On 2023-11-17 10:21:46, user EML wrote:

Really interesting paper, will dive into this deeper. Quick comment and question already:<br /> - would it be possible to do segregation testing in pedigree 1? As the affected individuals are still alive it might be possible to obtain DNA (no large amounts needed).<br /> - It would be helpful if the pedigrees in figure 6 would be redrawn in such a way that the first generation does not appear to have offspring of two females...<br /> best wishes,<br /> Elisabeth Lodder

On 2023-11-16 18:41:06, user Vernita Gordon wrote:

The Langmuir DOI for this article is 10.1021/acs.langmuir.3c01637.

On 2023-10-30 20:54:28, user Vernita Gordon wrote:

This manuscript has been accepted for publication in Langmuir.

On 2023-11-16 16:59:10, user MING YAN wrote:

The preprint is now published in Nature Communications. https://www.nature.com/arti...

On 2023-11-16 14:40:43, user disqus_46DQBV9D4A wrote:

Dear Wenderson,

thank you very much for the thorough feedback on our manuscript. We are very happy that you enjoyed the read! I think the preprint club in your lab is a great initiative!

As our manuscript was under review and ultimately accepted in the meantime (https://rdcu.be/drgAc), we did not manage to address all the issues you mentioned for the peer-reviewed version.

With respect to PC vs NMDS: we have adopted the method of doing both analysis since PC plots are limited to two variables in each iteration and this can cause statistical limitations with smaller sample sizes (as is the case here). NMDS collapses the variability to two dimensions and does not assume normal distribution. We use these two analyses in a complementary manner (one does not influence the outcome of the other, they are two independent methods). As our interest here was limited to inspect if the data is powerful enough to distinguish expression patterns at the respective time points, we did not focus on further discussion of these tests.

With respect to identifying secreted proteins, we used SignalP5 and TMHMM to identify candidate secreted soluble proteins. This was only mentioned int he methods section and we could have added this in the results section, as well.

We used the pipeline detailed in Figure 4 to identify the transcripts as noncoding. The tool CPC with a cutoff of an ORF length of 200 bp was used for that.

Once again, thank you for your feedback!<br /> Best regards,<br /> Stefan Kusch

On 2023-11-16 12:56:52, user Preprint Journal Club at MPIPZ wrote:

We recently reviewed this preprint in the PhD preprint journal club in the Department of Plant Microbe Interactions at the MPI for Plant Breeding Research. You can find the PREreview here: https://prereview.org/prepr...

On 2023-11-15 22:37:54, user Maxence Nachury wrote:

Just saw the paper in my Pubmed feed a few days ago and decided to present it in my journal club today. Superb imaging throughout. The evidence for 2 mechanistically distinct regimes of PC2-EV release is persuasive.<br /> Minor point: I was less convinced by the evidence of ciliary localization of the ∆4-desaturase. The signals are very weak in cilia. A few more images with increased contrast would help. The images in 4C and the counting in 4D suggest that the enzyme is required for both regimes. Could the differences I eyeballed com to significance when more data points are included? <br /> The major point has to do with the interpretation that the contact between the worm and bare glass generates a mechanical perturbation. If neither clear nor dense core vesicles are required for glass exposure to trigger PC2-EV release form the tip, then we could only see two surviving hypothesis: HYP1- the PC2-EV releasing cilia are intrinsically mechanosensitive. Can one tickle the worms with a non-glass micromanipulator? HYP2- the borosilicate surface triggers a physical response upon contact. Can one use plastic coverslips to squeeze the worms?

On 2023-11-15 21:29:07, user Arshia Zernab Hassan wrote:

Now published in Molecular Systems Biology https://doi.org/10.15252/ms...

On 2023-11-15 18:07:58, user Ryan Sheldon wrote:

This paper is now published in RNA Biology. https://www.tandfonline.com...

On 2023-11-14 21:36:11, user Sahar Melamed wrote:

Please note that the figures are incorrectly displayed in the preprint (some technical issue on BioRxiv). Please refer to the published version for the updated version and correct display of the figures.

On 2023-11-14 19:46:51, user M Fernanda Palominos wrote:

This paper has been accepted in Proceedings of the Royal Society B: Biological Sciences. Here's the link: https://royalsocietypublish...

Best fishes, <br /> feña

On 2023-11-14 16:49:20, user James Mallet wrote:

Congratulations on this provocative paper which I read with great interest.

However, I have some questions about the meaning of the results. Your paper suggests that previously, the prevailing belief has been that there is more hybridization, and therefore more gene flow between species, in plants than in animals. However, your preliminary discussion suggests that this is actually an artefact of “rely[ing] on morphological traits to arbitrarily define species (16),” where ref. 16 is Mallet 2005 in TREE. Although it is true that the data summarized in Mallet 2005 was indeed based largely on morphologically identified species (and their hybrids), it doesn’t rely on a morphological species concept. Anyone who knows taxonomy of any group of organisms knows also that morphology is a rather good, although not foolproof, guide to species status; two sister species, when they co-occur in sympatry, will typically display two modes in multivariate morphospace. Actually, Mallet in 1995 and 2005 argues for a genotypic cluster definition of species, which certainly applies to molecular markers as well as morphology. Two related species, if they co-occur in sympatry, will display a series of genetic differences that enables them to be identified, even if they hybridize. There are two modes in the multivariate genotypic distribution; the relationship with the classical taxonomist’s morphological identification of species is clear.

Then you argue “the emergence of molecular data ... enables substituting the human-made species concept with genetic clusters that quantitatively vary in their level of genetic distance (18),” where ref. 18 is Galtier 2019 in Evolutionary Applications. Now that is interesting, as I think Galtier proposes “Species are defined as entities sufficiently diverged such that gene flow (arrows) is very rare or inexistent” (his Fig. 1). In other words, he appears to have a species concept such that gene flow between species is zero. Any gene flow, he argues, would render the situation “ambiguous”.

Later, perhaps recognizing that this is too extreme, Galtier proposes using a reference species based system: “...to identify taxa in which large amounts of data are available, and species boundaries are consensual, or can be agreed on. Species delineation in any other taxon could thus be achieved so as to maximize consistency with the reference [taxa].”

Now perhaps this dickering about what is a species appears rather unreasonable, since I think we all know (and Nicolas Galtier certainly seems to agree) that there is a continuum between populations that are not species and those that are species. However, in order to disprove the prevailing narrative that plant species hybridize more than animal species, you really must take a stance on what you mean by a species, and what you mean by a population that is not a species. My natural history knowledge of flowering plants and animals such as insects and birds suggests that plant species that co-occur in sympatry really do have a higher rate of hybridization than animal species. Not only is a greater fraction of species involved, but when they do hybridize, there are usually a lot more hybrids.

But you will say perhaps: “that is not really the question we attempt to answer.” And indeed it is not, so perhaps you should not have complained that that finding about whether species hybridize was an artefact, which you appear to do.

The question you more attempt, I think, to answer is: “is introgression more common in plants than in animals for a given level of genetic divergence, DA?” Rather than a question about species, it seems to me you are asking a question here that is independent of what your (or the reader’s species) concept is (unless you argue that a species has a certain threshold level of genetic divergence).

After arguing that “the Tree of Life” is “interrupted by species barriers that are progressively established in their genome as the divergence between evolutionary lineages increases,” you then argue that “The consequences of reproductive isolation can therefore be captured through the long-term effect of barriers on reducing introgressing introgression locally in the genomes, which provides a useful quantitative metric applicable to any organism (4).”

Ref. 4 is Westram et al. (2022) J. Evol. Biol. “What is reproductive isolation?” Westram show that it’s actually very hard to measure overall reproductive isolation, RI, which they say is determined by the level of “effective migration” at neutral loci, or the fraction of the rate of neutral genes that actually establish (reduced due to species barriers) in the recipient population, me, divided by the rate of “potential gene flow,” m, into the population caused by the potential for hybridization and backcrossing, or RI = 1 - me/m. Effective gene flow depends on where in the genome you measure it; in which direction you measure gene flow; whether populations are parapatric or sympatric; whether you want to measure it using an “organismal” or “genetic” focus (in Westram et al.’s terminology). Furthermore, it depends on who is measuring it and how. Everyone who measures it seems to have somewhat different measures of reproductive isolation (Sobel, J. M., & Chen, G. F. (2014). Unification of methods for estimating the strength of reproductive isolation. Evolution, 68, 1511–1522). It doesn’t provide a very useful comparative measure applicable at the whole species level at all. My colleague from Boston University and I conclude from perusing the lengthy discussions in Sobel & Chen and Westram et al. that measuring overall reproductive isolation is unlikely to be useful, and we would be better off just accepting that it is a vague heuristic which expresses something about species (Mallet, J., & Mullen, S.P. 2022. J. Evol. Biol. 35:1175-1182). In contrast, one can readily measure some of its many components, such as “hybrid inviability”, “assortative mating” and so on, and these remain useful and interesting at the whole species level and as comparative indicators.

Again, it may seem a distraction that I am discussing what is reproductive isolation, but it seems important here, because you are using a measure of reproductive isolation, and then relating it to genetic distance. In Westram et al., the main concern was to develop an experimental measure of reproductive isolation. Westram et al cautioned against estimating reproductive isolation from sequence data, which is the method you employ here. Their reasoning is that sequence divergence is a consequence only of actual gene flow, me (after taking into account barriers to gene flow), and that there is no way of estimating “potential gene flow” from the same data. In the main part of the paper (e.g. the data points in Fig. 1A), there seems to be a non-continuous measure of reproductive isolation, such that “migration” has a value 1, whereas “isolation” has a value zero. It was not entirely clear to me why this should be so, since, whatever it is, it seems clear to me that reproductive isolation should surely be a continuous parameter. Delving into the supplement, I found that “genetic isolation” was indicated “when our ABC framework yields a posterior probability P(migration) < 0.1304. This threshold was empirically determined by the robustness test conducted in (Ref. 6).” Similarly, the same robustness test yielded “strong statistical support for ongoing migration ... when the posterior probability P(migration) > 0.6419.” Pairs of taxa with intermediate posterior probabilities were considered “ambiguous” and were discarded. Note that P(migration) is not the actual mixing rate of the populations, me, or the fraction of the genome exchanged, but, if I understand it correctly, the posterior probability that any gene flow at all occurs. This is a very different measure of reproductive isolation from that proposed by Sobel et al. or Westram et al., or anyone else.

I think the reason for your choice of a measure of reproductive isolation is indicated by the second question you ask in the introduction: “At what level of molecular divergence do species become fully isolated?” This is related to a common conception of species as irreversibly independent lineages, and the idea that speciation will be “complete” when gene flow becomes zero. But in fact, the “completion” of speciation in this sense seems rather unlikely. The progressive loss of compatibility between diverging lineages seems likely to follow some sort of continuous probabilistic failure law, similar to the way lightbulbs fail over time. The simplest failure law is log-linear with time, although more complex models such as the accelerating “snowball” model of hybrid incompatibility, or the likely “slowdown” model for selective reinforcement, are also possible (Gourbière, S., & Mallet, J. 2010. Are species real? The shape of the species boundary with exponential failure, reinforcement, and the "missing snowball". Evolution 64:1-24); but all have a long asymptotic tail. You seem to recognize this stretched out right-hand side timescale by plotting genetic divergence on a log scale in Fig. 1 (although why is “net divergence,” Nei’s DA, the correct scale on which to base such an analysis? You do not explain or justify this). Nonetheless, by making an argument for complete isolation as an endpoint, you ignore the asymptotic nature of compatibility decline to zero. Based on the data we analyzed, it is rather hard to estimate the shape of the failure curve, mainly because the accumulation of incompatibilities is so variable, even among closely related species, such as Drosophila fruit-flies, for example. This variability between pairs of species shows up only in the data, and not in the fitted curve in Fig. 1A, but is more evident from Fig. 1B.

Overall, I remain somewhat unconvinced that plants have a more rapid accumulation of species barriers than animals. I agree it is likely that many plants have “less efficient dispersal modalities” than most mobile animals, and that this might mean that actual gene flow becomes lower for plants at a distance from one another, but this is a little different from what I think one would mean by “species barriers.” Reproductive isolation and species barriers should generally be rather independent of geography; in other words reproductive isolation at close range is what we are primarily interested in. This is the problem of using a measure of reproductive isolation that depends purely on actual gene flow. I therefore remain unconvinced that my natural history observations of many plant hybrids in nature, and very few animal hybrids, are not reliable indicators of lower levels of reproductive isolation among plants than among animal species.

On 2023-11-14 09:58:04, user Thomas McCorvie wrote:

The authors should be aware that their FSC curves for two of CtRoco-NbRoco1-NbRoco2 maps indicate duplicate particles. This is shown by the FSC curves not dropping below a value of zero.These can easily be removed using the Remove Duplicate Particles in CryoSPARC. Testing different Minimum separation distance (A) values is recommended.

On 2023-11-14 06:19:25, user Malgorzata Biczysko wrote:

The article is now published https://link.springer.com/a...

On 2023-11-14 05:33:52, user HILA GELFER wrote:

I found the topic of your research incredibly fascinating and important as scientists try to better understand how to prevent cancer relapse among patients. Understanding the role of TAMs in preserving OvCSC presence serves an important purpose in identifying how to improve treatment responses to cancer patients. Here are some general comments I had on the statistics and figures in your paper.

1. Within Figure 3 (ex: 3B) you utilized medians as markers within your data, while earlier data utilized means. While both are proper statistical measures, the inconsistency in how the data is represented may be a little confusing for the readers. To improve coherence in their data, I would utilize either medians or means throughout the whole paper. Additionally, medians sometimes fail to note any skewness in the data. I think to further improve the representation of the data it may be a good idea to perform a Shapiro-Wilk test to determine the normality of the data.
2. Within several figures, including 3F, there were very few samples within each treatment group. Since no power analyses were performed, it may be difficult to determine the true statistical significance of the data. To improve confidence in your findings, I would recommend performing power analyses and adding more samples/replicates in future research to further increase confidence in your data as necessary.
3. Figure 1E indicates the percent survival of different cultured cells receiving different doses of treatments. Despite different cultures receiving different treatment doses, the points on the graph were connected making it difficult to decipher the differences between the samples. To increase clarity in the data, I would represent the data in the form of a bar graph or other similar form to better distinguish the differences between the samples.

Overall, I found your paper incredibly fascinating and hope to see further research on the topic to improve patient care!

On 2023-11-14 04:00:34, user JK Gamja wrote:

We represent our research about active microbial metabolite PLA produced by lactic acid bacterial. I hope this study help readers to provide the reason and mechanism of beneficial effects of probiotics.

On 2023-11-13 20:16:02, user Ebba Brakenhielm wrote:

Now published @CardiovascRes<br /> https://pubmed.ncbi.nlm.nih...

On 2023-11-13 20:14:26, user Ebba Brakenhielm wrote:

Now published @Naturecommunication<br /> https://pubmed.ncbi.nlm.nih...

On 2023-11-13 12:20:14, user paulanuneshasler wrote:

A peer-reviewed version of this article was published in Communications Biology on Oct 4th 2023 https://www.nature.com/arti...

On 2023-11-13 10:43:48, user Gary Mirams wrote:

This is a very nice use of a mathematical model of patch clamp compensations to account for artefacts in fast sodium recordings at 37C.

Just a little note that whilst Lei et al. (2020) introduced the artefact model without supercharging/prediction compensation, we expanded on that to include those effects in the artefact model version published in Chon Lok Lei's thesis: https://chonlei.github.io/t...

On 2023-11-13 02:04:39, user Partha Mondal wrote:

The paper is now published in Communications Biology (Narure Publishing). https://www.nature.com/arti...

On 2023-11-12 00:05:45, user Elizabeth Duncan wrote:

Recently, a group of trainees read and discussed this preprint as part of a journal club at the Markey Cancer Center at the University of Kentucky. We thought the findings suggesting that SETD1A may be driving the increase in H3K4me3 in MLL1 mutated cells (and possibly leukemic cells with MLL1 translocations) were very intriguing. However, we have several questions and suggestions:

In figure 2B (metagene analysis) and C (pie charts), you plot the mean read counts from H3K4me3 ChIP-seq. We interpret the unexpected lack of enrichment of H3K4me3 at gene TSSs in the WT sample as a reflection of the relatively significant increase of H3K4me3 at new gene loci in the MT1 and MT2 cell lines. Is this correct?<br /> If so, we believe this point could be made stronger by adding, for example, a Venn diagram of the genes with MAC2 peaks in the WT cells and those with peaks in the MT1 and MT2 cells. You could also create two separate metagene plots based on the data in Figure 2B: one looking at H3K4me3 in all three cells lines at genes with MACS2 peaks in WT, and one looking at H3K4me3 at genes with MACS2 peaks in MT1+MT2.<br /> Given that there is likely variability in the chromatin state in different iPSC lines, we also wonder if you performed these experiments and/or analyses using a separate iPSC line?<br /> It is unclear how you performed the differential expression analyses in figures 3, 4, and 5. The heatmaps show changes in both the WT and the mutated cell lines, even though we assume the differential expression is in relation to the WT cells? We appreciate there are many ways to perform these analyses, however we would like to understand the details of how they were done here to better understand their implications.<br /> What happens if you knock down SETD1 expression in the MLL1-R3765A cells?<br /> Do you see the same effects if you KO or KD MLL1? Versus this mutation that prevent association with WRAD?

We look forward to seeing your paper in publication.

On 2023-11-10 23:23:41, user Hui Wang wrote:

The study suffers from two serious flaws that undermine its credibility:

1. The authors overlook the fact that the aromatic ring of tyrosine 90 is essential for the SH3 domain hydrophobic pocket structure. They incorrectly present the Src90E mutation as mimicking phosphorylated tyrosine, ignoring the expected disruptive effect of almost any mutation at this site. This flaw raises doubts about the validity of their interpretation, as the observed data may be a result of the grossly disrupted SH3 domain binding site rather than of simulating Tyr90 phosphorylation.

2. The study neglects the tightly regulated nature of Src kinase activity through phosphorylation by Csk. Previous research by Erpel et al. 1995 has demonstrated that the mutation of Tyr90 to alanine impairs the interaction with Csk and the negative regulation of Src kinase. As the mutations Src90E and Src90A are supposed to disrupt the SH3 domain binding pocket in essentially the same way, the authors fail to acknowledge this crucial aspect. This oversight undermines the study's reliability and suggests that the similarities observed between Src90E and Src527F may be solely due to the impaired interaction with Csk.

These flaws significantly impact the study's findings and raise concerns about the thoroughness and accuracy of the authors' interpretation. Caution should be exercised when considering the conclusions presented in the study.

On 2023-11-10 16:44:10, user KJ Benjamin wrote:

Interesting approach, but I'm confused why the authors would model population instead of genetic ancestry? The authors use ADMIXTURE to show a great degree of mixed ancestry, but do not examine the effect of genetic ancestry, but "population grouping". This would be extremely influenced by environmental factors that are differences across and within continental groups.

On 2023-11-10 16:43:20, user Matt Jones wrote:

This manuscript has now been published in New Phytologist.<br /> https://nph.onlinelibrary.w...

On 2023-11-10 00:14:27, user Alan Rose wrote:

This is an impressive manuscript reporting a stunning amount of work that reveals an interesting and underappreciated feature of gene regulation in plants, namely that sequences downstream of the transcription start site (TSS) can have major roles in regulating expression. My only complaint is that it does not sufficiently cite previous work that reaches many of the same conclusions. Findings reported here that were previously published for Arabidopsis include the observation that sequences downstream of the TSS, in exons and introns, play a major role in controlling transcription (Rose and Gallegos, 2019, Scientific Reports 9:13777), that these sequences are unlike animal enhancers because they have no effect when moved upstream of the TSS (Rose, 2004, Plant Journal 40:744 and Gallegos and Rose, 2017, Plant Cell 29:843), that a motif containing the sequence GATC boosts expression in a dose-dependent manner and that mutating nucleotides within the GATC motif reduce its effect (Rose et al., 2016 Plant Molecular Biology 92:337). The Rose and Gallegos 2017 paper is cited but only as the reason for using the TRP1 promoter and for identifying the motif similar to GATC. I realize that the number of references is limited in some journals, especially those with a high profile (where I would really like to see this work published), but these seem too pertinent to omit.

On 2023-11-09 21:50:42, user lanka wrote:

very nice work! no supp table uploaded.

On 2023-11-09 17:10:12, user Reade wrote:

The biological concepts of the paper are easy to understand and follow as one experiment leads to another. The toy figures at the beginning of the figures outlining the experimental overview are very useful. There are some grammatical errors in the paper for example “casual relationship” which should be causal. Which statistical test is being used is unclear and at times I believe the wrong statistical analysis is used. For example, figure 1 states that Wilcoxon test or one-way ANOVA is used for comparison, but nothing indicates which analysis is used for which figure. Furthermore, when doing relative expression it is unclear what the expression is compared to, some graphs indicate it is relative to IRPL13a, but it looks like this is not true as in figure 2F the MFN2+/+ HEY1 and ID3 are both set to 1, suggesting that is what is being compared. The labeling of figures also makes it difficult to identify what is being compared, again in figure 2F it is unclear what the p values are indicating as they are over more than two groups. There are a few figures that I would like to see controls to compare against the date, example in figure 3A and 4I.

On 2023-11-09 15:17:53, user Bertram Klinger wrote:

Thank you for the nice explanation of expectation maximisation.

However, in my eyes your algorithm does not get rid of the spillover signal. <br /> In Fig3C the correlated distribution is the result of spillover from channel Yb172 into Yb173, as can be seen nicely in Fig3D where this correlation vanishes with the same antibody labeled to a channel which Yb172 does not spill into (Sm147D). Instead your algorithm seems to only set low signals to NA.

To undermine this point, Fig1a shows that the spill-in signal from Yb172 into the Yb173 channel is on average 2.7. Assuming the the mean of Yb172 bead to be of similar strength as Yb173 (~6.2) then for a cell population without Yb173 we would expect a difference of roughly 3.5 (in log scale) between the two channels if purely driven by spillover. Which is what can be seen in Fig3C for the CD3-low population ( i.e. they do not express CD3).

On 2023-11-09 15:12:20, user Matt Tudor wrote:

probably want to cite, discuss this prior work: https://doi.org/10.1371/jou...

On 2023-11-09 00:17:42, user Pooja Asthana wrote:

Summary:<br /> Identifying and modeling low occupancy structural changes and binding events is a major goal of protein crystallography. The most sensitive methods used to detect low occupancy changes require crystallographic datasets to be isomorphous, which often limits their applicability. To address this limitation, the authors have developed MatchMaps, an pipeline that performs map subtraction in real space rather than reciprocal space, thereby eliminating the need for isomorphous data. The MatchMaps approach takes measured structure factor amplitudes from two states: the ON state (the interesting/ligand-bound/perturbed state) and the OFF state (the ground/apo/unperturbed state). Next, the algorithm performs rigid body refinement of the OFF state model (e.g. a model built/refined using only the OFF state data) using both the ON and OFF structure factor amplitudes. The electron density maps are then aligned by a rotation-translation matrix derived from alignment of the ON and OFF refined models. The authors apply MatchMaps to four different cases studies and where applicable, compare the results with isomorphous difference maps

We tested the MatchMaps algorithm with some published datasets of the SARS-CoV-2 NSP3 macrodomain with ligand bound at 10-30% occupancy (​​https://zenodo.org/records/..., ligand-bound datasets UCSF-P0628, UCSF-P2193, UCSF-P2227 and apo dataset UCSF-P0110) along with some unpublished data. The program is well documented and easy to install. With some generous help from the authors, we successfully used MatchMaps to reproduce ligand density observed in isomorphous difference maps calculated using the same datasets. The initial issue we encountered had to do with the default solvent mask, but this was overcome based on their advice. We also successfully ran matchmaps.ncs to calculate a difference map between the two macrodomain protomers in the P43 crystal form (chain A and B of apo dataset UCSF-P0110).

Overall, the preprint is well written and the figures are clear and helpful. The major success of this work is the development of a method for the real-space subtraction of electron density maps to visualize structural changes between non-isomorphous datasets. This provides structural biologists with a powerful tool for visualizing structural differences between X-ray diffraction datasets and therefore will be of broad interest to the community. The major limitation is whether MatchMaps can be used to detect structural differences that are not detected using isomorphous difference maps, or to model structural differences that are not apparent by comparing refined coordinates. Although visualization is helpful, the real power in a tool such as this would be in its ability to detect and model low occupancy states.

Major points<br /> The manuscript could be strengthened by including an example where MatchMaps detects a structural change that was not detected by calculating isomorphous difference maps or by comparing refined coordinates. The authors show how MatchMaps removes artifacts due to misaligned models (Figure 3g), but it is unclear to us whether MatchMaps can detect new structural changes. Put another way, it’s unclear to us whether structural changes that result in non-isomorphous datasets would be better visualized using MatchMaps versus a simple comparison of coordinates.<br /> MatchMaps produces two maps by default, one with a solvent mask applied and one without. We are curious why a map with a solvent mask is calculated. This mask is based on the OFF model, so any features of the difference map corresponding to structural changes outside of the solvent mask will be removed. If the solvent mask is required to remove noise in the MatchMaps generated maps, then it would be helpful to discuss this and give examples where the solvent mask was necessary (because this goes somewhat against the claim made by the authors that MatchMaps maps are less susceptible to “uninteresting signal” - line 215). The solvent masking also was a challenge for us in detecting some fragments, but was resolved by working through different options with the authors. An expanded discussion of the merits and limitations of solvent masking (and when to depart from defaults) is therefore likely to be helpful to many users.<br /> Do the authors envisage that MatchMaps could be used to model structural changes or just to visualize them? Along these lines, a comparison with the PanDDA algorithm might be helpful (Pearce, N. M. et al. 2017, Nature Communications). PanDDA can be used to both detect and model low occupancy states, but is most effective when data sets are isomorphous (so is typically used to detect and model low occupancy ligands obtained by soaking). Can the authors imagine an extension to MatchMaps where multiple datasets are averaged to create the OFF map in a similar way to PanDDA? Improving the signal-to-noise of the OFF map might remove the need for the solvent mask.

Minor points<br /> Figure 3c/d. Can the authors comment on differences between the isomorphous difference map and the MatchMaps map? The density is similar but not identical. This is subjective, but to us the MatchMaps density looks a little noisier.<br /> Line 95. Are the data scaled with SCALEIT and then truncated (line 95)? Wouldn’t the reverse be more appropriate (e.g. truncation followed by scaling)?<br /> We were grateful for the -verbose flag in the command line, however, this only prints the output from SCALEIT/phenix.refine. Would it be possible to modify this flag to print the output from all the programs?<br /> Line 104. How are the maps placed on a common scale?<br /> Figure 2f. Is there positive difference density associated with the terminal ribose (or the unmodeled nicotinamide) in the MatchMaps? It would be helpful if the figure legend indicated what part of the model the maps are contoured around. <br /> Line 350. The text says ±2.5 σ but the figure says ±1.5 σ.<br /> Line 161-162 - figure references do not refer to the correct panels. <br /> Line 185-189 and fig. 4d label and text does not match- open:closed conformation/ H-bond

Review by Pooja Asthana, Galen J. Correy & James S. Fraser (UCSF)

On 2023-11-08 20:29:54, user P. Bryant Chase wrote:

Molecular basis for the "Abbott effect"? Bud Abbott was thrilled to know it was still being investigated in the 1980's, and would surely be thrilled to see this work if he was still living.<br /> Abbott BC & Aubert XM. (1952). The force exerted by active striated muscle during and after change of length. J Physiol 117, 77-86.

On 2023-11-08 11:55:26, user Philipp Rühl wrote:

An expression plasmid for rEstus is now available on Addgene:

https://www.addgene.org/209...

On 2023-11-08 09:39:25, user Patrik Simmler wrote:

An updated version of this manuscript has been peer-reviewed and published in eLife: https://doi.org/10.7554/eLi....

On 2023-11-08 06:55:56, user queeny dasgupta wrote:

A revised version of this manuscript is now published: https://www.nature.com/arti...

On 2023-11-07 13:19:53, user Pedro H. Oliveira wrote:

This is a very interesting manuscript.<br /> It was a pity however to not have seen discussed in this work the recent findings on defense systems' co-localization published here (https://www.biorxiv.org/con.... I believe the latter work will also be useful to update a few of the claims mentioned by Wu et al. in their Introduction.

On 2023-11-06 15:10:38, user Jo Wolfe wrote:

This paper is published in Systematic Biology, doi: 10.1093/sysbio/syad066

On 2023-11-06 14:56:20, user jfritscher wrote:

I question the practice to benchmark against a tool from 5+ years ago (MetaPhlAn2) that has massively improved in the meantime to demonstrate the own tool's performance. For the same reason I do not think the results in Fig 3 are in any way telling about the performance of MAGinator in the light of state-of-the-art tools. It is claimed that subspecies-level resolution is gained by using GTDB-tk. This is questionable has GTDB-tk resolves at species level and thus the increase in "resolution" is merely a result of using a different taxonomy and not because actual subspecies resolution (whatever that is anyway) is achieved. Further, I would not use "de novo identificiation" is this context.

On 2023-11-06 12:55:04, user Faraz K. Mardakheh wrote:

Congrats Mathias and the team. It is good to finally see this preprint out.

For any interested readers, I should also mention our preprint describing a very similar method (named TREX) which came out a few months before, since it is not cited in your preprint:

https://doi.org/10.1101/202...

On 2023-11-06 10:32:22, user MoMo wrote:

Hi,I like your work very much and wish you to publish the final version soon. I would like to point out that TP53 mutants may not be "unfunctional". Some missence point mutations (e.g. R175H, R273H) result in gain of function (GOF). You cited the paper by Escobar-Hoyos showing this. I also found that GOF mutant p53 regulate splicing of VEGFA, however the mechanism was different (Pruszko et al., 2017). It would be interesting to use your bioinformativ tools and skils to compare alternative splicing in GOF p53 mutants versus loss of function.

On 2023-11-06 09:30:19, user Mario Van Poucke wrote:

A revised version of this preprint has now been published in Journal of Veterinary Internal Medicine (http://doi.org/10.1111/jvim...:fbVTmMhy3YpqZyBD1yT9ZvImIPA "http://doi.org/10.1111/jvim.16892)").

On 2023-11-06 04:33:44, user Raghu Parthasarathy wrote:

The title really needs "in rats" (i.e. "in Female and Male Rats"). Otherwise, it is at best unclear and at worst suggests very general experiments about male and female animals of all sorts.

On 2023-11-06 00:38:13, user Sergio Contreras Liza wrote:

In this research we try to demostrate the effect of microbes (bacteria) on the production of potato seed tubers. Azotobacter sp.and Bacillus sp. were the most important genus in the form of consortia, for tuber number and weight.

On 2023-11-05 22:10:43, user Richard Cripps wrote:

This article is now published as:

https://journals.biologists...

On 2023-11-05 20:27:40, user Berna Devezer wrote:

This article has now been published (with a different title): https://doi.org/10.36850/mr9

On 2023-11-05 08:37:13, user Manuela Giovannetti wrote:

Dear Olga and co-authors,<br /> I have just read your paper and I want to compliment for the high level of your study. Your data are very interesting and worth of depth consideration. I have only a doubt, concerning the retrieval of bacteria other than endobacteria in your spore. As you may know, we have retrieved many bacteria strictly associated with AMF spores (after 15 washings). Actually, you performed a de-contamination of spores, with H2O2 and chloramine T, so you assumed that the retrieved bacteria were endobacteria. As our previous works described the occurrence of bacteria within the different layers of spore walls, I wonder whether they may have been protected from de-contaminating agents in such a peculiar niche. This is why we defined them as "stricty associated". With all my best wishes and regards, Manuela Giovannetti

On 2023-11-03 18:45:39, user Marouen Ben Guebila wrote:

scTranslator bioRxiv public review

Summary: Quackenbush lab journal club review of “A pre-trained large generative model for translating single-cell transcriptome to proteome by Liu et al., 2023.” This work is motivated by the lack of sc-proteomics data sets because they are limited by available sequencing technology. The paper presents a transformer-based model called sc-translator and employed 31 cancer data sets for training and validation. Training is based on a 2-stage process. Stage 1 is training on bulk (pre-training) and Stage 2 is training on sc data sets. The model is based on favor+ which is a transformer attention mechanism

Pre-training: In the isoforms prediction example, it seems that isoforms are predictive of each other e.g. CD49a and CD49b and not through model precision towards each isoforms. Correlation seems to be driven by a small number of data in the plot (Upper right portion of the correlation plot). Also, application on new dataset other than PBMC is warranted here to assess generalizability. Immune response is harder to predict: rare immune cells

The application on new data showed that pre-training is very important, however it is not clear why cosine is used and not correlation as an evaluation metric.

Downstream tasks:<br /> - More benchmarks on the attention interaction network are needed. It would be great to see a few examples of which genes regulate which proteins and their biological interpretations.<br /> - Pseudo-KO experiments also need to be benchmarked. There are many CRISPR knockout datasets which can be used for validation. Biological interpretation is missing for these experiments.<br /> - It would be nice to have a conduct GSEA in KO experiments<br /> - Finally for cell clustering (Figure 5a), it is likely that batch effects in true proteins are subpopulations for CD4 and not driven by batch.

Potential future use:<br /> - It would be nice to predict protein levels in a different setting such as drug response for example.<br /> - Conduct more benchmarks for KO experiments beyond EGFR and TP53

Additional comments:<br /> - Lack of evaluation in a real-life setting without the presence of protein data that can be used to fine tune the model first<br /> - Would it be possible to build a model for each protein or protein class which seems to make more sense because post-translational modifications vary between proteins and therefore fitting a single model can overlook these differences.

On 2023-11-03 15:51:53, user Corresponding Author wrote:

We - the authors of this manuscript - appreciate a Community Review of this manuscript posted here: https://zenodo.org/records/.... We agree with the overall assessment of the reviewers.<br /> 1) For the method description, we have cited previous publications and mentioned ‘as described previously’. Based on the reviewers' suggestion we will further describe the methods in detail to clarify the reviewers' concerns. In addition, we will include the age and sexes of mice in the legends of each figure. We will upload a revised version of this manuscript in a few months. eLife journal will publish the manuscript.<br /> 2) We agree with the reviewers that additional experiments are necessary for in-depth analyses of how elevated glycosuria increases compensatory glucose production. The goal of this project was to provide a foundation for future studies that will be informed by the list of secreted proteins identified using plasma proteomics, some of them may be correlative and others causal. At this time, it is not feasible to test each of the identified protein for its causal role in enhancing a compensatory glucose production. <br /> 3) eLife will publish a revised version of this manuscript in a few weeks.

On 2023-11-03 14:34:03, user Alex wrote:

It is not clear about the background of the used mutants. Some lines have WS background (for example, ahk3-1 and ahk3-2 - Wisconsin University lines WS-2) [Nishimura et al., 2004], while others - Col-0. <br /> Authors, however, always used only Col-0 as a control. <br /> Please, provide the proper background description for every used line.

On 2023-11-03 10:00:12, user Laura Dean wrote:

This article is now published at https://doi.org/10.1093/mol...

On 2023-11-02 17:48:33, user Adil Muneer wrote:

Published version of this article titled "Non-canonical function of histone methyltransferase G9a in the translational regulation of chronic inflammation" is now available: https://www.sciencedirect.c...

On 2023-11-02 10:03:14, user Katarina Gresova wrote:

This article have been now published at https://doi.org/10.1186/s12...

On 2023-11-01 16:33:32, user Jae Choi wrote:

Published in Research Article, J Mar Biol Oceanogr Vol: 12 Issue: 4 (https://www.scitechnol.com/...

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

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

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

On 2023-10-31 14:51:47, user Therese wrote:

Very interesting tool. We will test for biological applications

On 2023-10-31 11:39:45, user Graham wrote:

Published version available here:<br /> https://www.nature.com/arti...

On 2023-10-30 21:19:17, user Kyle Sander wrote:

Published: Biotechnology and Bioengineering. DOI: 10.1002/bit.28532

On 2023-10-30 08:40:11, user Estel Collado Camps wrote:

Dear authors,<br /> I've learned a lot from reading your pre-print! I'm intrigued to see what language models will mean for deeper understanding of complex biomedical data in the near future. <br /> I have noticed that a few UMAPs (see for example figures 3 and 4) have slightly different shapes in the differently colour-coded versions (a vs b, c vs d). As a non-expert, I can imagine that this can easily be missed while updating figures. I thought it would be beneficial to everyone to give a heads-up.

On 2023-10-30 08:19:18, user Sebastiaan Mathôt wrote:

A revised version of this preprint has been published and can be found here: https://doi.org/10.1016/j.n...

On 2023-10-29 09:08:48, user BBB Prair wrote:

Fascinating study as expected from the Yanai lab. I work on DTPs as well. I read the whole preprint and watched the Match Onco seminar by Prof. Yanai about this work. Maybe I missed some point in the paper but I wonder why the identified IC50 for drug-naive Kuramochi cell line is ~2 uM? In my own measurements, using both CellTiter-Glo and SRB assays in a 12-concentration range, 72-hr format, I always calculate an IC50 in the range of 150 to 200 uM in Kuramochi cell line for olaparib. These values are also supported by measurements in the GDSC (both versions 1 and 2) project. Did the authors check this? This might be an issue in the context of drug adaptability since the cell line, in bulk, is already poised to adapt by tolerating low uM olaparib concentrations used in the study (<160 uM).

On 2023-10-27 23:34:09, user CDSL JHSPH wrote:

Hello! I had a great time reading your paper as it is very important to the field of public health and very informative!

I was wondering, however, if there was enough data that was collected to show the immune response differences in those who had the vaccines separately. I assume that the closer you get the vaccines together, the better your IgG responses will be in the future, but I'd be interested to see if there is a weird window of time that the second vaccine becomes a catalyst for a more powerful IgG response (something random like 9 days after the second vaccine perhaps?) .

Again thank you so much for your effort that you put into this research as it is very important and helpful to so many!

On 2023-10-24 08:15:41, user CDSL JHSPH wrote:

Hello!

I found this paper very compelling. Nice work! It is really exciting to see that receiving both vaccines concurrently enhances the protectiveness of the COVID-19 booster. I have two questions:

1. Why is it that the group that received the two vaccines on different days were not administered the flu vaccine on the same number of days apart after the COVID vaccine? In the study it was stated that the flu shot just needed to be administered any time within 4 weeks from the time of the COVID booster. Could this have made the results more difficult to interpret? If you were to redo the study, do you think that the data would be more reliable if the second group all had the later flu shot administer on the same day, or does that not seem to matter?

2. Why was the Ebola vaccine used as the control vaccine in this case?

Thank you! And again, nice job!! :)

On 2023-10-23 04:34:17, user CDSL JHSPH wrote:

Greating Dr. Barouch and colleagues,

First, I want to commend you on investigating this timely research question regarding the immunogenicity of concurrent versus separate COVID-19 and influenza vaccination.

As I was reviewing your work, some aspects caught my attention which might further enhance its clarity and comprehensiveness. I understand that the study participants in MassCPR might have enrolled voluntarily. If this is the case, there could be potential selection bias to consider. It might be beneficial for readers to see a demographic table that provides baseline characteristics for both groups. Additionally, it would be helpful to understand the factors that influenced participants to either receive both vaccines simultaneously or at two separate intervals. Clarifying this could help readers discern if there might be any inherent differences between these two groups.

It also would be enlightening if you could expand on the potential mechanisms of the specific immune interactions that may be driving the increased IgG1 with concurrent vaccination? This could reveal important biology behind your findings.

I believe addressing these points could enhance the comprehensiveness of your paper. I hope these suggestions are helpful as you continue developing this research project.

Thank you for sharing your work and for your consideration.

On 2023-10-20 01:55:50, user CDSL JHSPH wrote:

Hello! I hope this message finds you well. I would like to express my sincere appreciation for your paper. Your research on the immunogenicity of these vaccines, especially in the context of concurrent versus separate administration, is of significant importance in the current landscape of emerging COVID-19 strains. I have a couple of queries that I hope you could kindly address:

1. Given that the influenza vaccine undergoes changes each year to adapt to evolving strains, I am curious about the potential impact of these changes on the observed results. Do you believe that the higher and more durable SARS-CoV-2 antibody responses associated with concurrent administration would remain consistent across different influenza seasons?

2. It appears that your study design involves a comparison between concurrent and separate administration, and the results are promising. Could you kindly provide more information on the research methodology? Specifically, was your study a randomized controlled trial (RCT), and if so, was blinding or randomization implemented?

Once again, I would like to express my gratitude for your valuable contribution to the field. I understand the dedication and effort that go into such research endeavors, and your work is commendable. I look forward to any insights you can provide regarding my queries.<br /> Thank you for your time, and I appreciate your consideration of these questions.

On 2023-10-27 19:48:12, user Cris Taniguti wrote:

Now published on GigaScience: https://doi.org/10.1093/gig...

On 2023-10-27 19:33:35, user Federico wrote:

The claim that you have generated brown adipocytes is overstated. There is no clear proof morphologically or by significant changes in gene expression (UCP1) that would support brown adipocyte character. I would revisit that experiment.

Showing tissue that has formed (and analyze it histomorphological) would make the in vivo work much more convincing. In line with that, survival for up to 28 weeks seems overstated as IVIS data thresholding doesn't seem to be corrected for background noise.

On 2023-10-27 15:59:31, user Ashraya Ravikumar wrote:

In this manuscript the authors have tested the hypothesis that the MSA constructed by AlphaFold2 (AF2) contains information about the distribution of different conformational states of a protein. Whereas the current way of thinking about AF2’s MSA-predicted Cβ–Cβ distance maps focuses on their power to provide binary classifications of inter-residue contacts, the authors propose that Cβ–Cβ distances should instead be thought of as a set of collective variables that approximate a Boltzmann distribution. This is a novel hypothesis that lends AF2 the ability to decipher the conformational Boltzmann distributions of proteins. The authors test this in the contexts of protein dynamics, mutation impacts, and protein-protein interactions. They start with analyzing the correlation between AF2 contact distance and spin label distance distributions obtained from EPR spectroscopy using T4 lysozyme as a model, finding a general agreement despite broader AF2 distributions. Following this, they explore if AF2 can approximate free energy changes in systems that contain multiple biologically important minima, using EGFR KD studies for this purpose. AF2 accurately identifies altered contact distance distributions corresponding to active or inactive conformations in several mutations, indicating a sensitivity to alterations that stabilize particular conformational states. Next, they assess sensitivity to thermodynamically destabilizing mutations. AF2 was able to predict different contact distance probabilities for disruptive mutations like L198R in UBA1, but was less sensitive for milder mutations like L198A. Lastly, AF2’s sensitivity to protein-protein interactions was explored using the μ-opioid receptor (μOR). Although the helix displacement distances observed in the predicted structure of isolated and complexed μOR do not exactly match with expected values, AF2 did successfully predict differences in select contact distance distributions of active/inactive-state μOR. Demonstrating that Cβ–Cβ distance probabilities from the same AF2-learned distribution reflect distances observed in differentially behaving domains of a protein lends strong support to the hypothesis that AF2 contact distance distributions can approximate conformational distributions.

The manuscript explores the correlations and sensitivities of AF2 predicted Cβ–Cβ distances across a variety of protein contexts, giving a broad view of its capabilities and limitations. Transitions between the various sections flowed well, and overall the writing was well worded and easily comprehensible. In addition, the presentation was balanced. It doesn’t just focus on the success of AF2, but also highlights where its sensitivities might vary or fall short, providing a balanced view of its capabilities. Given limited computational resources, the conformational space explored by MD and MCMC simulations is limited by their initial states. AI methods are instead limited by how informative their system definitions (MSAs and pre-set theoretical or experimental contact distance distributions) are, allowing AI methods, such as the AF2 method outlined by the authors, to more effectively sample conformational space. This is a very fascinating implication of their work which the authors have briefly mentioned in the discussion. This (and the connection to Figure 7 in the paper) warrants a deeper discussion, but the main conclusions the authors come to are within the scope of the manuscript, and are backed up by the evidence presented.

There are a few points we would like to bring to the attention of the authors to strengthen the manuscript further.

Major points:

1.There are some difficulties interpreting Figure 2. <br /> (a) It is important to mark the distances between the two chosen pairs of atoms in the active and inactive state. Without this information, the purpose of Figure 2D is unclear and Figure 2D, F and G are difficult to understand. <br /> (b) What is the threshold distance to classify a state as active or inactive?<br /> (c) Figure 2E seems confusing with different axis and ranges.<br /> 2. In case of DDR1, does the MD simulations reflect the peak distances (between 7.5 and 10.0 Å for DFG-in and between 16.0 and 18.0 Å for DFG-out) observed for AF2 distance distributions? Also, the probability distribution shift towards shorter distances for Y755A does not seem particularly strong at first glance. Is this why the double alanine mutant was included? Are there also MD simulations of the double mutant that show a reduced preference for the DFG-out conformation?<br /> 3. The overall results on EGFR mutants are striking. Many of these mutants (most notably L858R have structures deposited in the PDB (ID:2ITT and many others) that are potentially part of the overall training of AF2/OpenFold. Can you comment on how this might affect the results?

Minor Points:

1. There is some ambiguity in the statement, “The central hypothesis of this manuscript is that the collective contact distance distributions predicted by AF2 contain relevant information that can approximate Boltzmann distributions provided the relevant conformational states can be adequately described by these contact distances.” We suggest adding to this such that a stronger connection is formed between the theory section and the remainder of the paper. For example, the authors could explain that the contact distances specified in each section are the set of CVs you describe earlier, “we identify a set of CVs, ξ = (ξ1, ξ2, …, ξm)...”. It would also be helpful to clarify that the distributions predicted by AF2 represent the ensemble averaged observable, as described by equation 4. Lastly, the authors mention that these distributions can approximate Boltzmann distributions, but this is somewhat vague. This could be reworded to say that AF2 distributions can approximate experimentally derived Boltzmann distributions of the same distance.
2. The authors are comparing Cβ–Cβ distances determined by AF2 to spin label distances from EPR. This is explained in the methods section, but the procedure for adjusting the spin label distances to facilitate a meaningful comparison between them and the AF2 distances is somewhat unclear. To make a stronger justification for why these are comparable, the authors could clarify the procedure. For example, some context from the authors’ previous paper, De Novo High-Resolution Protein Structure Determination from Sparse Spin labeling EPR Data: “[distance from spin label] dSL is a starting point for the upper estimate of dCβ, and subtracting the effective distance of 6Å twice from dSL gives a starting point for the lower estimate of dCβ” could be beneficial. Including a rank correlation coefficient, as hinted above, could also help emphasize that the results demonstrate “similar relative probabilities among the contact distances for AF2 and EPR”
3. In the comparison of distance distributions between AF2 predictions and EPR measures, the major peaks of the two distributions are similar but in certain cases (127CB - 154CB, 120CB - 131CB), some additional peaks are found beyond 10A. A statistical comparison of the distributions, perhaps using a KS test, will help in evaluating the significance of the similarities.
4. Typo in Hamiltonian Equation 1 (should be momentum squared)
5. In the T4 Lysozyme example, how were the six contacts between the 12 unique residues found?
6. In Figure 5, the fourth row could have more discussion/explanation. What does the colorbar represent? There is no label.

7. As mentioned earlier, the connection between the Discussion and Figure 7 is not well established. The authors could expand on their writing and/or make the figure more simplified to match the discussion better.

8. Jessica Flowers, Angelica Lam, Ashraya Ravikumar, James Fraser

On 2023-10-27 15:13:53, user JO wrote:

Is it possible to show where the ecRNH and ttRNH would fall relative to the clusters in Fig. 9? Their identity to the AncA/B/C sequences? And key residues that vary between ecRNH and the most homologous Anc sequence?

On 2023-10-27 14:47:25, user Joseph H Vogel Beckert wrote:

"To add to this uncertainty, the pilot test coincided with international discussions on the fair and equitable sharing of benefits from the access and use of digital sequence information (i.e., genomic sequences) under the Nagoya Protocol adding increased uncertainty surrounding the legal compliance landscape57."

There should be some mention of "unencumbered access" through the proposed modality of "bounded openness over natural information". The sentence above references a Comment from Nature Communications that trumpets "de-coupling" access from benefit-sharing. "De-coupling" means independence and is probably not what its 41 authors meant. Similarly, any reference to a multilateral mechanism for ABS without recognition of the overarching implications of the economics of information, i.e. the justification of "economic rents", introduces bias and thus undercuts the presumed scientific neutrality of the manuscript..

On 2023-10-26 22:50:15, user ELSA COUVILLON wrote:

Dear authors,<br /> Overall, I felt your paper was interesting to read and highly relevant to the SARS-CoV-2 pandemic. These findings could be extremely valuable for identifying preventive measures for mitigating disease transmission, and I thought the experimental question addressed by your paper and the experimental design –incorporating a PSV entry assay and a syncytia formation assay– was cohesive. However, when reading and presenting at a journal club, some questions and comments came up that I would like to share with you.<br /> First off, I would like to point out a few simple fixes that I found. For one, Figure 4 is titled “Effect of turmeric extract and curcumin on PSV entry in 293/ACE2 cells,” however, the experimental results exhibited in Figure 4 only deal with curcumin, so I feel renaming that figure would be more accurate. <br /> Additionally, I’m curious as to why in Figure 4b, the PSV entry assay treatment conditions included the following:<br /> non-treated 30 mins/SARS-CoV-2 16-18h<br /> curcumin 30 mins/SARS-CoV-2 16-18h<br /> curcumin 30 mins/SARS-CoV-2+curcumin 16-18h<br /> but did not account for the effect of non-treated 30 mins/SARS-CoV-2+curcumin 16-18h? I was curious as to the logic for excluding that particular treatment combination, which I feel could’ve been a good control for comparing the infection rate of SARS-CoV-2 compared to SARS-CoV-2/curcumin without the additional variable/impact of the initial 30 min treatment. As a side note, it might be beneficial to readers to make more clear, what is meant by “curcumin pre-treatment”; does that refer to the 30 min “black arrow” segment, or the 16-18h “gray arrow” segment in which the virus has a “+ curcumin” label? <br /> One final thing I want to point out is the use of statistics in this paper. You state that a t-test is used throughout, however, I believe that an ANOVA might be more effective here, not only to reduce the number of tests you have to run on the data (and therefore reduce the risk of making a Type I error), but also so that you can show the readers comparisons between each and every group, and not just between each individual group and the control. Additionally, it would be helpful if there were asterisks or “n.s.” consistently shown for every statistic (for example, this is done on Figure 5b, but not Figure 4d), along with a key on every graph indicating the significance levels indicated by each asterisk, to help clear up some confusion about interpreting significance and statistics. Going back to the Figure 4 example, in the paper, it is stated that, “The results indicated that curcumin reduced PSV entry, especially for curcumin pretreatment before the addition of PSV (P = 0.035)” in reference to Figure 4d; however, in the actual graph, there isn’t sufficient statistical representation to confirm this conclusion (no statistics are shown for comparing Cur 1uM and the control) and additionally, I had a hard time determining what defined the pre-treatment when flipping between Figures 4b and 4d.<br /> Ultimately, I feel the paper is a great start and could mainly benefit from a few changes to encourage more clarity surrounding the ways in which different treatments were defined, the labeling and annotation of figures, and display/application of statistical tests. I look forward to following it through pre-print.

On 2023-10-26 22:41:47, user Srijit Seal wrote:

For more information about BioMorph and download the datasets used, please visit https://broad.io/BioMorph

On 2023-10-26 06:27:29, user Tom Hitch wrote:

You may wish to use the up-to-date taxonomy for these taxa. Prevotella copri was reclassified as Segatella copri last year; https://lpsn.dsmz.de/specie...

On 2023-10-26 06:22:08, user Pablo Rivera-Fuentes wrote:

This manuscript is now published in a peer-reviewed journal. I suspect that no link was made automatically because the last name of the first author changed during the peer review process. Here is the link to the published article: https://www.nature.com/arti...

On 2023-10-26 05:45:43, user Lindsay Halladay wrote:

This paper is now published online at Genes, Brain, and Behavior (open access). https://doi.org/10.1111/gbb...

On 2023-10-25 21:13:39, user Daniel Nelson wrote:

This manuscript has been published:<br /> Biology 2023, 12(10), 1277; https://doi.org/10.3390/bio...

On 2023-10-25 00:53:17, user CDSL JHSPH wrote:

Great work! I think this is very important for future research regarding T. cruzi and it's genome's contribution to Chagas disease. Sequencing this strain, whose whole genome has never been sequenced, is a significant contribution. Despite some limitations, this was done successfully and I think it will push others to work on sequencing genomes from other strains, particular those from field samples. I think this is a great step for further research in looking at the relation between transposable elements and multigene families. The methodology in this study was very convincing. I especially like the extra steps done to address the limitations of the Busco Score. I think comparing ORF length of assemblies from other strain with similar characteristics really helped improve your methodology. The paper had a logical flow, but I do believe limitations should have been further explained in the discussion. I think this would have really improved this paper. Overall, this was great work that opens up to more questions regarding this field which I hope this team or other researchers would look at in the future. Not only was a whole genome of this strain sequenced, but it was done using ONT nanopore sequence alone which can provide a less expensive method for sequencing T.cruzi genomes. A few questions that came to mind was how different do you think your results would have been if you used ONT nanopore sequencing with supplementation of other technologies. Also how different would your results have been if you used a field isolated samples instead of a lab strain? Do you think you would have found a greater correlation between transposable elements and multigene families?

On 2023-10-25 00:37:30, user Jessica Garvin wrote:

Hi! I found your research incredibly engaging to read about. The variety of test results that you shared was especially notable. Since publishing this paper, have you worked on any other projects similar to it? I think it would be an interesting find to see whether or not you would have similar findings with strains comparable to the Tulahuen strain. Wonderful job on your work!

On 2023-10-24 01:24:43, user Anshule Takyar wrote:

Hello! This is a good piece of work, and the value to the field is very evident. It is great to see novel sequencing methods like Oxford Nanopore sequencing being validated more and more, and by employing a Nanopore-only approach, you have probably helped to assuage some of the anxieties of others in the field regarding this technique. I had a few questions and recommendations regarding this technique. Have you sequenced T. cruzi, or the Tulahuen strain specifically, with short-read sequencing? Are there any hurdles involved with that? Also, do you think that by assembling this genome using the help of short-read sequencing, you would have gotten a better result? Additionally, I think that it would be helpful to show in a figure which coding regions are not impacted by transposable elements, as that would increase the significance of your work. Other than that, I really liked this work, and congratulations!

On 2023-10-24 17:32:53, user Jianhua Xing wrote:

It is nice to see more efforts on learning the governing equations of gene regulatory networks from single cell data, and thanks for mentioning our dynamo work. Congratulations on the work. I notice that some discussions on dynamo are not accurate --unfortunately it has happened repetitively in the literature such as stating dynamo requires data with metabolic labeling only and the vector field gives only lear relation between a regulator and its target gene. Related to what discussed here, with the dynamo vector field one can predict cell states NOT covered by the data. That is, dynamo is a generative model. So the criticism on using embedding is not justified. One uses low-dimensional manifold embedding (e.g. in Dynamo) to simplify the model (with reduced number of parameters to specify), and it is well-established that a dynamical system typically falls to a low-dimensional manifold after a transient period of time. A famous example is the 3-variable Lorenz model. Starting from any initial state, it falls to a strange attractor with dimensionality 2.06

On 2023-10-24 15:20:08, user kamounlab wrote:

We’ve discussed this note today and we have a question regarding Figure 1G. It's essential to ensure that RBA1 doesn't negatively affect the agroinfiltration process itself, which could potentially lead to reduced accumulation of the virus and reduced fluorescence, thereby impacting the interpretation of the results.

To address this issue, the experimental design should include appropriate controls to rule out any interference of RBA1 with agroinfiltration.

On 2023-10-24 12:41:18, user Ying Cao wrote:

Apparently, EMT means change of cellular states/properties but not of gene/protein symbols. In the case of EMT, what are epithelial and especially mesenchymal states/properties are not known. What it the scientific meaning of the so-called epithelial-mesenchymal transition?

On 2023-10-23 17:43:58, user Vernita Gordon wrote:

now published in npj Biofilms and Microbiomes! https://www.nature.com/arti...

On 2023-10-23 12:27:01, user Senthil-Kumar Muthappa wrote:

This preprint article is now published, please see: Priya P, Patil M, Pandey P, Singh A, Babu V, Senthil-Kumar M. (2023). Stress Combinations and their Interactions in Plants Database: A one-stop resource on combined stress responses in plants. The Plant Journal, https://doi.org/10.1111/tpj...

On 2023-10-23 08:13:46, user Richard Steeds wrote:

This is a really interesting study in an ultra-rare syndrome that kills a substantial number of patients through cardiovascular complications.<br /> 1. As the authors have acknowledged, we have never seen evidence in any of our studies of sexual dimorphism in adult presentation with disease or on cardiovascular imaging, either by echocardiography or cardiac MRI. We have seen young men and women suffer cardiovascular complications at similar age of onset in their 20s and 30s.<br /> 2. Changes in left atrial area, isovolumic relaxation time and ejection fraction without similar changes in myocardial performance index or global longitudinal strain worry me, as in humans I would expect both to be early indicators of restrictive cardiomyopathy. All of these values are affected by both acute changes in blood pressure (and especially during anaesthesia) and by the longer-term effects of hypertension, so this is an important confounder but again acknowledged.<br /> 3. One feature that is expected in human studies of restrictive cardiomyopathy would be corroborative evidence of pulmonary hypertension, occurring as a result of elevated LV end-diastolic pressure, high LA pressure and thereby pulmonary venous hypertension. Was there any TR in the mice model and any measure of TR maximal velocity?<br /> 4. I am an adult cardiovascular imaging specialist who practices both echo and CMR. I am only too aware of the variability of echo measures of cardiac function on an intra- and inter-observer basis. At heart rates of 350-450 BPM, I remain very concerned by the reproducibility in small numbers of animals - although I recognise these numbers are considered adequate in the animal physiology world. When I look at the box plots, there is often a wide spread of results, and no idea is given in the manuscript of the intra-observer measurement for example of ejection fraction - I understand that the person was measuring blind and was a single experienced sonographer...but in our practice, we recognise that in experienced hands, EF may vary in humans at up to 10% between scans at heart rates of 70BPM.

On 2023-10-21 20:16:36, user Bingxin Lu wrote:

Now published in Genome Biology, doi: 10.1186/s13059-023-02983-0

On 2023-10-20 16:06:57, user Alina Selega wrote:

Our paper is published in Transactions on Machine Learning Research and can be accessed here: https://openreview.net/pdf?...

On 2023-10-20 11:33:58, user Tom wrote:

Bacteroides vulgatus was renamed Phocaeicola vulgatus 3 years ago (https://lpsn.dsmz.de/specie...

On 2023-10-19 12:51:38, user Russell Cox wrote:

It would be good to know the actual titres of polyketides for the different systems in mg/L - then comparisons could be made with other related systems such as DEBS1-TE in various hosts including filamentous fungi (https://www.mdpi.com/1410198)

On 2023-10-19 01:02:59, user Jonathan Eisen wrote:

Minor comment - in many parts of the manuscript you refer to "16S rRNA sequencing data". It would be more accurate to refer to this as "16S rRNA gene sequencing data".

On 2023-10-12 08:46:22, user Michael Kuhn wrote:

Readers of this preprint may also be interested in https://microbeatlas.org/ , a similar resource with more than 2 million analysed samples from human, other host-associated and environmental samples.

On 2023-10-19 00:34:34, user Michael Polymenis wrote:

The paper has been published in GENETICS (see here https://pubmed.ncbi.nlm.nih....

On 2023-10-18 18:45:43, user Vanessa Staggemeier wrote:

Moura et al. evaluate the loss of suitability areas for non-flying mammals in the Caatinga in two future periods (2060-2100) under climate change effects and what would be the expected changes in the biotic composition of communities.

The authors employed an interesting approach with restrictions on species dispersal in the models and the results contribute to predicting the effects of climate change in this biome.

We see the importance of focusing on biotic changes and % of range loss, but it is our belief that adding the final predictions for each species in the supplementary material, in terms of maps and range shifts (direction of shift), it would be worth and informative because this information is important for managers and decision makers (those who manage conservation units but also to the researchers working on specific taxa).

We also think that including a more detailed discussion about some species that have been modelled in other previous studies could enrich the work and make some of the results obtained here clearer. For example, why species with a wide distribution such as Callicebus barbarabrownae would lose their entire area of suitability in 2060? Other studies, such as Barreto et al. 2021 and Gouveia et al. 2016 found different results, could you attribute this to the methodological choices?

We think the words used in the bibliographic review were not wide enough to include studies with mammals in the Caatinga because some important references are out of the included papers. The chosen words are mainly related to the biome or region. Maybe another approach would be to review occurrence records in a systematic way looking for articles with species names (as keyword) based on a preliminary list of mammals.

Including latitude and longitude in the maps it would be more informative and including political division of states could help to subside discussion for specific regions of Caatinga.

We wrote this comment during a meeting to discuss preprint papers that occurred by September, but I was able to post it just now.

I saw that the paper was accepted yesterday, so I am not sure if our suggestions/questions will have some worth to the authors (feel free to reply or not), but we decided to contribute with them anyway.

Many congratulations for your article! Although we think that some points could be different, we are sure that article is a nice contribution to understand potential effects of climate change in Caatinga :)

Comment written at the Laboratório de Ecologia Vegetal, Evolução e Síntese (LEVES) at the Universidade Federal do Rio Grande do Norte, RN, Brasil. Joined this meeting: Vanessa Staggemeier, Hercília Freitas, Víctor de Paiva, Yan Gabriel, Alexander Chasin, Rhuama Martins, Vitoria Alves, Jose Nilson dos Santos, Rafael Rocha dos Santos, Maria Luiza and João Paulo Câmara.

References<br /> 1) Barreto, H. F., Jerusalinsky, L., Eduardo, A. A., Alonso, A. C., Júnior, E. M. S., Beltrão-Mendes, R., ... & Gouveia, S. F. (2021). Viability meets suitability: distribution of the extinction risk of an imperiled titi monkey (Callicebus barbarabrownae) under multiple threats. International Journal of Primatology, 1-19.

2) Gouveia, S. F., Souza‐Alves, J. P., Rattis, L., Dobrovolski, R., Jerusalinsky, L., Beltrão‐Mendes, R., & Ferrari, S. F. (2016). Climate and land use changes will degrade the configuration of the landscape for titi monkeys in eastern Brazil. Global Change Biology, 22(6), 2003-2012.

On 2023-10-18 13:26:37, user Komal Patel wrote:

This manuscript is now published in Nature communications<br /> doi.org/10.1038/s41467-023-...

On 2023-10-18 05:35:02, user Yano_Lab wrote:

This manuscript has been recently published at ACS Chemical Neuroscience:<br /> https://pubs.acs.org/doi/fu...

On 2023-10-17 14:50:43, user charlesqzhou wrote:

Dear bioRxiv community @biorxivpreprint, please kindly note that this tutorial review has now been published open-access at Advances in Physics: X https://doi.org/10.1080/237...

On 2023-10-17 08:52:43, user DL wrote:

Very interesting paper and deep insight into the mechanism. However, no functional data regarding the detergent or DTT conditions are shown. I'd really like to see electrophysiological recordings of HCN1_wt, HCN1_CC mutation and HCN1_CCA mutation under a) DTT application and b) CHS/LMNG application/incubation to show the physiological/functional relevance of the resolved putative Intermediate and Open states.

On 2023-10-17 00:19:42, user Abram Magner wrote:

We, the authors of ``A Deep Learning Architecture for Metabolic Pathway Prediction'', thank the authors for pointing out the existence of duplicate entries in our datasets and for pointing out that we did not upload all of our code for data download from the KEGG database.

We have addressed the latter issue by uploading our data download script, keggpuller.py, to the project Github. This code was used to download molecule records from the KEGG database and store them in a commma-separated value format. This resulted in 6669 records. The dataset was then further processed to a simpler form to reduce each record to a SMILES string followed by a comma-separated list of letters indicating pathway class membership (this is smiles_property.txt). We refer to this as the multi-class dataset. We also considered the problem of classification of a compound as either being a member of a single, given pathway class or not. We refer to the resulting dataset as the single-class dataset.

The authors are correct that the resulting datasets contain duplicate entries. The single-class dataset contains six duplicates out of 4545, while the multi-class dataset contains 1740 out of 6669.

We have re-run our experiments on the datasets with duplicates removed. The results for single-class classification did not change. The table of results for multi-class classification can be found at this location.

We note that the accuracies of most methods dropped, including ours. The accuracy statistics for ensemble logistic regression increased.

However, we also note that the central results of our paper remain intact -- the relative ordering of accuracy of different machine learning methods (other than ensemble logistic regression) on the data remains the same, and the superiority of our method over the others that we evaluated remains. Indeed, this is expected because we ran all methods on the same datasets, using the same training/test split methodology.

We have uploaded the de-duplicated datasets to the Github page. The authors are correct to encourage the use of the de-duplicated datasets. We will also post a correction to our paper.

On 2023-10-16 16:21:21, user Kent wrote:

Now published https://doi.org/10.1186/s40...

On 2023-10-15 14:16:31, user Megan Gura wrote:

A revised version of this preprint has been published at https://doi.org/10.3389/fce...

On 2023-10-15 07:39:00, user Ben Dickie wrote:

Hi,

Really nice work. Please consider reporting your DCE/DSC methods using the new OSIPI lexicon to improve standardization.

https://onlinelibrary.wiley...

I’m very happy to help integrate the correct terminology (ben.dickie@manchester.ac.uk).

Best wishes,

Ben

On 2023-10-15 07:36:09, user Ben Dickie wrote:

Hi,

Really nice work. Please consider reporting your DCE methods using the new OSIPI lexicon to improve standardization. https://onlinelibrary.wiley.... I’m very happy to help integrate the correct terminology (ben.dickie@manchester.ac.uk).

Best wishes,

Ben

On 2023-10-14 20:57:10, user Christophe Leterrier wrote:

Figure 3 is repeated 2 times in the pdf file, Figure 2 being absent. Is it possible to upload a corrected manuscript as revision? Thank you.

On 2023-10-14 16:35:59, user Naveen Shankar wrote:

Published version of this article is now available. <br /> Shankar N, Sunkara P, Nath U (2023) A double-negative feedback loop between miR319c and JAW-TCPs establishes growth pattern in incipient leaf primordia in Arabidopsis thaliana. PLoS Genet 19(9): e1010978. https://doi.org/10.1371/jou...

On 2023-10-14 09:42:51, user Daisuke Kitamura wrote:

We did not provided you the cell line, "40L-MEF", whatever it is. By the way, we generated "40LB" based on Balb/c 3T3 cell line, not on MEF, as described in the Ref. 26.

On 2023-10-13 15:56:11, user Yichao Li wrote:

Regarding "we observed that features correlating with open chromatin or active genes (such as ATAC-seq,HDAC1/2/3, or H3K4me1/2/3)", if you look at your reference 14, it says "HATs have been associated with active and HDACs with inactive genes."

I'm wondering why HDAC is "active marker" in this paper?

Thanks,<br /> Yichao

On 2023-10-13 14:22:00, user Kareem Elsayad wrote:

Happy to share that this manuscript is accepted in principle pending minor revisions in Nature Photonics!

On 2023-10-13 09:27:43, user Tom Langton wrote:

Figure 5 and Table 2 now seem to have been muddled. It would be helpful for any changes to be given an alert.

On 2023-10-10 13:37:06, user Tom Langton wrote:

There is a distinct lack of transparency becasue data are not linked in supplementary material and neither is the stata code. Even if the code was there it would be difficult to reproduce – stata is not very widely used.

Does the analysis decisively link badger culling to decline in bovine TB in cattle? Culling is confounded with complex changes to gamma and other testing and other measures. There are places in the ms where a claim is made for a link ‘The effect of badger culling…’ but then later: ‘However, this data analysis cannot explicitly distinguish…….’ APHA have stated more than once that the breakdown data alone are insufficient to show an effect of culling.

However, the DID approach is inappropriate because the 52 cull and pre-cull study areas are surrounded by and in close contact with each other and so are not adequately discrete in space and time, breaking a DID requirement. Notably in the four-year period 2016/17 -2019/20 pre-cull and cull areas closely juxta position. Pre-cull and cull interventions may influence change in adjacent pre-cull and cull areas in an irregular and unpredictable manner, via the constant movement of cattle with different intervention histories.

Some of the very important bTB testing controls used and the timing of their introduction are not correctly described and some not even mentioned.

The approach mixes data from 46 High Risk Area study areas with 6 from the Edge Area. These have very different epidemiological and disease control history profiles and the reason for mixing them is not explained. Pre-cull gamma testing was intense in the Edge Area. While the manuscript mentions additional gamma testing from 2017, gamma testing was erratic between areas and over time. There were considerable numbers of gamma reactors in many of the cull areas in cull years 1 and 2 with similar disclosures to years 3 and 4 but pre-cull use in the HRA was generally low.<br /> In both HRA and Edge areas OTFW incidence was declining when many of the additional disease control rules were intensified, and badger culling introduced. The government chief scientist view was that distinguishing and determining with any precision any contribution of badger culling to disease control is not possible, is therefore maintained. It is impossible to distinguish between the effects of badger culling and disease control measures using the DID approach in any meaningful way.

The comment made on being unable to match cull and control is unevidenced and a 2022 published and peer reviewed study of this has not been cited, nor alternative approaches such as matching a series of individual farms in cull and control which would have been simpler and easier to monitor on a quarterly basis since 2018, as was the expectation of a High Court ruling.

The suggestion that incidence is the better indicator of true burden goes against APHA’s own epidemiology and the greatly clarified understanding of SICCT test sensitivity and specificity. The specificity change resulting from SICCT severe interpretation can be estimated and applied to OTFS, providing, with OTFW, a safe index of known infection. Gamma testing has shown numbers of unidentified reactors in both identified bTB infected and unidentified herds, and this ‘hidden’ reservoir remains undocumented although its size can be deduced.

OTFW relates by its nature to older infections with lesions, yet it is newer infections (OTFS) that indicate disease control success (elimination) more accurately, over time. There is no reason to believe that OTFS+OTFW (as above) is not the superior approach and that OTFW is half of the full picture. During the RBCT, prevalence was growing considerably and OTFS rising so the analogy made in this manuscript is inappropriate and trying unconvincingly to defend the historic reference source.

By reducing the pre-cull analysis to one data point the significant pre-cull decline is hidden and this also masks true trends.<br /> Because the methods are inappropriate, the results are flawed and the conclusions are flawed. There is no way to distinguish between different interventions and change in herd breakdowns since 2013 with the approach taken.

There is also need to see clarification of the analysis that is presented in the Appendix – this does not seem to be the output from the constrained DID analysis, but something else not fully described in methods which ‘matches’ it – which stata process was used ? It is the first analysis referred to in the main text. As well as showing that the BCP effect accounts for less than 2% of breakdowns it doesn’t look quite right that the BCP effect is based on error dof – pseudoreplication?

On 2023-10-12 15:56:16, user Michael McLaren wrote:

It would be useful to see a comparison to another method that also uses a Poisson / Multinomial distribution to handle issues associated with low + zero counts. In particular I would be very interested to see a comparison to Justin Silverman's fido package (https://jsilve24.github.io/..., though since fido is a Bayesian framework I imagine the comparison may not be as straightforward.

On 2023-10-12 01:59:38, user Nastassia Patin wrote:

This paper is relevant and should be cited: https://journals.asm.org/do...

On 2023-10-11 14:01:06, user Gilles wrote:

Is there any positive control for FoxP3/CD25 stainings ? the cytometry stainings are so poor it is difficult to conclude anything.

On 2023-10-11 10:42:55, user Vladimir Chubanov wrote:

The paper presents an interesting model. It would be great if the author would attempt to connect the model to available experimental evidence. For instance, clinical assessment of the patients with loss-of-function mutations in TRPM6 and conditional mutagenesis of Trpm6 in the kidney vs intestine of mice demonstrated the prime role of intestinal magnesium absorption in the systemic balance of magnesium.

On 2023-10-11 09:22:07, user Shigeyuki Koshikawa wrote:

Published in Development Genes and Evolution.<br /> https://doi.org/10.1007/s00...

On 2023-10-11 08:17:50, user XiaoFei Zhao wrote:

Just out of curiousity, did the authors compare performance (F-score, etc.) with the tool at https://github.com/genetron... ?

On 2023-10-11 03:38:39, user Pietro Roversi wrote:

The paper has appeared in https://doi.org/10.1016/j.i...

On 2023-10-10 23:01:20, user Alexander Chase wrote:

excited to see the incorporation of GH/CBM into BGCs - likely linking some of them to primary metabolism. should also check out our latest work on Salinispora here:<br /> https://doi.org/10.1128/mBi...

On 2023-10-10 00:39:54, user George Maiti wrote:

This article is now published in International Journal of Molecular Sciences. https://doi.org/10.3390/ijm...

On 2023-10-09 13:43:37, user Gregory Verdeil wrote:

This work is now published in Nature Immunology<br /> https://doi.org/10.1038/s41...

On 2023-10-09 12:16:03, user Taise Gonçalves wrote:

First, I would like to congratulate the authors, because this article is very well done. The diagram presented at the end of the introduction (Figure 1), exemplifying the expected results, adds a lot to understanding the text and it is an enriching differentiator for the manuscript. The data analyzed were obtained from environmental monitoring for 3 decades, which the authors were able to synthesize in very concise and accurate results. It certainly represents the best assessment to the hypotheses of pre-adaptation and limiting similarity to date.

Taíse Gonçalves - Master's student - Fungi, Algae & Plant Biology Program - UFSC - Brazil (On behalf of the PLENTBio Journal Club; plentbio.wixsite.com/alcant...:gXjqaj9KUVrPkIjzaJ8r_UkXvwc "plentbio.wixsite.com/alcantaralab)")

On 2023-10-09 12:13:10, user Taise Gonçalves wrote:

The main conclusion of this research states that oceanic islands showed higher phylogenetic endemism, based on both metrics used by the authors, i.e. denoting palaeoendemics and neoendemics. This pattern, however, have been already stated in a recent study (Veron et al. 2019). In this sense, the discussion about the processes involved in the origin of such floras could be improved by the ideas presented by Vasconcelos et al. (2021). In this study, the authors propose that the terms neoendemism and paleoendemism should be replaced by assessment in relation to the actual biogeographic and macroevolutionary processes that had occurred.

Moreover, although the authors provide a methodological discussion regarding the scale of the areas analyzed, a stronger emphasis should be put on it. Several large continental areas, for instance, represent large vegetation domains (i.e., Mata Atlântica and Cerrado), which include high number of endemism however unevenly scattered in island-like habitats throughout these areas.

1. Vasconcelos, Thais; O’Meara, Brian C.; Beaulieu, Jeremy M. Retiring “cradles” and “museums” of biodiversity. The American Naturalist, v. 199, n. 2, p. 194-205, 2022. DOI: https://doi.org/10.1086/717412

2. Veron, S., Haevermans, T., Govaerts, R. et al. Distribution and relative age of endemism across islands worldwide. Sci Rep 9, 11693 (2019). https://doi.org/10.1038/s41...

Taíse Gonçalves - Master's student - Fungi, Algae & Plant Biology Program - UFSC - Brazil (On behalf of the PLENTBio Journal Club; plentbio.wixsite.com/alcant...:gXjqaj9KUVrPkIjzaJ8r_UkXvwc "plentbio.wixsite.com/alcantaralab)")

On 2023-10-09 12:05:37, user Taise Gonçalves wrote:

I congratulate the authors for the study and would like to add a perspective that could improve the reach of the manuscript, as well as to clarify a specific point.

1. The three hypotheses proposed (lines 171 - 185) has been described as the predicted associations, reflecting correlation patterns instead of the causal mechanisms leading to those patterns. As an example of correlation, in discussion (line 437), the authors report that the higher taxonomic, functional, and spectral diversity found at middle elevations has already been tested all over the world. Could the focus of the hypotheses be stated as the process(es) behind these patterns instead of the correlations per se?

2. The species Phacelia secunda was selected to analyze intraspecific trends between traits and environment, based on the fact that it is present across the elevation gradient (line 224). It would be interesting to know if Phacelia secunda is the only species occurring along the all elevation gradient. If not, why the authors selected it specifically?

Taíse Gonçalves - Master's student - Fungi, Algae & Plant Biology Program - UFSC - Brazil (On behalf of the PLENTBio Journal Club; plentbio.wixsite.com/alcant...:gXjqaj9KUVrPkIjzaJ8r_UkXvwc "plentbio.wixsite.com/alcantaralab)")

On 2023-10-09 11:26:07, user Arda Sevkar wrote:

It appears that an incorrect strain was used for the alignment of Mycobacterium leprae. As indicated in the supplementary materials, the MRHRU-235-G strain was utilized for this purpose. Notably, the NCBI genome page designates this strain as the "reference strain". Unfortunately, that's not true. In the literature, alignment and genotyping are consistently carried out using the strain labeled as "TN" (NCBI Genbank accession number: AL450380.1)."<br /> Additional information regarding this subject is available in the following sources: Pfrengle2021, Krause-Kyora2018, Schuenemann2018/2013, Benjak2018, Monot2009

On 2023-10-09 08:48:33, user ALFONSO DARMAWAN wrote:

Hello,

First of all, I enjoyed reading through the whole paper and it was really interesting to see the single suspension cell specifically in the progress of FRT. I would say the paper has showed very thorough and clear figures overall; nonetheless, I would love to follow-up with some suggestions regarding some of the figures. In figure one, I have seen the visual profiles between the Diesterus and Estrus cycles; however, it would be great to see the whole visuals for all 4 cycles to have more thorough comparisons how the cells are phenotypically changed over each cycle. In figure 2, I have seen cell-expressions for BEpC, MAIT, and F were not showed in the b figure because I saw some significant changes in between phase for those expressions and it would be nice to see them within the plot as well. In addition to figure 3, might as well of showing the scRNA-seq data in other cell-type expressions of Tgfb2 and correlate them with their most corresponding expressions within each of the organ. Lastly in figure 4, it would be great to also show us the other expressions besides Alpl because it's been slightly confusing to change every expression from one figure to another without vivid and brief explanations alongside with it. Thank you!

On 2023-10-09 02:52:55, user Anacleto Silva de Souza wrote:

https://link.springer.com/a...

On 2023-10-06 11:03:43, user Anacleto Silva de Souza wrote:

This manuscript was modified and published in the Journal https://doi.org/10.1007/s10...

On 2023-10-09 00:23:05, user Joe wrote:

Published version of this preprint: https://esajournals.onlinel...

On 2023-10-06 12:37:11, user Ashok Palaniappan wrote:

An expanded, revised and advanced version post peer-review is now available [open-access]:<br /> Sarathi, A., Palaniappan, A. Novel significant stage-specific differentially expressed genes in hepatocellular carcinoma. BMC Cancer 19, 663 (2019). https://doi.org/10.1186/s12...

On 2023-10-05 22:27:50, user VK Ulaganathan PhD????????, M.Tech ( wrote:

Chimeric Cytokine Receptor expressing T cells (CCR-T cells)??

On 2023-10-05 22:14:42, user VK Ulaganathan PhD????????, M.Tech ( wrote:

It is published and open access.<br /> Here is the full text: https://doi.org/10.1038/s41...

On 2023-10-05 22:12:40, user VK Ulaganathan PhD????????, M.Tech ( wrote:

It is published and open access.<br /> Here is the full text https://doi.org/10.1002/cbi...

On 2023-10-05 17:58:21, user Rod Page wrote:

This preprint has now been published as https://doi.org/10.3897/bdj...