On 2018-02-08 10:49:54, user Lukas Fiederer wrote:
Dear authors,<br /> some small mistakes I have noticed while reading:<br /> - The captions of figure 9 and 10 have been switched.<br /> - The y-axis label of the green bar plot of figure 9 is currently a placeholder<br /> - Caption of figure 10 has a typo in 2nd word of last sentence.<br /> - Discussion, D, a), last sentence, CCN should spell CNN?<br /> - Multiple typos throughout the text, adding line numbers would ease a community review.
On 2017-03-11 15:13:06, user AdamMarblestone wrote:
-"Dynamics of cortical dendritic membrane potential and spikes in freely behaving rats" http://science.sciencemag.o...
On 2017-07-19 19:21:33, user AdamMarblestone wrote:
-"Neuroscience inspired artificial intelligence" http://www.cell.com/neuron/...
On 2017-04-10 01:52:09, user AdamMarblestone wrote:
-"Superficial layers of the medial entorhinal cortex replay independently of the hippocampus" http://science.sciencemag.o...
On 2017-10-21 19:00:44, user AdamMarblestone wrote:
-"Top-Down Feedback Controls Spatial Summation And Response Gain In Primate Visual Cortex" https://www.biorxiv.org/con...
On 2020-06-05 17:41:32, user Laurent Seroude wrote:
Manuscript has been peer-reviewed and has been accepted for publication in Genome on June 5th 2020.
On 2021-03-31 21:04:34, user Dr. Gener wrote:
I remember reading that bisulfite conversion wasn’t great with circular DNA. Y’all could try looking at this mtDNA with ONT as orthogonal validation.
On 2020-03-11 11:56:46, user Alexei Tsygvintsev wrote:
The manuscript will appear in J Proteins Proteom (2020). https://doi.org/10.1007/s42...
A view only link to article: https://rdcu.be/b2WBQ
On 2020-06-11 19:54:33, user Dave wrote:
From the manuscript:
The updated SPLITREADER source code, split in two parts (SPLITREADERbeta2.5_part1.sh and SPLITREADER-beta2.5_part2.sh) as well as the 4 following<br /> processing scripts are available on (https://github.com/baduelp/... "https://github.com/baduelp/public)").
Note that the source code you're referring to doesn't appear to be present in the repo at https://github.com/baduelp/...
On 2025-09-17 12:32:29, user manez wrote:
Information for readers: this preprint has been published and the link will be available soon.
On 2021-07-16 07:49:59, user Gerhard WINGENDER wrote:
This manuscript was used in class at IBG to practice peer-reviewing. This was the final version:
In this manuscript Classon et al. show that following the intestinal infection with the helminth H.polygyrus (Hp), Hp-specific CD4+ T cells enter the skin and become tissue-resident in the skin. This is an interesting finding. Furthermore, the authors show that the Hp-infected mice have a weaker recall-responses to intra-dermal injection of M. tuberculosis lysate (WCL). However, this point remains observational as no mechanistic explanation is offered. The authors suggest in their discussion that skin-resident Hp-specific Th2 cells would dampen the IFN? production in response to mycobacterial products locally in the skin, but no data to that effect are shown. Would Hp-specific CD4+ T cells produce IL-4/-13 after WCL, or at least, do more total skin CD4+ T cells do so? How do the authors exclude differences in WCL-induce priming in the skin-draining LNs (rather than local effects in the skin as proposed)? Given that it is known that intestinal helminth infection can dampen immune response at other sites of the body, the novel knowledge gained here appears limited. Moreover, several aspects require attention.
M&M section:<br /> - The B6 mice were 4-5 weeks of age at the start of the experiment. Can the authors show experimental results that similar data could be obtained with adult mice?<br /> - It is not clear if the authors used Fc-block for their flow cytometric staining.<br /> - The description of the BM-DCs+WCL cultures is not entirely clear and the experimental procedure is not explained in the main text or figure legends (only M&M), although that would be helpful. The BM-DCs were incubated with WCL over-night, but then the WCL was not washed off, but rather the leukocytes were added, is this correct? In that case a direct impact of WLC on the single cell suspensions from the skin cannot be excluded. What is the reasoning for this approach? This<br /> potential confounding factor needs at least be discussed.
• Figure 1 <br /> - Three of the dot-plots in fig.1b (and in other figures) do not show any dots. The authors should use density plots with outliers for all flow cytometry dot-plot data throughout the manuscript (including the supplements).<br /> - To the legend: (i) “Representative FACS plots illustrating the gating strategy” - the link to the supplement is missing; (ii) “BMDCs expressing WCL overnight“ - the DCs to not express WCL, they were incubated<br /> with it o/n, right?; <br /> - Why is the y-axis as CD4+ cells when the cells were gated for T cells? Writing ‘CD4+ T cells’ appears more appropriate. <br /> - The figure SF1b suggests that most of the cells purified from the ear are dead. Is this also the case for the CD45+ cells? Is the frequency of dead CD45+ skin cells comparable between the groups? What is the author’s argument that this would not skew the results?
• Figure 2 <br /> - The authors treated the mice twice for one week with DSS, which did not lead to changes in the numbers of CD4+ T cells in the skin. However, 2 weeks after the Hp infection there was no such difference either. Therefore, the conclusion that bacterial translocation does not lead to increased CD4+ T cells frequency in the skin cannot be made based on this DSS-timing alone. Furthermore, the levels of translocation (with Hp or DSS) would need to be comparable to make such conclusions even with identical timing. However, the sCD14-levels were not measured following the DSS treatment. In either case, the DSS-experiment is in its current form not sufficient to exclude the possibility that bacterial translocation is not involved in the skin homing. However, to this reviewer, excluding this option is a minor point that does not appear essential.<br /> - It is not clear to this reviewer why the cohousing of the dewormed mice with the chronically infected mice would not lead to reinfection.<br /> - As the experiment in fig.2j has only been performed once, the results are preliminary and should be moved to the supplements and the text should reflect the preliminary nature of the data. Furthermore, the relative percentage of CD4+ T cells should be given, additional to the numbers.<br /> - The figure legend lists figures n-p (for the replicates), which are not shown. Furthermore, the abbreviation ‘Dw’ is not explained. Please adjust.
• Figure 4 <br /> - For figures 4h+i the authors claim that the difference “was more pronounced in H. polygyrus-infected mice”. It is not clear how the authors arrive at this conclusion. Obviously, the difference in the p-values that compare ST2-pos and -neg cells is meaningless in this regard. One would need to perform an Anova analysis of the control groups (ST2-pos/-neg) vs. the Hp-infected groups. It appears, however,<br /> that this was not done. Please do so and include the values or adjust the language.<br /> - Furthermore, even if the Anova indicates a difference between controls and infected mice, the claim that “TH2 cells … especially targeted to the skin in worm-infected mice” cannot be kept, as the authors did not check other organs to see if this migration is specific to the skin.<br /> - Finally; the statement “ST2+ cells expressed higher levels of CCR4 and CCR10 … compared to ST2-cells” does only hold true for CCR4, not CCR10. The authors should be more careful not to claim things in the text that are not supported by their data.<br /> - The IL-4 cytokine increase in the ICCS appears questionable. A second method to identify IL-4 production should be included.
• Figure 5 <br /> - The authors wanted to show “the reactivity of skin-localized CD4+ T cells … and … analysed cytokine mRNA expression”. Obviously, the total-tissue mRNA response cannot be linked to a particular cell and does not show actual cytokines. This requires protein data on a single cell level. This applies similarly to suppl.fig.5 for which the authors claim in the text to have tested ‘cytokines’ when they actually only checked the mRNA.<br /> - The statement “CD4+ cells that accumulate in the skin of infected mice are H. polygyrus-specific” is not supported by the provided data as the authors did not clarify if indeed the majority of the accumulating skin CD4+ T cells are Hp-antigen-specific. All that can be said, is that the skin CD4+ T cells contained some antigen-specific cells. Either their relative frequency needs to be established or the claim in the text needs to be removed.<br /> - Similar, the data with N. brasiliensis cannot indicated that the skin CD4+ T cells were Hp-specific. All that this result indicate is that the resident CD4+ T cells are not cross-reactive against N. brasiliensis. The text should be adjusted.
On 2021-08-04 19:35:28, user Jing wrote:
A revised version of this story is now published at -
On 2020-05-03 01:17:28, user Gianguido Cianci wrote:
Could you share a reference for this please? Thanks!
On 2020-06-13 03:31:18, user Ray wrote:
Introduce both mutations into the virus and test if one can outcompete the other in cell culture. If yes repeat with model animals. Only then you can say that. Everything else is jumping on the COVID-19 train to get an easy publication. But I guess that's the only way to get money for your lab atm. Science has become a commercial product that is being milked by mass media and journals. The more scary the better it sells. This doesn't help. This just makes things worse.
On 2020-03-05 04:06:51, user Adam Taranto wrote:
This paper was a great read and will hopefully spark others in the field to look at TE dynamics in their own pathogen genome collections! It was exciting to see real data backing up the usually speculative relationships between TE dynamics and population level processes in fungi. Fantastic work by all involved - thanks for sharing this preprint.
There were a few points which were unclear to me. Notes included below.
163: Did you use the handful of existing TEs from RepBase or call everything from scratch with RepeatModeler?
215-216: Here you say that a TE was considered absent if "no evidence for spliced junction reads" was found, but on lines 207-208 you say "spliced reads are indicative of ... absence of the TE in a particular isolate".
246: I don't think consensi as a plural of consensus exists in english (it does in Italian though!). I might be wrong.
312: Does "singleton TE" mean that there is only one instance of that TE family across all isolates? or, that only one isolate has that particular TE at that particular locus?
Fig 1. D. ii) For the leftmost example (Splice junction reads in query isolate + no TE annotation in reference) the fig indicates that this is called as an TE absence in the query isolate. How did you rule out these loci as unannotated repeats in the reference?
351: Fig 2. E. caption. Should this be "mean" copy numbers?
On 2018-06-10 04:05:55, user Jahangheer Shaik wrote:
How does this deal with mixed infections? What if assemblies are not available for all species of interest? What if genomes are not syntenic? Intra-strain hybrids?
On 2022-03-07 15:26:54, user Rafael Yuste wrote:
We have some technical and theoretical concerns about the experiments. Hopefully our comments will help the authors to improve their manuscript and clarify the issue for the readers. <br /> There are some methodological issues that could be important in the interpretation of their results. Because the authors used wide-field imaging with one-photon excitation, which results in PSFs that are likely several times larger than the size of a spine (after tissue scattering), it is likely that the spine signals were contaminated by dendritic ones and by out of focus contributions. Also, since the experiments were performed in brain slices, and near the cut surface of the tissue, the studied spines may not have been in a physiological state. Moreover, the repeated patching of the neurons could lead to cellular damage, and the long periods of whole cell dialysis and high light exposure add serious risk of incident photo damage, affecting neuron’s biophysical properties. Indeed, dendritic beading, a classic sign of tissue damage already described by Cajal, is evident in most of their images. Also, although glutamate uncaging has been broadly validated by many groups, including our own, it is still not equivalent to the response of spines when synaptically activated in a physiological state, which could involve different set of receptors, conductances, inhibitory signals, etc.. <br /> With respect to the criticisms they raise about our recently published work (Cornejo et al, 2021), aside from major experimental differences in species (rat vs. mouse) and age (developing vs. adult animals) and without dwelling in the details of the analysis, we would simply encourage the authors to view our movies of voltage imaging of dendrites in vivo. In these movies, included as supplementary material in our recent paper (https://www.science.org/sto... "https://www.science.org/stoken/author-tokens/ST-255/full)"), one can directly see with one’s own eyes how individual spines are often activated in the absence of any significant depolarization in the dendrites. Also, while the authors suggest that the voltage compartmentalization could be an artifact of the slow sampling, in fact, the known frequency-depending filtering of electrical signals would predict the opposite. Finally, we would note that the authors own data (Fig. 1, for example) confirms the large amplitude of spine potentials made in our recent paper and in the intracellular recordings of Jayant et al. 2016, which they criticize. <br /> It is important to mention that many previous experimental or calculated explorations of spine voltage compartmentalization (Harnett et al., 2012; Tonnesen et al., 2014; Jayant at al., 2017; Kwon et al., 2017; Cornejo et al., 2022), have revealed a significant heterogeneity in spine responses. Indeed, even disregarding the technical issues mentioned above, inspections of the authors’ own data, for example Figure 1D (or in Popovic at al., 2015), demonstrates that a significant number of spines are compartmentalizing voltage to a similar extent than what we have measured in vivo. The authors ignore these data points and the heterogeneity that their results reveal. Given this, and the methodological problems of their approach, we would encourage the authors to soften their tone in their one-sided conclusions and do justice to their own data. <br /> We are looking forward to constructively conciliate different experiments and interpretations in order to better understand together spine physiology and biophysics.
On 2025-04-29 21:33:20, user Jalees Rehman wrote:
Dear Biorxiv,
we have published this paper here in Nature Communications.
https://www.nature.com/articles/s41467-025-57047-7
Best wishes,<br /> Jalees Rehman
On 2018-02-26 16:36:46, user Joseph Colorado wrote:
Wow ... 11 conferences and that is all they need to have a conclusion ... guess when I learned about research methods I was taught poorly in believing more is better when you want good data and less is better when you have an agenda.
On 2016-07-22 03:25:08, user Jong Wha Joo wrote:
Joint Fine Mapping of GWAS and eQTL Detects Target Gene and Relevant Tissue.
On 2018-05-01 19:51:12, user nanocyde wrote:
I wonder if we can tie this to the A0 haplogroup in Cameroon
On 2020-01-05 08:53:18, user sunburnt wrote:
This is great news.
Am I right in observing that it took a while to go from the original Yaminaka research from what.. the mid 2000s to now.. over 10 years later & we are just now trying to in vivo epigenetic resets?
What was the hold up? Was there a general assumption that all in-vivo resetting would cause teratomas?
Also, now that Lu, & Sinclair has shown it may be possible, can we now expect a global tidal wave of research into this area?
On 2021-07-07 15:33:36, user Antônio Medeiros wrote:
Now published in Science: https://doi.org/10.1126/sci...
On 2020-07-30 05:07:24, user ASM wrote:
Summary of this study:<br /> 1. We have sequenced 137 and analyzed 184 whole-genomes sequences of SARS-CoV-2 strains from different divisions of Bangladesh.<br /> 2. A total of 634 mutation sites across the SARS-CoV-2 genome and 274 non-synonymous amino acid substitutions were detected.<br /> 3. The mutation rate estimated to be 23.715 nucleotide substitutions per year.<br /> 4. Nine unique variants based on non-anonymous amino acid substitutions in spike protein were detected relative to the global SARS-CoV-2 strains.
On 2014-03-20 17:18:02, user Casey Brown wrote:
Great paper, thanks for posting. Given the controversy over the frequency of buffering vs. reinforcing effects of transcription and translation a little more discussion on the sources of this discrepancy. There are a bunch of variables here: MA vs OLS, ratios vs. denominators, mass spec vs. ribosome profiling, phyogenetic distance, etc.
-Casey
On 2015-03-17 12:48:40, user David Lovell wrote:
Published in PLoS Computational Biology <br /> Published: March 16, 2015<br /> DOI: 10.1371/journal.pcbi.1004075
On 2017-10-28 16:46:48, user Lionel Christiaen wrote:
Student #2<br /> Justin Crocker and his team examine how brief protein-DNA binding drive transcriptional regulation. Prior studies reveal low affinity binding is critical for transcription factors to interact with their targets, and increasing the strength of the DNA- protein binding drives gene expression dysregulation. To show how low-affinity DNA binding regulates transcription, the group utilize the shavenbaby (svb) enhancer locus, the Hox gene Ultraabithorax (Ubx) low- affinity binding, and the Ubx cofactor Homothorax (Hth). <br /> They first examine the Ubx distribution across the Drosophila embryos with immunofluorescence staining and super resolution confocal imaging. To better visualize the protein distribution, the team expanded the embryos four-fold. The expansion procedure could have distorted the distribution of clusters across the cells. To validate Ubx interaction with DNA, the Ubx transgene is mutated to disrupt DNA binding, which was an important control for the continuation of the story. They observe the spatial heterogeneity of Ubx clusters is dependent on DNA binding. Additionally, Ubx clusters were localized near active svb transcription sites. They creatively containing intronic mRNA and Ubx protein, they’re able to visualize the distribution of nascent transcripts as they relate to Ubx clustering. The article was concisely executed to tell a compelling story.
On 2018-12-18 00:49:46, user Peter Rogan wrote:
This has been published in F1000Research: https://f1000research.com/a...
On 2019-04-11 12:09:55, user Yiheng Hu wrote:
Nice paper! I am just curious that did you also do the 16S copy number adjustments?
On 2018-08-13 20:08:36, user Jeff Barrett wrote:
I was asked to review this manuscript for biOverlay, and I am posting my review here as well.
There has been a lot of attention to the role of de novo coding mutations in autism and other neurodevelopmental disorders, and a natural next question is whether it's possible to identify non-coding mutations that confer disease risk. One of the major challenges to this is how to understand the "regulatory code" and distinguish "synonymous" from "non-synonymous" mutations outside of genes. This paper applies a deep learning (as the authors note six times) algorithm, DeepSEA, to the this problem.
The major result is that DeepSEA successfully predicts a systematic difference between de novo mutations in individuals with autism and their unaffected siblings, which is an impressive achievement. One of my major suggestions for this paper is to provide more information about the input data (de novo mutations called from whole genome sequence), as this is notoriously tricky. I'm somewhat less concerned because the dataset provides a natural internal control between affected individuals and their siblings, but it would still be good to see more detail. For example:
DeepSEA predicts biochemical disruption, and these predictions were further trained on curated HGMD disease mutations and variants observed in 1000 Genomes. What happens if the predictions from DeepSEA are used directly in the autism data? The noncoding disease mutations in HGMD might be a problematic training set, as there are not that many known, and some may not be actually pathogenic, even in the curated set.
Further analyses (e.g. of tissue specific expression and enriched biological functions) provide additional support for the main findings.
For a follow-up paper, someone (the authors or another interested party) should run this analysis on the Deciphering Developmental Disorders dataset (which I was involved in), which should have good power to find specific causal mutations: https://www.nature.com/arti...
Minor comments:<br /> The authors suggest that 30% of simplex ASD probands have a de novo coding cause (and their point is that this is not very much), but I think that's high. I'm not sure where the number comes from, as ref 3 finds diagnostic mutations for 11%, and their Sup Note says 2.4%.
The comparison between versions of DeepSEA is described only fleetingly: "leading to significantly improved performance, p=6.7x10-123, Wilcoxon rank-sum test". More generally, one needs to read that paper to really understand what's going on. This is often the case, but a bit more of a summary of the method would help.
On page 18 there's a repeated word in "40 SSC families families".
On 2024-03-27 13:05:36, user Kai-jie Liu wrote:
This article has been published in nature chemical biology.<br /> Ziegler, M.J., Yserentant, K., Dunsing, V. et al. Mandipropamid as a chemical inducer of proximity for in vivo applications. Nat Chem Biol 18, 64–69 (2022). https://doi.org/10.1038/s41...
On 2025-02-03 17:44:55, user disqus_2m2wL3bGCz wrote:
I cannot find the methods part in the uploaded preprint ??????
On 2021-06-02 06:26:47, user Claudio Tennie wrote:
The following is PART 3 (of 3) of our response to Mielke (we had to split up our reply, due to character limitations here on disqus)
The claim that “the simulation rules used are undistinguishable from copying”.
Answer: This is an opinion that was also voiced by another participant in the original twitter debate and so we shall explain in more detail. In that twitter debate we already mentioned that they can be clearly distinguished, but that the difference underlying them is often not acknowledged or implemented. The resulting confusion is based on the tendency in other cultural modelling work (indeed, in most cultural modelling work) to blackbox and exclude the details of form copying. In our model, as all behaviours are latently present in the individuals (i.e. can individually be reinnovated) there is no need to copy new behaviours (and with it, no need to introduce new behaviours). This is because the simulation rules never implement the necessary copying. This exclusion of form copying is by design - in our model. The fact that the output of this our model of non-form-copying is indistinguishable from real life ape culture - or other cultures identified by the method of exclusion - is exactly the point that we are making.
Form copying is best defined as the causal copying of the actual details of demonstrated forms - i.e. their internal and/or linear structure to each other (Tennie et al. 2020 Bio & Phil). In other words, the know-how, must be causally transmitted. It is easy to see that the way in which most cultural models are implemented do not in fact model form copying in this way at all. In such a model, typically an agent might, for example, be faced with the problem of “copying” the production of a kayak. But the way that this is modelled then is usually by assigning a certain likelihood that a kayak is later produced by the observer. But then, none of the actual details of the kayaks are causally copied here - the kayak is either developed by this agent after observation, or it is not. But here is the catch. The kayak design cannot evolve using this type of model. But in real life the kayak design is even bound to (!) evolve culturally - via copying error alone (as this type of error is unavoidable). These models are often modelling a type of match that can in principle be solved either by copying or by socially mediated reinnovation - that is, by mere triggering. Another way of making clear the lack of actual form copying in these models is this: such models could not recreate the outcome even of the “telephone game” as played by children. This is because the initially whispered message (e.g. “the fox jumps over the fence”) cannot culturally evolve alongside the whispering chains in such models. The “funniest” outcome here would merely be the failure to pass on this message (after which there would be no more message at all transmitted further down the line). Therefore, the critique raised here by Mielke and others and in the original online twitter debate is not relevant. It is not, and cannot be, our responsibility that the field does not usually model real life form copying.
It would seem that the lack of form copying is a potential shortcoming of this field of modelling. When the explanatory target involves actual form copying in one way or another then such a model will no longer work, will no longer be useful (however, note that the explanatory target of many of these models are nevertheless unaffected by these particulars). Thus, for example, any good model of the telephone game must truly implement form copying (the details of the sentence must be attempted to be copied).
In our model, instead, we intentionally chose this very model design - i.e. one that is lacking form copying - and we choose it precisely because (!) it excludes form copying. We fully required the implementation of a mechanism that absolutely cannot copy form to test if form copying is and must be necessary before wild ape cultural patterns can be reproduced (or, more generally, before general positive outcomes of the method of exclusion can be reproduced). Ours is therefore a null-model. Contra Mielke’s claims, our socially mediated reinnovation is therefore not form copying - it can be clearly distinguished from form copying, by not transmitting the necessary details. Our model would not be able to produce real life outcomes of children's telephone games even. But again, this is intentional - it is so by design in our model. Our guided reinnovation of form A after social contact with form A is socially mediated reinnovation - it is a social triggering of this form in another. We know from experience that this implementation is unintuitive to us humans, precisely because humans base nearly everything they do instead on form copying. Were this not the case, and were human behavioural forms more often merely triggered, we would have a much easier time publishing our papers. Unfortunately for us, humans rarely show such triggering, and when we do, we do so in ways that do not closely match the types of learning underlying ape cultures (who are triggered in this way; see above). Nevertheless, it is still at least illustrative to consider such a case: a yawn merely triggering a yawn in another human does not transmit the yawn’s form (even blind-born people yawn). Again, this is being proof of principle, that not all culture needs form copying. Details in the ape case differ to yawning - but the outcome (triggering, not copying) is the same.
Needless to say, this difference (form copying yes or no) matters especially in the long run. With mere socially mediated reinnovation, a system is essentially restricted to the kinds of forms that it can self-produce (i.e. it is restricted to its zone of latent solutions; Tennie et al. 2009). Instead, humans are not restricted in this way, due to our reliance on actual form-copying. Here, error-copying alone will ensure, over time, and in a path-dependent way (!) that more and more forms are not only being produced, but also copied and maintained. This has two very strong consequences (e.g., Tennie et. al. 2020 Bio & Phil; Motes-Rodrigo & Tennie 2021). 1. This will produce forms that no individual could reinnovate anymore (as Richerson & Boyd have originally pointed out; or what we call copying-dependent forms; Reindl et al., 2018) 2. This will create a large number of these types of forms. Indeed, both effects can clearly be seen in humans - humans show billions (!) of copying-dependent forms by now (Motes-Rodrigo & Tennie 2021). This contrasts with the few thousands shown by the other apes combined, of which only zero to three show noteworthy indirect evidence for being copying-dependent (Motes-Rodrigo & Tennie 2021). Overall, ape behaviour is currently best explained instead via the ZLS account - in at least most cases, if not all - and our oranzee model is another piece of the puzzle that shows this to be the best explanation.
In summary - across all three parts of our response - we thank Mielke for correcting our ‘specifics claim’ (see Part 1 of our response), which we have therefore removed from our manuscript - but we disagree on the other claims Mielke raised, for all the reasons given in our remaining response (Parts 2 and 3).
Claudio Tennie and Alberto Acerbi
We thank Elisa Bandini for helpful comments on an earlier draft of our response.
On 2024-01-17 12:14:46, user Lihua Song wrote:
This preprint paper is being misinterpreted on social media. I would like to state the following facts:
The GX_P2V virus has been published in Nature in 2020 (doi: 10.1038/s41586-020-2169-0). It is not a brand-new virus.
The GX_P2V(short_3UTR) mutant was published in Emerging Microbes & Infections in 2022 (doi: 10.1080/22221751.2022.2151383). This cell-adapted mutant is the actual isolate published in the Nature paper. So, the original GX_P2V virus was not isolated. Clearly the original GX_P2V virus in the pangolin sample has severe growth deficiency in Vero cells.
The GX_P2V virus is not a human pathogen, although, based on molecular and animal infection experiments, it can infect a broad spectrum of host species, like human, cat, pig, golden hamster, mouse, rat et al. There is no evidence of the original GX_P2V virus circulating in these animals, not even consider this GX_P2V(short-3UTR) mutant. Please refer to publications: EMBO J, doi: 10.15252/embj.2021109962 and J Virol, doi: 10.1128/jvi.01719-22.
The GX_P2V(short_3UTR) isolate is highly attenuated in in vitro and in vivo models. In Vero, BGM, and Calu-3 cell lines, the virus induced only mild cytopathic effects, notably failing to produce viral plaques even on the human lung cell line Calu-3. In golden hamster and BALB/c mouse models, the virus can infect the animals' respiratory tracts but did not result in any observable disease symptoms. The attenuated nature of GX_P2V(short_3UTR) was also validated in two distinct human ACE2-transgenic mouse models. Please refer to publications: Emerging Microbes & Infections, doi: 10.1080/22221751.2022.2151383 and J Virol, doi: 10.1128/jvi.01719-22.
The attenuation of GX_P2V(short_3UTR) was also hinted in the Nature paper on the GX_P2V(short_3UTR) isolate (doi: 10.1038/s41586-020-2169-0). In Extended Data Figure 1, after infecting Vero cells for five days, GX_P2V caused noticeable cytopathic effects, but which were limited to cell rounding and mild cytolysis, which starkly contrasted with the severe cytopathic effects reported in SARS-CoV-2.
The public has developed a high level of population immunity against GX_P2V due to SARS-CoV-2 immunizations and infections. Collectively, the biological safety risk posed by GX_P2V(short_3UTR) is extremely low. I don’t think there is any immediate risk of spillover into the human population. Please refer to publication: J Med Virol, doi: 10.1002/jmv.29031.
Based on previous reports on ACE2 humanized mouse models with SARS-CoV-1 and SARS-CoV-2, there is significant variability in the outcomes of infection in these models, a topic extensively documented in the literature. A single ACE2 humanized mouse model does not constitute a reliable paradigm for evaluating viral pathogenicity. While GX_P2V(short_3UTR) proved lethal in our mouse model, it's important to consider that it did not cause disease upon infecting two other distinct ACE2 humanized mouse strains. The findings reported in this paper do not alter the fundamental nature of GX_P2V(short_3UTR) as being highly attenuated.
Several other research groups have repeatedly reported the spillover risk of this virus based on its spike protein binding to human ACE2. Those reports have not caught much attention. In our study, using a unique lethal model, we inadvertently reinforced the perception that this virus has a strong tropism for human brains and causes 100% mortality. We need to revise this in the subsequent revision of the paper and provide additional clarification on the intrinsic attenuated nature of the virus.
The GX_P2V(short_3UTR) mutant is a promising live attenuated vaccine against pan-SARS-CoV-2. Partial results can be found in this preprint paper: https://www.researchsquare.....
On 2020-05-18 14:30:58, user OxImmuno Literature Initiative wrote:
On 2022-10-14 11:40:10, user Zach Hensel wrote:
Most of the C/C sequences discussed in this manuscript come from a single study (Lin et al 2021 DOI: 10.1016/j.chom.2021.01.015) that reports methods inconsistent with Washburne et al concluding that associated GISAID records represent complete, full-length sequences. For example, the very first sequence shown in Table 1 in Washburne et al, EPI_ISL_451351, corresponds to sample SC-PHCC1-030. Table S2 shows that this sample has only 89.4% coverage with at least 1 read and only 63.2% coverage with at least 10 reads. Yet, the associated GISAID record is full length with zero Ns. Clearly these are consensus Wuhan-Hu-1 genomes modified by detected variations, and this is confirmed in the manuscript by Lin et al that is cited by Washburne et al:
For Nanopore sequencing data, the ARTIC bioinformatics pipeline for COVID (https://artic.network/ncov-... "https://artic.network/ncov-2019)") was used to call single nucleotide changes, deletions and insertions relative to the reference sequence. The final consensus genomes were generated for each sample based on the variants called in each position.
This is not limited to Sichuan sequences, but also to Wuhan samples from the same study.
Furthermore, Table 1 in Washburne et al includes a sample that was, in fact, considered in Pekar et al. EPI_ISL_453783 is a second record for EPI_ISL_452363 (identical sample ID, patient age, sampling date, and sequence).
Multiple authors of this manuscript have promoted their claimed discovery of new intermediate genomes on social media for the past several weeks and have been repeatedly been informed of these and other errors in their claims and have yet to make any corrections.
Edit 17/October/2022 -- Authors Washburne and Massey have responded that they are aware of this comment.<br /> Washburne: "I stand by every word."<br /> Massey: "grist to the mill lol"
On 2016-11-09 02:31:27, user Isaac Sánchez-Juárez wrote:
I am an economist, but I like to read this class of papers.
On 2025-04-02 17:03:58, user Arval wrote:
This paper has now since been published. Link at: https://doi.org/10.1128/mbio.03428-23
On 2021-05-19 15:03:33, user Peter Uetz wrote:
A revision of this paper has now been published in the Journal of Biological Chemistry: https://pubmed.ncbi.nlm.nih...
On 2022-06-18 19:15:13, user Alex Cope wrote:
Published in Plos Genetics: https://doi.org/10.1371/jou...
On 2018-05-06 12:02:11, user Layman wrote:
What is this Hamilton zuk hypothesis?
On 2025-05-17 03:17:17, user UrNewStepDaddy wrote:
In this study, the authors refine an established FDA method (FDA C010.02) originally developed for extracting PFAS from food to enable analysis of smaller volumes of Dolphin milk than previously possible, demonstrating that Dolphin milk may be a major source of PFAS for nursing calves. The major success of this paper was the ability to quantify the concentration for 13 targeted PFAS species and additional untargeted species in dolphin milk over a 2-year nursing period for the characterization of the PFAS most likely to be transferred from mother to calf. The major weakness of this study is the presence of several unsupported claims which undermine the rigor of the manuscript and weakens the credibility of the interpretations made. Nevertheless, this study provides important groundwork for future research on the transfer of accumulated PFAS from parent to offspring and the effect of PFAS on the development of aquatic newborns. Although the environmental accumulation of PFAS is well established, further research is needed to elucidate the horizontal transfer of these forever chemicals.
Major Points<br /> 1. The manuscript states that dolphin milk was stored at the Smithsonian’s Mammal Milk Repository at -20C for the past 30 years, but provides no detail regarding the type of containers used or whether potential contamination from storage materials was assessed. Since PFAS are hard to avoid and known to leach from plastics, contamination from the storage material could significantly impact results, leading to the question: Were these samples stored in a plastic container that could have leeched PFAS into the milk? According to pictures from the Smithsonian website ( https://nationalzoo.si.edu/conservation/news/making-sense-animal-milks) "https://nationalzoo.si.edu/conservation/news/making-sense-animal-milks)") the dolphin milk seems to be stored in plastic containers, however, the milk was harvested 30 years ago before the widespread knowledge of PFAS contamination. Were any blanks, storage container controls, or background correction measures collected to account for the PFAS introduced during storage? This study reports these chemicals in nanograms so even minimal leaching from storage materials could have introduced measurable contamination.
Minor points<br /> 1. Citations are needed for the sentences listed below:
-Line 43: “Since scientists have recently suggested that humanity has surpassed the planetary boundary for PFAS, major uncertainties must be addressed.”
-Line 217: “Additionally, most studies have only performed targeted quantitative PFAS analyses and not looked for new and unknown PFAS.”
-Line 283: “Previous studies have demonstrated that the lactational burden of POPs decreases following birth.”
-Line 386: “Although research on neonatal PFAS exposure is expanding, many epidemiological studies examine only one compound, failing to capture the complexity of mixtures encountered in the environment.”
-Line 467: “Although previous studies have linked traditional legacy PFAS, PFOS and PFOA, to adverse outcomes in dolphins and other marine mammals, there remains virtually no data on the impact of these chemicals or their replacement compounds on growth and development of neonatal marine mammals, especially with dosages of this magnitude.”
None of these claims are backed up by any evidence, which only helps to erode the work done within this study.
Some scattered typos are listed below:<br /> -In line 110, the title of the section has the method name wrong. It is correctly stated in the section as FDA C10.02.<br /> -In line 201, Administration does not need “ ’s ”.<br /> -In line 313, there is a red underline in the space in “illustrated that”.
Weekly tolerable intake of PFAS from the European Food Safety Authority and Food Standards Australia New Zealand is specifically stated twice within this paper (lines 22 and 368), however, it is only revealed during the 2nd mention that this is the weekly tolerable intake of PFAS for humans. From the source it could be surmised that the data was for humans, but having those numbers used for dolphins implies an equivalence between humans and dolphins that is not properly justified or supported with data (line 462). If human values are used for reference purposes, this should be clearly labeled or explicitly stated.
The wording in the methods section about sample collection and handling is unclear. In the sentence starting on line 90, does “During this time” refer to during the process of being milked or during the 603-day period in which Slooper was milked or during her life at the Naval station?
This manuscript would benefit from references to recent studies on PFAS amounts in dolphin carcasses: Sciancalepore G et al. (2021) and Foord CS et al. (2024) to name a couple. From this paper alone, PFAS do not seem like a huge problem, but put into the context of the papers I listed, it paints a more concerning picture.
On 2021-08-31 14:27:55, user Greg Keele wrote:
The final peer-reviewed paper is now available from Cell Genomics.
On 2017-09-21 23:45:21, user L Kushner wrote:
Any correlation or link between de novo variants at probands and the types of damage observed in the recent nature autism paper by Powell? Amazing what a contributing factor all of these are!
On 2017-12-19 23:04:26, user chloe goose wrote:
This is fascinating. Check it out. Clinically important sex differences in GBM biology revealed by analysis imaging, transcriptome and survival data https://t.co/z7PmAHDVr2
On 2021-10-25 18:16:45, user Peter Rogan wrote:
This article is in press in the International Journal of Radiation Biology. DOI: 10.1080/09553002.2021.1998709
On 2021-04-20 12:55:09, user Nelii cheps kips wrote:
Our recent work on bacterial attachment in high salt environment #brunlab #Caulobacterales #holdfast #polysaccharides
On 2018-05-16 12:54:35, user Michael Monaghan wrote:
When searching for primer and fragment-length information (I could not find either) I notice that (line 128) Sherwood and Presting (2007) is not in the References.
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 2017-02-06 10:39:29, user B C M Ramisetty wrote:
Kindly note an error made by us regarding disk diffusion assay results in the text. Thanks to Dr. Xavier Charpentier for spotting the errors.
Until a correction is posted, kindly read it as "We observed that zone of inhibition of MG1655 with ciprofloxacin (10 ug) was 3 cm (averages) while that of ?10 strain was 3.6 cm (Figure 3B)."
"With ampicillin (10 ug), the zones of inhibition for MG1655 and ?10 strain were 2.1 and 2.45 cm, respectively."
"With nalidixic acid, the zones of inhibition for MG1655 and ?10 strain were 1.78 and 1.98 cm, respectively."
Thank you
On 2021-03-29 19:30:31, user PaleFlesh wrote:
Interesting study. However I am concerned with the usefullness of these findings since only two species were tested, which is too small of a number to definitively say these findings are universal. It would be interesting to see if these results could be replicated with trees, grains, or even algae.
On 2021-06-04 18:54:07, user jasonrasgon wrote:
We are aware of an error in this manuscript regarding the small RNA analysis, the data have been reanalyzed and a corrected version will be uploaded shortly.
On 2022-03-17 17:00:47, user Joram wrote:
This work is now published in Frontiers in Digital Health: https://doi.org/10.3389/fdg...
On 2021-04-09 16:25:37, user Cyril Falentin wrote:
interesting paper, is it possible to get Supp Fig/Table and Supp Material 1 please ? can't find it.
On 2019-07-09 10:18:30, user Peter Manning wrote:
Hi Noemie- you're probably aware of it but we took a similar approach, but no SEM, in an old Ecotron experiment. Details here: https://www.sciencedirect.c...
On 2023-10-26 14:53:28, user Jordan Hamm wrote:
This paper is now published in PNAS. https://doi.org/10.1073/pna...
On 2020-06-25 16:59:55, user Jitin Singla wrote:
Please refer latest version
On 2022-05-13 17:10:25, user Prof. T. K. Wood wrote:
May wish to cite the literature relevant to MqsR/MqsA since we discovered it in biofilms, characterized it as a TA system, and got the structure for the toxin, antitoxin, and antitoxin binding DNA (all not cited here). Moreover, we linked it to resistance to bile acid in E. coli.
On 2022-09-30 22:29:03, user MIT Microbiome Club wrote:
It is know that the effect of a drug, and therefore perhaps a bacteria, can be non-linear with concentration, and that dose-additivity (Bliss) predicts drug combinations better than effect-additivity (DOI: 10.1038/s41564-018-0252-1). How might this impact the results? Of course, it can hard to half the concentration of the bacteria in the setup to create the Bliss model (although maybe something with lower glucose might help?) Might the fastest grower in each pair/trio reach the highest concentration, limit the growth of other species, and therefore provide an effect quite similar to itself in isolation? How would a "fastest grower" model compare to a "strongest" model? The authors note that growth rate did not correlate with effect size for "some" focal species, but might such a model work for the remaining focal species?
On 2023-07-20 09:13:24, user Dmitrii Kriukov wrote:
This is a great paper I found so long! Thank you for your work! You express many thoughs I had and even more.
Minor comments to your work:
Fig 3: "Black indicates observed variance; grey unobserved". - there is no grey entities, only black circles.
Fig 10: "MRL" in the legend
I would be excited to see also the experiment with KD method versus real data and its comparison to different MLRs.
On 2016-12-01 10:47:51, user Torbjørn Ergon wrote:
This paper has now been published as Open Access in Journal of Evolutionary Biology. Download at https://www.researchgate.ne...
On 2022-10-02 17:56:40, user Carrie Partch wrote:
The labs of Seth Rubin and Carrie Partch at UCSC jointly reviewed this preprint. This manuscript examines how the two transactivation domains (TADs) of ?-catenin interact with several domains of CBP/p300 to potentially control transcriptional activation. A combination of biochemistry, NMR, and ITC studies narrow down several binding sites for TAZ1 and TAZ2 domains. Overall, the manuscript is well organized and provides new details on these interactions that may play a role in ?-catenin function. We have some suggestions that might enhance the clarity of the work below. Thanks for an enjoyable read!<br /> Figure 1: <br /> • The schematics do not depict consistent widths/domain lengths and CBP/p300 is missing some domains, including one implicated in ?-catenin binding (Emami et al. 2004, PNAS).<br /> • It would be helpful if your schematic also illustrated all of the constructs used in the study and these names were used consistently.<br /> • If space allows, a simplified diagram of the pathway described in the text could be helpful.
Figure 2:<br /> • It would be helpful to add dashed line for predicted secondary structure cut-off at 0.3 in panel B.<br /> • It would enhance the rigor of the work to show expression levels for the constructs used in panel C.
Figure 3:<br /> • Labeling the MW markers, light and heavy IgG chains, and proteins (does the 666-781 fragment overlap with the light chain?) in panel A, along with the input, would make this figure easier to read.<br /> • The results of the pulldown seem pretty straightforward so quantification from n = 2 experiments seems unnecessary. If you do so, please define what ‘relative’ means in the quantification and make sure that your statistical methodologies are appropriate for this low n.
Figure 4:<br /> • There is some concern that the ITC data might be overfit to a two-site binding model without more information on the fits. Additional rationale or evidence that justifies use of the two-site model would also be welcome.
Figures 5 & 7:<br /> • It might be easier to interpret the binding interface if you used surface representation for the TAZ domains instead of ribbon.
Figure 8:<br /> • It was a bit confusing to show an analysis of the NMR data from the construct used in Fig. S3 in panel A, but then use data in panels B – E from a larger construct. We struggled a bit throughout the manuscript to match domain names and fragments (see Fig. 1 comment above) with the data.<br /> • It could be helpful to conclude with a cartoon or schematic that illustrates what was learned here.
Other:<br /> • Discussion text mentions a possible role for phosphorylation of serines; if citations for this exist, please add them or perhaps broaden this to a possible role for PTMs in general.<br /> • Consistent labeling of ITC data throughout the paper would help clarify which fragments of b-catenin were used in each experiment.
On 2017-03-02 23:39:01, user Stephen Williams wrote:
FYI the supplemental data is in a .tar ball not .txt.
On 2023-11-24 14:29:41, user eldad-a wrote:
Afik, E., Liu, T.J.B. <br /> & Meyerowitz, E.M. Macroscopic waves, biological clocks and <br /> morphogenesis driven by light in a giant unicellular green alga.<br /> Nat Commun 14, 6204 (2023). https://doi.org/10.1038/s41467-023-41813-6
On 2018-08-07 08:16:05, user matthewcobb wrote:
As an alumnus (1992, I think - the year Seymour Benzer came on the course as a student...), I thought this was a really interesting article. However, I think it could be strengthened quite a bit by being more critical/taking the long view and exploring some of the following questions:
How has *what* is taught changed over time, and *how* it is taught?
In retrospect, did the course get obsessed with certain techniques that turned out to be dead ends? How were the topics chosen? A summary of the topics taught in a table, together with some analysis, even hand-wavy, would be really useful.
How did the course contribute directly to the spreading of techniques, or did it only teach things that were well-established?
You describe how students are recruited, but what about the staff? You say where they are from, but what kind of 'social network' underpins staff selection? Has this changed over time? Why (not)?
CSHL has a tradition of highly influential summer courses (the Phage course in the 50s and 60s would be the obvious example). How does the fly course compare to this? Has it been as influential (probably not, although maybe in the early years), and if so how do you measure this?
Finally, I think you need to be a bit more critical about the claims for the role of the course in future careers. Understandably, you don't have a proper control group – as you explain, the students you recruit are generally the best/most motivated (I exclude myself from this ), so you can't compare their success with the overall grad school figures. You can make the comparison, but your suggestion that the course has 'a dramatic impact on trainee outcomes' isn't supported by it.
Good luck with the publication of this!
Matthew Cobb
On 2019-09-11 15:31:45, user Vesta Bahrami wrote:
Hi,
A question! What about those Zorastrians who converted to Islam recently? I also have a comment: I am from Iran and I have been told my family (from my mother side) were practicing this religion until 1900. My mom is from a place close to Hamedan in central Iran. My grandpa told us that they were from a big family (Bahrami) and they practiced persian religion not Islam until recent. They do not practice Islam now anyway, but are registered as muslims. They are from Persian_A group (I guess) since they live in that area. And also another comment. In Iran, in old days, people mixed mostly with local people who they shared similar genes with. Do u know if it is the same in other groups that Iranian Zartoshtis? And I know some Bahai people are mixed with zoroastrians in Iran.
On 2020-01-27 01:34:19, user Petting My Dog wrote:
So according to the formula a 1 year old dog would be aged to 31 Years right?
On 2022-10-24 23:44:43, user CDSL JHSPH wrote:
Thank you for giving us the opportunity to review your preprint article! I enjoyed reading the article and it was fun to learn a little more about whale songs and their potential influences. Understanding how vocal learning and conformity is especially important as the noise environment continues changing in the ocean. Overall, the article had a lot of information that supported how much fin whales depend on vocal learning and conformity.
I felt that your abstract and introduction requires additional information to understand the paper. There was a clear definition for vocal learning, but conformity and what a singing season is was not well defined. Additional information on fin whales would have also been nice to better understand their behavior not in the context of song. I liked how you included other examples of species to gain a better understanding and it also helped show that this study could potentially translate to those species as well. It was clear what questions you were trying to answer with your study, and you defined your results clearly without going too deep into it.
For the most part your methods and results were clear to understand even for someone who has no background in what was studied! In Figure 2, I thought that I had was to potentially add a comparison in Panel C to the 1998/1999 season. It would be interesting to see the change that occurred in all the locations in the ONA region instead of just seeing the one shown in Panel B. Figure 3 was clear to understand and it supported most of the claims that were made in the introduction. I thought it was very cool to see how many ways these figures could be interpreted. An additional suggestion that I have is for Figure 4, I felt like the figure caption was bare and was missing some information to make it easier to understand and there was not much detail into what this figure was supporting so I had to make my own inferences into what was being shown there. Additionally, frequency of note was spoken throughout the article so the frequency on the y-axis was confusing so potentially changing or clearly defining that axis title would be beneficial.
The discussion section in your paper went into a lot of detail and at times felt like too much. The discussion of the results that you obtained were lost in a lot of the extra information that was in it and at times were confusing since it felt like it was jumping around too much. At times it felt like I was reading a review article on animal songs instead of results from a study, but some of this information may be beneficial to have in the introduction section instead. Overall, I thoroughly enjoyed getting to understand whale songs a little bit better and the results that came out of your work are very interesting and hopefully this can form a basis for future studies in other animals that use song.
On 2025-03-17 14:47:47, user Gabriele Scheler wrote:
It looks like you omitted this paper https://pubmed.ncbi.nlm.nih.gov/29071065/ and the earlier paper by Koulakov etal., which showed that Hebbian learning alone - for instance in a homeostatic setting - is sufficient to result in lognormal distributions of synaptic strengths, also intrinsic strengths and spike rates. This was first discovered by SchelerSchumann2006, taken up by Hromadkaetal2008 and then explained by Scheler2017. With the exception of Buzsaki, the whole discussion is missing.
On 2021-02-05 18:43:15, user Morgan wrote:
Nice work from the Stavrou lab! I do have a question about the statement that the MARCH proteins addressed in this study target viral glycoproteins for degradation. Do you think MARCH proteins could be targeting various viral GPs through different mechanisms? For example, I noticed levels of cell lysate EBOV-GP2 was assessed in the presence of MARCH1/2/8, but did you assess the level of EBOV-GP0? Other studies on MARCH antiviral activity suggest EBOV-GP sequestration to the golgi and inhibiting processing of GP0 to GP1/2. How might you explain or reconcile conflicting reports? Also curious, do you have localization data or inhibitor experiments performed not only with MLV but also HIV-1, EBOV-GP, IAV, and the other viral GPs assessed in this study? I think those data would be interesting to see! Thanks for your time and efforts!
On 2020-05-18 13:31:12, user Jessica C Kissinger wrote:
My research group and collaborators are pleased to share our research on the complex & novel mitochondrial genome of Toxoplasma and related parasites with the larger parasite and evolution communities @WiParasitology @ISEPprotists @CTEGD. We welcome your feedback.
On 2019-04-01 07:40:24, user Jubin wrote:
The authors have missed to mention/compare a related tool called SVAMP that has already got a nice and user-friendly GUI: https://github.com/raeece/s...
The original paper for the same can be found here: https://www.ncbi.nlm.nih.go...
On 2019-03-31 00:38:04, user Gokul Rajan wrote:
Nice work. :) It's really interesting that the microbes were necessary for this learning task; but isn't it an overstatement to call them sufficient for cognition?
On 2021-03-18 21:47:49, user Raghu Parthasarathy wrote:
This looks useful, and I'm glad you found my work valuable! I strongly feel, though, that science doesn't need more acronyms (see e.g. here: https://elifesciences.org/a... "https://elifesciences.org/articles/60080)"), and simply combining my radial symmetry localization with FISH doesn't warrant a new term.
Also, by the way, I generalized my algorithm to 3D many years ago, but never formally published it (https://pages.uoregon.edu/r... "https://pages.uoregon.edu/raghu/Particle_tracking_files/radial_symmetry_3D.html)"). Other people also made a 3D version, as noted in the link above. If you had contacted me, I'd have happily told you about this and saved you some work.
On 2021-03-08 14:39:24, user @DrySci wrote:
Nice article @benjraphael et al... are there possibilities to apply in targeted sequencing data ?
On 2021-11-05 16:17:02, user Alizée Malnoë wrote:
The manuscript by Seydoux et al. investigates the role of proton potassium antiporter KEA3 in diatoms. The authors first demonstrated the pH dependence on photoprotection, specifically non photochemical quenching (NPQ) and showed that NPQ can be induced in the dark by acidic pH. They found that KEA3 modulates NPQ by impacting the proton motive force (PMF); indeed generated kea3 mutants showed increased partitioning into deltapH. Importantly they showed that diatom KEA3 in contrast to plant KEA3 possesses an EF hand motif which can bind Ca2+ and proposed that it controls KEA3 activity. The role of KEA3 and pH in affecting the NPQ response has been previously shown in other photosynthetic organisms however the novelty of this study lies in the demonstration that NPQ can be induced in the dark by acidic pH and the proposed role of Ca2+ in regulating KEA3 function.
Major comments<br /> - Page 5, you state that pH-induced quenching in the dark was accompanied by the conversion of DD into DT. Please provide de-epoxidation state (DES) at t15 time (Fig. 1B) to substantiate this statement. Starting DES would also be informative to ensure there was no retention of DT/zeaxanthin in the dark. <br /> - Also to ensure there is no sustained NPQ (and/or damage or disconnected antenna) at t0, please provide Fo and Fm levels for all NPQ kinetics experiments. Assessing PSII accumulation by D1 immunoblot could be done to ensure PSII damage does not occur.<br /> - In Fig. 2F, it is not clear which data points represent HL or ML treatment as well as which ones come from light or dark period. Please indicate them in different colors or symbols. Also clarify whether you have averaged data from the kea3 mutant alleles.<br /> - To confirm that lack of complementation by deltaEF is not due to mislocalization, please show whether deltaEF accumulates at the thylakoid membrane.
Minor comments<br /> - Page 3, Introduction, specify qE after NPQ response; PSBS should be written PsbS<br /> - Page 4, DD-dependent NPQ should be DT-dependent<br /> - Page 4, we suggest changing “crucial” to “Given the unknown role” if pH-dependence of NPQ in diatoms hasn’t been fully established before<br /> - Page 8, KEA3 most likely homolog, were there other homologs than the two shown in Fig. S5? also discuss conservation of other ion channels (is Phatr J11843 thylakoid-localised?) and if they could compensate for the absence of KEA3 in KO mutant (by being upregulated for instance).<br /> - Fig2B, comment on the band at ~80kDa in OE, is that from cleavage of GFP?<br /> - Fig2G, shouldn’t you expect a lower dpH in the OE? Please comment.<br /> - Page 13, for the statement that only dpH can modulate NPQ, we would suggest to tone down or specify that this is the assumption made here as it could be that dpsi modulates NPQ but has yet to be shown!<br /> - Most of the protein analyses were performed loading samples based on protein content, when possible please provide proof that chlorophyll levels are comparable between the genotypes (at least for the native gels)<br /> - Abstract, extra ‘of’ between capacity and via; page 23, extra ‘being’ between likely and less important<br /> - Define acronyms when used for the first time<br /> - There is a lot of ‘peculiar’ in the text ;-)<br /> - Fig. 2D, star symbol instead of square symbol, check consistency of symbols
Pushan Bag, Pierrick Bru (Umeå University) - not prompted by a journal; this review was written within a preprint journal club with input from group discussion including Alizée Malnoë, Maria Paola Puggioni, Jingfang Hao, Jack Forsman, Wolfgang Schröder, Emma Cocco, Jianli Duan.
On 2022-09-26 12:05:30, user Miha Moškon wrote:
The final version of this paper has been published at https://doi.org/10.1016/j.biosystems.2022.104778
On 2017-07-12 17:39:25, user guillemaud wrote:
PREPRINT PEER REVIEWED AND RECOMMENDED by PCI EVOL BIOL
This preprint by Clemente has been peer-reviewed by Michael D Greenfield and Joël Meunier and recommended by Vincent Calcagno for Peer Community in Evolutionary Biology. Peer-reviews, decisions, author's replies and the recommendation can be found here: https://evolbiol.peercommun...
On 2016-07-31 09:08:38, user Steven Wilson wrote:
This manuscript has been revised and accepted after peer-review, and should be available for open access reading in about a month!
On 2020-07-18 08:25:51, user Önder Kartal wrote:
Our paper has now been published by Genome Biology, <br /> see https://genomebiology.biome...
On 2023-09-26 21:25:26, user Victor M Faundez wrote:
Paper is now in press at Human Molecular Genetics<br /> https://doi.org/10.1093/hmg...
On 2018-12-11 22:42:03, user Andrew Leifer wrote:
Thank you for reading our manuscript closely and for sharing your comments with the community. We welcome a robust scientific discussion about our findings. In fact, this is one of the primary reasons why we post to the bioRxiv. Your group, in particular, has done pioneering work in this area and we value your thoughts. Below we provide a brief response to your note, highlight areas where we disagree, and discuss specific analyses to support our claims.
Extrapolating from your pioneering work, we had expected to see a dominant behavior signal in the first three PCs of neural activity, but we did not find such a signal. You express concern that perhaps noise may be present to such an extent during movement that it precludes drawing any conclusions from our PCA analysis. We do not think that is the case. Nonetheless, it is worth imagining what such noise would have to look like for it to both invalidate our PCA analysis yet simultaneously preserve our ability to successfully predict behavior. To be consistent with our measurements, such noise would have to have the following properties: 1) Be comprised of at least three independent orthogonal components that are the most dominant features in the recording. 2) Be distributed across many neurons (because otherwise these signals would not dominate in PCA, which involves z-scoring each neuron's signal). 3) Not overpower a signal that we observe in the first three PCs that is slightly predictive of the animal's velocity in GCaMP worms but absent in GFP control worms (Fig 2G) and 4) still preserve our ability to predict velocity and turning from the activity of a subset of neurons on held out data. While we cannot rule out noise with such unique properties, we think a much simpler explanation is that the first three PCs are not dominated by noise. We therefore merely conclude that the first three modes lack predominant behavior signals. In retrospect, it may not be surprising that moving worms have other signals dominating their neural dynamics. These could, for example, be related to sensory signals or to internal states.
https://uploads.disquscdn.c...
https://uploads.disquscdn.c...
We are aware that estimating neural IDs is extremely challenging. We have tried to be very transparent in our estimates. Table S2 and the supplementary methods give details. We are also happy to answer any specific questions you might have.
Sparse models have been successfully used before in neuroscience (Pillow et al., 2008; Tankus et al., 2012). We are aware of potential concerns with fitting sparse models. We mitigate them by 1) using elastic net which is suitable for highly collinear datasets such as ours, 2) using cross-validation to evaluate the robustness of our fits (Roberts et al., 2017), and 3) assessing model performance on held-out data, which we note is a higher standard than typically used for linear regression in the field. <br /> In fact, reference (Wu et al., 2007) that you mention in your note claims that LASSO performs best for datasets like ours where the variables have correlations, and we note that our elasticnet model incorporates the LASSO penalty.
Thank you for finding the typo in Fig 2D. We will fix it in future versions of the manuscript.
We appreciate your comments as they help us to strengthen the manuscript and anticipate reviewer comments.
Sincerely,<br /> Monika Scholz and Andrew Leifer
REFERENCES
Kato, S., Kaplan, H.S., Schrödel, T., Skora, S., Lindsay, T.H., Yemini, E., Lockery, S., and Zimmer, M. (2015). Global brain dynamics embed the motor command sequence of Caenorhabditis elegans. Cell 163, 656–669.
Pillow, J.W., Shlens, J., Paninski, L., Sher, A., Litke, A.M., Chichilnisky, E.J., and Simoncelli, E.P. (2008). Spatio-temporal correlations and visual signalling in a complete neuronal population. Nature 454, 995–999.
Roberts, D.R., Bahn, V., Ciuti, S., Boyce, M.S., Elith, J., Guillera-Arroita, G., Hauenstein, S., Lahoz-Monfort, J.J., Schröder, B., Thuiller, W., et al. (2017). Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure. Ecography 40, 913–929.
Scholz, M., Linder, A.N., Randi, F., Sharma, A.K., Yu, X., Shaevitz, J.W., and Leifer, A. (2018). Predicting natural behavior from whole-brain neural dynamics. BioRxiv 445643.
Tankus, A., Fried, I., and Shoham, S. (2012). Sparse decoding of multiple spike trains for brain–machine interfaces. J. Neural Eng. 9, 054001.
Wu, Y., Boos, D.D., and Stefanski, L.A. (2007). Controlling Variable Selection by the Addition of Pseudovariables. Journal of the American Statistical Association 102, 235–243.
On 2024-03-27 20:38:47, user Chris Buck wrote:
I've posted some further thoughts about this manuscript on my Substack page:<br /> https://open.substack.com/p...
On 2021-09-14 14:35:27, user Donald R. Forsdyke wrote:
The final peer-reviewed version of this paper may be found in Computational Biology and Chemistry 94:107570
On 2014-05-15 07:55:24, user Daniel Klevebring wrote:
Very nice work.
For some reason, I can't see the figures in the PDF of the main paper. The suppl figs show nicely, but only white boxes in the main PDF. Any idea why that is?
thanks
On 2017-04-20 18:45:48, user Loren Hauser wrote:
Interesting, but unfortunately this doesn't tell me if the variation is planned or just due to mis<br /> takes by the splicing system in cells. You really need to combine this with proteomic measurements that tell which splice variants are actually translated and inserted into the membrane.
On 2019-05-01 16:07:38, user Bert wrote:
Interesting, but there is no evidence yet that there is actually a TonB-dependent (outer membrane) transporter that imports these compounds.
On 2023-07-06 12:26:49, user Felix Hernandez wrote:
This MS has been published in International Journal of Molecular Sciences - MDPI
On 2018-05-09 17:37:56, user Uri David Akavia wrote:
Congratulations Dylan!<br /> Is the source code going to be available at some point?
On 2021-10-12 13:47:56, user Luisa Hugerth wrote:
The github link in the abstract leads to a 404 Page Not Found error. Maybe the repository is mistakenly marked as private?
On 2022-06-24 01:15:41, user Jichen Bao wrote:
The manuscript has been accepted by ACS synthetic biology. https://pubs.acs.org/doi/10...
On 2022-11-22 11:33:47, user Deepali Pal wrote:
Part of this study has been pre-print as below:<br /> https://www.cell.com/cell-r...
On 2020-07-22 19:33:46, user Richard Sanchez wrote:
interesting work from Ferdosi et al. It beautifully illustrates PTEN as a<br /> novel marker for neddylation inhibition as well as further exemplifying<br /> the integration of multi-omic data.
On 2018-07-31 11:29:06, user H. Etchevers wrote:
Accepted in a special issue of the journal "genesis" (Wiley) for the 150th anniversary of the neural crest, to appear at http://dx.doi.org/10.1002/d...
On 2018-10-02 19:55:41, user BU_Fall_BI598_G3 wrote:
Reinhard et al. performed transsynaptic tracing of retinal ganglion cells (RGCs) from targets in the superior colliculus (SC) projecting to either the lateral pulvinar (LP) or the parabigeminal nucleus (PBg), in order to determine how visual features are routed to the two areas. Analysis of dendritic morphology showed that retinal inputs to the parabigeminal and pulvinar circuits differ in size and stratification, and labelled RGCs were also characterized by their molecular identity. Morphological and molecular data was then used to identify twelve clusters of RGCs; each cluster contained cells of similar dendritic profile and molecular makeup. The LP and PBg receive input from distinct clusters, and the authors argue that each cluster contains a single cell type, which may encode a unique feature of the visual environment. They show that the SC demonstrates a relatively high degree of regularity in its guidance of inputs to various targets.
Overall, this study offers an in-depth look at the pulvinar and parabigeminal pathways. Reinhard et al. were successful in identifying which RGCs provide input to the LP and/or PBg, and the identifications made in this paper are helpful in indicating what type of input each nuclei is receiving. Another key strength of these experiments is the ability to express GCaMP in specific RGCs based on their downstream projection pathways. Such a technique is very useful for future experiments to precisely characterize ganglion cell response to a visual stimulus. Finally, Table 1 offers a nicely detailed summary of the structural and functional information on the pulvinar and parabigeminal circuits gathered in this study as well as in other studies. This summary allows clear understanding of the pathways and hints at possible future studies.
While we appreciate this manuscript’s strengths, we identified a few areas of major critiques, addressed below.<br /> Some important controls to verify the validity of the experimental setup are missing. Although using Cre-inducible viral cargo in injections to the lateral pulvinar is a good experimental design, it is not proven that the floxed TVA-G-mCherry is expressed in pulvinar-projecting neurons only. To verify this specific expression pattern, in a NSTR1-GN209-Cre brain injected with HSV-flox-TVA-G-mCherry and stained for pulvinar-projecting neurons, all fluorescent cells should be double-labelled. Additionally, does the experimental technique label cells in the superior colliculus that project to both the lateral pulvinar and parabigeminal nucleus? Such cells would take away from the claim that visual information is, for the most part, routed separately to these two areas. To test this possibility, we suggest injecting HSV-GFP into the pulvinar and HSV-mCherry into the parageminal nucleus and subsequently studying the superior colliculus for any double-labelled yellow cells. Also, rabies virus is known to destroy cells about ten days after infection. Therefore, significant variations in cell morphology created by the rabies virus could be present in the later stages of infection . We would like the authors to show that at the time of retinal extraction, the RGCs are still healthy and not yet negatively impacted by the rabies virus. Finally, though it is important to be able to extract out individual dendritic trees for analysis, does choosing cells only with non-overlapped dendrites create bias in the morphological or molecular identities of the chosen cells?<br /> Throughout the paper, techniques and ideas could be explained more clearly. Confusing wording and sentence structure make the paper difficult to follow at times (for example, the sentences starting on lines 112, 115, and 205). Additionally, the authors’ experimental setup is not entirely clear from the first few paragraphs of the paper. More detail about the viruses used, their cargo, and the expected expression patterns would be helpful for the reader to understand the overview of the experiment. A cartoon or an improved diagram of injection locations and expected labelling of the cells within the circuits could help with clarity. In other areas of the paper, the authors’ rationale for experiments is ambiguous. For example, with the description currently given in the text (lines 145-158), Figure 5 is very confusing. A more complete explanation of the rationale behind clustering the cells and brief descriptions of the three validation indices would make Figure 5 significantly more clear and highlight its importance for the rest of the paper. Another area that could benefit from further explanation is the comparison of 7 clusters to groups of PV cells in Figure 7. I.e., why were only 7 clusters shown and why were PV cells used for the comparison?<br /> The content and style of various figures made assessing the experimental design and results challenging. Small image/diagram size and low contrast was a recurring problem in several figures. For example, confirming injection specificity in Figures 1B and 1F is difficult simply due to image size. However, specific verification that fluorescent cells shown are truly in either the parabigeminal (1B) or pulvinar nucleus (1F) should be included also. Additional images verifying the placement of the EnvA-RV-GCamp6 superior colliculus injection would also be beneficial for readers. Image size and contrast was also an issue in Figures 1C,1G, 4A, and 4D. Low image magnification in figures 3C, 3G, and 4B made it difficult to see the colocalization of fluorescent labels. Similarly, to identify the lack of colocalization, Figures 4E and 4F should have been merged. Figure 2B should show percentages of cells rather than number, to provide more context for the data. Figure 1A and 1E depicting injection sites were confusing due to the color scheme chosen to represent HSV-TVA-G-mCherry and GCamp6. While we appreciated that two different colors were used to represent HSV-TVA-G-mCherry for the pulvinar injection (green) versus the floxed version for the parabigeminal injection (orange), this became confusing because the word GCamp is highlighted in green in the figure. Additionally, Figure 4 could be further improved if a quantification of CART+ cells was included, similar to what was done in Figure 3 for SMI32+ cells.
Additionally, there were a few minor weaknesses that, if addressed, could improve the quality of the paper. <br /> 1. The RGC clusters and their projections to the pulvinar and/or parabigeminal circuits identified in these experiments are useful in identifying the clustering of cells and subsequent circuitry implicated in routing visual inputs through the superior colliculus. However, an important distinction should be made between the identification of clusters versus cell types, as it cannot be necessarily concluded that cluster is equivalent to cell type. The 12 clusters identified in these experiments are referred to as putative cell types (line 161), and we do not believe the data presented in this paper is enough to make such a claim. Clusters may very well contain different types of cells that had similar morphology resulting in them being grouped into the same cluster, and the molecular data is not robust enough to validate these clusters as cell types. Moreover, the 3 clusters that were found to project input into both the pulvinar and parabigeminal circuits were referred to as ‘3 visual features’ (line 243). While separate clusters may represent distinct features of the visual space, this conclusion cannot be inferred from the data in this paper. <br /> 2. For both Figures 5 and 6, adding a color legend that identifies each cluster by the descriptive name given to it in the text (and Table 1) would help clarify the figures for readers. <br /> 3. In Figure 5, a 3D plot of the clusters could be added in the supplement for better visualization of the clusters along all three axes. <br /> 4. Use of PV-Cre mice as wild type mice is not discussed or validated.
On 2015-12-04 13:27:31, user Hiren Ghosh wrote:
Have any one tested this algorith with his/her own dataset ???
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 2016-05-13 00:51:27, user Donald R. Forsdyke wrote:
Please note that the case has been made that "BDM" should be "DM". See Heredity (2011) 106, 202 (http://post.queensu.ca/~for... ).
On 2025-07-15 22:07:24, user Åshild Telle wrote:
Links to movies (Supplementary Material):
https://www.dropbox.com/scl/fi/pa00vq7h29jlptegmkmp3/Patient1.m4v?rlkey=8xj6oula1ys68vdpzprhmlu61&dl=0 <br /> https://www.dropbox.com/scl/fi/7daf5rwkbh907lhxq03yk/Patient2.m4v?rlkey=8td1vwxhl4f70w2151owcmful&dl=0 <br /> https://www.dropbox.com/scl/fi/m0rd44brd5mhxllcm9cra/Patient3.m4v?rlkey=6t6er8qicg1t5pfgpuhnk05es&dl=0
On 2019-03-27 19:49:57, user Andrew Johnson wrote:
Nice work. A few minor comments: <br /> 1) the 1st report of IQGAP2 rare variant association with MPV was Ref. 49 - comment is that this was an Exome chip study rather than GWAS<br /> 2) rare variants in KALRN first reached genome-wide significance for MPV in Gieger et al., 2011 PMID 22139419 (not currently cited here), subsequently replicated in Eicher et al. Ref 49 and then Astle et al. Ref 29
On 2020-05-27 15:45:40, user OxImmuno Literature Initiative wrote:
On 2023-03-29 14:06:54, user Sarah Chellappa wrote:
Thanks for sharing these important insights. It is unfortunately not surprising that race/ethnicity and socioeconomic status are not frequently reported. Most sleep and circadian studies come from study samples comprised of individuals who are white, middle-class and from high-income countries. Hence, there is a historic imbalance that perpetuates until today. One of the perks of addressing this issue is that it will help foster more research into the socioeconomic determinants of health.
On 2021-01-05 00:58:07, user Charles Warden wrote:
Hi,
Thank you for posting this pre-print.
I see that this pre-print has both supplementary material and a link to code:
https://www.biorxiv.org/con...
https://github.com/OSU-BMBL...
However, the section for "Supplementary Materials" says "Supplementary Data are available at Science Advance online".
Is this intentional (perhaps part of an automatic submission from a journal?), or should the section say something else?
Best Wishes,<br /> Charles
On 2015-09-03 07:53:42, user Pierre Boursot wrote:
Spectacular finding. However I am surprised that the interspecific introgession hypothesis is not evoked. There are numerous examples of massive interspecific mitochondrial introgression not accompanied by any detectable nuclear introgression (and here with three nuclear fragments, the power to detect it is very limited anyway). This would in my opinion be a much more plausible explanation than long term high effective population size, which should anyway leave even more marked traces in the nuclear genome. There would remain to find the donor species, but they may be extinct. An analysis of the coalescent in each of the divergent mitochondrial lineages segregating in a given species might give hints about which lineage is likeky to be introgressed, since the most likely source of massive introgression is during range expansion into the territory of another species, and this is expected to leave a signature of expansion of the lineage that introgressed from the species whose territory has been invaded.<br /> Sorry for the self-citation, but you could read this paper and several references therein:<br /> Melo-Ferreira, J., L. Farelo, H. Freitas, F. Suchentrunk, P. Boursot, and P. C. Alves. 2014. Home-loving boreal hare mitochondria survived several invasions in Iberia: the relative roles of recurrent hybridisation and allele surfing. Heredity 112:265-273.
On 2018-04-11 02:12:06, user Emily Stephen wrote:
Great paper, thanks for sharing! I think you have a mistake in the methods: "Each 200 ms epoch was multiplied with a Hann taper, zero padded to 1 s, and Fourier transformed, resulting in an FFT spectrum with a frequency resolution of 1 Hz." -- actually, the frequency resolution should be more like 10 Hz. With 200 ms windows and one taper, the half-bandwidth will be 1/0.2 = 5Hz. Zero-padding doesn't affect the frequency resolution, just the interpolation of the frequency axis.
On 2018-07-06 11:00:09, user kamounlab wrote:
In response to some comments we received, here is a comparison of the sequence chromatograms corresponding to Fig. 3. They show that MoT3 reverse primer sequences were recovered in amplicons from M. oryzae isolates including Bangladeshi wheat and rice blast fungi.
We conclude that:
The reverse primer showed mis-priming despite the single mis-match at the 3' extension end (based on FR13 sequence), allowing WB12-like sequences to be amplified.
There could also be degradation of primer at the 3' extension end that enabled amplification in rare cases: there is only one putative evidence (the double peak in chromatogram from RB-11).
The wheat blast isolate BR32 lacks the WB12 region.
Joe Win and Sophien Kamoun
On 2019-04-29 17:55:26, user Charles Warden wrote:
I hope we can find a way to get more comments on bioXriv, so that they could be discussed prior to submission to a peer reviewed-journal:
https://www.nature.com/arti...
I would much rather have a revised pre-print than a correction / retraction in a peer-reviewed paper.
You can see any comments for an individual from their Disqus account, but I think this worked well in terms of keeping a friendly tone and asking questions that I think could improve reader understanding: https://www.biorxiv.org/con...
On 2022-02-21 19:22:36, user MA wrote:
The supplements tables are NOT included?!
On 2025-01-14 04:06:08, user Yi-Cheng Yang wrote:
This study contributes to the establishment of personalized treatment, such as considering the use of EZH2 inhibitors combined with PSMA-targeted therapy for NE type II patients, providing new treatment options for cancer patients.
On 2018-11-14 09:36:55, user Carlos Santiago wrote:
Quite interesting paper. Do you plan to extend it to evoked-potentials <br /> as well? There's another pipeline that deals with evoked and <br /> resting-data that has been validated in both healthy and schizophrenia <br /> patients that you guys could discuss: "An automatic pre-processing <br /> pipeline for EEG analysis (APP) based on robust statistics".
Further, there are the new guidelines for EEG pre-processing on https://osf.io/a8dhx that you could discuss. Nice work!
On 2019-05-07 01:17:57, user Keith Robison wrote:
I've written a pair of analyses on this -- some key criticisms are the lack of technical detail and that the analysis of errors is much less detailed than desired. There's also the lack of specificity in the text as to which data was deposited publicly -- only the E.coli is available. Also, the phred quality scores are overestimated at the high end by perhaps as many as 5 points.<br /> Poking at Genapsys Preprint<br /> Genapsys' Base Caller: Mysterious, But Not Ideal?
On 2023-01-11 04:42:12, user Leslie Vosshall wrote:
Konopka et al. produce a valuable reagent for the Anopheles mosquito community – a QF2 knock-in into the bruchpilot (brp) locus. They use this strain to quantify the number of neurons in the major sensory appendages using an existing QUAS-CD8:GFP reporter strain. The images in the paper are beautiful and the cell counts are a valuable resource for the field. No one has attempted to do cell counts like this before, and this reagent made this possible.<br /> A few suggestions to improve the paper:<br /> 1) The authors have not formally shown that the brp-QF2w line is in fact pan neuronal. It would require significant additional work to prove this, e.g. double label antibody staining with RNA in situ hybridization against a pan-neuronal gene to show a 1:1 correspondence. I do not suggest that they do this, but it’s important to note the caveat early in the paper that the genetic reagent is assumed to be pan neuronal because the targeted locus is a neuronal gene.<br /> 2) Speculation on phenotypic/behavioral variability caused by variation in cell number (Line 283-299) seems premature to me. It could be variation in the expression of the driver or reporter, rather than underlying variation in the number of chemosensory neurons. More experiments would need to be done to confirm that what is seen with the genetic reagents reflects actual biological variability. I might suggest that this paragraph be toned down or removed.<br /> 3) Figure 2E-F, Figure 3E: could the authors clarify how the data are plotted and what the sample sizes are. I have the impression that the small dots are individual experiments, but it is not clear. For transparency, it might be best to not use bar plots and instead use dot plots where all of the data points are clearly visible.<br /> Review prepared by Leslie Vosshall
On 2015-04-04 02:08:22, user Rat_Fink_Forever wrote:
What percentage is showing up? I have 4.4% Denisovan according to the Human Genome Project and my near term history is from the Baltics, with 50% northern European and a hefty percentage of SW Asian.
On 2023-08-16 13:06:21, user Pierre-Luc Germain wrote:
Very interesting contribution, I'd just like to make two comments.
First, it's wrong to write that scDblFinder is "formerly known as doubletCells". They're two methods developed independently, and it's simply that doubletCells was moved to the same package, but still as an independent method.
Second, your results are in contrast with other benchmarks, which you explain by more "realistic scRNA-seq datasets". I'm obviously not entirely disinterested here, but I think this is very misleading: you don't show any evidence that the traditional benchmark datasets do have unrealistic patient or batch effects, and omit to mention the critical fact that, as far as I know, the fatemap samples are homogeneous cell lines, which is far from being more realistic (people do scRNAseq on complex tissues much more often than on cell lines). I think a fairer description would be to abandon the "realistic/unrealistic" labels, describe your data as it is, and hence that your observations are basically about homotypic doublets, which the tested methods are very bad at detecting (but also don't claim to do). The lack of real difference between adjacent/distant seems to indicate pretty clearly that you're essentially dealing with homotypic doublets.
On 2020-02-01 15:40:37, user Levi Yant wrote:
Magdalena Bohutinska and Mark Alston contributed equally.
On 2015-07-27 15:52:01, user Maria Zavodszky wrote:
Hi, Thanks for sharing this manuscript. I have found that the manuscript mentions supplementary tables that I could not find in the word document posted here under Data Supplements. I would be interested in seeing them. Is is possible?<br /> Thanks,<br /> Maria Z.
On 2018-04-12 18:47:01, user H. Etchevers wrote:
Because this paper is behind a paywall, I will try to upload a revised version that includes the five references that had been missed in the first authors' version.
On 2019-11-25 09:30:11, user Daniel Žucha wrote:
Dear readers, <br /> I would like you to be notified that this preprint has been already published in the Clinical Chemistry journal with minor changes. It is accessible under DOI: 10.1373/clinchem.2019.307835. The direct link to this site is forthcoming shortly.
On the behalf of authors,<br /> Daniel Zucha
On 2020-07-07 11:41:45, user Rhyothemis wrote:
"SARS-CoV is known to repress ACE2 expression [4]. ACE2 regulation involves chromatin remodeling and structural chromatin changes [19]."
Could this type of change persist post-infection?
On 2021-02-18 06:24:22, user Ruoying Zheng wrote:
I totally enjoyed reading this paper. The experiment designs are great. It would be better if some parts of the article can be improved. In Fig 5, the biological model should be always mice instead of using human erythrocytes in 5A and using infected mice in 5C. Having a consistent biological model can rule out unnecessary variables. If both mice and human biological models are used here, then a vitro experiment about mice erythrocytes malarial infection and related treatments should be added here. In 5C, infected erythrocytes should have a higher FITC-A fluorescent binding properties than uninfected erythrocytes. More detailed explanation should be added in 5C to clarify the difference between each group. In the linear graphs of Fig 6A and 6C, different groups should have different colors, which would make it easier to read. As for the cell pictures in Fig 6A and 6B, the color scheme is not unified, the colors of the parasites should be the same in each picture so it would be less confusing. Besides, more background information about DHA(active metabolite of all artemisinin compounds) and why the author set up experiments to test efficacy of DHA+Alisporia should be explained.
On 2018-11-28 10:17:24, user Tanai Cardona Londoño wrote:
Hi, interesting proposal. I like it. A couple of comments.
The fossil heterocystous cyanobacteria reported by Pang et al., (2018) are not just akinetes. They are entire filaments with cells that do resemble heterocysts. I spent all of my PhD studying heterocystous cyanoabcteria, purifying them, extracting their thylakoids membrane, staining them, seeing them in a variety of microscopes... I have to say that those filaments are excellently preserved and are virtually indistinguishable from extant heterocystous cyanobacteria. I would dare to say that the fossils presented by Pang et al., (2018) are unequivocal fossils of heterocystous cyanobacteria. But I'm not a paleontologist.
You cited the review by Butterfield (2015) to validate your statement that the best fossil heterocystous cyanobacteria are from the Devonian, but in that paper that is Butterfield's own assessment, which predated Pang et al.'s paper. The Devonian fossil's cited by Butterfield are reported in a 1959 paper that I was not able to access. Were you able? Are those truly better preserved that Pang et al.'s? Butterfield does not show the Devonian fossils in his review... So, your argumentation there can be strengthened. Don't be so quick to dismiss Pang et al.'s fossils!
Your proposal also makes me wonder about the light-independent protochlorophyllide reductase. It is a nitrogenase-like enzyme and it is also oxygen sensitive (http://www.plantphysiol.org... "http://www.plantphysiol.org/content/142/3/911.short)").
Could it be that the oxygen sensitivity of protochlorophyllide-reductase limited the rise of oxygen prior to the Great Oxidation Event, before the diversification and expansion of today's taxa of cyanobacteria, and before the origin of the light-dependent protochlorophyllide reductase?
Thanks.
All the best,<br /> Tanai
On 2023-12-15 19:31:50, user Jordan C Rozum wrote:
This preprint has been accepted in PRX Life under a new title "Models of cell processes are far from the edge of chaos" and is openly accessible here: https://doi.org/10.1103/PRX...
On 2020-02-08 07:31:36, user jean-claude perez wrote:
Please read https://www.preprints.org/m...
On 2020-02-01 15:50:25, user ??? wrote:
.. Is this suggesting that the primary Coronavirus infects to HIV patients in Wuhan and mutated with transferring from hiv gene ?? Very aweful scenarios but possible.
On 2020-04-13 08:00:30, user Tartaglia Lab wrote:
This work is interesting and we find quite useful that the authors shared it. Thanks!
In our work (https://www.biorxiv.org/con... "https://www.biorxiv.org/content/10.1101/2020.03.28.013789v3)") we studied protein interactions with SARS-CoV-2 RNA using advanced computational approaches.
Just focusing on the RNA binding proteins present in the two studies, we found a significant overlap of genes such as Janus kinase and microtubule-interacting protein 1 JAKMIP1 (Q96N16), A-kinase anchor protein 8 AKAP8 (O43823) and A-kinase anchor protein 8-like AKAP8L (Q9ULX6), which in case of HIV- 1 infection is involved as a DEAD/H-box RNA helicase binding protein (among others).
It is very curious that our list of protein- RNA binding partners contains elements identified also in this protein-protein network analysis. Yet, it must be mentioned that ribonucleoprotein complexes evolve together and their components sustain each other through different types of interactions.
On 2025-04-04 09:28:15, user Volkmar Lessmann wrote:
Great work by the authors!! High profile research disentangling the role of phospholipid metabolism in rescuing AD phenotype with FTY720. Congratulations!
On 2021-02-18 21:31:48, user Dr. Tyeen C Taylor wrote:
This article is in review at Frontiers in Forests and Global Change
On 2015-06-03 14:54:48, user Kasper Hansen wrote:
This preprint was revised June 3rd, 2015. The major change in the revision is analysis of single cell epigenetic data (both ATAC-seq and WGBS). This has led to a change of abstract, introduction, a new results section and discussion; these changes simply reflect the additional data types. We have also included a very short analysis of the overlap of A/B compartments with various types of methylation domains (as has been previously done in for example Berman et al (2012) Nature Genetics. Typos etc. have been corrected and three new figures have been added.
On 2019-03-11 10:00:25, user Justin Andrushko wrote:
Fantastic paper investigating the cortical underpinnings of fatigability.The things I really like about this paper - no salami slicing! The authors conducted multiple experiments to investigate the observed mechanisms. The authors also included effect sizes on all statistical measures, something more people should consider to include. Effect sizes really help with data interpretation and meaningfulness of results. Great paper!
On 2020-03-06 22:44:50, user Luis Yáñez wrote:
this is a veyr useful paper, hopefuly will be published soon
On 2022-06-30 16:45:44, user Tatiana Mikhailova wrote:
Is raw .idat data from this study available for download?
On 2014-08-03 13:59:42, user Richard Rocca wrote:
Can you please post what where European samples ETR2, ETR5 and ETR9 are from and their archaeological context?
On 2018-03-30 13:00:18, user Markku Varjosalo wrote:
This preprint was published in Nature Communications on 22th of March titled as “An AP-MS- and BioID-compatible MAC-tag enables comprehensive mapping of protein interactions and subcellular localizations”
On 2016-11-22 07:13:13, user ???? wrote:
Thank you for your work,here I had some questions when I installed the package.When I install the package by :<br /> source("https://bioconductor.org/bi...") <br /> biocLite("fgsea")<br /> I got some errors like :<br /> fastGSEA.cpp: In function ‘Rcpp::IntegerVector combination(const int&, const int&, std::mt19937&)’:<br /> fastGSEA.cpp:318: error: ‘uniform_int_distribution’ is not a member of ‘std’<br /> fastGSEA.cpp:318: error: expected primary-expression before ‘int’<br /> fastGSEA.cpp:318: error: expected ‘;’ before ‘int’<br /> fastGSEA.cpp:324: error: ‘uni’ was not declared in this scope<br /> make: *** [fastGSEA.o] Error 1
Could you help me ? Thanks.
My R version is 3.3.1,and bioconductor is 3.4
On 2017-11-22 10:54:04, user Tristan Croll wrote:
Nice work! It will be very interesting to see how the results compare with our modelling (DOI 10.1080/07391102.2016.1165631).
On 2025-06-17 16:33:06, user Olivia Fromigue wrote:
Hello,<br /> This manuscript is now published:<br /> C-terminal binding protein-2 triggers CYR61-induced metastatic dissemination of osteosarcoma in a non-hypoxic microenvironment.<br /> Di Patria L, Habel N, Olaso R, Fernandes R, Brenner C, Stefanovska B, Fromigue O.<br /> J Exp Clin Cancer Res. 2025 Mar 5;44(1):83. doi: 10.1186/s13046-025-03350-6.<br /> PMID: 40038783
On 2020-08-26 13:28:16, user Qingtian Guan wrote:
This manuscript is now accepted in the International Journal of Infectious Diseases. We have updated the manuscript in pre-proof format, please refer to the following link for citation for the details: https://www.sciencedirect.c...
On 2016-08-23 15:43:05, user Danny wrote:
Raw data now available at http://www.ebi.ac.uk/pride/...
On 2017-08-25 16:20:58, user kamounlab wrote:
"...a signature of positive selection in candidate effector genes in S. reilianum is a poor indicator for the identification of genes with virulence functions"
Please see https://twitter.com/KamounL...
On 2025-05-17 06:44:22, user 11 wrote:
Summary<br /> The manuscript “An online GPCR drug discovery resource” describes an online resource of GPCR drugs, clinical trial agents, targets and disease indications. This resource offers unique reference data, analysis and visualization, and is availed as a new section, ‘Drugs and Agents in trial’ integrated in the GPCR database, GPCRdb. Furthermore, it includes a target selection tool for prioritization of receptors for future drug discovery.<br /> The major weakness of the paper is that the paper fails to include olfactory receptors in the database, the reliance on Open Targets disease association scores might miss novel hypotheses with weaker genetic backing. The validation of the newly introduced selection tools and visualizations, how the prioritization recommendations compare to existing approaches or human expert selection are unclear.<br /> This paper helps identify strategies and trends in current GPCR drug discovery and give insights into which already drugged, or yet untapped targets have the largest potential in specific diseases.<br /> Major Points<br /> 1. Validation of the Target Selection Tool<br /> The target selection tool offers a powerful yet swift means to prioritize GPCRs for future drug discovery based on disease indications, characterization, tissue expression and more. While there is no validation to show the tool’s performance. How does the tool perform when testing a now-successful GPCR target before it was clinically pursued? Consider revising by running the tool on historical data and show how it would have prioritized GIPR before the approval of tirzepatide.<br /> 2. Missing Method Comparison<br /> You described several new visualization methods (e.g., GPCRome wheel, intersecting Venns, Sankey plots) but how these tools are different from existing tools are not explained. Why are these tools better than existing tools such as those in Open Targets, ChEMBL, or DrugBank? consider adding a contextual comparison section to clarify the advantages and disadvantages of those tools, explain the innovations of the new visualization methods.<br /> 3. Disease Classification and Filtering<br /> The disease annotation relies on ICD-11 mapping from Open Targets (using EFO/HPO/MONDO). While manual curation of disease terms is not clearly defined. Adding a supplemental table listing these modifications would be great.<br /> 4.Exclusion of Olfactory Receptors Have Limitations<br /> Your research excludes olfactory receptors--a large family of GPCRs which play essential roles in cancer, reproduction, and metabolic regulation. Databas such as CORD (Comprehensive Olfactory Receptor Database) provides better annotations of olfactory receptors than those in GPCRdb, ligand binding and diseases linkage are also included. Including olfactory receptors in your research may reveal potential drug discovery opportunities.
Minor Points<br /> Technical Questions<br /> 1.Definition of "Agent" vs. "Drug" should be explained in the Abstract or Introduction section.<br /> 2.Explain why do you choose 0.5 as the association score cutoff from Open Targets.<br /> 3. The classification rules of “pharmacological modality” of agonist, antagonist and allosteric modulatorare not clear.<br /> 4. Non-human GPCR targets (e.g., mouse models) are not included.<br /> Stylistic Issues<br /> 1."the platform contains 516 drugs, 337 agents…" is later repeated again in Figure 1 text.<br /> 2. “bioactivity data” and “bio-activity” are used inconsistently.<br /> 3. PDSP Ki database is used without prior expansion.<br /> Unable to Assess:<br /> Data integration are made from tools like Pharos, GTEx, and the Human Protein Atlas, especially for expression-based filtering. I cannot offer expert feedback on the transcriptomic expression atlas validation and the correctness of the tissue expression normalization procedures.<br /> Final Reflection<br /> This paper made great contributions to the GPCRs field by offering an online resource of GPCR drugs, clinical trial agents, targets and disease indications. The platform will help streamline drug development pipelines, helps identify strategies and trends in current GPCR drug discovery and give insights into which already drugged, or yet untapped targets have the largest potential in specific diseases.
On 2018-08-22 14:18:07, user Skeptical Scientist wrote:
Interesting application of machine learning!
On 2023-04-17 07:08:00, user Lance wrote:
Hello,
I have looked through all of your dataset files, and did not see the cell type and subtype annotations (i.e., cluster labels) for the cells anywhere, including in the adata.obs field. Was there a mistake in uploading the data?
Also, I noticed that in Supp Table 2, all the entries are 0. Was this also a mistake?
Thank you! Looking forward to being able to access the actual cell type labels!
Best, <br /> Lance
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 2019-08-16 08:02:18, user WJR wrote:
Bug Report:
This (preprint) paper by Hickey and Golding relies on a software simulation. (Available at: https://github.com/gbgolding/evolutionSex)
There is a bug in the currently posted version of that software. It occurs in the file "sexual7.c" (seen August 16, 2019). The bug strongly decreases the computational efficiency of the simulation, and slightly affects the results.
In that file, there is a structure integer variable named '.sex', which is initialized to zero, and tested several times by IF-statements, but it is never changed. In other words, it does nothing. That is the first clue something is awry.
(Note: For some reason, when I try to post the offending code here, it gets displayed as an unreadable mess. I don't know why. I tried placing the appropriate code formatting brackets around it. So, instead I must here describe where the bug occurs.)
Lines 233 through 252 comprise a While-loop, which creates each progeny by a mating of some parent_i with some other parent. However, the loop then proceeds to OVERWRITE that progeny's allele data by a mating with EVERY OTHER parent. The original parent_i's allele data gets obliterated within that progeny, because it gets over-written many times. Each and every progeny is computed by mating each parent with EVERY OTHER parent. But only the last matings count, as the previous matings get over-written. This is exceedingly inefficient computationally. If the population size is n, then the computer time increases with n-squared, rather than n.
The end result is that each progeny is indeed the result of a random pairing of parents, but not the ones the simulation-writers intended. Moreover, the simulation aims that each parent be involved in AT LEAST a minimum number of reproductive events (given in the code by "FECUNDITY"), but that goal is not achieved. Due to randomness, some parents can mate numerous times, while others don't mate at all -- contrary to the stated design of the simulation. There is a disparity between what the code intends to do, and what it actually does, and this can affect the results.
I'm guessing the variable .sex is a vestigial remnant of code (now largely absent) that had originally matched each parent with exactly one other parent (i.e., for an obligate monogamy model). I imagine .sex was originally a flag, used to indicate that a given individual has (or has not) been used yet as a parent. This approach would be one of the simplest ways of guaranteeing that each parent has the fecundity claimed in the paper.
On 2022-03-08 02:07:09, user Jimin wrote:
Live demo of chromatin architecture prediction:<br /> https://nyu.c-origami-previ...
On 2022-03-08 02:03:59, user Jimin wrote:
Link to the project website:<br /> https://tanjimin.github.io/...
On 2020-06-13 20:44:48, user Henrique dos Santos Pereira wrote:
Would anyone answer my question, please? Would the variation in frequency of these more virulent variant of the virus correspond to a trade-off virulence x transmission over time and thus also explains why velocity of death declines (mortality) while morbidity keeps accelarating in the affected populations? henrique.pereira.ufam@gmail.com
On 2018-06-14 11:38:18, user GUILLAUME GAUTREAU wrote:
Little typo in algorithm 1, line 11: the indice and exponent ("y" at the top and "out" at the bottom) of delta are not coherent with the delta exponent and indice ("in" at the top and "i" at the bottom) in definition 1
On 2013-12-31 01:57:24, user Jacob G Scott wrote:
some discussion here: http://mathematicaloncology...
On 2019-12-29 08:15:34, user Levi Yant wrote:
Correspondence should go to James Higgins or Levi Yant.
On 2016-04-14 14:29:53, user lepisorus wrote:
if the authors can prove that the retrotransposon proliferation is one reason leading to their special reproduction way, that will be fantastic.
On 2017-01-20 19:39:06, user James Jun wrote:
Thanks for your interest all. The manuscript was uploaded in a hurry to meet the grant deadline and still there are some work left to do. JRCLUST will be professionally supported by Vidrio Technologies, creator of ScanImage, and will be maintained as free and open-source software based on Matlab.
On 2019-04-03 03:51:05, user Ann wrote:
The pitfalls are obvious, lacking in vivo validation and non-specific capturing.
On 2020-04-28 20:27:16, user Matthew Bogyo wrote:
This work is now published at Cell Reports Medicine : DOI:https://doi-org.stanford.id...
On 2021-05-05 11:07:02, user Milka Kostic, PhD wrote:
Dear authors, <br /> Thank you for sharing this interesting preprint with the community. Below are some more detailed comments that ay have others appreciate your work better. I congratulate you on an excellent piece of work. <br /> Kind regards, <br /> Milka
COMMENTS ON THE PREPRINT BY Dölle, Adhikari et al. <br /> Targeted protein degradation is a very active field at the moment. Many efforts in this area are focusing on transforming known ligands (binders, inhibitors) of proteins with a clear disease relevance into bifunctional (PROTAC-based) degrader molecules. Unlike the traditional antagonist/inhibitor based compounds in preclinical and clinical use that diminish (inhibit) activity of the target, these degrader molecules induce selective degradation of the target. Thus, they remove the target from the proteome. This type of pharmacological activity could be a real benefit when the target in question plays significant scaffolding roles, by engaging multiple binding partners using different regions and binding sites. In such a case, inhibiting individual protein-protein interactions would be highly impractical. However, if the target is degraded, all these PPIs would disappear together with the target!
Dölle, Adhikari et al. select one such target - WDR5, a protein that performs different scaffolding roles (i.e. binds different partners) in the context of epigenetic regulation. Because of this, WDR5 has been implicated as a target for drug development and couple of compounds that inhibit WDR5 mediated PPIs have been described. These compounds served here as a starting point for WDR5 selective degrader development. The authors used existing structures of WDR5 bound to the PPI inhibitors to identify surface exposed areas of the molecules that could be modified for degrader molecule development. In brief, each PROTAC (bifunctional degrader) includes a ligand for the target and a ligand that recruits an E3 ubiquitin ligase, connected via a linker. The linker is known to have an impact on the performance of PROTACs and the authors use three chemically distinct types of linkers (PEG based, aliphatic and aromatic). The nature of the E3 ligase is also a major factor that affects degraders' activity, and the authors start by incorporating ligands for cereblon (CRBN), VHL and MDM2. Altogether, they generate number of PROTAC-based degraders featuring different linkers and different ligases.
They describe detailed validation steps of their degraders which included: <br /> - Measuring in vitro (biochemical) affinity between WDR5 and degrader molecules using ITC, showing Kd values in low nM. They also tested binding via DSF and observed some differences between results from ITC and DSF, which they provide likely explanations for (I encourage you to read the preprint as the authors provide an important technical note).<br /> - The authors tested that degraders were cell permeable and that they engaged the target using BRET. This is an important step in validation as degrader molecules tend to be larger, leading to concerns that may have difficulty entering cells. <br /> - They provide evidence that their degraders induce target degradation in cells, including under endogenous conditions. Importantly, they show that negative control compounds (always critical to have on hand) show no activity, and that inhibiting proteasome rescues observed degradation. Additionally, they confirm that mRNA levels of WDR5 did not change, thus further validating that the reason for decrease in protein levels is due to degradation. (They also include additional pieces of evidence that effects on WDR5 protein levels are degradation dependent) <br /> - Also importantly, the authors show selectivity by quantitative proteomics and demonstrate that WDR5 is the only protein depleted out of more than 5800 identified after 9 hours of treatment (while treatment with individual ligands did not have this effect) - Lastly, they show anti-proliferative effects in MV4-11 cells of their best performing degraders (these compounds were VHL-based PROTACs). However, the concentration needed for cellular effects was high (10uM). The authors then showed that this is due to low levels of VHL present. When they overexpressed VHL, the growth inhibitory activity improved.
Overall, the work is of high quality and includes appropriate steps for degrader validation. This gives high confidence that WDR5 degraders described in this work are useful as probes for WDR5 biology. For example, what happens to histone methylation once WDR5 is removed? Does removal of WDR5 lead to destabilization (or stabilization) of some of its binding partners (proteomics results suggest that this may not be the case, but would be interesting to dig deeper into this question)? What happens to transcription? What effect does this have on MYC activity (MYC family is known to engage with WDR5)? I am sure the authors and the community have these and many other questions in mind, and I look forward to seeing what new biology they and others can discover with this new generation of tool compounds in hand.
On 2020-09-01 09:25:13, user Sean Munro wrote:
I not think that this paper reports an "interactome" - it reports a "proximityome". BioID will label many proteins that simpy happen to be in the same compartment as the protein tagged with the biotin ligase without neccessarily interacting with them. Thus a BioID version of a SARS-CoV-2 protein that is in the Golgi will biotinylate many Golgi residents even if it only interacts with a subset of with them.
On 2020-09-10 21:07:07, user F.Li wrote:
For cells mentioned in each figure, the figure legend provides hyperlinks to neuuPrint where more detailed information about these cells can be obtained. These hyperlinks work in the PDF version. So if you are interested, please check out the PDF version of this paper.
On 2024-01-03 20:27:28, user Peyton Lab wrote:
I think the links in the figure legends are broken - not working from the browser pdf.
On 2020-10-22 14:47:26, user Matthew Terry wrote:
Very interesting paper. Would it be possible to also take into account the cost of expressing the genome? I would imagine that this would also work in favour of gene transfer. For genes retained, is protein turnover as equally important as abundance in the energy equation?
On 2020-05-16 09:13:37, user standardito wrote:
A great paper about the spreading of a naturally selected variant of SARS-CoV-2.
On 2020-11-25 12:05:29, user Mark Hanson wrote:
The collaborative group of Huang et al. (in preparation) is now available online at the link below. This is a manuscript written by Jianqiong Huang and her supervisors and collaborators at the University of Strasbourg and Sino-French Hoffmann Institute, and also the Platform Biopark d'Archamps.
On 2018-10-20 04:51:55, user koooo wrote:
GWAS101: please share the list of GWAS hits in a table format.
On 2017-05-13 17:45:40, user Anthonie Muller wrote:
Outside mitochondria, but still in the cell, smaller temperature gradients of a few degrees have been detected, for instance:<br /> (1) Jui-Ming Y, Haw Y, Liwei L. Thermogenesis detection of single living cells via quantum dots. 2010:963-966.<br /> (2) Yang J, Yang H, Lin L. Quantum dot nano thermometers reveal heterogeneous local thermogenesis in living cells. ACS Nano 2011;5:5067-5071.<br /> (3) Okabe K, Inada N, Gota C, Harada Y, Funatsu T, Uchiyama S. Intracellular temperature mapping with a fluorescent polymeric thermometer and fluorescence lifetime imaging microscopy. Nature Communications 2012;3:705.<br /> (4) Kucsko G, Maurer P, Yao N, et al. Nanometre-scale thermometry in a living cell. Nature 2013;500:54-58.<br /> (5) Bai T, Gu N. Micro/nanoscale thermometry for cellular thermal sensing. Small 2016;12:4590-4610.<br /> (6) Qiao J, Mu X, Qi L. Construction of fluorescent polymeric nano-thermometers for intracellular temperature imaging: a review. Biosensors and Bioelectronics 2016;85:403-413.
Are these thermal gradients 'real'? Some argue that they cannot exist, given the known value of the thermal conductivity of water. So there is a controversy here:<br /> (1) Baffou G, Rigneault H, Marguet D, Jullien L. A critique of methods for temperature imaging in single cells. Nature Methods 2014;11:899-901.<br /> (2) Kiyonaka S, Sakaguchi R, Hamachi I, Morii T, Yoshizaki T, Mori Y. Validating subcellular thermal changes revealed by fluorescent thermosensors. Nature Methods 2015;12:801-802.<br /> (3) Suzuki M, Zeeb V, Arai S, Oyama K, Ishimata S. The 10^5 gap issue between calculation and measurement in single-cell thermometry. Nature Methods 2015;12:802-803.<br /> (4) Baffou G, Rigneault H, Marguet D, Jullien L. Reply to. Nature Methods 2015;12:803-803.<br /> The same arguments of Baffou et al may apply to this reported high temperature inside the mitochondrion.
On 2019-04-04 04:13:45, user Nimród Hunor Attila Árpád wrote:
Avars were the siberians, Hungarians were volga bolgars.
On 2023-01-17 21:10:46, user Gregory Way wrote:
Hi Daniel,
It was our pleasure to review. Thank you for posting!
Here are some comments regarding your very speedy reply:
Thanks again!<br /> Greg
On 2016-05-27 15:36:35, user Kevin Watson wrote:
6W/kg, 9 hours a day, for 2 years. With that level of exposure, I'm surprised their blood didn't boil first.
This relates to real-world circumstances how, exactly?
On 2018-12-05 09:19:23, user Julien Racle wrote:
Hi,<br /> great paper, a thorough and fair comparison of the deconvolution methods was indeed needed.
One important question that also arises in the field is to what extent the reference profiles that have been derived from tumor-infiltrating lymphocytes that infiltrate one tumor type (e.g. melanoma) can be used for samples from another tumor type (e.g. ovarian cancer), or even if reference profiles derived from blood are sufficient. In the paper from Schelker et al., Nat. Com., 2017, they argued that it was important to use reference profiles coming from the same tumor type than the bulk.<br /> But from your data it seems that it doesn't really matter so much (as most methods have reference profiles derived from blood or melanoma TILs and your analysis includes also ovarian tumors (in this optic, EPIC could additionally be tested with the blood-derived reference profiles)). To further verify this, it would therefore be interesting if you build some mixes where you take only the cells that originated from one of the tumor type at a time instead of mixing together the TILs from melanoma and ovarian.
Additionally, CAFs and endothelial cells have been less studied by the deconvolution methods, but due to their potential importance in cancer, it could be nice to include them also in your table 2, to help researchers interested by these cell types.
Best wishes,
Julien
On 2025-11-26 05:26:36, user Prof. T. K. Wood wrote:
Please cite the seminal paper that initiated research on the impact of bacteria-derived indole on human physiology: http://www.pnas.org/cgi/doi/10.1073/pnas.0906112107 <br /> This manuscript was the first to find indole tightens epithelial cell junctions.
On 2018-12-29 09:52:19, user Alexey Semyanov wrote:
Event based paradigm has been around for a while. In 2013 we have already discussed the weaknesses of ROI based imaging versus event based imaging https://www.ncbi.nlm.nih.go...
More recently I have reviewed the works in the filed https://www.ncbi.nlm.nih.go...
On 2023-05-16 09:25:26, user pierre wrote:
As the MRNA cannot enter the nucleus, there is absolutely no chance than it can interact with the DNA. Furthermore, even if MRNA was by some miracle be in presence of DNA, interaction would require a specific reverse transcriptase, as you know that there is not one RT, but one for any MRN, which come associated with the RNA of the retroviruses.
On 2020-12-13 23:25:13, user Alex Crits-Christoph wrote:
Could the authors release a BAM or list of mapped reads (including read quality information and mapping information) of the chimeric sequences they show? Ideally a BAM filtered to just chimeric sequences would be good. This would help evaluate whether these sequencing reads represent real biological events.
On 2020-04-05 16:08:32, user Ivan Kosik wrote:
Exciting paper, when do you guys plan to publish/upload sequences for Abs in genebank?
On 2021-08-24 19:42:40, user Julie Hanson Ostrander wrote:
Use this link to access a view-only version of the article for free and use Enhanced PDF features such as annotation tools, one-click supplements, citation file exports and article metrics. https://rdcu.be/cv1jC
On 2025-10-18 20:59:56, user CDSL JHSPH wrote:
This paper provides a clear and quantitative analysis of plasmid copy number (PCN) across thousands of bacterial genomes. The authors confirm a universal scaling law between plasmid size and PCN, showing that plasmid DNA load is conserved relative to chromosome size. I found the idea of “replicon dominance” in multi-replicon plasmids particularly interesting, as it explains how merged plasmids resolve replication conflicts. The study is significant because it formalizes long-standing assumptions about plasmid replication using a large comparative dataset. I wonder if the authors have performed any wet-lab experiments to test the “replicon dominance” rule in controlled conditions. I am looking forward to seeing how this line of research progresses!
On 2024-05-18 21:03:14, user Camilla Forsberg wrote:
This preprint has now been revised and published in CELL:<br /> https://www.cell.com/cell/f...
On 2020-06-26 16:00:55, user ChrisdeZilcho wrote:
The sensitivity of SARS-CoV-2 to Interferons is a very interesting observation with regard to its viral evolution. <br /> Type I - IFNs are normally produced by lymphocytes (NK cells, B cells and T cells), macrophages, fibroblasts and endothelial cells from all mammals as an important component of the immune response against viruses. Homologous IFN molecules have also been found in birds, reptiles, amphibians and fish species. IFN is therefore an essential part of an effective antiviral immune response. It activates surrounding virus-infected and non-infected cells, which consequently form proteins (RIG-I, MDA5, TLRs), which inhibit further (virus) protein synthesis in those cells and on the other hand cause the degradation of viral RNA. IFN-? has previously been used therapeutically in the treatment of chronic viral hepatitis for several years.
Bats were shown to elicit a particularly strong immune response against viruses through activation of IFN-pathways. (Cara E. Brook et al., eLife 2020;9:e48401): <br /> “The experiments and model helped reveal that the bats’ defenses may have a potential downside for other animals, including humans. In both bat species, the strongest antiviral responses were countered by the virus spreading more quickly from cell to cell. This suggests that bat immune defenses may drive the evolution of faster transmitting viruses, and while bats are well protected from the harmful effects of their own prolific viruses, other creatures like humans are not.” https://elifesciences.org/a...
So if the virus multiplied in a natural environment in mammals, bats in particular, it would be expected that it would have developed counter-mechanisms to IFN in its viral evolution. This is clearly the case with SARS-CoV. The "old" SARS-virus, which originates in bats and allegedly jumped to later intermediate hosts (civets/raccoon dogs), does not appear to be as sensitive to recombinant IFN as its "new" relative. SARS-CoV-2 is much more sensitive to recombinant Type 1 IFN in cell culture. This was similarly shown by Emily Mantlo et al. "Antiviral activities of type I interferons to SARS-CoV-2 infection", Antiviral Res. 2020 Jul; 179: 104811. https://www.ncbi.nlm.nih.go...
Interestingly, these studies, as well as in numerous publications before the outbreak in 2019, used the IFN-?/? -defective Vero E6 cells to cultivate SARS CoV. The kidney cells from African Green Monkeys lack the ability to produce Type I Interferon (IFN) (Naoki Osada et al., DNA Res. 2014 Dec 21(6): 673–683.). The cell line is popular not only due to its IFN-deficiency, but because of its ACE2 expression on the cell surface and similarity to human epithelial cells, many research laboratories worldwide have used them for years in the cultivation of natural and artificially generated SARS viruses in the laboratory.
It has been shown previously that the Vero E6 cell line proved to be particularly permissive towards SARS-CoV-2 - more than any other cell line tested with a standard CPE assay. The Vero E6 cell line is used not only in virus research, but also routinely in the production of vaccines for rotaviruses, inactivated polio vaccines, and for Japanese encephalitis vaccine.
The results above suggest that SARS-CoV-2, unlike its relative SARS-CoV-1, developed in an environment where IFN did not seem to play a role. However, since virtually all mammals use IFN in their immune response (bats in particular), why is CoV-2 so sensitive in contrast to CoV-1? What does it mean in terms of its evolution in mammals? Would that explain the lower virulence of CoV-2 compared to CoV-1 in most patients that actually develop mild symptoms? Would antiviral IFN-drugs prove to be effective against CoV-2 such as Avonex®, Rebif®, Plegridy®, Betaferon®, Extavia®, Intron-A®, Roferon®-A and is IFN-? more effective than IFN-??
I would be interested in the opinion of scientists in the field, since my only conclusion would be, that SARS-CoV-2 may not have developed in an environment, where Interferons play a major role in the hosts immune defence. Of course this is purely speculative.
On 2024-06-17 06:43:17, user Sebastian Gregoricchio wrote:
Now published on Nucleic Acids Research:<br /> https://doi.org/10.1093/nar/gkad267
On 2017-01-03 15:08:41, user John Didion wrote:
We reviewed this paper in our December preprint journal club. Overall, we found the paper to be well written and the conclusions to be convincing. We had only a few minor comments and suggestions:
· Please be more clear about what the coding score in Figure 3B and 4C means. It is difficult to move from the results to the methods to interpret the CS_hexamer_ equation, so it would help your readers if you give a more intuitive interpretation of this value right in the results. Also, how did you determine that 0.049 is the cutoff for high coding score?<br /> · It would have been nice to see two distinct tissues compared in figure 4B, given that one might expect “brain” and hippocampus to be fairly similar. If this would be an incorrect assumption, then it should be spelled out why, otherwise one or more confirmatory figures should be included in the supplement. Also, how did you choose 60% as the cutoff? Just by eye? <br /> · Please add coding genes to figure 4C.<br /> · Figure 2B could be improved by adding density plots in the margins with asterisks indicating significance (such as those provided in Figure 4E).<br /> · We were interested to see the effect of the algorithm for predicting coding potential. Do things change significantly if you use e.g. CPAT rather than CIPHER?<br /> · In the discussion, you focus on lncRNAs as a potential intermediate step leading to de novo protein coding genes. Isn’t it equally likely that lncRNAs (especially those that are highly conserved) were at some point functional and are degenerate in mouse? If yes, please consider this in the discussion, otherwise add a short explanation as to why this can’t be so.
Sofia de Pereira Barreira<br /> Steve Bond <br /> John Didion<br /> Tony Kirilusha<br /> Luli Zou
On 2018-03-21 18:56:40, user Sandeep wrote:
1) Fig 3 has the wrong gM -<br />
3-GGTGTACACGCCGGAGTAGTCGG-5 <br />
It is however correct in the search (SITable 3).
2) It has one mismatch with 2 diff (not 0)<br /> chr15: 99250216 (+ve strand) <br /> GGCTGATGAGGCCGACACACG <br /> GGCTGATGAGGCCG - CACATG
Neither s/w - nor CIRCLE-seq found this. Worrying.
3) "Several off-target sites contained mismatches in the protospacer adjacent motif ( PAM ) sequence, with NAG PAM as the most prevalent ( Fig. 1e and Supplementary Table 1 ), consistent with data from previously<br /> published studies" -
Several? Its 86%. Only 14% has the NGG.
Most would be more apt. Once again, very worrisome from an off-target point of view.
On 2021-10-15 20:43:00, user Swineheart wrote:
I am wondering why this paper is stuck in preprint status. I can review if needed, having already rerun all the analysis independently.
On 2018-01-28 23:42:40, user Blake Stamps wrote:
Thank you for the manuscript! One small thing- in your abstract, the link is incorrect, and leads to nowhere. Should it be http://microbiology.se/soft...
On 2020-05-07 15:21:51, user Jean-Michel Fustin wrote:
The data presented in this preprint is now published in Communications Biology at https://www.nature.com/arti...
On 2020-03-26 00:17:43, user Peter Dunn wrote:
Interesting study, but the abstract needs some editing. There are words missing. eg " <br /> SAK models more logistically feasible" should be SAK models WERE more logistically feasible.
On 2018-03-05 03:04:51, user PascualMarquiRD wrote:
^In disagreement with your suggested conclusion, we prefer another version, and note that there is no error in this conclusion either: "In Moiseev et al Neuroimage 2011, 2013, 2015, their Eq.2 with LCMVB weights, gives an estimate for the unknown amplitude of source 'i'. But this estimator is not good, you can't use this if your goal is localization. You can only use this if you know that only one source is active in the whole brain". <br /> ^Next: you are wrong. The bug you wish for in my code is not there. This does not exclude the possibility of other bugs.<br /> ^About the super-optimality of the MV filter: I find it funny that you have to start with the condition "…once the true location is established…". What does this mean? You need to know the unknown localization first, and then there is some sort of optimal property? But this defeats the whole aim of functional localization.<br /> ^One more thing: All the methods were compared under equal conditions, for the unknown current density orientation case. Where is the unfairness?<br /> ^And a final question/comment: CONNECTIVITY. What form of LCMVB signals are typically used for connectivity analysis? In two papers at least, (Hipp et al 2012 Nature neurosc. 15:884) and (Brookes et al 2012 Neuroimage, 63:910), they use Eq.2 from Moiseev et al Neuroimage 2011, 2013, 2015! And these LCMVB signals, under the assumption of distributed activity (not assuming 1 or 2 single dipoles), produce a very highly significant rate of false positive connections.
On 2018-01-25 21:34:43, user Casey Greene wrote:
This work appears to be interesting. However, it would have been nice to have some contextualization with the existing work in the field.
There's some work from our group on gene expression + autoencoders on bulk data.
Tan J, Ung M, Cheng C, and Greene, CS. Unsupervised feature construction and knowledge extraction from genome-wide assays of breast cancer with denoising autoencoders. Pac Symp Biocomput. 2015; 20:132-43. PMID:25592575
Tan J, Hammond JH, Hogan DA, Greene CS. ADAGE-based integration of publicly available pseudomonas aeruginosa gene expression data with denoising autoencoders illuminates microbe-host interactions. mSystems. 2016 1(1):e00025-15.
Tan J, Doing G, Lewis KA, Price CE, Chen KM, Cady KC, Perchuk B, Laub MT, Hogan DA, Greene CS. Unsupervised extraction of stable expression signatures from public compendia with an ensemble of neural networks. Cell Systems.
Way GP, Greene CS. Extracting a Biologically Relevant Latent Space from Cancer Transcriptomes with Variational Autoencoders. bioRxiv. 10.1101/174474
Tan J, Huyck M, Hu D, Zelaya RA, Hogan DA, Greene CS. ADAGE signature analysis: differential expression analysis with data-defined gene sets. BMC Bioinformatics.
Off hand I also know about some work by others on bulk data:
Learning structure in gene expression data using deep architectures, with an application to gene clustering<br /> Aman Gupta, Haohan Wang, Madhavi Ganapathiraju
Leveraging Stacked Denoising Autoencoder in Prediction of Pathogen-Host Protein-Protein Interactions<br /> Huaming Chen ; Jun Shen ; Lei Wang ; Jiangning Song
Learning a hierarchical representation of the yeast transcriptomic machinery using an autoencoder model<br /> Lujia Chen, Chunhui Cai, Vicky Chen and Xinghua Lu
Finally there's at least one paper on related approaches for single cell data:
Interpretable dimensionality reduction of single cell transcriptome data with deep generative models<br /> Jiarui Ding, Anne E. Condon, Sohrab P. Shah
As a reader, it would be very helpful if at least some of this work, especially the prior single cell work, was provided to help contextualize the advance described in this paper.
On 2021-01-09 14:43:25, user Arlin Stoltzfus wrote:
Nice work. I have some questions. The main argument of the paper is that a case of extreme parallelism is caused by extreme non-uniformity of rates of mutation, rather than by extreme non-uniformity of fixation probabilities caused by fitness differences. (1) Why is there no measurement of the rate of the A289C mutation with and without the enhancing context? (2) Why does the title of the paper refer to synonymous sequences? Saying that synonymous sequences facilitate parallel evolution is a very strange way of reporting extreme parallelism caused by a mutational hotspot.
On 2017-06-27 13:17:02, user Myron Best wrote:
In reply to the follow-up comments, we feel the arguments raised by Dr. Chakraborty do not make sense, thereby once more erroneously interpreting our work.
First, we once more aim to emphasize that the biological mechanisms contributing to the TEP RNA profiles are not exclusively based on RNA sequestration from both cancer, immune and stromal cells, but also splicing of endogenously expressed RNAs. As we only included intron-spanning RNA-seq reads for our analyses, the increase of F13A1, a bonafide platelet gene, can be explained by enhanced splicing of this RNA in platelets of patients with cancer. We observed low levels of MET using the shallow thromboSeq protocol. However, sequestration of RNAs by TEPs does likely only contribute minorly to the RNA profiles, as compared to induction of RNA splicing, and such MET transcripts will only be confidently detected once you sequence deeper. Second, to ensure the specificity of our classification algorithms, esp. the pan-cancer classification algorithm, we performed independent validation of the classifier in a cohort of which samples were not involved in algorithm development. In addition, to ensure specificity of the gene panels and classification algorithms, we randomly permutated the group labels of the samples assigned to the training cohorts resulting in random classifications in the validation cohort / left-out samples in LOOCV. This contradicts the suggestion by Dr. Chakraborty that the classifications are caused by random assignment of counts. Finally, in Dr. Chakraborty's first comment we aim to highlight the following sentence: 'RNA-seq values certainly show no over-expression (on the contrary - but leaving that aside, since surrogacy does not require them to be there)'. This urges us to conclude that indeed MET or other tumor-derived biomarkers do not necessarily need to be detected in TEPs via the current thromboSeq protocol, thereby undoubtedly suggesting that all three manuscript posted by Dr. Chakraborty should be retracted.
Best wishes,<br /> Myron Best<br /> Thomas Wurdinger
On 2020-05-27 15:22:47, user Esmeralda R. wrote:
This is a great preprint. <br /> Can't wait to see the final version of it. But I'm already planning to use it to discuss with my labpeers in a virtual journal club<br /> Grama Esmeralda
On 2025-05-10 17:20:18, user sebastiangregoricchiophd wrote:
Now published on Genome Biology:<br /> https://doi.org/10.1186/s13059-025-03596-5
On 2021-04-15 08:31:36, user Alexander Kastaniotis wrote:
In contrast to inactivation of the ACP1 gene encoding acyl carrier protein in yeast, a knockout of the PPT2 gene encoding phosphopantetheine transferase in Saccharomyces cerevisiae is viable (e.g. Merz S, Westermann B. 2009. Genome-wide deletion mutant analysis reveals genes required for respiratory growth, mitochondrial genome maintenance and mitochondrial protein synthesis in Saccharomyces cerevisiae. Genome Biol10:R95-2009-10-9-r95). This would argue for a non-essential role of the PPT group also in yeast. We discussed this in a recent review (Kastaniotis AJ, Autio KJ, R Nair R.Mitochondrial Fatty Acids and Neurodegenerative Disorders.Neuroscientist. 2021 Apr;27(2):143-158).
On 2024-12-27 18:54:27, user ML Gaynor wrote:
This preprint was 'Just Accepted' (12/17/2024) and will be available here: <br /> https://doi.org/10.1086/734411
On 2020-04-22 08:41:51, user Ural Yunusbaev wrote:
Which viewer did you use to create Figure 2?
On 2018-01-15 09:41:54, user David Curtis wrote:
Overall, this seems a very interesting paper. However there are two relevant analyses of the Swedish dataset which are not cited:
https://www.biorxiv.org/con...<br /> https://www.biorxiv.org/con...
The first of these is now published:
http://onlinelibrary.wiley....
Both papers show that missense and non-singleton variants contribute to risk in the Swedish dataset and also point out an important problem. There is an excess of subjects with a substantial Finnish ancestry component among cases compared with controls. What this means is that if one includes all subjects and does a burden test using rare variants then a variant which is common in Finnish subjects will be commoner in cases. Here is an example from the second paper: "An example was COMT, with SLP=7.4. On inspection, it seemed that this gene-wise result was largely driven by SNP rs6267, which was heterozygous in 51/6242 controls and 94/4962 cases (OR=2.3, p=8*10-7). However this variant is noted in ExAC to have MAF=0.002 in non-Finnish Europeans but MAF=0.05 in Finns. Hence, its increased frequency among cases appeared to be due to the excess of cases with Finnish ancestry."
Another problem with the Swedish dataset is that there are in general fewer singleton variants among the subjects with Finnish ancestry. This produces significant complications when trying to interpret the results of singleton analyses. (Again, this is discussed in the second paper.)
In order to address these issues, we used a reduced dataset in which subjects with substantial Finnish ancestry were excluded. However it is not clear to me that the methods of analysis used in the current paper would be robust against this potential source of artefact. It would be reassuring if this issue could be explicitly addressed.
On 2020-05-10 01:31:42, user Chris Enitan wrote:
This was a good read.<br /> I Wonder how many threads/electrodes would be needed to map the entire human brain in the long run and what physical constraints that might bring. For now though, this is very exciting stuff.
On 2023-08-31 13:39:20, user Gregory Voth wrote:
Dear Authors,
We congratulate you for your work on simulating lipid droplet biogenesis at the MARTINI coarse-grained resolution. We also thank you for citing three papers from our group. However, I am leaving this comment because our papers were not adequately nor accurately cited in your manuscript.
First, we have already shown that asymmetric tension decides a budding direction in J Phys Chem B 126 (2022): 453-462 using our simulations. This is consistent with your findings, and none of your text mentioned this.
Second, we have already carried out a large-scale coarse-grained simulation of lipid droplet biogenesis with seipin, published in Elife 11 (2022): e75808. This includes not only nucleation but also maturation and budding. We have further found and discussed the critical role of seipin transmembrane segments in maintaining a neck structure. In particular, based on our simulations, we proposed a mutant construct, which was further validated by experiment in our paper. The final structure of our CG molecular dynamics simulations is consistent with the experimental structure. In that regard, our work has been cited in your paper only for nucleation but did not receive proper credit for budding and maturation. In particular, we disagree with the following two sentences in your manuscript:
"The function of seipin is also not completely clear: simulations and experiments suggested that it may trap triglycerides (13-15), therefore affecting LD nucleation and growth by ripening, but its localization at the LD-ER contact site raises questions on a possible role also in the budding process.”
"LD nucleation and phase separation were observed in simulations before (7,13,22,23,38), and occur on fast time scales (below the microsecond); in contrast, the budding process has never been observed so far, neither in simulations nor experimentally."
I hope our concerns are properly addressed during revision so that we do not have to write a comment to the journal in which your paper will be published. Thank you.
Gregory Voth
On 2020-03-25 15:30:06, user Sinai Immunol Review Project wrote:
Rhesus macaques were immunized intramuscularly twice (week 0 and week 4) with SV8000 carrying the information to express a S1-orf8 fusion protein and the N protein from the BJ01 strain of SARS-CoV-1. By week 8, immunized animals had signs of immunological protection (IgG and neutralization titers) against SARS-CoV-1 and were protected against challenge with the PUMC-1 strain, with fewer detectable symptoms of respiratory distress, lower viral load, shorter periods of viral persistence, and less pathology in the lungs compared to non-immunized animals.
The authors should write clearer descriptions of the methods used in this article. They do not describe how the IgG titers or neutralization titers were determined. There are some issues with the presentation of data, for example, in Figure 1a, y-axis should not be Vmax; forming cells and 1d would benefit from showing error bars. Furthermore, although I inferred that the animals were challenged at week 8, the authors did not explicitly detail when the animals were challenged. The authors should explain the design of their vaccine, including the choice of antigens and vector. The authors also do not include a description of the ethical use of animals in their study.
The authors describe a vaccine for SARS-CoV-1 that could benefit from a discussion of possible implications for the current SARS-CoV-2 pandemic. Could a similar vaccine be designed to protect against SARS-CoV-2 and would the concerns regarding emerging viral mutations that the authors describe as a limitation for SARS-CoV-1 also be true in the context of SARS-CoV-2?
On 2018-04-24 20:14:39, user John Goertz wrote:
Interesting article, it's an intriguing approach. I'm always excited about ways to get around microfluidics!
Some thoughts:
Figure 2b is very hard to interpret. The gel beads are nearly invisible, so it's hard to tell what's oil, what's water, and what's bead. It would benefit from fluorescence imaging, maybe FAM-labeled hydrogel, aqueous-phase Texas Red, and oil-phase Nile Red, Oil Red-O, or DiD. This would also enable estimation of the thickness of the aqueous "shell" around the beads.
This shell thickness is important, since the shell volume, not total engulfed volume, limits target genome (and cell) capture. Let's say your 50 um diameter beads are engulfed in 55 um diameter droplets, and that satellites form 3% the total aqueous volume (note, in the text you say <3%, in Figure 2 you say <5%). Then the satellite fraction of the total satellite and shell volume, i.e. the volume inhabited by the target genome, is (0.03*27.5^3/0.97)/(27.5^3-25^3) = 12%. At 5% total volume fraction, this becomes (0.05*27.5^3/0.95)/(27.5^3-25^3) = 21%. Still viable for ddPCR, but now it's contributing a non-negligible error to your concentration estimates, and is a larger contribution than the variation in encapsulating-droplet volume.
Standard error may not be appropriate for Poisson estimations given the assumption of normality. Better would be to propagate the Poisson error (and the satellite-volume error) of each individual measurement across the averaging.
On 2018-03-06 14:21:30, user Banks Lab wrote:
Suggestions, comments or other feedback welcome.
On 2024-06-18 15:19:14, user Todd Gaines wrote:
This preprint has now been published in Genome Biology:<br /> https://link.springer.com/a...
On 2017-10-12 20:50:26, user Jonathan Dry wrote:
FYI we have released (as part of a DREAM challenge) a very large cell line combination screening dataset. I believe a biology-led approach to predict response could have an edge in these data, and it would be very interesting to see how this mechanistic model performs. You can request the data here https://openinnovation.astr.... Also a chance to benchmark vs the machine learning methods used within the challenge itself https://www.biorxiv.org/con....