On 2022-11-09 09:15:00, user Hauser Kronenberg DZNE wrote:

This work is an important resource and characterisation of culture media conditions for anyone doing in vitro iPSC-microglial monocultures.

1. We were curious if the authors had performed immunocytochemistry for some of the most common microglial markers (eg. Pu.1, IBA1, Tmem119, or similar) with the different media conditions?

2. Did the authors also ever test the effect of the frequently used poly-D-Lysine or poly-L-ornithin coatings for example? Or play/adjust the density of plated cells?

3. Unfortunately it wasn’t quite clear to us, if one or all iPSCs lines were used in each round of differentiation and which line is represented in the brightfield images.

On 2025-06-04 14:51:37, user Michelle Anderson wrote:

This is now published: https://doi.org/10.1038/s41598-023-47777-3

On 2025-08-06 17:18:29, user Swathy Babu wrote:

@bioRxiv<br /> This manuscript is now published. https://www.nature.com/articles/s41467-025-61937-1 <br /> Please add a link to the published version.

On 2021-03-15 12:59:15, user Gadi Schuster wrote:

This manuscript is now accepted for publication in The Plant Journal.

On 2021-03-19 17:44:20, user fischmidtlab wrote:

Great study and important contribution! Quick remark: Our animals are not 'invariably culled' after a successful immunization campaign. After some resting time, they can be immunized again (also no need to cull them when they retire).

On 2019-12-12 16:55:37, user Andrew Mitz wrote:

Nice study. Lots to review. Note, some of the gene names in your spreadsheet have been converted to dates. You might with to make gene columns text type, then re-enter the gene names.

On 2017-07-13 13:47:49, user Justin Ashworth wrote:

Looks cool. Is Fig. 1D real? Is that a real regression that just happens look like hand-drawn lines?

On 2019-04-11 20:43:27, user Robert wrote:

Your R-squared is 129? Isn't it usually between 0 and 1?

On 2019-09-20 16:03:47, user Ian Coombs wrote:

The final version of this BioRxiv submission was published in Nature Communications on 20th Sept, 2019. https://rdcu.be/bRvro

On 2023-06-08 13:45:21, user Angela Fuentes Pardo wrote:

Please note that this preprint was already peer-reviewed and a link will be forthcoming in biorxiv. Meanwhile, this manuscript is publicly available here: https://doi.org/10.1111/eva...

On 2025-05-07 11:52:46, user Amanda Veiga Sardeli wrote:

This preprint has been accepted for publication on Aging Cell and I will update here when it is published.

On 2020-05-08 13:14:17, user Lucia Theodoro Freitas wrote:

https://jvi.asm.org/content... - maybe would be better take a look in this paper - they say something different and they have a consistent proof ...

On 2015-09-09 15:24:05, user Jean Manco wrote:

You cite Nielsen, R. et al (2015), Population genomics of Bronze Age Eurasia, Nature 522:167?172.

This should be Allentoft, M. et al. (2015), Population genomics of Bronze Age Eurasia Nature 522:167-172. See http://www.nature.com/natur...

You cite Pääbo, S. et al (2014), Ancient human genomes suggest three ancestral populations for present-day Europeans, Nature, 513, pp 409-413

This should be Lazaridis, I et al (2014), Ancient human genomes suggest three ancestral populations for present-day Europeans, Nature, 513, pp 409-413

http://www.nature.com/natur...

Neither found R1a1a in Yamnaya cemeteries. Is there another paper to come Pääbo et al 2015?

On 2025-01-17 06:41:26, user yewon wrote:

hi i am a student very interested in your study. Is GRAF workflow currently posted in Cavatica?

On 2021-07-31 05:50:33, user Stephen Fairweather wrote:

The discovery of the essential L-lysine plasma membrane transporter from the human intracellular parasite T.gondii. Large amount of data from various experiment systems and in vivo and very rigorous demonstration of all conclusions. A paper worked on since 2015.

On 2015-04-20 05:14:10, user Manolis Dermitzakis wrote:

Regarding replication on Fig 3: the most reliable replication is when a discovery in small sample is replicated in larger sample size. The 373CEU to YRI89 is a discovery in a large sample size (373) and replication in a small sample size (89). It was presented for people's information. Already 53% and 69% are very low replication rates and not reliable stimates of true postives. The best estimate is the of YRI (89 indivs) replicated in CEU (373 indivs). There the estimates are 97% for both methods., which is where (plus more analysis to be presented in the final version of the paper) we base our conclusion overall that methods are similar is confidence of discovery but complementary in terms of what type of asQTLs they discover.

On 2025-02-19 03:28:56, user Yue Jia-Xing wrote:

Author comment:<br /> This work has been formally published in Genome Research recently under a different title:<br /> "Interactive visualization and interpretation of pangenome graphs by linear reference–based coordinate projection and annotation integration".<br /> Advanced online on 2025.01.13, formal publication in 2025.02.<br /> DOI: 10.1101/gr.279461.124 <br /> Link: https://genome.cshlp.org/content/35/2/296.full

On 2016-05-27 16:23:03, user Karl Muller wrote:

One day, Louis Slesin's article in Microwave News announcing these findings will be seen in exactly the same light as that guy in the Titanic movie, shouting: "Iceberg! Iceberg! Straight ahead!"

This ship cannot be turned around. Motorola, in its SEC filings on business risks for 2011:<br /> https://www.facebook.com/EM...<br /> ... said that "adverse factual developments" regarding mobile phones on health could severely impact its business. It has since dropped that warning from its filings, even as the iceberg approached. This is going to devastate shares, and devastate shareholders like the pension funds, and they are going to ask: *what* did the industry know, and *when* did they know it?

This is where the case of Murray vs Motorola also becomes critical:<br /> http://www.saferemr.com/201...<br /> ... a class action suit that has been in the courts for nearly 20 years is about to go into discovery, where we really do here in a court, for the first time -- what *did* the industry know? And when did they know it?

Now, this whole case will be carried out in the light of this finding: *mobile phone radiation causes cancer*. Oddly enough, it causes the exact cancers we see being caused.

This is the end of the wireless industry as we know it. Iceberg! Iceberg! Straight ahead!

On 2021-06-26 15:16:11, user Raymond .Enke wrote:

I love the idea for a National Center for Science Engagement. Groundwork laid by the SEA-CUREs, DNA Barcoding, GEP, etc have proven that various CUREs can be implemented with a reasonable per student cost. Organization at the national level would only further decrease cost of implementation as the efficiency of implementing these CUREs increases. How do I get involved?

On 2019-11-04 07:49:30, user Nikhil Ratna wrote:

Extremely important research. I have a question.<br /> In the methods it is given as, "We removed 133 individuals with a<br /> comorbid diagnosis of bipolar disorder, schizophrenia, schizotypy or schizoaffective disorder (since these are likely to share risk genes for psychiatric disorders independently of their HD status)"<br /> What is the basis of classifying people as psychiatric patients of HD independent of HD status? Since psychiatric symptoms in HD are of broadest range and can present in any stage of the disease, how is it possible to say that the psychiatric symptoms in an individual are related to or not specifically to CAG status.

On 2017-04-23 02:49:30, user Keith Robison wrote:

Discovered a small hitch in one thing I suggested -- the Baikal seal in the Beklemisheva analysis has 2n=32 but Hawaiian monk seals have 2n=34 (Lu et al 2000). According to Arnason 1974 the 2n=34 karyotype is probably ancestral with a single fusion generating the 2n=32 karyotype. Fronicke et al 1997 would make the fused chromosome "S", which is homologous to human chromosomes 17 and 5. Some more musings over on the blog

On 2017-08-08 11:32:05, user Inigo Martincorena wrote:

Hi David. Thanks for your comments! I absolutely agree. We are measuring selection at the level of cohorts of samples not at the level of a phylogeny within a single tumour. They are very different things indeed. An analogy with standard phylogenetics would be measuring selection within a population (as you suggest) or running dN/dS across species, using one reference genome per species. Cohort analysis of somatic mutations across cancer samples is conceptually more similar to the latter. This is also where dN/dS is most frequently used in phylogenetics.

Regarding your specific questions, we are very much aware of the importance of using appropriate substitution models. In fact, this is one of the key factors required to obtain unbiased dN/dS estimates, as we show in the Suppl Material. In the revised version of the paper, which is not available in BioRxiv, we have added a number of analyses that you will find of interest, including AIC and simulations of cohorts of samples with heterogeneous mixtures of mutation rates, signatures and selection. To answer your specific question, AIC shows that the 192-parameter model is vastly superior to a model of 12 parameters without context-dependence in all datasets analysed in our study (e.g. AIC values in the breast dataset are 3,689 and 39,750 respectively). But more details will be available in the revised manuscript. I have also included the ability to do AIC model selection in the package that I will release very soon.

Finally, you ask about mixture of negative and positive selection across samples (the same could also be said about sites). We already discuss this in the manuscript. This is of course possible in any dN/dS study. However, this appears highly unlikely to be a relevant factor here. When removing known cancer genes, we find dN/dS~1 across any group of genes studied in any group of samples analysed (see Fig. 3). Also, the distributions of dN/dS values at gene level (Fig. 3A-B) confirm that the vast majority of genes display a dN/dS=1. For this to be the result of mixtures of positive and negative selection across samples or across sites, we would require a perfect balance of the extent of positive and negative selection in each gene, including in non-expressed genes or likely-passenger genes (unlikely to have significant positive selection). This is unlikely to be a significant factor.

Thanks for the interesting comments!

On 2020-04-03 22:11:40, user Gerald Carter wrote:

The figures are a bit too low resolution. So here's a temporary link to a higher resolution version: https://www.dropbox.com/s/w...

On 2017-01-18 03:41:12, user Keith Robison wrote:

The notorious P.flourescens has slipped twice into your manuscript. Please reconsider citing Mikheyev & Tin - it's an awful paper & not a remotely representative portrayal of the furrent state of the platform (put another way, you've clearly ignored their conclusion and gone ahead and used MinION, so why pick that paper?)

On 2025-07-29 17:20:59, user Kristin G wrote:

???? AI ? Bio: Testing the Physics Beneath the Predictions<br /> Beyond RMSD: What AlphaFold3 Really Understands

Dan Herschlag was my postdoc advisor. Dan taught me how to think mechanistically, how to test assumptions, and how to pursue scientific clarity with unflinching rigor.

That legacy is all over this paper. And is incredibly needed in this era of AI hype where data volume often sidelines careful model-driven science.

Herschlag et al. show that while AlphaFold3 predicts protein structures with backbone-level structural precision, it struggles to capture the physical rules that govern biological function.

But this paper isn’t a takedown: it’s a roadmap for how to go further.

What They Did<br /> Rather than rely on RMSD alone, the team evaluated AlphaFold2 and AlphaFold3 against:<br /> ->Energetic rules: bond torsions, hydrogen bonds, and van der Waals contacts<br /> ->Experimental ensembles: from multi-temperature crystallography<br /> ->Model confidence: comparing pLDDT to physical plausibility

What They Found:

~30% of side-chain interactions deviated from experimental observations, often with incorrect partners or implausible geometries<br /> High-confidence predictions (>90 pLDDT) still showed strained or physically invalid conformations<br /> Energetically-favorable conformations could increase RMSD and be penalized by the model<br /> AlphaFold3 missed ~85% of conformational variability seen in real experimental ensembles

Key Insight: Physics != Proximity<br /> AlphaFold can recapitulate Ramachandran and Lennard-Jones-like patterns. But that doesn’t mean it understands physical constraints.<br /> To improve these tools, we need evaluation metrics grounded in molecular energetics, not just geometry.

???? Takeaway for Scientists<br /> This paper is a reminder that progress requires more than prettier predictions. It demands models that reflect the physics that drive biology.

On 2025-09-23 20:28:30, user Gennady Gorin wrote:

I read through this with some interest. If you are not yet aware of it, you may be interested in our paper from several years ago: https://www.cell.com/cell-systems/fulltext/S2405-4712(23)00244-2 . Basically, although the moment-based estimates are useful (and almost mandatory for inference over non-iid samples), it is also relatively straightforward to compute the full likelihoods by quadrature. As the cells within a cell type are assumed iid here, this is more or less compatible with the framework.

The low-beta regime can also be represented as a two-state model with bursty (and potentially leaky) expression in the active state, slightly more straightforward to solve numerically.

For the connection to ATAC, please see https://journals.aps.org/pre/abstract/10.1103/PhysRevE.110.064405 . The same model is straightforward to generalize to some latent regulator or a coupled series thereof (although the best way to define such coupling is still obscure).

The joint modeling of these quantities is a somewhat underexplored problem. One key issue we raise in the PRE paper is that noise modeling for ATAC is unusually challenging. There are, of course, other challenges, like the slightly non-iid nature of measurements across supposedly homogeneous cells ("cell size" effects) and the usual DNA/gene mapping issues: how should one map a peak to a particular gene? How should one represent the relationship between multiple peaks that all ostensibly control or overlap a single gene?

It is exciting to see people pursuing mechanistic approaches for this problem, and I look forward to future work.

On 2021-05-25 15:41:08, user Ian Hastings wrote:

I have two major comments on this preprint.

Firstly, the conclusions are not novel but echo those we previously reported several years ago from similar pharmacological considerations [1] i.e. that selection against ACTs will likely occur in two phases i.e. “Phase 1 is characterised by resistance eroding the therapeutic capacity of the partner drug…….. Phase 2 then starts because both the artemisinin and partner drugs have similar therapeutic capacities so both contribute to cure and hence selection pressure exists for resistance to each drug”. The current preprint essentially repeats this and demonstrates that the more phase 1 has progressed, the faster phase 2 proceed, see their abstract i.e “Higher frequencies of pre-existing partner-drug resistant genotypes lead to earlier establishment of artemisinin resistance”. This is the same conclusion we reached previously i.e. we stated “The main threat to antimalarial drug effectiveness and control comes from resistance evolving to the partner drugs”. I made the corresponding author aware of our previous work [1] and it is disappointing that they choose not to make this previous work available to readers, nor to interpret there results within this context. In fact, the very title of the preprint states it is rediscovering our Phase 2. Two publication reaching the same conclusion are obviously more compelling than one, hence the need to cross-cite

Secondly, they have modelled three partner drugs: amodiaquine (AQ), piperaquine (PPQ) and lumefantrine (LF). It is important to note their modelling differs from the real drugs’ pharmacology in some key respects. Their parametrisation assumes one-compartment (1-c) pharmacokinetics for all drugs and calibration between drugs differs solely in their half-lives and maximum killing rates; EC50 values are selected to obtain their chosen failure rates and resistance mutations affect IC50. In reality, PPQ and LF follow 2- or 3- compartment pharmacokinetics (depending on the source publication) while AQ is even more complex (it is converted to its active metabolite DEAQ and both AQ and DEAQ have antimalarial activity and 2 compartment pharmacokinetics). The calibrations are in arbitrary units (e.g. dosage per patient in mg/Kg are not given); compare this to, for example, the extensive pharmacological calibrations tabulated in [2]. The pharmacological details are not obvious in the preprint. They cite their ref #17, but this back-cites to the SI of a previous paper [3]. Essentially ,they use 3 different calibration of a 1-compartment drug. There is nothing inherently wrong in using a PK approximation using simple 1-c dynamics provided this is clearly stated. However, calling them PPQ, LF and AQ without making this clear gives an aura of pharmacological accuracy and precision which is simply not present in their models. This is a vital caveat given recent realisation that the evolution of drug resistance needs to be placed firmly within its pharmacological context (e.g. discussed in these reviews [2, 4]).<br /> There is nothing inherently wrong in this work, it is essentially a question of transparency i.e. (1) to acknowledge previous work and that their results are consistent with previous analyses rather than being novel (2) to provide explicit description and discussion of their pharmacological models of specific antimalaria drugs.

Ian Hastings, Liverpool School of Tropical Medicine.

1. Hastings IM, Hodel EM, Kay K. Quantifying the pharmacology of antimalarial drug combination therapy. Scientific Reports 2016; 6:32762.
2. Zaloumis S, Humberstone A, Charman S, et al. Assessing the utility of an anti-malarial pharmacokinetic-pharmacodynamic model for aiding drug clinical development. Malaria J 2012; 11:303.
3. Nguyen TD, Olliaro P, Dondorp AM, et al. Optimum population-level use of artemisinin combination therapies: a modelling study. The Lancet Global Health 2015; 3:e758-e66.
4. Simpson JA, Zaloumis S, DeLivera AM, Price RN, McCaw JM. Making the Most of Clinical Data: Reviewing the Role of Pharmacokinetic-Pharmacodynamic Models of Anti-malarial Drugs. The AAPS Journal 2014.

On 2016-11-08 17:46:31, user jdudley wrote:

Nice work. I noticed the paper is missing references to several key papers in this field from Sudhir Kumar’s group:

https://www.ncbi.nlm.nih.go...

https://www.ncbi.nlm.nih.go...

www.ncbi.nlm.nih.gov/pubmed...

On 2017-07-23 14:51:37, user kamounlab wrote:

A related paper is Gladieux, Condon et al. Gene flow between divergent cereal- and grass-specific lineages of the rice blast fungus Magnaporthe oryzae http://www.biorxiv.org/cont...

On 2020-10-23 02:59:54, user Mario Aguirre wrote:

Hi Gus. <br /> Excellent article, good job. Have you information regarding thermal shock (temperature increase for short time)applied to tilapines as virus control in Ghana?.

On 2025-05-06 21:52:34, user Young Cho wrote:

Summary

In my opinion, Nanopore sequencing incorporated with 3 modes in a single instrument is great. I understand that in this work, the authors sequenced large genomes, so it requires PromethION. If working with smaller genome-size sample (e.g. shrimp), does MinION work as well (since MinION is more cost-effective for majority of labs). Also, from the Table 1, it looked like more parameters are better when using trio than Pore-C. Therefore, I was wondering if Pore-C is a good choice over trio in this case?

Introduction

• Authors stated that Pacbio HiFi provides 99.5% accuracy and Oxford nanopore (ONT) for ultra long reads gives 95% accuracy. To my knowledge, ONT provides higher accuracy rate than this number. Therefore, it could be better if you could provide references for these information.

Results

• Figures and captions: figure resolution was not great

Discussion

• Authors mentioned that there is a large variability in yields over times due to pre-released versions of Duplex sequencing. Is there any way to solve this? Because from my point of view, this is an important point.

Methods

• Authors did not mention how they used Pore-C data to assemble human genome.

On 2021-02-10 18:35:55, user Arnab Ghosh wrote:

The references for "worldwide dominant Asp614Gly<br /> variant, introduced a new elastase proteolysis site in the spike protein (2, 21)." needs to be updated as the statement is not mentioned in the referred papers. The following paper talked about elastase cleavage site at Spike:614G it for first time:<br /> https://www.biorxiv.org/con... (May 05, 2020); now published: https://doi.org/10.1016/j.m...

On 2017-10-06 12:58:23, user Michael Lichten wrote:

Would be useful to have a brief summary of what's changed in the most recent version.

On 2019-01-02 08:37:46, user naseeb singh wrote:

Excellent work done by the group. congrats and keep it up

On 2019-11-27 10:14:09, user Hangyu Zheng wrote:

looks like the matrix-binder peptides that were not included in previous studies are the key point in this study

On 2020-09-14 17:16:21, user Arlin Stoltzfus wrote:

The text says that "the original residue was randomly mutated to one of the 19 other amino acids." But the number of alternative amino acids accessible by mutating one nucleotide of a codon is typically 6 or 7, not 19. The other 12 or 13 require double or triple mutations. Furthermore, any heterogeneity in mutation rates, e.g., transition bias, necessarily increases the chance of parallelism. If effects like this are not taken into account, then the null distribution is mis-specified and greatly under-estimates the extent of parallelism.

On 2019-08-03 03:32:22, user Andrew Bernstein wrote:

Very interesting! Is this in the process of being peer-reviewed?

On 2023-03-08 21:13:53, user Elizabeth Duncan wrote:

We read your impressive study as part of a student journal club at the Markey Cancer Center (University of Kentucky). We appreciate how many controls and orthogonal experiments you did to test your hypotheses rigorously.

One question we have is regarding the genetic background of the cell lines used e.g., A2780, A2058, HCT116, AGS, etc. Given that you made an important point about the mutual exclusivity between the 1q aneuploidy and TP53 mutations in patient cancer samples, we wondered if the cell lines used to manipulate chromosome ploidy were wild-type for TP53. Upon searching the literature and ATTC webpage, it seems like all of the above lines are WT for TP53 except for melanoma line A2058. Notably, this is also the line in which you show significant regain of chromosome 1q after deletion. We see that you tested the role of p53 in aneuploid addiction by mutating TP53 in the TP53-WT line A2780, but what role might the TP53 mutant status of A2058 cells play in their robust re-acquisition of 1q aneuploidy? Have you tried restoring the TP53 gene to its WT sequence in this cell line and comparing their ability to regain chromosome 1q?

On 2020-04-01 15:10:57, user Maria Llamazares wrote:

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

very interesting manuscript!<br /> In a first glance seems that Table 1 labelling is incorrect since you say you are showing the PPE/saline ratio and actually you are doing the other way around: Saline/PPE. Could you double check?

On 2017-06-24 07:54:37, user sandeep chakraborty wrote:

I thank the authors (https://www.ncbi.nlm.nih.go... "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4644263/)") for engaging in a public debate on the TEP-paper.

I understand their critical view on my pre-prints - "and once more highlights the critical need for peer-reviewing" and " we wish to point at the importance of peer review".

However, I would like to point simultaneously that biorxiv provides a platform to ask `impertinent' questions in case the human (and fallible) peer-review process might have been missed out on asking those.<br /> "That is the essence of science: ask an impertinent question, and you are on the way to a pertinent answer." - Jacob Bronowski.<br /> So, I hope readers judge the data I am providing - and not my credentials.

I will try to prove the statement "the author clearly has no understanding of the general principle of surrogate signatures" wrong below<br /> in details.

My problems are with:<br /> 1) low counts indicating degraded RNA (or low sample amount) - and lack of supporting information about real values.<br /> 2) application of "deep learning" techniques in lieu of the low counts.

The problems with measuring low counts have been critiqued by Sinha, et al, 2017.<br /> http://biorxiv.org/content/...

And noted in a news item:<br /> https://www.wired.com/2017/...<br /> "Sinha and other academic researchers aren’t the only ones who need that kind of needle-in-a-haystack sensitivity.<br /> Precision medicine—like spotting a piece of tumor DNA in a drop of blood or finding a rare variant among the 3 billion base pairs in the human genome1—also requires high-resolution sequencing. Clinical researchers and biotech start-ups that need that kind of resolving power are increasingly using Illumina’s ExAmp chemistry and the machines that employ it, including its newest line, the NovaSeq."

Since the TEP-study is using the same "needle-in-a-haystack sensitivity", it needs to provide the data that shows over-expression of MET.<br /> "Overexpression of MET protein in tumor tissue relative to adjacent normal tissues occurs in 25-75% of NSCLC" - https://www.mycancergenome..... Is it 25%, or is it 75%, in the current 60 sample size - a wide range. So, it is very very important to see that the classification has been done properly, before providing statistics based on surrogate biomarkers.

RNA-seq values certainly show no over-expression (on the contrary - but leaving that aside, since surrogacy does not require them to be there)<br /> Healthy MET = [287 197 61 142 178 127 7 176 2 133 188 156 23 2 185 170 104 23 28 71 108]<br /> NSCLC MET = [0 14 24 5 11 9 45 1 5 11 9 12 5 2 34 3 7 42 6 16 12 2 5 2]

Instead, the paper summarily states:<br /> "Assessment of MET overexpression in non-small cell lung cancer FFPE slides was performed by immunohistochemistry (anti-Total cMET SP44 Rabit mono-clonal antibody (mAb), Ventana, or the A2H2-3 anti-human MET mAb (Gruver et al., 2014)).

The challenges in FFPE include:<br /> 1) Degraded/fragmented DNA or RNA:<br /> 2) Insufficient amount of sample<br /> https://www.promega.com/-/m...

And similarly, the EGFR mutations were determined using FFPE (what EGFR mutations? there are so many, see below).

Is this not the same problem being stated by Sinha, et. al, 2017, albeit for a different sequencer?

In the refutation to my analysis, Dr Best states:<br /> "Surrogate signatures are composed of indirect biomarkers, and as stated in the publication the direct markers were not detected using thromboSeq (shallow sequencing), in contrast to amplicon sequencing (deep sequencing), which did allow for the detection of such direct markers in tumor-educated platelets."<br /> I guess it refers to the line in the manuscript:<br /> "We subsequently compared the diagnostic accuracy of the TEP mRNA classification method with a targeted KRAS (exon 12 and 13) and EGFR (exon 20 and 21) amplicon deep sequencing strategy ($5,0003 coverage) on the Illumina Miseq platform using prospectively collected blood samples of patients with localized or metastasized cancer.".

Two questions:<br /> (A) Where is the supporting data? With the zero counts of EGFR in the RNA-seq sample, is it not important to see that?<br /> EGFR in NSCLC = [1 4 1 0 14 9 1 0 0 0 2 0 0 0 19 0 0 21 0 0 6 0 0 0]

(B) EGFR (exon 20 and 21) gives a subset of possible EGFR mutations. Does its absence suffice to infer wildtype?<br /> Here is a comprehensive list from https://www.mycancergenome....<br /> Kinase Domain Duplication<br /> c.2156G>C (G719A)<br /> c.2155G>T (G719C)<br /> c.2155G>A (G719S)<br /> Exon 19 Deletion<br /> Exon 19 Insertion<br /> Exon 20 Insertion<br /> c.2290_2291ins (A763_Y764insFQEA)<br /> c.2303G>T (S768I)<br /> c.2369C>T (T790M)<br /> c.2573T>G (L858R)<br /> c.2582T>A (L861Q)<br /> Just to specify, Exon 20 and 21 spans from "2541 to 2881". And includes only a couple of possible mutations.

How does all these different mutations modify the surrogate signatures in the same manner?

About the Kappa statistics - Table S7 talks about "EGFR mut" in "4/39 (10%)".<br /> Since only 36 samples are annotated (Table S1), how does one know about the remaining 3?

"the author clearly has no understanding of the general principle of surrogate signatures" - here is what I understand. Please correct me if I am wrong.

Platelets circulating through the body when in proximity to tumor cells get their mRNA profile changed. "A total of 1,453 out of 5,003 mRNAs were increased and 793 out of 5,003 mRNAs were decreased in TEPs as compared to platelet samples of healthy<br /> donors". (See below for questions on the validity of the statistics).<br /> Now, these are passed onto the SVM algorithm.<br /> I have used the perl version http://search.cpan.org/~kwi..., so I know a bit.<br /> Essentially, this is some sort of machine learning (there are features with values, binary or otherwise, and training sets) and then validation (10 fold, for example : (a) Partition dataset into 10 sets of size n/10. (b) Train on 9 datasets and test on 1. (c) Repeat 10 times and take a mean<br /> accuracy). As mentioned in the TEP-paper, "The algorithms we developed use a limited number of different spliced RNAs for sample classification". And finally, comes out a set of genes that classifies between different diseases or between disease and healthy (and this is final problem, see my closing statements). I understand it does not even have to be the genes implicated in the cancer (for example, MET and EGFR in NSCLC).<br /> Thats the "surrogate signature" theory.

Moving on, take one gene (TRAT1) from the set of 1072 genes which is supposed to discriminate between healthy and pan-cancer (Fig 1F and G column in TableS4).

Healthy = [158 75 39 88 98 96 0 242 0 92 359 53 7 3 53 51 103 11 1801 48 67]<br /> NSCLC = [0 1 8 0 40 1 855 0 8 10 2 25 1 288 19 0 0 3 1 3 2 0 13 0]

An empirical glance shows some difference - most NSCLC have<br /> low counts, and most healthy samples have higher counts (though few go the other way, still I will say it shows difference).<br /> But, the raw reads are too low for discrimination, and as in the case of MET if small reads can finally translate into much larger numbers by using FFPE sequencing, then this can go either way.

Because for MET these are the values as shown above are :<br /> Healthy MET = [287 197 61 142 178 127 7 176 2 133 188 156 23 2 185 170 104 23 28 71 108]<br /> NSCLC MET = [0 14 24 5 11 9 45 1 5 11 9 12 5 2 34 3 7 42 6 16 12 2 5 2]

If FFPE analysis finds over-expression in NSCLC from such RNA-seq data, what is to say of the real expression levels of other genes which have expression in the same levels (like TRAT1) - they could be anything !!

2) The P-values of the over-expression values seem doubtful. Take the ribosomal gene RPSA (Table S2) - another gene used for discrimination.

Healthy (21 samples) = [490 323 160 783 306 342 531 593 241 283 846 364 44 572 253 372 701 54 1416 214 546]

Pan-cancer<br /> (98 samples) = [118 342 381 328 122 991 151 191 242 347 265 580 195 88 279 354 213 366 92 118 317 292 74 206 157 171 343 117 194 136 209 189 170 160 15 155 300 734 46 173 288 140 119 762 1156 341 223 363 508 416 709 73 40 227 204 174 156 148 147 255 324 298 121 194 295 103 58 1078 206 190 50 140 156 457 577 152 260 175 147 105 179 178 201 275 134 277 19 315 215 71 120 189 102 175 102 444 139 133]

Visually, I dont see any difference to justify a P-value of 1E-38 (this is ranked the second), and will find it hard to believe any statistic that comes up with that number.<br /> Again, these counts are too low - and as mentioned above with MET, can go either way - when amplified.

Note, the lowest RPKM of RPSA in set of tissues is 83 (+-9) in liver - https://www.ncbi.nlm.nih.go....<br /> The values above from the TEP-study are raw reads - dividing them number of million reads per kilobase of transcript,<br /> would give very low values.<br /> "Platelet RNA sequencing yielded a mean read count of $22 million reads per sample". And the gene is 1186 long. So divide by about 22 to get the RPKM above.

As conclusion, I can describe the basic problem of this flow in this way - assume<br /> 1) One randomly assign values to each gene count,<br /> 2) Remove genes where the random generator has assigned equal values or too random values<br /> 3) Give it to a classifier to do its MAGIC, which will finally give a set of genes which will separate out the classes.<br /> Thats its job, and we have made it easier for it by step 2.<br /> Each time one does these steps, she/he will get a different answer - with no biological significance.

Here, RNA-seq is not a random number generator (platelet markers have huge counts) - but for low values genes, its counts cannot be trusted, and becomes almost random.

best regards,<br /> Sandeep

On 2020-06-01 17:48:07, user Kendra Sirak wrote:

Genotype data for the newly reported 184 individuals in this study are now available at: https://reich.hms.harvard.e...

On 2017-02-28 15:43:50, user Lee Albee wrote:

The mtDNA call on PitV72.435B seems out of place---X2a is a Native American haplogroup<br /> This is likely X2j not X2a

On 2024-10-30 14:15:51, user Anonymous wrote:

Dear authors,

as part of a group activity in our lab we discussed your very interesting paper with the goal to review it. The below review is the result of this exercise and therefor reflects the thoughts and concerns of several people. We hope this helps you with your way forward to publish the paper in a good journal.

Review:

The manuscript by De Tito et al. reports a hitherto unknown role for ATG9 in recruitment of PI4K2A to damaged lysosomes to control the levels of PI(4)P, lysosomal membrane contact site formation with the ER, and lipid transfer from ER to damaged lysosomes. They report that this mechanism is controlled by ARFIP2, which shuttles between the Golgi where it anchors ATG9 and lysosomes, and by the AP-3 complex, which mediates retrieval of ATG9 from lysosomes. Finally, the authors show a role for their proposed mechanism in lysosome damage induced by Salmonella infection. The manuscript deals with the timely topic of lysosomal damage repair and PI4K2A was recently emerging as a key player in this process. Mechanisms for PI4K2A recruitment to the sites of damaged lysosomes were so far elusive. Although the manuscript is certainly of interest for the field, the presented evidence on which the claims and the proposed model are based on seem not always substantial enough and further work is required.

Major concerns<br /> • Novelty and generalization: Although the findings suggest new functions for ATG9A and ARFIP2, they overlap significantly with previous work on autophagic and lysosomal repair pathways. The novelty of these findings, in comparison to earlier research, could be emphasized more clearly.<br /> • The claim that ARFIP2 "modulates lipid transfer for lysosomal repair" could benefit from additional direct evidence linking lipid transfer to lysosomal recovery and ARFIP2's specific role in this process. There is only one in vitro experiment directly showing lipid transfer modulation by ARFIP2 (Fig. 5K). The authors should use the ARFIP2 W99A mutant in this experiment to test whether lipid transfer modulation is specific and according to their model.<br /> • A central claim of the manuscript is a role for ARFIP2 in the repair of damaged lysosomes. The gold standard assay in the field is recovery of lysotracker fluorescence after LLOME-induced damage as shown in Fig. 2G. The way the experiment is presented does not instill confidence. The effects are relatively modest, it is unclear how the statistics were done, the representative images in the supplement only remotely resemble lysotracker stainings, how a reliable lysosome number from these images could be extracted is unclear, it is not shown whether and how much the cells express GFP-ARFIP2, the time points of the images do not match the relevant time points in the quantification, and the lysosome number in the different samples before LLOME treatment is not factored in. <br /> • The major lysosome repair assay in the manuscript is Galectin staining. This is a rather indirect assay as compared to the lysotracker recovery assay as it shows the damage rather than the repair. Could there be repaired lysosomes that still are positive for Galectin? Is the total Galectin expression level the same in the cell lines and conditions used? Furthermore, spot quantification for lysosomal damage markers like LGALS3 (Figure 2f) does not account for cell size or density, potentially leading to misinterpretation of the data. The authors further need to show lysotracker recovery in ATG9 loss of function to substantiate their claims.<br /> • The authors show that ARFIP2 and ATG9 affect PI4K2A localization in cells. The “delivery” of PI4K2A to lysosomes, however, which is a central claim in this manuscript, is insufficiently demonstrated. Same is true for the “retrieval” of ATG9: The authors claim that AP-3 and ARFIP2 are important for the retrieval of ATG9A vesicles. Even though they show effects of these proteins on ATG9A presence at the lysosomes, they never manage to show that retrieval really is impaired. I am not fully sure if it is possible to make videos that convincingly show this. Either they should try that, or they should not hammer so hard on the word retrieval when they never show it specifically. <br /> • The dynamic interactions of the relevant components need to be better demonstrated and characterized. E.g. interactions ATG9-PI4K2A interaction needs to be proven and characterized better (e.g. by co-IP, immunogold or similar techniques). Otherwise the role of ATG9 is much secondary, and should be a ARFIP2-mainly focused paper. The experiments in figure 4H and 4G seem ideally suited for this purpose but the authors fail to show the relevant components in the same blot.<br /> • The increased formation of membrane contact sites is insufficiently demonstrated. The authors need to use electron microscopy, super resolution microscopy, SPLICS or other appropriate techniques to make this claim<br /> • The way of quantifying protein localization to lysosomes (intensity lysosomes/total) seems heavily dependent on the lysosome coverage of the cells. In conditions where to my impression the overall number of lysosomes also changes, I would like to see a negative control that demonstrates that the enhanced lysosome localization is not just by chance, since there are more lysosomes. This applies for instance to Figure 4E+F and 5A+B.<br /> • I am not satisfied with the data analysis they presented. The authors should indicate the statistical test for each piece of data they analysed including an explanation, why did they chose particular test. For instance, in fig. 2g they compare the amount of puncta or lysosomes (it is not clear as well) in the cell in a pairwise manner. It would be more appropriate to implement a statistical test that can compare the curves fitted with the data points or a test which can compare each time point individually, i.e. Kaplan-Meier plot.

Minor comments<br /> • The zooms generally miss scalebars<br /> • Which part of the picture the zoom is from is often not indicated<br /> • Many of the images are hard to interpret because of low contrast, I would recommend to put their individual channels in grey<br /> • Fig3f. requires a better representative image and although there is a line indicated there is no line plot<br /> • Fig. 1c: show quantification for pS6K<br /> • Fig. 2b: data points are the same as figure 1b<br /> • Fig. 3: Live-imaging of ATG9A with AP-3D1 is needed

On 2021-12-06 15:49:22, user Simon wrote:

Many experts in the fields are skeptical of the validity and accuracy of the methods used in this work and therefore of the conclusions drawn in the article.

Criticisms:

The structure of the Omicron RBD made using mutations in Pymol cannot be trusted without proper structural data on the omicron RBD, because Omicron contains a high amount of mutations, the mutations cluster in a very disordered region of the RBD and influence of the glycans is disregarded (See e.g for their importance https://pubs.acs.org/doi/10... "https://pubs.acs.org/doi/10.1021/acscentsci.0c01056)").

The methods used to predict stability generally tend to work better on decreased stability than for gain of function. There is no benchmark against existing biophysical assays (e.g from deep mutational scanning) to show that the used method (i-mutant3.0) works on the RBD. In addition, the effect of multiple mutations are likely not additive but subject to epistasis. The used predictor is quite old and should be compared with newer approaches.

In general, protein docking is unreliable to estimate binding affinity in absolute terms. Especially in this case because the structures used were obtained by simple mutagenesis in PyMol without any equilibration/relaxation. The conclusions are not be trusted without additional experimental or more reliable computational analysis that includes the proper glycan shield of the protein and relaxed structures.

It is unclear how a table of amino acid composition and corresponding secondary structure prediction is useful or has any meaning for the conclusions of the article. The fact that the RBD is mainly alpha helical is not an indicator for high structural stability. The differences between the predicted fractions of alpha helices are meaningless and very small.

Some experts expressing their criticisms:

https://twitter.com/ElisaTe...<br /> https://twitter.com/RolandD...<br /> https://twitter.com/jpglmro...

On 2024-10-08 19:41:38, user Reena Sharma wrote:

The key message of the study is that heavy metals like cadmium (Cd) and mercury (Hg) pose significant threats to plant health, but legumes, including Medicago truncatula, exhibit genetic variation in their ability to tolerate and accumulate these toxic metals. By conducting a transcriptomic analysis of plants with varying levels of tolerance to Cd and Hg, the study identified tissue-specific, genotype-specific, and metal-specific gene expression patterns.

Notably, plants inoculated with mercury-tolerant rhizobia strains carrying a mercury reductase (Mer) operon experienced less reduction in nodule number, plant biomass, and iron distribution under Hg stress. This suggests that Hg-tolerant rhizobia can mitigate Hg toxicity in plants, enhancing resilience in contaminated environments. These findings highlight the potential to optimize legume-rhizobia interactions for improving plant tolerance to heavy metals and reducing heavy metal transport to edible parts of the plant, which is critical for food safety.

On 2020-04-04 03:29:03, user eugnene ioanid wrote:

Prof. Darian-Smith's lab has produced most interesting research seeking out the role of sprouting in recovery of digital grasping of a pellet. Our own work indicated that precision grip, as reported and explained by Vierk was lost after dorsal column section. We found that the problems in such a task is quite different for a rhyzotomized monkey (C2-T12), bilat or unilat [b/deaff or u/deaff), as opposed to a monkey with dorsal column or dorsal column nuclei lesion. The latter's deficit is totally in manipulative skills and after 3 yrs of trying daily never returns, replaced by a crude power grip or raking whole hand movement. By contrast the d or u deaffs eventually acquire exquisite digital coordination when only a precision grip provides reward. Their reach, however, in contrast to dorsal column or nuclei lesion (as reported by Vierck) is severely dysmetric as so well demonstrated by Gilman. However, dorsal rhizotomy superimposed on dorsal columns section produces severe dymetria but coordinated digital precision grip recovers faster than in deaff alone. We hypothesized that loss of dorsal columns causes severe noise in the somatosensory system from which deaff monkeys are liberated by the total removal of sensory input. It also seemed to us that somatosensory input is far more crucial for the axial and torso large muscles for posture and arm stabilization over the target. Crowley et al add a most important component, CorticoSpinal sprouting, by indicating that sprouting is not critical to manual sensorimotor coordination, suggesting that the descending signal does more to release spinal synergies and primitives as so elegantly indicated by BIzzi and colleagues.

On 2025-01-20 14:53:29, user Mathias Witte Paz wrote:

This paper has been published at NAR Genomics and Bioinformatics <br /> https://academic.oup.com/nargab/article/6/4/lqae168/7928173

On 2018-01-16 01:44:35, user QW wrote:

This manuscript is highly flawed. Data in Sup fig 3 contradicts the conclusions of the paper. The RNA is present every where. <br /> No quantifications make it hard to trust that the images were not cheery picked.

On 2015-06-13 13:32:53, user Casey Bergman wrote:

Could you post the supplemental materials here as well?

On 2017-11-24 13:40:01, user Kirti Prakash wrote:

Dear Readers, <br /> Please note that #132571 is the original version of the paper and #121061 is the revised version.<br /> The most recent version of the article here: <br /> https://www.biorxiv.org/con... <br /> Feel free to get in touch if you would have any questions. <br /> Sincerely, <br /> Kirti Prakash

On 2022-05-09 20:31:44, user Harmen Draisma wrote:

Many thanks for sharing this -- on page 2 you write that "GWAS signals ... are enriched in gene regulatory elements and eQTLs", and I'm wondering why then in the rest of the paper you make such an ostensibly stark delineation between "GWAS hits" and "eQTLs", i.e. if these eQTLs and GWAS hits can colocalize "i.e., indicate the same [causal] genetic variant" as per page 2 also? Thanks

On 2019-03-29 14:31:31, user S. Biffo wrote:

Nice work, from what I understood upon a fast reading. I have a couple of observations, one more phylosophical than a critic, the other is a question. Point one, in order to really know that modified RACK1 leads to a functional ribosome one should really do a knock-in mouse. We did not expect that in mice RACK1 loss, due to an hypomorphic allele was lethal, to the point that we could not rescue it in the p53 -/- background and we could not even arrive to the MEFs! In other terms, since people describe "ribosomal heterogeneity" in animal organs, I think that in vitro studies can be misleading when dealing with a property of ribosomes that we may suspect "perhaps" relevant in vivo. The other is a question, man proteomic studies identify Tyr phosphorylation of RACK at Y52. In your study, is p-Y52 likely to play a role in the off-rate? Bye, bye and thanks for posting it (Biffo)

On 2017-04-12 00:13:38, user Kyle Elliott Mathewson wrote:

Interesting finding, but needs more validation in my opinion. A concern for me would be the variation in the recording location across the groups. Where were the participants seated? what was going on around them? We find large decreases in the peak of alpha when people move from inside to outside the lab

On 2020-01-24 09:36:54, user Dirk Jochmans wrote:

Great paper, a lot of info on the nCoV and the first patients. Thanks!

On 2020-01-24 13:44:23, user Yen Shu Chen wrote:

The authors did not share (no GenBank/GISAID accession number are <br /> provided) the genome sequence of the critical bat-CoV that represents a <br /> close relative to human 2019-nCoV. <br /> No way to access/reproduce/further use their result. Do scientific journals accept such practice?

On 2018-04-30 20:54:01, user Hunter Rice wrote:

Hello,<br /> My name is Hunter and I am a graduate student in the Microbiology department at the University of Tennessee in Knoxville. Recently, we discussed your preprint article in a journal club on computational biology, and I wrote up my personal review of the article and included the comments posed by the class. We thought if there were any chance this might be of benefit to you, we should share it, so I’ve posted the review and comments below. I hope it is at least a little useful. Thank you for your contribution!

Review of Muraro et al, 2018 (preprint), ‘Chronic inflammation delays cell migration to intestinal villi’

Paper describes three alternative models, and their comparisons and implications, of the migration of epithelial cells from the recessed crypts of the small intestine (duodenum and ileum) toward the tips of adjacent villi. Specifically, they model the migration rate across three conditions: without disease, after acute inflammation (possibly wounding), and after and during chronic inflammation.

Data for parameter estimation comes from experiments done with C57BL/6 mice. Cell proliferation and migration was tracked with BrdU. Experimental conditions were: 1) mice given 0.5mg/mL TNFa, 50mg/mL BrdU, sacrificed and sectioned/stained from 1-48hr pi, 2) mice given TNFa expression plasmid, monitored for 2 weeks for chronically high TNFa expression, then given BrdU, sacrificed and sectioned/stained 1-48hr post-BrdU. Number of cells stained for BrdU and position in villus quantified (Fig 2)

Strengths of this paper:

Conclusions from three models converged, supporting hypothesis that chronic inflammation can slow cell migration across the villus. This is significant because it suggests a mechanism by which chronic inflammation might be self-perpetuating or at least induce a positive-feedback loop, as the breakdown of the epithelium due to a decrease in cell flux would likely lead to further inflammation.

This mathematical model combined with the experimental model may be useful in future attempts to dampen chronic inflammation specifically by targeting the factors involved in reducing cell migration in situations of chronic inflammation.

Weaknesses of this paper:

It seems that the major conclusion (Chronic inflammation delays cell migration to villus) could be arrived at without the mathematical model. Not that it is not useful, but if the paper did more to accentuate the novel conclusions facilitated by the modelling side, it might be easier to convince the reader of the importance of the computational approach.

Comments (made by journal club):<br /> • Why age selection of mice? Clarification would be good for non-expert’s sake<br /> • Showing staining in figure would strengthen representation of methods in paper<br /> • Heaviside function representation is confusing on first viewing<br /> • Use of ‘BrdU’ instead of ‘control’ is confusing in both the text and Fig 2.<br /> • How were cells quantified? How did you control for differences in crypt-villus units?<br /> • Figure formats (no subfigure labels in fig2)

On 2020-11-05 14:27:03, user Luping2020 wrote:

This paper has been accepted for publication in Experimental Biology and Medicine.

On 2025-05-23 18:27:25, user Minwoo Kang wrote:

This preprint has been published in a journal.<br /> https://biosignaling.biomedcentral.com/articles/10.1186/s12964-025-02227-7

On 2018-02-19 18:20:07, user J.J. Emerson wrote:

Casey Bergman points out via Twitter (http://bit.ly/2HtIKOt) "http://bit.ly/2HtIKOt)") three previous examples of TE variation in substrains of ISO1 using various different methods:

FISH: http://go.nature.com/2sDWW46<br /> Southerns/FISH/RT-PCR: http://bit.ly/2sDX03Q<br /> Genomic sequencing: http://bit.ly/2ED5RbU

We will be incorporating these references into subsequent revisions of the manuscript. Thanks, Casey!

On 2021-01-14 01:01:51, user Avant_Garde_1917 wrote:

The proposed neurofeedback model was implemented on Python (with deep learning models implemented using pyTorch library) and run on a machine with an Intel i7 processor, NVIDIA GeForce 1050Ti GPU and 8 Gb RAM

What on earth is this? Please repeat the experiment on modern technology. I checked the specs on the 1050Ti and this would be horrible at real time AI calculations.

CUDA Cores 768 Graphics Clock 1290 MHz Processor Clock 1392 MHz Memory Interface GDDR5 VRAM 4 GB Memory Interface Width 128-bit Memory Bandwidth 112 GBps Tensor Cores No

Compare that to an RTX 2080 Super, which is not even the current generation 30 series

CUDA Cores 3072 Boost Clock 1815 MHz Memory Interface GDDR6 VRAM 8 GB Memory Interface Width 256-bit Memory Bandwidth 496 GBps Tensor Cores Yes

I know everyone must have heard about new video cards. I would hope you have bought them by now. If not, then please do it. And if you have already bought them, then please repeat the experiment so that you can do a far more real time, higher quality image, at a higher resolution with less loss.

On 2019-10-18 22:59:52, user Charles Warden wrote:

I am still not entirely sure what I think about these metrics, but I think some of your experiences might be relevant to this discussion:

https://www.biostars.org/p/...

For example, are there situations that can increase the variability of reads obtained per sample? I think this mostly tends to happen when the desired number of reads per sample decreases, but that is not the only reason why reads might have to be combined between runs. However, if you are trying to barcode larger numbers of samples, then that seems relevant.

Are you keeping track of the number of reads that go to unassigned barcodes (particularity if you make sure not to allow any index mismatches)? For example, do you consider it a "red flag" if you are getting 10,000s of reads for several unassigned barcodes and you only want 1,000 reads?

As you design custom adapters, does that have any effect on the index quality scores? Are there situations where you have decided that a lane and/or run needed to be thrown out?

Have you found common causes of situations that seem to make cross-contamination more likely? While it won't be a fair representation for recombination between similar sequences, have you tried things like having a unique spike-in for each sample (and then measuring how much of that spike in can actually be found in other samples)? I think this can noticeably vary between batches, but I don't believe that I have a satisfactory explanation that could predict / prevent this from happening (in general).

It looks like all of the deposited data is for MiSeq. You also mention an issue with a tandem barcode limitation for NovaSeq, which I was not previously aware of.

So, am I correct that you are primarily interested in decreasing the sample preparation costs (rather than trying to increase the number of samples processed per-lane)? If so, perhaps my initial impression is off. However, does that then mean that part of the goal is to decrease sample preparation costs (and avoid unnecessarily high sequencing depth) to make sequencing with a lower-throughput sequencer (probably within a lab) more appealing?

More directly related to the paper, if you use this strategy to prepare MiSeq libraries, have you seen any indication of a maximal number of samples that you think should be processed per run? Based upon Figure 1, it seems like the biggest cost benefit occurs around 1000 samples.

I apologize if I have missed something, but I am trying to get a sense of the robustness of the metrics provided for Hackflex over the course of 10-20 runs (and, ideally, if variation in metrics can occur as a function of the total number of samples that you try to process, or the types of libraries that you process). I am having a hard time answering the later question myself, but it seems like this might somewhat match your interests.

Also, Supplemental Table 2 only goes up to 192 samples (and I only see Supplemental Table 2 and 8). So, I apologize, but I am not sure where I should be looking to see that there is no compromise in data quality if you process 1000 samples over 100-200 samples.

On 2017-11-13 08:54:50, user James Lloyd wrote:

Thank you for posting this interesting article about short ORFs in moss onto a pre-print server. I think that it is great that you have been able to identify sORFs throughout the transcriptome, classify them as uORFs, dORF etc and examine conservation and even find evidence for protein products for some, and function for one.

Comments and questions:

In Fig 1B, it would be nice to see another column describing how many sORFs were found in each classification from the computational pipeline, without evidence of expression or translation. Also, it would be best to right-align the numbers in the columns so that it is more obvious the magnitude of the differences at a glance.

I like how Figures are integrated with the main text in this pre-print, it is rarely done, but I think that it would be better to change the legend text so it does not get confused with the main text so easily.

dORFs are not explicitly described in the main text of the manuscript. There are times when genic-sORF is used, and others when CDS-sORFs is used - I think that they refer to the same group, if not, make that clearer. If they are the same group, they should be consistent. It is not quite clear what “intergenic-sORFs” are when intergenic-sORFs are also a group.

CDS-sORFs are an interesting group that I had not considered. I take it that they use internal start codons in a different frame from the main ORF. Is there much evidence from other systems that they are real (eg ribo-seq data focusing on start codons to suggest those types of start codons are used)? Are the peptides from these vastly different from the protein of the main ORF? This is unclear in the manuscript and I think that a clearer explanation would help the reader appreciate the importance and validity of this.

Line 143 has "(REF)", it was not clear initially that this was a abbreviation rather than shorthand for citation. Perhaps it is just me but changing this in some way might aid the reader, especially as it is most frequently used much later in the manuscript, after initial definition. I'm not actually sure randomly selected exon fragments needs to be shorten; not doing so would aid readability.

Lines 175-176 "significantly fewer uORFs and dORFs in the two closest species", do you think that this could relate to the poorer genome/transcriptome assembly of these organisms relative to the others in the study? Perhaps ends of transcripts are less reliability reconstructed?

In Fig 5, it might be worth adding "WT", "OX" and "KO" to the respective panels to save the reading having to look between the legend and the images.

Line 681 and other places: "custom-made python scripts (available upon request)", it would be ideal if these scripts could be placed on GitHub or deposited on something like Zenodo (or maybe FigShare)? With Zenodo you can get a DOI to reference. Even if the code has not been tidied up for maximum re-use, I still think that this would be the best thing to do.

I could not see any list of global sORF classification (uORF, dORF etc), position (genomic coordinates and linked gene when possible) and whether it is conserved in other species or not. I think that such a list would be extremely useful. I know that I would use it to aid some of my future analysis in moss data.

On 2021-04-18 23:46:37, user Yury Barbitoff wrote:

Dear Charles,

thank you for your interest in our research!

The filtering settings used for "hard filtering" in our work are similar to the ones you provided in your comment; however, we use more parameters for filtering as suggested by the GATK Best Practices workflow. SNPs and indels are processed separately accoring to this guideline: https://gatk.broadinstitute...

It is possible that your settings allow for an increase in the F1 Score - and we may check using our dataset. At the same time, we believe that our work is not the first one to demonstrate that unfiltered GATK calls show higher F1 score (DeepVariant authors came to the same conclusion in their comparison, and so did the Zhao et al., 2020 study; 10.1038/s41598-020-77218-4). It is possible, however, that these observations are made because of the same "overtuning" issue - it may apply not only to deep learning models such as DeepVariant, but to conventional calling methods as well (because all evaluation is basically performed using the same set of samples :)). I can tell from my personal experience in working with patients' exome data that filtering in GATK does substantially decrease the recall, at least when using the setiings we've tried - we had several cases with the causal variant being filtered out, and these were rather unpleasant cases. That's one reason why we try to pay the reader's attention to the filtering issues for medical purposes.

As for your suggestion regarding the direct proof of overfitting/overtuning problem - this is something we're working on at the moment, so thank you for your suggestions! We have not yet found a perfect dataset to work with yet, so testing the issue may take some time.

Sincerely,<br /> Yury

On 2016-07-19 05:30:05, user Frederic Bastian wrote:

Great work! <br /> p.3: "In addition to lexical criteria, we use ontology structure criteria." "The rules above do not exhaustively cover all cases" "code on GitHub for details"<br /> I would be interested in getting more details about the lexical/ontology structure criteria used to generate prior probabilities. Could you point to where to find this information in the github project?

On 2019-11-26 16:58:25, user Vahe Demirjian wrote:

The chasmosaurine specimens YPM 2016 and AMNH 5402 are interpreted as more similar to Vagaceratops irvinensis by Campbell et al. (2019), who remove these specimens from Chasmosaurus belli and place Vagaceratops back in Chasmosaurus.

Campbell, J. A., Ryan, M. J., Schroder-Adams, C. J., Holmes, R. B., & Evans, D. C. (2019). Temporal range extension and evolution of the chasmosaurine ceratopsid ‘Vagaceratops’ irvinensis (Dinosauria: Ornithischia) in the Upper Cretaceous (Campanian) Dinosaur Park Formation of Alberta. Vertebrate Anatomy Morphology Palaeontology, 7, 83-100. https://doi.org/10.18435/va...

On 2020-09-01 17:37:34, user Marcos Fernando Basso wrote:

Dear authors,

Please, let me know how I can access the genome database of the brachiaria.<br /> Thank you so much.

On 2023-02-22 12:31:33, user Felix Bäuerlein wrote:

Review on manuscript

The intention of this review is to improve the scientific approach and to help the authors improve the manuscript and not to attack anyone personally!

It is a very important challenge, to develop cryo-FocusedIonBeam/ElectronTomography (cryo-FIB/ET) technology further to be able to investigate not only single cells but also patient derived tissues. <br /> The authors attempt in their manuscript to address mouse and human brain tissue by cryo-ElectronMicroscopy (cryo-EM) - tomography is aimed in the future.<br /> Several groups attempt to do cryo-FIB/ET on tissue with different technical and sample preparation techniques. In our recent review [Bäuerlein & Baumeister (2021) JMB https://doi.org/10.1016/j.j...] we address the challenges that need to be taken.

In this present manuscript the authors successfully manage a lamella preparation in brain tissue with an approach developed in 2014. <br /> Despite a technical solid approach the sample choice, retrieval and preparation are key to the molecular interpretation of cryo-EM studies! Here I see serious major issues with the sample retrieval and preparation. Besides a technical good approach, the sample preparation is at least as important, to be able to draw high resolution conclusions from the imaging data.

MAJOR ISSUE 1: <br /> In the presented manuscript here, the way of preparation of human brain (post-mortem, ischemic due to long lasting anoxia (8.3h!), chemical fixation) doesn’t allow conclusive interpretations at molecular resolution - particularly not investigating neurodegeneration, which the authors project to do! In our recent review [Bäuerlein & Fernandez-Busnadiego & Baumeister, (2020) TICB https://doi.org/10.1016/j.t...] we argue why post-mortem, anoxic brain tissue should NOT be chosen for the interpretation at high-resolution for the investigation of neurodegenerative diseases:

„However, postmortem tissue is not suitable for high-resolution studies, as brain tissue is extremely sensitive to anoxia and rapidly develops into brain death due to global ischemia in the dying patient. Irreversible cerebral damage develops rapidly, leading to a drastic decrease of survival in minutes if an individual is not resuscitated [92]. Furthermore, the chance of a favorable neurological outcome of a surviving patient after 30 min of resuscitation is vanishingly low [93], indicating major neuronal damage. Substantial structural alterations, affecting most organelles and cytosolic structures, have been observed in the first minutes to hours after global ischemia [94–97]. And importantly, cerebral ischemic injury and neurodegenerative disorders share many commonalities [94]. Thus, studies using high-resolution methods on postmortem tissue [98,99] image a cellular situation in which the neurodegenerative pathologies cannot be distinguished from postmortem artifacts and should thus be taken with caution.“

Brain tissue is the most sensitive tissue to anoxia in the human body - several minutes of hypoxia (stroke, heart arrest) cause first structural changes in minutes and typically causes irreversible ischemic damage to neurons. Thus the term „excellent tissue quality“ is more than questionable, many hours of anoxia like in this study (8.3h). Since cryo-EM offers molecular resolution the condition of the sample must be strictly close-to-physiological, which can reasonably not be expected many hours under anoxic conditions.

The authors conclude however an „excellent tissue quality“ with stating a tissue pH of 6.3. I wonder what the term ‚quality‘ here refers to!? <br /> Under physiological conditions the intracellular pH is narrowly buffered to typically 7.2, the cells surrounding CSF (an ultra-filtrate of the blood) is typically around 7.35. An acidosis with values below a pH of 6.8 is incompatible with life - so how can a brain tissue pH of 6.3 be interpreted as “excellent tissue quality” in terms of physiological vitality, which is essential if high-resolution interpretations want to be made?

In contrast to the authors interpretation I suspect this brain sample to be seriously ischemic, degraded, acidic and simply dead, which are normal processes post-morten and thus a more than suboptimal target for high resolution analyses! And importantly, cerebral ischemic injury and neurodegenerative disorders share many commonalities [Nikonenko (2009) Anat. Rec.].

MAJOR ISSUE 2: <br /> In it’s adequate use - by cryo-fixation - cryo-EM enables the investigation of the cellular context at molecular resolution in a close-to-native state.<br /> The authors chemically fix the human post-mortem brain, which causes structural alterations at the molecular level [Gilkey & Staehelin (1986) J. Electron Microsc. Tech] and thus dissipates any of the favorable advantages of cryo-EM. In Fig 5 all panels show ‚empty‘ white spaces in the micrographs indicating precipitatation of soluble proteins on membranes and the cytoskeleton. Fig. 5 actually has an appearance almost like a dehydrated, resin embedded sample and looks quite untypical for cryo-preserved tissue. Also the contrast of these micrographs appears quite untypical for cryo-EM micrographs. The comparison of chemically fixed brain in Fig 5 to the not fixed mouse brain sample in Fig 4 visualizes the strong alterations chemical fixation introduces on the molecular level. Thus taken together the title of this Fig 5 „Ultrastructural preservation“ is likely exaggerated!

MAJOR ISSUE 3: <br /> The major prerequisite for the ultrastructural preservation of intact cells and tissues is the vitrification of the biological matter. This means the absence of water-ice crystallinity. Sadly the authors offer no proof of absence of hexagonal ice crystallinity in their manuscript - this definitely is a must. In Fig 4B there are indications of crystalline water ice: corresponding black and white diffraction patterns also called Bragg diffraction patterns seem to be present here. This indicates that larger parts of the mouse brain sample might not be vitrified.

MAJOR ISSUE 4: <br /> The standard way to represent microscopic data in the cryo-ET field is at least a slice of a tomographic reconstruction to not be biased by superimposing structures in the three-dimensional lamella or membranes that are only visible when looking at an angle which is near parallel to the membrane sheets. Here I am very surprised to exclusively see 2D projection images - this is very untypical and doesn’t support the fact, that the whole aimed cryo-ET pipeline works. Why is there not a single tomogram shown? The figure caption in Fig 4 claims the left panel as tomographic slices however they very much appear as simple projection micrographs. I would definitely show at least slices from 3D reconstructions and not 2D projections - this is minimal standard in the cryo-ET community.<br /> For example, conclusions drawn like in Fig 5 C) are not substantiated: How can the authors be certain in the interpretation that this structure is a ribosome-associated VESICLE without any 3D information - this might just be an ER tube extending in the z-direction which was cut off to top and bottom by FIB-milling?

Besides this I am stunned to read in the methods section that for a single projection, an excessive dose of 100 e/Å^2 was necessary - this is a dose typical for an entire Tilt-series acquisition with about 30-50 projections - is that maybe the reason why a tilt-series could not be recorded to reconstruct a tomogram?

MINOR POINTS:<br /> Furthermore I would love to see some typical overviews of entire lamellae - this could give a nice impression of the cellular context and also show how well the H-bar cryo-FIB preparation works.

It would also be helpful for the community, if the authors share their most representative data on EMDB (Electron Microscopy Data Bank) - this is common practice in the field.

CONCLUSION:<br /> Thus, in conclusion the technical approach is interesting but as lined out there are several major issues - leading the use of ischemic, post-mortem, heavily anoxic brain tissue, and chemical fixation which each separately are incompatible sample preparations for high resolution imaging as intended with cryo-ET. We emphasized the importance of the sample quality in our review in Trends in Cell Biology.

Major issue with the perspective of the authors future intentions:<br /> The authors plan to do sub-tomogram averaging of post-mortem patients brains with this work-flow - a very well-meant advise: this is not going to be successful! Chemical fixation denaturates proteins which alone will make sub-tomogram averaging rather meaningless.

Furthermore, the biological/medical conclusions drawn from protein functional states, concentrations, distributions and so on, will be very questionable when imaged in the dying, heavily ischemic brain (after some hours, there will likely not be a single neuron alive). Necrosis, apoptosis, breakdown of all membrane potentials and gradients, depletion of ATP, protein degradation and many more processes will be governed by this - so how does one want to separate the effects of death from disease mechanisms during the lifetime of the patient?

Thus this approach needs to be profoundly reevaluated, when questions about disease mechanisms of neurological and psychiatric conditions are the target!

On 2024-10-09 16:05:10, user Rosângela Maria Rodrigues Barb wrote:

Very interesting. I wonder if it works with urban pests (mosquitoes)

On 2018-11-27 08:10:26, user Klaus Fiedler wrote:

The similarity of Wnt - p24-GOLD domain interaction shown here to XWnt8 -<br /> Fz8-CRD interaction seen in the cited work [32] can be observed when <br /> comparing the figure 2A of Janda et al.

On 2024-01-22 17:35:49, user Mohieddin Jafari wrote:

After reviewing your paper, I found it to be a little bit confusing. In the first Figure, you reference Fargpipe and DIA-NN, but the case studies appear to be centered around Spectronut. Moreover, it's unclear in the "Mass Spectrophotometry Methods" section which specific case study you are addressing. Do you have any updated version of this manuscript?

On 2018-06-12 20:25:38, user Luke Evans wrote:

Dear Drs. Balding and Speed,

Thank you for your comments on our preprint. We agree that LD can play an important role in phenotypic variation. In our revision, now published in Nature Genetics (link above), we performed a wide range of simulations to explore the influence of LD-dependent architecture on estimates of SNP-heritability across a wide range of estimation models, including the Speed et al. 2017 and Yang et al. 2015 models. Importantly, we included simulations based on an LD-based model (Gazal et al. 2017 Nature Genetics) that is independent of the Yang or the Speed models.

We confirmed that under a range of conditions, all the models can be biased. However, stratifying the markers by MAF and individual SNP LD scores (GREML-LDMS-I, which we introduce in the published manuscript) provides the least biased estimates across all simulated phenotypes, and provides the most accurate picture of genetic architecture across the entire range of LD-dependent and LD-independent architectures.

We believe that these additional simulations, included in the revised and published manuscript, address the LD issues you raised in your comments.

We look forward to continuing the discussion of SNP-heritability and genetic architecture.

Luke Evans, Institute for Behavioral Genetics, University of Colorado

On 2018-08-29 14:11:11, user ofoxofox wrote:

The paper has just been accepted for publication with Nature Sci Rep. More developments on this topic will be coming out soon enough.

On 2023-07-01 18:19:52, user Rod Page wrote:

Notes to self, other relevant papers include The big four of plant taxonomy – a comparison of global checklists of vascular plant names and Fungal names: a comprehensive nomenclatural repository and knowledge base for fungal taxonomy

On 2022-08-14 20:38:52, user Ricardo M. Biondi wrote:

With my colleague Alejandro E. Leroux we have written an extended commentary with our opinion on the work, which can be found in the link: https://www.qeios.com/read/....<br /> In short, we find that the authors do not properly cite previous work, notably the Gao and Harris paper (2006) that reaches similar conclusions. In addition, the introduction fails to acknowledge even basic issues. For example, the classical PKCs are constitutively phosphorylated by PDK1 without growth factor signaling. Akt/PKB becomes phosphorylated by PDK1 in a PI3-kinase dependent manner but also has been described to become phosphorylated by PDK1 in a PI3-kinase INDEPENDENT manner. In contrast to what Levina et al. indicate in the introduction, a model to explain PDK1 phosphorylation of substrates must take into consideration that some substrates are phosphorylated in a PIP3-independent manner! <br /> For a detailed commentary on the results section, again I recommend that you go to the qeios link above. Most of the hard biochemistry in the paper is dedicated to describing the dimer that must be formed along the very very slow process of trans-autophosphorylation in vitro. The hard-core biochemical studies are based on a fusion of PDK1 to PIF. It is difficult to understand what useful information can be obtained from those "dimers"... PIF binds with high affinity to PDK1: what would be the sense of crosslinking GST-PIF to PDK1? would you obtain any information about the GST/ PDK1 heterodimers??? If the model was correct, PIFtide should inhibit trans-autophosphorylation. The authors did not do this control experiment. But it was done previously: this was NOT observed in the paper by Frödin et al (2000). So the dimer model with an important hydrophobic motif binding to the PIF-pocket in the neighbour molecule is very likely incorrect. Finally the authors claim autoinhibition by the PH domain and release of this autoinhibition by PIP3. I have not yet seen any convincing data to support the existence of an autoinhibited PDK1. Please, refer to the qeios link for further details. In short, I believe that the conclusion of this part of the work is also not supported by their data nor by 25 years of careful work by different laboratories.

On 2019-10-18 02:23:52, user Ural Yunusbaev wrote:

Wow! Very nice manuscript! But I didn't catch what is the Hi-C?

On 2025-06-17 07:28:32, user Valters Abolins wrote:

Very interesting article - thank you for investigating this topic. I have a comment on the methodology that, in my opinion, might affect your results. In previous studies, it has been shown that finger interdependence tends to increase during isometric steady-state force production tasks (e.g., Hirose et al., 2020; Abolins et al., 2020, 2023). In your analysis, the time window used to calculate forces appears to vary from trial to trial. This variability may affect the assessment of interdependence and related force measures. Have you examined whether finger interdependence changes over time in your data? I believe this would be an important point to consider when interpreting your findings.

References:

Hirose, J., Cuadra, C., Walter, C., & Latash, M. L. (2020). Finger interdependence and unintentional force drifts: Lessons from manipulations of visual feedback. Human Movement Science, 74, 102714.

Abolins, V., Stremoukhov, A., Walter, C., & Latash, M. L. (2020). On the origin of finger enslaving: Control with referent coordinates and effects of visual feedback. Journal of Neurophysiology, 124(6), 1625–1636.

Abolins, V., Ormanis, J., & Latash, M. L. (2023). Unintentional drifts in performance during one-hand and two-hand finger force production. Experimental Brain Research, 241(3), 699–712.

On 2020-08-15 23:26:06, user Peng He wrote:

It seems that the html format is massed up. But please check out the pdf which should be intact! And of course the paper is now officially online

On 2019-01-24 09:15:18, user Diego di Bernardo wrote:

very interesting results. In a 2016 Oncotarget publication we confirmed the effects of Niclosamide on cancer cells with the constitutive activation of the PI3K/P70S6K signalling axis: https://www.ncbi.nlm.nih.gov/pubmed/27542212

On 2016-10-30 19:33:46, user ohwilleke wrote:

There are a few grammatical issues with the abstract: "schizophrenia brain" should be "schizophrenic brain"; "while participants performing the working memory task" should read "while participants are performing the working memory task"; "discrimination performances of pattern classifier machine trained" should read "the discrimination performance of the pattern classifier machine trained"; "classifier machine trained by time-lagging patterns of low frequency fluctuation (LFF) produced highest classifying accuracy than the machines" should read "the classifier machine trained by time-lagging patterns of low frequency fluctuation (LFF) produced the highest classification accuracy relative to the machines"; "classifier machine trained by coherence pattern in LFF band also made better performance than the machine trained by correlation-based connectivity pattern.", should read "the classifier machine trained by coherence patterns in the LFF band also performed better than the machine trained by a correlation-based connectivity pattern"; "These results indicate that there have been unwatched but important features in the functional connectivity pattern of schizophrenia brain on which traditional emphasis on correlation analysis could not capture." should read "These results indicate that there are unobserved but important features in the functional connectivity patterns of schizophrenic brains which the traditional emphasis on correlation analysis does not capture."

On 2025-02-21 00:46:47, user Hurrian Fan wrote:

Congratulations to the team on some amazing work! The steppe component visible in East_steppe_Sets in Chalcolithic and Bronze Age western Anatolia is modeled with Yamnaya, CWC, and Bell Beaker, which are chronologically and geographically somewhat improbable for the early samples. <br /> Have the authors considered checking potentially more proximal sources of potential steppe ancestry, such as the Kartal A & B clusters from Penske et al 2023?

The subclades of y-hg I-L699 found in the Kulluoba and Kalehoyuk samples are not only shared with Serednii Stih individuals, but also Cernavoda (KTL001 & KTL006) and Thracian EBA individuals (Bul4 & I2165), which might lend some plausibility to this source.

On 2020-08-10 21:58:05, user Mariana Souza da Silveira wrote:

It would be great if we could see the Suppl figures.

On 2021-06-27 14:05:02, user Jasun wrote:

The new version of this preprint is available at https://www.nature.com/arti...<br /> 21-021-00282-1, Thanks for your attention!

On 2023-08-12 19:35:33, user Charles Warden wrote:

Hi,

Thank you very much for posting this preprint.

This comment relates to the methodology and my personal experience.

Essentially, I have a number of data types (SNP Chip, Exome, Illumina Whole Genome Sequencing, and PacBio HiFi Whole Genome Sequencing for myself). You can see part of those results if you scroll down to "Raw Re-Analysis for HLA Typing" on this page.

My HLA-A, HLA-B, and HLA-C (which I believe are the "class I" HLA genes) had consistent results that I believe can be reliable.

However, at least for myself, I had concerns about the SNP chip imputations for HLA-DRB1, HLA-DQA1, and HLA-DQB1 (which I believe are the "class II" HLA genes). The Introduction and Supplemental Tables have HLA-DRB1 results, and that is part of why I wanted to post this comment.

If I am correctly understanding that this paper makes noticeable use of SNP chip imputations, then I have the following questions:

1) If I do a quick literature search, I think my own results may be consistent with Pappas et al. 2018 (but perhaps less so with Karnes et al. 2017). While I had to learn more about HLA/MHC genes, I think it may make sense that it should be easier to make assignments for some genes over others. Do you agree or disagree with that conclusion?

2) I thought SNP2HLA and HIBAG were relatively common methods for HLA imputations from SNP chip data. However, is there another method available where I can test generating HLA imputations for my sample and see if they are more consistent with the sequencing results (for the "class II" HLA genes)?

If I understand correctly, the GitHub page for this publication describes using SNP2HLA (which I don't think gave reliable imputations for HLA-DRB1, HLA-DQA1, and HLA-DQB1 with my SNP chip data, either for 23andMe or Genes for Good). However, even if that is true, I don't know if the precise settings can have a noticeable effect on the HLA imputation results?

Thank you very much!

Sincerely,<br /> Charles

On 2017-11-01 17:33:06, user Lin-xing Chen wrote:

Congrats to Evan!

On 2024-02-01 02:33:33, user Tania Gonzalez wrote:

This pre-print is now peer-reviewed, edited, and published at Biology of Reproduction. Main data stays the same so these pre-print supplemental spreadsheets are safe to use. For the final version, we added details on the specific genes used to identify decidua "contamination" during quality control, combined numbers of protein coding and long noncoding genes (the pre-print mostly focused on protein coding only), included a comparison to our single cell RNA-seq (Sun et al 2020), added immunofluorescence for select genes, and added more about the biological significance of our results. [PMID: 38271627] https://doi.org/10.1093/bio...

On 2024-01-19 12:10:39, user Rob wrote:

https://github.com/GreiderLab code empty at moment, will it be public soon?

On 2020-05-17 04:59:32, user Fraser Lab wrote:

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

The goal of this paper is to engineer an optogenetic circuit that provides low noise and allows single-cell gene expression control in mammalian cells. The authors 1) engineered the light-inducible tuner (LITer) system and illustrated that incorporating negative feedback into optogenetic circuits can drastically reduce noise, and 2) applied the LITer system to achieve expression control of KRAS gene and explored the biological functions of KRAS in cell proliferation.

The authors characterized the LITer system using different light intensities and illumination conditions, exploring the dose-response, the linearity, and the efficiency of the system. One other important and distinct feature of the optogenetic system is reversibility that can provide flexible control of gene expression. The authors didn’t mention or do any characterization of the reversibility of the system in the paper. Adding a reversibility test would thoroughly highlight a potential advantage of the LITer system.

By comparing the performance of the LITer system with a benchmark tool LightOn, the authors observed 4- to 5-fold noise reduction in the LITer system. The authors proposed three sources of the noise reduction, including negative feedback, fast kinetics of LOV2, and the advantage of monomer over dimer. I think the authors could provide more evidence about each of their hypotheses. For example, to validate how the kinetics of optogenetic tools affect the noise reduction, they can use computational modeling to explore the different kinetic parameters, combining with experimental validation to see how changing those parameters will affect the performance of the system.

To test their new tool, the authors adapted the LITer system to control the expression of KRAS, where they showed that the level of KRAS and the downstream ERK phosphorylation could be fine regulated by different light intensities. By doing a cell count experiment, they found that after light illumination, the cell number decreases. The authors thus concluded that low KRAS level may maximize cell growth, while higher KRAS may lead to senescence. Several controls and supporting experiments could strengthen this claim:

They could measure KRAS and phosphorylated ERK concentration in parental cells to make sure the range of KRAS level in LITer-KRAS cells is comparable with that of the parental cells. <br /> They could perform cell number counting before the light stimulation as a control to make sure cells of different cell types or treated with different light intensities have the same initial count. Otherwise, the cell number count after illumination isn’t normalized. Moreover, they showed in the supplement the cell counting results under different light intensities from 0 up to 500 g.s. The number of parental cells fluctuates a lot under different light conditions, which makes their claim that light does not affect the growth of parental cells questionable. The authors tried to normalize the cell count results by calculating the ratio of the LITer-KRAS cells to the parental cells, but they didn’t do any statistical analysis. <br /> They pointed out in the paper that they tried to validate that the observed effects were due to KRAS induction and not light alone by using chemical inductions. Quite interestingly they only showed that doxycycline can induce KRAS expression and phospho-ERK, but didn’t put any cell proliferation results. Adding these results might be a convincing argument, if they also observe decreased cell number after doxycycline induction.

In general, the major success of the paper is that they engineered and characterized the LITer system that showed significant noise reduction compared to the benchmark LightOn system. Moreover, through computational modeling they found the reason for the high expression level of the system and made further improvements leading to LITer2.0, which shows lower basal expression level and better linearity. The major weakness of the paper is that they lack some important control and supporting experiments to support their conclusion about the biological functions of KRAS.

In summary, the LITer system that the authors engineered will allow precise and spatiotemporal control of gene expression for biological researches, with potential improvement to the dynamic fold change by using more efficient optogenetic tools.

Minor points:<br /> 1. Why not use the same duration of illumination for both LITer and LightOn systems when doing the comparison? Both systems seem to reach saturation after 12h illumination.<br /> 2. The deterministic and stochastic model seem to have similar results according to the paper. Is there a reason why they want to use both methods for computational modeling?<br /> 3. It would help the reader understand the circuit they engineered better if they can put the detailed gene circuits shown in supporting Figure S1 into Figure 1 and 3 in the paper.<br /> 4. The x-axes of Figure 2J and Figure 5H should be 0, 50, and 100 since the unit is percent (%).<br /> 5. In the supplement where they explain the deterministic models, R should stand for ???????????????? or Tet Repressor but not TetR Repressor.

On 2020-01-30 13:43:45, user frédéric cadet wrote:

Dear Authors, Supplementary Figure 12b is not correct. Indeed, Cadet (2018) is clearly "Extrapolative". See Figure 10 for Ref 32. Best regards.

On 2017-05-17 18:05:53, user Chris McCown wrote:

Is there any chance that you're interpreting the mixture data backwards?<br /> Everything about the R1a/R1b split and their subsequent phylogeny <br /> suggests that it migrated outward from NW Europe to the Steppe and not <br /> from the Steppe to Europe. For example, you attempt to connect R1b to <br /> Beaker migration approximately 4K YBP, yet R1b1 aDNA was found in <br /> Northern Italy 14K years ago. If I understand your paper correctly, you<br /> say that Iberians are missing Steppe ancestry. I contend that is <br /> because admixing further east in Europe then migrated to the Steppe <br /> rather than Steppe migrating to Europe Have you considered this <br /> possibility? See Big Picture Migration Map: <br /> http://www.anthrogenica.com...

On 2018-04-29 17:16:53, user Premendra wrote:

Deeper study of the article poses major difficulties. Let us see this paragraph:

“Third, between 3100-2200 BCE we observe an outlier at the BMAC site of Gonur, as well as two outliers from the eastern Iranian site of Shahr-i-Sokhta, all with an ancestry profile similar to 41 ancient individuals from northern Pakistan who lived approximately a millennium later in the isolated Swat region of the northern Indus Valley (1200-800 BCE). These individuals had between 14-42% of their ancestry related to the AASI and the rest related to early Iranian agriculturalists and West_Siberian_HG. Like contemporary and earlier samples from Iran/Turan we find no evidence of Steppe-pastoralist-related ancestry in these samples. In contrast to all other Iran/Turan samples, we find that these individuals also had negligible Anatolian agriculturalist-related admixture, suggesting that they might be migrants from a population further east along the cline of decreasing Anatolian agriculturalist ancestry.” (Narasimhan 2018 bioRxiv: page 9 lines 276 to 285).

This paragraph provides us with two crucial pieces of information:<br /> 1. The Gonur and Shahr-i-Sokhta samples dated from 3100 BC to 2200 BC had no evidence of the Steppe-pastoralist-related ancestry in them.<br /> 2. These Gonur and Shahr-i-Sokhta people had the same ancestry profile as the 41 ancient individuals from northern Pakistan living between 1200 BC and 800 BC.

Impression from these two findings: This gives the most parsimonious impression that the ancestry or the genetic profile of the people from North Pakistan, Indus-Harappa proper and the Greater Indus Valley which included the regions up to east Iran and southern Turkmenistan had a genetic continuum in space and time, and they all were the same people. There is nothing in the article to prevent this conclusion being accepted to be as valid as the one considered by the authors.

1. These people (Gunur, Shahr-i-Sokhta, Swat etc North Pakistan, henceforth called GSP) had negligible ancestry from Anatolia.

2. These people (GSP) had not arrived from steppe-pastoralist culture of the Late Bronze Age. (explicitly expressed).

Impression from points 3 and 4 : There was no arrival from either the Neolithic Anatolian farmers, nor had been any arrivals from the steppe-pastoralist cultural location, prior to or up to 800 BC.

Now let us look at the definition of the word AASI used in the quoted paragraph. It is ““Ancient Ancestral South Indian (AASI)-related”: a hypothesized South Asian Hunter-Gatherer lineage related deeply to present-day indigenous Andaman Islanders” (lines 204-205). This means that the Indians (both North and South) had a hunter-gatherer population whose ancestry had been exactly the same as the present day Andaman islanders before 8th millennium BC, the time of arrival of Neolithic in India. It also by implication means that Andaman Islanders and the Hunter-Gatherer Ancient Indians had not diverged genetically at all in spite of having been separated genetically and spatially for 30,000 to 60,000 years. Another important thing to understand here is that this new name AASI means the same thing as the ASI coined by Reich (2010). In other words, Narasimhan assumes that the Andaman Islanders like people (ASI) had occupied the whole of India, and were not restricted to the south India before the Neolithic, and hence they have been given a new name AASI replacing the older name ASI.

However this assumption cannot be supported on the basis of received information so far. We know from the data supplied by the Narasimhan article as well as earlier articles by various authors that the Y-DNA haplogroup of the Andaman Islanders had stayed the same--the oldest Asian ones---D1 and C2. On the other hand people who had stayed in the mainland India had developed newer haplogroups like F*, C5, H1 etc in their Y-DNA profile, and these newer Y-DNAs have largely replaced the oldest lineages D1 and C2 in the mainland India by this time. Hence the identification of the pre-Neolithic Indians by modern Andaman Islanders gene is essentially flawed, and is fraught with the dander of misleading the entire study towards wrong conclusions.

Now we should examine another statement regarding the GSP population: “These individuals had between 14-42% of their ancestry related to the AASI and the rest related to early Iranian agriculturalists and West_Siberian_HG.” (lines 279 to 281).

This statement at least confirms that the early Iranian agriculturists were genetically related to the GPS (Bronze Age Gonur, Shahr-i-Sokhta, North Pakistan) people. Although Narasimhan et al assume that the Zagros Iranian agriculturists (ZIA) were ancestral to the GPS, there is another possibility that that the GPS and Zagros IA had descended from a common ancestor who was located more likely in Pakistan or Eastern Iran than in the Zagros.

The latter possibility is supported by stouter evidence. It has been noted that there was a genetic discontinuity, a break in the cline, between the Zagros people and the Anatolian farmers of the 7th millennium BC (Lazaridis 2016; Broushaki 2016). Such break is produced always by either a new arrival of a population, or an insurmountable long time geographical barrier between two adjacent populations. Broushaki had studied the Wezmeh sample from another Zagros cave. Broushaki noted,

“We sequenced Early Neolithic genomes from the Zagros region of Iran (eastern Fertile Crescent), where some of the earliest evidence for farming is found, and identify a previously uncharacterized population that is neither ancestral to the first European farmers nor has contributed significantly to the ancestry of modern Europeans. These people are estimated to have separated from Early Neolithic farmers in Anatolia some 46-77,000 years ago and show affinities to modern day Pakistani and Afghan populations, but particularly to Iranian Zoroastrians.” (Abstract). Thus, the Wezmeh DNA seems to be a part of wider Indo-Iranian ancient pool, having maximum concentration in Pakistan as in this picture.”

Narasimhan and his colleagues have not assimilated this finding into their discussion, which could have changed the conclusions.

On 2018-11-28 11:44:17, user Northern Finland Birth Cohorts wrote:

Seems like LEPR is important in infancy BMI - <br /> See ours posted a year ago <br /> https://www.biorxiv.org/con...

On 2019-12-12 13:56:01, user Rosemary Noblin wrote:

Altered phenotypes is the key term in this article. It’s as simple as understanding that every living thing carries its own vibration. It’s stands to reason based on that alone that altering a living state will change its forces. Has NOTHING to do with being a vegan or “tree hugger” for those that comment ignorantly.

On 2022-05-12 15:43:02, user L. Collado Torres wrote:

Hi,

Congratulations on getting this project to the pre-print finish line! Kudos to you!

Given some of my research projects, I'll need to read in detail your pre-print as I find it very interesting. That's why I made a feature request (FR) on GitHub asking for a documentation website or information on how to use GPSA https://github.com/andrewch.... I might have missed it, and look forward to further interacting with you. As you are likely acutely aware, sometimes testing software in a different computational system or dataset might reveal some bugs or potential new feature requests. I recognized that you have implemented a GitHub Actions workflow and automatically test your software https://github.com/andrewch... on Python 3.8, which is formidable. As I'm a Python novice, I don't know if there's an equivalent to covr in R (https://CRAN.R-project.org/... "https://CRAN.R-project.org/package=covr)") for code coverage.

On the pre-print itself, I greatly appreciate how you've shared all appendixes, and in particular, I love sections 6.1 and 6.2 where you described where you got the datasets you analyzed and link to the code you used, respectively. I would further encourage you to deposit your code at a permanent repository like Zenodo or Figshare (or even bioRxiv) since code can be deleted from GitHub. You'll get a DOI that you can cite in an update pre-print or peer-reviewed version of your manuscript.

While I'll need external help and/or quite a bit of time to understand your mathematical models, I also like how you have described it in detail.

I'll repeat here (and edit) some of my questions I asked publicly on Twitter & live during Andrew Jones' talk at #BoG22:

* Would you be interested in trying out your method in our 2021 data with spatially-adjacent replicates?<br /> * How far can you go in µm? We have some replicates 300 µm apart. (Jean Fan from JHU BME asked the same question framing it as a Z-axis distance question).<br /> * Can you combine H&E + smFISH images?<br /> * For the future studies that you described, what contingencies are you considering for cases where an intermediate tissue slide has a technical problem like tissue folding?

I recognize that these questions are beyond the code of this pre-print and will likely be answered elsewhere.

If it helps, we would be happy to chat with you about our data we have publicly available and some that we are also generating.

Best,<br /> Leonardo

On 2023-09-14 13:46:17, user Andres Marcoleta wrote:

Dear all, this preprint was published as a peer-reviewed enriched version in Science of the Total Environment journal:

https://www.sciencedirect.c...

On 2023-09-12 12:46:54, user LEVIn - Unicamp wrote:

We discussed this preprint in our journal club and enjoyed reading it. We collectively agreed it's a stimulating contribution to understanding how individual specialization emerges and is maintained in natural populations. Most of the literature on the causes of individual specialization focuses on the ecological (i.e., extrinsic) causes of this phenomenon (sensu Araújo et al. 2011 Ecology Letters), while the proximate causes (e.g., functional trade-offs, social status) have surprisingly been little studied. Part of this discrepancy is due to the challenges of testing whether and how individuals' intrinsic traits influence their trophic preferences. This preprint adds a novel level of complexity to the field by quantifying (i) the relative contribution of largely overlooked proximate causes (social learning, maternal effects, genetic factors) and (ii) the simultaneous effects of ecological (i.e., environment context) and proximate causes. We were positively impressed by the quality of the data and statistical analyses during our discussion. The resolution and temporal extent of the data used are unprecedented in the literature, and the Bayesian framework implemented is thorough. As we appreciated the quantitative approaches, our discussion focused mainly on how the question is motivated in the introduction and the major implications of the results. We agreed that the introduction outlines well how the measured factors are expected to drive individual heterogeneity. Still, a more general framing of the research questions could make the manuscript more appealing to a broader and more diverse readership. For instance, the introduction begins by explaining that environmental factors are key drivers of trophic niche variation. However, unraveling how individuality emerges in natural systems, by nature and/or nurture, is a general question that is still widely open in different areas of science - and we believe this manuscript provides exciting results in this regard. In the discussion, the fact that maternal learning, maternal effect, and environment combined explain most of the variance in trophic position (lines 297-299) could be further explored to emphasize the importance of simultaneously studying proximate and ecological causes of individual specialization. Also, the sizable residual observed in the "Maternal learning" model suggests that understanding what generates trophic diversity within populations is far more complex than initially thought, particularly in species with complex social structures, creating a stimulating challenge for future studies. Congrats on this excellent manuscript, and good luck with the next steps of this work!

Laboratório de Ecologia das Variações Individuais (LEVIn), State University of Campinas (Unicamp), Brazil

On 2025-10-22 11:53:45, user Simona Huwiler wrote:

Today, this preprint was published at Communications Biology and is available at https://doi.org/10.1038/s42003-025-09010-x. An official update of this preprint with linked DOI is forthcoming.

On 2017-04-11 15:14:46, user Rahul Nahar wrote:

We have seen such a phenomenon even on Hiseq2500 which also uses bridge amplification and thus I think it might be present on Nextseq 500 as well though may be to a slightly lower extent than Hiseq4000.

On 2021-07-28 14:33:17, user CircaReader wrote:

Interesting paper with exciting results. There are certainly valuable implications from these findings. After reading through, I had several comments that I thought I would share.

1. Add legend to figure 1 x axis, “Days”. Perhaps p-day would be useful.
2. Is it standard to have text labels describing the results as seen in figure 2?
3. What is the purpose of the colored arrows in figure 2?
4. What is meant by ‘significant trend’ in figure 2/4? What test was used to determine this? Probably will need to clarify this statement

On 2016-04-13 14:19:37, user Peter Menzel wrote:

The paper is now published here: http://www.nature.com/ncomm...

On 2019-03-26 04:45:11, user Richard Mills wrote:

This study is very similar to our study in PNAS, (Mills et al, 2017, https://www.pnas.org/conten... "https://www.pnas.org/content/114/40/e8372)"), which the authors do not appear to have cited. It would be appreciated if this work was recognized in this study.

On 2021-08-25 07:27:33, user nemo peeters wrote:

Dear Readers, A colleague has spotted a copy/paste error resulting in a yeast spot duplication in Fig1A. The new figure will be uploaded soon, together with a new supplementary material showing the raw Y2H matrices.

On 2016-08-09 16:15:24, user Hansi Weißensteiner wrote:

There's something wrong with that huge amount of NF. Not sure if related to the previously issues in the VCF file (not present anymore on the 1000G ftp site - I was in contact with the 1000G Project Helpdesk last year in this regard, find here the file: https://dbis-owncloud.uibk.... ). A simple check: download http://ftp.1000genomes.ebi.... - upload it in HaploGrep2: http://haplogrep.uibk.ac.at/ Export as "Haplogroup Extended (txt)" The column "Not_Found_Polys" contains the "false negatives" - 8860 however not found as the most contributing one to the high number of FNs. Reflected in this report is the high fluctuation rate of 16189, 152, ... best, Hansi

On 2021-10-26 02:41:51, user CDSL JHSPH wrote:

Thank you for sharing your research. I found the paper to be very well-structured and I think the organized layout of the paper helps build a narrative that can be read by a wider, non-scientific audience as well. <br /> It was interesting to find that neutrophils in older adults had increased uptake and oxidative capacity, compared to younger adults (Fig 2B). I liked that you referenced research articles with findings that were in contrast to your own finding regarding neutrophil oxidative capacity in older adults. I think including such references is a great way to avoid confirmation bias in research.<br /> I hope to see future studies that build upon this research theme by using cohorts that span the entire age range of 1-80 years. It would also be interesting to see the variation in dendritic cells and B cells at human nasal mucosa with age. In addition to the challenge of obtaining human tissue samples, did you face any other challenges while investigating the immune cell composition at nasal mucosa? Also, were there any factors that led you to focus mainly on T cells and neutrophils for this study?

On 2019-01-24 06:05:11, user Huxley Mae wrote:

In my opinion, aflatoxins contamination to foodstuff is critical to human health, hence, fast and accurate methods of for aflatoxins production are inevitable, This method represents an alternative approach to determine aflatoxin production from Aspergillus species and can be impactful. Once improved it can be helpful in assessing the Aspergillus spp. interaction in terms of toxins production that is essential prior to use of such fungal spp. as biological control

On 2021-05-05 16:10:41, user Bruce Kohorn wrote:

Interesting new data on the adhesion ELMO family in Arabidopsis, will add nicely to the work recently published on the family https://doi.org/10.1242/dev...

On 2020-12-01 02:41:26, user Sg Kim wrote:

Figure 3 legend: "p>.05 FWE" should read "p<.05 FWE", obviously.

On 2018-05-29 06:58:51, user Thomas Kallehauge wrote:

Hey. Will Shoelaces work with bacterial ribosome profiling data?

On 2023-10-04 18:00:04, user VK Ulaganathan PhD????????, M.Tech ( wrote:

This paper is published.

https://doi.org/10.1021/acs...

On 2019-02-01 00:41:30, user euan ashley wrote:

Nice paper! Agree with many of your points. Hopefully graph references and long reads will organically take us away from reliance on the reference sequence. We explored some aspects you discuss a few years ago in a paper in PLoS Genetics: https://journals.plos.org/p... Might be of interest.

On 2025-04-04 13:16:05, user Eva-Maria Geigl wrote:

We agree with the conclusion that the Bell Beaker groups formed locally in (north)western Europe. We proposed last year a similar model in our Science Advances article “Parasayan, O., Laurelut, C., Bole, C., Bonnabel, L., Corona, A., Domenech-Jaulneau, C., Paresys, C., Richard, I., Grange, T., Geigl, E.-M. (2024) Late Neolithic collective burial reveals admixture dynamics during the third millennium BCE and the shaping of the European genome Sci. Adv. 10, eadl2468 (2024). Doi: 10.1126/sciadv.adl2468”. We proposed local formation of Bell Beaker groups in northern France starting ~2600 BCE in a Late Neolithic archaeological context (“Néolithique récent”, formerly called SOM, comprising in its final stage a few dispersed AOC burials) as a result of the merging of (1) north-western CW-associated steppe-ancestry carriers (corresponding to two of your Vlaardingen/CW individuals), (2) individuals associated with the “Néolithique récent” and (3) individuals associated with Maritime Bell Beakers originating from southwestern Europe, the latter two lacking steppe-ancestry. A discussion of our model and a citation of our article is missing in the present manuscript given the similarity of the conclusions drawn herein.

On 2020-12-20 15:05:12, user andreagradidge wrote:

Please see https://www.microbe.tv/twiv... for explanation, debunk, plus a really good scold for lack of data.

On 2021-10-21 17:34:05, user Iklwa wrote:

Chimera formation during PCR is common.

On 2022-04-11 19:41:33, user Chen wrote:

Very interesting preprint. Supplemental tables 3 and 4 are missing.

On 2021-10-08 01:28:43, user Adrian Flierl ???????? wrote:

There is no question that ANTs are essential in mitigating environmental and cellular stress.<br /> Regarding the hypothesis of functional ANT at the epithelium cell membranes, these extraordinary claims require extraordinary evidence. <br /> In general, there are several technical and methodological points of concerns:<br /> Overexpression of ANT (mitochondrial ANT content is tightly regulated) can lead to miss-targeting, sorting or even excretion from cells, especially in cells with a high secretory capacity. It also has been shown that a significant portion of mitochondrial proteins are excreted through vesicles and Exosomes. <br /> Technically, there is the possibility of imaging immuno-histo/cyto-chemistry artifacts due to unspecific binding of primary and secondary antibodies (entrapment), when employing fluorescence-immunocytochemistry in this notoriously difficult cell type.<br /> It would have been nice to have additional evidence for ANT localization to the cell membranes, either by immunohisto/cyto staining controls, a secondary detection method (higher mag or EM) or simple biophysical cellular fractionation (lipid fraction) and protein detection (western).

As much as I'd like to see ANT to also fulfill a role of regulating airway epithelial cell membrane function, I would have to see additional evidence that would support the significant functional presence of ANTs at cytoplasmic membranes.

On 2017-09-25 00:07:43, user John Smith wrote:

It seems that data is not available in ArrayExpress. I wonder if the accession number is correct.

On 2019-11-13 11:38:26, user Aaqib Sohail wrote:

Dear Travaglini,<br /> Thank you for sharing the article, very comprehensive study. Really liked it.<br /> I was trying to access the script from github, but the link is not work. Can you check if it is valid?<br /> Aaqib

On 2018-03-08 17:16:32, user Dan Quang wrote:

As one of the ENCODE-DREAM competitors you compared against, I'd like to give my two cents. First, with the exception of CTCF, your method only displays competitive performance against the other models when you trained on CISTROME data. CISTROME data was not allowed in the challenge. Second, you used PhastCons conservation data. Conservation data was explicitly forbidden in the challenge.

Anyways, I wish you the best of luck in the review process! Given my luck, you will likely get published well before my manuscript ever gets accepted anywhere!

On 2015-08-05 02:04:56, user Elsie Jacobson wrote:

Hey, was looking for the tool and found this instead http://www.targetfinder.org... (not updated since 2010). Is this an update to the original targetfinder or not related at all?

On 2021-05-06 14:00:58, user Artem Barski wrote:

Given the quick developments in this area, this robust comparison of various WfMS will be very useful for the field.<br /> I noticed that in your discussion of CWL, you mostly used the reference implementation (cwl-tool), rather than a number of excellent pipeline managers that were developed for CWL. The reference implementation is just that- a reference for the language. The key “workflow manager” features are coming from WfMSs, such as CWL-Airflow, Toil and others. For this reason, the discussion of WfMS features, such as resources, data staging, parallelization, retries, etc. is centered on WDL/Cromwell and Nextflow but is missing CWL WfMS, making the comparison incomplete. Even for the test case, Cromwell (originally a WDL WfMS) is used as a CWL WfMS. I believe, adding a discussion and testing CWL runners will greatly enhance the paper.<br /> Separately, I noticed that our CWL pipeline manager, CWL-Airflow is mentioned in one of your tables as tedious to setup. While we provide a simple pip install option for CWL-Airflow, we would very much appreciate feedback on what caused difficulties in setting it up. We would also be happy to help you troubleshoot the setup if this will be helpful.

On 2016-05-09 12:42:23, user Dirk Jochmans wrote:

Favipiravir resistance (minor) has already been described for CHIKV. So possibly needed to adapt the text.<br /> Mutations in the chikungunya virus non-structural proteins cause resistance to favipiravir (T-705), a broad-spectrum antiviral.<br /> Delang L et al<br /> Antimicrob Chemother. 2014 Oct;69(10):2770-84. doi: 10.1093/jac/dku209. Epub 2014 Jun 20

On 2021-01-27 20:35:37, user Rath R. Weird wrote:

A few simple takeaways from this work:<br /> 1) structural biologists unconcerned with functional (e.g. biochemical) analysis of their samples are liable to determining the structure of catalytically dead enzymes;<br /> 2) there is such thing as over-optimization of expression vector;<br /> 3) soluble protein isn't necessarily the native one (just like non-denaturing doesn't always mean native, as in purification);<br /> 4) it naturally follows from the differential impact of synonymic codons on folding and activity of SARS-CoV-2/hCoV-19 RdRp in one heterologous host (E. coli) that they'd have similar (in magnitude) impact on its expression in another heterologous host (human). Which calls for as careful examination of SARS-CoV-2/hCoV-19 mutations in terms of codon replacement, as received by amino acid substitutions. Furthermore, the non-synonymic mutations may impact fitness not only through the impact of amino acid change, but the codon frequency alteration also.

On 2020-09-27 04:32:46, user John Philip Vaughen wrote:

Excited to see this mechanism for non-autonomous GBA action and aggregate spread! Sharing a concern I've seen using multiple independent UAS-Gba1b transgenes (including those in attp2/attp40): GAL4-independent rescue of Gba-null fly phenotypes when UAS-Gba1b is present. Your FigS1 is reassuring, so I was wondering what temperature you do rescues at, and if you've noticed GAL4-independent rescue in other experiments? We think the fly brain is especially sensitive to GAL4-independent leak from UAS-GBA1b constructs, which makes cell-type specific rescue challenging (but is in line with human sensitivity to Enzyme Replacement Therapy)

On 2020-06-21 12:39:31, user OxImmuno Literature Initiative wrote:

https://www.immunology.ox.a...

On 2016-07-20 17:13:25, user Atanas Kumbarov wrote:

I want to join the ones complaining about the Y-DNA haplogroup assignments. This happens with every single ancient DNA publication - haplogroup assignments are extremely conservative and often completely wrong. For example, the haplogroup diagram on Fu et. al. (2016) was completely messed up.

I just came back from vacation an it took me just four or five hours to download the data for this study, process it and get haplogroup assignments which go much deeper than original ones. If I had a better PC and faster downlink, it could be done twice as fast.

I wonder how people who spend their entire time on this and make a living out of this fail to assign haplogroups more accurately.

On 2016-08-03 02:17:30, user Charles Simmins wrote:

Chikungunya reached levels approaching 38% of the population on Reunion. When it moved into the Caribbean, Martinique and Guadeloupe reached reported levels over 15% in the first wave. These numbers are much lower.

There is data on microcephaly related to an infection from Brazil. In the first 1,500 reports, a study found 652 confirmed newborns with this defect. [ http://www.thelancet.com/jo... ] As of the latest report from the Ministry, through July 23, a maximum of 1,749 reports were confirmed to be microcephaly related to an infection. It appears, in Brazil, that your first column in Figure S7 is closest to the real world data. In fact, that estimate clearly is in excess of that reported as your estimate is of first trimester infections alone. The real world data is for all trimesters.