On 2021-03-13 21:22:34, user Givi Koberidze wrote:

Has this paper been peer-reviewed and published?

On 2025-08-26 14:13:25, user Darren Thomson wrote:

Can you please provide more details about your image analysis pipeline? Simply citing the software won't help anyone reproduce this assay.

On 2018-04-04 16:53:18, user bennedose wrote:

Here are some further thoughts on the findings presented in the paper. I will first quote lines 276 to 282 from the paper:

"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."

The paper clearly states that earlier samples from Swat (taken 1200-800 BCE) did not show steppe ancestry, but had AASI. This must be correlated with what is known from history. By 500 BC Emperor Darius had erected the Behistun monument in Iran which was recorded in Old Persian - which developed after the earlier "Iranian" language Avestan. Avestan was spoken by early Zoroastrians and dates back to 1000 BCE in the Punjab region. Scholars like Darmetester and Mary Boyce show that the Zend Avesta - the holy Zoroastrian book post dated the Vedas and was almost identical to the Atharva Veda. That means that Indo European languages were already there in North West India (Gandhara/Swat area) by 1000 BC and earlier (since the Vedas are dated earlier)

Now the genetic picture uncovered by this paper shows that the people of the Swat area DID NOT have steppe ancestry in that period (1200-800 BCE). But Indo European Languages were already present in the area. This rules out the connection between steppe ancestry and Indo-European languages. In India, Indo-European languages pre-dated steppe ancestry.

On 2020-10-01 15:17:11, user Muhammad Zakiruddin Chowdhury wrote:

This research was conducted by members of Globe Biotech and Globe Biotech is presenting this as a publication in International Journal. They conveniently skipped mentioning that it has not been peer reviewed. Please google Bangladesh English News portals on 01 and 02 October and you will find more.

On 2021-07-21 14:37:48, user Taras Oleksyk wrote:

A modified and improved <br /> version of this article has been published in GigaScience. doi: 10.1093/gigascience/giaa159

On 2021-06-08 02:23:10, user Daniel J Paluh wrote:

The peer-reviewed paper is now published at eLife: https://doi.org/10.7554/eLi...

On 2022-09-19 07:58:28, user zhljude wrote:

Hi Thomas Burger:

This article is a nice work. However, the low resolution of the Figures makes it confused to understand the content of the article. Could you provide clearer Figures ?

Best regards<br /> Jude

On 2017-11-10 18:07:39, user James Lloyd wrote:

I would like to thank you for the interesting pre-print and tool. I really enjoyed reading about this tool for fast quantification of alternative splicing events, including complex events not often captured by other approaches.

Comments:<br /> Page 5: I wanted to know more about the identification of novel AS events. There is mention of "pseudo-de novo AS event identification (see below)" but then it is not discussed again, as far as I could see. I think discussing what sort of de novo events, with junctions not explicit in the transcriptome annotation provided Whippet can find would be really helpful.

Page 10: "HeLa whole cell, nuclear, and cytosolic fractions, as well as mono- and polysomes", it is worth pointing out that the RNA in the polysomes used in study #5 were from HEK293T cells.

Page 30: "Additional nuclear and whole-cell HeLa fractions originating from a different paper were also analysed as a comparison" what paper is the HeLa fractions from?

PS, as a Yorkshireman, I appreciate the name Whippet.

On 2020-12-24 18:56:48, user Charles Warden wrote:

Thank you for posting this preprint.

I noticed that there was highlighting of sentences throughput the manuscript. Was this intentional, or should those be removed after editing and discussion?

On 2020-10-02 16:28:20, user David Ross wrote:

This preprint is related to an earlier preprint with the same title (https://www.biorxiv.org/con...: "https://www.biorxiv.org/content/10.1101/2020.07.10.197574v1):") We received feedback that the earlier manuscript contained too many ideas for one paper. So, we split the story into two parts. The part contained in this manuscript includes a description of the measurement and a discussion of what the results can tell us about LacI allostery. A subsequent manuscript will focus on the use of the results for precision engineering of genetic sensors.

On 2015-08-22 15:37:34, user Edouard Debonneuil wrote:

format suggestion: at the end of the first sentenceof thediscussion, list many articles rather than only 2 (eg list of references embedded in Sven Bulterijs and David Gems' articles)

On 2021-09-01 00:46:56, user Tom J wrote:

Please check Fig 1d relative to the text on page 4...seems contradictory. If the Alpha spike/Delta backbone replicated less efficiently than Alpha, the ratios should be >1. Is the y-axis in the figure correct?

On 2017-03-12 18:31:48, user Jean Manco wrote:

@ David Smurkovic. I am not one of the authors, but I have entered BOVO1b in my online table as H27. The markers are 263G 750G 1438G 4769G 8860G 15326G 16093C 16519C<br /> http://www.ancestraljourney...

On 2017-06-10 08:42:33, user imroze khan wrote:

We provide the first experimental evidence for the rapid evolution of immune memory in insects

On 2017-05-21 20:53:25, user Carl Mesarich wrote:

The tomato accession names/numbers are now correct (May 21, 2017).

On 2024-06-28 13:20:02, user Jo Wolfe wrote:

Interesting preprint! Regarding the intro, indeed the oldest direct fossil evidence is Jurassic...but we recently found that the crown group of Brachyura are probably Triassic<br /> https://academic.oup.com/sy...

Also, in our 2021 Bioessays paper, we did suggest the pleon folding in metamorphosis may be due to Abd-A repression, so it's cool that you found support for that result

On 2014-02-14 23:56:27, user Casey Brown wrote:

This is a very nice paper.

A have a few questions/comments:

1. On the classifier. Did you attempt any other<br /> approaches? Could you provide more details on how the cell type specific probes<br /> were selected? Were they simply the 58 most correlated probes? Were they<br /> selected in any way to maximize independent signals? Did you consider the NNLS<br /> approach used by Battle et al.?

2. Limiting search to previously identified,<br /> additive cis-eQTLs. As the authors note,<br /> limiting the search for interaction-eQTLs (as opposed to searching the full set<br /> of cis-SNPs) almost certainly downwardly biases the fraction of<br /> interaction-eQTLs identified, because they previously selected for SNPs that<br /> explain a large proportion of the variance in an additive model. Of course, this effect will be even more<br /> dramatic for the eQTLs with larger effects in the minority cell<br /> population. Could the authors perform<br /> the analysis on the full set of SNPs on at least a subset of the data?

3. Characteristics of interaction-eQTLs. The analysis of GWAS overlap is nice, but it<br /> would be great if the authors pushed this a bit further. Are these genes enriched for genes that are<br /> differentially expressed between cell types? Are the distributions of SNP to<br /> TSS distances different between the different classes? Are there cell type<br /> specific regulatory element (e.g. DHS?) datasets that can be used to interpret<br /> these findings?

4. Cell type gene expression signature. Are there<br /> genetic variants that are associated with the proportion of neutrophils? When<br /> you include the cell type term in the model, do you increase the number of<br /> identified additive cis-eQTLs? (I.e., does heterogeneity add noise to the<br /> standard analysis?) Relative to uncorrected expression data? Relative to covariate-naïve latent variable<br /> removal?

5. Examples. It would be nice to see a few plotted<br /> examples of particular gene-SNP combinations that have significant interaction<br /> eQTLs. In particular, it would be nice to see the data from one or both of the<br /> datasets where the cell type counts were directly quantified.

<cross posted="" on="" haldane's="" sieve,="" bc="" i'm="" not="" sure="" what="" the="" protocol="" is="" in="" the="" pppr="" world="">

On 2020-07-21 16:05:48, user OxImmuno Literature Initiative wrote:

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

On 2017-04-24 21:18:41, user Benjamin Zinszer wrote:

Published version of this article (with substantial revisions) is available from PLoS ONE: http://journals.plos.org/pl...

Emberson LL, Zinszer BD, Raizada RDS, Aslin RN (2017) Decoding the infant mind: Multivariate pattern analysis (MVPA) using fNIRS. PLoS ONE 12(4): e0172500. https://doi.org/10.1371/jou...

On 2017-05-05 11:53:48, user Harry wrote:

Quiet an interesting investigation. Looking forward for your future works.

On 2020-05-09 23:10:52, user Dima Shvartsman wrote:

Excellent and thorough work. Limiting the proliferation of non-committed cells is very important for the safety of transplanted cells and a reduction of heterogeneity in the cell population.

On 2018-08-26 12:56:55, user Maier Lab wrote:

Detailed, important analysis on protein denaturation at air-water interfaces - and rescue options. Critical for cryoEM sample preparation but important also beyond cryoEM.

On 2019-11-15 21:12:39, user Tyler Square wrote:

Cyclostomes (lampreys and hagfishes) are not established as being 2R, and they are unaddressed here. It seems like you are actually addressing gnathostomes and the gnathostome common ancestor, not "all vertebrates" and their ancestor (per the first sentence of your abstract).

On 2021-04-05 03:48:58, user Zohreh Khosravi wrote:

Congratulations with your amazing paper. I have a question and really appreciate you if you could help me in this matter.<br /> when You performed a whole genome CRISPR screen in cancer cells to identify regulators of T cell killing. One of your hits leads to increased killing of your cancer cells in presence of T cells but it represents an uncharacterised protein. What is the hypothesis and which three experiments would you carry out to elucidate the function of your hit?<br /> Best Regards,<br /> Zohreh

On 2019-02-12 18:23:11, user Eve Wurtele wrote:

In Arabidopsis, in silico predictions followed by experimental evidence indicate that the de novo orphan QQS gene was quickly integrated into the metabolic network affecting carbon and nitrogen partitioning, and into a network conferring broad-spectrum resistance; most significantly, because QQS interacts with conserved network elements, introduction of QQS into other plant species confers the same effects (Li et al .,2015, PMID: 26554020 ; Qi et al., 2019, PMID: 29878511 : Li et al., 2009, PMID: 19154206; Arendsee et al, 2014, PMID: 25151064)

On 2018-04-05 15:18:51, user Andrew Millar wrote:

Smallwood et al. tested lipid synthesis in nutrient-limited conditions with added glycerol, in a preprint published a day before this one, https://doi.org/10.1101/293704. Sadly we did not see this or their January preprint on lipid droplet release prior to our publication, so will take their work into account during revision.

Intriguingly, the three, unknown proteins that are highlighted as being up-regulated in nutrient-limited conditions in their paper were also up-regulated in prolonged darkness in our results (their RefSeq protein identifiers below), suggesting that what we propose as a quiescent 'dark state' might be similar to their lipid-release state:

XP_003078347 is now ostta03g04500, an abundant protein in our Fig. 2a, Fig. 4B.

XP_003078347 is now ostta09g00670, an unknown protein, in Fig. 4A.

XP_003078347 is now ostta02g03680, unkown protein with a putative BAR domain, in Fig. 4A and EVFig. 7a,7b.

On 2021-04-19 14:22:39, user Milka Kostic, PhD wrote:

Dear authors,<br /> Thank you for sharing this preprint with the community. I read the preprint with interest, and I am sharing the comments below in hope they will be helpful to you as you go forward with publishing and sharing your new findings further.<br /> Kind regards,<br /> Milka

Comments to the authors:<br /> In this preprint by Shao, Yang, Ding et al. the authors describe an expansion of the PROTAC (Proteolysis Targeting Chimera) concept into a new direction: using chimeric molecules that combine a DNA sequence fragment (used here to recruit DNA binding proteins) and an E3 ubiquitin ligase binding warhead (used here to recruit either VHL or cereblon (CRBN)). The resulting hybrid molecules are referred to in this preprint as O’PROTAC, short for oligonucleotide PROTACs. The big motivation for this work is the lack of strategies to target majority of transcription factors (TFs). <br /> TFs represent a large and diverse class of proteins that are critical for many different aspects of biological regulation. They could also be viewed as essential targets for drug development; and yet, outside targeting nuclear receptors (NRs), which represent a subfamily of TFs that are endogenously regulated through small molecule binding and have therefore evolved to bind drug-like molecules, efforts to develop chemical tool compounds and/or drug leads that target TFs has been difficult. <br /> Couple of relatively recent breakthroughs in this are IMiD compounds (immunomodulatory imid drugs) - small molecules that serve not to inhibit protein-protein interactions, but rather to promote complex formation between an E3 ubiquitn ligase and different TFs (such as SALL4, IKZF1, IKZF3), resulting in fully functionally competent E3 complex that marks these TFs for proteasomal degradation. In many ways this is similar to the effect of PROTACs, another kind of small molecule degraders, that feature two warheads connected via a linker. One warhead binds an E3 ubiquitin ligase (usually VHL or CRBN) and the other is a ligand for a protein of interest. By now, PROTACs targeting many different targets have been developed, but when it comes to TFs finding the ligand that can be transformed into the PROTAC warhead remains a major bottleneck.<br /> Enter Shao, Yang, Ding et al. - these authors exploit the fact that TFs bind specific DNA sequences, usually short-ish oligonucleotide sequences. They design O’PROTACs to include double-stranded oligonucleotides, on one hand, and a VHL or a CRBN warhead on the other. The two proof of concept target TFs they focused on are ERG transcription factor and Lymphoid enhancer-binding factor 1 (LEF1), both clinically relevant.

Dealing with nucleic acid based reagents requires special delivery methods (which is the down side of this strategy), so the authors used lipid-mediated transfection. They were able to observe:<br /> - degradation of exogenously expressed HA-ERG in 293T cells as monitored by western blotting<br /> - CRBN-based O'PROTACs had a stronger effect than VHL-based ones<br /> - ERG degradation could be achieved in prostate cancer cell line VCaP that overexpresses ERG as well as its truncated form (TMPRSS2-ERG)<br /> - degradation of ERG has the expected downstream effect on its transcriptional targets<br /> - similar behavior was noted for LEF1, and LEF1 targeting O'PROTACs were able to inhibit prostate cancer cell line proliferation; however some of the validation steps done for ERG O'PROTACs (ERG pulldowns, and proteasome dependence) do not seem to be included for LEF1.

I think this is an important proof-of-concept work, albeit a bit preliminary. What authors could have done a bit differently is:<br /> - try to be more quantitative (it's unclear how large the observed effects are)<br /> - use a negative control (create O'PROTACs that don't bind to the ligase, or feature an oligonucleotide that has no target binding); negative controls are essential and some work around developing a high quality negative controls for O'PROTACs would be useful<br /> - have the authors tried to see if their O'PROTACs have an effect on cells where ERG (or LEF1) have been deleted? These experiments are important when validating new modalities.<br /> - provide some commentary about and/or evidence that they affect their target cleanly (selectively). Are there any other TFs that would potentially bind to the oligonucleotide motifs they used here?<br /> - provide a more useful discussion of the design consideration for O'PROTACs, potential limitations of this strategy, and how to get the most out of using them as a research tool; in the current form the Discussion is not necessarily all that useful for anyone interested in using this technology. <br /> - (in the future) it would be cool to see what happens in cells that don't overexpress ERG. Have the authors tried those experiments?

Congratulations on driving forward this interesting new concept of O'PROTACs and I hope my comments help you strengthen your technology further.

On 2020-05-12 08:46:49, user Theodor Sperlea wrote:

The findings reported this preprint - and some additional results - have now been published in Scientific Reports (https://www.nature.com/arti... "https://www.nature.com/articles/s41598-020-63424-7)").

On 2022-07-06 11:54:24, user Luca Jovine wrote:

Now published: https://doi.org/10.1038/s41...

On 2019-05-10 17:15:35, user Leslie Vosshall wrote:

We discussed this interesting paper at the Vosshall Lab Olfaction and Behavior Journal Club on May 8, 2019. The use of GCaMP for peripheral antennal imaging is really exciting because it opens up Anopheles mosquitoes to comprehensive investigation of mechanisms of olfaction. The paper also takes on the thorny question of the mechanism of action of insect repellents. The big ideas out there are: 1. DEET smells bad and repels insects. 2. DEET soaks up human body odor, making you invisible to them. 3. DEET scrambles the mosquito odor code so that humans smell like pizza-vomit-coffee-gasoline or something rather than just humans. This paper provides more evidence for model 2. Syed and Leal (PMID: 18711137) first pointed to DEET as binding odorants as the mechanism for blocking mosquito biting (e.g. if you coat yourself with DEET you become invisible) (model 2). Syed and Leal also provided evidence that DEET smells and repels, by activating olfactory neurons (model 1). Our group published a response to this and showed contrary evidence that at the concentrations we tested that DEET did not prevent odorants from volatilizing (PMID: 21937991). Our current data in fly and mosquito are consistent with model 3. This paper shows that DEET does NOT activate olfactory neurons but bind odorants (model 2), so neither model 1 nor model 3.

So which model is correct? DEET is a seductive molecule to study scientifically; we are still no closer to closure on its mechanism of action and this paper adds an additional wrinkle that is worthy of further investigation by the field.

We had the following feedback and questions (in no particular order):

1. In Figure 1, is it possible to do an overlay to estimate which of the 7 identified cells reliably respond to which of the 6 tested odorants? This would extract more information from the figure and give some initial glimpses into mosquito odor coding.
2. Scale Bar missing Figure S1
3. The graphics would be easier to “read” if the Tufte “chart junk” of background grids were removed to let the data take center stage (example Figure S3a)
4. The 1-octen-3-ol in Figure 1d and Figure 2a appear to be the same image, duplicated in different figures. It would be ideal to provide a new image or disclose this in the Figure 2 legend.
5. We wondered if the odor activation code is really consistent across all segments, such that focusing on one segment would give a universal answer for all antennal segments? Or is there some zonal nature or functional specification that would alter the conclusions? The whole antenna images in Figure S1b suggest some variability in how many neurons are activated in a given segment.
6. Do the natural repellents block/change odorant responses as DEET does to 1-octen-3-ol? We could not find this experiment in the paper.
7. The paper focuses on one odor 1-octen-3-ol to build the case that DEET acts merely to reduce drastically the volatility of this odorant, thus reducing/eliminating the delivery of this odorant to olfactory neurons. Is this the case for other odorants? How would this one DEET molecule be able to reduce, mechanistically, the volatility of the hundreds of different molecules emitted by the skin? We are not chemists but DEET does not seem to be particularly reactive. Is it a covalent attachment promiscuously to every odorant or more hydrophobic van der Waals mechanism that blocks odorants from volatilizing when mixed with DEET? How could this work given the enormous range in the chemistry of human odor volatiles?
8. Is Anopheles coluzzi repelled by DEET behaviorally?
9. PIDs measure bulk ionized molecules but cannot identify them. What are the prospects for repeating this with GC-MS?
10. Finally, if DEET acts by binding odorants on our skin rather than acting to repel (model 1) or confuse (model 3) wouldn’t you be bitten if you had a swath of skin that was not coated with DEET that was giving off human odor fumes?

On 2017-06-21 20:15:29, user Lindsay Cowell wrote:

Nice paper. You may be interested to see this paper which has prediction of shared sequences as a use case PMC4698203.

On 2022-04-10 11:06:58, user uv wrote:

More credit could have been given to the CATCH method. this is basically exactly the same enrichment method

On 2019-02-06 18:02:11, user Wen Li wrote:

cool! our recent shows the alpha tACS effects can last up to 24 hrs. Mostly in alpha connectivity. https://academic.oup.com/sc...

On 2018-11-15 22:28:30, user Ravishankar Palanivelu wrote:

The Supplemental information is not loading up. I get an error message.

On 2023-08-15 21:00:41, user Elijah Taeckens wrote:

This has now been published as part of the conference proceedings for the IEEE Conference on Neural Engineering.

DOI: 10.1109/NER52421.2023.10123745 <br /> Link: https://ieeexplore.ieee.org...

Please update accordingly.

On 2024-05-10 08:11:47, user Stefano Vianello wrote:

Dear Dr. Blotenburg,

I'm Stefano, the author of REF 20 re endoderm-rich gastruloids. In the Discussion section of your manuscript you write that

[REF20] maintained mESCs in 2i-medium and reported faithful emergence of endoderm cells

. Given the importance of mESC culture conditions in your analyses and possible future interpretations (at least, re endoderm), I wanted to point out that — following the practice of the lab I was working in at the time — mESCs were not grown in the classic 2i medium (2i in N2B27), but in fact in a 2i in ES+LIF medium (exact recipe in REF20's Materials & Methods > Cell culture). Based on gastruloid end-phenotype alone (of those shown in FigS1), I would guess this atypical mESCs culture medium is most closely matched by your culture condition 3 (and possibly condition 4), and that those conditions (though they were not selected for scRNAseq) are giving rise to endoderm-rich gastruloids.

Sincerely,<br /> Stefano Vianello

On 2025-05-06 17:52:12, user Alizée Malnoë wrote:

In this manuscript, Miyazaki et al. studied the interactions between EcLptM and EcLptD/E, by performing mutagenesis experiments, immunoblotting, crosslinking assays and solving the structure of the EcLptD/E/M complex via cryoEM, to further understand the role of LptM in LptD assembly and maturation. This study revealed that LptM has an essential region (C20GLKGPLYF28) within its N-terminal domain that interacts with LptD, although possibly slightly longer as discussed below. Consistent with the resolved cryoEM structure of EcLptD/E/M, in vivo disulfide crosslinking experiments revealed that LptM residue F28 is involved in the interaction with the LptD barrel domain. Additionally, residue G21 was shown to be critical for the function of LptM in the maturation of LptD. Also, the authors revealed the timing at which LptM interacts with LptD, showing that it acts at the late maturation step of LptD, after the action of BepA. The manuscript is well-written, and the conclusions made are supported by the data presented. We provide major and minor comments to help clarify some of the data and interpretations made.

Major comments<br /> - Figure 1C. Could you explain why erythromycin sensitivity increased in the ?bepA strain, while it resembled that of WT in the ?lptM strain? Does it mean that maintaining a well-folded ?-barrel domain is sufficient to maintain OM integrity? This is beyond the scope of this study, but do you think that would also be the case in ?dsbA strain?<br /> - Figure 1E-F. Narita et al., 2016 concluded that BepA might play a role in facilitating the interaction of LptD and LptE at the BAM complex. This conclusion was based on the observed suppression of erythromycin sensitivity when overexpressing lptE in a ?bepA strain which you also observed. Consider including this hypothesis as part of the discussion.<br /> - Figure 2B. Could you explain the accumulation of LptDC in K33amb and A35amb strains? Could it mean that LptM becomes less stable, or are these residues essential for LptM function? <br /> Lines 174-183 and lines 248-250: The interpretation that LptM in the cryoEM model stabilizes the folding of LptD due to the observed tight closure of the ?-barrel junction is not fully convincing. It was based on comparisons made with the crystal structure of S. flexneri and an Alpha-fold model. Would it be possible to perform the comparison with a cryoEM structure of EcLptD/E without LptM instead, to see whether the junction is tightly closed in the absence of LptM or not? Or maybe do the comparison with the solved crystal structure (only ?-barrel domain) of E. coli (PDB ID: 4RHB)?<br /> - Figure 4B. Consider showing the +ME lane between 15 and 20 KDa with ?FLAG antibody to control that LptM is present.

Minor comments<br /> - Introduction (Lines 80-88): Could you add to the LptM paragraph that it interacts with the LptD/E translocon by mimicking LPS binding?<br /> - Figure S4A (middle). The crystal structure presented (PDB ID: 4q35) is for Shigella flexneri and not E. coli.<br /> - Lines 176-177: The crystal structure solved in Qiao et al., 2014 is for Shigella flexneri and not E. coli.<br /> - Figure 5 and Lines 208-209: Could you clarify why alanine, cysteine and tryptophan were chosen for the mutagenesis experiment? Is it because they represent a non-polar (small), polar and non-polar (bulky) amino acids, respectively? <br /> Also, the data shows that only when G21 was mutated to Trp (and not to Ala or Cys), was the LptM activity affected. Could you explain why? And whether it has to do with steric hinderance or that the bulkiness of Trp obscures an essential interaction between other two amino acid residues? Could you show with what residues of the ?-barrel domain G21 interacts in the cryoEM structure?<br /> Also, could you show an alignment of the conserved region with LptM homologs in the Enterobacteriaceae family and show whether G21 is conserved?<br /> - Lines 243-245: “Considering that this short essential region tightly interacts with the ?-barrel domain of LptD (Figure 3, 4), it is unlikely to serve as a recruiter for the disulfide oxidase DsbA or disulfide isomerase DsbC to the LptD intermediate”, Could you explain why being in tight interaction with the ?-barrel domain rules out the possibility that LptM recruits either DsbA or DsbC? Also, could you re-type “Considering that this short essential region....” to “considering the short essential region of LptM ....”, because it is not clear to the reader if you were referring to LptM. <br /> - Section 5 (Lines 218-250): Since this section describes the model, could you make the model a main figure instead of a supporting figure?<br /> - Lines 261-263: Could you remove “consisting almost entirely of signal sequences”, because, for example, LptM is not made entirely from a signal sequence or clarify that you are referring to the secreted proteins.

Sally Abulaila, Kim Kissoon and Habib Ogunyemi (Indiana University Bloomington) - not prompted by a journal; this review was written within a Peer Review in Life Sciences graduate course led by Alizée Malnoë with input from group discussion including Michael Kwakye, Madaline McPherson, Madison McReynolds, Mandkhai Molomjamts, Octavio Origel and Warren Wilson.

On 2025-08-26 09:30:10, user Constant VINATIER wrote:

Feedbacks about your preprint : https://doi.org/10.1101/2025.08.01.668136

About registration: <br /> We could not find any information about the pre-registration of your study in the pre-print. Pre-registration involves documenting the hypotheses, methods, and/or analyses of a scientific study prior to its conduct (10.1073/pnas.1708274114; 10.1038/s41562-021-01269-4). If your study was pre-registered, we strongly encourage you to include the registration number in the pre-print, ideally in the abstract make this important information easy to retrieve, as this practice enhances transparency and reproducibility. If the study was not pre-registered, this should be acknowledged as a limitation. For future studies, we recommend pre-registering on an appropriate repository.<br /> About Protocol Sharing: <br /> We did not find the protocol for your study. If you have one, we encourage you to share it as supplementary material or deposit it in a publicly available repository such as the Open Science Framework ( https://osf.io ) or Zenodo ( https://zenodo.org/) "https://zenodo.org/)") . You can then include a statement in the Methods section indicating that your protocol is openly available (e.g., 'The protocol for this study is available at (link)/ in the supplementary'). Sharing your protocol will help readers better understand your study and enable them to reproduce it if they wish to test it.<br /> About the Statistical Analysis Plan Sharing: <br /> We did not find the Statistical Analysis Plan (SAP) for your study. If you have one, we encourage you to share it as supplementary material or deposit it in a repository such as the Open Science Framework ( https://osf.io ). You can then include a statement in the Methods section indicating that your protocol is openly available (e.g., 'The SAP for this study is available at (link)/ in the supplementary'). Sharing your SAP will help readers better understand your study and enable them to reproduce it if they wish to test it.<br /> About Deviations and/or changes<br /> We could not find any information about potential deviations or changes to the protocol in your pre-print. Since such deviations are common, if this applies to your study, we strongly encourage you to include a subsection titled Changes to the Initial Protocol in the Methods' section and discuss these changes as a potential limitation of your results. If any deviations occurred during your study, please specify them in this new subsection.<br /> About Data sharing / FAIR Data<br /> We found insufficient information about your data sharing approach. Data should be findable, i.e. data are to assigned a globally unique and persistent identifier (for instance there is a DOI assigned to the dataset, or data are registered or indexed in a searchable resource). Data should also be accessible, i.e. data are retrievable by their identifier and can be accessed following an open, free, and universally implementable protocol. As your data id not sensitive data, we encourage you to share it openly on a data sharing repository (Dryad, etc.) and include the Digital Object Identifier (DOI) in the Methods section. If you want more information about good practices of data sharing, visit https://www.go-fair.org/ <br /> About Code sharing<br /> We could not find any information about your (statistical) code. Sharing code is important for enhancing transparency and reproducibility, especially since it does not contain sensitive information. We encourage you to openly share it on a code sharing platform (Github, Codepen, CodShare, etc.) and include the Digital Object Identifier (DOI) in the Methods section. If you want more information about Code sharing https://fair-software.nl/ .

On 2017-12-19 22:04:47, user Chris Gorgolewski wrote:

Thanks for your reply. This is a great conversation to have.

I remain skeptical about Point 3 - especially if Bob decides to test a hypothesis not reported in the original paper. Such analysis might not yield expected results even if the hash and and order was correct indicating a problem with data and experimental design which would be then conflated with academic fraud (an accusation one should be very careful with).

As for the range of possible pseudo random sequences please mind that fMRI design efficiency will impose some constraints on the item sequence that would be usable in a given experiment. This combined with the fact that two different sequences could yield very similar results (making it very difficult for Bob to be able to say if differences he sees in his results are due to differences in processing pipeline, differences in hypothesis he is testing, or fraud). I'm afraid that limiting the entropy of the sequence generator will be the easiest way to game this system.

BTW I could not find the part of the example where Bob evaluates entropy of the sequence generator in your manuscript.

PS Love the photoshop rebuttal.

On 2017-07-20 20:08:48, user Néstor Saiz wrote:

Nice work in looking at the role of transcription/protein degradation in this fate decision.

However, it would be good if you discuss on the paper how your treatment regimes compare to those we did in our Nature Comms 2016 paper. Especially since a lot of your conclusions seem to confirm our observations, although this is not acknowledged. We already propose that it is the double positive cells (NANOG+, GATA6+) the ones whose fate is affected by FGF signaling modulation, and not the single positives. As a consequence of their progressive specification towards either epiblast or PrE, the response to the treatment at the population level changes over time.

Also, regarding your quantifications in the methods, it isn't currently clear how cell populations were scored - I seem to both understand that cell types were assigned manually but also that there was some sort of segmentation to measure protein? At this point, it would be worthwhile elaborating on how measurements were done, whether any correction or transformation was done to the data, etc., as this affects the statistical analysis. Also, maybe automate the fate assignment based on measured protein expression, or justify why not.

Nestor

On 2018-08-07 15:42:17, user cplaisier wrote:

Very cool and intersting work. Had one issue and one suggestion:

Issue. Adding a spike-in won't help with determining the fraction of RNA-seq reads coming from ambient RNA in the soup. The spike-in would be generated from a known distribution because you spike-in a given amount. The ambient RNA in the soup would be as you say experiment and channel specific. The two distributions are not related and as such a spike-in would not be a great solution to the determining the amount of ambient RNA contamination. Am I missing something?

Suggestion. Could you use the transcripts which show up in the less than 10 UMI cells to catalog transcripts which will have contamination. Coming up with a genes likely to be unexpressed is potentially difficult and dagerous if the assumption is in correct. It seems like coming up with a more standardized approach for this would be highly beneficial.

On 2022-01-01 13:22:23, user ??? wrote:

Please check out this paper also: https://www.osapublishing.o...

On 2019-10-24 16:35:01, user Ian Fiebelkorn wrote:

Traveling waves and rhythmic sampling during attention. The authors describe traveling waves in the marmoset visual cortex (area MT) associated with both elevated neuronal responses and elevated perceptual sensitivity during a difficult visual detection task. I very much enjoyed reading this excellent manuscript, which describes a perceptually important phenomenon. Near the end of the manuscript, the authors speculate that this traveling wave phenomenon might explain previous reports of attention-related relationships between pre-target theta (3-8 Hz)/alpha (9-14 Hz) phase and behavioral performance (e.g. Busch and VanRullen, 2010, PNAS; Fiebelkorn et al., 2018, Neuron; Gaillard et al., 2019, BioRxiv; Helfrich et al., 2018, Neuron; Fiebelkorn & Kastner, 2019, TICS). This is an intriguing possibility that remains to be tested. Here, I will point out some of the differences between what is described in the present manuscript and the attention-related effects that we recently described based on recordings in the fronto-parietal network.

(1) The authors contend that these traveling waves are much more predictive of perceptual sensitivity than previous reports of links between visual-target detection and either pre-target alpha or theta phase. First, it is difficult to compare the overall magnitude of effects given differences between the tasks, differences in recording techniques, differences in species, differences in brain regions (sensory cortex vs. higher-order cortex), and differences in how the data were analyzed. For example, we used a broad phase window (of either 90 or 180 degrees, see Figures 3 and S7 in Fiebelkorn et al., 2018, Neuron) when calculating phase-dependent hit rates. This smoothed the data (i.e., phase-detection relationships), biasing the behavioral effects downward. Second, whereas the present manuscript measures the phase of a wideband signal (5-40 Hz), we measured frequency-specific phases. Moreover, our results demonstrated that higher-frequency activity (e.g., in the beta band) was modulated by the phase of theta-band activity. When we accounted for the phase of both higher and lower frequencies there was a stronger link to the likelihood of visual-target detection. Third, whereas performance in the present task was at approximately 50% detection, performance in our task was at approximately 80%, leaving less room for behavioral modulation.

(2) Unlike the present manuscript, which describes a link between visual-target detection and the phase of spontaneous events. We describe a link between visual-target detection and what appear to be ongoing theta-dependent changes in neural activity. That is, we report evidence that higher-frequency activity is associated with visual-target detection at various time points (from -500 to 0 ms) prior to target presentation (Figure S11 in Fiebelkorn et al., 2018, Neuron), not only the time period just prior to target presentation. To be more specific, our data show, e.g., alternating periods when either higher or lower beta-band activity is associated with better behavioral performance. That is, higher beta-band activity occurring at either -500 or -250 ms (relative to target presentation) is associated with a higher likelihood of detection, while lower beta-band activity occurring at either -375 or -125 ms is also associated with a higher likelihood of visual target detection. In our data, higher beta-band activity (in FEF) just prior to target presentation is generally associated with better detection, so why would lower beta-band activity, e.g., at -375 ms also be associated with better detection? Because the strength of beta-band activity oscillates at a theta frequency, and a period of lower beta-band activity at -375 ms indicates that the strength of beta-band activity will oscillate back to a high point just prior to target presentation. This is just one example, but our data generally indicate behaviorally relevant, theta-dependent structure in the ongoing neural activity, rather than spontaneous events (see also, e.g., Figure 2 in Helfrich et al., 2018, Neuron).

(3) The present manuscript links behavioral performance to pre-target phase in a wideband (5-40 Hz) signal, finding no relationship to behavior when the data were filtered to isolate either alpha- or beta-band activity. In comparison, we report links between behavioral performance and narrow-band activity, in the theta, alpha, and beta bands (Figure 3 in Fiebelkorn et al., 2018, Neuron). Moreover, we show that these frequency-bands are functionally distinct by linking them to specific, functionally defined cell types.

(4) While it remains to be tested whether traveling waves are coordinated across brain regions, we have shown (Fiebelkorn et al., 2019, Nature Communications) that attention-related rhythmic sampling is characterized by theta-dependent changes in between-region functional connectivity (across cortical and subcortical nodes of the attention network).

On 2019-07-19 00:23:12, user Guillermo Parada wrote:

In the discussion you said "There is no evidence for RNA editing to modify splice sites yet", however at least in vertebrates there is evidence of GT-AA introns that are activated by ADAR and transformed to GT-AI (read by the spliceosome as GT-AG). In 2014 we found 7 putative splice sites that can be activated by A-to-I editing and one of them, located at ADARB1, was previously found by other researchers (see Table 2; https://academic.oup.com/na... "https://academic.oup.com/nar/article/42/16/10564/2903109)"). It might not be a very frequent event as non-canonical introns are very rare, but it's very interesting how different RNA processing events interplay during transcription.

On 2024-10-07 14:40:02, user BindCraft Enjoyer wrote:

I like the ‘Design-Until’ architecture of the BindCraft pipeline, but one thing I couldn’t find in the paper is any quantification of BindCraft’s in silico design success rate. In the Introduction, you note that one drawback of RFDiffusion/MPNN-based pipelines is the need to screen thousands to tens of thousands of designs in silico before finding the 10-100 that pass the quality metrics and can be tested experimentally with good success rates. Does BindCraft also require screening of thousands to tens of thousands of designs, or is it more efficient in silico than an RFDiffusion pipeline? You mention in the paper that BindCraft outputs statistics from each design run, and that biasing away from alpha helical binders reduces the in silico design success rate; so it sounds like you have the statistics ready to hand, at least for the targets reported in the paper. I’d love to see these design success rates added to a table, either in the main paper or the SI.

Another thing I’d like to see is some quantification of the compute time and cost required to run the 4-step pipeline until 100 designs pass the in silico filters. I understand this cost scales with target/binder size and target difficulty, but I would imagine you have the data required to calculate these metrics at least for the design campaigns reported in the paper. I saw on Twitter that you’re working on a direct BindCraft / RFDiffusion pipeline comparison; I hope you’ll include the computational hardware and total CPU/GPU time for each side of that design campaign.

Great work!

On 2019-11-01 10:13:27, user Rudolf Meier wrote:

We used two different primer pairs to check for consistency. With regard to the pig DNA, it is consistently found with both primers for the products of a certain supplier. Upscaling is certainly needed, but it's arguably even more important to have a sampling scheme that is tied to specific targets (e.g., volume of sales; sale of endangered species; preventing fraud in mixed-species samples through regular checking, etc.). MinION is a technique that is quite versatile and one can now design with regulatory targets in mind instead of having to think too hard about how to sequence it all with Sanger sequencing without spending too much money (and Sanger sequencing would fail for mixed-species samples).

On 2020-04-21 08:04:39, user AspiringPolymath wrote:

Citation 45 (https://www.nature.com/arti... "https://www.nature.com/articles/nature12426)") is cited and used within this article as microfossil evidence — it is not...

On 2024-01-03 17:20:48, user Francesco Iorio wrote:

Now published in The CRISPR Journal:<br /> https://www.liebertpub.com/...

On 2020-11-17 18:26:38, user Patrick Shaw Stewart wrote:

Typical body temp of minks is 39C. Many promoters are temp-sensitive and "RNA thermometers" are important in many species.

Any mutations in "promoter" regions & regions affecting RNA secondary structure could have profound effects on viral pathogenicity.

Easy intro: https://youtu.be/l_jmqGvdeB...

Review: https://www.douglas.co.uk/f...

On 2025-10-02 06:19:23, user Wolfgang Graier wrote:

This is a thrilling work! Thank you for sharing it with the community. Did you had a chance to test for differences in mitochondrial dynamics and motility upon various ratios of L-MFN2 and S-MFN2? Good luck with publishing.<br /> Best, Wolfgang

On 2020-06-10 18:25:05, user Simon Drescher wrote:

To add some more literature which is not mentioned in the manuscript:

A review about asymmetric phospholipids: Huang and Mason, 1986 BBA 864, 423-470; "Structure and properties of mixed-chain phospholipid assemblies".

And some original work about the SDPC including x-ray studies and freeze-fracture electron micrographs: Hui et al. 1984 Biochem. 23, 5570-5577; "Acyl Chain Interdigitation in Saturated Mixed-Chain Phosphatidylcholine Bilayer Dispersions".

Finally, McIntosh et al. 1984 Biochem. 23, 4038-4044; "New Structural Model for Mixed-Chain Phosphatidylcholine Bilayers" - a whole paper about SDPC using x-ray.

Hence, the biophysical properties of SDPC are known for >35 years ...

On 2020-06-08 13:55:57, user Simon Drescher wrote:

SDPC should be 1-stearoyl-2-decanoyl-sn-glycero-3-phosphatidylcholine instead of "2-decyl" - since two ester bonds are in SDPC.

Futher, I think the relevant literature is not mentioned in this article, for example:

Lewis et al. 1994 Biophys. J. 66, 207-216; "EnigmaticThermotropic Phase Behavior of Highly Asymmetric Mixed-Chain Phosphatidylcholines That Form Mixed-lnterdigitated Gel Phases" describing the the phase behavior of SDPC, the lipid used in this study.<br /> and

Lewis et al. 1994 Biophys. J. 67, 197-207; "Studies of Highly Asymmetric Mixed-Chain Diacyl Phosphatidylcholines that Form Mixed-Interdigitated Gel Phases: Fourier Transform Infrared and 2H NMR Spectroscopic Studies of Hydrocarbon Chain Conformation and Orientational Order in the Liquid-Crystalline State"<br /> ...to name only two.

Finally, statements such as "It is not currently known what biophysical properties membranes composed of asymmetric lipids will display" are not true, since these properties are already known. Also, the fact that asymmetrical phospholipids show chain interdigitation is not new.

On 2020-11-25 14:39:12, user Richard Zimmermann wrote:

Dear Sarah and Steve, congratulations, that’s a really cool and timely study with milestone potential for both drug screening strategies and fight against viral infections.<br /> We all have been following news on the search for small molecules as potential antivirals in the fight against COVID-19 and related clinical trials with great interest for almost the entire year 2020. We realize that there are trials with RNA-polymerase- and viral protease-inhibitors and that there are great efforts under way to develop e.g. TMPRSS2-inhibitors as well as new viral protease-inhibitors. What we have not yet seen to be systematically addressed as potential small molecule SARS-CoV-2 antivirals, however, are the Sec61-inhibitors. The last decade has led to the discovery of a constantly growing list of Sec61 targeting small molecules and toxins, including cyclic heptadepsipeptides (such as cotransin 8 and CAM741), eeyarestatins, apratoxin A, mycolactone, Ipomoeassin-F, and Coibamide A, which can be expected to inhibit the biogenesis of viral membrane proteins in infected human cells. I am convinced that it is worthwhile to pursue Sec61 inhibitors as potential antivirals. Therefore, it is fantastic that you have started to tackle this subject with your study on the biogenesis of CoV-2 membrane proteins in a cell free system. I realize that there is a long way to go with this project, as you correctly point out in your exciting manuscript. But even if it does not lead to a therapeutic approach or combination therapy in the current crisis, it may well do so for the next viral pandemic.

On 2017-10-16 22:04:21, user Tamara Bidone wrote:

VERY interesting paper

On 2020-05-21 22:35:56, user GG Anderson wrote:

excellent work. Is the inhibition mechanism known? It seems that ORF3b could either block transcription by binding to TF region that controls IFN-1, or impede translation by binding to mRNA.

On 2016-09-13 15:50:19, user Andrew Jaffe wrote:

The software is currently available in the sva-devel branch of SVA and will hopefully be available in the next Bioconductor development branch release:<br /> https://github.com/jtleek/s...

On 2019-04-01 18:29:51, user Stefan Stender wrote:

Thank you for sharing this interesting work. One question: MARC1 p.Arg200Ter appears to be about 70-fold more common in Ashkenazi Jewish than in European populations: https://gnomad.broadinstitu...<br /> Do the 5 principal components of ancestry used in the analysis account for this?

On 2024-01-10 09:49:46, user Todd Vision wrote:

The peer-reviewed version is available at Methods in Ecology and Evolution 14, 2531-2540.<br /> https://doi.org/10.1111/204...

On 2021-01-20 12:09:56, user S Ramasubramanian wrote:

It's very promising to see non injectable intra nasal single dose vaccine for covid-19, which will be boon to india..hope to see it in use very soon..

On 2019-05-01 21:54:01, user Brian DeVeale wrote:

Awesome work! Perhaps the wrong forum, but it looks like the command for converting v2 objects to v3 is 'UpdateSeuratObject' in R and listed as 'UpgradeSeuratObject' in the FAQ on your website.

On 2020-03-16 22:12:55, user Laura Sanchez wrote:

Dear Yu and Petrick, this preprint was discussed in a lab meeting and we would like to offer the following for review. Thank you for posting this very interesting manuscript. Best, The Sanchez Lab:

As frequent users of molecular networking tools which are based on tandem MS data, we enjoyed reviewing a method not commonly used in our lab. It is clear that reactomics networks may help to identify analogs of compounds that are not easily matched by MS2 networking, especially for molecules with poor fragmentation patterns. We are very interested to see the application of linking enzymes to disease states through metabolomics approaches. That said, we have also included a list of critiques and suggestions for the preprint.<br /> Major:

• Networking tools incorporating mass shift and KEGG integration such as MetaNetter 2 (Cytoscape plugin) and MetaMapR (R package) exist. The manuscript could benefit from clarifying what makes this approach unique.

• Overall, the manuscript assumes a high level of knowledge. There are processes that go unexplained, such as the full extent of pre-processing accomplished in CAMERA and RAMclust, and why the Pearson’s correlation coefficients threshold was set to 0.6, etc. The pre-processing is extremely important for this technique to work due to the issues that were stated in the text. E.g. how is the difference between an isotope and a PMD determined and how do you differentiate between in-source fragments and neutral losses and the PMDs reported.

• As it stands, self-loops in figure 1 are confusing and add a lot of visual clutter. Based on the source code, it appears that retention time is taken into account when defining peaks, so we are assuming what’s happening is two peaks with the same accurate mass have different retention times. However, this should be explicitly stated within the text.

• In “Source appointment of unknown compounds”, the second paragraph is very difficult to understand. It sounds as if all compounds plotted are carcinogenic, but then “carcinogenic compounds were not connected by high frequency PMDs”? This then doesn’t seem to align with the next sentence discussing the average degree of connection, for which the values of 8.1 and 2.3 arealso not explained. The carcinogenic 1, 2A, and 2B could be briefly explained or perhaps just calling them level 1, level 2A etc. would help. In the same vein, the Figure 3 legend could benefit from changing labels from “Endogenous compound 2” to “Level 2 endogenous compound” or something else to clarify that it is referring to the toxicity level. Even then it is not sure if including that information is relevant to the figure.

• The final example in “Biomarker Reactions” feels underwhelming in its current state. Even though it’s claimed to have a high significance, it is not convincing that the +2H mass shift is a significant biomarker when it’s such a common reaction. If this could be tied to a specific enzyme that has been implicated in lung cancer, or some other biological evidence to support this claim, it would be more convincing. Otherwise, it could be worth making networks based off of the biomarker metabolites found in the original publication e.g. NANA?

• Figure 4 does not effectively communicate the significance of the data points as it is explained, the plots look very similar to the naked eye. Perhaps there is a statistical visualization that would make the point more clearly.
• Finally, it might be worth adding a discussion regarding the resolving power needed and limitations. The described workflow seems to highly depend on the ability to accurately gather three decimal places. This will take a higher mass resolving as the molecular weight of the analytes of interest increase. It is unclear right now what the targeted mass ranges were for the measured compounds and what resolving power may have been used. Given that this is an experimental variable users can designate on an Orbitrap for instance, is there a recommended number or do the authors envision leaving this to the mass spectrometrist to define? Depending on the target audience this could greatly aid in implementation and general understanding. <br /> Minor:

• The first sentence of the manuscript defines metabolomics using specifically an untargeted metabolomics definition. Some clarification of targeted vs untargeted or a focus on untargeted would make this clearer.

• Methylation, oxidation, are common artefacts from the extraction process and analysis of biological samples. These are unavoidable facts of metabolomics, but perhaps the authors could comment on accounting for sample degradation processes.

• As a proof-of-concept, we would have liked to see orthogonal identification for some molecules to prove that the molecules are related. An exact mass is only a “Level 5” identification (doi/10.1021/es5002105).

• The two matrices might be clarified by being presented as tables or figures with abbreviations as an element of explanation. For instance, adding in “Ethylnitronate (S1), Oxygen (S2)” would help clarify and remove the necessity for the sentence starting in “For KEGG reaction R00025..”. The following sentence could be made into the table caption.

• Use of the phrase “Topological structure” in the section “PMD Network Analysis” is confusing when it’s used in proximity with a compound’s chemical structure. It is unclear whether stating that chemicals with similar topological structure have similar biological activity refers to the topological properties of the network representation or if it is referring to the chemical structure itself. The reference to Figure 1 does not clarify this and either way this is a statement that requires further experimental or literature support.

• Figure 2 is very low resolution and the yellow and peach colors are hard to make out. Overall we’d suggest changing edges to have a text reference to what reaction is being used as opposed to color. Addition of the chemical structures for TBBPA and some of the analogs into this figure would help to visually make the point that what you are seeing is a representation of molecular analogs. We also offer that the node color could be based on what has been confirmed in the previous study and what was newly annotated by this analysis to more clearly make the point that there is new information to be gleaned by running this analysis

• Would it be possible to re-examine tandem data collected from the original paper to match fragmentation and confirm that the secondary network is related to the confirmed network?

On 2020-05-20 12:39:54, user Crayfarmer wrote:

There is no market for such a small crayfish except in developing countries. Marbled crayfish is on average considerably smaller than Procambarus clarkii. Therefore, culture of marbled crayfish in closed systems will never be profitable.

On 2019-10-20 03:57:17, user David Acton wrote:

A very interesting study that sheds some light on the cutaneous information that gates itch. However, more work needs to be done to discriminate between pre and postsynaptic effects, particularly with reference to GRPR+ neurons. I would also like to point out that animals were not shaved in the study by Acton et al., as stated in the discussion.

On 2025-03-04 21:05:38, user Simone Picelli wrote:

Hi, I think there is a mistake in the name of the company used to make the modified TSO. It's not Biosyn Corporation ( http://biosyncorp.com ), as you wrote, but rather Bio-Synthesis ( http://biosyn.com ). <br /> Moreover, in the TSO sequence: "/5Biosg/" is the acronym used by IDT for a 5' biotin. The "g" has nothing to do with deoxyguanosine (G), but you write in the paper "5BiosG/" and this can be confusing. The standard 10x TSO sequence is, in fact: 5’-AAGCAGTGGTATCAACGCAGAGTACATrGrGrG-3’<br /> so no G at the 5' (like there was never a G at the 5' of the SMART-seq oligo, from which 10x took their sequence).<br /> The code for biotin at Biosyn is [Btn] (standard C6 spacer, which I assume is the one you mean here).

On 2016-07-08 18:14:40, user Hamed Seyed-allaei wrote:

Nice job!

I have a suggestion regarding Fig 4. This figure is the spotlight of your work. But it is noisy, especially at the tails. This is natural, because there are few highly cited works. This can be improved using one of the following methods:

1. You can use logarithmic bins to construct the histograms: 0,1,2,4,8, ...
2. You can use cumulative density/histogram instead.

This reduces noises at the tails of the distributions so one can compares the performance of journals around highly cited works.

On 2016-09-15 21:52:20, user Francisco De La Vega wrote:

Please indicate the version of RTG tools that you used to compare varmatch with vcfeval. These tools are in active development and your timing and comparison results may change if you use a different version and thus the reader (and I assume the reviewers) need to know that.

On 2024-11-15 07:55:21, user Giovanni Bussi wrote:

Authors of the review

Olivier Languin-Cattoën, Giovanni Bussi

Summary

The authors use Molecular Dynamics with enhanced sampling techniques to gain insight in the directional catch-bond mechanism of Vinculin tail (Vt) interaction with F-actin. They construct two models of the Vt-actin complex that are hypothesized to represent a weak state and a strong state in a force-activated allostery model of the catch bond. They use enhanced sampling techniques to estimate the free-energy landscape of the unbinding process in each state, as well as the unbinding kinetics, in absence and presence of pulling forces of variable intensities and directions along the actin filament axis. Their results demonstrate higher kinetic stability for the strong state with respect to the hypothesized weak one, with unbinding kinetics in range of experimental expectations, confirming the viability of a “3-state” (2 bound states, 4 unbinding pathways) kinetic model of the bidirectional catch bond observed in single-molecule experiments. They additionally observe an increase in bond lifetimes for moderate constant pulling force (10-20 pN) in both directions, indicating an intrinsic catch-bond behavior within each “allosteric” state that may superimpose to the overall allosteric one. They show how an external pulling force affects the positioning of the H1 ?-helix believed to act as a regulatory motif of the weak-to-strong transition, providing a compelling structural hypothesis for a force-induced allosteric mechanism. Finally, they provide molecular insight on the difference in stability between both states, highlighting the role of a C-terminal extension (CTE) and the redistribution of Vt-actin contacts under force.

Comments

* We wonder if the nomenclature “Holo” can be confusing at first glance for the reader, given the historical usage of the holo- and apo- prefixes to designate protein constructs with and without their constitutive prosthetic groups (usually non-proteic cofactors)

* We suggest that the authors make clearer the composition of the Holo and Aligned protein sequences given the different numbering in 6UPW and 1QKR (that we guess is due to the presence of Metavinculin instead of Vinculin), that in our understanding are identical except for the absence of the leading H1 helix.

* In FES calculations, since the constant force (that is, a linear bias) is applied on the same coordinate Q_?, we expect that the resulting FES could be entirely predicted from the FES at zero force by simple addition of the linear slope, given sufficient exploration of the Q_? direction during the OPES-MetaD sampling. This fact could be used by the authors to assess the consistency between the FES computed at different forces. Alternatively, one may want to first aggregate the OPES-MetaD simulations at all forces using appropriate reweighting, and then estimate minimum free-energy paths and free-energy barriers at arbitrary force using the aggregated FES. This approach might lead to a better statistical use of the vast amount of simulations and a smoother estimate of the FES at all forces within the studied range. Finally, the multiple trajectories (20 replicates x 9 force values) could be used in a single bootstrap to assess the statistical uncertainty of the results (see below).

* Given the coarse nature of the set of chosen CVs (Q_? and Q_?) it is unclear whether the Vt is able to regain its canonical binding site during OPES-MetaD, notably because of free rotation with respect to the actin filament. <br /> - The authors acknowledge the difficulty in the methods (sec. 5.5) without explicitly stating whether such rebinding events happen at all in their simulations. We believe this is an important piece of information for proper understanding of the FES presented. We would suggest showing time series to clarify how many binding/unbinding events are observed.<br /> - One might expect that the absence of recrossing lead to a poor estimate of the free energy difference between the bound and unbound states as well as the height of the binding free energy barrier. On the bright side, the estimate of the unbinding barrier – which is the one they are the most interested in – can still be expected to be reliable.<br /> - The authors suggest to run multiple (20) separate OPES-MetaD simulations to compensate for this limitation. It should be acknowledged that independent runs starting from the bound state will not correct for the systematic bias caused by the absence of rebinding events. A bootstrap on these replicas would anyway estimate their statistical error.<br /> - We wonder if the use of the more specific CV Q_contact might allow for such recrossing to happen within OPES-MetaD, without the need of aggregating a high number of independent trajectories. In our understanding the authors only used Q_contact to assess the robustness of the free-energy barrier height to the precise choice of the projection space, but did not try to perform OPES-MetaD directly on this CV space, which could be instructive.

* The authors analyze the FES by determining a minimum free-energy path using the “String method” as a post-processing method. <br /> - The Methods section might benefit from some information about the use of the method, in particular that it is directly applied on the 2D CV-space projected FES (as opposed to a search of a minimum energy path on the full potential energy surface as originally proposed in [50]), and provide details about initialization (choice of end points for the string, number of nodes, initial interpolation) and robustness to these parameters in the converged paths and corresponding barrier estimates.<br /> - Since the FES are aggregated from 20 independent OPES-MetaD runs, it might be relatively straightforward to estimate errors (for example using bootstrapping) and provide error bars on Fig 2c. We believe this would strengthen the significance of the observed barrier difference.

* One may be concerned about the significance and reliability of the constructed “Aligned” state, since this state was constructed by aligning Vt to another conformation (in what we could refer to as “docking-by-homology”) with little experimental confirmation that such a state is stable in vitro. We understand that the model constitutes the core hypothesis of the whole computational approach, and that the consistency of the computational outcomes with single-molecule experiments themselves validates its plausibility. Nevertheless, it could be argued that lower binding affinity and lifetime are to be expected from a suboptimal binding partner in a suboptimal binding pose. This raises the question of whether the proposed model corresponds to a specific binding mode in reality, or if the results could be reproduced with a different alignment. Is this ruled out by the stability observed in the 500 ns simulations shown in Fig S1?

* In Sec 3.2 §4, the authors say “these results [...] do not quantitatively explain the observed experimental results, since the experimental changes in lifetime shown in Fig. 1D reflect a net 1.4 kcal/mol change in barrier in the negative direction, and 0.6 kcal/mol in the positive direction (if one assumes a constant prefactor the kinetic rate constant)”. It was somewhat unclear to us where these values come from (Are they computed from the fitted 3-state model in SI S1? At a specific value of the pulling force?) and how exactly they are compared to the computed barriers for Holo and Aligned to conclude to a discrepancy (Overestimated?)

* In Sec 3.4 §3, the authors convincingly remark that in two out of five simulations of Holo+H1 state pulled towards the barbed end, the conformation of H2–H5 becomes more similar to the Aligned (unbound Vt) structure, suggesting a first step in the strong -> weak allosteric transition. We wonder if (i) the specific contacts made with actin and (ii) the specific intra-domain contacts of H1 with the H2–H5 bundle are also indicative of a displacement toward the Aligned state, since this would be an even stronger argument validating the proposed model.

* Given the relative simplicity of the supposed allosteric motif and the suggestive results of the Holo+H1 simulations, we cannot help but wonder whether the authors also tried to "unfold" the H1 helix in the Aligned model with a N-terminal pulling force since this seems a very natural test to look for equally suggestive indications of a weak -> strong allosteric transition under force.

* Typos or writing remarks:<br /> - Fig 1B: The caption is inconsistent with the figure. In the caption, p1 denotes the COM of Vt helices H2–H5, p2 the COM of actin A1/A2 and p3 the COM of actin A4/A5. On the figure instead (p1, p2, p3) -> (p3, p1, p2).<br /> - Some repetitions that might be elegantly avoided<br /> x “capture the difficult-to-capture” (abstract)<br /> x “protein of interest [...] on our molecule of interest” (introduction §1)<br /> x “takes into account a Boltzmann weighted average over all possible configurations [...] a Boltzmann weighted average over all possible configurations” (introduction §4)<br /> - Typos<br /> x “FimH-manose” -> “FimH-mannose” (introduction §8)<br /> x “which is the the direction” (3.1 §1)<br /> x “These approximate one-dimensional free energy pathways also give us a way to define when the system has crossed into the unfolded state” -> Maybe the authors meant “unbound state” (3.1 §1)<br /> x “not all of the catch bond need come from” -> “need to come from” (3.2 §3)<br /> x “we note that these results are suggestive, they do not quantitatively” -> We wonder if the authors intended a formulation along the lines of: “we note that despite being suggestive, these results do not quantitatively” (3.2 §4)<br /> x “a constant prefactor the kinetic rate constant” -> “a constant prefactor for the kinetic rate constant” (3.2 §4)<br /> x “grafted to our Holo structure random orientation” -> “grafted to our Holo structure with a random orientation” (3.4 §2)<br /> x “TIP3 water” -> “TIP3P water” (5.1 §2)

Acknowledgements

This report was written after a journal club given by OLC in the bussilab group meeting. All the members of the group, including external guests, are acknowledged for participating in the discussion and providing feedback that was useful to prepare this report. The corresponding authors of the original manuscript were consulted before posting this report.

On 2018-05-02 16:13:26, user Alan VanArsdale wrote:

The record for Homo erectus now appears to be badly truncated. The dating here coincides with the timing of first appearances in the fossil record of Homo erectus in Eurasia and Africa, which likely was a major expansion from some unknown place for Homo erectus. During this expansion morphologies from encountered popualtions of Homo (with just one species of Homo ever existing at any one time), were picked up. There is no evidence in the fossil record of any lineages of Homo erectus being "dead end" morphologies. Instead it appears early appearances of Homo erectus in both Asia and Africa were morphologically African from the hip and below, and Asian above the neck, as intraspecific hybrids. .........................The best candidate for denisovans in the fossil record, and the best match morphologically for the known denisovan teeth, is Homo sapiens heidelbergensis, which is now known from Asia and South Asia (the cranium). The closest match morphologically for the trachiolus foot prints / track ways, reliably dated at about 5.7 mya, is Homo floresiensis (so H. floresiensis could have walked to Flores when last thought to have been connected to Asia about 5 mya). With Homo floresiensis being the closest known morphological match to whatever unknown Asiatic grades of Homo "erectus" contributed morphologies to AMH not seen (usually, except in a few early neandertal females), in African origins neandertals. Especially relatively small nasal bones and eye ridges, P4 relatively small compared to the molars, high domed heads, and presumably ancestral ARHGAP11A (which is thought to have enhanced intelligence but not led yet to increased brain size). .................................................. That populations such as Red Deer Cave people, Homo tsaichangensis and Homo floresiensis are late survivals needs to be allowed for. However, there is no evidence that as late survivors they in some way became genetically isolated from the single species existing in the genus Homo. Selective pressure not only maintains genetic diversity (as with neandertal genes as they enter Africa to be selected out there), they also maintain morphological diversity within interbreeding populations spread over great distances. With great morphological diversity, by about 1.9mya Homo was able to occupy diverse environments. Asia for the genus Homo long being more arboreal in morphology relative to Homo in Africa, on average, as reflected in lower body morphology when known. In Africa and Europe australopithicinae Western gorillinae and Pan, unlike in Asia, at first gave strong competition in all environments, and over time retreated more to arboreal environments limiting arboreal adaptations in Homo in Africa. Gigantopithicines (Asiatic gorillinae), may have been somewhat environmentally restricted in their competition with Homo and late surviving Lufengpithecus. ''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''Nor is the Asiatic stream of morphology coming into AMH limited to a little less than 2 mya, it goes back at least 10mya, just it can not be seen with what is known in genetics back more than about 2mya (for now). The "remarkable" convergence of Homo tsaichangensis with Javanese Gigantopithecus is not convergence at all, this is shared primitive character states (from initial divergene of Homo and gorillinae about 9mya), just as the lingual groove of gigantopithecus is a shared primitive character state with orangs. And in modern humans (as was published in the 1952 paper), Gigantopithecus dental character states are seen, with highest frequencies and strength in those places where Gigantopithecus is known in the fossil record. This can seem incredible, to those who imagine typologically all hominins at some time are derived from just one morphology or holotype. However, this is not supported by the evidence in primates. Instead, it is supported that in hominin evolution there has at any time always been high morphological diversity. With morphological diversity usually being preserved in the phenotype (even if not seen in the spotty and intermittent fossil record), or sometimes hidden to be expressed later in the genotype.

On 2018-11-02 03:56:36, user Stuart Cook wrote:

Great study. The finding that IL-11 is specifically increased in activated fibroblasts in colitis and predicts resistance to anti-TNF therapy is consistent with a previous study (PMID: 19700435). Notably, OSM and IL-13 signalling - both also predicting response to anti-TNF therapy in this study - have an absolute requirement for IL-11 to stimulate fibroblast activation (PMID: 15699166, PMID: 29160304).

On 2019-03-26 20:29:36, user Vincent Denef wrote:

We read and discussed this preprint as part of an upper-level microbial ecology class at the University of Michigan (EEB 446, Winter 2019) that I am teaching and below I am posting some of the thoughts the students had after reading the preprint:

1. General thoughts. Brito (2019) poses the question, “Do we exchange oral and gut commensals with our closest family and friends?” in order to understand the impact of routine interpersonal contacts in shaping the microbiome composition. Addressing this question is important because the gut microbiome is so impactful to the normal health of humans, any information about how it is affected is important and useful, although the students would have liked to see a more explicit explanation of how knowledge of transmission impacts how we understand how microbiomes are shaped, function, and affect human health. The structure of the study focused on isolated, non-industrialized communities. This enabled the authors to focus in on interpersonal interactions while reducing potential influences of external factors. However, it also makes it challenging to relate findings to other communities that are industrialized. While this is acknowledged in the study, they felt that this should be treated as a case-study and the data should not be used to draw conclusions between other populations and geographic regions.

2. They had some thoughts regarding the authors’ definition of 'transmission'. Brito et al. loosely define transmission by shared inferred genotypes. Their work provides insight into the correlation of community patterns for individuals in a household or family unit. As it is difficult to determine the exact mechanism for transmission, the approach used allowed the authors to identify trends in oral and gut microbiome similarities across individuals without understanding how or why this might be. Yet, this does mean they can’t necessarily rule out similar environmental factors among close family members to lead to similar ecological selection rather than transmission, thus there is a risk of overinterpreting correlative data.

3. Use of social network data. Some students had some suggestions regarding the methodology used for social network construction and wondered if other analyses of the network structure could have been added to gain deeper insights. Specifically, within the social networks constructed, simplified approaches of defining an individual and establishing a single connection and then utilizing that network for analysis is very broad in nature and only skims the surface with respect to extracting information from a social network. Understanding centrality measures of individuals within the village i.e., degree of individuals tied to, geodesic betweenness (how often is an individual on the shortest path to another individual), closeness of an individual (how easily can they reach other actors within the network), and eigenvector measures (how well-connected are individuals that an individual is connected to) would all have allowed for a stronger utilization of microbiome sample data for the usage of predictive models. Each of these different centrality measures come with an attributable value associated with the strength of centrality. After determination of centrality measure values for each individual, correlation of network centrality values and similarity in both oral and gut microbiome compositions could be measured.

4. Ethical concerns. Student questions: Did the participants in this study give consent, and furthermore, did they have informed consent about the study that they were taking part in? There were also questions whether the exact form used to gain informed consent could be shared to preempt any of these concerns readers may have. Further questions they had were whether the participants in this study will benefit in any way from the knowledge obtained in this study? Also, one student group wondered whether knowing about the microbiota composition of certain populations could help drug companies target certain populations with certain products?

5. Other methodological, interpretation, or presentation concerns. The students wondered why they inferred the amount of years couples were married based on the age of their oldest children. It’s interesting why they didn’t just ask the couple how long they had been married also it’s not always true that children are directly related to amount of time lived together. Another question related to the data availability section, where they didn’t quite understand why the authors discuss mislabeling of a sample in the database. Could they change the database to reflect this labeling issue?

On 2021-03-03 23:23:32, user William Longabaugh wrote:

The above manuscript has been published as a book chapter: Desai R.M., Longabaugh W.J.R., Hayes W.B. (2021) BioFabric Visualization of Network Alignments. In: Yoon BJ., Qian X. (eds) Recent Advances in Biological Network Analysis. Springer, Cham. https://doi.org/10.1007/978...

On 2019-05-28 21:23:35, user Kristof Theys wrote:

We intend to make all code used for this study available to anyone that can benefit from it. We plan to use the repository https://github.com/ktheyss to share the framework.

On 2017-01-11 20:21:34, user Stephen Van Hooser wrote:

Question: is it certain the calcium activity in the bouton reflects only the presynaptic signals? If you patched a cortical neuron and fired it, is truly no signal observed in the presynaptic boutons? (One might imagine responses derived via presynaptic NMDA receptors, etc.)

On 2020-01-31 22:02:47, user Dave Baltrus wrote:

It is likely that the evolutionary relationships found between these two protein sequences of both viruses are due to a complete coincidence and, stepping back, do not appear "uncanny" to multiple experts that have also examined the sequences. In short, the authors base their analysis on a short sequence of the spike protein from 2019-nCoV, but a much more comprehensive search outside fo the viral sequences queried in the manuscript demonstrates that this sequence is also found in *many* *many* other places than HIV. Thus, while the 2019-nCoV strain does appear to have a sequence difference from other closely related viruses, there is not enough resolution to clearly demonstrate the evolutionary history of this change let alone trace it to HIV.

see analysis here for instance: https://twitter.com/trvrb/s...

On 2024-10-17 01:51:59, user Kazuki Iijima wrote:

This manuscript has been peer-reviewd and published in Communications Biology as "Erroneous predictive coding across brain hierarchies in a non-human primate model of autism spectrum disorder" after peer review. https://doi.org/10.1038/s42003-024-06545-3

On 2021-04-07 10:21:36, user Jake Wintermute wrote:

All the raw data and python scripts for this study are available on Zenodo: https://doi.org/10.5281/zen...

On 2021-03-23 00:06:38, user Clara B Jones wrote:

... thinking of Ants, all of which are classified, Eusocial [e.g., see Holldobler & Wilson 1990] ... [1] are we going to accept the distinction between "primitively" and "advanced" eusocial? ... [2] among ant species, traits are highly variable, for example, with re: presence or absence of "castes;" patterns of task, role, and/or morphological specialization; &/or "totipotency;" ... [3] however, "reproductive division-of-labor" is a universal ... [4] since Cooperatively Breeding taxa exhibit [a] "reproductive division-of-labor;" [b] 1 or a few "pure" breeders; and, [c] "helpers," can we classify "cooperative breeders," "primitively" eusocial? ... [5] social mole-rats exhibit "reproductive division-of-labor;" "pure" breeders; totipotency; "helpers" [role specialization]; and, unless i am mistaken, "temporal division-of-labor" ["age polyethism:" Damaraland mole-rats] and are, generally, classified, "primitively" eusocial [eusociality including "reproductive division-of-labor;" totipotency; but without (more or less) "sterile casts" and, usually, without morphological specialization] ... how does this new report deviate from a classification, "primitively" eusocial for social mole-rats or from the highly variable traits reported for Ants?

On 2025-10-04 19:58:19, user annonymous wrote:

The authors claim that fitting a DDPM to a single MD dataset and subsequently generating samples from the trained model results in ‘enhanced sampling’. This claim is highly dubious as the results seem to indicate that the ‘enhanced sampling’ they refer to is equivalent to adding small amounts of guassian noise to samples already present in the MD training data. In fact, this result is expected - when generating samples from a DDPM, a simple prior distribution is iteratively transformed to a sample from the data distribution through noisey purturbations in the direction of maximum likelihood. Consequently, it comes as no surprise that generated samples are highly similar but not exactly identical to samples from the training data. Upon additional training of a DDPM on a single dataset, one expects that these deviations should asymtotically decrease until the DDPM ‘memorizes’ the training data and generate nearly exact copies of the training data. In the context of MD, adding gaussian noise to pre-existing samples from long-time MD is generally not considered a useful enhanced sampling method, unless noise perturbed structures are subsequently evaluated with a potential energy function and are subject to some aceptance criteria, as is done in metropolis hasting / MCMC sampling methods. I do not belive that the ‘enhanced sampling’ the authors claim to obtain from over-fitting DDPMs on single MD datasets is of the same nature as that expected from standard enhaced sampling methods for MD like replica exchange or metadynamics, which aim to explore completely new regions of phase space that are otherwise difficult to access and not yet characterized. The authors present no evidence that their procedure is able to generate samples that are substantially different than those already present in the MD training data - therefore, I would not consider this an enhanced sampling method in any sense. Moreover, the DDPMs presented here are trained to maximize the likelihood of generating samples that are similar to the training data distribution - there is no incentive for the model to explore new regions of phase space and one could argue that if their model were producing samples highly dissimilar to the traing data - it would suggest that the model is either under-fit or systematically incapable of appropriately modeling the training data.

On 2020-11-09 21:31:18, user Clemantine wrote:

Why would they use aborted fetal tissues lines to experiment with? Using any cell line after the 1947 Nuremberg code requires the informed consent of the Subject and the ability to withdraw from the experiment, both denied to aborted human babies. All cell lines derived after 1947 using aborted fetal tissue is immoral, unethical and illegal and must be discontinued!

On 2017-10-26 15:34:38, user Allen Collins Zoologist wrote:

Another minor taxonomic edit: The correct name for Lucernariopsis campanulata is Calvadosia campanulata (Lamouroux, 1815). Cheers,

On 2019-08-18 23:35:24, user Hunter D. Young wrote:

Im very intrigued. And i also hope they start human clinical trials.

On 2019-02-27 16:55:56, user Huiwang Ai wrote:

The final version of this paper is availiable from ACS Biochemistry: https://pubs.acs.org/doi/10...<br /> Please refer to the ACS Biochemistry version for the most acurate presentation.

On 2023-03-01 11:05:33, user KS wrote:

Dear author, <br /> I attempted to access PyCalibrate, but received an error message indicating that the processing frame on the web page failed to load. I attempted to resolve the issue by trying multiple browsers and devices, but without success.<br /> If you could kindly check the frame, it may resolve the issue. However, if the issue still persists, please let me know, and I would be happy to help you troubleshoot the issue further.<br /> Best,

On 2025-03-26 08:51:39, user Shigeaki Saitoh wrote:

The revised version of this article is published in Journal of Cell Science ( https://doi.org/10.1242/jcs.263747 ).

On 2019-11-29 13:58:11, user Yang Yu wrote:

An earlier GoldCLIP paper by Hannon, Yu and colleagues with essential the same idea published last year should be cited (GoldCLIP: Gel-omitted Ligation-dependent CLIP. https://www.ncbi.nlm.nih.go... "https://www.ncbi.nlm.nih.gov/pubmed/29709556)").

On 2021-01-30 22:18:10, user Dittmer Lab wrote:

Would this be considered a gain of function experiment?

On 2025-02-04 22:16:48, user Bill Mallet wrote:

To assist interpretation, it would be helpful to plot the dose-response curves in Fig. 2a with a log10 X-axis.

On 2017-06-02 15:26:16, user Debbie Kennett wrote:

I think you have a typo in line 295. The third word should be "our" not "or".

On 2017-05-17 14:51:59, user Jean Manco wrote:

Another little typo, this time in the supplementary table. Pabbay Mor should be corrected to Pabay Mor, as in the Supplementary Info.

On 2017-08-29 13:38:48, user Bernard Secher wrote:

Good work for this new version of the paper!

On 2017-09-29 04:09:39, user Roy Q wrote:

Has this been accepted anywhere? I keep seeing blogs on it but cannot it anywhere. If someone has a link thanks, nothing on pubmed

On 2025-10-21 03:59:01, user CDSL JHSPH wrote:

Thanks for the very interesting preprint. I had a few questions:

Since the analysis reuses public datasets, could upstream biases affect PCN estimates? Did you try any sensitivity checks?

Many assemblies don’t include clear isolation/source info. If metadata are available, would stratifying by source (clinical vs environmental, host/body site) change the main patterns?

Some taxa (e.g., E. coli/Enterobacterales) seem over-represented. Do the size–PCN trend and the ~2.5% DNA-load rule still hold after down-sampling or re-weighting?

Do you plan a small wet-lab validation or tests in more distant taxa to see how broadly these rules generalize?

Thanks again—really exciting direction!

On 2023-08-04 16:32:57, user Alex wrote:

The final version of this paper is available here in Scientific Reports under the title "A universal tool for predicting differentially active features in single-cell and spatial genomics data"

On 2016-12-30 09:57:32, user Alan Carter wrote:

Good to see this reanalysis of the D4h3a ancient DNA . It is becoming clear that there was no no Beringia Holdover population, there is no, repeat no archeological data to support this - see Buvit 2016. I have spent the last 18 months looking at evidence for migration and depopulation in Siberia and Japan. I hypothesize that the Amerindian Homeland was Japan, and that it formed in Kanto Honshu between 21 and 16 kya. There were no further gene flows into Honshu and Hokkaido until 2300 years ago. This population served as a source population for both the recolonisation of Siberia via the Amur Valley and Trans Baikal from 22 cal kya and more rapidly from 16 kya and migration by boat to the Americas. This Kanto population was the source population from NE Asia to the Americas in a series of near continuous migrations. This dual migration to Siberia and the Americas from the same source population explains the long noted similaraties between Siberia and the Americas. They both came from the same homeland. The failure of the holdover model frees us to better interpret gene flow and reconciles the clear actual lithic archaeology as people migrated through time from Japan bringing their current lithics with them. Interestingly I was going to write to Brian K about this and this article stimulated this opening comment

On 2020-06-03 11:57:56, user N.Mallikarjunarao1 wrote:

At molecular level covid spike protein interactions with ACE2 are explored

On 2024-11-18 13:41:59, user Min-Ho Nam wrote:

This manuscript has been published in the Crispr Journal.<br /> DOI: 10.1089/crispr.2021.0025

On 2024-09-22 21:30:29, user Christian Helker wrote:

Beautiful work!!! :)<br /> I would like to bring to your attention our publication (“Apelin signaling drives vascular endothelial cells toward a pro-angiogenic state”; https://elifesciences.org/articles/55589) "https://elifesciences.org/articles/55589)") , which explores the function of Apelin on the vasculature. I believe it could provide additional context or complementary insights to your work.

On 2018-06-12 14:48:10, user Ole Jørgen Benedictow wrote:

This is all a consequence of not reading the studies on the behaviour of rats. Rats are cannibals, they eat sick fellow rats that cannot defend themselves more or less alive. When rats get ill or dying that hide away as best they can for this very good reason, as it is stated: they die in unusual and inaccessible places. This subject is commented on by all standard works on plague and are summarized and updated in Chapter 3 of my 2010 monograph on the alternative theories. See especially subchapters 'The question of the presence of Rats and the Methodological Fallacy of Inference ex silentio', and 'Ars Moriendi Rattorum: Where have all the Dead Rats Gone?, pp. 85-97. See also Chapter 8 in my 2016 monograph, pp. 395-451.I provide copies of Chapter 3 and 8 or parts of them for the orientation of those who wish to have a wider and less biased version of the history and life of rats.

Kind regards Ole J. Benedictow<br /> o.j.benedictow@iakh.uio.no

On 2020-11-10 10:05:17, user linguist wrote:

No, the authors (the linguists among them) never make their data available, apart from an obscure table of +'s and -'s ... You have to take their word about their method and their accuracy. It's no surprise that most linguists don't.

On 2023-06-23 04:35:02, user Stephanie Wankowicz wrote:

The mineralocorticoid receptor forms higher-order oligomers upon DNA binding

Summary:

This paper aims to answer the question of the oligomeric states of the mineralocorticoid (MR) in the nucleus and when bound to DNA hormone response elements (HREs) in vivo. Using Number & Brightness (N&B) analysis, they investigate the oligomeric state of MR in the presence of different ligands and mutations/truncations to identify what controls different oligomeric states. While they comparisons they performed between different ligands and constructs of MR show qualitative differences, this paper is missing some key controls, particularly in the localization of the nucleus and MMTV array, which prevent us from thoroughly assessing the paper.

Major Comments:

1) Provide details and controls on identifying the nucleus versus cytoplasmic versus DNA binding/MMTV array. Only labeling these with the molecule of interest (MR) is inappropriate. To assess if MR is congregating at the MMTV or some other location in the nucleus, the MMTV array must be labeled with something other than GFP, allowing simultaneous visualization of the array and the MR oligomerization state.

2) Explain the varying oligomerization states you observe across your dataset. Can you provide ranges of oligomerization states across your results? How should we interpret mixed populations? What were your criteria to decide whether a construct dimerizes, oligomerizes etc.

3) The manuscript has varying points for each condition (for example, in Figure 1B, there are 490 single cells for one condition, with 36 single cells for another condition). Please explain why there is so much variety in the number of data points.

Minor Comments:

1) The introduction could be improved by expanding on details on the transcriptional crosstalk of MR/GR and the observations of GR at MMTV (and clarify this is the data the rest of this paper is compared to).

2) Please clarify the construct of the cell line and if endogenous MR is knocked out.

3) In Fig. 4 C, the MR-N579/GC-470C mutant array displays only 11 data points, while the figure legend says it contains 22.

4) The figures with agonist or antagonist would be clearer if the agonist or antagonists were labeled.

5) In the section ‘MR and GR do not share the same dimerization interfaces’. Please provide some context for the D-loop and P-loop. Figure 3A could be improved by showing where these are structurally or among the entire sequence.

6) Please specify how many independent experiments were run for each condition.

8) The authors describe that imaging happened 30min - 2h after ligand adding. Please specify what experiment was incubated with ligand and for how long. Is it possible that the signal is increasing proportionally with longer incubation times? A comparison in the Supplementary would be helpful.

Reviewed by Stephanie Wankowicz, Lena Bergmann, and James Fraser (UCSF) <br /> 10.5281/zenodo.8072766

On 2020-05-29 16:37:34, user Thomas Perkmann wrote:

Dear authors,

Many thanks for sharing this exciting work. In the publication, the negative samples of the specificity cohort display median values of 2.2 -2.4 AU/ml. In the manufacturer's IFU, there is a LOD of 3.8 AU/ml (i.e., the device does not give values below 3.8 AU/ml). How were these low values measured?

Thanks for answering this question.

Best regards Thomas Perkmann

On 2018-05-10 15:27:34, user Priyanka Sharma wrote:

Mechanistic and detailed view of CTD citrullination .

On 2024-09-19 20:21:02, user Danve Castroverde wrote:

Final peer-reviewed version has been published: https://doi.org/10.1111/pce.15098

On 2020-03-25 19:33:23, user Rohit Satyam wrote:

Hi Authors. <br /> Referring to line "We also used psRNATarget server to compare the predicted targets by the<br /> two methods"

I am unable to understand why you have used a plant small RNA target analysis server, psRNATarget in your study?@COVID-19 is not a plant virus.

I hope there are many differences in the prediction as mentioned https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3125753/

"For example, an animal miRNA generally requires loose complementarity in about first eight nucleotides of the miRNA, while a plant miRNA demands the whole miRNA mature sequence to be near perfectly aligned with its mRNA target. Secondly, an animal miRNA tends to inhibit target gene’s expression at the translational level, whereas a plant miRNA directly cleaves its target transcript"

That would make your predictions wrong.

On 2020-08-17 18:59:05, user OxImmuno Literature Initiative wrote:

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

On 2019-10-24 18:38:45, user Martin Hölzer wrote:

Dear readers,

the preprint is accepted and published now at NAR GAB:<br /> https://doi.org/10.1093/nar...

Unfortunately, one URL in the HTML abstract shown here is not working because of a missing '/' (Thanks, Charles for this hint):<br /> https://rna.uni-jena.de/sup...

This can not be changed because the preprint is accepted already.

Thanks for your interest in our study, <br /> Martin

On 2021-02-09 17:01:32, user Virginia Savova wrote:

Now updated with comparison to other methods

On 2021-12-09 20:59:29, user Siddhartha Joshi wrote:

We recorded the activity of locus coeruleus (LC) and anterior cingulate cortical neurons in awake rhesus macaques. During passive fixation, ACC pairwise correlations tended to be reduced when baseline firing rate of LC neurons increased. But, when external events drove transient increases in LC activity, ACC pairwise correlations increased. Both relationships covaried with changes in pupil size. Therefore, modulation of coordinated cortical activity might result, at least in part, from ongoing,<br /> context-dependent, arousal-related changes in LC activity (- From the author).

On 2021-03-06 17:47:29, user Clement Kent wrote:

Thanks for a careful and thoughtful paper. After first reading, I see no reason to question the results or interpretations presented.

I note several minor typos. Line 386 has GG>AT when GC>AT is meant. In Figures 3 and 4, you have what looks like an R-related blip: the Greek characters gamma and kappa have been replaced by an empty box. You must use the "expression" command to get correct use of special characters and math symbols in R plots - see for example https://stats.idre.ucla.edu.... Similarly in Fig 5 B, the text at top right reads r sub(S box W) rather than your intended r sub(S>W).

You are cautious in your discussion of how differences in kappa and gamma may have arisen between simulans and melanogaster. You do mention GC methylation being low in Drosophila, but you don't discuss some recent evidence (e.g. Deshmukh et al 2018, PubMed 30002967) for dramatic differences between Drosophilia species in cytosine methylation. In particular, simulans and yakuba both were estimated as having ~50 times higher 5mC levels than melanogaster, which latter species stands out as an exception among 12 drosophilids tested (op. cit., Figure 2). Some discussion of this might be worthwhile. This presumably effects kappa because of known mutational mechanisms.

The curious case of negative gamma for W>S in some bins is also stimulating. You did not discuss possible mechanisms for this. Have any been suggested in the literature? If low-GC introns are found in low recombination areas, then could interference or Hill Robertson effects from selection on exons which are after all only 8 bp away from the beginning of your SI introns have an effect? Did you at any point consider estimating various substitution rates as a function of position in the 8-30 bp region - perhaps as measured by distance of each bp from the nearest exon?

I enjoyed this paper and hope to see it in its final form soon.

Clement Kent<br /> Dept of Biology,<br /> York University, Toronto

On 2019-01-28 10:32:45, user Marc Gf wrote:

It is good to have a pathway-like graphical notation for rule-based modeling. The fact that the notation has a general view of a pathway combined with the fact that you can click on each specie to reach deeper insights of it, such as site-specific details and state patterns, provides the user with a powerful tool to sinthesize a huge range of knowledge. It may be useful in manny ways, to share some results in a understandable way, to have a visual support to check the errors while building a model...

Nevertheless, as it happens with SBGN, there is information that this graphical notation systems cannot catch and there is some loss in the conversion to machine-readible formats. Do you think that with this notation, the loss when converting is improved? Do you think would it be possible to update this approach and incorporate some of this specifications in the reaction without creating a tedious graphical model?

Marc.

On 2023-06-03 14:55:56, user Andrew Borchert wrote:

Very interesting and useful work. I am wondering: how do you reconcile your observation of a correlation between ATP and heat output with prior observations that heat shock itself can lead to an increase in ATP concentration for E. coli?

https://microbialcellfactor...

I think you touch on this with some of the mutant analysis, but I wonder if you can describe in more detail how you can distinguish between high ATP being the cause of increased heat output vs. higher ATP being in response to increased heat output?

On 2017-09-19 13:12:24, user Martin Modrák wrote:

Really liked the approach. I toyed with similar problems recently, but could not get over some of the road blocks you managed to solve. Good job!

On 2024-08-30 21:57:17, user CruzVeralab wrote:

I like this paper, missing some TnaC references: https://pubmed.ncbi.nlm.nih.gov/16061180/ ; https://pubmed.ncbi.nlm.nih.gov/34504068/ ; Also, I am wondering how the results could explain previous data with uL22 (K90D) mutant proteins: https://pubmed.ncbi.nlm.nih.gov/35311583/

On 2018-09-21 00:05:23, user Chad Rohlfsen wrote:

Have you tried Levant N and CHG in the pright to model the AHG as Iron Gates, Natufian and Iran N? I'm pretty sure you'll find Iranian already there in qpAdm and qpGraph. Thanks!

On 2025-06-13 13:20:50, user Anonymous wrote:

Dear authors,<br /> as part of a group activity in our lab we discussed your very interesting manuscript with the goal of reviewing it as well as improving our reviewing skills. The below review is the result of this exercise and reflects thoughts and comments of several people. We hope this helps you with your way forward to publish the paper in a good journal.

Summary, strengths and limitations of the paper<br /> The manuscript by Lee et al. investigates the mechanisms behind lysosomal damage in CLN4, a form of neuronal ceroid lipofuscinosis caused by dominant mutations in the DNAJC5 gene. The authors demonstrate that CLN4-associated DNAJC5 mutants aggregate on lysosomal membranes, leading to membrane disruption and severe lysosomal damage in neurons derived from human iPSCs and in a Drosophila disease model.<br /> In non-neuronal cells, a protective ubiquitin-dependent microautophagy pathway is activated, helping degrade these toxic aggregates and preventing lysotoxicity. Through CRISPR screens, the ubiquitin ligase CHIP (STUB1) was identified as a critical regulator of this pathway. CHIP facilitates the ubiquitination and lysosomal degradation of CLN4 aggregates, thus preserving lysosomal membrane integrity and preventing the lysosomal-driven cell toxicity.<br /> Importantly, overexpression of CHIP in CLN4 mutant neurons and flies restores lysosomal function, reduces lipofuscin accumulation, and rescues neurodegeneration, highlighting CHIP as a potential therapeutic target for CLN4 and, potentially, other lysosome-related neurodegenerative diseases.<br /> A strength of this study is its comprehensive mechanistic investigation into CLN4 disease, identifying lysosomal membrane damage as a key pathological feature and clarifying the still open question about how DNAJC5 aggregates cause neurodegeneration. In addition, the identification of the ubiquitin ligase CHIP as a key regulator of a protective microautophagy pathway represents a key discovery. CHIP-mediated ubiquitination of DNAJC5 aggregates enables their lysosomal degradation, effectively preserving lysosomal integrity. These findings are of important translational relevance, since CHIP overexpression restored lysosomal function and reduced neurodegeneration in both human neurons and flies, highlighting a promising therapeutic application.<br /> However, the study also displays some limitations. While the neuron-specific vulnerability to CLN4 aggregates is a central focus, the mechanistic basis for this selective sensitivity remains only partially explained. Additionally, the therapeutic relevance of CHIP modulation is based on genetic overexpression, which, while illustrative, does not yet translate to practical interventions such as small molecules or gene therapy strategies. The broad role of CHIP in cellular protein quality control also raises questions about potential off-target effects of systemic modulation. Despite these challenges, the paper makes a strong contribution to the field by establishing a mechanistic link between lysosomal membrane damage and CLN4 pathology and identifying CHIP-mediated microautophagy as a potential neuroprotective pathway.

Major Comments<br /> 1. In Figure 1A, why do monomeric DNAJC5 levels change in the mutants? L116?HT seems to have comparable levels to WT, which is not the case for the other heterozygous mutant nor for the homozygous of the same mutation. Are the overall DNAJC5 levels changed in the different lines? Maybe testing by qPCR or checking the fully solubilized protein by WB can be options.<br /> 2. In Figure 1D, can the authors prove that the loss of lysotracker signal in day16 neurons is not simply because these cells are dying? Can they stain cells with calcein-AM as well?<br /> 3. In Figure 1L, why do they not find CHIP in the proteome? Based on figure 5A, they should find a difference in its levels between WT and L116? HM. Do they check the proteome at a timepoint that is too early, or are CHIP levels overall too low to pick up changes? Minor comment: in the text, they define a log fold change cut-off of 1.5. Please illustrate this in figure 1L.<br /> 4. In Extended Data Figure 2E, where does the HMW form of DNAJC5 fractionate? And does the organelles fraction change in mutants? It would be of help to see the fractionation of the mutant too, showing also the HMW DNAJC5. This will also help to understand why the authors see the downregulation of DNAJC5 in mutants in mass spec (Figure 1L). <br /> 5. The authors show that the lysotracker phenotype in mutants is likely not linked to V-ATPase dysfunction. However, it is not fully clear what is happening to V-ATPase. In Extended Data Figure 3A, the WB shows a decrease in the interaction between mutants FLAG-DNAJC5 and membrane-bound ATP6V1G2. What is the authors’ hypothesis of this phenotype?<br /> 6. In Figure 3, the authors conclude that ubiquitin, HGS and DNAJC5 colocalize on lysosomes. Can they add a lysosomal staining (e.g. LAMP1) to really prove this point and show the lysosomal localisation of mutants? And, more in general, the authors should include a colocalization analysis (e.g. Pearson’s everytime they claim it - Fig. 1H, 3A, 3C, 4F, 4H and 6C).<br /> 7. In Figure 3E,F, it seems that also the monomeric version of DNAJC5 accumulates inside lysosomes and this is impaired when microautophagy is blocked. Can the authors comment and expand in the result section about the WT phenotype? <br /> 8. In Figure 3H,I, the treatment with TAK-243 induces the reduction of lysotracker signal also in cells overexpressing the WT isoform. Why? The authors can include the quantification of untreated cells too. <br /> 9. In Figure 4 (A and B) and Extended data Fig.5, the authors employed CRISPR screens and identified CHIP as a candidate regulator and then they further supported this finding through KO and rescue experiments. The claims they made here can be sufficiently supported by the data they showed. However, one concern is the noticeable difference between the screening results of the WT and ?J conditions, as there are relatively few overlapping hits. What could explain this divergence? How feasible is it to use Keima-DNAJC5 with L116? for CRISPR screen instead of using DNAJC5 WT and ?J? Does the Keima-DNAJC5?J mutant have the same aggregation and/or lyso-toxicity phenotype as observed with the L116? mutation? Considering that both DNAJC5 WT and the ?J mutant are involved in misfolding-associated protein secretion (MAPS) and microautophagy (PMID: 35506243), but the ?J mutant lacks MAPS activity, is it appropriate to use the ?J mutant as a substitute for the L116? mutation? How is this choice justified in the context of the study?<br /> 10. In Figure 4D, is the observed leftward shift, particularly in case of sh-CHIP, substantial enough to confidently conclude that there is a decrease in the association of Keima-DNAJC5 WT with lysosomes? It would strengthen the claim if this was quantitatively assessed and supported by statistical analysis.<br /> 11. In Figure 4E, we would expect similar immunoprecipitation efficiency for all FLAG-tagged proteins using FLAG beads. The recruitment of the various FLAG-DNAJC5 constructs to the beads should be comparable—consistent with what is shown for FLAG-DNAJC5 WT, L115R, and L116?—in order to confidently conclude that the co-immunoprecipitation demonstrates CHIP can bind both WT DNAJC5 and the CLN4 mutants independently of the J domain. Alternatively, if transfection efficiency or expression levels of FLAG-DNAJC5 ?J present an issue, the protein level of FLAG-DNAJC5 ?J in the input should be provided to clarify this point.<br /> 12. In Figure 4F, from the representative confocal images, Ci-L116? mutant in CHIP-KO appears to be localized also on cell periphery or boundary along with punctate localization. Also, it would be better to show the status of ubiquitin and HGS staining in CHIP’KO cells without any over-expression of CLN4 mutants to appreciate the role of CHIP in microautophagy of CLN4 mutants.<br /> 13. In Figure 4G, I and K, the figure legends for the graphs do not clarify how the normalization of the Ub and HGS areas was done with respect to their untransfected (UT) cells. Did the authors use neighbouring untransfected cells from the same coverslip or did they use a common untransfected control for all the samples? Also, it would be more informative to add the untransfected column in the graphs shown in Fig. 4G and Fig.4I, similar to Fig.4K, to have a better comparison in the data.<br /> 14. In Figure 5, the overall rescue effect of CHIP in this system is weak. Maybe the fact that their promoter only activates from d8 onwards is part of the problem? Would it be possible to start expressing CHIP earlier?<br /> 15. In Figure 5A, the authors overinterpret their results and claim from only the fact that CHIP is in the NP40-insoluble fraction that it must be inactive. Could they check whether it really ends up in the HMW L116? aggregates, and maybe even perform an in vitro assay to determine its activity in either version?<br /> 16. In Figure 5G, the authors show increased cell death in immature neurons which lack the lysosomal damage phenotype. What is the authors’ explanation for this phenotype? Is it linked to CHIP aggregates accumulation?<br /> 17. In Figure 6, how do the different levels of lysosomal translocation make sense with their model? Shouldn’t the brain have the lowest level of translocation, since this is the only tissue where a phenotype occurs?<br /> 18. We would recommend moving Figure 7 to the extended data, and spend more time in the text to explain the relevance of Tsg101. Currently the figure comes a bit unexpected and does not allow the authors a strong finish to the paper, since the phenotypes are less convincing than the ones in Figure 6. In addition, is there any quantitative analysis of the rough eye phenotype that can give a more objective assessment for the phenotype?<br /> Minor comments<br /> ? In Figure 2F, the authors should also show the WT DNAJC5-treated cells. It will make the data more complete and solid, confirming that the WT isoform is not interfering with lysosomal homeostasis.<br /> ? In Figure 2H, in the L116?mono panel a control cell (intact) is missing. <br /> ? There are no supporting images for Figure 5H (also not in extended figure).<br /> ? In Extended Figure 6B and C, why does the WT go down too? Showing the graph like this is a little confusing, and also the normalisation.. and they should add also earlier time point to see if at d12 something is happening<br /> ? Try to avoid using red-green as a combination in figures, to make the paper accessible to colorblind people.<br /> ? Authors can homogenize how they show statistics in their graphs, either deciding to not show the p-value when it is not significant or to include it every time. Also, why do they often use n=2 and do statistics on individual data points? Why not add n=3 and do statistics on experiments?<br /> ? The authors claim a microautophagy-based system that cleans up the DNAJC5 aggregates, which end up inside lysosomes. However, if the aggregates can damage the lysosome membrane from outside, why would they not do the same from the inside?

On 2020-02-03 17:15:55, user ncc wrote:

Hi

Nice setup, and well described. I enjoyed reading the paper. However, I disagree with the overall focus: that high-throughput, or obtaining the maximum number of replicates, is a desirable goal in respirometry.

I have built similar IFT setups, as have *many* others. I would suggest the only really "innovative" aspect of this setup is that you are utilising the downtime during flushes to measure oxygen in different chambers. That is, getting maximum utility out of your 10 channels. However, this is only useful in **very** limited circumstances where the measurement period is comparable in length to the flush period. That is, species with very high metabolic rates and/or at high temperatures.

However, i would question the entire practice of doing flushes so frequently if this is the case. It suggests to me your chambers are not large enough if the oxygen is being depleted so rapidly.

High throughput should not be an end in and of itself if the resulting data are not representative of SMR or RMR. This is especially true of experiments where the specimen may be easily disturbed, as in fish respirometry. Are you absolutely sure your fish were not disturbed by the pumps coming on every 8 minutes, either via increased vibration, sensing the water has been changed, or changes to the water flow patterns? Is the 1 minute of data you exclude from the start of each measurement period sufficient time for the specimen to resume "normal" behaviour after a flush?

I have recently run IFR flow experiments on a fish. These were on a temperate species, in a fairly large relative volume and took roughly 2-3h to show a decrease of around 10%, whereas a flush took 5 minutes. However, we found that for around 2h after a flush, the fish's metabolic rate was still decreasing, that is, it was still elevated and had not yet reached what could be defined as RMR, let alone SMR. As a result we decreased our replicates from 4x 2h replicates to to 2x 3/4h ones, and as a result got much more consistent data. In this case, high throughput, numerous replicates would not have given us a better estimate of RMR, in fact would have provided a much worse one. Every species is different, but i would 100% **always** choose fewer longer duration replicates, than numerous high-throughput replicates as described here.

A few other points:

There is NO fundamental difference between closed and IFR respirometry. IFR is simply having an apparatus that allows for multiple, sequential closed respirometry experiments to be run easily, minimising disturbance to the specimen. They are otherwise identical in nature. IFR respirometry is simply multiple closed respirometry experiments, and comes with *exactly* the same drawbacks that you suggest for "closed" respirometry. How important these are or if they are of no consequence at all depends on multiple factors in the experiment: the organism, water volume, duration, temperature etc, but most importantly the oxygen saturation level the experiment is allowed to reach. It is **completely incorrect** to say closed chamber respirometry is inherently associated with accumulation of nitrogenous waste and carbon dioxide, and increased stress, and that IFR is not. You can have these occur in both methods depending on how low long the experiment proceeds.

The article you cite here (Snyder et al. 2016) is concerned with a completely different question, that of critical oxygen tensions, and the difference between *methods of inducing hypoxia*, either via degassing with nitrogen or via the animals own metabolism. This study is *not* a comparison of these two methods for determining SMR or RMR, but for determining hypoxia tolerance.

I have run many "closed" respirometry experiments over long durations where oxygen decreased by only a few percent, and there was negligible build-up of waste or CO2. Given these experiments allowed specimens to be completely undisturbed for many hours, I would argue this is more likely to provide better estimates of SMR or RMR than any number of high-throughput, replicates that this or other IFR methods may produce.

Whether or not to use closed or IFR methods is a mostly practical question, but fundamentally these methods are exactly the same.

Other comments:

• You mention no correction for tubing volume. The water volume of each experimental loop consists of the water in the chamber plus that in the tubing in the loop. If this was exactly the same for all chambers then that is an easy correction. However the fact that (according to your schematic) your recirculation pump was at one end of the apparatus suggests a possibility there might have been different lengths for the close chambers than the ones furthest away, which will cause a systemic error. Happy to hear otherwise, but either way it is a necessary correction (i don't see it mentioned in the R script either, but there the volume is 0.375 not 0.300, so maybe this is it?)

• You also mention no correction for fish displacement volume. Your 300mL chamber does not contain 300mL once you put the fish in. The fish displaces some of the volume, and bigger fish will displace relatively more, so this leads to systemic error across body size ranges. Working from your data sheet, this is anything from 2-8% of the volume (assuming the fish are roughly neutrally buoyant) which would cause a misestimate of oxygen use, directly biased towards larger specimens. Your true "effective volume" is the chamber volume, plus tubing volume, minus fish volume.

• There are at least two open-source software solutions for conducting and reporting respirometry analyses (full disclosure - i am developer of one of them) which you should mention:

Harianto, J., Carey, N. & Byrne, M. respR -An R package for the manipulation and analysis of respirometry data. Methods Ecol. Evol. 10, 912–920 (2019).

Morozov, S., McCairns, R. J. S. & Merilä, J. FishResp: R package and GUI application for analysis of aquatic respirometry data. Conserv. Physiol. 7, (2019).

These allow investigators to report their analyses transparently and in reproducible form. Investigators who are skilled coders might choose to use their own workflows, but these are aimed at those who are not. I have another package with some utility functions: https://github.com/nicholas...

Please do get in touch if this was useful. Happy to discuss these aspects more!

Regards, Nick

On 2016-03-17 19:29:25, user Fabien Campagne wrote:

I disagree with the recommendation to use BioConductor as stated by the authors (section 3, page 11, frameworks). BioConductor is a great option in R, but it is not easy to obtain previous releases of BioConductor and the packages that it offers. If you need computational reproducibility, it is not trivial at all to obtain specific versions of a BioConductor environment. I recommend that the authors try to put their solutions to the test before recommending them. My group experienced many dependency installation issues with BioConductor, including the inability of the release servers to tag URLs with versions, so that even source code cannot be retrieved reliably in the future. <br /> We now routinely create docker images that contain R, BioConductor and a specific set of packages. This is the best way we found to achieve computational reproducibility with R.

On 2017-01-13 19:53:58, user Manuel Tardáguila wrote:

This is one of the best comparisons I have ever seen for a cutting edge technique. I don't know why it has not made it yet to the major journals.

On 2020-07-29 13:23:01, user Jamie Carpenter wrote:

This ensemble ML/AI method sounds really interesting as it appears to handle large multi-dimensional data by compressive-sensing-like stochastic sampling of subsets of manageable size. The reported convergence is very encouraging and hopefully this can be backed up with a rigorous mathematical derivation (e.g., upper bounds on bias, prevision, variances, information, etc.) I wonder if the code is available for community testing and independent validation.

On 2021-01-19 16:53:03, user antonio_j_p_rez wrote:

It has been already publish in Briefings in Bioinformatics:<br /> https://academic.oup.com/bi...

We hope that some of the proposed regulators can be useful as a putative target in the fighting against SARS-CoV-2.

On 2021-10-07 19:41:10, user aquape wrote:

Congratulations with this paper that beautifully explains *how* we lost our tail. The *why* is perhaps less difficult: Miocene Hominoidea were "aquarboreal" (aqua=water, arbor=tree) in swamp forests: they frequently waded bipedally with stretched legs, and climbed arms overhead in the branches above their head. Nasalis larvatus (proboscis monkeys) often wade upright in mangrove forests, and already evolved shortened tails. Aquarborealism also helps explain why we became much larger than monkeys, why hominoids are also called Latisternalia ("broad-breastboned ones"), why humans & apes have broad thorax & pelvis, with dorsal scapulas, lateral movements of arms & legs, and more centrally-placed vertebral spines (monkeys have narrow bodies, laterally-places scapulas, dorsally-placed spines etc.). See e.g. our Trends paper (TREE 17:212-217), google "Aquarboreal Ancestors".

On 2015-05-24 02:44:16, user Matthew Kosak wrote:

My question is regarding the methodology behind the paper, an "upstream" issue as to why the authors chose to select only certain genes such as the SLC...A5 and SLC...A2 as opposed to looking at many other genes that may have been available in the samples. A central working hypothesis is that these genes in question gave some advantage, that led to skin lightening and possibly greater Vitamin D production. The question is, how can other genes like bcl-2 and P53 genes be excluded as being extremely important, to "Eight thousand years of Nat Selection..." since these are critical to modulating cancer susceptibility (see Genta studies etc on cancer) and would be it seems, more activated when skin lost pigmentation? Would the risk of higher cancer rates not offset advantage of Vitamin D production? It is not a mutually exclusive issue, it is a matter of why as I ask above, the methodology does not require looking at these other critical genes in the skin, and their impact. So how can they be excluded from the study?

On 2019-03-26 21:12:48, user Charles Warden wrote:

Interesting study - it caught my eye that the impact factor was not significantly correlated with the overall reporting score ("?=-0.07, p=0.52; Figure 2C"). I think this is true, but I don't believe I've seen anybody show that before.

On 2020-09-24 17:54:01, user Michael wrote:

According to NYULH policy, when using data or tools generated in the core in publications, talks, or grant applications, please acknowledge the Microscopy Core at New York University Langone Health. <br /> Please amend the Acknowledgements accordingly.

On 2020-10-26 11:44:57, user PlantGen Lab wrote:

Dear authors,<br /> thank you for sharing your preprint on source-sink relationships in wild vs. cultivated rice and its impact on vegetative vs. reproductive growth. While discussing your manuscript in our journal club, we have noticed that the investigated wild species, O. australiensis, has a perennial growth habit. There are several studies describing differences in the source-sink balance between annual vs. perennial plants. Could you comment on how your presented results align with previous findings on annuals vs. perennials? In line, we were also wondering if you considered the root as major storage organ?

We would gladly hear your view on this topic.

Kind regards from the Plant Genetics lab at the Heinrich Heine University in Düsseldorf

On 2021-07-13 03:19:08, user shenzheng mo wrote:

The results are different from previous studies:Summersgill H, England H, Lopez-Castejon G, Lawrence CB, Luheshi NM, Pahle J, Mendes P, Brough D. Zinc depletion regulates the processing and secretion of IL-1?. Cell Death Dis. 2014 Jan 30;5(1):e1040. doi: 10.1038/cddis.2013.547. PMID: 24481454; PMCID: PMC4040701.