On 2020-05-20 12:30:22, user Paul Conduit wrote:
Note that this is an updated version of our preprint originally posted in 2019. The changes are largely in response to Reviewers' comments and do not affect our original conclusions.<br /> Paul Conduit
On 2021-01-17 22:08:49, user Peter Cabal wrote:
if this current vaccination program fails, this drug might stop/slow down this endemic pandemic
On 2022-03-16 09:06:46, user yael grunwald wrote:
This paper has been accepted to The Plant Cell and can be viewed at https://doi.org/10.1093/plc...
On 2020-02-17 08:42:05, user Nellie Song wrote:
A detailed version of a method used in this article can be found here, doi.org/10.21769/BioProtoc....
On 2020-03-16 22:10:40, user Yes Consulting wrote:
please try this on coronavirus COVID-19 too
On 2023-11-20 09:22:39, user David S Gardner wrote:
Hi, just reading with interest as investigating phenotypes associated with carnivory. You have categorised Dog as carnivore, same score as obligated carnivore - cat ?; almost certainly dog is an omnivore. Doubt it'll change your conclusions but just sayin.... https://pubmed.ncbi.nlm.nih...
On 2021-11-22 09:54:23, user Tanai Cardona Londoño wrote:
Hi, thank you. Fascinating stuff. I love the many interesting ways in which ASR can be applied!
Just wanted to comment on the following statement:
“Photosystems, however, are complicated, specific structures with a relatively limited capacity for functional variability or spectral tunability.”
All photosystems have a common origin. And from that origin you have the emergence of a photosystem that can split water to oxygen generating over one volt of oxidative power, reducing quinones. On the other hand, you have a second photosystem that evolved to generate over -1 volt of reductive power to reduce ferredoxins and at a potential that allows the fixing of carbon dioxide, both spectrally tuned to a level of precision that still blows the mind of scientists. These can be “spectrally tuned” to do the same function as efficiently using far-red red light, shifted about 40nm beyond the standard PAR, in what’s known as the FaRLiP, widespread in cyanobacteria. You have the evolution of the spectrally tuned photosystems of the Prochlorococcus optimized to work with just blue light.
From the same origin, you have the anoxygenic photosystems that have been spectrally tuned to use infrared light all the way from 800 to 1000 nm. They all have evolved to use different type of pigments and cofactors as it is characteristic of each phyla or group.
Within cyanobacteria, you have other mechanisms of tunability and adaptability that allows the photosystem to be dynamically optimized to changing functions. So, for example, a cyanobacteria may carry encoded in their genomes a set of subunits that can be replaced to optimize the photosystem to low light, or high light, or low oxygen, and even far-red light. In fact, Photosystem II can be changed from water oxidation to chlorophyll-f synthesis. A single cyanobacteria strain, like Chroococcidiopsis or Nostoc can encode in their genomes the capacity to assemble over a dozen of differently of optimized photosystems II and photosystem I. Some of these photosystem II versions may have functions beyond water oxidation. The vast majority of these variant forms have not even been characterized yet.
So, what you say in the statement, is not really accurate… and in comparison with rhodopsins, it may be the exact opposite. I could argue that the functional variability and spectral tunability of rhodopsins pale in comparison with what photosystems can actually do... but a single pigment can only take you so far! :D
Think about it ;)
Again, thank you for the fascinating work!
Tanai
On 2023-09-26 03:19:45, user MichaelNeville wrote:
Where can we listen to soil eco-acoustics? We teach healthy, living soils through composting, worm farming and mulching. being able to listen would be a fantastic teaching tool.
On 2018-06-12 15:40:55, user guillemaud wrote:
A PREPRINT PEER REVIEWED AND RECOMMENDED by PCI EVOL BIOL
This preprint by Duncan et al has been peer-reviewed by 2 anonymous reviewers and recommended by Dries Bonte for Peer Community in Evolutionary Biology. Peer-reviews, decisions, author's replies and the recommendation can be found here: https://evolbiol.peercommun...
On 2021-09-21 02:14:28, user Kshitish Acharya wrote:
Published in the Journal of Biomedical Research: https://www.sciencedirect.c...
On 2016-09-20 19:18:23, user Jesse Bloom wrote:
The HIV replicate-replicate correlations are only slightly worse than we got in our first influenza study with HA (eLife, 2014). However, the HIV correlations are much worse than our newer influenza HA study (Viruses, 2016).
We think we can eventually improve the HIV replicate-replicate correlations with some additional experimental modifications, but aren't sure yet...
One of the challenges is that HIV grows to much lower titers, so larger volumes are needed to passage enough infectious units to avoid bottlenecking the library.
On 2020-08-31 13:30:11, user Ben Sprung wrote:
"In the context of a linear regression, maximizing the variation in the predictor variable will bias the estimated slope downwards, while maximizing the variation in the dependent variable will bias the estimated slope downwards (Hayashi et al. 2018)" -- is one of those two "bias the estimated slope downwards" supposed to read differently, as implied by the "while"?
On 2024-11-01 13:06:13, user Maxim Greenberg wrote:
Hello, this paper has now been published in Nat Struct Mol Bio. https://doi.org/10.1038/s41594-024-01405-4 Thank you!
On 2020-10-02 07:27:13, user jvarga wrote:
Nice work! By what criteria did you identified proteins? Did you use only unique peptides? How much per protein?
On 2025-11-15 03:01:19, user Brian Swann wrote:
You need to get this work evaluated by the Crick, for example. If they say it is credible, it will go somewhere. It needs an institution with bioinformatics folk and statisticians to look at this. I just wonder where this is all going - do we end up proving that unless we are identical twins we are all different. We know that already by looking in our faces. What difference does it make to human disease in the long term, if anything. There is crosstalk between the nuclear and mitochondrial genomes. How is that organised.
On 2021-03-21 13:27:01, user Anonymous wrote:
Given the coincidence in timing between the emergence of the pandemic in late 2019 with the published separate episode of theorized Pneumonic Plague diagnosis in Beijing in early November 2019, investigation should be made of whether this Beijing incident was in fact the earliest but unrecognized incident of Covid19 human to human transmission. Please see this article: https://www.telegraphindia....
On 2020-02-03 17:39:23, user Mohammed David Zhang-Singh wrote:
How's abaout a T cell epitopes and generating memory T cells? A much faster approach.
On 2020-05-08 17:57:38, user Mraugmented wrote:
The approach is very intriguing, I believe in order to reach the next level of human intelligence and life on this planet.. this is the necessary step.
On 2017-01-12 16:08:22, user E Harris wrote:
What a nice, thorough, well-written, data-rich paper! It answers many questions about LTP at Schaffer collateral synapses. Just one question: in previous studies of the pharmacology of LTP induction, blockade of only NMDA-sensitive receptors could completely block the induction of LTP by high-frequency stimulation. In your study, you've blocked NMDA-, AMPA- and KA-sensitive receptors and mGluRs, yet upon wash out you see some LTP - those results seem at odds, and the former (no LTP) can't be explained by reduction of post-synaptic depolarization if the latter (LTP) occurs in the presence of MORE antagonism - or am I missing something? Looking forward to your response
On 2018-05-17 01:18:22, user ConservationBytes wrote:
Now peer-reviewed and published in Rethinking Ecology (https://doi.org/10.3897/ret... "https://doi.org/10.3897/rethinkingecology.3.24333)")
On 2019-08-01 07:46:23, user Ionel Sandovici wrote:
New preprint from our lab together with our collaborators. We uncover novel roles for IGF2 in pancreas development.
On 2020-01-02 18:29:46, user Richard McKenney wrote:
The work presented in this manuscript confirms published observations from our own lab (Tan et al. Nat. Cell Biol. 2019), in which we found that tau molecules cannot bind as densely along GMP-CPP (a GTP-like state) microtubules, as compared to taxol or native GDP microtubule lattices. Both our earlier work, and the current manuscript, support previous observations from the Goodson lab showing that tau has a higher affinity for GDP, versus GTP-like microtubule lattices (Duan et al. J. Mol. Biol. 2017). It should also be noted that work from the Berger lab has previously shown that tau does not have the same effects on processive kinesin transport depending the nucleotide state of the microtubule lattice (McVicker et al. J. Biol. Chem. 2011). Together, these studies lead to the picture that the tau molecule intrinsically recognizes the nucleotide state of the microtubule lattice, an effect likely mediated by changes in the spacing of tubulin dimers within the assembled microtubule. Further support for this conclusion comes from earlier observations that tau preferentially binds highly curved regions of microtubules in living cells (Samsonov et al. J. Cell Sci. 2004, Ettinger et al. Curr. Biol. 2016, and this manuscript), and in vitro (Tan et al. Nat. Cell Biol. 2019). It will be interesting to further delineate the molecular mechanism of tau’s intrinsic ability to distinguish different conformations of the microtubule lattice.
On 2021-10-29 13:45:32, user Angelino Carta wrote:
our preprint has been accepted for publication and a link will be forthcoming
On 2016-09-26 00:42:27, user Nagarjun Vijay wrote:
This is a very comprehensive study.
On 2017-06-07 16:41:34, user bruno wrote:
Accepted manuscript here with many more goodies and a few minor edits: https://elifesciences.org/a...
On 2017-06-12 17:42:49, user Leonid Schneider wrote:
Well, some of us have a lot to learn about FACS data acquisition from the esteemed authors of this preprint: https://forbetterscience.co...
On 2018-02-08 09:33:23, user Yao He wrote:
That is a really insightful way to look at scRNA signatures ! <br /> I would appreicatie it if I can ask two further questions :<br /> 1.<br /> Does the logistic regresson incorporating transcript quantifications as<br /> covariates work well on full-length scRNA protocol by using <br /> transcript-level counts/TPM calculated from kallisto ? Is there any <br /> example from real dataset ? <br /> 2. What is the reason to filter out extremely high UMIs counts cells (outside the interval [2K-20K]) ?
On 2020-07-23 11:02:57, user Renard Henri-François wrote:
Very interesting manuscript! It challenges concepts considered as accepted.
However, I have a few concerns:
1°) How is endophilin-A1 organized around your neutral tubules when the recruitment is forced by TIL? Does it make a proper scaffold? Maybe I didn't check carefully, but I didn't find any data regarding this aspect in the manuscript.
If endoA1 is just randomly recruited to the membrane without any specific organization, then my second question arises:
2°) How could you exclude that your tubulation effect is not just due to protein crowding? Have you tried to fuse the SH3 domain to something else than endoA1 BAR domain and look if you observe the same effect? You could use a globular protein unrelated to BAR<br /> domains, but also mutants of endophilin BAR domains (lacking H0 helix, etc). Maybe I missed them in the manuscript, but these controls would be necessary.
3°) In these in vitro experiments, how can you be sure that the density and the orientation of TIL are physiologically relevant?
On 2021-01-08 23:14:39, user Martin wrote:
Hello there, I think that it should be possible to fool the virus to mutate to its next variation more quickly in the lab to see what it transforms into next. By doing this you should be able to jump one step ahead to modify the vaccine to work against the next mutation. I think of virus' DNA/RNA as a computer program that must go down a certain route i.e. it has routines & subroutines when it is attacked in certain ways. By getting the virus to change to its next mutation quicker, we can get one or two jumps in front of it to produce a new vaccine to stop it before it mutates in the real time world situation.
On 2025-01-03 07:27:48, user Sam Danziger wrote:
This is an enormously interesting dataset.
Are you willing to also share some of the cell-type annotations for the data deposited in GEO?
Thank you,<br /> -Sam
On 2025-05-05 18:21:42, user jorge rocha wrote:
this was published in a peer reviewed journal<br /> https://journals.asm.org/doi/full/10.1128/jb.00747-19
On 2025-02-27 20:34:24, user Michael Roswell wrote:
This paper was published in January, 2025 https://onlinelibrary.wiley.com/doi/10.1111/ddi.13969
On 2025-02-25 00:16:29, user Meet Zandawala wrote:
This study investigates the dynamics of neuropeptide release and activity following blood feeding in female Aedes aegypti mosquitoes. Sajadi et al. provide valuable insights into the temporal signaling dynamics of diuretic and anti-diuretic hormones, significantly enhancing our understanding of excretory physiology in mosquitoes. The authors employ heterologous expression of receptors to quantify circulating peptide levels, a robust methodological approach that strengthens receptor-ligand interaction validation and hormone quantification. Notably, the study demonstrates a synergistic interaction between DH31 and kinin-like peptides on Malpighian tubules, contributing novel findings to the existing literature on neuropeptide-mediated excretory regulation in mosquitoes. Overall, the study presents an innovative and well-executed investigation with high methodological rigor.
The authors have presented their findings in a clear and well-structured manner. We only have a few questions and comments/suggestions that could help improve the clarity of the manuscript:<br /> 1. Can the authors clarify the exact number of female mosquitoes used in each condition (blood-fed vs. non-blood-fed) at each time point, specifically in methodology section 3.2 for the haemolymph collection?<br /> 2. Were there any biological replicates across different mosquito cohorts? Did the authors observe variability between different mosquito batches? Sample sizes have been provided in the figure captions but what does n signify? <br /> 3. The study utilizes specific time points for haemolymph collection post-blood feeding. Can the authors provide justification for selecting these intervals? Were preliminary studies conducted to determine these as the most informative time-points, or were they based on prior literature?<br /> 4. The authors acknowledge the roles of serotonin (5HT) and DH44 in regulating fluid secretion but focus their experimental analysis on DH31, kinin, and CAPA peptides. Could they clarify why 5HT and DH44 were not included in their haemolymph quantification assays? Were there methodological limitations, or were these hormones not expected to show significant post-bloodmeal changes, or is this something they plan to address in the future?<br /> 5. The study quantifies neuropeptide levels over time but does not discuss the mechanisms responsible for their clearance from haemolymph. Can the authors speculate on whether DH31, kinin, and CAPA peptides are degraded enzymatically, removed through receptor-mediated internalization, or cleared by the Malpighian tubules? Can they use in silico approaches to predict the half-life of these peptides based on their sequence composition? Alternatively, can they extract hemolymph when the peptide has peak activity and test this extract after different times to see how much bioactivity they retain over time? Addressing this would enhance our understanding of neuropeptide turnover and regulation.<br /> 6. Including the individual data points in the bar graphs can provide more information on the spread of the data.
Comments prepared by Bilal Amir (on behalf of Zandawala lab)
On 2020-06-18 01:47:08, user kathydopp wrote:
QUESTION: What does this research imply regarding the possible development of herd immunity against the SARS-COV2 virus? Could it possibly imply that persons having mild COVID19 cases could be infected more than once and, possibly, infect persons susceptible to developing serious cases who have not yet been infected?
I hope not but would like to know what you think.
On 2020-02-25 20:02:21, user Jory Goldsmith wrote:
I really like that you've formatted it to make it readable. It seems it would only take 10-20 minutes but it makes a huge difference. There is no real reason the preprint should be in the journal submission format.
On 2023-01-23 20:42:50, user Elizabeth Cooper wrote:
This paper is now published in a special issue Frontiers in Plant Science dedicated to resources for bioenergy crops. The published version can be found at: https://www.frontiersin.org...
On 2020-03-23 12:47:00, user Ben Berman wrote:
is sgRNA the typical way to refer to subgenomic RNA? For people new to the field , it is confusing with CRISPR sgRNA (single guide RNA). Thanks
On 2020-12-28 15:58:00, user Dilip De Sarker wrote:
It will open new vistas in rice breeding
On 2021-05-07 15:19:43, user Steven Sutcliffe wrote:
Very cool! I appreciate the acknowledging of biases towards Caudovirales genomes in databases and emphasizing running it on complete phage genomes. Despite that, the first thing I want to do is run it on samples that break these assumptions! So I would be curious to see if you took the training data set and/or testing data set and turned them into fragments like viral contigs found in metagenomic samples how well it would work. Others have predicted temperate lifestyle from these types of samples by presence of integrases and recombinases or similar criteria (Shkoporov et al 2019 and Minot et al 2013 for example). I'd be happy to know if this tool outperformed these simple predictions on these datasets. I would be okay with less accurate results (re your point: incomplete genomes can increase false-predictions of strictly lytic phages), and just acknowledging the assumptions. Regardless, great work! Thanks for putting this tool together and releasing it.
On 2018-06-19 22:43:56, user JZ25 wrote:
This is an exciting study revealing the effect of AGEs on mechanics independent of DM, and it is very interesting to see the sex difference in AGE accumulation from your previous research confirmed. I'm hoping you can confirm whether your imaging and histology were performed on samples subjected to your mechanical testing protocol or if these were "unloaded" samples. Thanks!
On 2018-09-04 03:08:37, user Nilay S wrote:
To the reader, there are some differences between the draft of this manuscript originally uploaded to biorxiv in March 2018 and the final version accepted by Neoplasia in August 2018 and epublished that month. In addition to some copyediting, there are some differences such as in the format that the Fa was originally displayed and described in this version as compared to the final version. I would advise you to use the final, published version as that best displays the data best and included additional data that was requested by the reviewers, including the appropriate way to discuss Fa with regard to synergistic effects, evidence of cytostatic vs cytotoxic effects of the drugs, and expanded data on all four cell lines.
On 2017-12-04 16:32:43, user David Rimm wrote:
The Rimm lab group, in an effort led by Balazs Acs will present data from the International Ki67 Working group using this software at SABCS. This software appears equivalent or better than similar commercial platforms
On 2025-12-02 10:54:18, user Babür Erdem wrote:
This article was published in a peer-reviewed journal. Citation:<br /> Babur Erdem, Ayben Ince, Sedat Sevin, Okan Can Arslan, Ayse Gul Gozen, Tugrul Giray, Hande Alemdar,<br /> Api-TRACE: A system for honey bee tracking in a constrained environment to study bee learning process and the effect of lithium on learning,<br /> Computers and Electronics in Agriculture,<br /> Volume 241,<br /> 2026,<br /> 111236,<br /> ISSN 0168-1699,<br /> https://doi.org/10.1016/j.compag.2025.111236. <br /> ( https://www.sciencedirect.com/science/article/pii/S0168169925013420) "https://www.sciencedirect.com/science/article/pii/S0168169925013420)")
On 2020-02-09 05:26:04, user Anon wrote:
I do not see much value in this manuscript. The first publication discussed in this paper (“Uncanny Similarity...”) has already been retracted and its flaws have been pointed out in much depth. Similarly, the second paper has also been discussed much elsewhere, and alternative mammals have already been proposed with supporting evidence to contradict the snake hypothesis. Disappointment and waste of funds for this paper.
On 2018-12-20 21:42:04, user rooeikim wrote:
new genome-wide crispr screen data
On 2023-10-06 12:37:11, user Ashok Palaniappan wrote:
An expanded, revised and advanced version post peer-review is now available [open-access]:<br /> Sarathi, A., Palaniappan, A. Novel significant stage-specific differentially expressed genes in hepatocellular carcinoma. BMC Cancer 19, 663 (2019). https://doi.org/10.1186/s12...
On 2022-01-17 12:56:11, user Nicolas Rivron wrote:
Dear Cheng Zhao et al.,
Thank you for this first attempt at unbiasedly comparing the transcriptomic state of the cells of early human embryo models. Merging datasets to create reference maps, and applying multiple methods to identify cell fates is really important and it is crucial to ensure an appropriate understanding of the predictivity of the model. Your analysis matches some of the original analyses by the articles you study, despite using a different reference map. We were happy that you come to the same conclusion as us that about 3% of our cells are not matching EPI, trophoblast, or extraembryonic endoderm lineages, which is encouraging (note that we did not use the dataset from Zheng et al. in our merged reference map). Please find below a couple of remarks that we hope will help you with the revision process.
Our first main concern is that you are focusing the interpretation of your analysis on one study (Liu et al. 2021). Instead, we think that it would be better to propose tools and data that everyone can work with and use to interpret data and form conclusions (your website is a good start in that direction). The scientific process is fallible by nature and we think it is important to openly give the results without dismissing the original proposals, in order to gradually improve our understanding of the cell states produced within these models. We understand the specificities of the publication format but we think that this first concern can be easily addressed by adapting the title and the text.
The second main concern is that you are not taking into account the embryonic stages. A primary goal of an embryo model is to form cells reflecting a specific stage, in that case, the human blastocyst stage (days 5 to 7). After, from day 8, the embryo starts implanting into the uterus and forms the stages following the blastocyst one. During these transitions, cell states change rapidly to fulfil a sequence of different functions (e.g., the TE becomes polar TE to mediate the attachment to the endometrium). As such, ensuring the predictive power of the model requires that the model forms blastocyst-like cells. The lack of assessment of the stages of the Trophoblast-like cells (TLCs), Epiblast-like cells (ELC), and Hypoblast-like cells (HLC, a.k.a. PrE) lead to ambiguous interpretations.
These stages might be evaluated by increasing the number of Seurat clusters and affiliating a stage to each cluster based on the percentage of staged embryo cells within each cluster. This affiliation can be controlled by looking at the expression of stage-specific markers: the zones of the blastocyst-stage are well defined on your map by some markers of the EPI (e.g., SUSD2, KLF17 [PMID: 29361568]) or of the trophoblasts (e.g., CDX2).
Also, the annotation of the TE in Figure 1A is misleading. The term TE is generally used to describe the trophoblastic tissue that forms the wall of the blastocyst (E5-7). After implantation, the TE generates other trophoblast cell types including syncytiotrophoblasts, or cytotrophoblasts, this later state being captured by the hTSCs derived by Okae et al., which were also included in your UMAPs. In this figure 1A, you used the term TE for trophoblasts harvested from blastocysts cultured in vitro for up to E14. We think it prevents clarifying the limit of the blastocyst trophoblasts. Regarding the EPI, your Seurat cluster C0 is partly representative of the blastocyst EPI but also clearly captures early PrE cells and post-implantation EPI (E10). Increasing the cluster resolution would be essential to obtain clusters that better capture the blastocyst EPI. Although it is not clear if the cells of in vitro cultured blastocyst truly progress as expected, we believe that including the C7 and C9 space to define ELCs, when you are evaluating a model of the blastocyst, is misleading.
Instead of focusing on the stages, you have rather focused on the lineages. However, the amniotic and mesoderm cells originate from the EPI. As such, treating the amnion and mesoderm (although it includes some extraembryonic mesoderm that might not originate from the EPI) as cell types outside of the 3 lineages makes your analysis ambiguous. This is especially true for Extended Data Fig.2b, in which you classify the cells into the following families: ELC, HLC, TLC, MeLC, AMLC, and Undefined. This classification mixes both the stages and the lineages. For example, MeLCs is the descendant of the EPI lineage at a post-implantation stage and thus represent the same lineage, but they are represented in an independent bar. On the contrary, the bar with the TLCs includes both pre- and post-implantation trophoblasts from the same lineage but different stages. It would be better to use only one parameter, either lineage or stage, for this graph. If you use lineages, the MeLC bar, and arguably the AMLCs, bar should be added on top of the EPI bar.
Overall, we think that identifying the stages and the tissues, including the polar TE, the mural TE, the blastocyst EPI, the post-implantation/pre-gastrulation EPI would be a great achievement to benchmark the models. We think it might need a feasible refinement of your analysis, similar to the analysis that led you to uncover X dampening and the polar/mural states in human blastocyst (Petropoulos et al. 2016).
A third concern is that you are making statements about the quality of the models based on the ratios between the different cell types (e.g., EPI, TE, PrE) present in the scRNAseq datasets. It is known that blastocysts contain more TE than EPI, and more EPI than PrE (Niakan and Eggan 2013). This was also measured and recapitulated in our model (see Figure 1H, ~60%/35%/5%) using immunofluorescence, and thus based on the expression of selected marker proteins. However, these ratios are not similar in the scRNAseq datasets, with a prominence of EPI as compared to TE. This unbalance cannot be due to a delay in trophoblast specification because the number of trophoblasts was measured within blastoids at the protein level and the RNA precedes the protein. A most likely explanation is that the numbers and ratios in the scRNAseq data do not reflect the ones in the blastoids because there are biases due to the dissociation and sorting processes that precede the sequencing. We believe you have also observed in the blastocysts that, upon dissociation, they release a majority of TE and only a few EPI. As such, evaluating the quality of blastoids based on the numbers in the scRNAseq dataset is probably not justified, unless you have another explanation that we did not grasp from your text.
In figure 2C, it would make it more clear if the title would explain that this pseudo-bulk is based on 187 TE, TSC and Amnion genes.
Finally, the neural network (NN) approach was confusing for us. We definitely commend you for using new tools, however, we think it should be better explained in order to clearly understand the results that are presented. We understand that space constraints might limit the expansion of this part of your manuscript. As it is presently presented, we understand that your NN analysis yields somehow contradictory results than the UMAPs. The data points at the possibility that we have formed cells transcriptionally resembling the blastocyst EPI. For example, on your UMAP, our ELCs are shifting from the PXGL hPSCs space to the blastocyst EPI space, which suggests that the blastoid environment might normalize their transcriptome. These ELC are also within the KLF17/SUSD2/IFITM1/PRDM14-high space, which are blastocyst-stage EPI markers [PMID: 29361568]. We are surprised that, using your NN, some of those cells were attributed a primitive streak identity. It would be highly informative to know which cells have changed fates, and the gene signatures associated with this. Doing so systematically for all cells “re-annotated” seems an important improvement to guide the biologist through your analysis (just like you did to explain how you re-annotated some of Tyser et al or Xiang et al samples).<br /> We are convinced you have thought about it, but it might be suited to add figures showing that the NN did not make use of other parameters to attribute cell fates, such as the read depth, which varies a lot between datasets. For example, Yanagida et al., comprises cells with >6000 genes/cell whereas the Kagawa et al. dataset includes cells >2000 genes/cells. The datasets used for the training are typically using cells with > 3000 genes per cell. The percentage of mitochondrial genes could also have had an important influence. Overall, this might lead to non-comparable datasets.<br /> Also, it is difficult to have a clear idea of “off-target” samples. It would help if you would provide a number next to the predicted off-target cells reflecting the percentage as compared to the total number of cells.<br /> Finally, how did you control that your reference samples were large enough? Specifically, using ~25 cells from the Tyser et al. dataset as a reference for the amnion is probably not sufficient. Also, the depth of identification of amnion cells or extraembryonic mesoderm cells is really sparse in the peri-implantation dataset, which might be a better reference, as they are closer to the fate transitions that are being modeled.<br /> In summary, although the NN is an important tool for the future, we are wondering if the current quality of both the embryo and the models datasets are sufficient.
Thank you for considering those points.
Best wishes,<br /> Nicolas Rivron, Laurent David, Alok Javali, Harunobu Kagawa, Heidar Heidari.
On 2020-12-21 11:49:28, user Shi Huang wrote:
Why ignore the glaring inconsistencies! Check the data file, find rs1973664. All present day E1b have the alt allele C but the ancient Mota-E1b has ref T. We have this reported in a submitted manuscript a month ago. Ancient DNAs are supposed to be more informative than extant DNAs with regard to past events and could thus serve as the best evidence to either verify or invalidate any phylogenetic trees that are built by using extant DNAs. It is therefore surprising that the field has yet to use the now abundant ancient DNAs to verify the standard model of modern human origins, the OoA model. Is it because the model cannot pass the ancient DNA test? We found it can't!
On 2025-11-27 15:35:01, user Manuel Mendoza wrote:
Peer-reviewed version available in JCB https://doi.org/10.1083/jcb.202502014
On 2016-11-04 21:20:27, user Lutz Froenicke wrote:
This nanopore data based genome assembly certainly represents a nice progress. However, the manuscripts fails to compare the results to typical PacBio data for fungal genomes. Both the read metrics and the genome assembly metrics are significantly inferior to what can be expected from sequencing of 3 or 4 PacBio RSII SMRT-cells using the current chemistry (P6C4). For example you could compare the results to the metrics in this paper that used older now two year outdated PacBio chemistries.<br /> ( Faino L, Seidl MF, Datema E, van den Berg GCM, Janssen A, Wittenberg AHJ, Thomma BPHJ. 2015. Single-molecule real-time sequencing combined with optical mapping yields completely finished fungal genome. )
On 2024-12-16 22:03:01, user Alexander Scheffold wrote:
The specificity of the so called AIM assay is a highly relevant topic since it is now used by many labs. And the manuscript nicely addresses the signals leading to marker upregulation and identifies bystander stimulation to be a major confounding factor after 20 hours of stimulation.<br /> However, I wonder why the authors do not compare with the initial protocol using CD154 upregulation after only 7 hours. There it is convincingly shown that CD154 after 7 hours is absolutely specific for TCR activated T cells (e.g. Frentsch et al Nat Med 2005 PMID: 16186818).<br /> A simple but very reliable confirmation is also AIM combined with single cell sorting, cloning and restimulation. Again for CD154+ T cells after 7 hours for several antigens at least 80-90% specificity has been demonstrated (Bacher et al JI 2013 PMID: 23479226). We have confirmed that for many different antigens since then.<br /> Best<br /> Alex Scheffold
On 2020-06-23 08:20:49, user Fabian wrote:
Interesting body of work which is timely for the understanding of potential intermediate host. It is great that the mRNA levels for ACE2 and TMPRSS2 has been evaluated based on organs and age. However, what was left disappointing was that there was no IHC or IF images of the host proteins despite the abstract describing "Histological expression showed that ACE2 and TMPRSS2 are co-expressed with viral RNA." Histology imply anatomical evaluation under the light microscope. Co-expression under microscopy would also require concurrent labeling of multiple protein of interest.
On 2020-12-27 11:23:23, user Joachim Berner wrote:
Could Bemcentinib from BerGenBio stabilise AXL and promote epithelial-mesenchymal transition?
On 2022-08-02 22:02:47, user Tania Gonzalez wrote:
The miRNA-seq data was deposited to NCBI GEO under accession GSE184860. Link here: https://www.ncbi.nlm.nih.go...
On 2021-04-30 03:26:00, user StreuthCobber wrote:
Phytopthora Agathadicida is not a "recent" introduction. It was discovered by Dr Peter Gadgil in 1972. And then renamed due to morphology. Kauri are not going extinct and Gadgil's site is regenerating. Kauri are still thriving on Great Barrier Island. Gadgil found P. Hevae (now known as agathadicida) under both sick and healthy trees. This is the same as P. Cinnamomi which also causes kauri dieback and is found under sick and healthy trees but only causes diseas under certain climatic conditions (eg drought years).
On 2017-07-21 10:57:29, user Jun Wang wrote:
Thanks for your comment. Although our study showed that adding more scores into the integration did not necessarily increase the prediction power, we will look into this option, as this increases the data dimension contributed from evolution based approaches, like fitCons which is included in the current study.
On 2018-02-03 17:14:02, user Mehraveh Salehi wrote:
Very interesting! In line with our recent in NeuroImage spacial issue, where we show that individualized functional networks predict sex: https://www.sciencedirect.c...
On 2021-01-15 11:30:54, user Wiep Klaas Smits wrote:
Interesting work! I think you may find the following reference relevant for your analysis: https://pubmed.ncbi.nlm.nih...
On 2017-06-21 16:32:29, user Leslie Vosshall wrote:
The Vosshall Lab discussed this paper on June 21, 2017 in our Olfaction and Behavior (Preprint) Journal Club. Here is a summary of our discussion:
This is an important technical achievement, and a really interesting paper. We found it astonishing that the Y chromosome can “work” across these large evolutionary distances.<br /> 1. Supplemental data were not available for this pre-print. Would the authors consider using the versioning function of bioRxiv to upload the missing content. We were particularly interested in seeing Supplementary Table 1B showing that X:A ratios cannot account for sex determination in these species.<br /> 2. If you do revise, it would be really helpful to label the figures with Figure #.<br /> 3. How does Anopheles swarming and mating behavior, particularly cross-species, look in small laboratory cages (30x30x30)? Obviously it does work, but we found it interesting that this works at all.<br /> 4. We were interested in apparent differences in tissue-specific expression between body and head in Figure 4. What are these genes? Also, what are the open dots that are neither red circles nor purple triangles? Anything of interest on the Y chromosome that is actually regulating autosomal loci (open dots?).<br /> 5. Next steps: what are the physical and functional differences in male-determining gene across these species? Can you use CRISPR-Cas9 to edit the Y to narrow this down?<br /> 6. Figure 5: are there any morphological, physical, behavioral, metabolic differences between Arabiensis males vs Y-introgressed Arabiensis males?
On 2022-10-13 04:34:53, user W John Martin wrote:
A Figure was inadvertantly ommited from the uploaded article. I will work on having it included: The Legend of the Figure is included below
Legend: Photo of an ethidium bromide-stained 8-laned agarose gel electrophoresis. The arrow points to lane 4 and shows the migration of a portion of the DNA that was extracted from the filtered and ultracentrifuged supernatant of stealth virus-1 infected MRHF cells. Lane 3 directly beneath the arrow shows the migration of another portion of the extracted DNA that was digested using EcoRI enzyme prior to electrophoresis. Lane is EcoRI digested DNA obtained from the lysate of the infected MRHF cells. Lanes 1 and 3 are HindIII and Bst-II lambda phage DNA markers, the largest of which are 23,130 and 8,454 nucleotide base pairs, respectively. The lower staining material in lanes 3, 4, and 6 is RNA. Reproduced from reference (1) with permissio
On 2020-02-01 22:10:51, user Zhenguo Zhang wrote:
The study shows something interesting, but the title is just exaggeration. Furthermore, if you look at Table 1, the inserts are not so similar to HIV proteins except insert 1 and 2. Also these inserts have been found in the bat virus (Fig. S2). But these findings has potential to explain the contagiousness of this new virus.
On 2025-06-05 11:10:50, user Nicolas João Silva wrote:
This study is now accepted and available at Animal Behaviour: https://doi.org/10.1016/j.anbehav.2025.123223
On 2025-08-04 17:29:56, user Jon Fisher wrote:
Does anyone know if this paper was ever submitted for peer review and published?
On 2018-10-18 02:57:10, user Abhinav Jain wrote:
What is the ethnic background of your patients? Are they all Canadian or from different region ?
On 2019-11-26 20:40:16, user Noah Strom wrote:
See the published version on Applied Soil Ecology: https://doi.org/10.1016/j.a...
On 2020-10-05 17:16:32, user Esin B Sozer wrote:
The peer-reviewed and updated version of this article is now online at biophysical journal https://doi.org/10.1016/j.bpj.2020.09.028
On 2017-10-26 18:45:03, user Nicole KING wrote:
This paper has now been published at Cell: http://www.cell.com/cell/ab...
On 2022-10-16 16:15:13, user Alex Crits-Christoph wrote:
In this preprint, Washburrne and colleagues put forth some reasoning and basic analysis that they believe suggests the viral genomic data from the early SARS-CoV-2 pandemic is consistent with a single spillover event. This is in contrast to the work of Pekar et al. 2022 Science, which concluded that the genomic data from the early pandemic is best explained by multiple independent spillover events from an animal population. However, this preprint misrepresents the findings of Pekar et al. 2022, and makes several conceptual errors that fundamentally undermine their conclusions.
There are 4 basic features of the early SARS-CoV-2 phylogeny that are each largely inconsistent with a single spillover event:
A Lineage A ancestral haplotype is inconsistent with the molecular clock: Lineage B exhibits more divergence from the root of the tree than would be expected if lineage A were the ancestral virus in humans (Pekar Fig S20, S19).
Two basal polytomies of lineages A and B were formed at the start of the SARS-CoV-2 epidemic, whereas most single introductions within a city, location, or event are characterized by a single polytomy.
There are no plausible candidates for intermediate genomes observed for lineages A and B.
Both Lineage A and Lineage B are connected to and were present during the outbreak at the Huanan Seafood Market, and there was sustained case transmission within the market for up to a month.
The authors have *attempted* (unsuccessfully) to address points 2 and 3, but they have entirely ignored points 1 and 4, which are still highly pertinent. All four of these observations need to be explained by any hypothesis of SARS-CoV-2 origins.
Now, on to specific scientific errors in this work:
New Zealand https://www.nature.com/arti...<br /> Lombardy https://www.nature.com/arti...<br /> Louisiana (Mardi Gras superspreading event) https://www.sciencedirect.c...<br /> Xinfadi market in Beijing https://academic.oup.com/ns...
In none of the above cases of introduction/superspreader events do we observe two basal polytomies separated by two mutations with no intermediates as we do for early SARS-CoV-2 in Wuhan.
Ironically, the authors cite Popa et al. 2020 Nature Communications on the spread of SARS-CoV-2 in Austria as an example of how polytomies can be linked to superspreader events. However, this work elegantly describes how each polytomy results from a separate introduction event into Austria:
Vienna-1 clade/polytomy: connected to an index patient from Italy.<br /> Tyrrol-1 clade/polytomy: phylogenetically linked to North America.<br /> Vienna-3 clade/polytomy: connected to Cluster OG, an independent travel-associated cluster.<br /> Tyrrol-3 clade/polytomy: connected to Cluster D, an independent travel-associated cluster.
So indeed, the cited work is actually more strong evidence that introduction events — including those of a ‘superspreader’ nature — are characterized by a single polytomy. We see no instances of a single superspreader event creating two concurrent polytomies, separated by two or more mutations, as we observe with the rise of lineages A and B in Wuhan. It is not merely the existence of polytomies in a phylogeny that is relevant, but the observed ratio of polytomy frequency and size, which Pekar et al. simulations predict would arise very infrequently with a single introduction.
Further, the authors are incorrect in their characterization of the FAVITES models used by Pekar et al. FAVITES has been modified to accurately recapitulate SARS-CoV-2 superspreading nature; see Worobey et al. 2020 Science, Figure S2. Washburne et al. say:
“and the transmission model of FAVITES will extend superspreading events over timescales that within-host evolution can occur”. However, the simulations in Pekar et al., 2022, and in FAVITES more broadly, account for within-host evolution: the coalescent process and subsequent mutational evolution are agnostic to subsampling and within-host evolution.
The lineage A/B split and the basal polytomies of SARS-CoV-2 are still obvious in any phylogeny of early SARS-CoV-2 even when excluding genomes from the city of Wuhan: this phylogenetic structure is factually not an artifact of sampling, and anyone is welcome to build a tree of sequences before April 2020 excluding those from Wuhan and demonstrate this.
Likewise, lineage A is still incompatible with the molecular clock when genomes linked to the Huanan Market are excluded. Even in sequences from February 2020 can you see a ‘lag’ in the evolution of lineage A from its root compared to lineage B (Pekar Fig S20).
The authors propose no explanation of how contact tracing of patients connected to one market could produce a phylogenetic artifact of two large, basal polytomies: indeed, their simple analysis in Fig 2 shows that contact tracing will preferentially sample just one lineage, not two. Small polytomies are common throughout the SARS-CoV-2 phylogeny.
A contact tracing bias cannot explain a lack of intermediate genomes between lineages A and B into itself. Firstly, if the evolution between the lineages occurred in humans, the patients with intermediate genomes should be contact traceable from normal lineage B patients. Second, even if they were missed in Wuhan, we would see the phylogenetic descendents of the intermediate genotype spread to other countries, unless this lineage just happened to be wiped out very quickly.
As discussed by the Worobey et al. 2021 Science perspective, several of the earliest known SARS-CoV-2 patients were emphatically not contact traced from others — they were independently noticed in different hospitals throughout the city. This includes the earliest known case of lineage A, who was not contact traced, and had no noted connection to the Huanan Seafood Market, but after the fact was realized to live just a few blocks away (and shopped at a nearby market).
Several other data points that together point towards the known early case data in Wuhan not being strongly characterized by ascertainment bias are discussed in the supplementary text of Worobey et al. 2022 Science section on this topic.
They propose that a set of genomes obtained from Sichuan may constitute C/C intermediate haplotypes between lineages A and B. However, the data does not support this, as elegantly explained by Zach Hensel on Twitter: <br /> https://twitter.com/alchemy...<br /> https://twitter.com/alchemy...
Washburne writes: "It is difficult to see how sequencing errors, which are random, could occur at exactly the same position in these 12 early outbreak genomes."
However, what they do not understand is that several of these genomes were plagued by systematic bioinformatics errors, not random sequencing errors. This was likely due to a known issue with a pipeline that imputed the reference genotype in loci with no read support, instead of replacing those positions with N characters. As demonstrated by Hensel above, for this particular dataset with poor coverage, that included the vast majority of samples which had no coverage at the relevant sites.
Further, the authors misunderstand why certain genomes have been excluded from Pekar et al. The deciding observation is not the quality of the underlying sequencing data — although that is certainly likely the hidden cause — but the observation that some genomes share multiple polymorphisms with derived lineages in A and B, strongly indicating that they are phylogenetically aberrant. In all scenarios in which underlying data are available, it has been confirmed that these phylogenetic outliers are plagued by poor data quality issues, with missing data that has often been incorrectly imputed. In cases without the underlying data, the only alternative explanation would have to be a highly unusual degree of recurrent mutations. As this is fully explained in Pekar et al. 2022, I highly suggest the authors attempt a re-read to understand the reasoning of how we can identify these incorrect genomes.
There are two more “minor” (in the grand scheme of things) errors in this section:
“Lineage A and Lineage B, are separated by only two defining single nucleotide changes (SNCs), at positions 8782 and 21844”
This is incorrect - the second position should be 28144, not 21844. This is wrong throughout the manuscript.
"Intermediate sequences suggest there may not be two basal polytomies"
Polytomies can be separated by a single mutation and still be polytomies: there is a basal polytomy in lineage A, and a separate basal polytomy in lineage B. The existence of intermediate genomes would not preclude the presence of these two polytomies.
In sum, neither of the three points raised by Washburne and colleagues are in fact relevant to the hypothesis of multiple spillovers of SARS-CoV-2. Finally, it is also important to briefly discuss a broader conceptual error made by the authors. As they write:
"Far from being able to conclude two spillover events, both hypotheses - natural origin and lab origin - are still on the table."
This quote (along with knowledge of their past works) makes evident the aim of the authors: to reject the possibility of multiple SARS-CoV-2 spillovers because it is a finding largely inconsistent with their preferred laboratory origin hypothesis. They are correct in thinking that multiple spillovers of SARS-CoV-2 cannot easily be explained by a hypothesis of laboratory emergence. They are, however, incorrect in their statement that a lack of evidence for multiple spillovers would “put the lab origin hypothesis on the table”. There is an astounding degree of evidence against the possibility of laboratory emergence, primarily:
(1) the complete lack of epidemiological contacts traced to the WIV, and the March 2020 seronegativity of Shi Zhengli’s group, <br /> (2) the geographic epicenter of the pandemic was in Hankou, Wuhan, not Wuchang, where the WIV resides, <br /> (3) the detailed insight we have into the research ongoing at the WIV in 2018-2019, including CoV sequences submitted to GenBank in 2018 (Yu Ping et al.) and Latinne et al. 2020 (submitted Oct 6 2019), multiple publicly available theses and papers, interviews, collaborator emails, US intelligence investigations, and unfunded grant proposals: all of which has so far indicated a lack of a SARS-CoV-2 progenitor at WIV, <br /> (4) the preponderance of evidence from the known early cases within the city of Wuhan, which were either linked to or centered around the Huanan Seafood Market, including the very first cases first identified in hospitals as reported by independent journalists as described in Worobey 2021 Science perspective,<br /> (5) the positive viral samples from an animal cage, a freezer, a defeathering machine, and the drains and ground of wildlife selling stalls within the western half of the Huanan Seafood Market, the half to which most human cases were also linked, and <br /> (6) direct and geographic links of patients and environmental sampling firmly establishing that both early SARS-CoV-2 lineages A and B were first identified in connection to the Huanan Seafood Market.
Put otherwise, it is clear that the authors misrepresent and misunderstand the reasons why multiple spillovers have been proposed. Contrary to their beliefs, it is not to undermine or reject the laboratory hypothesis. The clear evidence against that hypothesis is well described in Holmes et al. 2021 Cell, The WHO Mission Report, and Worobey et al. 2022 Science— it is entirely incidental that the likelihood of multiple spillovers also happens to be inconsistent with their hypothesis.
Why then has the possibility of multiple spillovers been proposed? Because the genomic data from the early SARS-CoV-2 pandemic is *peculiar*, and these peculiarities have so far only been adequately explained by models incorporating multiple spillovers. It is as simple as that.
On 2018-07-10 22:21:53, user Mareen Engel wrote:
Dear all,
Please note that during the work we have done on this since posting here, we found some issues with the data presented in Figure 1 (I.e. a major part of the originally reported m6A-peaks was supported by none or only a few biological replicates). The analysis has been corrected in the final version which also includes several other data sets as m6A-Seq of the cKO animals and human cell lines. Unfortunately, I can’t correct/upload the new version here. The final version will be online soon & linked asap. Please don’t hesitate to contact us for the corrected version.
I'm sorry!
Best, <br /> Mareen Engel
Please also note the updated accession numbers for the data included here: <br /> GEO SuperSeries<br /> https://www.ncbi.nlm.nih.go...<br /> inlcuding SubSeries<br /> https://www.ncbi.nlm.nih.go...<br /> https://www.ncbi.nlm.nih.go...<br /> https://www.ncbi.nlm.nih.go...<br /> https://www.ncbi.nlm.nih.go...<br /> https://www.ncbi.nlm.nih.go...
On 2025-05-27 15:30:22, user Inaam A. Nakchbandi wrote:
An international patent application has been filed (WO2011097401) for the peptide molecule GLQGE and its use in the treatment of fibrotic conditions characterized by an excess accumulation of extracellular matrix in a tissue and/or an organ. It was granted in CN and JP, and is under examination in EP, US and IN. For more information, please contact Max-Planck-Innovation ( http://www.max-planck-innovation.de ).
On 2017-03-12 09:28:18, user Assaf Grunwald wrote:
This is an updated version to the preprint at page doi.org/10.1101/113522.<https: doi.org="" 10.1101="" 113522.="">
On 2020-10-07 06:08:28, user H Eric Xu wrote:
The PDB and map files are attached as supplementary information for those want to look into the structure immediately, and we are looking forward to feedback on the paper.
On 2023-02-22 11:57:34, user Hanne Poulsen wrote:
Beautiful!! Should the supplementary tables be (somew)here?
On 2024-08-01 10:03:57, user Tim Dreßler wrote:
I might be wrong but isn't there a typo in the second to last sentence of the introduction: "We also found that neural activity was sensitive to natural variability in vocal acoustics, though less than during altered feedback, suggesting that vocal sensory prediction may be relatively course (--> coarse?).
Best,
Tim
On 2022-08-19 20:54:50, user Stephanie Wankowicz wrote:
Summary: In this paper the authors set out to develop new methods for refinement of models into cryo–EM density maps. There are three primary interrelated contributions:
-Assigning “responsibility” for different regions of the map to a model and then fitting GMM as a real space B-factor. This is a new way to model atomic B-factors, since it is done in real space, compared to reciprocal space in most other software.<br /> -Sampling an ensemble based on those B-factors. The major success of this paper was that the authors created a new ensemble method that samples within the B-factors to improve the fit of hundreds of cyro-EM maps, demonstrating that their method is robust and can be done in a high throughput manner.<br /> -Refinement procedures for composite maps based on smoothing of responsibility. The examples all seem to be from individual maps with different levels of resolution across the map, not from true composite maps (calculated from different masking procedures for example). This part was very confusing for us to follow and although there are methodological links to the B-factor assignment/ensemble modeling parts of the paper, it might be better explained in a separate manuscript.
Major comments:<br /> 1. The introduction only briefly discusses B-factors and doesn’t lay out what is distinct about this method. For a contrast, sampling is discussed with references and contrast:<br /> “ The sampling itself is usually based on either molecular dynamics (MD)4,9, minimisation10, normal mode analysis and/or gradient following techniques11,12, or Fourier-space based methods2.”<br /> Similarly, B-factor refinement should be discussed. The way Phenix and Refmac handle it (real vs. reciprocal space), the limitations that the GMM addresses, etc.
With regard to sampling, there are other methods that are now similar for generating ensembles (the EMMI work from Vendruscolo and Bonomi for example). It would be useful to contrast the limitations of those methods and how this method is distinct. For example, this method seems likely to be much more computationally simple to run. It would also be good to benchmark against examples of those ensemble methods in terms of RMSF/inferred B-factors.
When you refer to the TEMPy-REFF models in each case study are they always ensemble models using segmentation?
How are the weights for each focus map decided for when creating a composite map? Stated in ‘combining focused maps into a single overall composite map, with optimal weights of the focused maps.’ (page 3)<br /> We think that more information on how you are generating ensembles belongs in the results section which will help clarify the paper. Some additional specifics we think would make this section strong include: Are the ensembles being created for different segments of the model (based on map segmentation) or the entire model? When creating an ensemble, what is the input model? Has it already gone through iterations of the map to model fitting? How are ensemble models represented? Please provide examples and discuss how you would like these models interpreted.
Please clarify how b-factors are represented in your ensemble models and input into maps. Furthermore, in the discussion you state ‘We address this challenge using B-factor estimation. We find, as previously shown by us and others, that an ensemble of equally-well fitted models represents this local variability better than a single model.’ (page 16). However, it is unclear how the b-factors integrate with the ensemble model to represent local resolution. Please clarify which part of your model correlates with local resolution.
On average, how many models were included in an ensemble? Please provide a graph of CCC values versus number of models in an ensemble for more examples (ie more than SI Figure 7). How are you thinking about the trade-off between a more complex model versus a small gain in CCC? How deterministic is this procedure? Can you repeat and compare at least one dataset? If you generate multiple ensembles starting from the same structure - do you get the same number of models out and are they similar?
If we understand the calculations correctly, the increase in CCC comes from those models being refined independently, not collectively (which makes the increase all the more impressive). Does this suggest the ensemble captures both precision and accuracy (as discussed here: https://pubmed.ncbi.nlm.nih... and therefore the sampling allows escaping of local minima in a clever way. Are there other examples like the His alternative conformation that can help speak to this?
When assigning responsibility for a part of the map that may be able to similarly explain two parts of the model, how does the method decide which part of the model should fit in that segment of the map?<br /> Please provide more insight on the interpretation of uncertainty of discrete positions of different sidechains as described in the sentence ‘ensemble adopting either (bottom inset), or uncertainty in the exact side chain confirmation (bottom inset) of two residues (Y76 and L78)’. How is uncertainty measured? Is the RMSF similar or comparable to what would be inferred by B-factors? Please compare the numbers you are reporting to other traditional refinement softwares such as REFMAC and Phenix. It’s unclear whether this is capturing anharmonic motions in a really different way or just sampling the B-factor harmonic component.
Minor comments:<br /> 1. In Figure 1a, please provide more description about what you are representing with the blue and orange circles in the responsibility estimation.<br /> 2. How does your method represent very high resolution structures with low b-factors but high numbers of alternative conformers (specifically looking at PDBs: 7A4M, 7A5V of Apoferritin and GABA receptor).<br /> 3. In Figure 5a, please clarify how you are normalizing the B-factor.<br /> 4. Please deposit output models in Zonodo or some other public repository.<br /> 5. What does SMOCf stand for? Please introduce this briefly in the results.
Review by Stephanie Wankowicz & James Fraser
On 2018-01-11 21:05:15, user Jonas Bille Nielsen wrote:
Amendment to the acknowledgement section:<br /> This research has been conducted using the UK Biobank Resource under application number 24460.
On 2020-10-22 16:40:44, user Abeer Sayeed wrote:
Dear authors,<br /> Thank your for sharing your research. My main critique would be on your figure displaying quantification using the three assays. You argue that PCR is effective in detecting low density infections which you define as <200 parasites/ml. However, it appears that you are quantifying samples at much higher parasitemia. It would be convincing if you could include samples within the threshold of your definition of LDMI. Also, why did you normalize your qRT-PCR results to the uPCR results? Wouldn't a direct comparison be more effective in determining the efficacy of qRT-PCR? Thank you.
On 2020-06-26 17:30:26, user Paul Robustelli wrote:
Additionally, to be consistent with the wide literature on chemical shift prediction from IDP ensembles, one should probably just report the RMSD of each shift-type prediction, since that value has a clear meaning and is commonly what is reported.
It would also be great to see some free energy landscapes for the lowest temperature replicas (Ie. Rg vs. Helical Contact or AlphaRMSD (https://www.plumed.org/doc-... "https://www.plumed.org/doc-v2.5/user-doc/html/_a_l_p_h_a_r_m_s_d.html)"). I'm guilty of not including them in a99SB-disp paper, but going forward I'm hoping this becomes more standard
On 2020-10-25 15:15:59, user Ruben L Gonzalez Jr wrote:
This preprint has already been published in Journal of Biological Chemistry: Haizel SA, Bhardwaj U, Gonzalez Jr RL, Mitra S, and Goss DJ (2020) 5'-UTR recruitment<br /> of the translation initiation factor eIF4GI or DAP5 drives cap-independent translation<br /> of a subset of human mRNAs. J Biol Chem. 295, 11693-11706 (DOI:<br /> 10.1074/jbc.RA120.013678)
On 2021-09-23 12:04:27, user Edward Emmott wrote:
This preprint has now been published in Nature Communications - please see https://www.nature.com/arti...
On 2017-01-12 18:49:13, user Fabien Campagne wrote:
Now with much improved performance. See https://twitter.com/FabienC...
On 2023-11-22 15:04:58, user Mel Symeonides wrote:
Amazing work! This is an ingenious approach. I have a technical question regarding the insertion of the targeting sequences into the provirus. I see that you transformed the library into DH5alpha E. coli (or, well, NEB's version of it). In my experience, even when grown at 30C, proviral plasmids grown in this strain (as well as in NEB10beta) can exhibit LTR-LTR recombination, resulting in a heavily truncated form of the plasmid that excludes the entire provirus but retains the antibiotic resistance gene, which grows competitively against the full-length plasmid. The result of these recombination events is that you end up isolating what might look like "good plasmid" in terms of DNA yield, but actually makes very poor virus stocks as the majority of the plasmid in the prep does not even contain the proviral sequence.
These recombination events can be monitored by simply running the uncut plasmids on a gel and looking for a prominent band around ~3 kb (corresponding to the supercoiled form of the truncated plasmid which should be ~5 kb in length). Long-read (e.g. Nanopore) whole-plasmid sequencing is another great way to detect and quantify the frequency of these recombination events. Switching to NEB Stable E. coli (also grown at 30C) is a fantastic way to reduce the frequency of LTR-LTR recombination.
You did a great job dealing with the repetitive sequences in nef to prevent those smaller recombination events, but I am wondering if you have looked for any such LTR-LTR recombination events that would happen already in the E. coli transformants, and if so, do you know how frequent they might be?
Mel Symeonides<br /> University of Vermont
On 2016-05-28 14:51:02, user boletistan wrote:
How many rats in the control group? Cancer rate in the control group?
On 2017-08-29 04:33:38, user Claudio Casola wrote:
I hope the authors will use these data to also find CNVs. I wonder if that's possible given that only two samples were used for assembly, though there could be another red oak genome available to map reads onto?! In any case this is already a super cool work!
On 2017-10-20 21:03:53, user Matthew Akamatsu wrote:
Our lab presented this manuscript in our journal club, and we decided to share our comments to encourage more discussion about the data.<br /> The manuscript by Sathe and Muthukrishnan et al. uses time lapse fluorescence imaging and electron microscopy to identify two new components of the CLIC/GEEC (non-clathrin non-dynamin) endocytic pathway and provide a time line of the accumulation of several CLIC/GEEC proteins at these sites prior to scission of the vesicle. Importantly they provide strong evidence for the participation of branched actin assembly via Arp2/3 complex in this endocytic process.<br /> The somewhat stereotyped and sequential accumulation of proteins to these GPI-rich sites points to the CLIC/GEEC pathway being a regular celluar process, which could be as stereotyped as other types of endocytosis. Even without a unique marker for their event (GPI-anchored GFP derivative), they are able to build a nice time line of some of the proteins that assemble during the event. This time line includes two new proteins: IRSp53, a protein involved in filopodia formation that contains an inverse BAR domain, CRIB/GBD (Cdc42-binding) domain, SH3 domain, and PDZ-binding domain; and PICK1, a BAR domain-containing protein with a PDZ domain, which inhibits activation of the Arp2/3 complex. One can start to envision a hierarchical pathway that accounts for the timing, predicted pairwise binding of domains, and activation/inhibition relationship between pairs of proteins.<br /> In the spirit of futhering discussion about the fascinating data presented, we have some comments and questions about this version of the manuscript.<br /> 1) Which controls were carried out to check for the effect of the pH switching on their endocytic process? What accounts for the relatively stereotyped jumping of fluorescence intensity for, say, IRSp53 or PICK1 at times far before and after the scission event? Is this due to a small number of tracks being averaged? Or is it a reproducible response from the pH shift?<br /> 2) Are the authors confident they can accurately measure the fluorescence of a concentric ring around the signal in Figure 5? The plot is also hard to read and interpret. Perhaps to report this result they can measure the standard deviation of a Gaussian fit or a FWHM over time?<br /> 3) Was a negative control carried out for the EM experiment; i.e. DAB treatment with nonspecific APEX2 added? Or without APEX2?<br /> 4) Data presentation. How did the authors choose their standard to normalize against, e.g. in Fig. 3b? We suggest that the result would be clearer if data were normalized to the control (i.e. wild type cells). This approach would show the extent to which this process was inhibited in the absence of e.g. IRSp53.
On 2018-11-20 05:29:43, user X. Chen wrote:
the github url of VariantKey in the paper is unreachable
On 2021-01-16 17:58:54, user xuboniu wrote:
Nice work! This story is very good, and it is consistent with our previous work (Tendon Cell Regeneration Is Mediated by Attachment Site-Resident Progenitors and BMP Signaling. Curr Biol. 2020 Sep 7;30(17):3277-3292), which also showed tendon cell ablation in zebrafish disrupted muscle morphology.
On 2020-12-15 19:21:33, user johnLK wrote:
Surprised that there's not more mention of hippocampal maps. As I see it, the "contexts" long-described in the place-cell literature are essentially "models", making virtually all notions of how hippocampal place cells contribute to navigation, "model-based" theories. Pfiefer and Foster points that direction, but the literature is deeper than that.
On 2019-05-15 11:05:14, user Guest wrote:
It makes no sense that Tuscans have South Asian admixture, and no other study shows that. Something's wrong. And in Figure S9C, ALL of the European samples show almost as much non-European (mostly South Asian) admixture as the Afrikaners. That makes no sense either.
On 2024-02-27 08:48:43, user Herman van Eck wrote:
This is an interesting manuscript! However, the manuscript typically describes parallelism. It is not about convergent evolution.
On 2025-06-26 18:10:26, user Amartya Mitra wrote:
Now published:<br /> https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0325229
On 2017-10-03 23:02:53, user AdamMarblestone wrote:
-"Predicting non-linear dynamics by stable local learning in a recurrent spiking neural network" https://arxiv.org/abs/1702....
On 2017-07-14 14:18:37, user AdamMarblestone wrote:
-"Why Does the Neocortex Have Layers and Columns, A Theory of Learning the 3D Structure of the World" http://www.biorxiv.org/cont...
On 2025-09-08 06:07:48, user Ashutosh Tiwari Psychologist wrote:
Good job, waiting to see the next stage of this research. Thanks
On 2020-12-20 12:03:45, user GoranM wrote:
You write that "The study cohort consists of 147 unrelated individuals from Serbia". However, Serbia has a significantly diverse population (nationally) and it would be of interest to know from which regions of Serbia they were. Were the recruited only from the Belgrade region or did you also recruit subjects from Northern Serbia, Eastern Serbia, South-East Serbia, South Serbia (of Romanian, Bulgarian, Albanian, Hungarian, Czech ... descent)? If yes, that would mean that "Serbian" genom, which you showed nicely, overlap perfectly with the European, and that observed changes and differences might mainly be attributed to the epigenetic changes. Serbia is, unfortunately, one of the mots polluted European countries with no enforcement of environment protection laws and very rudimentary public health protection, and with extremely high air pollution and the country with the most active cigarette smokers.
On 2020-12-11 18:07:03, user Georgie Metters wrote:
Interesting stuff, looks like this measurement could be of use in my work!
On 2020-08-11 18:12:38, user Xavier Jenkins wrote:
Would it be possible to create a Phylomorphospace plot using the Gauthier et al matrix? Certainly, the taxa included in the Pritchard and Nesbitt taxa are distant enough that any ecological signal would be low at best, if detectable at all. This would probably better elucidate how Oculudentavis fits within squamates more broadly, rather than just the several Toxicoferid taxa (Uromastyx, Shinisaurus, and Iguana) within the Pritchard dataset.
If not, the inclusion of more plesiomorphic species with Pygopodomorpha (or Gekkota more broadly) and Scinomorpha (if even just two taxa) could benefit the analysis, if not just visually.
On 2023-03-13 08:07:03, user Toman Bortecen wrote:
A PDF with detailed LC-MS method summary has been added to the GitHub repository (https://github.com/krijgsve... "https://github.com/krijgsveld-lab/QuaNPA)").
On 2021-06-26 20:46:44, user Stefano Vianello wrote:
Hello, I was looking for the list of VE and AVE markers you mention in the text but I could not find the Supplementary Tables you reference. I wanted to ask whether these will be uploaded on biorxiv too. Thank you!
On 2020-03-08 13:32:50, user Jun Yan wrote:
I have problem installing ATHENA on both mac and pc. Could you put the executable files on the github as well?
On 2020-04-17 19:12:10, user KR wrote:
I was wondering if you had any thoughts about why the bat coronavirus RBD doesn't bind nor enter the cells expressing the bat ACE2 protein?
On 2021-05-14 13:11:33, user Eloisa wrote:
This article has been peer reviewed and published in Frontiers in Immunology, 10.3389/fimmu.2021.648064
On 2021-06-17 21:04:42, user Huanglab wrote:
Final version has been published on J Med Chem. https://pubs.acs.org/doi/pd...
On 2021-09-26 17:35:19, user Fernando Barroso wrote:
Great job! Happy to see the agreement with our previous theoretical predictions! https://doi.org/10.1101/202...
On 2017-10-28 16:48:16, user Lionel Christiaen wrote:
Student #8<br /> It has been well described that genomic regions near and far away from coding DNA have direct roles in regulating gene expression. Usually these sequences, called enhancers, have binding sequences that function to recruit either activating or repressive transcription factors (TFs). TFs have been shown to bind DNA transiently, which has been suggested as a means to prevent non-specific binding between TFs and enhancer sequences. The local binding of TFs may be caused by increased local concentrations of TF in ‘clusters’. Crocker et al. explored this question via the shavenbaby locus since it has multiple previously described enhancers. Further, the svb locus has known binding factors such as the Hox gene Ultrabithorax (Ubx) and its cofactor Homothorax (Hth). To develop their model, the authors produced a number of novel reporter systems to quantify levels of Ubx within individual nuclei. Specifically they quantified concentrations of Ubx in proximty to the active svb locus. They next mutated relevant known enhancer binding sites in the svb locus to understand how their role and binding affinity to Ubx effects transcription. Finally they observe that Ubx and its cofactor Hth co-localize to regions of active svb transcription.<br /> Other than minor formatting issues, I believe that this is a fantastic paper worthy of publication as it is. It is clear that the authors went though great lengths to develop their experimental design, and in doing so their hypotheses and conclusions are extremely clear. I thought it was creative to simply expand their Drosophila embryos to attain higher resolution of nuclei. To be nitpicky, they could have created artifacts in this procedure, however they make up for this with their subsequent experiments. Other than that, I am convinced by their model. I personally cannot think of anything specific that they would additionally need to do. One minor typo that they should fix is that line 80 references supplemental fig 3 however I believe they mean to reference supplemental fig. 2
On 2018-12-13 00:15:58, user aquape wrote:
Fantastic find, thanks a lot. The rel.long legs, however, don't suggest a fully humanlike locomotion: very long legs are typically seen in wading-birds, e.g. ostriches have rel.shorter legs than herons or flamingoes, and they also have horizontal spines (bipedal mammals also run with horizontal spines), whereas wading-birds hold their spines often more upright.<br /> Little Foot seems to confirm the aquarboreal theory: (1) frequent bipedal wading in forest swamps, not unlike wading apes, such as lowland gorillas in search for aquatic herbaceous vegetation (AHV), google e.g. "gorilla bai" & "bonobo wading", but also (2) frequent vertical climbing in the branches above the swamps. See our 2002 paper in TREE (Verhaegen, Puech & Munro "Aquarboral Ancestors? Trends Ecol.Evol.17:212-217) or google "Ape and Human Evolution 2018 biology vs anthropocentrism".
On 2020-04-03 02:11:33, user Tamara Terzian wrote:
University of Colorado Anschutz Medical Campus ran a story on our article.
On 2020-02-16 23:07:56, user Yuan Xue wrote:
We have updated our preprint "A single-parasite transcriptional atlas of Toxoplasma gondii reveals novel control of antigen expression" to include additional analysis and experiments. Below are some major takeaways:
On 2019-04-16 20:29:02, user Kasthuri wrote:
Please see the revised version of this article at:<br /> https://www.biorxiv.org/con...
On 2025-01-22 19:52:04, user Leonardo Chicaybam wrote:
I peer-reviewed this manuscript on ResearchHub: https://www.researchhub.com/paper/6716382/car-t-cells-targeting-ccr9-and-cd1a-for-the-treatment-of-t-cell-acute-lymphoblastic-leukemia/conversation
On 2024-10-10 22:14:48, user Delphine Destoumieux-Garzon wrote:
Now published in PNAS: <br /> Oyanedel D, Lagorce A, Bruto M, Haffner P, Morot A, Labreuche Y, Dorant Y, de La Forest Divonne S, Delavat F, Inguimbert N, Montagnani C, Morga B, Toulza E, Chaparro C, Escoubas JM, Gueguen Y, Vidal-Dupiol J, de Lorgeril J, Petton B, Degremont L, Tourbiez D, Pimparé LL, Leroy M, Romatif O, Pouzadoux J, Mitta G, Le Roux F, Charrière GM, Travers MA, Destoumieux-Garzón D. Cooperation and cheating orchestrate Vibrio assemblages and polymicrobial synergy in oysters infected with OsHV-1 virus. Proc Natl Acad Sci U S A. 2023 Oct 3;120(40):e2305195120. doi: 10.1073/pnas.2305195120.
On 2018-12-08 02:52:01, user Christoph Nowak wrote:
"... 15% lower risk of coronary heart disease (OR 0.87 ..."<br /> - 13% ?<br /> - I've also been wondering if we shouldn't usually write x % lower odds (OR ..., instead of risk (guilty myself)?
On 2018-03-20 14:55:19, user Gary W Miller wrote:
I know of a journal that would like to see this manuscript. Just saying.
On 2020-02-26 09:38:01, user Stefano Campanaro wrote:
Dear bioRxiv reader,<br /> The preprint was published in Biotechnology for Biofuels with the title "New insights from the biogas microbiome by comprehensive genome-resolved metagenomics of nearly 1600 species originating from multiple anaerobic digesters" (Biotechnology for Biofuels volume 13, Article number: 25; 2020).<br /> You can find the full-text at this link:<br /> https://biotechnologyforbio...<br /> The paper is open access, free of charge.<br /> Thanks in advance for your interest in our publication.
Stefano Campanaro
On 2018-12-14 18:33:41, user Joshua Harrison wrote:
Cool paper and interesting findings! Another relevant citation for interested readers:
Whitaker, M. R., Salzman, S., Sanders,<br /> J., Kaltenpoth, M., & Pierce, N. E. (2016). Microbial communities <br /> of lycaenid butterflies do not correlate with larval diet. Frontiers in microbiology, 7, 1920.
On 2022-11-10 14:22:35, user Anacleto Souza wrote:
The manuscript was published in https://www.tandfonline.com...
On 2017-04-09 09:26:57, user Doug Speed wrote:
The authors find that GCTA performs best among the methods they compare, but this is because their simulation model is unrealistic in one important aspect, and it matches an unrealistic assumption in the heritability model assumed by GCTA. (The authors refer to GCTA as GREML, which is confusing as there are many GREML methods (including both GCTA and LDAK), which differ in the assumed heritability model.)
L71 "Genetic architecture refers to the number, frequencies, effect sizes, and locations of causal variants (CVs) underlying trait variation"
What is missing here is linkage disequilibrium (LD). The GCTA model assumes that the heritability of SNPs is distributed independently of LD, whereas LDAK assumes an inverse relationship between heritability and LD. Across 42 human GWAS we have shown that the LDAK model provides a much closer fit to reality (1), whereas the Evans et al simulations are based on the GCTA model and not the LDAK model.
We previously showed (2,3) that GCTA can give highly biased estimates in regions of low or high LD, whereas LDAK adjusts appropriately. Our recent work (1) was spurred by the misleading results presented in (4), which also compared the performance of GCTA and LDAK using phenotype simulations based on the GCTA model but not the LDAK model. We have pointed out this deficiency to some authors common to both (4) and Evans et al, so it is disappointing to see the same kind of unfair and unrealistic comparisons being made again.
L123 "... simulate phenotypes with differing genomic architectures under realistic patterns of LD structure"
While strictly correct, this is a potentially misleading claim because there was no attempt to include in the simulations a realistic relationship between LD and phenotype. It is true that the genotype data include realistic levels of LD, which is presumably what the authors intended to claim, but the LD-phenotype relationship might be understood to be covered by this claim, and is much more important yet has been ignored.
While we do not currently have a precise model for the relationship between LD and per-SNP heritability, in (1) we showed the LDAK model to be more realistic than the GCTA model across a wide-range of traits, and hence at a minimum any comparison of SNP heritability methods should consider results from simulations under models reflecting both the GCTA and the LDAK models.
Doug Speed, UCL Genetics Institute, London<br /> David Balding, University of Melbourne and UCL
(1) Speed D, Cai N, UCLEB Consortium, Johnson M, Nejentsev S, Balding D (2017) Re-evaluation of SNP heritability in complex human traits. To appear Nat Genet, preprint: BioR?iv doi: 10.1101/074310
(2) Speed D, Hemani G, Johnson M, Balding D (2012) Improved Heritability Estimation from Genome- Wide SNPs. Am J Human Genet, 91(6): 1011-1021. doi: 10.1016/j.ajhg.2012.10.010
(3) Speed D, Hemani G, Johnson M, Balding D (2013) Response to Lee et al: SNP-Based Heritability Analysis with Dense Data. Am J Human Genet, 93(6): 1155-57.
(4) Yang, J. et al. Genetic variance estimation with imputed variants finds negligible missing heritability for human height and body mass index. Nat. Genet. 47, 1114–1120 (2015).
On 2020-05-08 18:11:57, user Zineb El kharouf wrote:
Very important article it answer to a lot of scientific questions, really very thankful to the team for their important efforts, we need this study to identify the virus and to know what are we dealing with.
On 2020-08-20 20:57:15, user Shashank Patel wrote:
Nice study. An indepth followup from our early study in melanoma.
On 2015-10-14 23:26:16, user Val Bykovsky wrote:
Nice technique. A quick question: why don't use electrical patterns to be setup by proper voltages on the micro-electrodes?<br /> Thanks, Val
On 2025-11-16 07:07:24, user Lucian Parvulescu wrote:
A paper that may be of interest to you: http://doi.org/10.7717/peerj.18229
On 2020-10-14 11:46:13, user Yoann wrote:
Why not simply breed Charolaise cows? They are plain white. https://fr.wikipedia.org/wi...
On 2025-09-26 16:52:17, user Elzi wrote:
To clarify, the sample pool consisted of all males, no women?
On 2025-04-01 17:52:03, user Mathurin Dorel wrote:
Published as https://link.springer.com/article/10.1186/s13046-024-03075-y
On 2021-05-10 15:31:03, user Alok Javali wrote:
Dear authors,
Thank you for performing this meta-analysis of the single cell sequencing data from the 2 recently published human blastoid papers. It is extremely beneficial for the community to be able to benchmark results to high-quality reference maps using adequate bioinformatic pipelines. Information on the level of similarity between blastocyst and blastoid cells will decide on the potential for these models to predict the molecular aspects of human early development.
It is quite evident from your analysis that several cell populations in <br /> both the iBlastoids and stem blastoids have signatures corresponding to post-implantation cell types as exemplified by an overlap with streak-stage and mesoderm cells. In the stem blastoid, we wonder if you have also looked at the presence of post-implantation-like<br /> trophoblasts within the trophoblast cluster. Would you be able to use <br /> gene sets for Extra-villous Trophoblasts or Syncytiotrophoblasts (for <br /> example as defined by Castel et al. PMID: 33238118 ) to evaluate the <br /> percentage of pre- vs. post-implantation trophoblasts? Because of the limitations of the UMAPs, it is also difficult to evaluate the percentage of blastoid cells that overlap with the pre-implantation epiblast, and, more generally, the total percentage of blastoid cells resembling the pre-implantation blastocyst stage.
Merging datasets is prone to biases and we wonder how to evaluate and limit them. What kind of controls do you consider the best to evaluate the proper merging between datasets? Do you value anchoring points based on common physical samples (e.g. sequencing few embryo cells along with your stem cells) in order to enhance reliability? Moreover, have you performed multiple analysis with different batch correction and integration methods in order to evaluate the biological significance of the merging?
On a separate note, we were wondering why you mentioned that the higher ratio of epiblast/trophoblast suggests that blastoids represent a <br /> post-implantation-like state. While appropriate ratios between three founding lineages is important at all stages, in our experience the proportions in the scRNAseq datasets do not reflect the proportions in blastoids due to biases in dissociation, data filtering, etc... We think that immunofluorescence might allow for measurements more closely reflecting the truth, although it must rely on well-selected markers.
Let us know what you think!
Best wishes,<br /> Alok Javali, Harunobu Kagawa, Heidar Heidari, Theresa Sommer, Giovanni Sestini, Nicolas Rivron
On 2023-09-22 23:48:41, user Duncan Muir wrote:
Summary:<br /> Arrhenius/Eyring behavior for enzymes (i.e., a linear increase in kcat with temperature) is predicted by transition state theory. Nevertheless, deviations from this minimal model have been observed over the years[4]. Work from groups including Klinmman[27,28] and Daniel and Danson[21] have exemplified this unexpected, non-Arrhenius behavior of enzymes and have sought to apply models to these observations, including an equilibrium between active and inactive states of the substrate-bound enzyme[21]. Åqvist and colleagues [5] have sought to distinguish between competing models, including the activation heat capacity model from this manuscript, and the equilibrium model. In the literature, there are contradictory reports regarding the role of activation heat capacity [22,8], leaving the debate on the origin of non-linear temperature dependence of activity unresolved.
This paper aims to characterize conformational changes during catalysis through the lens of activation heat capacity. The authors used MalL as a model system and conducted kinetics experiments over a range of temperatures to calculate changes in the heat capacity of activation. Using molecular dynamics, the authors simulate a narrowed conformational landscape of a transition state-like MalL in comparison to wild-type. Based on the observed kinetic behavior of MalL across a wide range of temperatures, the authors presented MMRT-2S, a modified version of Macromolecular Rate Theory (MMRT) that accounts for a cooperative transition between the enzyme-substrate conformation and the transition state-like conformation. The major strength of this paper is high quality temperature-dependent kinetic data collected over a range of temperatures typically not explored, which allows us to evaluate complex models to describe unexpected enzymatic behavior. The major confusion we have with this paper is the lack of details needed to aid the reader in interpreting the data: namely not presenting the hydrogen bond measurements and statistics to demonstrate significance, and a lack of controls or citations for kinetic assay assumptions. Overall, this paper presents an intriguing explanation for the role of conformational changes in catalysis to reconcile diverse observations noted in enzyme literature.
Major points:<br /> Substrate Saturation, Denaturation Controls, and Assay Set-UpAccording to the methods section, saturating concentrations of p-nitrophenyl-?-D-glucopyranoside were assayed. It would be helpful if exact concentrations were reported for reproducibility of the work. Furthermore, it is important to mention if Km has been reported at the range of temperatures assayed for this substrate, to confirm saturation.
The text mentions that nonlinearity has been reported in this absence of denaturation; this statement could be strengthened if controls or references were included to corroborate this.
From the description in the methods section, we believe the enzyme is not incubated at the assay temperature prior to the reaction, so over the course of the limited reaction time (45 seconds) we are unsure if the enzyme has reached a folded/conformational equilibrium. Furthermore, we think it would be informative for readers if the authors include discussion about foldedness being unlikely to be a contributor to changes in kcat.
Mutant Structure RationaleThe rationale for creating the S536R mutant is unclear to us. Although we agree that “introducing new hydrogen bond networks at the surface of the protein” is one way to perturb conformational dynamics and rates, we are unsure why arginine was selected as the residue for this. We are also unsure why the X-ray structures were determined in the presence of urea.
Mutant / TLC ActivityThe mutant structure shows a distinct ensemble via the PCA projection. It is important to discuss if activity data collected for this mutant should be expectedly different from WT based on the MMRT-2S model.
Hydrogen Bond MeasurementGiven the difference in crystal packing between the wild-type and mutant structures, it is important to identify if any of the hydrogen bonds of interest are proximal to a gained or lost crystal contact between the two structures.<br /> Additionally, while truncating the data to a similar resolution will remove high resolution reflections from the mutant structure, the reflections around 2.3Å will likely have higher signal:noise, which complicates analysis. <br /> It would be helpful for readers to have more details on the hydrogen bonds mentioned in Figure 5A – for example, by creating a table of the residues involved in each bond and the amount that the bond was shortened. Further interpretation of the shortened hydrogen bonds would also be helpful; for example, we are curious if the authors consider all the shortened hydrogen bonds as equal contributors to the restricted conformational landscape of the TLC, or if there are certain ones that the authors believe are more impactful.
Additionally, for those unfamiliar with MD, more commentary on the interplay between structural data and MD simulations would be instructive. For example, are residue motions observed in MD simulations consistent with the hydrogen bonding differences observed in the static structure?
ADH ComparisonWe are unsure if the authors’ claims contrasting MalL and ADH at different temperatures are supported by the data, given that there was no MD performed on ADH. For MalL, we were able to visualize the restricted conformational landscape of the TLC via a combination of MD simulations and structural data. For ADH, only Cp is calculated from kinetics data, and there is no structure or MD data presented. Arguing that there is a more restricted conformational landscape based on Cp alone seems insufficient compared to the amount of work done on MalL. Perhaps citing more previous literature on ADH in detail would be helpful for strengthening the contrast between MalL and ADH.
Minor points:<br /> Language / Word Choice:Use of word “significant” should be reserved for demonstrated statistical significance<br /> Qualifiers like “very” were used frequently and distract from the data and claims in themselves, particularly in section headers i.e., “Very High Resolution Structure...”<br /> “Accurate” should be replaced with “precise” when describing collected data, as the data themselves are observations of ground truth, and the narrow distribution of replicates implies high precision
Scope<br /> The claim “The importance of activation heat capacity for enzyme kinetics has been the subject of debate recently” is supported by 3 self-references, out of four total.
In previous work, the authors identify that MMRT applies to chemically-limited catalysis, which should be mentioned in the discussion regarding the applicability of the model, and cite Kern and Hilser’s works on adenylate kinase as an edge case as in the author’s previous reviews.
Figures <br /> In general, the addition of more labels/legends on the plots would help with interpretation.<br /> Specific examples:<br /> Figure 2: labeling the color that corresponds to each mutant <br /> Figure 3: labeling deuterated vs protiated <br /> Figure 3D: labeling which curves correspond to ?H and ?S<br /> Figures 4A & 4B: For clarity, the different regions of MalL (lid, active site, loop, etc.) could be labeled here. Furthermore, arrows depicting the motions observed in PCA1 and PCA2 would be informative.<br /> Figure 5A: For clarity, the S536R mutation could be labeled here.
On 2019-02-21 14:56:28, user Matheus LC wrote:
Congratulations Ricardo Righetto
On 2022-09-27 09:53:01, user Emmanuel Gaquerel wrote:
Hi, thanks for the feedback. The corresponding zenodo link should work now.
On 2016-11-30 16:57:32, user Peter K Rogan wrote:
The published article in Nucleic Acids Research indicates that this preprint at bioRxiv contains our responses to the reviewers' comments on the paper. BioRxiv would not published the revised preprint (with these responses). <br /> We have therefore published the responses on our company website: http://www.cytognomix.com/w...
On 2020-01-13 12:16:02, user Phil wrote:
Seems like this paper is now published in Nature Structural & Molecular Biology<br /> https://www.nature.com/arti...
On 2025-11-29 18:12:35, user Agata Leszczuk wrote:
The paper is currently published in Plant Science: https://doi.org/10.1016/j.plantsci.2025.112906
On 2017-05-16 19:24:45, user Matt wrote:
Good paper, very interesting. One possible inconsistency: Vucedol Tell sample I4175 is listed as a "15-17 year old male, found in a double burial" in Supplementary Notes and marked as female in the Supplementary Table, while Vucedol Tell sample I2792 marked as "40-45 year old female, found in a multiple burial" in notes is marked as male in the table. Are these two mixed up somewhere, and how do these corresponds to the samples marked in the autosomal analysis?
On 2025-01-09 06:09:05, user Chun Wai Wong wrote:
This has been published in Nature Communications ( https://www.nature.com/articles/s41467-023-41737-1#citeas )
On 2020-02-05 11:13:06, user Pei-Hui Wang wrote:
Prof. Wang Pei-Hui Lab in Shandong University, China, all orf clones of 2019-nCoV / SARS in pcDNA6B-FLAG can be freely requested, please contact him. Email: wphlab@163.com covering the following orfs: nsp1-16, S, 3, E, M, orf6, orf7a/b, orf8, orf9, orf14, N; and ACE2
On 2018-02-22 06:40:29, user Silas Kieser wrote:
It doesn't work without the 3D structure, isn't it?<br /> What abiut speed?
On 2017-06-16 22:13:56, user John Belmont wrote:
Replication cohorts?
On 2020-02-29 15:44:02, user Aurora Fontes wrote:
This is the peer-reviewed final version: https://www.frontiersin.org...
On 2020-06-17 10:39:45, user Markus Pfenninger wrote:
The questions "Can biodiversity adapt to increasing anthropogenic pressures?", "Is evolutionary adaptation rapid enough for the current rate of environmental change?" or similar issues are key questions in many areas of current biology. To answer these, it would be paramount to know how many different selection pressures organisms can adapt to simultaneously and at which speed these adaptations can proceed. Yet our knowledge on this vital issue is currently rather limited.
Our manuscript opens up a new approach to qualitatively and quantitatively address this important problem. The application to a natural population of a non-biting midge shows that adaptation in natural populations can be almost instantaneous, affect most of the genome and react to many selection pressures. Like several recent findings, our study thus challenges classical paradigms in population genetics.
On 2020-09-02 04:35:47, user Nafisa Jadavji wrote:
This is an interesting study. Have you looked other classes? What about hands on labs? Was there a movement to using journals JoVE?
On 2024-02-29 22:41:31, user Michael White wrote:
This manuscript is now published in PLOS Computational Biology as of Jan 16, 2024. https://journals.plos.org/p...
On 2025-01-22 08:26:37, user PreOmics wrote:
As representatives of PreOmics, we would like to highlight a key observation in the submitted manuscript for readers to consider.
Supplementary Figure 1: The authors describe the injection of 300 ng peptides, but Supplementary Figure 1 shows variations in TIC signal intensities for the compared techniques. These differences may stem from the two different peptide quantification assays employed and described in the study. From a mass spectrometry perspective, such discrepancies make it challenging to technically compare different enrichment techniques due to the influence of signal intensity variations on the S/N ratio, peak-picking algorithms, peptide quantities, and sequence coverage. We recommend to repeat the study and use our recommendations for peptide quantification compatible to ENRICHplus.
In addition, it is important to acknowledge that the ENRICHplus pre-release version was employed for the comparison, which differs from the commercial version scheduled for release on February 22, 2025.
PreOmics is always supportive if customers struggle to achieve expected results with our solutions and we are keen to support you and provide our recommendations to achieve the best results possible.
On 2024-04-15 15:15:55, user Vojtech Cermák wrote:
This work has been published in Frontier in Plant Science: https://doi.org/10.3389/fpl...
On 2021-12-06 20:58:47, user disqus_8AVEuorTBu wrote:
It is worth noting that the LKGG sequence matching MYH6/7 discussed here is the exact site where SARS-CoV-2 PLpro cleaves its polyprotein. Reynolds et al. used an equivalent method of searching homologous sequences and then experimentally verified that this MYH6/7 sequence is cleaved by SARS-CoV-2 PLpro and more slowly by MERS PLpro (doi: 10.1021/acsinfecdis.0c00866). This cleavage prediction was, however, not replicated by any neural networks trained on more diverse PLpro cleavages (doi: 10.1101/2021.10.04.462902). Without any evidence of autoantibodies against this site, I think it’s much more likely that COVID-associated myocarditis is caused by cleavage and not mimicry causing rare genetic variant-dependent autoimmunity.
On 2023-03-30 14:34:49, user Nikola wrote:
a) Increase the sample size: The results may not be as generalizable as they could be because the study only included a small sample of mice. The probability of false-positive or false-negative results may be decreased and statistical power may be increased by increasing the sample size.<br /> b) Incorporate extra controls: The study utilized wild-type and knockout mice as controls. Including other control groups, however, such as mice with a heterozygous deletion of the PCYT2 gene or mice with a knockout of a different gene, may be advantageous. This would assist in eliminating any possible confusing effects.<br /> c) The study used mice as a model organism, and it is uncertain whether the results can be generalized to human populations to validate the findings. By examining gene expression patterns in muscle biopsies or carrying out clinical trials with PCYT2 modulators, additional studies could examine the function of PCYT2 in muscle health and aging in human populations.<br /> d) Employ more sophisticated methods for data analysis: The study examined levels of gene and protein expression using Western blotting and qPCR. Yet, further approaches like RNA sequencing, proteomics, or metabolomics may offer a more thorough comprehension of the molecular pathways governing muscle health and aging.<br /> e) Perform functional studies: The study did not look into the underlying mechanisms, despite demonstrating the importance of PCYT2 in muscle health and aging. Functional assays could be used in future research to examine PCYT2's effects on muscle structure and function, including muscle fiber size, contractile characteristics, and fatigue resistance.
On 2019-05-27 14:41:04, user Florian Freimoser wrote:
This work has now been published in Molecular Microbiology:<br /> https://onlinelibrary.wiley.com/doi/full/10.1111/mmi.14272
On 2018-09-17 11:56:56, user Robin Beaven wrote:
These elegant and insightful experiments are a great contribution to the ongoing debate about Wnt/Wingless long-range function (we hope our paper is too! https://elifesciences.org/a... as well as a fascinating recent study in C elegans https://elifesciences.org/a... ).
I have one question regarding figure 2. This is interesting to me as Nrt-Wg is clearly at higher levels, at least in fixed tissue. This is consistent with the idea that the protein is more stable as previously suggested, and we see a similar staining difference in the embryonic midgut (https://elifesciences.org/a... "https://elifesciences.org/articles/35373#fig4s1)"). What I don't quite follow is that you state "these experiments showed a much broader domain of extracellular NRT-Wg protein than<br /> the two stripes of cells that actively express wg at the late third instar larval stage (Figure<br /> 2A’)". To me Figure 2 seems to show rather the opposite ie. a more restricted spatial Nrt-Wg domain? You may want to consider clarifying this point.
On 2020-07-17 09:47:55, user Mestan Sahin Pir wrote:
The article is very well-written and it is easy to follow. In addition, the article well discusses the topic of interest, and deals with a topic with many applications in practice.
In my opinion, the article has its merit and is of interest for the PLOS ONE readership. The length of the paper is appropriate.
It does not include unnecessary extra information. The authors used a data set, which is current and complete.
The data spans 34 years, from 1980 to 2013. The authors indicate the STATISTICS PROGRAM used for the analysis in the paper.
On 2021-03-16 15:30:48, user Rudolf A Roemer wrote:
Excellent paper, just what I was waiting for to extend our own study on flexibility and mobility of SARS-CoV-2 proteins [Sci. Rep. 11, 4257 (2021), https://rdcu.be/cfvcQ] to the mutated spikes. I hope that the PDB structures will soon be released to general use. Do you have an estimate when this might be done?
On 2024-08-03 16:25:52, user James Hurley wrote:
The community should be aware that the structure that is reported to be predicted here was already solved by cryo-EM and preprinted on Jun. 1, 2023 https://www.biorxiv.org/content/10.1101/2023.06.01.543278v3 and the cryo-EM coordinates of the same complex were released on Jun. 23, 2023 https://rcsb.org/structure/8SOI .
On 2024-02-19 15:17:57, user Mathis Riehle wrote:
super nice - congratulations conceptional very persuasive paper!
Q: is there a link to the movies to have a look? Would love to use them in class in the future - nothing like seeing is believing.
slight critique - the bar graph overlays on top of the data in figs 2B, D & F, 3D, 4C & E, 5C & D & 6C are OK - if you think that you need them - but consider toning their intensity down to allow one to 'see the data' - they are a bit 'in the way'/too strong imho.
Not quite sure if the n numbers given in the figures for 'cells' are for independent experiments or neighbouring cells? If they are in the same dish it would quite difficult in dens(ish) cultures as shown to be assured of independence; in your methods you talk about 3-8 independent experiments - here exemplary analysis showing the data as visualised via e.g. SuperPlotsofData by J Goedhard (https://huygens.science.uva... & DOI: 10.1083/jcb.202001064) would give assurance that these datasets are good to be assembled/pooled together?
On 2016-08-31 10:46:53, user Iain Johnston wrote:
IMPORTANT! There's a production-related typo in the abstract here. We don't find differences of 2080 SNPs between mtDNAs -- this should read 20-80 SNPs.
On 2023-12-01 19:08:37, user nbrake wrote:
The content in this manuscript related to sodium channel gating has now been published.<br /> Niklas Brake, Adamo S. Mancino, Yuhao Yan, Takushi Shimomura, Yoshihiro Kubo, Anmar Khadra, Derek Bowie; Closed-state inactivation of cardiac, skeletal, and neuronal sodium channels is isoform specific. J Gen Physiol 4 July 2022; 154 (7): e202112921. doi: https://doi.org/10.1085/jgp...
On 2025-09-15 07:51:30, user Thorsten Schnurbusch wrote:
Figures are missing in the text!?
On 2025-05-28 16:48:42, user Jonathan Eisen wrote:
Nice work. One quick comment - I think the use of the term "green algae" in reference to Ostreococcus and Bathycoccus is not ideal. I suggest either replacing this or at least adding the scientific name for the group in which these taxa are found (Chlorophyta).
On 2025-11-11 17:59:30, user Erin Sauer wrote:
This manuscript is now published:<br /> https://doi.org/10.1093/icb/icaf086
On 2022-01-13 02:08:19, user Seth Winfree wrote:
Dataset for this manuscript is now available:<br /> https://doi.org/10.5281/zen...<br /> Demonstrations coming soon at:<br /> https://vtea.wiki
On 2016-08-02 10:34:50, user Marco Pontoglio wrote:
Very interesting paper. I think that all these results very nicely complement our recently published study:
http://nar.oxfordjournals.o...
1) Formaldehyde fixation is responsible for the artefactual dissociation of HNF1Beta from mitotic chromatin. On the other hand, a simple methanol fixation can preserve the mitotic localisation.
2) Human mutations (affecting DNA binding in a temperature dependent way) are defective in mitotic localisation and display a very dynamic temperature dependent (citoplasm/mitotic chromatin) shuttling
3) Importazole ( an Importin beta inhibitor) blocks the relocalisation on mitotic chromatin
Marco Pontoglio <br /> Inserm U1016/CNRS UMR 8024/UP5/ Cochin Institute<br /> Paris / France
On 2020-06-08 12:28:54, user OxImmuno Literature Initiative wrote:
On 2021-04-22 12:32:41, user JohnPravinKing wrote:
'Plate Reader with 390-400 nm excitation and 580-510 nm emission wavelengths'. <br /> Is the emission wavelengths correct for the substrate used in FAP activity?
On 2018-09-04 14:42:10, user vkrishna wrote:
Hi,
A peer reviewed version of this preprint is available at
https://journals.plos.org/p...
The version published in the journal is substantially revised and improved based on comments received during the review process.
Vinod Krishna (one of the authors).
On 2021-01-15 17:24:31, user Richard Stopforth wrote:
Dear Haizhang and colleagues,
First of all, congratulations on this work and very interesting data.
I wanted to comment that I have previously developed an assay capable of detecting soluble immune complexes, by measuring SHIP-1 recruitment to human FcgRIIb (https://pubmed.ncbi.nlm.nih... "https://pubmed.ncbi.nlm.nih.gov/29351998/)"). Although employing a different system (detection of inhibitory as opposed to activatory FcgR signalling), I think this paper is worthy of a reference, if only to highlight its differences or limitations.
Your observation that FcgRIIA is non-responsive to soluble immune complexes is very interesting, as I found FcgRIIA (H131) to pair very effectively with FcgRIIb in the activation of SHIP-1 recruitment to FcgRIIb in response to soluble immune complexes. This is presumably caused by the licensing of inhibitory signalling by activatory FcgRIIA-inhibitory FcgRIIb co-ligation. Altogether, this possibly indicates that activatory FcgRs may have differing roles in the regulation of activatory or inhibitory signalling in response to soluble immune complexes? In relation to this, I was wondering if you co-expressed your FcgRIIb/c and FcgRIIa constructs in your reporter cells, whether you could crosslink these receptors (i.e. with anti-human FcgRIIa/b, AT-10) and detect any differences in comparison to both 1) individual receptors alone and 2) soluble immune complexes?
Kind regards,
Richard Stopforth
University of Southampton<br /> R.J.Stopforth@soton.ac.uk
On 2024-11-17 04:42:42, user De Novo Enzyme Enjoyer wrote:
Fantastic paper, breathtaking results. Just one minor suggestion—when I searched for RFam on GitHub, I found a repo with an almost identical name (Rfam) already exists, for an RNA bioinformatics tool. So to avoid confusion/difficulty locating the Rosetta Flow Atomic Matching codebase (and paper, whenever Ahern et al comes out), it might be a good idea to change the shorthand nickname for the model before publishing the repo (and the papers). RFlam, RoFAM and RoFlam all appear to have more unique namespace than RFam, while still being just two syllables to pronounce. Just a thought!
On 2017-03-16 15:01:39, user Ploulack wrote:
Small typo page 7: Experiments confirm this prediction (Figure 4C), I believe it should be 3C here.
On 2017-11-13 13:30:24, user N.K.Mishra wrote:
Did the RNAs have any complementarity with noncding regions of nucleolar DNA that codes for three of the four classes of ribosomal RNA?
On 2018-03-22 02:34:15, user jvkohl wrote:
Use of the term enhancer is confusing. Please consider the link from bioinformatics to energy-dependent top-down causation via microRNAs (miRNA expression). See for instance: https://www.ncbi.nlm.nih.go... (2011)
Cis-regulatory elements of gene expression linked matrix-attachment regions (MARs) and miRNA expression by tethering the chromatin to the nuclear matrix.
"This study implies that the association of MAR binding proteins to MARs could dictate the tissue/context specific regulation of miRNA genes by serving as a boundary element signaling the transcriptional activation."
The boundary gets blurred when the virus-driven theft of quantized energy as information changes the miRNA/messenger RNA balance. The effect on "...tethering the chromatin to the nuclear matrix..." changes to a less structured supercoiled DNA, which is also less functional. The degradation of messenger RNA links negative supercoiling to all pathology.
On 2024-02-23 23:44:15, user Doris Loh wrote:
The preprint stated that "taurine (TCI Chemicals) was dissolved directly into complete cell culture media at a concentration of 40 mg/mL", which is 319.62 mM. However, throughout the entire study, the only concentration of taurine used in various experiments was either 160 mM or 100 uM. Was 40 mg/mL a typo or did the authors use 320 mM instead of 160 mM?
On 2018-09-20 00:12:06, user Arlin Stoltzfus wrote:
Though this paper cites McCandlish and Stoltzfus 2014, a lengthy historical and mathematical review of origin-fixation dynamics, the contents of that paper clearly have not been digested. They explain the history and also the logical structure of derivations, and they raise the same questions about whether the assumptions of origin-fixation dynamics are actually justified for natural populations. Multiple things you say in the introduction are not justifiable, e.g., the "weak mutation" assumption underlying origin-fixation dynamics is not at all "classical," being completely divorced from the views of Fisher, Haldane and Wright. Origin-fixation dynamics are presented explicitly in two different papers in 1969.
On 2020-08-19 12:37:40, user Tomáš Hluska wrote:
Hi, if you have still chance to change the manuscript, here are a couple of things I noticed:<br /> - p. 7 at the very end sentence starting "Compared to wild type" contains twice the concentrations<br /> - p. 9 first line - should be Suppl. Fig. 4B<br /> - You don't use consistently the abbreviations. Mostly, you use LOT, but e.g. in Fig. 6 description you use KO.<br /> - Fig. 7 - what are the standard conditions? Are they the same for both transporters or do they differ? BTW in the text you say (and I would agree) that the Ade uptake decreases in absence of E source, while in the Fig description you say "its activity is independent of an energy source.". Yeah, it may be less affected but there is still some effect.<br /> p. 15 - may transports - should be may transport
Honestly, I don't see much difference in the azg2 ko and AZG2 OE plants on 8-aza-Ade (Suppl. Fig. 4B).
On 2017-04-11 09:52:55, user Yu-Chen Liu wrote:
The authors appreciate the insightful feedbacks and agree with prospect that hypothesis derived from small RNA-seq data analysis deserve examination in skeptical views and further experimental validation. Regarding the skeptical view of Prof. Witwer on this issue, whether a specific sequence were indeed originate from plant can be validated through examining the 2’-O-methylation on their 3’ end (Chin, et al., 2016; Yu, et al., 2005). The threshold of potential copy per cell for plant miRNAs to affect human gene expression was also discussed in previous researches (Chin, et al., 2016; Zhang, et al., 2012).
Some apparent misunderstandings are needed to be clarified:
? In the commentary of Prof. Witwer:<br /> “A cross-check of the source files and articles shows that the plasma data evaluated by Liu et al were from 198 plasma samples, not 410 as reported. Ninomiya et al sequenced six human plasma samples, six PBMC samples, and 11 cultured cell lines 19. Yuan et al sequenced 192 human plasma libraries (prepared from polymer-precipitated plasma particles). Each library was sequenced once, and then a second time to increase total reads.”
Authors’ response:
First of all, the statement "410 samples" within the article was meant to the amount of runs of small RNA-seq run conducted in the referred researches. Whether multiple NGS runs conducted on same plasma sample should be count as individual experiment replicates is debatable. The analysis of each small RNA-seq run was conduct independently. The authors appreciate the kind comments for the potential confusion that can be made in this issue.
? In the commentary of Prof. Witwer:
“Strikingly, the putative MIR2910 sequence is not only a fragment of plant rRNA; it has a 100% coverage, 100% identity match in the human 18S rRNA (see NR_003286.2 in GenBank; Table 3). These matches of putative plant RNAs with human sequences are difficult to reconcile with the statement of Liu et al that BLAST of putative plant miRNAs "resulted in zero alignment hit", suggesting that perhaps a mistake was made, and that the BLAST procedure was performed incorrectly.”
Authors’ response:
The precursor sequences of the plant miRNAs, including the stem loop sequences (precursor sequences) were utilized in the BLAST sequence alignment in this work. The precursor sequence of peu-MIR2910, “UAGUUGGUGGAGCGAUUUGUCUGGUUAAUUCCGUUAACGAACGAGACCUCAGCCUGCUA” was used. The alignment was not performed merely with the mature sequence, “UAGUUGGUGGAGCGAUUUGUC”. <br /> The stem loop sequences, as well as the alignment of the sequences against the plant genomes, was taken into consideration by using miRDeep2 (Friedländer, et al., 2012). As illustrated in the provided figures, sequencing reads were mapped to the precursor sequences of MIR2910 and MIR2916. <br /> As listed in the table below, a lot of sequencing reads can be aligned to other regions within the precursor sequences except the sequencing reads aligned to mature sequences. For instance, in small RNA-seq data of DRR023286, 5369 reads were mapped to peu-MIR2910, and 4010 reads were mapped to the other regions in the precursor sequences.
https://uploads.disquscdn.c...
(Check the file MIR2910_in_DRR023286.pdf, MIR2910_in_SRR2105454.pdf, MIR2914_in_DRR023286 and MIR2916_in_SRR2105342.pdf) <br /> The pictures are available in the URL:
<https: <a href="www.dropbox.com" title="www.dropbox.com">www.dropbox.com="" sh="" 9r7oiybju8g7wq2="" aadw0zkugsdsti3aa\_4x6r8ua?dl="0">
As described in the article, all reported reads mapped onto the plant miRNA sequences were also mapped onto the five conserve plant genomes. Within the provided link a compressed folder file “miRNA_read.tar.gz” is available. Results of the analysis through miRDeep2, were summarized in these pdf files. Each figure file was named according to the summarized reads, sequence run and the mapped plant genome. For example, reads from the run SRR2105181 aligned onto both Zea mays genome and peu-MIR2910 precursor sequences are summarized in the figure file “SRR2105181_Zea_mays_peu-MIR2910.pdf”.
? In the commentary of Prof. Witwer:
“Curiously, several sequences did not map to the species to which they were ascribed by the PMRD. Unfortunately, the PMRD could not be accessed directly during this study; however, other databases appear to provide access to its contents.”
Authors’ response:
All the stem loop sequences of plant miRNAs were acquired from the 2016 updated version of PMRD (Zhang, et al., 2010), which was not properly referred. The used data were provided in the previously mentioned URL.
? In the commentary of Prof. Witwer:
“Counts were presented as reads per million mapped reads (rpm). In contrast, Liu et al appear to have reported total mapped reads in their data table. Yuan et al also set an expression cutoff of 32 rpm (log2 rpm of 5 or above). With an average 12.5 million reads per sample (the sum of the two runs per library), and, on average, about half of the sequences mapped, the 32 rpm cutoff would translate to around 200 total reads in the average sample as mapped by Liu et al.”
Authors’ response:
Regarding the concern of reads per million mapped reads (rpm) threshold, the author appreciate the kind remind of the need to normalize sequence reads count into the unit in reads per million mapped reads (rpm) for proper comparison between samples of different sequence depth. However the comparison was unfortunately not conducted in this work. Given the fact that the reads were mapped onto plant genome instead of human genome, the normalization would be rather pointless, considering the overall mapped putative plant reads only consist of ~3% of the overall reads. On the other hand, the general amount of cell free RNA present in plasma samples was meant to be generally lower than within cellar samples (Schwarzenbach, et al., 2011).
Reference
Chin, A.R., et al. Cross-kingdom inhibition of breast cancer growth by plant miR159. Cell research 2016;26(2):217-228.<br /> Friedländer, M.R., et al. miRDeep2 accurately identifies known and hundreds of novel microRNA genes in seven animal clades. Nucleic acids research 2012;40(1):37-52.<br /> Schwarzenbach, H., Hoon, D.S. and Pantel, K. Cell-free nucleic acids as biomarkers in cancer patients. Nature Reviews Cancer 2011;11(6):426-437.<br /> Yu, B., et al. Methylation as a crucial step in plant microRNA biogenesis. Science 2005;307(5711):932-935.<br /> Zhang, L., et al. Exogenous plant MIR168a specifically targets mammalian LDLRAP1: evidence of cross-kingdom regulation by microRNA. Cell research 2012;22(1):107-126.<br /> Zhang, Z., et al. PMRD: plant microRNA database. Nucleic acids research 2010;38(suppl 1):D806-D813.
On 2015-11-03 01:55:13, user Fabien Campagne wrote:
Here's a video to illustrate Figure 6. The video shows the interactive capabilities when combining composable R with the biomart micro-language. https://youtu.be/ZwGj1RPOODQ
On 2020-06-29 11:48:04, user Grigorios Koulouras wrote:
We recently posted a highly-related article: https://doi.org/10.1101/2020.06.25.170431<br /> It seems we neglected to cite this article. We apologize for this omission, which we will fix at our next revision stage.
On 2024-08-26 01:58:52, user Pravesh Gupta wrote:
This article has been published in Neuro-Oncology and can be accessed https://doi.org/10.1093/neuonc/noae139 .
On 2019-01-15 19:15:29, user Michel Thiebaut de S wrote:
This is so cool. I always wondered whether there was some similarities in cytoarchitecture accross the brain! Looks like there is!!!
On 2019-05-29 14:39:32, user Victoria wrote:
I appreciate that the authors do acknowledge that the recombinant and native enzymes may possess different properties. This assumption is often underestimated, especially by biochemists with chemical background. Yet I would like to pinpoint that the statement of the manuscript that "The biochemical and enzymatic properties of the native KADHC in mammalian tissues have not been characterized", is not true. In 2017, a paper was published (PMID: 28601082), where it was shown that a rather high levels of OGDH activity in the brain homogenates are devoid of the OADH activity. That said, one cannot exclude formation of the hybrid complexes in the cell lysates as an artefact of the detergent extraction and/or further purification procedures. Regarding the OADH activity of the purified OGDH complexes, one could add other couple of references to the discussion: PMID: 10848975; PMID: 8495733
On 2020-04-03 14:46:22, user Maria Llamazares wrote:
It was a pleasure, a challenge and a very positive scientific experience collaborating with Big Pharma (Boehringer Ingelheim, our sponsors), Academia in Germany and US (EMBL, DKFZ, McGovern Medical School), clinics (Thorax Clinic Heidelberg, Asklepios Biobank) from our Innovation Center (BioMed X). Big thanks to all the authors for their excellent work and input!! Hope you can implement succesfully our workflow for the profiling of human samples!!!
On 2018-08-31 05:31:31, user Harel Weinstein wrote:
Glad to see the data in this preprint https://doi.org/10.1101/362970 . So many different ways have been suggested for membrane modulation of GPCR function, especially by stabilization of various states. Not much was said or cited about these in this manuscript, but the literature is there. Without energy calculations and appropriate comparisons, it must be difficult to discern the specific mechanism and the relative importance of the different modes of modulation by the membrane. Binding to a protein state is a competition with the surrounding membrane, and, if the composition changes, so will the apparent affinity of the binding lipid (Chol, Pip2, etc). Even if it still adheres, the stabilizing effect may be insignificant. How best to interpret this? We are still trying to decide what can be learned from the identity of lipids adhering to the protein in certain conditions and how the hydrophobic mismatch energy on the one hand, and the segregation free energy of the particular binding lipid, on the other, balances this. Any good ideas from the in-vivo-like membrane results about likely concentrations required of potential modulators? It's a fascinating problem but may be more complex than it appears.
On 2019-12-30 14:59:49, user Lei Khiang Tswian wrote:
It would be great if Japanese and Siberian samples are included in the Admixture analysis. The purple source in Mongolic and Tungusic populations cannot be the same as that of Sino-Tibetans. It should be splitted into at least three sources including Northern Asian (Siberian), Eastern Asian (Japanese) and Sino-Tibetan (such as Yi/Naxi).
On 2020-05-13 19:27:51, user Sinai Immunol Review Project wrote:
Keywords: SARS-CoV2, Human lungs, Transcriptomics
Main findings: In this preprint, the authors used bulk and single-cell transcriptomics in human lungs to study possible interactions between age-associated host genetic factors and genes regulated by SARS-CoV-2/SARS-CoV infection. Their transcriptomics data suggest that an aging lung has an increased vascular smooth muscle contraction, reduced mitochondrial activity, and decreased lipid metabolism though expression of cellular receptors for SARS-CoV2 was not age dependent. Combining both bulk- and single-cell sequencing data, they found that the number of lung epithelial cells, macrophages, and Th1 cells decrease with age, while those of fibroblasts and pericytes increase with age. The authors speculate that these age related changes in tissue composition and cell interactions could potentially predispose the ageing lung to pathological contraction seen in COVID-19 infections. Authors suggest a larger overlap of genetic pathways between the aging lung and SARS-CoV-2 infection compared to younger population, making the elderly more susceptible to COVID-19 infection.
Critical Analyses: <br /> 1. The study did not include samples from COVID-19 positive patients.<br /> 2. Aging is a complex multifaceted phenomenon, making clear deductions would be difficult and further studies for cross-validating these observations will be needed.
Relevance: Since elderly populations are worse hit by SARS-CoV2 infections, it is of immense importance that we understand the underlying mechanisms of this increased vulnerability and accordingly develop rational therapies for COVID-19.
Reviewed by Divya Jha, PhD and edited by Robert Samstein, MD PhD, as part of a project by students, postdocs and faculty at the Immunology Institute of the Icahn School of Medicine, Mount Sinai.
On 2021-01-08 23:46:13, user Alejandro Marquez wrote:
Hello, honestoy I don't get how these primers can be specific for the new variant, I only found SNP variations in the middle of the primer and not in the 3'-end. Based on my experience these primers will amplify with the original and the new variant... can you explaint better...?
On 2021-05-02 21:38:54, user Marina Duarte wrote:
Very interesting paper, congratulations. I suggest to delete "animal" of the title because soundscape is a term related to all the sound components of a landscape. There is also a new paper about the topic: https://www.sciencedirect.c...
On 2017-05-27 01:36:11, user Davidski wrote:
Hello authors,
You refer to ancient populations from what is now Iran as Iranians and to potential admixture from such populations as Iranian ancestry.
This is an anachronistic approach and probably not a good idea.
Please note that Iran is a modern-day country, not a geographical region, and Iranians are a modern-day nationality, and at best the term Iranian can be extended to the speakers of Iranian languages.
So it might be better to say the South Caspian region or even the Zagros Mountains instead of Iran. And instead of using Iranian it's probably more sensible to say something like Chalcolithic farmers from modern-day Iran or the Zagros range.
On 2025-08-19 19:07:24, user Eric Miller wrote:
The authors present interesting information showing that a KD could enhance glutamine uptake when glucose is reduced through KD thus suggesting that glutamine targeting would be effective for managing pancreatic cancer when employed together with KD therapy. Mukherjee et al. presented a similar therapeutic strategy but with an explanation for the therapeutic effect different from that of the author’s ( https://doi.org/10.1038/s42003-019-0455-x) "https://doi.org/10.1038/s42003-019-0455-x)") . It is important for the authors to address the different explanations for the similar findings in their discussion.
Besides an anapleurotic explanation for the role of glutamine in driving tumor growth, the authors should also address a role for mitochondrial substrate level phosphorylation in the glutaminolysis pathway as an additional explanation for the effect ( https://doi.org/10.1080/17590914.2024.2422268) "https://doi.org/10.1080/17590914.2024.2422268)") .
The authors assume that ketone bodies and fatty acids are critical fuel sources for pancreatic tumor cells based on their finding of labeled TCA metabolites derived from beta-hydroxybutyrate and caprylic acid. No evidence is presented showing that the pancreatic tumor cells can survive on these fuels in the absence of glucose and glutamine. Caution is needed in considering labeled TCA metabolites as evidence for fuel utilization in the absence of viability studies.
Abnormalities in mitochondria structure and function are found in pancreatic cancer (https://doi. org/ 10. 3109/ 01913 123. 2013. 788306; https:// www. ncbi. nlm. nih. gov/ pubmed/ 6185201; https:// www. ncbi. nlm. nih. gov/ pubmed/ 968802). Such abnormalities would obstruct efficient utilization of fatty acids and ketone bodies for ATP synthesis. The authors must address the question of how ketone bodies and fatty acids can be critical fuel sources in pancreatic cancer showing abnormalities in mitochondria structure and function. Support for the author’s assumption would come from bioenergetic evidence showing that the MIA-PaCa2 cells die in hypoxia and cyanide, which would be expected for cells dependent on fatty acids & ketone bodies for fuel. No credible evidence is presented showing that the MIA-PaCa2 have normal mitochondrial function.
The authors mention (reference 22) as an example of a "push-pulse" strategy, where a ketogenic diet nudges a system towards greater dependency on a specific metabolic program, which in turn exposes new dependencies. This paper is incorrectly cited as there is no mention of a "push-pulse" strategy in the paper. The correct terminology is “Press-Pulse and was first presented in the following paper, which is the correct citation (DOI 10.1186/s12986-017-0178-2).
On 2018-02-22 21:03:55, user Hyunmin Kim wrote:
I am sure this is current evolution of genotyping
On 2019-07-25 07:38:15, user Guillaume Méric wrote:
Thanks for your comments and interest in our work.
2,3,4,6. The study presented here showing a link between classification performance and the size/composition of indices was performed using GTDB release 86. We have also recently created an index based on the GTDB release 89 (https://github.com/rrwick/M... "https://github.com/rrwick/Metagenomics-Index-Correction)"). The number of MAGs in r89 is higher than in r86, which will contribute to an even better classification using r89.
Regarding your other points: Rather than the classifier tool, the reason why the prokaryotic fraction of environmental metagenomes is still not fully classified even with corrected indices is indeed the general misrepresentation of non-clinical environmental sequences in public repositories. More generally, it is correct that fine-tuning specific taxa in context-specific databases to specific needs of a study (environment/host/etc) will improve future work, and our paper highlights this possibility. The more reference genomes from a particular environment/host (MAGs or not) that you will add to the index, the more reads you will classify from any metagenomic sample from that environment/host. Notwithstanding, our corrected indices classify up to 6 times more reads/samples in environmental samples as compared to default indices, which is a promising example. In the future, better and more accurate databases (built using MAGs or not) will of course improve this even more.
On 2020-10-14 10:01:24, user isthereanyhope wrote:
Nice work! Does this mean V1 -> RSC visual input goes first to place cells and then to grid cells, given that gain manipulations in VR show place cell firing has greater influence of vision and grid cell firing greater influence of path integration (Chen et al., 2019, Nature Comms 10:630)?
On 2020-07-28 14:54:58, user @MRI_phys_KS wrote:
Thanks for this toolbox.
I'm sure you know that the thresholded k-space division (TKD) methods of Shmueli et al., 2009 and Wharton et al., 2010 give different results. Which one have you implemented as method (1) in SEPIA's QSM panel?
If you need an implementation of Shmueli's TKD you can find it here: https://xip.uclb.com/i/soft... along with a nice iterative Tikhonov regularisation method described in more detail here: https://pubmed.ncbi.nlm.nih...
On 2021-10-21 14:07:25, user Iris Chen wrote:
Great analysis and synthesis of ideas pertaining to the mechanism by which macrophages stimulate cancer cell proliferation. The findings are interesting and innovative, and the arrange of figures and conclusions is also great! And I am just a little bit confused with Figure2A, so why you chose to observe the recipient cancer cells for 15 hours? Because I find in some experiments, the data was collected after cells were co-cultured for 24 hours, so maybe you can also lengthen the observation time in 2A to 24 hours, in this way I think your conclusions will be better supported. And another possible advice is for Figure4D, I think here you can also add a figure to show the number of mito fragments with in the artifact macrophages, just like Figure4B. And I also think you could add more details in the introduction part, like the proliferation of breast cancer cells (why you chose to get the data after 24 hours), the relation of ROS and ERK (is there any previous studies about this pathway? are there other possible pathways?), and the mitochondrial network in macrophages (has anyone also found M2 with smaller mitochondrial fragments before? and does the mitochondrial phenotype influence the cell functions?). Anyway I think this is a really good preprint, thank you for presenting your work here.
On 2016-06-15 16:11:21, user Dwight Read wrote:
The local isotope ratios are determined as follows: "Archaeologists construct local reference ranges of strontium isotope ratios by sampling sediments, waters, plants, and/or animal bones/teeth (both archaeological and modern) in the geographic areas surrounding particular sites." What we also need to know is the geographic scale over which the local isotope ratio extends. How far from the community in question could someone be raised, then migrate to this community, and still have an isotope ratio comparable to the reference ratio used for classifying someone as a local? In other words, how rapidly, and to what extent, does the background isotope ratio change as one moves away from the community? How does this compare with the likely distance between neighboring communities?
On 2021-04-13 03:49:38, user Dave Roe wrote:
I don't see where approach was demonstrated on KIR. Did I miss it?
On 2014-08-09 19:44:19, user Jamie Cate wrote:
To the authors,
Interesting results, and thanks for posting. I forwarded the link to my group.
An implication of your findings, correct me if I'm wrong, is that the translational efficiency of a specific mRNA should vary depending on the species of ribosome which is translating it. Further, there must be a mechanism that partitions more translationally active ribosomes from less active ribosomes on a given mRNA, for the ribosome species to be separated by polysome position. In other words, why would a specific mRNA equally present in the yeast trisome fraction and tetrasome fraction have different ribosome species bound? What are your thoughts on this?
As a note of caution, in refs. 24-25, the authors were a bit concerned about the translational activity of lighter polysomes being relatively lower for artifactual reasons, i.e. monosome contamination, inactivation of oligomers, and ribosome dimerization in high magnesium ion concentrations (ref. 24, p. 636, left column). As an orthogonal measure, is there information embedded in some of the ribosome profiling data to address this? For example, do mRNAs with a higher translational efficiency (TE) have faster rates of translation as inferred from chase experiments with harringtonine?
Jamie
On 2017-08-24 16:12:11, user Leslie Vosshall wrote:
Thank you for posting this preprint. The Vosshall lab read Vinauger, Lahondere et. al. for our lab journal club and wanted to share our collective thoughts below. Because of our expertise in neurogenetics, we focused on the genetic mutants below, followed by more general comments and questions about the rest of the paper.
CRISPR Mutagenesis
It is exciting to see a wider adoption of CRISPR-based mutagenesis in mosquitoes, part of the larger push to bring precise, targeted genetic manipulations to non-traditional model organisms with fascinating ethology and critically important public health relevance. Here, CRISPR is used to perform targeted mutagenesis of the Dop1 receptor (Dop1R1) in Ae. aegypti mosquitoes to explore the genetics of dopamine receptor signaling in host seeking behaviors, and represents an important complement to dsRNA and pharmacological approaches to support the conclusions of the paper.
Co-injecting two (or more) sgRNAs is a potent mutagenesis strategy in many animals, including Ae. aegypti, and was used here to isolate an allele with an 18bp in-frame deletion relatively early in the open reading frame of Dop1R1.
• Did these injections lead to additional distinct alleles from different founders? If so, what was the rationale for choosing the 18bp (6aa) in-frame deletion reported here for further study? If there were viability or other fitness effects noted in other lines predicted to be more destructive to the gene product, that is important to note (and an interesting finding in its own right!)
• Can the authors say anything about whether this protein is expressed and trafficked at levels similar to wild-type? For example, could the authors stain Dop1 mutant brains with their anti-Dop1 antibody? If so, can they make any predictions or speculations on the function of the mutant receptor based on the location of the deletion in the predicted protein sequence? Is there evidence to suggest the allele is a hypomorph vs. a full loss-of-function?
• We had some questions about genetic background. In addition to the potential for off-target mutations induced by CRISPR/Cas9, the Aedes aegypti genome is replete with recessive deleterious mutations and large regions of low recombination. Accordingly, inbreeding can cause fitness defects that manifest themselves non-specifically across a variety of behavioral assays (lower activation and overall activity, for example, as hinted at in the CRISPR data points in Figure S2). Overall, this makes the choice of outbreeding scheme and allelic combinations critical when using genetically modified mosquitoes in behavioral experiments. We are not sure if this is possible, but these concerns would be addressed if behavioral experiments were performed with a heteroallelic combination of independently isolated, outbred, and homozygosed alleles and compared to appropriate parental strain and heterozygous control genotypes. If this is not possible, then it would be great to see more independent validation of the fitness of this homozygous mutant line outside the context of learning. We have had some nightmare scenarios where homozygous mutants of orco (and other genes we have targeted) had intriguing behavioral problems that went away when we tested heteroallelic combinations. We think background effects confounded out initial results.
• It was not clear from the text in the methods and the figure how outcrossing and homozygosing was performed, and these details should be clarified to note the number of generations that animals were outcrossed back to the wild-type (parental) strain as opposed to intercrosses between heterozygous mutant animals designed to generate a homozygous mutant line.
• We had some questions about other aspects of Dop1R1 mutants. Do mutant animals lay the same number of eggs? Are their circadian rhythms unaltered? It would be nice to see them in the CO2 assay in Figure 3, as well as to have a more detailed description of their baseline activity in the CO2 and host-seeking behavioral assays when compared to the appropriate parental and heterozygous controls.
Additional comments and questions
We found the mosquito learning behavior shown here very interesting and are looking forward to further studies from the authors in the future.
The ability of mosquitoes to selectively learn to avoid certain odor stimuli including 1-octen-3-ol and rat odor (but not chicken odor) is an interesting observation. We found it interesting that learned avoidance of the human odor blend was not as strong, since in Figure 1D there was no difference between unpaired and trained mosquitoes. It would be helpful to include the average percentages of mosquitoes attracted into each arm (in addition to preference index) within the main text.
It would be interesting to see for at least some experiments the additional controls of odor (conditioned stimulus) and mechanical perturbation (unconditioned stimulus) alone to determine how these affect choice.
The increase in frequency and amplitude in response to CO2 in the tethered flight arena is a neat result. We are wondering what the change in wingbeat frequency and amplitude means for the free-flight behavior of a mosquito?
The electrophysiology prep is a significant advance in technique for the mosquito. It is great that similar types of compounds cluster together in the PCA odor map and seems to validate the approach.
The results in Figure 4A are confusing because attraction to lactic acid increases after aversive training. It is unclear if this is indicative of learning because the unconditioned stimulus is aversive and the learned response is attraction. Perhaps this is some sort of sensitization to different odors, as lactic acid is generally reported to be an innately attractive compound to the mosquito. The unpaired control would be useful in this case. .
On 2020-12-01 09:33:16, user nboccard wrote:
If, as i read in Ruigrok et al., brain volume is about 11% bigger among males wrt. females, then SD male is mechanically 11% larger too. Thus your finding of larger SD by 27% (per my computation of your ratios) is decreased by as much. If so, the significance test may not pass.
On 2018-07-11 23:55:50, user Olin Silander wrote:
Thanks for the comment. We're getting 2nd opinions to double check, I have very little experience in rat taxonomy.
On 2022-08-18 15:08:16, user Ludovic Spaeth wrote:
Thank you for sharing this elegant work on bioRxiv.
I'd like to discuss the network inductive bias. The assumption about the normal distribution of inputs within the input layer might be correct in some discrete regions of the cerebellar cortex (https://pubmed.ncbi.nlm.nih... "https://pubmed.ncbi.nlm.nih.gov/19571137/)"). We performed systemic spatial mappings of granule cells to Purkinje cells (GC-PC) functional connectivity in a given module of the mouse anterior vermis (https://pubmed.ncbi.nlm.nih... "https://pubmed.ncbi.nlm.nih.gov/35102165/)"). In this particular region, however, GC inputs to PC are not normally distributed along the input layer, rather dispatched as strongly connected nodes intermingled with silent granule cell patches. <br /> Nonetheless, our data supports the idea that task-dependent optimal coding level should be somehow nested in a relatively invariant anatomical frame defined by the scattered mossy fiber - granule cell - Purkinje cell pathway.
It is clear that the level of activation within the granule cell layer, when observed experimentally, seems to be much denser than theoretically expected. We previously discussed how one may reconciliate this contradiction between theoretical work and experimental data (https://pubmed.ncbi.nlm.nih... "https://pubmed.ncbi.nlm.nih.gov/35391650/)").
On 2016-04-22 16:13:03, user Vladimir Seplyarskiy wrote:
Dear Jonathan and colleagues,
I have read your article and find it very interesting as it highlights violations of infinite site model. I would like to suggest a comment about interpretation of shifted SFS in sites considering mutations in other species. You observe that mutation rate varies within mutation class, thus you might expect higher mutation rate in sites containing mutations in other species.
I would like to point out that we and others have already shown that mutation rate is several times higher in sites containing mutations in other species (Seplyarskiy et al 2012; Johnson & Hellmann 2011; Hodgkinson et al 2009). Variation in mutation rate may likely explain the decrease fraction of singletons among variants with mutations in other species. In our study we found that mutations in other species even on variants other than alleles in human population increase expected mutation rate. Moreover, transversion in one species is a better predictor for the transversion than for transition in other species.
Good luck publication,<br /> Vladimir B. Seplyarskiy
On 2020-04-06 16:25:34, user Hugh Gannon wrote:
I noticed GART is a hit in the siRNA screen, which is also involved in purine biosynthesis. Do other members of the purine biosyntheis pathway (ATIC, PPAT, PAICS) come up as second-tier hits in the CRISPR or siRNA screens? Might lend more biological relevance to the pathway as a whole.
On 2020-08-11 20:41:00, user Yoram Burak wrote:
A revised version of this manuscript is available as an article in eLife.
On 2017-09-15 14:39:00, user benjamin vincent wrote:
Hello Serghei, This looks like a nice analysis. For TCR, did you compare the performance of ImReP with TRUST (Li et al., Nature Genetics 49, 482–483 (2017)) and/or MiXCR RNA-seq mode (which in its latest iteration is optimized for RNA-seq data) ? For BCR, did you compare with VDJer (Mose et al, Bioinformatics 2016 15;32(24):3729-3734. - Disclosure: I was an author on the VDJer paper)? Also did you compare against amplicon datasets from Adaptive for biological validation? -Benjamin Vincent
On 2019-11-12 17:06:42, user Lobding wrote:
Our first BioRxiv preprint reveals the in vivo existence of RNA G-quadruplex structures! Plants are so powerful!
On 2024-03-26 12:05:47, user Miguelo wrote:
Now published in Thyroid doi: 10.1089/thy.2023.0638