highly enriched in cystic structures although SOX17 did not follow this trend (Fig. 3a). In order to confirm these results spatially and on a protein level, we performed immunofluorescence analysis with well-known lineage markers. In accord with the qRT-PCR results, we observed enrichment for KRT18 in the outside TE-like layer, and expression of OCT4/SOX17 in the Epi/Hypo-like inner compartment (Fig. 3b)”. Is Sox17 expressed or not?and, if it is expressed, how often and with what variability in pattern?. Again, a comparison with a human blastocyst would be helpful as contrasting figure 3B with published images of natural blastocysts suggests that these structures have different arrangements.

      In the same paragraph we are told that “At later time-points in culture (D6), some structures maintained GATA3 expression in the TE-like outside layer”. By now the issue of numbers becomes very important if<br /> this is to be a useful experimental system. How many of the initial aggregates cavitate? How many of these exhibit the three lineages by, say, D6? Of those with the three lineages, what is the organization of their Primitive Endoderm/hypoblast in the structure? The manuscript has an inconsistent and variable use of markers which makes it difficult to assess the relationship of these structures to the normal blastocyst.

      The experiment to test the further developmental potential of the hESC derived structures is a good one but the results are not very hopeful, at least in what is shown. A comparison of the images from Figure 4 from those of the structures generated in ref 15, and also other published<br /> similar experiments, show that after plating, the structures appear not to proliferate (have very few cells) and lack the organization of an embryo. There is no proper assessment of markers nor a comparison with a conceptus under the same conditions,

      Importantly, there is no evidence for an amniotic cavity. What the<br /> authors call amniotic cavity is, most likely, a response of epithelial cells to the culture conditions as it is well known that under conditions that provide a matrix or a substrate of sorts, hESCs will form cysts similar to those shown here (see e,g PMID: 26626176). Furthermore, there is no evidence for an amnion and one cannot have an amniotic cavity without an amnion.

      On these basis, the drawings in Fig 4A are not accurate as they represent a structure with more organization and numbers of cells than what the experiment produces (compare with Figures 4D and E). It might be a good idea to draw a more accurate representation of the experiment.

      On the basis of the evidence shown, while the structures shown here resulting from the aggregation of hESCs bear some features of a human blastocyst, there is no evidence to suggest that they resemble one; at least in my opinion. These differences increase with the culture time and are manifest afetr D6. Neverthelss, it is good progress towards the development of such structures in vitro.

    1. On 2019-01-08 19:38:54, user Alfonso Araya wrote:

      A really good work because this try to answer one of the currents problems where there are no standardized measures of data for validation and assessment of the quality of the integration methods. I would recomend to include in some future work the use of non negative matrix factorization, because it does not require any data transformation, or any special matrix construction, but instead, it integrates networks naturally represented by adjacency matrices. This will prevent the loss of data in comparasion with other methods. also it has great accuracy, even superior over Kernel-Based methods.

    1. On 2022-12-03 14:54:44, user Alex Cope wrote:

      If you're interested in this manuscript, you may be interested in our manuscript looking at this method: https://www.biorxiv.org/con.... We applied the original pipeline described by Rosenberg et al. to simulated protein-coding sequences that should remove any correlation between the bond angles and synonymous codon usage, provided this general relationship exists. However, we get pretty much the exact same results when using either the simulated protein-coding sequences or the real protein-coding sequences. We suspect there may be some underlying biases in the data that are not accounted for in their original analysis.

    1. On 2019-07-24 15:13:55, user David Curtis wrote:

      I think maybe the same concerns apply re synaptic activity and intrinsic excitability. It looks like you've tested a few things and some are normal while others are abnormal if one makes no correction for multiple testing.

    1. On 2021-10-26 01:54:16, user CDSL JHSPH wrote:

      This was an interesting study to read, and a great analysis of how the immune system reacts as people age in their mucosal surfaces. After reading this study, I have a couple of questions regarding your experiment. In Figure.3 I understand that the data focuses on Spn colonization causes of inflammation in children. This test included young adults, but I noticed older adults were not included. Is there a specific reason older adults were not tested for this part of the experiment? Figure. 4G also omitted the older adults from this study. Lastly, the discussion noted that children ages 6 to 17 years old were not included in this study. Was there a particular reason for the age gap in subjects? Thank you for your responses in advance!

    1. On 2019-06-06 18:27:31, user Amrit Singh wrote:

      Hi Y-h. Taguchi, this is interesting. The datasets in the mixOmics R-library are a small subset of the original data used in the manuscript (https://www-ncbi-nlm-nih-go... "https://www-ncbi-nlm-nih-gov.ezproxy.library.ubc.ca/pubmed/30657866)"). Please see https://github.com/singha53... for the entire breast cancer data (train and test). I wonder if there is a sparse version of HOSVD, in order to perform variable selection which is already implemented in DIABLO using soft-thresholding.<br /> Best,<br /> Amrit

    1. On 2019-06-28 18:15:59, user Fraser Lab wrote:

      This manuscript by Rocchio and co-workers investigates the structural basis for the interaction between the molecular chaperone Spy and the client protein Im7. Although the Spy-Im7 complex crystallizes, only the Spy portion is readily modelable and there is only residual electron density for the Im7 molecule. This attribute of the cyrstal system means that investigating the structural basis for the interaction is challenging for traditional X-ray crystallography methods. In a previous publication (Horowitz et al., 2016), an approach was designed to overcome this challenge; 4-iodophenylalanine (pI-Phe) was introduced into the Im7 peptide at one of eight positions, and anomalous data was collected for the Spy-Im7 complexes. The Spy-Im7 interaction was mapped using a combination of the anomalous signals from pI-Phe, the residual electron density from Im7, with plausible Im7 conformations generated using molecular dynamics simulations. The original work was the subject of criticism (Wang, 2018), which focused mainly on whether or not the interpretation of the weak anomalous signal was valid. The authors have acknowledged aspects of the criticism (Horowitz et al., 2018), while emphasizing the cross-validation build into their approach, and that other biophysical methods (e.g. NMR) corroborate their proposed model for the Spy-Im7 interaction. The major successes of the present manuscript are that: i) it increases the sensitivity of pI-Phe assignment, validating parts of the previous paper, and ii) it contributes to the development of anomalous scattering as a tool for interrogating protein structure and function.

      In the present work, Rocchio and co-workers focus on optimizing data collection and analysis for anomalous signals from pI-Phe. They placed pI-Phe at one of three positions (only one of which cleanly overlaps with the previous publication) in the Im7 peptide and collected diffraction data around the iodine L absorption edge (? = 2.38 Å and 2.76 Å). Because the iodine scattering factor is 3.9-fold lower at the longer wavelength, peaks in the anomalous density map that are present at 3.28 Å but absent at 2.76 Å, were assigned to iodine. To test signal reproducibility, they adjusted the goniometer ? angle, and collected additional datasets from the same crystals, and also collected repeat datasets from different crystals. The reproducibility was moderate; three signals were observed in all the datasets, one signal was observed only in one of the datasets, and two signals were observed in three out of four datasets. The authors used a cut-off of 6 ? for assigning peaks. Why was this cut-off chosen? Were peaks present at site I3 (e.g. Fig. 1A/C) at lower electron density thresholds? We suggest that showing the anomalous difference maps at various thresholds is a good idea for a supplemental figure to appreciate the sparsity of the signal at high sigma values and the choice of the cut-off at 6 ? .

      The manuscript could be strengthened if the authors addressed the lack of reproducibility for some of the iodine signals in more detail. They state that it is “…clear that the ability to detect low intensity signals is highly dependent on the crystal and the resolution.” Could it be that different crystallization conditions have shifted the binding mode of Im7? I noticed that the concentration of zinc acetate was listed as between 70-270 mM – could this have contributed to the difference? The authors also mention that three out of the four binding sites were identified in the previous work. To make a fair comparison, it might be worth showing all of the proposed binding sites from the previous paper (Horowitz et al., 2016) (e.g. on Fig. 5). On a related note, how confident are the authors in the refined occupancies for the iodine atoms? Is it possible to perform refinement with different initial occupancies to see if they converge on a particular value?

      The major weakness of the manuscript lies in its failure to extend the increased sensitivity of iodine assignment to an improved understanding of the Spy-Im7 interaction. The authors begin the manuscript by introducing the READ algorithm – why hasn’t the more sensitive assignment of pI-Phe been fed back into READ? It would be interesting to see if whether the ensembles generated by the two anomalous data collection strategies agree with each other.

      Overall this is a well written article that will be of interest to a wide section of the structural biology community. The improved capability of modern beamlines to collect anomalous data at long wavelengths, as described in this paper by Rocchio et al., may help to interrogate the structure and function of macromolecules that were previously intractable to traditional approaches.

      Minor points

      1. Consider revising the manuscript title – it is possible for a residue to be partially occupied and not dynamic - “conformationally heterogeneous” is probably more accurate. Or leave it as is, but clearly define the differences and the ability of crystallography to inform between dynamics and heterogeneity somewhere in the manuscript

      2. It might be helpful to add a couple of sentences in the introduction to describe what is known about the Spy-Im7 interaction from orthogonal methods (see point 6).

      3. Please include a section in the methods describing how the ITC experiment was performed.

      4. Page 4 line 31: Typo - “we reasoned that we should be able to specifically distinguish iodine anomalous signals from other with elements”

      5. Page 2 line 14: Shouldn’t it be Residual Electron and Anomalous Density not Residual Anomalous and Electron Density?

      6. Please either label Fig 1D with the location of the “crook of Spy’s cradle” or include a new figure. It might be helpful to have a “cartoon” type schematic in the introduction to illustrate what is known about the Spy-Im7 interaction.

      7. Fig S4-6: Please label the iodine peaks with the labels used in Fig. 1 (e.g. I1, I2, I3, I4).

      8. PDB 6OWX: Is imidazole 214 in chain A modelled correctly?

      9. It’s notable that they have co-crystalized Spy with 10 peptides (8 in the previous publication, 1 overlapping in both, and 2 novel ones in this publication) - have soaks been attempted? This could also add an interesting experimental control where the anomalous signal should be displaced by excess unlabeled client peptide but maintained (or enhanced) by labeled peptide.

      10. Peak height is a good measure and relatively unbiased, but the anomalous maps have the potential to inform (in a relatively model unbiased manner) on the occupancy and B-factor directly. Showing multiple concentric contours or plotting density as a distance from peak center along a pseudo atomic radius will help to clarify the differences in the profiles. For example, in table 4, the peak heights for the 4th and 5th rows are the same, but the occupancy is 2-fold different.

      11 - Figure 4 is pretty difficult to follow, even for us. We could do with a bit more annotation of where the disordered peptide backbone is predicted to trace. It also seems like the aromatic group attached to the I is a fairly strong modeling constraint that could help guide the eye in this figure.

      12 - These data are especially valuable for methods developers and given the issues raised by Wang on the previous paper, it is especially important to put as raw data as possible out into the public. Papers like this should be held to a higher data deposition standard (mtzs are not enough!): the authors should deposit their raw diffraction data at SBGrid Data Bank (https://data.sbgrid.org/) "https://data.sbgrid.org/)") or an equivalent resource to enable future re-use and validation.

      We review non-anonymously, James Fraser and Galen Correy (UCSF), and will have posted this review as a public comment on the preprint: https://www.biorxiv.org/con...

      References<br /> Horowitz, S., Salmon, L., Koldewey, P., Ahlstrom, L., Martin, R., Quan, S., Afoine, P., van den Bedem, H., Wang, L., Xu, Q., Trievel, R., Brooks, C. & Bardwell, J. (2018). Nat. Struct. Mol. Biol. 25, 989–991.<br /> Horowitz, S., Salmon, L., Koldewey, P., Ahlstrom, L. S., Martin, R., Quan, S., Afonine, P. V., Van Den Bedem, H., Wang, L., Xu, Q., Trievel, R. C., Brooks, C. L. & Bardwell, J. C. A. (2016). Nat. Struct. Mol. Biol. 23, 691–697.<br /> Wang, J. (2018). Nat. Struct. Mol. Biol. 25, 989–991.

    1. On 2021-06-23 07:38:24, user Alex Crits-Christoph wrote:

      This work by Professor Jesse Bloom details a phylogenetic analysis of a set of SARS-CoV-2 genomic sequences originally submitted to the Sequence Read Archive, likely by the authors of Wang et al. 2020, in February - March, 2020. Both a preprint, and a later paper (by Wang et al.), were publicly available describing these sequences, including key aspects of their genomic features. However, independently of the preprint or paper, which make no direct reference to an NCBI submission, the sequences been marked for deletion on NCBI, but were re-obtained by Bloom via internet backups of the data. While sharing more genomic data from the early epidemic can be valuable, I believe the current version of this work makes several errors that are important to address in both scientific content and critically, in scientific communication.

      In general, the work is vague or remiss about extremely important context and details about the sequences in question. To begin, the genomes obtained are consistently referred throughout the title and abstract (and indeed most of the text) as being from the "early" epidemic. This terminology is too vague, as it may invite the reader to assume that the genomes include the earliest cases known - in fact, they are not, and were originally reported by Wang et al. 2020 to be from January 2020. Over 25 genomes had previously been obtained during 2019 for SARS-CoV-2. To properly interpret the context of this work, an emphasis on the timing is critical and should not be avoided in the abstract or title.

      Secondly, the abstract and title both strongly give the inaccurate impression that the re-obtained sequences alone newly demonstrate that the known and reported genetic diversity of viruses in the Huanan market was not representative of all circulating SARS-CoV-2 variants at the time. This is alluded to in the title, which says they "shed more light" on the early epidemic. Yet, this is not supported by the data in the manuscript: it was already known that the genetic diversity of viruses obtained from Huanan market cases was not representative of all genetic variants circulating at that time. As can be seen most clearly in Figs 3 and 5, the new sequences obtained were not completely unique in sequence or unusual in case timing. There were already several reported publicly available similar genomes with for the most part identical sequences from the early epidemic. The author is certainly aware of this, as it is evident from the data presented and from the works cited (both Garry 2021 and Kumar et al. 2021 discuss this at length), but this key point is not emphasized in the title or abstract, and indeed is a weakness of the importance of the work itself.

      Thirdly, in the introduction the work makes several key omissions about the timing of cases. The introduction directly suggests that the Huanan market is unlikely to be a site of zoonosis because a number of early cases could not be confirmed to be connected to the market; However, this was true of SARS-CoV-1 in Fushan as well. The author also neglects to mention that a significant fraction of cases were connected to *other* markets in the city, including the earliest cases (as described in the WHO report). And indeed, if there were multiple spillover events, e.g. from a shared wildlife supply chain that connected multiple markets throughout the city, even with perfect contact tracing data one would not expect all cases to be connected to one market.

      Fourthly, I believe the inclusion of Figure 2 in this paper is highly unprofessional and misleading. This is a screenshot of an email from a scientist who is not associated with the papers or data described in the data; indeed, the only relevance between them seems to be a shared nationality of the author(s). Additionally, as of June 2021 the data described as 'deleted' in this screenshot is publicly available again on NCBI. While I understand that Bloom likely did not realize this mistake, it is difficult to justify the inclusion of this figure in a scientific work.

      Fifthly, and perhaps most importantly, I think the previous point happens to provide a fortuitous lesson about a key error that Bloom makes in assuming misconduct on the part of the Wang et al. 2020 authors. He writes "It therefore seems the sequences were deleted to obscure their existence". It is implicitly assumed that Bloom believed that to also be true of the data described in Figure 2 - hence its inclusion - but its recent re-publication on NCBI makes it abundantly clear that was not the case. To be honest, this is not surprising. Claims of scientific misconduct made from a distance, without knowledge of the details and circumstance surrounding the issue, have the potential to appear convincing despite having perfectly ordinary (or perhaps slightly less than ideal, but not nefarious) scientific rationale.

      Similarly, there is also evidence that scientific misconduct is also highly unlikely to be the case with the dataset that is described in this paper. Primarily, this is because the paper describing these sequences was published in a journal several months later. If the authors were indeed trying to obscure their data, they would simply also not publish their paper in a public journal, or request it to not be published after submitting for review. If these sequences were removed for the purpose of obscurity, it is also worth noting that such an effort clearly flopped - because as described above, these sequences do not immediately provide any completely new knowledge about the genetic diversity of SARS-CoV-2 in the early pandemic. In both the preprint and the paper (the first submitted before data removal; the second published likely after), no mention of a BioProject accession is made, and instead it is said that the data is available upon reasonable request from the authors. If the writers initially intended for the data to be made public, standard practice would have included the BioProject reference (or even a placeholder) in the text. The fact that there was never such a reference makes it quite evident that there likely was a miscommunication between co-authors about whether the data was intended to be released to NCBI.

      The reality is that minor scientific missteps and less-than-ideal circumstances surround the sharing of scientific data all of the time. The process of publishing on scientific data is fraught with tiny details and hard work, the trainees responsible for it are often overworked, and there are often unfortunate but relatively ordinary scientific incentives to avoid making as much data public as should be. The rationale behind what data is and is not shared for scientific works are often nuanced, and sometimes quite personal. The scientific process would be better if it were not the case, but these forces are almost universal, and everyone encounters them in scientific work. In this circumstance, like in the circumstance outlined in Figure 2 of this work, these ordinary forces of scientific circumstance are almost certainly more likely the case than direct censorship or misconduct. It is unfortunate and alarming that this work as it currently stands baselessly contributes to an environment where many readers assume the latter depending on the country of origin of the original authors.

      Minor point:

      At the bottom of page 5: "But although there are unusual aspects of RatG13's primary sequencing data". This statement is vague, the language is unscientific, and many interpretations of it would not be supported by the citations provided. I would recommend that Bloom change this sentence to present a coherent scientific statement if indeed there is a point about genome quality to be made there that is relevant to the text.

    1. On 2020-04-13 21:24:43, user Mutasem Taha wrote:

      The supplementary table of this preprint raises doubts about the findings, for example, Enoxacin is given a high index of 1.18 while its very close analogue Norfloxacin is considered inactive and given index of 0.07. Similarly, Prothionamide is considered potent with an index of 1.14 while its close analogue ethionamide is totally inactive according to the study with activity index of 0.12. <br /> The preprint considered Nitazoxanide to be inactive (index 0.6) although it is currently in clinical trials as treatment for COVID-19 (https://clinicaltrials.gov/... "https://clinicaltrials.gov/ct2/show/NCT04341493)") and was reported to have anti SARS-Cov-2 IC50 of 2.1 microM in a peer reviewed paper (Cell Research (2020) 30:269–271; https://doi.org/10.1038/s41... "https://doi.org/10.1038/s41422-020-0282-0)").

    1. On 2024-12-06 17:54:14, user Malte Elson wrote:

      The remarks below are a summary of the points discussed during the Cake Club of the Psychology of Digitalisation lab at University of Bern ( https://www.dig.psy.unibe.ch/studies/cake_club_/index_eng.html ). They do not reflect the opinions of each individual journal club participant. Any responses to these points should be addressed to Malte Elson.

      In their preprint, Spiess et al. (2024) illustrate the impact of influential data points on statistical significance in linear regression analyses. The authors reanalyzed data from three high-impact journals by searching for the term "linear regression” and digitizing graphs of the included papers (due to the absence of raw data). Their findings revealed that excluding influential data points often rendered previously significant results non-significant. The simulations included in the study largely confirmed expected outcomes, supporting the overall argument for incorporating leave-one-out analyses in data analyses practices. The authors ultimately advocate for broader adoption of such methods to enhance the robustness of statistical conclusions.

      We found the paper to be interesting and an illustrative contribution to statistical education, both in terms of the potential fragility of published claims and as an illustration of an intuitive but underused outlier detection method. We identified points that might allow the authors to strengthen future versions of the manuscript, including some critical points about potential weaknesses or absences in the current version of the manuscript.

      1) TERMINOLOGY CONFUSION AND REPORTING ISSUES<br /> * Graphs vs. Papers: There is some confusion regarding the unit of analyses, and probably some reporting errors: On p. 4, l. 115, the paper states that the sample was 24 + 30 + 46 = 100 graphs, whereas on p. 6, l. 170 the authors state they examined 100 publications (going by Table 1, this is a simple clerical error, and should say graphs).

      * Similarly, the description of the columns in Table 1 (p. 11) is confusing, and we think has at least one reporting error:

      * It is unclear what “Hits” represent: Are these unique papers, or do the search engines of Science/Nature/PNAS return the same paper multiple times for each instance of the search term (“linear regression”)?

      * What does "number of graphs that were not shown" mean? We think these are instances of linear regressions that simply were not reported with a corresponding graph in the original publication, but they could also be graphs missing, inaccessible, or excluded <br /> * The “Articles” column is described as “number of Articles in which the analyzable graphs were found” (p. 11, l. 314), but we think these are the 21 articles in which the 29 “influential variables” were found. The number of articles with analyzable graphs is not reported. It thus remains unclear how many papers were included, and how many graphs were analyzed from each paper.

      * On p. 6, the authors report having identified 29 graphs in 21 papers in which the removal of one datapoint changes the result of a linear regression (see also Figure 1). On p. 6, l. 179 the “incidence” (should be prevalence instead) of changes in papers is reported as ~20%. However, this puts papers (21) in the numerator and graphs in the denominator (100), which underestimates the prevalence. On the graph-level, it should be 29/100 = 29%. The paper-level prevalence cannot be calculated because the authors do not report the number of papers with analyzable graphs (see above).

      * We strongly recommend reporting a Prisma flowchart to clarify the inclusion/exclusion of graphs and papers. In the same vein, the paper lacks basic information about the included studies, such as sample sizes or the distribution of p-values. Other information would also help emphasizing the importance of the present study, e.g. citation metrics.

      * The authors refer to “Supplementary Data 1” (p. 4, l. 121) but provide no link.

      2) SAMPLING STRATEGY <br /> * The study focuses on digitizable graphs without overlapping data points, inherently excluding studies with (1) larger samples and (2) homogeneous effects, where overlapping data points should be more frequent. This selection skews the included papers towards studies with smaller samples and p-values near 0.05 (due to lower power and publication bias / p-hacking), which are more susceptible to the illustrated effects. This is not a problem per se, but means the findings (including the prevalence rate) are about a narrower population of studies. Either way, the selection effects should be discussed in the paper.

      * It is not fully clear how it was decided which graphs are analyzable and which are not. Moreover, on p. 4, l. 127-130 the authors state that the obtained regression parameters match those reported in the paper closely, but they do not further explain what exactly this means, or what happened when they did not match

      3) ANALYSES AND CONCLUSIONS <br /> * The analysis does not account for dependencies when multiple graphs from the same paper, which will likely be based on the same data (which are then susceptible to the exclusion effects), are included.

      * In a way, the susceptibility of findings to the removal of a single data point is a restatement of issues related to small samples. Small samples are inherently more fragile, and larger sample sizes are more robust to the influence of removing (or adding) single data points and render p-values (and other estimates) more stable. This is not to say that the findings reported are not interesting; however, we were wondering whether a table of all included studies sorted by observed p-value and sample size would have flagged the same fragile papers. This is also not to say that dfstat is redundant, and we absolutely see the pedagogical value in being able to point at individual data points that “cause” a finding to be significant. Rather, we would be interested to what extent dfstat converges with common heuristics.

      * Relatedly, the authors decry that influence measures such as dfstat are largely ignored, even by statisticians (p. 4, l. 139). This may well be, but of course, statisticians (and non-statisticians) are obviously aware of issues related to low power and small samples, and one of these issues is the problem of spurious findings (e.g. due to few, extreme data points).

      * The authors largely blame frequentist statistics, particularly on p. 10, where e.g. they state that “[a]s long as stating significance or not is still based on the ubiquitous ? = 0.05 threshold, these statements can be sensitive to the presence of a single data point.” (l. 282-284). However, it is unclear how this follows from their findings. Any inference (not just ? = 0.05) could be susceptible to the influence of single data points when the estimate is close to the criterion. Moreover, particularly when the sample size is low, any metric’s value (e.g. point estimates) will vary as a function of the removal of individual data points, regardless of whether the inference is threshold-based or not. This is simply a property of statistical models fit to a limited amount of data. So again, the issue seems to be with small sample sizes.

      4) RECOMMENDATIONS AND FUTURE DIRECTIONS<br /> Things we would have liked to see:

      * Additional analyses, such as leave-two-out or leave-k-out methods. The leave-one-out analyses are providing a good intuition of how fragile some small-sample study results are. Additional leave-k-out analyses would provide further information about the fragility of the entire sample.

      * So far, the authors are concerned with the fragility of results as an outcome of removing data points. An additional study exploring the reverse scenario would be valuable. Specifically, it could investigate how extreme an additional data point would need to be to alter results, and how adding non-extreme data points could mitigate the relative weight of extreme data points.

      * Discussing dfstat as a robustness metric (“How many individual data points would have to be removed/added to render a significant result nonsignificant or vice versa”)

      * A discussion of how dfstat could be used for p-hacking by showing researchers which data points they would have to remove to turn a nonsignificant study result into a significant one.

      * The authors graciously and immediately shared data and code with one of us who requested it, and we thank them for this. We would like to see this data and code provided in a public repository and linked to in a future version of the manuscript.

      * We note that the authors chose to anonymise their data so that the reader cannot tell which original study’s results are robust or not. Personally, we think that meta-scientific interests are best served by making this information public; that is, we would like this data to not merely be used to illustrate the method but also inform the reader about the fragility or robustness of those publications’ results. Of course, not everyone agrees with this practice - perhaps the authors could comment on their perspective on this issue in a future version of the manuscript.

    1. On 2022-03-15 10:42:31, user sagar khare wrote:

      Thank you, Roberto and James for taking the time to engage with our work and for these helpful comments! We are revising the manuscript in the light of these comments (among others). We will post a detailed response to your comments in time.

      Re. why we used AF2 when exptl structures are available: we generated these models in early December and posted this preprint on Dec 13 before any experimental data were available. When the first structures came out in early Jan, we performed a comparison with our models and submitted to journal in mid-January. (what can I say, that's the pace of scientific publishing :))

    1. On 2016-08-20 15:44:28, user Raquel Tobes wrote:

      It is good to know new things about bacterial intergenic regions!<br /> Escherichia coli, Salmonella enterica and Klebsiella pneumoniae are plenty of REP (Repetitive Extragenic Palindromes) in their intergenic regions. Staphylococcus aureus and Streptococcus pneumoniae have many intergenic tandem repeats and Mycobacterium tuberculosis has REP-like intergenic sequences and some tandem repeats. <br /> Even considering that probably the short reads assemblies in many cases collapse the regions with repeats, it would be interesting to find some analysis about repeats in your study.

    1. On 2025-02-19 09:08:52, user Gennady Gorin wrote:

      This is a novel and interesting consideration, though I believe less fundamental than 1. the definition of "neighbor" is somewhat arbitrary because the metric is arbitrary and 2. the definition of "neighbor" is ill-specified because the uncertainty in (imperfect) data is not properly propagated.

    1. On 2018-11-29 09:06:32, user Conrad Mullineaux wrote:

      Speculative hypothesis papers can be fun and good for stimulating debate. But, to be useful, I think they need to present a plausible and coherent scenario (something that at least has a chance of being true) and they need to pay reasonable attention to the facts. I’m not sure that’s the case here. My main concerns are:<br /> 1. Fig. 3. The feedback loop looks neat, but it ignores the fact that the local [O2] around the nitrogenase need not correlate to any significant extent with the global atmospheric [O2]. Huge discrepancies could occur, due to local environmental conditions, and also due to the metabolic activity of the cell itself. Considering only the latter factor, the intracellular [O2] could be much higher than ambient (due to PSII activity) or much lower than ambient (due to respiration). If the nitrogenase doesn’t actually see the global atmospheric [O2], such a feedback loop could not clamp global [O2] at any particular level as proposed.<br /> 2. P.5 “If diazotrophic cyanobacteria are grown under conditions where they have sufficient CO2 and light, and with N2 as the sole N source, then they grow and accumulate no more than 2% oxygen in their culture atmosphere (16). The 2% O2 remains constant during prolonged culture growth because this is the O2 partial pressure beyond which nitrogenase activity becomes inhibited. With greater O2, nitrogenase is inactivated and there is no fixed N to support further biomass accumulation. With less O2, nitrogenase outpaces CO2 fixation until the latter catches up, returning O2 to 2% in the culture.” The outcome of this experiment will come as a surprise to anyone who has observed diazotrophic cyanobacteria happily growing without a combined nitrogen source at 21% ambient O2 (it depends on the cyanobacterium, of course). The result is a key plank of the authors’ argument, but it’s not clear if, when or how the experiment has been carried out. It’s not as straightforward as it seems, and nothing like that is described in the cited reference (16: Berman-Frank et al 2003). The nearest thing in that paper is a statement that a specific cyanobacterium, Plectonema boryanum, is unable to fix nitrogen above certain ambient [O2] levels. The limits are actually rather lower that the 2% quoted: 0.5% in the light and 1.5% in the dark (16). Plectonema is a specialist for microaerobic environments, and most other diazotrophic cyanobacteria are not so susceptible to O2 inhibition. <br /> 3. P.5 “Cyanobacteria have evolved mechanisms to avoid nitrogenase inhibition by oxygen, including N2 fixation in the dark, heterocysts or filament bundles as in Trichodesmium. Critics might counter that any one of those mechanisms could have bypassed O2 feedback inhibition.” Indeed they might. The authors go on to brush aside their imaginary critic on 3 grounds, none of which seem valid. “First, evolution operates without foresight”. Foresight isn’t needed: there would have been an immediate selective advantage to acquiring an O2 protection mechanism. “Second, the mechanisms that cyanobacteria use to deal with modern O2 levels appear to have arisen independently in diverse phylogenetic lineages, not at the base of cyanobacterial evolution when water oxidation had just been discovered”. Very likely so, but what about the next 2 billion years? “Third, the oldest uncontroversial fossil heterocysts trace to land ecosystems of the Rhynie chert”. It may or may not be the case that heterocysts evolved late, but, in any case, heterocysts are not significant contributors to marine nitrogen fixation: in extant cyanobacteria it’s the other protection mechanisms that allow cyanobacteria to make a huge contribution to oceanic nitrogen fixation even in the presence of 21% atmospheric O2. What about those other mechanisms? The fact that different lineages of cyanobacteria have independently come up with at least 3 different ways to protect their nitrogenase from O2 indicates that evolving such mechanisms is not really such a big deal. The authors’ scenario suggests that for a period approaching 2 billion years there was a nitrogen-limited biosphere with cyanobacterial nitrogenase operating right up against an inhibitory concentration of O2. There would have been intensive selective pressure for adaptations to protect the nitrogenase from oxygen. The scenario depends on the assumption that no cyanobacterium was able to develop a protection mechanism that would allow nitrogen fixation at >2% O2, despite selective pressure operating over a period of about 2 billion years and the availability of multiple solutions to the problem, as seen in extant cyanobacteria. I’m afraid that’s implausible, and I suggest that we need to look elsewhere for an explanation of the low O2 level through the Proterozoic.

    1. On 2020-07-08 13:37:25, user Anders Vahlne wrote:

      Some comments about the reactivity of the blood donors from 2019 (BD2019) and 2020 (BD2020):

      The results are shown in Figures 3A and 4B, F and G showing interferon-gamma-producing cells after stimulation with SARS-CoV-2 peptides.

      In Figur 3A one can see that six BD2019 react with S (spike) and four with M (membrane) but none with N (nukleocapsid). Tthe cut-off may have been chosen high enough to get a desired outcome. In the histogram to the right the authors decide that in order to be T-cell reactive the cells have to react with N plus S or M. There is no explanation why. One might suspect that the reason is to have all BD2019 negative and still have BD2020 positive.

      In Figur 4B they depict those that are CD4+ and CD8+. Now two CD8+ BD2019 are positive also with N.

      In Figur 4F they depict those those that are antobody positive and antibody negative. The BD2019 were not tested and thus omitted in Fig 4F.

      In Figur 4G there are two histograms. The one to the right summarizes T-cell positives in respective patient group. Again, the authors decide that in order to be T-cell reactive the cells have to react with N plus S or M so they get zero reactivity with the BD2019s.

    1. On 2021-03-26 09:24:57, user Tijawi wrote:

      Fascinating stuff, need to read through again. Note that UBP9 is now class Xenobia. Also, I hate to be pedantic, but be sure to check that the names of your nereid-based taxa are correctly combined: Eudoromicrobium, Autonoimicrobium, and Amphithoimicrobium.

    1. On 2019-07-09 09:29:52, user Martin Steppan wrote:

      A very fascinating study and topic with interesting implications! If I understand the results and design correctly, the genetic instruments quantifying the risk for early menarche also grasp a considerable amount of socioeconomic information (by being associated with educational attainment). Would it not be straight forward to control outcome variables (like age at first birth) for socioeconomic differences on the observational level first, before conducting Mendelian Randomization? MR is powerful, but if you have real confounding variables, is there a rationale not use them?

    1. On 2020-12-02 15:40:43, user Ryan wrote:

      NE 598 Group 2<br /> Social isolation impairs the prefrontal-nucleus accumbens circuit subserving social recognition in mice. https://doi.org/10.1101/202...<br /> Ryan Senne, Evan Mackie, Patlapa Sompolpong, Anthony Khoudary

      Introduction

      We are a group of Boston University students enrolled in a course focused on understanding neural circuits, including cortical information processing, guided behavior and cognition. To further engage with current research in the field and to gain experience in the process of peer-review, we present the following critique of the currently unpublished manuscript from Park et al. posted on biorxiv.org on November 12, 2020.

      Summary <br /> The medial prefrontal cortex (mPFC) has been shown to activate in response to social behaviors in both humans and rodents. Recent studies have revealed a corticothalamic circuit affected by social isolation; however, whether social isolation affects mPFC projections to other subcortical regions involved in social behaviors remains unclear. To this end, Park et al. investigate the role of projections from the mPFC to the nucleus accumbens shell (NAcSh) in the social recognition deficit observed in mice following social isolation. Through retrograde viral tracing, electrophysiological, chemogenetic and behavioral experiments they identified a novel circuit projecting from the prefrontal infralimbic cortex (IL) to the NAcSh affected by early social isolation. They found IL neurons to have decreased excitability in single housed (SH) mice compared to normally group housed (GH) mice. NAcSh-projecting IL neurons were activated when the GH mice interacted with a familiar mouse, but this activation was not observed in SH mice. Furthermore, inhibition of IL neurons in GH mice impaired social recognition without affecting social interaction in GH mice. Similarly, activation of IL neurons rescued social recognition in SH mice. These findings corroborate the social recognition defects observed in models of ASD and schizophrenia, which may reflect problems in human patients. Overall we recommend comparison of results to data collected before the re-socialization period, non-parametric data analysis and improved IHC imaging. Additionally, we recommend consistency between figures in the manuscript and the extended data, alternative anxiety measurements and in vivo electrophysiology recordings. We believe these recommendations will strengthen the argument for the role of this novel circuit subserving social recognition.

      Figure 1 serves to establish the experimental timeline and demonstrate the social recognition deficit induced by social isolation. Mice were housed either singly or in groups for 8 weeks post weaning. SH mice were then regrouped for 4 weeks with their littermates. At the end of this 12-week period, experiments were conducted. Mice from both cohorts were subjected to three chamber tests assessing social preference and social recognition. Both GH and SH mice spent significantly more time with a novel mouse than an inanimate plastic mouse; indicating no change in social preference due to isolation (Fig. 1c). GH mice spent significantly more time with a novel mouse compared to a familiar one in the social recognition test. Constratingly, SH mice spent comparable time with both the novel and familiar mouse suggesting a deficit in social recognition (Fig. 1d). Both cohorts showed no significant deficits in general recognition memory or hippocampal dependent memory (Fig. 1e, f). SH and GH mice also showed similar body mass changes, basal locomotor activity and anxiety levels (Extended Data Fig. 1).

      The authors hypothesized that projections from mPFC to NAcSh may be involved in social recognition. To test this the authors injected a retrograde enhanced green fluorescent protein (eGFP) virus into the NAcSh. Neurons in the deep layer of the IL were heavily labeled with eGFP. There was a significant difference in the number of eGFP+ cells in the IL compared to the PL (Fig. 2b). This observation led the authors to focus their study on mPFC-IL projections. Ex vivo brain slice whole cell patch clamp recordings revealed a significant decrease in excitability of NAcSh-projecting mPFC IL neurons in SH mice compared to GH mice (Fig. 2c). This decrease in excitability was not observed in mPFC PL projections to NAcSh, suggesting cell specific modulation of this circuit by social isolation (Fig. 2d). Other electrophysiological properties of NAcSh projecting IL/PL neurons were similar in both GH and SH mice (Extended Data Fig. 4).

      The goal of the next experiment was to determine if IL-NAcSh projections were activated by familiar mice in a different behavioral paradigm. Mice from both cohorts were habituated to a target mouse (Fig 3a). Interestingly, both GH and SH mice spent significantly less time interacting with the target mouse on consecutive social habituation trials (Fig. 3b). In the social recognition test SH mice again spent comparable time interacting with both novel and familiar mice, indicating the social recognition deficit (Fig 3c). Post mortem slice histology was used to quantify the activity of IL-NAcSh projections in response to a familiar or novel mouse. A retrograde eGFP virus was injected into NAcSh in both GH and SH mice; eGFP+ cells co-labeled with c-Fos staining were used as a proxy for activation of this circuit (Fig. 3d, e). Quantification of this labeling revealed that GH mice had a significant increase in the ratio of c-Fos+/eGPF+ cells after interacting with a familiar mouse compared to a novel mouse (Fig. 3f). This increase in activity was not observed in SH mice, supporting the claim that this circuit is activated by a familiar conspecific.

      To confirm the findings in Fig. 3, the authors conducted chemogenetic experiments in normal GH mice. A retrograde eGFP-Cre virus was injected into the NAcSh and a Cre dependent hM4Di receptor virus or mCherry control vector was injected into the IL (Fig. 4a, b). Intraperitoneal injection with CNO confirmed the inhibitory effect of hM4Di (Fig, 4d). Mice were then subjected to the social preference and social recognition tests following CNO injection. Inhibition of IL-NAcSh projections did not affect social preference, but did result in a significant decrease in social recognition (Fig. 4e, f). To further investigate this effect, mice were subjected to the social recognition test with the choice between a cage mate (in place of a target mouse) and a novel mouse. When IL neurons were inhibited, mice were unable to distinguish their cage mate (Extended Data Fig. 5). These findings support the claim that activation of this IL-NAcSh circuit is necessary for social recognition.

      In an attempt to solidify this claim, further chemogenetic experiments were conducted in SH mice. The previously mentioned experimental approach was used; however, a Cre dependent hM3Dq or mCherry control vector injected into the IL (Fig. 4a, b, c). CNO injections confirmed the activation of IL neurons (Fig 4d). Activation of IL-NAcSh projections did not affect social preference but did rescue social recognition (Fig. 4e, f). These findings demonstrated that activation of this IL-NAcSh circuit is both necessary and sufficient for social recognition.

      Major Criticisms

      The authors claim that regrouping SH mice in the model is insufficient to rescue social recognition. White the first experiment showed that SH mice spent relatively similar time with both the novel and familiar mouse, suggesting a social recognition deficit, all behavior tests were done following resocialization of SH mice (Fig. 1d). Adding another SH cohort without resocialization prior to administering behavioral tests would be beneficial to determine whether there is a significant change between the performance of regrouped SH mice and non-regrouped SH mice.

      In the second experiment, the authors found the projections from the prelimbic cortex (PL) to the NAcSh to have less neuronal density when compared to IL-NAcSh projections, therefore decided to conduct subsequent experiments only looking at the IL (Fig. 2b). Relatively less dense neuronal density in the PL does not equate to low activity in the PL and is not sufficient to rule out the role of the PL in social behavior, especially because previous papers have found projections from the PL to contribute to social behavior. There was no information on how eGFP-positive cells in the IL and PL were quantified. The cell numbers in the IL and PL were compared using an unpaired t-test, however, the IL cells appear to have a normal distribution while the PL cells do not. Using a parametric test may therefore be inappropriate for comparing the two populations. In Figure 2, there was also minimal physiological data to confidently conclude that excitability in the IL of SH mice is significantly reduced (Fig. 2c). Incorporating in vivo data would be beneficial.

      In the third experiment, c-Fos immunohistochemistry was performed as a proxy of recent synaptic activity. The ratio of quantified c-Fos+ cells in the IL to GFP+ cells was used to prove that GH mice show a significant increase in c-Fos positive NAcSh-projecting IL neurons while encountering familiar conspecifics. The method behind quantifying the overlaps are unclear in the paper. The major issue with this approach is that separately quantifying c-Fos+ cells and comparing it to the quantified number of GFP+ cells is that there is a possibility that there are quantified neurons that are not co-labeled with c-Fos and GFP. A one-way ANOVA and Tukey's multiple comparisons test was used to analyze the data, however, all of the data does not appear to follow a normal distribution (Fig. 3f).

      Apart from data in Figures 2 and 3 that are not appropriate for parametric statistical tests, data from other figures such as Figure 1 exhibit a binomial distribution and also do not fit the criteria for parametric tests (Fig. 1c). The distribution of the data in all experiments should be taken into consideration when running analyses <br /> While the viral stain in Figure 2 appears to be non-nuclear, the stains in Figures 4 and 5 appear to be nuclear (Fig. 2a; Fig. 4b; Fig.5b). It would be more standard to use the same virus for labeling throughout the experiments. The figures state that a retrograde adeno-associated virus (AAVrg) expressing eGFP was used, but the expression patterns are not consistent with this.

      Minor Criticisms <br /> Many of the summary bar graphs in the figures have error bars that are obscured by the individual data points, specifically figures 3b and 3f, 4e and 4f, and 5e and 5f. Changing the color of the error bars would help with better visualization of the data and its distribution. Additionally, in figure 1b and all three chamber tests, it would be worth noting whether or not the tests were counterbalanced with the stimulus mice in different chambers. This would control for the SH mouse simply memorizing the location of a preferred stimulus rather than true social recognition or preference.

      In the first experiment, it would have been worth titrating the length of juvenile isolation in order to find the critical period where its effect is strongest. The referenced paper determined 8 weeks to be effective, but an experiment to prove that 8 weeks is ideal would have been beneficial to the study as a whole. Another useful tool would have been in-vivo electrophysiology to selectively measure activity in IL-NAcSh projecting neurons during socialization and confirm the results shown by the c-Fos immunohistochemistry. Optogenetics also could have been used to measure social preference or recognition during the inhibition of these IL-NAcSh projections.

      Merits <br /> The panels in Figure 1 are incredibly well made and very easily communicate the experiments and data. The heatplots used throughout the paper are incredibly parsimonious in their representation.The novel object and object place controls on the three-chamber test often get ignored so this experiment was very well controlled. <br /> The behavioral schedule in Figure 3 is incredibly erudite and can be recycled by other researchers for these types of experiments. <br /> One of the biggest mishaps in chemogenetic experiments is a lack of proper controls. The researchers were incredibly thorough in their DREADD’s experiemnts and included all the necessary control groups including CNO in WT mice and using a saline vehicle in a DREADD injected animal. This type of comprehensive experimental schedule ensures that the data has a considerable level of confidence attached to it.

      In the supplemental figures the authors chose to include several controls which are necessary for the confidence of their results. Their inclusions of anxiety controls, often overlooked electrophysiology metrics, object controls, and cagemate controls inspires confidence in the results. Overall, a very well controlled paper.

      Future Directions <br /> One of the most important future experiments could involve dissecting between cell subtypes within the IL. A recent paper has shown that somatostatin interneurons house social memory within the PL and such cells could be necessary and sufficient for proper memory expression. The authors coils also determine the receptor subtypes the pyramidal neurons they focused on contained. For example, a recent article showed that Pl neurons which projected to the NAc shell were D1R+ and it would be interesting to see if similar neurotransmitter systems were prevalent in both mPFC areas.

      With respect to Figure 2, outside of the IL, the PL, and vCA1 have also been shown to be necessary for the expression of proper social cognition and behavior. These other areas have been shown to project tohe NAc shell. A follow up study that highlighted the unique contributions of these distinct areas and how possible neural circuitry links them together would be a valuable funding for the social neuroscience community. The electrophysiology in this figure is solid from a technical standpoint but whether this difference in excitability translates to meaningful behavioral phenotypes isn’t characterized. To this end, in vivo physiology during epochs of social interaction may more aptly furnish the narrative that Il cells are preferentially affected by social isolation.

      With respect to Figure 3, one of the most crucial aspects of this paper is that socially isolated mice have functioning social recognition on a short time scale as shown in 3A and 3B. The authors supply two reasonable hypotheses that this then could be a deficit in consolidation or retrieval memory mechanisms. This would be a crucial discovery for the field of social and memory neuroscience. One possible set of experiments the authors could pursue in a future paper would be to use the TRAP2 or tet-tag viral system to tag cells active at the encoding of a social epoch with ChR2 and eYFP within vCA1 or the PL, two areas shown to be important for the social engram. The next day the researchers could perform a 90-minute transcardial perfusion and quantify overlap. If there is an above chance overlap between the “tagged” cells and endogenous c-Fos this would rule out consolidation as the faulty mechanism. In this hypothetical scenario the researchers could then use a subsequent cohort to see if chronic activation of this memory ensemble could be enough to rescue the behavior if it were a failure of retrieval.

      Works Cited

      1.)Yamamuro K, et al. A prefrontal–paraventricular thalamus circuit requires juvenile social experience to regulate adult sociability in mice. Nature Neuroscience, (2020).<br /> 2.)Murugan M, et al. (2017) Combined Social and Spatial Coding in a Descending Projection from the Prefrontal Cortex. Cell 171(7), 1663-1677.<br /> 3.)Cummings K. and Clem R. (2020) Prefrontal somatostatin interneurons encode fear memory. Nature Neuroscience 23(1):61-74<br /> 4.) Xing B. et al (2020) A subpopulation of Prefrontal Cortical Neurons Is Required for Social Memory. Biological Psychiatry in press.<br /> 5.) Okuyama T et al. (2016) Ventral CA1 neurons store social memory. Science. 129:17-23.

    1. On 2018-11-30 13:12:24, user theempiricalmage wrote:

      And of course, lack of Iranian samples from the paleolithic. "Caucasus", nowadays, has become the choice euphemism for Iranian. Note the lack of 25kybp R1a diversification in the Caucasus compared to Iran (likely origin). Difficult to reconcile that with this study. The Iranian anthropological record is impeccable, and it has long been established that the plateau was well populated by that time.

    1. On 2016-11-09 02:34:34, user James Hane wrote:

      An interesting read, but excluding scaffold N50s and using only contig N50 in assembly comparison tables versus the other paired-end/mate-paired assemblies is misleading. Adding scaffold N50 and total unknown(N) bp to those tables would address this.

      Fleshing out a discussion on how the nanopore platform resolves heterozygous regions would also be appropriate.

    1. On 2019-02-05 10:21:11, user Sascha Rösner wrote:

      https://uploads.disquscdn.c...

      The results of this study are very interesting. Especially as we are dealing here with a long lived habitat specialist with very limited dispersal abilities. The ecological or conservation consequences might be crucial in the case of e.g. increased habitat fragmentation. In a study, where similar questions were asked, we found somewhat similar result in terms of relationship with significant closer relationship of males up to 5km I guess.

      https://www.researchgate.ne...

    1. On 2020-02-07 11:42:41, user mlhnrca wrote:

      "Among acylcarnitines, acetylcarnitine (C2) and the hexanoylcarnitine (C6-DC/C8-OH) showed the greatest absolute differences in baseline values between cases and controls (7.54 µM versus 9.92 µM (p=0.54) and 0.12 µM versus 0.08 218 µM, respectively)."

      Those p-values are not significantly different, so it's misleading to state that there were concentration differences.

    1. On 2021-06-01 06:06:21, user Prof. T. K. Wood wrote:

      Unfortunately, much literature is missing from this manuscript:

      Manuscript should cite the first persister proteome study; one that even used a similar technique, overproducing toxin YafQ (doi:10.1111/1462-2920.12567).

      Manuscript should cite the first single cell work showing the importance of ribosomes and heterogeneity in persistence resuscitation (doi:10.1111/1462-2920.14093).

      Manuscript should cite the first resuscitation study with single cells which showed the importance of HflX (https://doi.org/10.1016/j.i... "https://doi.org/10.1016/j.isci.2019.100792)").

      Manuscript should cite the persister formation work with single cells showing the importance of RaiA (https://doi.org/10.1016/j.b... "https://doi.org/10.1016/j.bbrc.2020.01.102)").