On 2020-03-11 22:35:54, user SRIDHAR MANI wrote:

This paper is now published in full format Open Access (final) here:

https://www.embopress.org/d...

On 2022-11-16 05:53:33, user disqus_8AVEuorTBu wrote:

You did clearly state the 8kb max, which is why I never suggested removing the 3rd BsmBI site. However, removing the adjacent BsaI does not increase the max fragment length above 8kb or in fact at all (max remains 7578bp).<br /> To summarize, I argued that only 1 site is required here and that 2 would make engineering more difficult. You half responded by stating that removing the 1st of these 2 sites wouldn't work, yet you omitted the obvious next option of what if the 2nd of these 2 sites were removed. Again, please explain why any engineer would want or need to separate this tiny, highly conserved fragment.

On 2021-05-04 15:06:24, user AAAAAAAAAA wrote:

I noticed that you did the high salt tagmentation (300mM NaCl) for PBMC mixing experiments, which I think is the "right" way to avoid the open chromatin bias but for other experiments, you did the tagmentation in 10X ATAC buffer (10mM NaCl). Is there a particular reason for this? I thought the low salt would have serious ATAC signals, which is demonstrated in the original CUT&Tag paper.....

On 2024-03-26 10:05:44, user Davidski wrote:

Hello authors,

Your preprint claims that present-day Hungarians are genetically similar to Scythians, and that this is consistent with the arrival of Magyars, Avars and other eastern groups in this part of Europe.

However, present-day Hungarians are overwhelmingly derived from Slavic and German peasants from nearby Hungary. This is not a controversial claim on my part; it's backed up by historical sources and a wide range of genetic analyses.

Hungarians still show some minor ancestry from Hungarian Conquerors (early Magyars), but this signal only reliably shows up in large surveys of Y-chromosome samples.

The Scythians that you used to model the ancestry of present-day Hungarians are of local, Pannonian origin, and they don't show any eastern nomad ancestry. So they're either acculturated Scythians, or, more likely, wrongly classified as Scythians by archeologists.

And since these so-called Scythians lack eastern nomad ancestry, the similarity between them and present-day Hungarians is not a sign of the impact from Avars, Hungarian Conquerors and the like, but rather a lack of significant input from such groups in present-day Hungarians.

I've done a rather long blog post about your analysis of Medieval Poles and present-day Hungarians at the link below. Hopefully you'll find it useful.

https://eurogenes.blogspot....

On 2020-12-30 01:04:52, user Manuel Ricci wrote:

This can confirm that these functional mutations can happen in long term infections under a selective pressure applied by antiviral drugs, plasma, etc...not in normal infections that resolve, for the best or the worst, within 30 day

On 2021-03-14 10:28:06, user Charly wrote:

To prevent mutations from occurring by calculating them in advance, quantum physics is required.

On 2021-02-27 03:36:52, user Manwa Li wrote:

Japan has developed a Antiviral Coating Agent Prestige Shield ( http://www.darg.com.hk/page... ) which contain copper nanoparticles in the market mainly on contruction site and hotels. Mr. Miyamoto start studing Copper against influenza virus in 1998.

Let's fight against COVID-19 together! Please contact me email: manwa@darg.com.hk

On 2019-05-06 13:37:42, user Ruslan wrote:

Hi<br /> It is clear that having an isotropic dataset makes life much easier.<br /> But which algorithm did you use to downsample the XY images to 3um?<br /> As far as I understood, you original pixel size was 1.625m, how did you get 3um (and not 3,25um)?<br /> Best regards<br /> Ruslan<br /> P.S: contrast-based microCT is already able to show it :)

On 2018-12-05 17:08:23, user ezra smith wrote:

Have you considered examining low (~9 Hz) vs high (~11 Hz) alpha subbands? I wonder if it is the case that low alpha corresponds to your occipital source, and high alpha corresponds to the parietal source. Interesting work!

On 2022-03-29 09:25:53, user Daniel Baldauf wrote:

Congrats, nice study! I was curious about your analysis of the speech tracking, especially in the experimental condition where the speech signal was mixed with noise. I wondered though what role attentional processes might play in this task, particularly, when it comes to parsing works in the noisy environment. I was a bit surprised that you don’t quite discuss those. For example, recently Marinato & Baldauf (2019, Sci.Rep.) used similar stimuli, ie. a speech signal mixed with an environmental 'sound-scene', and showed that top-down object-based attention has a strong effect on the parsing of the language stream. DeVries et al. (2021, JN) then also recorded MEG during such a task, showing that it is particularly the alpha band in a fronto-temporal network that mediates these functions of object-based attention to words, and that allows for the trial-wise decoding of the locus of attention. Maybe that is helpful.

On 2020-11-03 04:48:03, user Naveen v wrote:

We have recently published the structural work on SARS-COV-2 Nsp1 and 5'UTR complex. It is good to see the consistency in the work. https://pubs.acs.org/doi/fu...

On 2015-04-03 14:10:26, user D Samuel Schwarzkopf wrote:

I reformatted this manuscript to have Methods between Intro and Results as any proper paper should be. I further tried to clarify a few points that may have been confusing. I also replaced Figure 2 with a version in which the X-axes are fixed. I hope this makes the difference between the three examples clearer.

On 2015-11-13 13:13:44, user Mike Taylor wrote:

Dear authors,

Please revise your abstract to say what the RCR actually IS. It's not enough to tell us how wonderful it is.

Thanks.

On 2017-11-09 12:09:26, user Pat Schloss wrote:

The preprint by Robin Rowher and colleagues seeks to develop a workflow that complements methods for classifying 16S rRNA gene sequences with greater precision than is found in the Wang naive Bayesian classifier. This is an issue that many people have raised with me. A lack of classification for a sequence can be blamed on inadequacies of the taxa represented in the database, lack of taxonomic data (e.g. at the species level) within the database, and the selection of the region within the 16S rRNA gene to classify. This paper seems primarily concerned with the first problem by supplementing ecosystem-specific sequences and touches on the second problem by adding finer taxonomic information for the ecosystem-specific sequences. I felt like the authors were a bit conflicted over what they wanted this manuscript to be. Is it a description/announcement of a new method, TaxAss? Is it a validation study? Is it a benchmarking study? Overall, it is a description of a new method that is being used by the authors and others. However, I feel like it needs some help to improve the description as there are points in the manuscript that are not clear. Furthermore, I felt that the validation and benchmarking could use some help to quantify the need for the method and to demonstrate that the method overcomes that problem.

General comments...

1. As described in Figure 1, sequences that fall below an empirically determined threshold when compared to an ecosystem-specific database are classified using a comprehensive database and those that are above the threshold are classified against the ecosystem-specific database. Perhaps it's because I'm familiar with people using blastn to classify sequences, as I read the manuscript, it was not clear whether the sequences in the two arms were then classified using the Wang method or blastn. Reading through the source code, it looks like blastn is only used to split the dataset and once split, the data are classified using the Wang method. Perhaps this could be clarified in the text.

2. It is not clear how the FreshTrain database was developed or how it is curated to add finer taxonomic names to the sequences. The authors have done this for the readers who are interested in fresh water bacteria, but what steps should someone interested in gut microbiota take to recreate the database to classify their data? More importantly, how did the authors decide on their taxonomic levels of lineage, clade, and tribe? Why not follow the phylogenetic approach used by the greengenes developers for defining family, genus, and species for environmental sequences?

3. I am also not clear why the authors did not want to pool FreshTrain with one of the comprehensive databases. A simple cat command would pool the two files producing a file that could then be used as a single reference. The downside of this would be that they would need to add the same level of taxonomic detail that is in the FreshTrain database to the greengenes database. Also, a downside of the greengenes database is that the core reference appears to be moth balled going forward while RDP and SILVA are still actively being developed.

4. One motivation that the authors state for the method is the issue of "forcing". I would call these "false positives", but I get their point. The authors raise this issue numerous times. Yet I was unable to find a citation that quantifies forcing and the authors do not appear to measure the amount of forcing in their data. Perhaps this is what they were getting at in Figure 3? If that is the case, then I am a bit troubled because they are accepting the FreshTrain data as the ground truth, when it has not been validated yet. I could also imagine that even with FreshTrain, there might be forcing if a taxonomic name is set for the full length sequence, but two variable region sequences are identical even though their parent sequences have different taxonomies. More importantly, the source code indicates that the authors are using any confidence score with out applying a filter. The suggested confidence score is 80%, not 0%. I don't think that the problem with classifications from the Wang method is forcing, rather, it's that the classifications don't go deep enough. Something may classify as a Bacillus with 20% confidence and so researchers should work their way up the taxonomy until the classification is above 80%, which might be Firmicutes. In offline conversations with the authors, they reassured me that they are applying an 80% threshold in separate scripts. It would probably be worth adding that they are using 80% as a threshold in the Methods seciton.

5. Related to this point, at L122 the authors state that "In a large database an OTU dissimilar to any reference sequences will not be classified repeatably as any one taxon, resulting in a low bootstrap confidence." This is correct, but is a bit misleading. I would suggest saying "...repeatedly as any one genus, resulting in a low bootstrap confidence and reclassification at a higher taxonomic level where there is sufficient bootstrap confidence". I am concerned that the results and the discussion of forcing are based on not using a confidence threshold rather than the default 80% threshold.

6. To measure forcing, I would like to see the authors run the greengenes and FreshTrain databases back through the classifier using a leave-one-out testing procedure and quantify how many times the incorrect classification is given, when using the 80% (or even the 0%) threshold. Again, I suspect the results would indicate that the problem isn't one of forcing, but of "holding back". To be clear, this isn't necessarily a problem with the Wang method, but the databases. Addressing this point is where I think the authors could really do the field a service. It would be a really helpful contribution to show the percentage of forcing (false positives) and holding back (false negatives?) in a leave-one-out scheme and on a real dataset when classifying with (1) each of the comprehensive databases, (2) using TaxAss with the comprehensive databases and FreshTrain, (3) merging the comprehensive databases with FreshTrain and running them through the Wang classifier.

7. I am not sure what the authors mean by "maintaining richness" as they use it in the manuscript. Could the problem they are trying to address be described better? Also, I would ask whether they know what the *true* richness is and if not, why they think that one value of richness is better than another. Perhaps this corresponds to what I might call "underclassifciation" or "false negatives".

L25 - why not include the RDP reference database in this list?

L49 - "Course" should be "Coarse"

On 2020-08-22 04:43:33, user Masahiro Nakano wrote:

Reference for Gilbert and Li (2013) is missing!

On 2019-05-28 16:04:37, user Mia Shin wrote:

Members of the Lander Lab at Scripps Research in La Jolla, California discussed this manuscript at a Journal Club and would like to share our thoughts with the authors as well as the broader scientific community.

In this manuscript, Rubinstein et al introduce “Shake-it-off,” a cryo-EM specimen preparation device assembled by the authors using parts from an ultrasonic humidifier, homemade self-wicking EM grids, 3D printed parts, and a Raspberry Pi single board computer. Notably, all components can be manufactured using open-source files shared on the internet or readily purchased. Rubinstein stated via Twitter (@RubinsteinJohn) that the SIO device was constructed for ~$1000 Canadian dollars (approximately $740 US dollars). He added, “This is #frugalscience.” Indeed, SIO is remarkably less expensive than the cryo-EM specimen preparation devices that are commonly used by the cryo-EM community, indicating that this device could readily be adopted in-house by any lab interested in pursuing cryo-EM.

The SIO device attempts to address several substantial limitations that the community currently faces during cryo-EM specimen preparation using traditional blot-plunge devices: 1) more than 99.9% of the (often precious) sample applied to the EM grid is blotted away and trashed, 2) the plunge-freezing process occurs on the timescale of several seconds to minutes, which can lead to problematic air-water interface interactions for macromolecules (preferred orientation, complex disassembly, denaturation/aggregation, etc.), and 3) questionable reproducibility of ice thickness from grid to grid. According to the authors of the manuscript, only 1 uL of sample is required for applying sample to the ultrasonic humidifier, a 3-4-fold reduction in wasted starting material. The authors report plunge-freezing by SIO is on the timescale of 100 ms, which should substantially reduce hydrophic effects from the air-water interface compared to traditional blotting. Additionally, the resulting grids appear to consistently have a large “mountain” of frozen sample with a ring of optimal ice for data acquisition at its periphery, thereby reproducibly delineating where to image on each grid.

The authors used SIO to prepare samples of equine apoferritin that resolved to better than 3 Å resolution, demonstrating the device’s ability to vitrify robust samples for high resolution cryo-EM analyses. As SIO appears to address long-standing specimen preparation problems faced by the cryo-EM community for a fraction (a very small fraction!) of the price of the sophisticated Spotiton/Chameleon devices, we are excited about this promising design and its potential to revolutionize specimen preparation for cryo-EM labs worldwide.

However, we have several points we’d like the authors to address prior to this manuscript’s publication in a peer-reviewed journal:

Major Points:<br /> 1. Figure 4 shows two atlases of apoferritin grids prepared using SIO. Both grids have a circular “mountain” of ice that occupies ~25% of the grid area circumvented by a narrow region of suitable ice for high-resolution imaging. We are curious about the reproducibility of these types of grids. Although these two grids appear nearly identical, do grids containing different protein sample with different buffer conditions (e.g. salts and detergents) produce the same “mountain” of ice phenotype? Additionally, we invite the authors to speculate as to the origins of this region of thick ice, and whether or not it can be correlated to a region of the ultrasonic spray that may emit larger droplets than other regions. Presumably the drops released from the piezo are smaller than this mountain, suggesting that the drops puddle together during the plunging? Could this happen within 100 ms? Perhaps if this is the case, the ultrasonic spray can be re-positioned or improved such that a larger region of the grid may be amenable for high-resolution imaging.<br /> 2. While the authors were able to resolve samples of equine apoferritin prepared using SIO to sub-3A resolution, we would really like to know whether its purported plunge-freezing speed is able to overcome preferred specimen orientation at the air-water interface, as has been reported for Spotiton/Chameleon. In addition to the benefits of using less sample and reproducibility, minimizing air-water interactions would likely be one of the primary motivations for other groups to build their own SIO plunge-freezing device. We request that the authors include images of hemagglutinin, a sample that exhibits pathological preferred orientation (Tan et al., Nat. Methods 2017), to test SIO’s ability to overcome preferred specimen orientation. Additionally, the inclusion of tilt-series to assess the percentage of proteins associated with the air-water interface (a la Noble et al., eLife 2018) would be greatly informative.

Minor Points:<br /> 1. Is the micrograph shown in Figure 4C representative for the dataset or is the best micrograph. Near the bottom left quadrant, there is a circular area characteristic of protein denaturation. While we see this routinely in our micrographs from a variety of samples and datasets, particularly in areas where the ice is very thin, it would be troubling if this type of pathology was present in many or most micrographs (or worse).<br /> 2. How is the piezo device is cleaned after each sample is prepared? We are able to see in the GUI that there is a button for cleaning the piezo device, but no description of the mechanism was found.<br /> 3. The 1 uL droplet that is applied to the piezo device is obviously much smaller than the surface area of the ultrasonic humidifier. Does one achieve different results depending on location of sample placement on the piezo device? If this is the case, perhaps there can be modifications to the design of the piezo device to identify the optimal location for sample placement for spraying.<br /> 4. Lastly, is there a potential health hazard associated with the ultrasonic spray emitting sample. Do you recommend the user to use a mask or placing SIO into a covered chamber to avoid any aerosol contamination being emitted from the device, particularly in the case of BSL-2 level samples (i.e. prions)?

On 2019-10-15 23:21:53, user sheep wrote:

Figure1C, almost all the Math5-Brn3b-GFP infected MGs reprogrammed into RGCs (as there is no GFP in inner nuclear layer, also no GFP+ process reach to outer nuclear layer), however, based on their Sox9 staining on Math5-Brn3b-GFP infected samples, almost all the MGs still at the INL, and they are still Sox9+. The authors were trying to hard!!!

On 2022-09-14 20:55:58, user Hannah wrote:

Really interesting integration of single-cell and MPRA techniques. More information about how these CRS's were chosen would be helpful.

I would be curious to see how these promoter activity changes you observe in the HEK293 cells and K562 cells also carry over to changes in the expression of the genes that these promoters control.

For the cell cycle analyses, have you looked at what transcription factors might be driving these changes in promoter activity?

On 2019-03-27 12:42:22, user Abhishek Dutta wrote:

The current version of the paper does not come with the figures? I mean some of us may just click on the current version and not look at the older version that contained them. Just a minor inconvenience.

On 2017-10-30 08:10:06, user Florian Heigwer wrote:

Dear Aritra Chowdhury,

I read your paper and found that you took a very interesting approach. Sadly, I could not find any link, software or explanation how I could realize your method in practice. Are you planning to provide this as a imageJ plug-in or rather a stand alone?

I would be very happy to be an alpha tester.

Best,<br /> Florian

On 2019-05-28 09:14:29, user Mikko Rautiainen wrote:

Please make it clear in the text that the GraphAligner in your comparison is not actually GraphAligner, but just the bit-parallel DP extension algorithm. I recommend using terms like "bit-parallel" or "Rautiainen et al." or something else that won't get confused with GraphAligner.

I ran GraphAligner (version 1.0.7, bioconda, "/usr/bin/time -v GraphAligner -g MHC1.vg -f M3.fastq -a alns.gam -t 4") on your MHC1 graph and M3 reads. It aligned all reads in 2 minutes cpu-time (30 sec wall-time) on my laptop. This might be an another software for your seed-and-extend heuristic comparison. Since the alignments are outputted in .gam format, you can even reuse your pipeline for comparing to vg.

On 2021-03-13 01:52:43, user Arturo Liñan wrote:

Dear,

Could you please provide the supplementary material?

On 2021-07-08 02:23:10, user Lifeng Kang wrote:

This work has been published on International Journal of Pharmaceutics. Please find the peer-reviewed version here: https://doi.org/10.1016/j.i...

On 2017-03-06 10:31:55, user Alissa Mittnik wrote:

Hi Davidski,

thanks for your comment!

"So much so that your Baltic foragers cluster with modern Europeans, which contradicts your own formal statistics."

Are you referring to the Baltic BA, since the Baltic foragers cluster with WHG far from any modern Europeans? However the BA are quite recent with good coverage and shouldn't be affected by shrinkage as much.

"Try using a subset of the modern samples as references, and then project<br /> both the ancient samples and another subset of modern samples using <br /> lsqproject."

This is actually how it was done, the plotted modern populations are only the reference subset used to create the PCs, while a larger set of moderns and ancients are projected. Other projected modern Eurasians fall as expected in PCA space.

Cheers,<br /> Alissa

On 2016-04-08 17:06:17, user Tim Triche, Jr. wrote:

We might need to revisit the name. Thank you for the feedback on that. Suggestions are certainly welcome though not necessarily expected. This manuscript will see substantial revision; perhaps the name as well.

On 2019-04-24 09:07:25, user Johannes Soeding wrote:

The parameters for the program were optimized on the same 54 genomes that were also used as test set. This is very problematic as it can give too optimistic results. You need a test set that is independent of the set used for optimisation.

On 2021-09-22 10:23:04, user Kathy.Dibley wrote:

Hello, I have read this and the Custódio et al pre-print with much interest- thank you for sharing this research here.The finding of a Cl binding site is of particular interest. <br /> Can you please keep us updated here as to when this work has been published via peer review, as we intend to cite both in an upcoming research article on STP function.

On 2020-03-29 23:12:09, user Tyler A. Elliott wrote:

Was interesting to hear about the classification discordance between different databases, it would be great to have more details on that, especially so that the databases or users of the databases can be aware of this. Also having more details on possible mis-classification from the confusion matrices might be useful in a similar sense. Very impressive results though.

On 2016-05-19 20:45:21, user Arpiar Saunders wrote:

What a creative, wonderful tool and impressive proof-of-concept application to boot. I'm new to biorxiv, so not sure this is where I'm supposed to post nit picky things or just email the authors directly. Anyway, I noticed in Fig. 1 B and D the "number of cells" i believe should be "number of reads" as no UMIs were used and would be more consistent with the legend.

On 2017-10-28 16:52:07, user Lionel Christiaen wrote:

Student #7<br /> Previously shown that distal enhancers and promoters have physical, as well as functional interactions using 3C and genetic approaches. However, the dynamics of these interactions and the requirement of enhancer-promoter proximity for transcriptional activation was not well understood. The authors wanted to determine if proximity was required for transcription or whether it was a result of transcription, as well as if transient or sustained contact was required for activation. To address these questions, they used genome editing, genetics, and live single-cell imaging of transgenic embryos using MS2, PP7 and parS.<br /> They found that enhancer-promoter proximity is required for transcriptional activation, rather than being a consequence of activation. Similarly, they found that sustained physical association of the enhancer and promoter is required for activation, and that upon their dissociation, transcriptional activity stops. In addition to the requirement of close proximity, they found that the topological arrangement also plays a large role in whether or not transcription will be activated. Lastly, they found that there is a competition between promoters using the same enhancer, which can cause developmental defects by reducing expression from the endogenous loci.<br /> They conclude that enhancer-promoter proximity is required for transcriptional activation and that a sustained, rather than transient, contact is required for activation. Similarly, they provide evidence that the topology of the chromatin plays a large role in activation and that TADs are likely involved in this process. They also state that physical interactions between enhancers and promoters could be a key rate-limiting step in gene regulation.

Innovation<br /> Technically innovative in their combination of MS2, PP7 and parS to mark active transcription and the eve and lacZ loci.

Major<br /> Why does distance between eve and parS differ for each enhancer if homie is responsible for bringing them in proximity? Shouldn’t the distance be invariable if dependent on homie-homie interactions?

Is there any way to show interactions between the different eve enhancers and the lacZ transgene?<br /> TF-GFP fusions if there are TFs specific to each enhancer?

Minor<br /> Results, 1st paragraph: missing the ’ in 3’ end<br /> 1c is not in figure 1 legend, it says bottom panel for B, when it is actually labelled C.<br /> Page 3, 1st paragraph: missing the ’ in 5’ end<br /> Page 3, 2nd paragraph: 2-hour* old<br /> Page 5, last paragraph: single* nuclei<br /> Page 8, 2nd paragraph, missing the ’ in 3’ end<br /> Page 8, last paragraph: change “inactivate to active” to inactive to active

On 2019-10-02 16:14:12, user Michael Lichten wrote:

Note that another bioRxiv posting (https://www.biorxiv.org/con... "https://www.biorxiv.org/content/10.1101/778100v1)") observes similar structures in meiotic yeast cells but identifies them as actin filaments, observes them in both nucleus and cytoplasm.

On 2019-11-15 10:10:14, user Alexis Verger wrote:

This work is a nice addition to the previous work by the Hahn and Klevit's labs deciphering the molecular mechanisms of Mediator complex subunits recruitment by transactivation domains (TADs). Here they show that despite no clear sequence homology, the Gal4 and Gcn4 TADs bind the same surface of Med15 ABDs domains. In addition the same region of Gal4 TAD was known to interact with Gal80 via a tight structured complex, suggesting that the structured binding partner of an intrinsically disordered protein (IDP) dictates the type of interaction.

Please find below some general comments that I hope will be of some interest.

• I am not completely convinced that the interfaces of the Med15 ABDs with Gcn4 and Gal4 is sequence-independent. Analysis of the backbone chemical shifts for the Gal4/ABD1 complex indicate that residues 861-869 adopt helical structure upon binding (Figure 3D). This is reminiscent of the situation observed between Gcn4 cTAD/ABD1 that adopts helical character upon binding Med15 (Brzovic et al. 2011). Intriguingly, as stated by the authors, region 2 of Gal4 TAD contains a sequence (YNYLF) included in the helical structure and that resembles the short motif in GCN4 cTAD (WXXLF) critical to bind ABD1 hydrophobic cleft. Interestingly mutation of Gal4 TAD YLF to AAA has a strong effect in transcription activation (Figure 2) and Y865W (mimicking WXXLF of GCN4) possesses higher transcription activity compared to wt. It could be interesting to test in FP and/or ITC the effects of YLF ->AAA and Y865W mutations and see if there is a correlation between transcriptional activation potential and Med15 binding.

• Given that the affinity and binding mode of GCN4 and Gal4 TADs for MED15 are similar (Table 1), did the authors try competition experiments to investigate whether GCN4 TAD can compete with Gal4 TAD for binding to Med15 (and maybe implying the WXXLF motif )?

• Concerning Table 1 - it could be nice to add 1 or 2 ITC curves for better illustration. My understanding is that protein concentrations used for ITC are quite high (mM) 500X above the Kd.

• I understand that Gal4 TAD 828-881 is soluble but not 840-881. Did you try a shorter version 855-870 (corresponding roughly to the helical structure) in NMR ?

• The Gal4 region that binds to Gal80 overlaps with the one that binds to Med15. Is the Gal80 interface very different from the ABDs interface ?

On 2022-08-26 13:40:17, user Matt Higgins wrote:

We were extremely interested to see these impressive structures of a chimeric Sec translocon, held in a state ready for post-translational translocation through interaction with Sec62/63, bound to eight different inhibitors. Particularly noteworthy to us is that the inhibitor mycolactone binds in a different location in this study when compared with our previous structure of the same inhibitor bound to a ribosome-bound translocon, primed for co-translational translocation (Gerard et al Molecular Cell 79 406-15).

The authors speculate that “Our data suggest that the density feature previously assigned as mycolactone is unlikely to be mycolactone.” While it is true that our previous structure has a resolution of ~5A in the region of the map attributed to mycolactone, and therefore also true that we cannot unambiguously place mycolactone in this density, we remain confident that this density is mycolactone for the following reasons:

(i) Our procedure involved incubation of microsomes with mycolactone at a concentration of ~0.3µM (compared with 100µM used by Itskanov) before detergent/digitonin treatment and purification of ribosome-associated Sec complexes. A similar sample was prepared without mycolactone. When these two protein complexes were studied by cryo-electron microscopy, the Sec translocon adopted a substantially different conformation when mycolactone-bound compared with free. The only difference between these two samples was the presence of mycolactone, indicating that this structural difference is due to mycolactone binding.

(ii) We confirmed the presence of mycolactone in our mycolactone-bound purified using mass spectrometry of a sample taken immediately before addition to grids for structural analysis.

(iii) Analysis of the electron density for the mycolactone-bound translocon did not reveal any density feature in the mycolactone-bound sample in the location of the binding site observed by Itskanov. Therefore ribosome-bound, mycolactone-bound translocon is different from Sec62/63-bound, mycolactone-bound translocon.

(iv) The only additional density feature observed in the ribosome-bound, mycolactone-bound translocon is that which we have attributed to mycolactone and molecular dynamics simulations confirm that mycolactone is stable in this binding site.

It is therefore our view that we did not misattribute the electron density into which we have placed mycolactone. Instead, it is our view that the difference between these two structures is likely to be genuine and mechanistically interesting.

There are possible technical differences which could account for the different binding sites observed when comparing our structure with that of Itskanov:

• While we added mycolactone to the Sec translocon while still in the native membrane environment of microsomes, and then extracted the mycolactone-bound complex, Itskanov added to mycolactone to translocon after its purification and integration into a non-lipid peptidisc. Our model for how mycolactone reaches its binding site in our system relies on translocon “breathing” within the physiological situation of a lipid bilayer, and mycolactone itself being present in this bilayer. It is not known if the translocon within a peptidisc is able to undertake similar “breathing”, nor how highly hydrophobic mycolactone may interact with this material.

• While we used native canine microsomes, Itzkanov et al used a hybrid translocon, comprised of human transmembrane regions and yeast extracellular regions. It is not known if this hybrid translocon is functional for translocation, or whether the translocation of model substrates by it is inhibited by mycolactone.

• There is also a large difference in mycolactone concentration used in the different studies. Mycolactone is effective at sub-nM concentrations on live cells. To provide sufficient molar ratios of mycolactone in concentrated microsomes, we used ~300nM in our studies, while Itskanov used the much higher concentration of 100µM mycolactone. It would be interesting to know whether this was the minimal concentration required for them to see binding, indicating a lower affinity binding site, or was simply the concentration selected.

While there are technical differences which might account for the different binding sites observed, there is also the far more interesting possibility that both studies have correctly identified binding sites for mycolactone and that this inhibitor acts differently in post-translational and co-translational translocation.

The Sec translocon can act through either a post-translational (involving Sec62/63) or a co-translational (involving ribosomes) mechanism. McKenna, Simmonds and High have previously shown (PMID 26869228) that mycolactone-mediated blockade is different in these two systems. While mycolactone shows a broad effect, preventing co-translational translocation of a wide range of substrates, it has a more restricted effect during post-translational translocation, only affecting translocation of a subset of substrates. Together with the differences in mycolactone binding between these two structures, this suggests the intriguing possibility mycolactone might have two different binding sites; perhaps one site which occurs during co-translational translocation where mycolactone is stably wedged into the cytosolic side of the lateral gate (Gerard et al), and one site which operates in post-translational translocation and is more easily overcome by signal peptide binding (Itskanov et al). Future studies will be required to test this intriguing possibility.

Sam Gerard, Matt Higgins and Rachel Simmonds

On 2019-03-26 17:36:07, user Charles Warden wrote:

I am a little hesitant to add a confident comment here, but I thought there were at least a couple Twitter threads that I should provide links to:

https://twitter.com/cwarden...

https://twitter.com/manuelr...

On 2016-01-25 10:47:07, user Andrew Warren wrote:

Attempting to download this pdf crashed my system in Chrome and in Firefox.

On 2020-08-08 16:14:24, user UAB BPJC wrote:

Review of Benda et al., “The YtrBCDEF ABC transporter is involved in the control of social activities in Bacillus subtilis. <br /> University of Alabama at Birmingham Bacterial Pathogenesis and Physiology Journal Club

Summary<br /> The model organism for genetic competence, Bacillus subtilis, has many uncharacterized methods of regulating its various alternative lifestyles, such as competence, sporulation, and biofilm formation. As far as competence goes, most of these methods interact in some way with the transcription factor ComK, whether directly or indirectly. This paper focuses on the characterization of the ytr operon and its role in the development of competence. <br /> Overall, the paper takes a sound and methodical approach to the genetic testing of the operon, and the role of the ytr operon it proposes is convincing. However, we do have some comments that may be beneficial to the paper if they are addressed.

General Comments<br /> • There seems to be a general lack of statistical analysis throughout the tables in the paper. This may be just a lack of explanation of the stats that were performed, but we found ourselves asking what the “+/-“ was referring to in the data points. Is this the standard deviation? A slightly more in-depth explanation of this would be helpful.<br /> • Towards the end of the paper, namely in the last two figures, we felt that the data seemed a little scarce in comparison to earlier tables. Since a wide variety of ytr mutants were created, would it be possible to analyze biofilm formation and cell wall thickness with them?

Specific Comments<br /> • In Table 4, we feel it would be very helpful to include a representative image of the fluorescence microscopy.<br /> • As mentioned in above, the cell wall thickness assay only includes two mutants in comparison to the wild-type, but it would be beneficial to include a wide range of the ytr mutants (especially the ytrAF knockout) and others that exhibited a low number of transformants to potentially determine a correlation of cell wall to transformation efficiency.<br /> • Also on Figure 3, it may be worth staining and imaging pili on the ytrA mutant, since it is speculated that the thicker cell wall phenotype could be inhibiting them.<br /> • In the text leading up to Table 4, it is mentioned that the RNases were not included because of their tendency to form chains. However, the nrnA mutant is till imaged. Is there a reason to believe it won’t form a chain unlike the mutants that aren’t included?

On 2022-01-06 16:15:09, user Dr. Martin Stoermer wrote:

Haven't taken it all in yet but I do have a quick Q. Why do you refer to the catalytic dyad as non-canonical when His41/Cys145 is pretty standard for most (all?) betacoronaviruses

On 2016-05-30 20:36:13, user Starman wrote:

Yawn. Some humans are rats, but rats aren't human. Good insights over here: http://theness.com/neurolog...

On 2019-06-11 01:47:01, user John Osei wrote:

Excellent, but I have some questions, although I am planning to adopt this procedure for my microbiome analysis: <br /> 1. Can the same approach be used for meta-transcriptomics of the microbiome i.e., after using the saponin treatment, can total RNA be isolated without being degraded or affected?<br /> 2. Can this be extended to the gut and other microbiome besides the lower respiratory microbiome?

On 2019-07-08 17:22:54, user Anna Schwabe wrote:

In response to George Weiblen

George Weiblen wrote: We suggest that readers of this article might also consider reasons to question the central claim that NIDA cannabis is genetically closer to hemp than to marijuana.

We respond: In our sampling, NIDA was found to be genetically closer to the hemp-type samples than the marijuana-type samples. This was seen in multiple analyses. We do not claim that NIDA is supplying hemp for marijuana research, but we are confident that our analyses show that the research grade marijuana supplied by NIDA is genetically different from the retail marijuana samples analyzed in this study.

“Our results clearly demonstrate that NIDA Cannabis samples are substantially different from most commercially available drug-type strains, sharing a genetic affinity with hemp samples in most analyses.”

“…this study highlights the genetic difference between research grade marijuana provided by NIDA and commercial Cannabis available to medical and recreational users.”

Given both genetic and chemotypic investigations have concluded that NIDA is supplying product that does not align with what is available for consumers, our hope is that NIH, NIDA, and the University of Mississippi take this into consideration. Medical practitioners, researchers and patients deserve access to marijuana products that reflect the products available on the legal market.

George Weiblen wrote: First, the authors do not report the cannabinoid profiles of their samples, so it is unclear whether the NIDA samples are marijuana-type, hemp type, or intermediate, nor did they specify the batch number. The NIDA Drug Supply Program has materials available from all major varieties of cannabis.

We respond: The lack of inclusion of sample batch numbers was an oversight on our part. The “research grade marijuana” plant material samples were labeled as: <br /> 1. High THC: RTI log number 13494-22, reference number SAF 027355.<br /> 2. High THC/CBD: RTI log number 13784-1114-18-6, reference number SAF 027355.

One of the aims of the study was to determine where the NIDA samples fell on the genetic spectrum of Cannabis types. The phytochemical content was not considered in this study because it is widely known that phytochemical constituents change due to environmental conditions, which include age, storage conditions, and storage temperature.

Furthermore, the samples from NIDA were ordered and are labeled as “research grade marijuana”, which should need no further investigation into whether the samples received were indeed marijuana-type, hemp type, or intermediate.

George Weiblen wrote: Second, there is inconsistency between the individual-based metrics and population-based metrics. Statistics for population subdivision (Fst) and genetic distance (Nei's D) in Table 1 do not agree with Figures 1-4 in supporting the central claim. For example, the Fst values of the NIDA samples are more differentiated from hemp than they are from the three drug-type subclasses. According to Nei's D, the NIDA samples are more similar to "hybrid" and "indica" drug-types than they are to hemp. The authors point out that the small sample size (N = 2) of NIDA varieties in their study is not sufficient to accurately estimate population-level parameters so they emphasize the individual-based results instead. This represents a bias on the part of the authors, who could request more samples from the NIDA Program to improve their sample size.

We reply: The “populations” are not true populations per se, but rather are commonly referenced usage groups. Given the high degree of hybridization among these groups, we do not necessarily consider the six groups as unique and separate populations. Hemp and drug-type cannabis groups have consistently been found by several studies to be genetically separate, and we feel these may be considered populations, but the rise in cannabidiol popularity has led to the development of several lines that are hybrids between the two types.

There were only two types of Cannabis from NIDA because that is what we had access to through one of our co-authors. We are not opposed to incorporating more NIDA samples into our analyses if NIDA would like to provide them. However, we feel that the two samples we examined have an interesting genetic profile given this is what was supplied to researchers conducting marijuana research and will possibly inspire further investigation of additional material supplied by NIDA.

George Weiblen wrote: The authors place more rhetorical weight on the individual-based approach by devoting four figures to it. It also possible, however, that the greater number of similar individuals in the drug-type samples could exaggerate their separation from much smaller numbers of NIDA and hemp samples of individuals across all four individual-based metrics.

We respond: The drug-type and hemp-type samples are grouped as such because that is how they were presented. However, given hybridization levels and wide variation in THC/CBD levels, as well as over-reporting of these levels, we feel that, even though we grouped them as such, examining genetic relationships at the individual level rather than population level was appropriate for this investigative study.

In some cases, drug types fell out with the hemp type samples, and is likely an ancestral artifact given these analyses are among individuals within species. The opposite is not true of the hemp group- no sample designated as hemp had substantial genetic signal associated with the drug-types (<15%).

The individuals in the drug-type group are not all that similar in description, reported THC content, or genetically. We sampled a wide range of available strains and feel this appropriately represents the groups. We have 9 hemp samples (including ruderalis), 11 sativa, 14 hybrid, 10 indica, which is a good representation of each of these groups. The 3 CBD samples we expected to be hybrids of hemp and drug-types, which they were, and we feel although this group is small, we again reiterate the groups are artificial.

George Weiblen wrote: An even stronger potential artifact has to do with the microsatellite genotypes themselves as presented in the supplementary table. The hemp samples all have considerable missing data whereas no data is missing from the drug-type samples and the two NIDA samples have a large number of private alleles. It appears that most of the signal assigning the NIDA samples to hemp are due to alleles at only three of the ten loci. Complete microsatellite panels and preferably more NIDA samples are needed to evaluate the preferred interpretation.

We respond: We did not assume to assign the samples from NIDA as hemp, but rather made the observation and conclusion that the plant material supplied from NIDA labeled as “research grade marijuana” does not align genetically with marijuana available on the retail market. In fact, it is quite different, as indicated by the presence of private alleles. We are aware that there are three loci are contributing to the majority of the divergence between NIDA and drug-type samples. Considering that 3 loci represent 30% of our marker regions, this divergence is substantial. Private alleles are commonly used in population genetic studies to identify divergent groups. Although the inclusion of additional NIDA samples would be beneficial, additional sampling would in no way change the genotypes of the samples we have included in this study.

Regarding the missing data, we are attempting another round of reruns to fill in some of the missing data, some of which we have retrieved. We will include this data prior to publication.

Anna Schwabe and Mitchell McGlaughlin, University of Northern Colorado

On 2025-10-30 22:22:50, user Agent Mahone wrote:

Can you please share the supplementary tables mentioned in the paper.

On 2016-07-01 20:54:27, user Sergey Kornilov wrote:

Congratulations on the intriguing set of findings!

I have a small clarification regarding two claims:

1) On p. 3, "additional risk loci and variations are beginning to be suggested by genome-wide association scans (GWAS, reviewed by (22)), but few have exceeded accepted thresholds for significance, and they have yet to be validated by independent replication studies".

2) On p. "In this study, we used exome sequencing followed by Sanger validations and segregation analyses, to perform a first characterization of exome variants of likely aetiological relevance in SLI".

I would like to point out the recent paper in Pediatrics that combined GWAS and WES of SLI/DLD and both provided a novel candidate (SETBP1, replicated in an independent cohort) and described a set of exome variants of potential relevance.

Best,

Sergey

Kornilov, S.A., Rakhlin, N., Koposov, R., Lee, M., Yrigollen, C., Caglayan, A., Magnuson, J.S., Mane, S., Chang, J., & Grigorenko, E.L. (2016). Genome-wide association and exome sequencing study of language disorder in an isolated population. Pediatrics, 137(4), e20152469. doi:10.1542/peds.2015-2469

http://pediatrics.aappublic...

On 2016-08-12 13:07:01, user Pat Schloss wrote:

This manuscript has been accepted to mBio and will be available online around 8/23/2016

On 2018-04-24 08:54:48, user siwei wang wrote:

minor issue: the fig 7 Bi is missing y=x/2 and y=x/3 as the figure caption described, or is there an updated version?

On 2017-08-15 17:27:32, user Anjana Ramnath wrote:

http://garmiregroup.org/gra...

link doesnt work

On 2018-09-13 04:41:25, user Follicle Thought.com wrote:

Glad to see new research completed by the Rendl lab. Let me know if you wanna do that interview again :)

On 2019-11-21 21:11:47, user Mark M wrote:

One needs to cautious interpreting this study, as it utilizes gremlien whole-body AKT2 ko mice. Given the role of insulin signaling via AKT in liver and fat, the cell-autonomous effects of AKT signaling in muscle cannot be ascertained using an whole-body insulin-resistant AKT2 KO model.

On 2018-02-26 11:37:47, user drg1985 wrote:

I should add - an accepted and updated version of this manuscript is available at Royal Society Open Science, - thank you all for your valuable input. http://rsos.royalsocietypub...

On 2017-05-19 16:43:45, user Bernd Pulverer wrote:

Good to see basic modelling of the scientific rewards system is consistent with the intuitive conclusion that ‘if publishing is the sole criteria under which academics are judged, then dubious conduct can thrive’ - along the lines articulated by DORA [http://www.ascb.org/dora/]. <br /> Would be important to see in how this theoretical model maps onto real world data.

The crux seems to be, as so often, carrot vs. stick: to balance the incentives for diligence with the apparent need for some penalty for bad conduct.<br /> Currently, if a young researcher obtains a result s/he knows is unreliable or even flawed, but that may further their career if published, they can decide not to publish and forfeit their grant/tenure/career or publish and risk later retraction which might lead to the same outcome. Can we expect this researcher resist the publish or perish pressures?

On the other hand, as discussed at the METRICS workshop (https://metrics.stanford.ed... "https://metrics.stanford.edu/research/towards-lasting-innovations-literature-amendments)"), we want to encourage self correction and penalties may counteract that.<br /> Can the model help inform what sort of penalties are constructive?

Two minor comments:<br /> 1) ’top-tier journals possess a limited number of publication slots’ [p. 2]: top-tier journals have in principle infinite space online and especially OA ones would actually benefit financially from publishing more, but they decide not to publish more as selectivity is seen as a valuable service to the community.<br /> 2) ‘rewarding diligence improves the proportion of funding allocated to diligent groups’ [p. 13]: seems to be a little circular

On 2020-09-03 12:02:56, user Vinod Singh wrote:

Dear Author, your article reports," human fibroblast cells (García-Nieto et al., 2017) and observed that inactive TADs acquire significantly higher damage compared to active TAD ", based on repair-seq data. Whereas our study and some other studies showed that DNA damage due to UV radiation is uniform across all genomic contexts, it is only the repair mechanism's efficiency that varies across various genomic contexts (https://www.pnas.org/conten... "https://www.pnas.org/content/114/26/6758)") .

On 2020-04-10 16:05:48, user Jubin Rodriguez wrote:

I find it odd that coronavirus sequences from bat RaTG13 and pangolin have not been included here despite them being the closest (known) phylogenetic relatives to SARS-CoV-2

On 2021-10-10 11:22:11, user Zarul Hanifah wrote:

In the abstract, it says "77% of the SAR11 community was compromised of a small number of ASVs (7 of 106 in total). In this phrase, are you saying 77% of the SAR11 community by relative abundance? Which means the remaining 99 SAR11 ASVs made up the remaining 23% of SAR11 community relative abundance?

Also, should it be comprised or compromised?

On 2020-07-13 13:55:43, user GBRC wrote:

Dear Sir,<br /> Please refer to the link given in the data access section.<br /> Thank you.

On 2023-06-27 18:54:33, user Beth wrote:

Beautiful work! Do you think these methods are applicable to study of decalcified rodent skull?

On 2018-04-27 00:47:32, user Swati Mishra wrote:

Hi, this a review from a Journal club where we discussed the above mentioned paper. We have the following questions/comments for the authors. .

1) Why is the BST used to formulate model equations? Is it because it is the most widely used method to model biochemical systems or there is a specific reason for its use? How unique is the model currently considered and if interactions were not given as mass-action like terms, would predictions of the model change?

2) Same argument goes for the use of Monte Carlo method to search for identifiable parameters. As a reader with a non-modeling background, I would appreciate if these methods and the reason behind their use can be explained within the text. This is also important if you want to target a larger audience, those outside the mathematical modeling domain.

3) A lot of results or data is referenced to supplementary material but there is no link to these supplement data files which makes it difficult to make informed inferences.

4) The authors identify the shortcomings of their model and clearly cite where the model results deviate from those in literature. This is a very positive attitude that many authors don’t adopt.

5) I would strongly suggest placing the figures and the legends at the place where they are mentioned in the text to make it easier to read for the reader/reviewer.

6) The overall language of the paper in describing the model and the obtained results is good as the authors refrain from using flashy words to describe their model.

7) How confident are the authors in quantifying the pathway (fig 1)? Of example, are there biological reasons that going from PI(4)P to PI(5)P impossible? It would be nice to have some intuitive explanations of why the model predicts some pathways but not others.

On 2020-02-11 18:34:42, user Richard G. Lanzara wrote:

This type of analysis may show that the "net shift" is a better measure for receptor activity than either the fractional or proportional response.

On 2025-06-06 09:00:46, user Michal Mokry wrote:

Peer reviewed article is now published: https://doi.org/10.1093/cvr/cvaf077

On 2020-01-23 08:05:54, user Charlie wrote:

I worry about your interpretation of the Dot1l inhibitor result. Since there is no active demethylase, it actually takes a number of days before the histone modification is lost passively through cell division. Therefore, your results with pre-treatment with Dot1l inhibitor are likely to be at least in part explained by the slow kinetics of these histone marks, rather than the specific order in which the drugs are added.

On 2019-03-06 17:18:38, user Marc Scheetz wrote:

First time publishing a pre-print... interested to see how this goes. Vancomycin + PipTazo increased AKI is a myth! interested in the discussion!

On 2021-11-09 16:29:05, user Lainie Brice wrote:

The preprint has been published in Ecology Letters, and the final article will be linked to the preprint soon. In the meantime, you can find the article here: https://onlinelibrary.wiley....

On 2020-03-10 16:52:36, user Jef Vizentin-Bugoni wrote:

"The transition from trait-based to abundance-based linkage rules corresponds with a decline in floral trait diversity" corroborates predictions of the 'neutral-niche continuum model' (Vizentin-Bugoni, J., Maruyama, P. K., de Souza, C. S., Ollerton, J., Rech, A. R., & Sazima, M. (2018). Plant-pollinator networks in the tropics: a review. In Ecological networks in the tropics (pp. 73-91). Springer, Cham.)

Based on similar insights, we produced (in the review above) a simplified model where we specifically predict that in communities with high trait variation, niche-based processes (or trait-based, as you call) tend to be more important than neutral-based processes (or abundance-based, as you call) as drivers of species interactions. The underlying mechanism we propose for the first scenario is that more biological constraints (morphological, phenological, chemical, etc) exist, limiting species interaction. In contrast, random change of encounter should prevail prevails when trait diversity is low and, therefore, traits do not importantly constrain species interactions. I think your work may be the first formal test of this model which is, however, overlooked in this preprint. Hopefully this could be amended in a further version. Otherwise, this is a great work.

Jef

On 2017-06-06 21:19:27, user jaap wrote:

But then: what about 'coincide'?? Mark this, for what's going on here is actually interesting!

On 2021-03-20 11:44:40, user Linus R Shao wrote:

Now published in J Endocrinol. 2020 Sep;246(3):247-263. doi: 10.1530/JOE-20-0155.

On 2020-06-28 10:50:48, user Waseem El-Huneidi wrote:

The reported findings are interesting, the findings suggest that transient hyperglycemia associated with COVID 19 is related to low insulin secretion due to affected exocytosis, so i am wondering if there is any data about C-peptide levels in COVID 19 patients which reflect low insulin level (and can eliminate other potential extra-pancreatic sources of hyperglycemia, e.g. insulin resistance). another concern, what if the observed hyperglycemia is related to Glucagon/ insulin ratio, i.e. what if the glucagon concentration was affected, taking into consideration that the findings are based on pancreatic Islets (which include alpha and beta cells), i mean is there a possibility that the effect was on glucagon secretion, peering in mind that alpha cells exhibit similar exocytotic mechanism as of beta cells.<br /> thank you for the interesting findings

On 2020-06-13 12:24:57, user Michael Polymenis wrote:

The paper has now been published in eLife (https://elifesciences.org/a... "https://elifesciences.org/articles/53127)")

On 2016-11-22 11:47:22, user Charles Oppenheim wrote:

Sorry, but the claim made by Eglof et al is nonsense, and I fully agree with Rod Page's comments. In UK law (maybe not in other countries), one needs an absolute minimum amount of skill and judgement to get copyright in a photograph/image. It is probably irrelevant whether the image is presented in a standardised way or not. Database right, much restricted in scope after the William Hill versus British HorseRacing Board case, might also apply. The real question is: who owns the rights in such images? It will be the original authors unless they (foolishly) assigned copyright in their article to a publisher. If they did assign to the publisher, one can guess the reaction of some publishers to unauthorised use of the images.

On 2021-01-05 09:29:51, user Lillian Kofod wrote:

Thank you for your good work. It is so important to understand the indoor environment and how it infect our health. We need more nature into the indoor living to become more healthy.

On 2017-08-09 04:48:13, user Christian M Schuerch wrote:

Hi<br /> I'm wondering what Figure 3B means. You show here small intestinal epithelium, not PDAC. Please provide some H&E slides so that pathologists can interpret what exactly you show.<br /> Thank you, Christian

On 2018-12-28 04:32:36, user Alessio Peracchi wrote:

In the text, the product of the C. crispus CHC_T00009480001 gene is tentatively proposed to act as a dehydratase in the biosynthesis of certain mycosporine-like amino acids (MAAs). Although the gene product is almost certainly a pyridoxal phosphate (PLP-) dependent enzyme, most similar to functionally validated serine dehydratases, the proposed substrates in MAAs biosynthesis would be serine/threonine derivatives blocked at the amino group (Figure 8). Such compounds cannot be transformed by PLP-dependent enzymes, which require substrates with a primary amino group in order to interact with their cofactor. Hence, participation of CHC_T00009480001 in the proposed dehydration reactions, outlined in Fig. 8, is mechanistically untenable.

Alessio Peracchi<br /> Associate Professor of Biochemistry<br /> Department of Chemistry, Life Sciences and Environmental Sustainability<br /> University of Parma<br /> 43124 Parma, Italy<br /> ORCID: http://orcid.org/0000-0003-...

On 2020-10-08 13:58:21, user Saoirse Foley wrote:

The published version of this manuscript is available at the following doi:

10.1098/rspb.2020.1688

On 2023-08-05 15:10:27, user Flo Débarre wrote:

In case someone else is confused about what happened to the data in the email shown in Figure 2 of this version of the preprint ("EXAMPLE SRA DELETION FROM ANOTHER STUDY" in the previous comment):

SRR11119760 and SRR11119761 were made public again on June 16, 2021; on that day, they were also synchronised on the other INSDC repositories, ENA and DDJB. June 18 was the date at which the data were pushed to the cloud. <br /> The data were therefore public before the preprint was even sent to bioRxiv, and not, like the previous comment could indicate, as a response to the preprint being shared.

On 2019-09-04 02:36:12, user Md Rana Sarkar wrote:

Please,Would you send me dataset of this paper?

On 2022-01-31 10:59:31, user James Fellows Yates wrote:

I would like to commend the authors for revisiting the subset of the results from the original paper that were quite dubious - something that many in the field have been quite skeptical of.

In that vein, I would like to make a small recommendation to cite the following paper from already in 2017, where the consumption of moss claim was already argued to be extremely unlikely:

Dickson, J. H., Oeggl, K., & Stanton, D. (2017). “Forest Moss”: no part of the European Neanderthal diet. Antiquity, 91(359). https://doi.org/10.15184/aq...

This could be cited for example around llines 41-45. This would add further support to the results of this preprint.

Please note the following:

1. You're missing a citation and version for the for Tera-BLASTn software (and settings, if not default)
2. You have a small typo on line 92: 'resent' should be 'recent'

On 2016-05-03 02:34:43, user Mark Farman wrote:

As stated by the authors: The Oryzae and Triticum pathotypes of P. oryzae from Brazil display a level of differentiation comparable to that reported between accepted species such as P. grisea and P. oryzae (Couch & Kohn 2002, Couch et al. 2005), or the new cryptic species recently identified within Pyricularia (Hirata et al. 2007, Choi et al. 2013, Klaubauf et al. 2014). In truth, they know this not to be true because the data below have been shared with them on multiple occasions (Oryzae and Triticum strains are in the dense group at the top. Scale is % divergence). They choose to ignore these data because they do not support the new species hypothesis.

On 2016-05-03 02:44:27, user Mark Farman wrote:

Let's see how the proposed new Pgt species maps on a phylogenetic tree based on whole genome data. Below is a neighbor-joining tree built using genetic distances assessed across the whole genome. Gray shaded ovals encompass strains that fall under the proposed Pgt umbrella. Placements seem kind of arbitrary, don't they?

On 2019-01-10 18:18:27, user Jakob Trendel wrote:

A peer-reviewed version of this manuscript is now published in Cell under https://doi.org/10.1016/j.c....

On 2024-06-15 11:45:28, user Jiashun wrote:

Great works!Cogratulations, Dr Cheng. While I found there may be a small mistake. "Of the sixteen F1 double heterozygotes we derived from the cross between gpgp and the TILLING mutant heterozygotes, half had yellow pods, and all of these yellow podded F1s carried the ChlGW121* null allele (Fig. 3i-j)". I checked the figures and found 10 yellow pods rather than the half of sixteen (8). Please let me know if I misunderstood.

On 2019-11-27 10:05:53, user German Leparc wrote:

Thank you for looking into this - it has been one of our questions after getting a NovaSeq. Some comments: maybe I missed it, but you do not cite the paper by Chhangwala, et al regarding the impact of read length on the quantification of differentially expressed genes and splice junction detection. Also you make this claim that short paired end reads are better, but base it solely on comparing to the chosen gold standard of 150-bp paired ends, and not on other empirical measurements of splice site detection, read mapping rates, and other metrics as described by the Chhangwala paper. It is no surprise that paired end reads of any length correlate better than to each other than single reads to paired end reads. Thus the only conclusion that can really be drawn here is really that "paired end reads of different read lengths correlate very well together." What I'm saying is that you conclude that an Apples to Apples comparison correlates better than an Apple to Pears comparison.

On 2024-04-10 17:36:11, user VN wrote:

This pre-print has been accepted for publication in Molecular Ecology (https://onlinelibrary.wiley... "https://onlinelibrary.wiley.com/doi/10.1111/mec.17341)").

On 2021-03-03 07:03:52, user Sameen Mahmood wrote:

My peers in my research journal club enjoyed discussing your paper and wanted to highlight strengths as well as areas for growth. We felt that the enhanced immune response observed and potential for greater efficacy via a needle-free system were both highly promising. We were particularly intrigued by the use of a DNA vaccine over other vaccine platforms, but we also wanted to know greater detail regarding the Th1/Th2-skewed responses and their specific known or proposed mechanisms in the case of coronaviruses. Additionally, the development of a needle-free VIU-1005 vaccine could be further supported by incorporating the following feedback: firstly, the images for the vector plasmid construct should have a higher resolution and the image should highlight the S-gene. The fluorescent IHC results should be supplemented with controls (i.e., knockout for S-gene with just secondary antibody, transfected cells without primary, etc.) and can be conveyed further via quantification of control vs. plasmid staining as it’s not extremely clear in the figure. It would also be helpful to elaborate on why exactly the measurement points in Figure 5 were taken given that there were three separate doses administered in the beginning on an even timeline, whereas each subsequent measurement is not spaced out evenly, possibly leaving room for variation. Figure 6 is convoluted due to the variety of symbols in close proximity, so spacing the data or breaking it into more digestible figures would prove useful.

On 2022-06-22 06:31:57, user Jakub Gemperle wrote:

Now published in eLife<br /> https://doi.org/10.7554/eLi...

On 2019-02-19 08:26:19, user Guyguy wrote:

Overall comments, discuss strengths/weaknesses of the study, novelty, significance, general execution and scholarship. Additional comments including concerns about dual publication, research ethics, or publication ethics: <br /> The reviewers appreciated the attention to an important topic. Based on the reviews below may help us to revise the manuscript for another submission. This work has the merit of honoring the memory of a supervisor who has worked a lot in the fight against sleeping sickness. Beyond this tribute, we are therefore recommended to go through more scientific literature, and to consult the following useful sources for learning how to write:<br /> -Writing Workshop: PLOS and PLOS Neglected Tropical Diseases. Ppt presentation. http://journals.plos.org/pl....<br /> -San Francisco edit newsletters: www.sfedit.net.<br /> -Docherty & Smith. The case for structuring the discussion of scientific papers. BMJ 1999;318:1224–5. <br /> -Kallestinova E.D. How to Write Your First Research Paper. Yale Journal of Biology and Medicine 84 (2011), pp.181-190. <br /> We appreciate those reasons and we are working on them. In addition, the English used in the writing of this article needs to be significantly improved. Also make clear how in 2019, while we approach elimination of sleeping sickness, a comparison in the situation in 2002 versus 2003 is still relevant.<br /> -Are the objectives of the study clearly articulated with a clear testable hypothesis stated?<br /> -Is the study design appropriate to address the stated objectives?<br /> -Is the population clearly described and appropriate for the hypothesis being tested?<br /> -Is the sample size sufficient to ensure adequate power to address the hypothesis being tested?<br /> -Were correct statistical analysis used to support conclusions?<br /> -Are there concerns about ethical or regulatory requirements being met?<br /> Results<br /> -Does the analysis presented match the analysis plan?<br /> -Are the results clearly and completely presented?<br /> -Are the figures (Tables, Images) of sufficient quality for clarity?<br /> Conclusions<br /> -Are the conclusions supported by the data presented?<br /> -Are the limitations of analysis clearly described?<br /> -Do the authors discuss how these data can be helpful to advance our understanding of the topic under study?<br /> -Is public health relevance addressed?

On 2021-03-05 19:59:58, user James Gorley, PhD wrote:

This is a fascinating paper, but can be improved with addition of a methods section relating to the brain specimen. Was the brain freshly extracted post-mortem? Was it preserved in formalin? What were the weight, dimension, etc of the brain specimen? This information, currently missing from the manuscript is important as it affects the tissue properties of the specimen and therefore its morphological/structural/connectomic appearance. Freshly extracted brains have the advantage of mimicking in situ brain structural properties, but subject to degradation. Conversely, formalin-preserved brains can be maintained indefinitely but are much more stiff and lack the in vivo properties of in situ brain

On 2021-05-07 20:28:09, user Kevin McKernan wrote:

Excellent work. <br /> I would encourage having a look at a reference with a THCAS contig, a more complete CBCAS contig and a Y chromosome. I think you are not finding any signal in THCAS as the reference you are mapping against doesn't have these three regions. As a result, your THCAS (assuming you are sequencing a type I plant) reads are likely mis-mapping to CBCAS/CBDAS. This can create spurious SNP calls.

There is an annotated male and female Jamaican Lion reference in NCBI. <br /> https://www.ncbi.nlm.nih.go...

There is an even better HiFi assembly but not yet annotated. <br /> https://www.medicinalgenomi...

On 2019-12-19 11:00:58, user Xiaoran Lai wrote:

A. Köhn-Luque would like to acknowledge that the research leading to these results has received funding from the European Union Seventh Framework Programme (FP7-PEOPLE-2013-COFUND) under grant agreement number 609020 - Scientia Fellows, and M. E. Rognes would like to acknowledge the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme under grant agreement 714892 for its support. We also would like to acknowledge the help received from the Department for Research Computing at USIT, the University of Oslo IT-department

On 2016-05-20 18:13:30, user Martin Gühmann wrote:

Very nice!

"This differs from previous reports which divide cnidarian opsins into one ((Suga et al. 2008; Porter et al. 2012; Feuda et al. 2012; R. Feuda et al. 2014; Hering & Mayer 2014; Liegertová et al. 2015), two (Plachetzki et al. 2007) or three groups (Suga et al. 2008; Porter et al. 2012; Feuda et al. 2012; R. Feuda et al. 2014; Hering & Mayer 2014; Liegertová et al. 2015)."

Well as far as I can see, Suga et al. (2008), Feuda et al. (2012), and Feuda et al. (2014), divide the cnidarian opsins into three groups. And Porter et al. 2012, Hering & Mayer (2014), and Liegertová et al. (2015) have just one group. Liegertová et al. (2015) subdivide also their cnidarian opsins, but none of them groups with the other bilaterian groups. Hering & Mayer (2014) say that their cnidarian opsins form three groups, but they did not mark them in their phylogenetic tree, so I assume that their three groups are more related to each other than to the bilaterian opsin groups of Hering & Mayer (2014).

On 2025-05-20 15:59:51, user Young-geun Kim wrote:

The author name "Xinyuan Zheng" is incorrectly listed as "Xinyuan Zhang" on the preprint page, but it appears correctly in the published version: https://www.frontiersin.org/journals/psychiatry/articles/10.3389/fpsyt.2024.1397093/full .

On 2019-05-09 02:59:04, user Yanmei Huang wrote:

Hi, Thanks for creating a very nice tool! One question: what is the definition of "Noise" and "Signal" in Figure 2 and Figure 3? How are they calculated?

On 2020-09-22 02:46:27, user yi wang wrote:

Dear Readers: please notice that the information for ethyl acetate production in this version of the manuscript is not accurate; please refer to our revised version of the manuscript (https://www.biorxiv.org/con... "https://www.biorxiv.org/content/10.1101/2020.03.29.014746v2.article-info)") for details. Please let us know if you have questions. Best,

--Yi Wang, Corresponding Author.

On 2016-12-20 20:38:49, user jdudley wrote:

Curious why the authors did not do a head-to-head comparison with the Cornell prOCTOR tool to see if there was any increased value by using deep learning vs. prOCTOR RF approach.

On 2017-03-24 10:44:22, user Elizabeth (Liz) Wager wrote:

I can see many benefits from separating the process of investigating misconduct and alerting readers to unreliable work. However, one important recommendation of this new proposal is that "If misconduct or fraud has occurred, this should be reported on, but such reporting should be considered as distinct from the process of correcting the literature." But we know that universities in many countries are often unwilling to share information about misconduct investigations, so I hope the authors will provide some more detail (and research institutions will also comment on) how this might work in practice. Also, editors report cases in which institutions refuse to investigate well-founded allegations (which is one reason why the current COPE guidelines suggest that Expressions of Concern are sometimes needed) and I'm not clear how an Amendment would handle this -- if authors and institutions are uncooperative, what level of evidence do journal editors require before posting an Amendment?

On 2020-02-14 18:01:43, user Keith wrote:

FYI the HCP retinotopy dataset consists of twin pairs (106 individuals are identical twins, 68 fraternal twins, and 7 non-twins or whose twin isn't included in the dataset). From your paper, all 10 of the "test" subjects have a twin in the "training" set (5 identical, 5 fraternal). Similarly, 7 of your 10 "development" have a twin in the training set, and 2 of the "development" subjects are actually a pair of identical twins.

You might need to address this confound, since your tuning,training,and testing sets are not independent!

On 2024-09-06 12:37:55, user Ashim Kumar Dubey wrote:

This article has now been published - https://pubs.acs.org/doi/full/10.1021/acsinfecdis.4c00249

On 2021-03-21 13:53:24, user Vicent Pelechano wrote:

Dear colleagues,

I read with great interest your manuscript. Unfortunately, I fear that the main conclusion derived from your work might be a technical artifact associated to intrinsic biases of your method. In particular, the likely inclusion of untemplated C during the reverse transcription step. As all methods have biases, I would recommend performing control experiments with your own protocol using as input randomly fragmented RNAs. I am sure, that will help you better identifying the biases associated to TRESeq.

I also found confusing your use of “cleavage sites” in your manuscript when in reality you are measuring the bulk of cap-less 5’RNA boundaries. And of course, we know that XRN1 mediated degradation is key to remove RNAs and controls its stability and abundance. In addition to checking our previous work, I would advise for example to check the nice paper from Harigaya and Parker (PNAS 2012). Using samples where one inhibits the 5’-3’ exonucleolitic decay would be more suitable to answer the question you ask.

I normally do not comment publicly on preprints, but after reading your paper I felt with the obligation to do so. I hope you find my comments useful to improve your work

Best regards,

Vicent Pelechano, PhD<br /> http://pelechanolab.com/

On 2018-04-05 15:52:49, user Pedro Mendes wrote:

When you log transform, does this mean that you log transform the <br /> equations? Or is the parameter search done in log space (ie sampling of<br /> random numbers)? This could be better described in the manuscript...

In COPASI we automatically use log scale for random <br /> number sampling when the interval spans more than 1.5 orders of <br /> magnitude (of course this only affects algorithms that use random numbers).

On 2022-12-17 14:56:50, user Tim Breitenbach wrote:

I think the work could benefit from discussing also related work in the field of CRE selection, like https://academic.oup.com/bi..., and at least in an outlook show how the strengths of all methods could be combined and might act together.

On 2017-05-17 17:52:25, user hansdb wrote:

Does anyone know why the Esperstedt sample I0114 is not included - a pitty since this L39+ sample represents the often ignored I2a2b hapogroup.

On 2020-10-27 06:36:17, user DR. NELSON PHIRI wrote:

The Paper has been peer reviewed and published on the following link: https://doi.org/10.5430/jer....

On 2020-07-01 11:10:42, user Andrea Luchetti wrote:

Very interesting paper, thank you for sharing as pre-print! However, a citation error in the branchiopods section: in fact, Anostraca DO NOT exhibit lower mitochondrial substitution rate than Notostraca and Diplostraca, anostracans actually showed a higher substitution rate (please, check on Luchetti et al., 2019 - Zool Letter 5:15).

On 2022-08-29 12:05:44, user Manuel Ruedi wrote:

This is a very fine new piece of evidence that the Myotis radiation is both quick... and complexe. I have a single comment regarding the place of M.brandtii within the Old World clade, rather than within the New World (as evidenced elsewhere, incl. in large phylogenies using 1610 UCE to recover that topology): The branch linking brandtii to the few other Old W taxa is very short, so that the root of the whole Myotis tree is very fragile. The authors used distant Vespertilionids to place this root (instead, they could have used Kerivoulinae or Muriniae representatives, i.e. the sister-group of Myotinae, which would have been more effective in placing this root of Myotis). Also because they used only few Old World species, they gave little chance for that group to represent its full diversity.<br /> But what is clear from this brilliant study is that the brandtii lineage appears more basal to the New World radiation than previously reported.

On 2023-01-30 14:42:31, user Leduc cécile wrote:

"Vincente et al. have measured the periodicity between C-to-C terminal and N-to-N terminal from ULF inside cells, but, here, we observe this periodicity in the actual filaments which is formed by the WT vimentin, not the mutant."<br /> We did it in the filaments too. Just check the figure 3 of Vicente et al.

On 2020-05-21 09:07:14, user Andre Goffinet wrote:

Human virus goes to ferret, then from ferrets to ferrets, then probably back to human. Could it mutate in between. Recently, Harbin virus generated a disease somewhat distinct from original covid19 (https://www.globaltimes.cn/... "https://www.globaltimes.cn/content/1188898.shtml)"). Since Harbin first inoculated several animals, I am afraid virus jumped back from animals to man, in a different form with longer incubation and more directly inflammatory pneumonia. All this is playing with fire, and those experiments must be done with utmost care.

On 2024-06-19 00:31:10, user Rajan K C wrote:

This article has been published. Please update the article.

K. C. R, Patel NR, Shenoy A, Scallan JP, Chiang MY, et al. (2024) Zmiz1 is a novel regulator of lymphatic endothelial cell gene expression and function. PLOS ONE 19(5): e0302926. https://doi.org/10.1371/jou...

Thank you!

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

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

On 2025-05-30 13:49:38, user Haiyue Hou wrote:

Great work! But how broadly applicable is this CCS library in glycan structure sequencing? As we can see, the examples cited in the paper are cases with significant CCS differences in glyco-epitope fragments (Fig. 4). However, many fragments exhibit minimal CCS distinction—within 2% (e.g., F1 and F2)—which falls within the typical error range for CCS measurements. In such cases, determining glyco-epitopes might be challenging?

On 2019-05-22 14:18:52, user A. S. wrote:

When you write "Mitochondria evolved from archaebacteria", you are probably confusing alphaproteobacteria (which are Eubacteria) with archaebacteria.

On 2018-11-16 16:25:46, user Eryn McFarlane wrote:

Dear Authors,

We are a group of Phd students and Postdocs at the University of Edinburgh that meet weekly to discuss life history papers. We noticed that the tone of our discussions could be a little negative, so, to counteract this, we decided that the most positive thing would be to review pre-print papers, and then share our reviews with the authors. Hopefully, this acts to both give us experience as reviewers, and provide feedback to researchers who have posted their manuscripts on bioRkiv.

We hope that this review is useful to you, and will help to improve your paper. Please feel free to contact us if you have any questions or clarifications.

Best wishes,<br /> Eryn McFarlane<br /> eryn.mcfarlane@ed.ac.uk<br /> on behalf of UoE Life History Journal Club

Major comments on ‘Loci, genes and gene networks associated with life history variation in a model ecological organism, Daphnia pulex (complex)’.

This paper asks some interesting questions about the genomic and transcriptomic underpinning of life history traits in wild caught Daphnia pulex. Malcom et al’s linking of the genotype, transcriptome, phenotype in ecologically relevant traits in a model system is a thorough exploration of this on-going problem in ecological genomics. Below, in no particular order, are our main suggestions to improve this manuscript.

Description of methods in main text: In general, we found the paper a bit difficult to follow with the methods after the discussion. We would suggest that either all results are reported with a brief description of the method used, or the methods be incorporated early in the ms (i.e. after the introduction). Ideally, if there were to be increased narration of the methods in the results section, this would include a description of sample sizes. In general, we were not clear on the experimental design until we had read the methods, which made much of the paper difficult to follow.

Quantitative genetics: We think that the authors have used a mix of appropriate and inappropriate quantitative genetics techniques. For example, we agree with how they have estimates H2. However, the genetic covariances described don’t account for error around the breeding value estimates. This is problematic, and can lead to anticonservative estimates (Hadfield et al. 2010 Am Nat 175(1):116-125). We suggest that, instead, the authors use multivariate statistics that estimate variance covariance matrices, with error. Further, we don’t agree that the authors have accounted for maternal (genetic) effects using their experimental design (line 443 – 445). A standardised environment over several generations does not preclude (heritable) differences in maternal investment. If there are maternal genetic effects that co-vary with environment or clone, then we expect that these will lead to an inflated H2 estimate.

Hybridization: the presence of unidentified hybrids among the clones is concerning to us. If this is a hybrid complex, then many of the downstream genomic and transcriptomic analyses are inappropriate, as they assume populations, rather than hybrid zones. For example, LD is imagined to be very high in recent hybrids, which could lead to GWAS hits that are representative of large portions of the genome. We suggest that STRUCTURE (Pritchard et al. 2000 Genetics 155(2): 945-959 or similar) is used to determine the admixture score of each clone between the 2 species in the complex. Then, admixture mapping could be applied which would take advantage of this hybridization. Similarly, if there are hybrids among the samples, then they could be utilized to examine allele specific expression, which should not be disregarded. These are some interesting additional questions that could be posed using a dataset that includes hybrid individuals.

GWAS: 96 individuals for a GWAS analyses is a quite low sample size (although might be more reasonable for admixture mapping, see below). Additionally, this is a tall order with only 4000 markers. As all clones are wild caught, we wonder what the LD is between the 4000 makers. Kardos et al. (Molecular Ecology Resources 2016 16:727-741) have a description of the problem of quickly dropping LD with few markers and small individual sample sizes which illustrates the problem of GWAS on data sets such as this one.

GO term analyses: These analyses should demonstrate enrichment of GO terms, not just presence of significant GO terms. Would these GO terms come up at this frequency just by chance because they are representative of the GO annotation of this species?

On 2024-11-04 22:58:21, user Makenna Thomas wrote:

Hello! My journal club recently read your paper, and we were very impressed by how thorough your experiments were. We also appreciated how much converging evidence you included for elucidating the role of the ADGRG1 gene in Alzheimer’s disease pathology. Here are some comments I wanted to provide as possible room for improvement.

Starting in Figure 1 but present in the whole paper— is there a reason why female mice were not included in your inducible knockout line P2ry12? Without female mice included from this genotype, it seems hard to make conclusions about the data that also extend to females.

In Figure 3, I was a bit confused why you used unpaired two-tailed t-tests to analyze this data. The data doesn’t appear to be normally distributed (specifically in figures 3b and 3d), and t-tests assume the data is normally distributed. Additionally, it was unclear whether you accounted for technical replicates in dendrites from the same animal. I would recommend a nested ANOVA be performed to better analyze this data set because it could account for dependency in your dendrite measurements.

In Figure 4, it was difficult to interpret the results of the Morris water maze given that ANOVAs do not perfectly analyze percentages. It would be helpful if, in this section, the data was analyzed by a chi-square test or another statistical test that can more accurately depict frequency differences. As another suggestion, it may be more easily understandable if the target quadrant were compared to each of the three non-target quadrants separately as opposed to all at once. It currently seems as though the non-target quadrant results are skewed by the 3:1 ratio of non-target to target quadrant presence.

Overall, I really enjoyed reading this paper. I can tell a lot of time was put into this research and I wish you the best with your work moving forward!

On 2020-06-08 21:08:18, user rlsheets wrote:

You discuss that one of your researcher's samples caused a false positive due to ovulation. You argue that the male dogs may have become excited by the secreted sex hormone metabolites. Have you considered that since LH and PRL are significantly elevated in male Covid-19 patients, that they might secrete a catabolome similar to those of ovulating women? This might be another explanation as to why the dogs were confused.

On 2024-09-06 20:37:49, user Noam Kaplan wrote:

Hi-C data publicly available at GSE276554

On 2019-02-06 01:04:41, user Charles Warden wrote:

Thank you for providing a detailed set of results.

One minor thing: before going to peer-reviewed publication, I think Figure S1 is sideways (right side should be bottom)

On 2018-02-01 15:21:09, user Katsuhiko Murakami wrote:

This manuscript is in press in Molecular Cell.

On 2014-09-15 13:21:14, user d wrote:

1) The easier solution to the 'minor problem' raised would be to use the scientific notation, rather than the italicized 0s: 1.2*10^4 +/- 5.7*10^3 would work just fine.<br /> 2) Would it be a valid argument to say that you want the same number of digits behind the decimal point for both the mean and the SEM? That would make it easier at a glance to relate the two to each other (for this, it would probably make more sense to add a digit than to remove one). For the example give above, it would now be: (1.23 +/- 0.57) *10^4.

On 2015-08-24 05:27:43, user Ilia Stambler wrote:

Excellent article.

Completely agree with your point that the main problem is the deficit (or even absence) of scientifically grounded or clinically applicable “diagnosis of aging”.

As you write “there is no universal set of biomarkers and guidelines for measuring aging as a system" and “to successfully evaluate the effect of any drug that influences aging, it is essential have a measureable endpoint, such as biomarkers”.

It seems that even biomarkers as such are not enough; there is a need to precisely define measurable *clinical* end points. Without their definition, it seems unlikely that aging can be recognized as a treatable medical condition (or disease). This seems to be a recognized problem for Alzheimer’s disease. Treatments may seem to work on biomarkers (e.g. clear amyloid), but seem to give no clear clinical benefits – and billions of dollars are gone.. But at least in Alzheimer’s there is a more or less clear clinical definition – unlike aging, apparently... As one author writes about Alzheimer’s disease:

«Regulatory agencies are unlikely to provide accelerated approval for a presymptomatic treatment based solely on biomarker (i.e., surrogate marker) endpoints without additional evidence to show that a treatment’s biomarker effects are “reasonably likely” to predict a clinical benefit.»<br /> http://www.ncbi.nlm.nih.gov...

It seems this is largely a problem of scientific, clinical and even mathematical definition of aging – perhaps even less of its “socially constructed” perception (as in the case of mental illnesses or obesity). In the article "Estimation of Heterogeneity in Diagnostic Parameters of Age-related Diseases" we explored the question of some possible formal mathematical, yet clinically applicable, definition (p. 223) http://www.aginganddisease....

Also generally, the methods of determining risk factors for mortality (including the factor of aging) appear problematic. For example, even in the authoritative Global Burden of Disease (GBD) study, it appears that the risk of death from various factors can exceed hundreds percents (when in fact it should be no more than 100% ...). And of course, in GBD, aging is not even considered anywhere close to a risk factor (though there are factors like “injuries by pedal cycle vehicles”...)<br /> http://www.sciencedirect.co...

In the article “Information-theoretical analysis of aging as a risk factor for heart disease" we explored the question of a correct definition of risk factors and their combinations (p. 204) http://www.aginganddisease....

Indeed, "senility" is already a part of ICD classification – as recognized by some GBD statisticians.<br /> http://www.icd10data.com/IC...

Yet, it is unlikely to affect policy makers, as it is considered a “garbage code” – when there is no clear clinical or biological definition. So in order to successfully use this code, it seems there is again the need to develop the evidential basis of biomarkers and clinical end points.

Here is, for example, how the same article about the Global Burden of Disease speaks about that “garbage code” (pp. 2099-2100).<br /> http://www.sciencedirect.co...

«Murray and Lopez introduced the notion of “garbage codes” in the GBD and proposed methods to redistribute deaths assigned to garbage codes to probable underlying causes of death. Garbage codes are causes of death that should not be identified as underlying causes of death but have been entered as the underlying cause of death on death certificates. Classic examples of garbage codes include senility or cardiopulmonary arrest. In the GBD 1990, major garbage codes were identified and simple algorithms proposed to redistribute these proportionately to various causes (called “target codes”) that were the likely underlying causes of death. A similar approach was applied for the GBD 2000 and subsequent WHO updates».

And another consideration that seems very important – a treatment may improve the biomarkers and even clinical or functional end points of aging – but shorten the lifespan!!! (as in the case of some stimulants) It seems there is a need for long term analysis (ideally establishing the effect on the actual lifespan, or at least long term effects on mortality). Yet it seems this kind of research may not be very popular with investors or politicians. But without it, our “cures against aging” may shorten people’s lives…

Thank you

Ilia Stambler, PhD <br /> www.longevityhistory.com

On 2025-10-28 04:59:06, user Xiangyu Luo wrote:

This paper has been published in Biometrics with the link https://doi.org/10.1093/biomtc/ujaf127

On 2016-06-20 01:41:36, user Warrick Nelson wrote:

Revision was just to correct the order of photographs in Figure 3 - thanks John for noting.

On 2018-04-02 16:55:01, user Balaji wrote:

Your model supports Kuzmina's identification of the Andronovo as the progenitors of the “Indo-Iranians” and Max Mueller's date of around 1500 BC. for the Aryans to enter India.

You have stated, “we do not have access to any DNA directly sampled from the Indus Valley Civilization (IVC)”. But surely such aDNA results are in the pipeline. If they show significant “steppe-related” ancestry in 2500 BC. Harappan sites, then your model of Andronovo-mediated steppe ancestry entering the Indian Subcontinent around 1500 BC. will no longer be tenable.

I urge you to also consider alternate models. One such is the following qpGraph generated<br /> by “Davidski” of the Eurogenes Blog at my request.

https://drive.google.com/fi...

The idea of this model is that populations represented by nodes A, B, C and D were all resident in different parts of the Indian Subcontinent and diverged by isolation by distance. Population C was from the Eastern part and Populations B and D in the western part (what is now Pakistan). Out-of-India migrations at different times in the Mesolithic gave rise to EHG, CHG and Iran_Neolithic. Bronze-age migration of a population related to ANI gave rise to Yamnaya and the spread of Indo-European languages out of India and into Europe.

On 2021-05-09 22:52:50, user Benjamin Feng wrote:

I really enjoyed reading this paper and thought it was interesting. The color scheme was consistent and most of the graph layouts were really clear. I appreciated including images of the tumors as it helped to visualize. In 1A, it may help to group the graphs by what you were trying to measure rather than the cell line, similar to 1J to get the point across. In Fig 2, the data is good, but making all of them larger would help to view them easier. You could also remove the actual image from 2G and enlarge the fluorescence as it doesn’t add much to the figure. For 2F, is it supposed to be .2% or 20% extravasation efficiency. In both cases, it would be helpful to show it at different timepoints to ensure that it was not only at this time. The legends in 4A aren’t really helpful as the colors, although consistent, blend in with the expression levels. Try moving it to direct text over the top. The relative expression for 4C also is really long and unhelpful since you can’t get the full view of it all at once. Labeling each row could be helpful. Figure 5 does a good job of showing the hypothesized pathway.

Finally, what’s the purpose of the different cell lines. Why was only one used in follow-up experiments? I think it would be helpful to have at least 1 of both naturally expressing and ectopically expressing Ecad line to inhibit in the figure.

On 2021-11-02 17:55:46, user Nicole Reynolds wrote:

Lichtheimia is not in the Zoopagomycota, it is in the Mucoromycota. Please fix this before publication.

On 2022-05-18 09:14:54, user Magnus Palmblad wrote:

The name ("PROPOSE") is great, and consistent in the PDF version of the preprint. But it appears as "PROIOSE" and "IROIOSE" in the Abstract and Full Text. Perhaps something went wrong when generating or uploading this text?

On 2024-02-20 09:47:16, user Nils Schuergers wrote:

Nice work! Instead of reference 42 you probably wanted to cite "Nils Schuergers, Tchern Lenn, Ronald Kampmann, Markus V Meissner, Tiago Esteves, Maja Temerinac-Ott, Jan G Korvink, Alan R Lowe, Conrad W Mullineaux, Annegret Wilde (2016) Cyanobacteria use micro-optics to sense light direction eLife 5:e12620"

On 2019-11-16 01:35:23, user Toru Takumi wrote:

Social cells in the insular cortex

On 2020-05-18 13:37:49, user OxImmuno Literature Initiative wrote:

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

On 2024-06-20 16:05:39, user Kishore Babu wrote:

1. I would also agree that the term “archetypical” in the title is wrong as the first structure of this class of proteins (PP2 family proteins) was published in 2023 (see Bobbili, KB et al. (2023) Structure, 31, 1-16) which reported the structure of Cus17 from the phloem exudate of Cucumis sativus. Therefore, the title should be modified by removing this word and reference should be made to the above publication and the structure of Cus17 in the Introduction as well as in the Discussion.

2. SEC- MALLS experiment (Supplementary Fig1a) appears strange: (a) While Nictaba is eluting much later than BSA monomer (Mr = 66,000) the authors claim Nictaba to be a tetramer in solution (Mr = 76,000; subunit mol.wt. = 19,000 Da), so that they can claim a difference in their protein from that of PP2 gene family of proteins all of which have been shown in at least dozen other studies to exist as dimers only. (b) Only two molecular weight markers have been used as the standards for calibrating the column. (c) Nictaba a PP2 gene family protein is expected to be impeded on the gel media of their column as in a number of studies in the past on PP2 gene family of proteins they have been shown to get retarded on on gel media ranging from Sephadex, Acrylamide, Superdex etc.(Read, SM and Northcote, DH (1983) Planta 158, 119-127; Anantharam, V. et al. (1986) J. Biol.Chem. 261,14621-27 and Bobbili, KB et al. (2023) Structure 31,1-16).

3. The location, geometry of the binding site, the stereochemistry of the bound chitotriose and its interaction in Nictaba are identical to that reported for Cus17- the founding member of the PP2 gene family fold (Ref. Structure (2023) vol 31 pp1-16). Moreover ,the key residues tethering chitotriose to Nictaba are Thr14, Trp15, Tyr21, Val39, Ala40 and Trp151 are identical and correspond with Thr18, Trp19, Tyr25, Val46, Ser47, Trp48 and Trp141. Given this remarkably striking level of identities of the binding residues and the groups in the sugar one fails to see any novelty in Nictaba-sugar interactions as compared to the fold founding member of the family, namely Cus17. In this context, the authors should discuss their results in comparison with the structure of Cus17.

4. Even the backbone C? atoms of the subunit of Nictaba overlap within 1.06A of the C? atoms of Cus17 indicating that Nictaba fold is not new and is a faithful copy of Cus17. This should be stated in the Results and Discussion sections of the manuscript as appropriate.

5. The InterPro site that curates protein folds has created a separate folder for PP2 gene family of proteins since the appearance of Cus17 structure recognising it as a novel fold. It is therefore not surprising that Nictaba fold is curated and subsequent to the fold of Cus17.

6. Authors do not report on study on the stoichiometry of binding by any method including ITC but they claim Nictaba has a single binding site per subunit for the sugar perhaps based on crystal structure which is not a conclusive evidence for their assumption as there are numerous examples of differences for the number of binding sites seen in crystal structure or modeling vis-a-vis what are found in solution. Extensive ITC studies on several PP2 type lectins have given a wealth of information on the binding constants and thermodynamic factors associated with the binding of chitooligosaccharides to them as well as on the binding stoichiometry (see Nareddy, PK et al. (2017) Int. J. Biol. Macromol. 95, 910-919; Bobbili, KB et al. (2018) Int. J. Biol. Macromol. 108, 1227-1236; Bobbili, KB et al. (2019) Int. J. Biol. Macromol. 137, 774-782).

7. Nearly 40% of the 60 references cited in this manuscript are citations to the publications of the corresponding author! On the other hand many important, relevant publications of other scientists (mentioned above) are not cited.

On 2019-12-12 09:22:14, user Rohit Satyam wrote:

Hi Eleanor

While going through your paper, I read in result section that you predicted subtelomeric sequences in-silico:

"In our case, these maps are then compared with in silico-generated maps of subtelomeric reference sequences"

Can you guide me a bit how you did that?

On 2020-09-19 22:36:55, user samhuff wrote:

Bad news is the microbes use the carbon for food and produce CO[2] or methane perhaps.

On 2022-06-27 17:36:28, user Gibs wrote:

Seems interesting (great name also)! Currently the GitHub link does not work. I imagine the repo is private - would it be possible to make it public now that the pre-print is out?