On 2017-04-27 23:15:25, user Misha Koksharov wrote:

It would also be interesting to analyze why people subconsciously or deliberately misinterpret their experimental data.

I presume it happens quite often that a lot of time/resources is wasted to collect experimental data in inappropriate or wrong way and then people just have to publish it somehow (even when the data turns out to be essentially useless)...

On 2017-11-12 16:27:28, user Michael Hall wrote:

The DOI on this article doesn't seem to work?

On 2018-04-04 08:52:22, user guillemaud wrote:

A PREPRINT PEER REVIEWED AND RECOMMENDED by PCI EVOL BIOL

This preprint by Bacigalupe et al. has been peer-reviewed by Nadia Aubin-Horth, Wolf Blanckenhorn and Dries Bonte and recommended by Wolf Blanckenhorn for Peer Community in Evolutionary Biology. Peer-reviews, decisions, author's replies and the recommendation can be found here: https://evolbiol.peercommun...

On 2023-04-04 16:57:11, user Wenxi Xu wrote:

Hi, I found the supplementary materials by the way. thank you! I do have one question about the Figure 1C. For CD45 expression, fibroblasts showed the highest expression level and it is even higher than immune cells. It is quite different from what I know that fibroblasts tend to show low/negative expression of CD45. Could authors add some comments about it if it is possible? Thank you!

On 2019-10-23 09:13:00, user ahmad alali wrote:

Hi Zhiya,

Congratulations for the great job. How could we get the vector? Is it available?<br /> Thanks.

On 2018-08-02 09:32:01, user DKlug wrote:

Interesting study on fucosylation in Toxoplasma highlighting the importance of this translational modificiation during the life cycle of apicomplexan parasites. However, I want to point out that the conclusion to state that O-fucosylation of thrombospondin-like repeats of MIC2 are important for processing and efficient host cell invasion is very ambitious. From the data you presented you can, in my opinion, only say that the absence of fucoslylation in Toxoplasma negatively effects trafficking (of micronemal proteins). I would highly recommend to make a MIC2 mutant that can not be fucosylated anymore by mutating the respective residues (mutations shouldn't affect TSR folding). By this way you could also show if the added sugar moieties have an additive effect or if there is one modification that plays a dominant role.

On 2025-02-05 10:44:20, user pablo RANEA ROBLES wrote:

Hi! This is a very interesting study, and very relevant considering the debate on omega-6 PUFAs on metabolic health. However, when we were going to read it for a journal club we missed the methods section on this preprint.

On 2023-09-01 16:20:46, user Bernard Costa wrote:

This review resulted from the graduate-level course "How to Read and Evaluate Scientific Papers and Preprints" from the University of São Paulo, which aimed to provide students with the opportunity to review scientific articles, develop critical and constructive discussions on the endless frontiers of knowledge, and understand the peer review process.<br /> The study "Pain reflects the informational value of nociceptive inputs" by Michel-Pierre Coll et al. examines the potentiality for the central nervous system to optimize pain’s learning function by being modulated by pain perception. Moreover, the author's hypothesized that nociceptive processing could be augmented when pain is surprising to<br /> facilitate predictive learning about pain.<br /> To see if this occurs, the authors record nociceptive flexion reflex amplitude combined with electroencephalography to link nociceptive flexion reflex modulation with the influx of ascending nociceptive signals to the cortex. <br /> On the electroencephalography they use two indices of expected pain. The first is the late positive potential (peaks around 400-600 ms after the pain-predictive cue). The second is a decrease of oscillatory power in the alpha and beta bands. This seems to be a very powerful take on how pain learning is associated with the expectation of pain. And the combinations of different methods of recording the perception of pain is a novelty in this area, combining the recording of nociceptive flexion reflex modulation amplitude with electroencephalography to link the modulation with the influx of ascending nociceptive information to the cortex. That way the authors can distinguish between the nociceptive responses and perception of pain in the participants and that the predictions errors between anticipated predictions and actual outcomes are associated with the nociceptive flexion reflex.<br /> The procedure of the experiment is very well delineated and explained in the text.<br /> The statistical analysis is very well explained and appropriate for the problem in hand. Then again, the authors did not specify why they use the three computational models that they choose to use. Although those three computational models are frequently used in studies about uncertainty in perception takes in volatile environments, the justification for<br /> their uses are intrinsically different. The justification for the use of a computational model must be a priori unless the study is about the models themselves. All that said, the interpretation for the basis why the Rescorla-Wagner model provided a better fit for the data is very good. Maintaining the environmental volatility relatively high and constant throughout the task due to the regular introduction of new cues is a very good way of simulating actual environmental conditions. <br /> Also, they did not say why they used thirty-five participants. Is always recommended to do a pilot to calculate effect and sample size or to justify this quantity from the literature or previous works from the group, published or not. It is argued that this is good enough to reach meaningful conclusions in this specific case.<br /> A limitation of this study is the dependence on the connection of the electroencephalography markers used for pain processing to aversive prediction errors that still don't have strong evidence in the literature. The authors make that clear in the text. Meanwhile, combining electroencephalography with functional magnetic resonance imaging or functional near-infrared spectroscopy may give more insights and give more evidence on pain processing to aversive prediction.<br /> The results of prediction errors being associated with increased pain perception and physiological pain responses and also with increased anticipatory cortical responses shows that this study is well done and very promising reliability and validity.

On 2018-12-11 23:02:30, user Sebastian Aguiar Brunemeier wrote:

D+Q may not be the best way to clear SnCs, though. Better senolytics/morphics are needed. The Unity compounds can't be administered systemically due to toxicity, so hopefully someone comes out with a better senolytic soon.

On 2016-06-23 17:35:05, user Simon Schultz wrote:

Please note that this paper was published as:

S. Reynolds, J. Oñativia, C. S. Copeland, S. R. Schultz and P. L. Dragotti (2015). Spike Detection Using FRI Methods and Protein Calcium Sensors: Performance Analysis and Comparisons. Proceedings of the 11th IEEE International Conference on Sampling and its Applications (SampTA), Washington, DC, May 25-29, 2015, pp 533-537.

On 2020-07-22 14:08:17, user Tom Williams wrote:

Dear Tanai,

Thanks for your comment and interest in the study. You raise an intriguing point. One thing to note is that for computational tractability, we used broad but sparse taxon sampling in these analyses, and as such included a fairly limited set of photosynthesizers (in either dataset). The relevant gene families were therefore too small to compute PPs as we do for the more widespread families. I think to really nail down the origins of those genes, a much denser sampling, or a sort of targeted oversampling of the key lineages, would be needed. However, we will take a close look at the genes you highlighted and will include the results in the next version of the ms if we find anything!

Best,

Tom

On 2021-10-24 15:04:18, user Kayla Hess wrote:

This investigation will surely revolutionize the field of terrestrial biomonitoring as it establishes the successful use of airDNA outside of a controlled laboratory setting. Collecting samples at multiple locations inside and outside of the park provided a large and diverse pool of data, thus supporting the validity of the investigation. The read count variability of airDNA inside the enclosures in comparison to outside seemed unexpected. Especially since there was so much drift between the closures, I expected there to be a similar amount of drift to outside the park as well. What might account for this difference? Wouldn’t the same weather and climate patterns that caused airDNA to drift between the enclosures also cause it to drift outside? I figured that maybe most of the drift could have been due to human activity rather than weather events but I could be mistaken. In addition, how might human activity affect the accuracy and precision of airDNA terrestrial biomonitoring in the wild?

On 2019-12-19 02:04:15, user Richard Rocca wrote:

Please add Supplementary information referenced in the pre-print.

On 2017-03-14 15:12:26, user Constantin Cretan wrote:

I propose you a mental experiment:

We<br /> have a population with 10,000 SNP of common polymorphism on <br /> intelligence. An individual with an average genotypic intelligence has <br /> 4,000 common SNP that favor intelligence (F) and 6,000 common SNP that <br /> are unfavorable for intelligence (U). The polygenic score is 40%.

An<br /> individual with an average genotypic intelligence have also 8,000 rare <br /> SNP that favor intelligence (f) and 12,000 rare SNP unfavorable for <br /> intelligence (u).<br /> By<br /> the average 70 de novo mutations per individual, one slightly increases the <br /> intelligence (like a common SNP-F), 21 slightly decrease the <br /> intelligence (the same magnitude as for 21 common SNP-U) and two <br /> decrease the intelligence more (each having the effect of 10 SNP-U).<br /> The average decrease by de novo mutations equates 40 SNP-U, equating 0.3 IQ points (40:(4,000F+8,000f)=0.3%).<br /> We<br /> suppose the selection pressure on intelligence is not negative or zero,<br /> but the strength of this pressure is not enough to mantain the actual <br /> level of intelligence. This strength is enough to eliminate only the <br /> equivalent of 30 SNP-U.<br /> Because<br /> there are 12,000 SNP-u and 6,000 SNP-U, the selection will eliminate 20<br /> SNP-u and 10 SNP-U. The polygenic score on common SNP will increase <br /> with 10 SNP-F, equating with 0.25% (even if the the genotypic <br /> intelligence will decrease with 10 SNP-U) per generation.<br /> After<br /> 100 generations (3,000 years), the polygenic score will increase with <br /> 1,000 SNP-F, an average individual will have 5,000 SNP-F and 5,000 <br /> SNP-U, and a polygenic score of 50% (a relative increase of 25%, like in<br /> the paper of Woodley&Piffer). But the genotypic intelligence will <br /> decrease with 1,000 SNP-u, equating with 8 IQ points (1,000: <br /> (4,000F+8,000f)=0.08).

If<br /> there are more common SNP than rare SNP that increase/decrease the <br /> intelligence, the polygenic score on common SNP will increase even <br /> faster and more, despite the decrease of the genotypic intelligence.

I<br /> believe the increase of polygenic score is a proof for a positive <br /> selection on intelligence, but it is not a proof for the increase of <br /> genotypic intelligence.

On 2024-07-05 13:00:12, user Xuan Zhang wrote:

Now published in Genome Biology: https://genomebiology.biome...

On 2021-10-24 17:46:08, user banksinoma spinifera wrote:

This is an interesting study about the effect of propionate on diabetes-induced neurological dysfunction.

Two small suggestions for the authors:<br /> - The title is a bit misleading. The role of PI3K-AKT-eNOS is not demonstrated. The changes are associated with the improvement but there is no loss-of-function or gain-of-function study to demonstrate it.<br /> - In Fig. 13a, the blots for p-PI3K and p-AKT are identical. Authors should check that out.

Best regards!

On 2022-11-03 02:07:05, user Nathan Pearson wrote:

Thanks for so quickly posting these findings for the community.

Can you clarify (in main text, and perhaps in figure and/or yet unposted supplemental material) how many of the bivalent recipients had been boosted once (or twice) previously with monovalent -- and, ideally, a basic profile of age, sex, etc. among the study cohorts?

And, of obvious interest, can you report which analogous titer measures differ significantly -between- cohorts (rather than merely within cohorts), including after accounting for multiple testing?

On 2017-11-14 12:56:32, user Tobias Rose wrote:

This article is now formally peer reviewed and published: <br /> http://dx.doi.org/10.1038/s...

Thanks a lot to all who provided us with input on our preprint!

On 2019-01-08 01:43:14, user Denise Montell wrote:

A sensitive and specific reporter for RasGTP in vivo.

On 2019-07-18 13:02:35, user Sylvia Kornherr wrote:

This is very compelling research. Looking forward to more work in this area. EPC Solution

On 2021-04-04 14:03:47, user Damian_VA wrote:

DOI to the journal pre-proof version of this article: https://doi.org/10.1016/j.j...

On 2022-09-03 02:22:59, user Babu Sudhamalla wrote:

Now published in ACS Biochemistry doi: 10.1021/acs.biochem.2c00297.

On 2020-04-18 19:45:23, user Chung-chih Lin wrote:

Dear Dr. Pachitarium,

I use web-based Cellpose to segment my image and it showed the error as the following figure after submssion.<br /> Even I try the images of the web page, it still showed error.<br /> Can you help me this?

Best Regards,

Chung-Chih<br /> https://uploads.disquscdn.c...

On 2022-04-01 20:33:17, user Joel McManus wrote:

Please see the McManus Lab's response to the commentary from Byeon, Leppek and Barna<br /> https://drive.google.com/file/d/11PmIIZ4xiq1CYVWAx1imA71Qdpv08DmD/view?usp=sharing

On 2015-07-14 21:16:36, user FreeTim wrote:

Thank you so much for this article--something I think everyone has been thinking for a very long time and desperately needs addressing. It is also surprising that with all the extra panels and supplementary data, there are still so many papers that can't be reproduced (very high in Cancer Biology!). I think a big problem is inexperience and insecurity of the editors, who allow nit-picky reviews to make authors spend $40-50K making insignificant additions to the papers. Instead, they should really ask the reviewers to ask for only changes that are absolutely necessary to make the point and consider what they expect the cost for each to be. It is ironic that with less funding come more expectations.

On 2020-05-07 03:16:49, user Kristin Cunnar wrote:

Here is an interesting publication regarding a need to investigate more on Coronavirus 19 patients for fungus and bacteria. There is a paucity of information regarding fungus and Coronavirus 19 patients. Basically, unless the blood sample is drawn on day 1, there could be arguments made that any fungus or bacteria grown from a blood culture could have arisen by the environment from the Hospital instead of the Coronavirus 19 patient already arriving at the hospital with the hidden fungus and/or bacteria. Coronavirus 19 is a mycovirus. In my opnion, the Coronavirus 19 originally began as a fungal spore that got attacked by a virus which changes the genome, and can hide the fact that it is a fungus as an origin. Important to get blood sample on day 1.

https://academic.oup.com/ci...

On 2021-02-04 00:02:39, user Melody Zaki wrote:

Hi Dr. Alkhatib et al.,

Thank you so much for conducting this research on triple-negative breast cancer. It was so interesting to read your methodology as you approached this looming problem in the way that we treat cancer patients. I am so glad that I had the opportunity to not only learn more about TNBC through reading your paper but that I was also exposed to the surprisal analysis technique you used to evaluate single-cell responses to RT and CT. The figures that you used to describe your methodology were very helpful for students, like me, who may not have understood the nuances of your experimental approach upon first glance of the paper.

As an undergraduate student, however, there were still some things that I was left wondering and confused about after reading through your research. For example, I thought it would have been very helpful to clarify the demographics of the patients used in this study. Especially given that you were studying single-cell variations in breast cancer tumors, it may have been interesting to compare whether certain races suffered similar mutations or if they had similar cell-specific signaling signatures. I also struggled to understand some of the figures that you used to display your results because of the color changes and the inconsistent axes labeling. For example, in figure 4g I was confused as to why you decided to switch to black and white labeling of the active processes and believed it would have been more helpful to label the vertical axis as ‘process number’ and the horizontal axis as ‘CSSS.’ At first glance, it seemed as though you were now looking at 14 different CSSS instead of the CSSS in the fourteen different processes. It was also unclear why you decided to switch up the order of the CSSS (as they were no longer in alphabetical order). Small changes to the organization in your figures would be very beneficial in allowing those who do not have as much experience in research to gain a lot from your writing.

Overall, I learned so much from your work and I'm so excited to see how others will respond to your research! I also hope that others will apply your methodology to other types of cancer research as it could shed a lot of light in how we are approaching treatment options.

Thank you again!

On 2023-02-13 07:44:05, user Srimoyee Mukherjee wrote:

https://pubmed.ncbi.nlm.nih....

On 2018-05-08 14:38:21, user Ruibang wrote:

As reminded by Jason Chin, the link to the GRCh38 reference genome has recently changed to 'ftp://ftp.ncbi.nlm.nih.gov/....

On 2023-05-19 00:23:53, user Zhou Chen wrote:

Thrilled to share that our first known client-loaded EMC, a real multiple TM segment channel:chaperone structure is out now @Nature. Check the link below:<br /> https://doi.org/10.1038/s41...

On 2021-03-02 23:50:04, user Sarah Nelson wrote:

Related to Cassandra's comment - I suggest re-casting the PLINK sex check as a sex check rather than "gender" check. Chromosomes cannot tell you about gender.

On 2019-04-13 17:11:43, user Andrei I Ivanov wrote:

We have published a paper a few years ago that shows the roles of anillin in regulating apical junctions and the perijectonal actomyosin in human epithelila cells https://www.ncbi.nlm.nih.go...<br /> I am surprised that the authors did not refer to our study.

On 2019-10-11 19:37:55, user David Marjanovic wrote:

This is a manuscript submitted for the third round of peer review at PCI Paleo.

On 2020-09-15 14:47:18, user Rosalina Fonseca wrote:

This manuscript is now published. Here is the link to the manuscript in Cerebral Cortex

https://academic.oup.com/ce...

On 2021-01-28 02:51:54, user LarsN wrote:

Someone forgot to include the proper link to the software. Find it here on GitHub https://github.com/LarsNauheimer/HybPhaser

On 2021-04-18 13:14:31, user Anandi Krishnan wrote:

Posting here a unique context to this work that might be helpful for some:<br /> this preprint is the first and major culmination of a unique research re-entry award to A.K. by the NIH (specifically, the National Center for Advancing Translational Sciences, NCATS but also available from other institutes). The research re-entry awards are designed for those experiencing life-related interruptions to their careers – please visit this link if this is of interest: https://ncats.nih.gov/ctsa/....<br /> This work would not have been possible without this unique NIH mechanism (that then facilitated a subsequent NIH/NHGRI career development award to A.K.). More of the backstory here: https://twitter.com/anandi_...

On 2023-08-22 17:17:49, user Rory O'Keeffe wrote:

The DOI of the published version in IEEE Transactions on Neural Systems and Rehabilitation Engineering:<br /> 10.1109/TNSRE.2023.3291748

On 2023-04-19 00:11:43, user Peter H Uetz wrote:

Can the authors please comment on some taxonomic implications? I see that Ninia atrata nests within Sibon and Tropidodipsas is paraphyletic with Sibon in their tree. There is probably other stuff like this ... (haven't checked carefully).

On 2024-09-30 08:04:04, user Ema Nymton wrote:

Zach's reply does not really address the points in my previous comment. I note that it also does not address the points raised in Bloom 2024.

It only tangentially addresses 2 points in my post, both of which are apparently misconstrued.<br /> The first point that was addressed was about the p-values- however this response did not acurately portray the argument.<br /> My post never said it was misleading, but rather that the sampling bias towards the stalls would concentrate the highest p-values near the stalls, even more so than a simple relative-risk heatmap would, making the result of elevated p-values near the stalls expected whether or not the stalls were the origin of the outbreak.<br /> Compare: https://i.imgur.com/iDD6D93.jpeg to https://i.imgur.com/2YmYlZp.png

The second point "an argument that implicitly assumes all expectations for all coronaviruses are identical regardless of their hosts and modes of transmission,"<br /> No, it does not assume they are "identical", but rather that there are general similarities. I am asking for an explanation why the distribution is so different.<br /> Neither mode of transmission nor host explain this - nor does date of sampling.<br /> While not explicitly stated, there is a substantial focus on Raccoon dogs.<br /> You show the distribution of a virus with the same putative host (Raccoon dogs) and same transmission methods (respiratory). The distributions are strikingly different.<br /> Compare https://i.imgur.com/iDD6D93.jpeg to https://i.imgur.com/OFXpCIf.jpeg

This paper argued (in preprint), and it still argues )in final published form) that the 12 Jan RNA had more time to decay. Considering the market was closed at the same time, why did the SARS-CoV-2 reads decay to such lower values than other CoVs in samples from the same dates, and within the same samples, as shown by Bloom?<br /> See here: https://i.imgur.com/kIOTTYq.jpeg <br /> -even when stratifying by collection date, clear negative correlations still come up.

Overall, I find that a lot of speculation is offered, but it comes accross as excuses for the data not actually showing that the virus originated from the wildlife stalls.

I'll also raise additional critiques now:<br /> 1) The heat maps do not colocalize with the potential hosts:<br /> SARS-CoV-2 (RR p-values) and Raccoon dog reads shown here: https://i.imgur.com/AyAiag9.jpeg

In the paper, instead the focus is on the south-west portion of the market.<br /> As I see it, there are two possibilities:<br /> i) The analysis DOES have sufficient resolution: in which case the resulting heat map not co-localizing with the wildlife stalls would exonerate them<br /> ii) The analysis DOES NOT have sufficient resolution: in which case it must be noted that the eastern and northern parts of the market were very poorly sampled. In this case, finding more positive samples where more samples were taken does not allow any conclusions to be drawn.

2) The analysis of the SNPs of the Raccoon dogs clearly aligns with the the SNPs of locally caught wildlife (the C14859A + A15304G genome from Hubei, and the C14372T, C15102A, C15252A, T15306C genome from Hubei). Despite the evidence clearly pointing to locally caught raccoon dogs, speculation is offered (again) that maybe racoon dogs with these SNPs are found further south.<br /> Notably, Hubei (the local province) is not a plausible origin of the progenitor. Pekar seemingly agrees: https://www.biorxiv.org/content/10.1101/2023.07.12.548617v1

In essence, this paper shows:<br /> i) 3 lineage B sequences 1 low quality lineage A sequence have a (very very wide) tMRCA confidence interval that overlaps with a tMRCA calculated from many more A and B lineage genomes: hardly anything surprising or something that conclusions can be drawn from

ii) Within the heavily sampled area of the market, the areas of elevated relative risk don't actually overlap with the locations of the wildlife stalls<br /> -There is just one stall on the periphery of an area of elevated risk, the one closest to the entrance/mah jong rooms/bathrooms, which is where the higest elevated risk is.

iii) The raccoon dog genomes from that stall suggest that they are locally caught, and do not come from areas with potential reservoirs of SARS-CoV-2's precursors

On balance, the sum of this evidence seems to suggest that the HSM wildlife stalls in general, and raccoon dogs specifically, were not the origin of this virus.

Much speculation and many excuses are offered, but it must be reiterated that these excuses and speculation, not evidence.

The correlation analysis of Bloom can point right to the hosts of other viruses. The heat maps produced for this paper can point right to the hosts of other viruses. SARS-CoV-2 continuously fails to produce evidence and associations that other animal viruses at the market produced.

The way these findings are presented in the paper and to the general public through the media seems to be at odds with what the data actually shows.

On 2018-04-23 18:57:44, user Francesco Brighenti wrote:

There is no certainty at all that the Sanskrit term samudra in RV 7.95.2 ("[Sarasvati River,] pure in her course from the mountains to the samudra") indicates the Indian Ocean. In fact, the primary meaning of samudra in the RV is 'a gathering of waters', 'a con-fluence' (?ud + sam).

A recent geochemical study, published in November 2017, has shown that the Himalayan-fed Sutlej river once flowed into the present Ghaggar-Hakra river channel (the Sarasvati of the RV) but changed course and joined the Indus in the Panjab region in the late Pleistocene-early Holocene (between 15,000-12,000 years and 8,000 years ago):

https://www.nature.com/arti...

Thus, the "Sarasvati" (actually the paleo-Sutlej) may have flowed to the Indian Ocean till 8kya at best. Yet the RV just says (in one verse) it reached the *samudra* (= a large body of water, for instance an inland lake or delta, or a playa), not the Indian Ocean!

There is no way the composers of RV 7.95 (a hymn mentioning metal fortresses and chariots!) lived on the banks of that river 8kya or earlier.

On 2018-04-05 01:56:00, user IMMCHR wrote:

Dear Dr. Reich & his esteemed team,

This is a wonderful paper for two things :-

1. The massive amount of new data that has been published including first samples from Central Asia & Eastern Iran & also to a limited extent - South Asia.

2. The huge collaborative effort of scientists of so many institutes spread across various continents. Truly mindboggling.

Nevertheless, I do not believe that the paper has been able to prove a steppe migration into South Asia. A steppe migration cannot truly be proven to have taken place unless we have aDNA from South Asia which is prior to that suppossed migration and which happens to lack the steppe input that later South Asian populations have.

I will divide my comment in 2 parts :-

PART ONE

I take strong objection to the theory that it was only AASI ancestry which was native to South Asia and that Iranian Neolithic ancestry is not native but intrusive. I would also like to take this opportunity to put forward a model which can explain a possible migration out of South Asia in the Holocene to various surrounding regions.

One of the facts made bare in this paper is that the early Iranian Neolithic farmers were goat herders and they did not keep either sheep or cattle. This is in quite contrast to the Anatolian Neolithic farmers who were the domesticators of Taurine cattle.

Yet it is argued that it is these very Iranian Neolithic goat herders who spread into South Asia and established its Neolithic. Quite puzzlingly, South Asian Neolithic also had domestic sheep and domestic Zebu cattle. Infact, South Asia is an independent center of cattle domestication and the Zebu cattle is native to South Asia.

You state in your paper that perhaps with the Anatolian admixture into Iranian Neolithic goat herders, the individuals of Anatolian ancestry may have contributed to spreading agriculturalist economies across Iran and further east. But quite clearly, Iranian goat herders never took to cattle and sheep until much later.

Below is a paper that argues that domestic cattle suddenly appeared in the eastern fertile crescent (Iran Neolithic) around the middle of the 6th millenium BC

https://scholar.harvard.edu...

However, the domestication of South Asian Zebu cattle had already occured by the 8th millenium BC as suggested by two widely separated sites of Mehrgarh in Baluchistan and Bhiranna in Haryana.

http://archaeology.up.nic.i...

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

Now if we argue that the agricultural economies were spread by the people of Anatolian ancestry into Iran and eastwards, we are left with the question of no Anatolian Neolithic ancestry in ancient South Asia.

Nor could it be argued that Iranian goat herders spread it because they themselves could only start using cattle in the middle of 6th millenium BC which is almost 2 millenia later than the 1st appearance of the indigenous Bos indicus cattle at South Asian Neolithic sites.<br /> Undoubtedly there is a link between the Neolithic site of Mehrgarh and the early Iranian Neolithic sites in Zagros. But there is no proof that the farmers from Zagros settled at Mehrgarh. It could just as well be that Neolithic farmers from around Baluchistan settled in Zagros and due to unfavourable conditions could not bring along their cattle and sheep.

The implication of the above is that Iranian Neolithic like ancestry is not necessarily intrusive into South Asia during the Neolithic but could be much older than that.

The following papers give strong supporting evidence for such a probability

http://www.cell.com/ajhg/fu...

https://www.nature.com/arti...

https://bmcevolbiol.biomedc...

https://www.nature.com/arti...

https://bmcevolbiol.biomedc...

All the above papers prove that CHG or Iran Neolithic are unlikely to have contributed to Indian ancestry and that they are good vectors for South Asian ancestry because they are related to an ancestral South Asian population that split off from them quite likely in the Mesolithic.

In other words, an Iranian Neolithic like population with more ANE affinity is most likely to have been native to South Asia atleast since the Mesolithic. It is pertinent to analyse the aDNA from Neolithic and Mesolithic samples from South Asia to verify this. It is also necessary to throw off the notion that the hunter gatherers of South Asia were all AASI like. South Asia is a big place. An Iranian Neolithic like hunter gatherer could have lived in South Asia in the North & Northwest at the same time as a AASI type hunter gatherer may have lived in the Peninsular & more interior regions.

The paper has also argued in one of its Supplementary sections that Iranian Neolithic like populations could not have possibly moved out from South Asia because they lacked the AASI admixture. This is an argument of fallacy. As I have stated above, an Iranian Neolithic like hunter gatherer with no AASI admixture most likely lived in the regions of North India / Pakistan. Such a population could definitely have moved out of South Asia into Iran or Central Asia before admixing with the AASI. Therefore a lack of AASI in neolithic farmers or chalcolithic people in Iran does not prove that they had no ancestry from South Asia.

On 2017-02-15 15:31:22, user Marius Pachitariu wrote:

Dear Eftychios and Andrea,

I’m trying to understand the relationship between the registration algorithm you are proposing here, and the one we introduced in Suite2p (http://biorxiv.org/content/... "http://biorxiv.org/content/early/2016/06/30/061507)"). They appear to be very similar: they both divide the FOV into overlapping blocks, compute rigid offsets for each block by cross-correlation, and then interpolate the offsets to generate a full field of continuous and smooth motion vectors.

At present, I can see only the following differences between the two approaches: 1) the precise method of tapering for smoothing between blocks (we use Gaussian, you use linear); 2) the use of Fourier-domain upsampling to avoid smearing.

This apparent similarity of the two algorithms raises a question: do the performance differences you report arise from algorithmic differences, or from parameter choices? In Suite2p one can specify the block size, overlap, and tapering length, but it seems you chose different values for suite2P than for your implementation (for example, no splitting in X). An accurate comparison of two methods certainly requires use of appropriate parameters for both.

I also wonder how confident you are in your performance benchmarks, given that you don't have ground truth. Optic flow may be a problematic performance metric: activity dependence in neurons, and particularly in neuropil, can appear to induce optic flow, which could contaminate or swamp any real movements. Furthermore, both correlation between frames and crispness can be decreased simply by adding noise.

In summary, I fully agree that nonrigid registration will be an important tool for 2-photon microscopy. In order to accurately distinguish between the performance of different approaches, however, the community would benefit from standardized datasets and performance metrics, similar to the CellFinder and SpikeFinder challenges.

Best,<br /> Marius

On 2024-12-05 00:13:55, user Krista Longnecker wrote:

This has now been published as: https://aslopubs.onlinelibrary.wiley.com/doi/10.1002/lno.12275

On 2020-05-13 17:51:23, user Egon Willighagen wrote:

I found a minor typo: AEC2 (PS interesting work!)

On 2016-07-14 14:39:17, user hasearcy wrote:

This is a nice study but the manuscript somewhat overstates the novelty of their PR-DUB findings. Virtually every component of the complex that they found has already been shown. They fail to cite an important article: Sowa, M. E., et al. (2009). "Defining the human deubiquitinating enzyme interaction landscape." Cell 138(2): 389-403.

On 2020-05-11 13:26:37, user Liz Miller wrote:

This paper was the subject of the Miller lab weekly journal club and, following a fun discussion of the findings, we have the following comments to make. Please bear in mind that we do not study lipid droplets, but enjoyed reading the manuscript nonetheless.

Lipid droplets (LD) are one of the key players in ER stress response. Among the proteins involved, the fat-induced transmembrane proteins (FIT) comprise a protein family that provides support for LD formation. Of the two sub-families, FIT1 and FIT2, yeast have two FIT2 family homologs, namely Scs3 and Yft1. It has been shown previously that the yeast FIT2 homologs are dispensable for the formation of LD, but are necessary for budding of the LD from ER membrane. It has also been shown that FIT2 homologs in yeast may be involved in lipid homeostasis and crosstalk with other ER-stress pathways including unfolded protein response (UPR). However, an accurate description of the molecular mechanism of the function of Scs3 and Yft1 still remains to be seen.

In this work, Shyu et. al. attempted to answer this question by investigation of lipid metabolism and protein homeostasis under ER stress conditions involving yeast FIT2 knock-out strains. They also performed membrane yeast-two-hybrid screening to dissect protein-protein interactions associated with FIT2 homologs. They found that loss of FIT2 homologs affect both lipid homeostasis and protein homeostasis, providing extensive and solid evidence on the importance of FIT2 function in ER stress response.

Following our group discussion, here are some brief comments:

The temperature sensitive strain used in this work, scs3-1, provides a nice handle for genetic and biochemical studies. It would be very helpful if the authors can report some details on this strain, including the sequence of scs3-1 mutant and how it perturbs the UPR pathway and lipid homeostasis under normal growth condition.

It would be nice to have ire1? strain as a control for Figure 2A.

As inositol and choline are both important precursors for lipid biosynthesis, it would be very interesting to perform lipidomics on the scs3? strains under different growth conditions with or without them. As lipidomics may be a little bit demanding to perform, it would be nice if lipid abundance including PS and PE could also be traced according to the same procedure as PC and PI represented in Figure 2.

The MYTH assay revealed a more promiscuous interaction pattern for Scs3 and specific interactions of Yft2 (high enrichment in Pho88 and Shr3). Further investigation of the difference between these two homologs will be interesting.

The interplay between lipid precursor availability, FIT2 homologs and ERAD pathway illustrates the complexity involving the function of FIT2 in lipid and protein homeostasis. Reflection of this complexity in the title could be really helpful.

On 2020-12-22 23:40:21, user Atulya Iyengar wrote:

Specific point-by-point response to eLife Peer Review

Response to #1

The study by Iyengar et al describes age- and temperature-depended changes in the neurophysiology of the giant fiber (GF) system in adult wild type and superoxide dismutase 1 mutant flies (SOD[1]). While the main GF circuit and downstream circuits exhibit little change when flies areared at 25C, GF inputs and other circuits driving motoneuron activities show age-dependent alterations consistent with earlier studies. Rearing flies at 29C temperatures had no additional effects except that age-dependent progression of defects were accelerated, as it was expected from previous studies. In SOD[1] mutants, which are short lived, changes in the neurophysiology of the GF system were different than those induced by high temperature.<br /> Overall this technically challenging, and well executed study provides a nice description of the effects of aging, high activity (induced by higher temperature), and loss of SOD function on the neurophysiology of the GF system. However, most of the described effects have been observed in other systems and are thus not entirely novel. Moreover, the study does not provide any insight into the mechanisms underlying the age-depended alterations of the examined neurons. Thus, the overall significance of the described findings is limited.<br /> We thank the reviewer for their recognition of “this technically challenging, and well executed study provides a nice description of the effects of aging, high activity and loss of SOD function on the GF system”. We would like to stress that our study goes beyond the GF system, including its input circuit in the brain and other thoracic circuits that drive the motorneuron, bypassing the GF pathway. Furthermore, we provide the first identification of age-related changes in the flight motor circuits and those recruited during seizure discharges.

We believe that the following comment misrepresents the essence of our work: “described effects have been observed in other systems and are thus not entirely novel.” To our knowledge, ours is the first study describing the aging trajectories of the flight motor circuit, afferent inputs to the GF system, and seizure discharge properties. Conceptually, this is the first study in Drosophila aging to elucidate the age trajectories of different motor circuits, and the first to identify functionally resilient vs. vulnerable circuit elements. <br /> The idea that high-temperature rearing “accelerates” aging has been generally assumed for many decades. As such, it has been a general practice of aging studies in flies to rear flies at 29 C to expedite the experiments. This study is the first direct and quantitative comparisons of neurophysiological properties between the two temperatures. Our results provide justification and basis for proper interpretation for experiments conducted at 29C.

Fig 1 D. The quantified wing beat frequency is hardly visible in the shown graph. Please change the scale of the y-axis appropriately.

We thank the reviewer for the suggestion. We will re-scale the figure to a narrower range accordingly in the next version. Wing beat frequencies are largely stable during flight bouts, but can be quite variable between individuals (see Curtsinger 1984 and Iyengar & Wu 2014).

Fig 2C-D. What is the difference between the light and strong colored dots in shown graphs? I assume one is the mean and the other are all data points. If so, please describe this in the legend.

Yes, lighter points represent individual data datapoints, while the darker points represent mean +/- SEM. We will clarify the figure legend in the next version.

Response to #2

This is longitudinal aging study of the physiological changes in a specific Drosophila neural circuit that participates in flight and escape responses. To date there have been few examples of longitudinal aging studies looking at the vulnerability or resilience of neurophysiology at the resolution presented in this study. The analyses have revealed different trajectories for individual neural components of the studied behaviors during aging. The study also reveals different sensitivities of neural components to stressors that are known to alter lifespan (temperature, oxidative stress). The study is well-written and the experiments are performed at a high level. A concern is that the study is highly descriptive and provides very little mechanism to explain the differences in the vulnerability or resilience of neural functions observed. In addition, the authors present little evidence other than lifespan to support their interpretation of the effects of the experimental conditions at the cellular level. For these reasons, I do not support publication in eLife at this time and I suggest significant revisions before consideration.

We appreciate that the reviewer recognizes the novelty of our approach and the level at which we performed the experiments and prepared the manuscript. We are certainly interested in the cellular bases of aging and Sod mutant phenotypes. We have been performing experiments using embryonic culture system, larval neuromuscular preparations in addition to the adult physiological characterization reported here. We are currently preparing publications on the findings accumulated over more than a decade. (See for example a recent bioRxiv pre-print https://doi.org/10.1101/202... O’Harrow et al, 2020). It is outside the scope of this paper to describe some of the adult neuromuscular junction work we are conducting. We will nevertheless try to make reasonable inferences in the Discussion to the cellular mechanisms discovered in larval and adult NMJ systems, even though they may not “explain” the aging trajectories characteristic of individual circuits. Within the scope of this paper, we aim to establish the functional changes of individual circuits and neural elements underlying aging progression of behavioral performance. With this knowledge well-established, the cellular and biochemical mechanisms could be more effectively elucidated.

Major Critiques:

1-Overall, the study is highly descriptive and there is a lack of experiments aimed at understanding the cellular effects of aging on neural function.

We seek to provide a detailed and comparative analysis of aging in different motor circuits. As mentioned above, we attempt to also study ‘mechanisms’ of aging by manipulating genetic, molecular, and cellular properties in Drosophila and examining behavioral/physiological properties. However, identification of salient neurophysiological properties can established most effective readouts for further molecular and cellular studies. To date, aging studies have largely examined the properties of the of the GF neuron and its efferent elements. Our findings establish the suitability of certain neural elements (e.g. habituation of the GF neuron afferents, properties of thoracic circuits driving DLM activity like flight and seizures) to serve as markers of the aging process.

2-There is a lack of supporting data or discussion about the expected cellular mechanisms of the high temperature manipulations or SOD mutants. While it is true that both of these manipulations shorten lifespan, their relationship in the natural process of aging remains controversial. The ability to extend the resilience of the neural components studied by a manipulation that extends lifespan would be very supportive (i.e. diet, insulin signaling mutants).

We thank the reviewer for their helpful comment. We will expand our discussion on potential mechanisms underlying accelerated habituation and increased seizure susceptibility in the next version of the manuscript. Any relevant discoveries in our larval and adult NMJ work will be cited.<br /> We also believe analysis of long-lived flies (e.g. chico) would be a beneficial experiment, and will mention this in revised version of the Discussion. However, such experimental extensions are not likely to be included within the current scope. The present study represents the cumulative findings of 15+ years of experimental work. To add another category of mutants will require recordings from a large number of flies, and would be prohibitively time consuming.

2-The data from the current study demonstrates that the major effect of SOD mutants on neural function and mortality exists in newly eclosed animals suggesting significant issues during development in SOD mutants. This complicates the comparison of this condition to the other conditions or even considering it a manipulation of aging. The authors should also considering showing that the effects on neural function by SOD mutants is mimicked by other conditions that alter ROS more acutely such as paraquat exposure or test mutations in insulin signaling (i.e. chico) which have been shown to increase anti-oxidant expression.

Throughout the manuscript, we extensively point out developmental issues in Sod mutants. As Figure 7 demonstrates (Init. Column), the 1-3 d old Sod examined often showed the biggest phenotypic differences. It is clear that sod does not simply “accelerate” aging, and have clear embryonic/larval/pupal developmental defects, which have been uncovered in our embryonic culture and larval NMJ work (see above). Please see also our comments to above (point #2, chico).<br /> We thank the reviewer for the suggestion to examine paraquat-exposed flies, and are currently extending our paraquat feeding experiments from characterization of lifespan to neurophysiological effects.

3-The authors contend that the changes in neural function, particularly in regards to seizure susceptibility, provide indices for age progression. It is unclear to this author how these neural functions described in this study, including the appearance of seizures, contribute to lifespan of the flies. One could imagine that changes in flight distance or escape response could contribute to lifespan in the wild, but do changes in flight, jump response, or seizure susceptibility have any bearing on the lifespan of flies in vials? Why would seizure susceptibility be predictive of mortality?

We thank the reviewer for raising an important point regarding the interpretation of “indices for age progression” and the relationship between lifespan and susceptibility to seizure discharges in our study. We merely refer to certain neurophysiological parameters as potential “indices” or “markers” because they show a clear and robust age-dependent trends. (see ~line 575). We submit that our work is akin to the first establishment of a “standard growth curve” in child development for a particular population. Therefore, the simple correlation between physiological states and stages of aging should not be interpreted as causal. We never meant that the changes directly cause shortened lifespans. Rather, the age-related physiological changes are best illustrated by the trajectory of the relevant parameters along the lifespan. For example, the ECS Threshold (Figure 5) displays a monotonic reduction which scales according to the % Mortality along the lifespan curve. This finding does not imply that the reduced seizure threshold causes shortened lifespans. Conceivably, an underlying cause e.g. dysregulation of neuronal- or circuit-level excitability could lead to reduced ECS threshold and abnormal behavioral traits contributing to shortened lifespan.

We found the spike pattern during the initial seizure discharge (ID) that correlates with the death rate when lagged by a certain amount (i.e. the procedure for determining cross-correlation). As such this finding indicates a potential suitability of the initial seizure discharge spike pattern as leading indicator for mortality. Again, this observation in no way implies that seizure activity causes mortality. As we point out (Figure 6 results), this phenomenon we point out is that in population with heterogeneous risk of death, the fraction about to die display neuronal hyperexcitability manifest as extra ID spikes. Conceivably, the associated neuronal excitability could promote death, although more work will be required to delineate the precise relationship.

In addition, the assays presented here utilize experimental conditions (intense whole head stimulation) that are seemingly non-physiological so it is unclear what the declines represent in a normal aging fly. The authors need to discuss this.

As we note in the discussion (~line 471) the motor sequence recruited during ECS discharges is reminiscent of the sequence of seizures and paralysis induced by mechanical shock in bang-sensitive mutants. Conceivably, there may be overlap between age-related physiological changes and mutation-induced disruptions in bang-sensitive mutants (e.g. mitochondrial disfunction associated with the bang-sensitive mutant ATP? , see Palladino et al., 2003 J. Neurosci 23(4).).

It is also interesting to point out that the spike pattern during the initial seizure discharge (ID) correlates with the mortality (death) rate with a defined lag (see Fig. 6, the procedure for determining the best cross-correlation). As such, this finding indicates a potential suitability of ID spike pattern as time-locked indicator for mortality. Again, this observation in no way implies that seizure activity causes mortality. Conceivably, the increased neuronal excitability could promote death, although more work will be required to delineate the precise relationship.

4-There are no experiments aimed at understanding the cellular or molecular nature of the functional declines presented.

Please see our comments to the general comment and to major point #1.

Minor Critiques:

1-It would be supportive of their conditions if the authors showed an increase in oxidation in the SOD mutants compared to the other conditions. There are a number of relatively simple assays for the production of oxidation products such as 4-hydroxynonenal that have been used successfully in Drosophila.

Although measurement and comparison of oxidation in Sod mutants and aged WT flies would be interesting, it is beyond our capabilities. As pointed out in #2, we are comparing Sod mutants with paraqat-fed WT flies.

2-The discussion of overly long and lacking of discussion related to the age-related mechanisms. In addition, it is surprising that there is nearly no discussion as to the potential age-related mechanisms underlying the observed differences in neural performance between high temperature and SOD mutants.

See our response to the general comment above. Although beyond the scope of this manuscript, we are currently conducting studies on potential cellular/biochemical mechanisms.

3-It is surprising that there is no discussion of how this work compares to similar work from Aplysia that has provided detailed cellular mechanisms for age-related declines in gill withdrawal habituation (see Kempsell and Fieber, 2016).

We thank the reviewer for pointing out this work. We will cite and discussed the paper accordingly

Reviewer #2 (Minor Comments):

The authors should include the husbandry conditions in regards to humidity, light and dark cycles, and the number of cohorts included in their lifespan analyses.

We will add the information in the next version. We thank the reviewer for their suggestion.

Reviewer #2 (Additional data files and statistical comments):

The authors present mortality data but do not describe how this data is determined. Furthermore, the mortality presented is actually the death rate. The authors should run a standard Gompertz mortality analysis on their data and present this in the figure.

Based on A dictionary of epidemiology (Ed. Porta), mortality rate and death rate are synonymous. As we state in Figure 6 legend, the curves are ascertained by finding the negative slope of the lifespan curve (i.e. Fraction alive (t1) – Fraction alive (t0)/(t1 – t0)). <br /> We agree that a Gompertz plot is important to interpret the lifespan curves. We will add such an analysis as a figure supplement in the next version of the manuscript. Our preliminary analysis indicates both 25 and 29 °C-reared CS population lifespan curves are fit to a reasonable degree. However, we found a significant late-life mortality deceleration, as other groups (e.g. Curtsinger 1992) have noted in Drosophila.

Response to #3

1) Standards for conducting ageing studies in Drosophila and other model systems have gone significantly up in the last ~15 years following experimental evidence that genetic background can (and does) have a significant effect on the outcome of 'ageing' experiments (see Partridge and Gems, Nature, 2007). Today, 'backcrossing' relevant lines into a reference wild-type strain multiple times (to remove any second-site mutations) is a gold standard for virtually all ageing studies in Drosophila. Furthermore, this approach is being widely adopted even in the studies investigating physiological properties in developing flies (for example, in Imlach, Cell, 2012, the authors obtained very different electrophysiological results after 'isogenizing' the genetic background via backcrossing, and concluded that "the previous finding may have been due to a second site mutation"). As this important step is not mentioned in either the main text or in 'Methods' section, it is reasonable to conclude that the authors did not perform this step prior to conducting the experiments. Recent papers, one of which was referenced by the authors (Augustin et al PloSBiol 2017 and NeuroAging 2018) repeatedly demonstrated a significant, age-associated increase in the short-response (TTM and DLM) latency in the GF circuit following a strong stimulation of the GF cell bodies in the brain. It is likely that these age-related changes in the GF circuit remained undetected in the flies with non-uniform genetic background likely used in this work. The same problem affects the paper (Martinez, 2007) referenced by the authors throughout the manuscript.<br /> It is difficult to say which of the findings reported here are most affected by the variability in the genetic background, but any kind of correlation between the lifespans (Figure 1B) and physiological parameters should be taken with a high dose of scepticism.

This manuscript reflects findings over a 15+ year period of studies of widely used strains of Drosophila, the CS line, which originates from the Benzer lab. This line has been used as a reference/control strain in countless neurogenetic studies (including the Nobel Prize-winning studies on circadian rhythms in Drosophila). Our analysis of a range of neurophysiological properties has identified several parameters, way beyond the “short-response (TTM and DLM) latency” (SLR, or short-latency response in the literature since the 1980’s), to establish for the first time the clear age-dependent trends in age-vulnerable circuit elements including (flight, GF inputs and circuits recruited in seizures, see Figs. 4, 5 and 7). It is somewhat surprising that the review emphasis the “age-resilient” property of the SLR latency. As stated in the text, the reported age-dependent change is within a fraction of a millisecond, while the behaviorally relevant long-latency response (LLR) takes ~4-5 ms (Engel & Wu, 1992; von Reyn 2014). Therefore, the essence of this work in distinguishing aging-resiliency and -vulnerability of circuit components seems to be overlooked in the review.

We firmly believe that the data collected from the widely used canton-special (CS) line is important for the Drosophila neurogenetics researchers. In this tradition, the most reliable approach to avoiding “second-site” mutational effects is to generate multiple independent isolates. With the consistent phenotypes across independent alleles firmly establishes the identity of the gene responsible the phenotype of interest. We have studied multiple Sod alleles, including the original allele n108 highlighted in this paper. Our work also includes additional alleles such as x39, n58, n64 (Ruan & Wu, 2008).

Only for newly isolated mutant lines, we carry out “cantonization” by backcrossing to CS. In this work the Sod line has been established since 1989, and it is important that we use the same line to generate data rather than to other Sod studies by many other labs.

2) The manuscript is entirely 'phenomenological' in the sense that it does not investigate the causes of the observed physiological changes. The manuscript (with minor exceptions) does not discuss the possible reasons behind the functional readouts or speculate about what makes the (sub)circuits differentially susceptible to the effect of ageing. For example, when mentioning the effects of temperature and Sod mutation on the fly physiology, the authors limit their comments to generic and obvious statements such as 'oxidative stress exerts strong influences differentially on some of the physiological parameters and the outcomes are distinct from the consequences of high-temperature rearing'. Some of the possible questions the authors could ask are: could changes in the kinetics of relevant ion channels explain some of the results obtained under different temperatures; could the previously demonstrated effect of ROS on voltage-gated sodium channels explain some of the Sod1 phenotypes, etc?

Please see our general response to Reviewer #2. We are surprised at the comment that it is obvious to state that 'oxidative stress exerts strong influences differentially on some of the physiological parameters and the outcomes are distinct from the consequences of high-temperature rearing'. Scientifically, the assumption of distinct effects between high-temperature and oxidative-stress cannot accepted without direct experimental verification. A major aim of the current paper is to verify empirically how these two forms of stress affect different circuit aging trajectories. As summarized in Figure 7, there are different degrees of distinctions from little difference in LLR Habituation, to totally opposite direction trends in SLR refractory period.

On 2016-12-15 13:36:33, user Faiza tebbji wrote:

New cell size paper from Tyers and Sellam Labs! It was a pleasure to participate in this remarkable work! Thanks everyone.

On 2018-02-20 14:20:03, user Ruben Milla wrote:

this paper might be useful: Li et al. 2014. New Phytologist 203: 63–69

also, i missed information on what does the region Chr4@16.92-17.23 code for

On 2020-04-05 11:35:49, user Andres Romanowski wrote:

Very cool Claude and team! I will give it a go. Thank you for pre-printing!

On 2018-08-09 10:01:45, user Pan Pantziarka wrote:

This is a really interesting set of findings. The licofelone result is especially intriguing. Given the role of chronic inflammation in the process of carcinogenesis (see https://www.ncbi.nlm.nih.go... for a discussion related particularly to Li Fraumeni Syndrome) the impact of licofelone in tackling inflammatory pathways and p53 function would be of particular interest. Do you intend to explore this further, particularly in vivo?

On 2024-08-31 00:56:13, user Alonso Favela wrote:

Published version here: https://link.springer.com/article/10.1007/s11104-024-06720-9

On 2023-12-13 15:16:28, user Jan-Ulrich Kreft wrote:

This preprint has been published in Bioinformatics:

Moradigaravand D, Li L, Dechesne A, Nesme J, de la Cruz R, Ahmad H, Banzhaf M, Sørensen SJ, Smets BF, Kreft J-U (2023). Plasmid Permissiveness of Wastewater Microbiomes can be Predicted from 16S rRNA Sequences by Machine Learning. Bioinformatics 39: btad400

On 2017-03-29 09:10:36, user Markus List wrote:

Nice shiny app! Very useful for exploratory analyses. You might want to consider including a feature comparison to similar approaches, e.g. HiTSeekR (doi:10.1093/nar/gkw554) and CARD (doi:10.1038/ncomms11214). Disclaimer: I am the developer of HiTSeekR.

On 2020-05-27 01:35:17, user Mahdis Monajemi wrote:

Where can I find the supplementary tables for the paper? (They are not included in Supplementary Material)

On 2020-07-13 17:59:53, user Roosevelt Silva wrote:

Excellent work showing the importance of NSP1 protein. The authors could cite our work that was recently published, where we determined potential compounds to bind in this region of NSP1 described in the article. Paper: <br /> "Identification of potential drugs against SARS-CoV-2 non-structural protein 1 (nsp1)"<br /> Journal of biomolecular Structure & Dynamics<br /> DOI: 10.1080/07391102.2020.1792992<br /> July 2020

https://www.tandfonline.com...

On 2020-10-21 17:00:38, user Jen Skuban wrote:

This is amazing. I have had a theory similar to this since April, have not found any studies this close to it. This may be a long shot but since the virus does enter the body through the small intestine, could the rotavirus vaccine be the reason why kids are less affected by the virus? Since it would stimulate the immune response in the small intestine, and also because rotavirus is in the reovirus family?

On 2017-04-01 20:29:07, user AdamMarblestone wrote:

-"Attractor dynamics of network UP states in the neocortex" http://www.nature.com/natur...

On 2017-04-16 16:31:09, user AdamMarblestone wrote:

-"Functioning of Circuits Connecting Thalamus and Cortex" https://www.ncbi.nlm.nih.go...

On 2017-04-12 14:30:17, user AdamMarblestone wrote:

-"Associative content-addressable networks with exponentially many robust stable states" https://arxiv.org/abs/1704....

On 2016-12-22 20:47:54, user AdamMarblestone wrote:

-"Biologically plausible learning in recurrent neural networks for cognitive tasks" http://biorxiv.org/content/...<br /> -"Loss Is Its Own Reward: Self-Supervision for Reinforcement Learning" https://arxiv.org/abs/1612....

On 2018-09-21 16:22:53, user Amy Holdorf wrote:

This study was published in Cell Systems,Volume 22,<br /> ISSUE 12, P3126-3133, March 20, 2018. https://doi.org/10.1016/j.c...

On 2019-04-24 23:47:23, user Andrew Morgenthaler wrote:

page 3, line 3 should say "...in the non-synonymous set that presumably produce truncated, non-functional protein." (remove the "a" before "produce")

On 2021-05-15 20:13:19, user MINH BUI wrote:

Hello! I really enjoyed reading the paper and learned a lot from it. Below are some of my comments and suggestions for the paper: <br /> 1. Figure 1: I really like the labelling of the heart and hypothalamus in the images. Very clear. <br /> 2. Figure legends: italic fonts are not dyslexic friendly and can be hard to read for people, perhaps using a smaller straight font would retain the purpose of figure legends and make it more readable. <br /> 3. Figure 3 and 4: it would be better if they have similar layout of the graphs. <br /> 4. All figures: it would be clearer if there are asterisks for any significance on the graphs, instead of using letters like A,B,C to define significance, which makes it confusing because there are also different groups labelled with letters in the figures. <br /> Thank you so much for the paper and I hope these suggestions help!

On 2020-04-20 04:58:40, user Kavi att wrote:

Where did you find the datasets for this study?

On 2025-07-10 14:19:16, user Huanyuan Zhang wrote:

Hi This manuscript has been published here: https://www.nature.com/articles/s42003-025-08420-1

On 2025-04-24 10:22:51, user Juha Hulmi wrote:

The article is published in Journal of Physiology 2025:<br /> https://physoc.onlinelibrary.wiley.com/doi/10.1113/JP288104

On 2020-02-19 18:50:51, user Nathanael Rollins wrote:

Without doing new wet-lab experiments, here are some ways to roughly estimate whether a natseq model may have worked or not: (1) See if natseq models could have accurately predicted good VS bad mutants you've measured. (2) For a set of unbiased mutants -e.g. a comprehensive or fully random virtual mutscan- see how your model scores VS natural seq model- maybe they strongly agree! (3) See where good and bad tested mutants fall in a score distribution of seqs sampled from natseq models, with similar distance from wt as those tested. Are scores of the best expt mutants among the best I might've sampled from an unsupervised model? if yes, the natseq model should've given you similar results- if no, then measure or somehow estimate what the hit-rate would've been for those sampled seqs that score higher.

On 2019-02-24 08:03:23, user Afif Elghraoui wrote:

Can you achieve a similar effect with single-pass reads if you use the consensus of the subreads coming from the same ZMW for alignment/assembly?

On 2020-11-10 13:50:01, user Darren Norris wrote:

Interesting and timely. <br /> But maybe useful to expand the Discussion in relation to the importance of examining population demographics (what % of the comprehensively assessed species have population trend data available?), particularly as different life stages can be more or less sensitive to "sustainable" or "unsustainable" uses e.g. timber vs non-timber forest products.<br /> Our findings from a global scale analysis of turtles showing early stages (eggs) are perhaps potential candidates for use whereas adults are unlikely to be so may be of interest:<br /> Population dynamics and biological feasibility of sustainable harvesting as a conservation strategy for tropical and temperate freshwater turtles: <br /> https://doi.org/10.1371/jou...

On 2024-05-03 21:48:51, user Michael L. wrote:

This manuscript has now been combined with https://www.biorxiv.org/con... and published as https://pubmed.ncbi.nlm.nih...

On 2020-03-25 15:04:57, user Matteo Paccagnella wrote:

Hi I'd like to know if it'll be possible use a DESI ionization technique to avoid cromatography separation, maybe it could be faster processing larger number of samples? Maybe spectra would be too complicated...

On 2022-06-09 13:45:55, user C.S. Raman wrote:

This preprint is now in press at ACS Catalysis. A link will be posted soon.

On 2024-02-09 07:24:14, user Osvaldo Chara wrote:

This bioRxiv preprint was published in the Royal Society Open Science in 2023:<br /> https://royalsocietypublish...

On 2019-11-28 03:32:30, user yumeng liu wrote:

In this paper, the authors uses 33 datasets from n=9,312 samples. How these datasets are acquired?

On 2017-09-16 17:01:49, user R. Ahmad wrote:

Thank you for putting this online! Given the prevalence among so many different groups, studies like this are crucial

On 2021-05-03 19:15:13, user Syevda Tagirova wrote:

Dear Readers, our preprint has been published and a link will be forthcoming.

Authors.

On 2020-12-15 18:38:48, user Angelica Gaona wrote:

BI598 Group 3 Paper Review<br /> Jamie Dela Cruz1, Angélica Gaona1, John Axiotakis1, Guangmei Liu2

1Senior undergraduate in Neurobiology, Boston University. 21st-year PhD student in Neurobiology, Boston University.

Microglial activation results in neuron-type-specific increase in mPFC GABAergic transmission and abnormal behavior in mice Binliang Tang, Jinxiang Jiang, Lei Wang, Afzal Misrani, Qingwei Huo, Yuanyuan Han, Cheng Long, Li Yang doi: https://doi.org/10.1101/202...

Introduction<br /> We are university students taking an upper-level neurobiology course (with professor Cruz-Martín) that centers on understanding neural circuits and modern research techniques through in-depth discussions of recent literature. To fully immerse ourselves in current scientific discourse, we have written this review of the manuscript from Tang et al. posted on biorxiv.org (version: June 14, 2020)

Summary<br /> Microglia are known to mediate activity-dependent synaptic plasticity and neurogenesis in the fully mature CNS and to release inflammatory cytokines and neuroprotective factors in response to inflammatory signals. However, there is currently little understanding of how microglia-associated neuroinflammation regulates neuronal activity. To investigate this, Tang et al. used a single-dose injection of lipopolysaccharide (LPS), a proinflammatory cytokine inducer, to induce acute neuroinflammation and therefore microglial activation in the medial prefrontal cortex (mPFC) of 1-2 month-old male and female mice. They performed a variety of electrophysiological, biochemical, and behavioral evaluations two hours after the LPS injection (referring to these mice as 2h-LPS mice). To study the effects of blocking microglial activation, they also had a subset of mice undergo a minocycline pretreatment, injecting them with minocycline once daily for three days and performing the LPS challenge on the third day. They found that activating microglia leads to significantly increased miniature inhibitory (but not excitatory) response in the mPFC pyramidal neurons. Accordingly, minocycline alleviated the LPS-induced abnormal mIPSCs and the associated abnormal protein expression and behavior. Therefore, this paper shows that acute neuroinflammation via activated microglia affects GABAergic synaptic transmission in the mPFC and proposes minocycline as a possible treatment for neuroinflammation-induced abnormal neural function and behavior. Overall, we recommend that the authors provide more clarification for certain steps in their procedure, such as their reasoning for choosing the given timepoints, compounds, and concentrations. We also suggest that the authors provide some stratification of their measurements within the mPFC and reconsider the direct claims of minocycline’s specificity. Additionally, our review asks the authors to tweak certain figures for improved clarity and to provide further comment on their findings concerning mIPSC half-width and open field test results.

In Figure 1, the authors looked at how systemic inflammation affects synaptic transmission in the mPFC. They performed whole-cell patch clamp recordings of mEPSCs and mIPSCs in acute in vitro mPFC cortical slices of mice injected with 0.5 mg/kg LPS or PBS (control). There were no significant differences in the amplitude and frequency of mEPSCs observed in control and experimental mice, but LPS-treated mice showed significantly increased mIPSC amplitude and frequency (Figure 1C-F). This is visually shown through example traces of mEPSCs and mIPSCs from control and 2h-LPS mice (Figure 1A-B).

Given this observed increase in mIPSC amplitude and frequency, the authors suspected that there were alterations in the pre- and postsynaptic activities of mPFC pyramidal neurons in 2h-LPS mice. For Figure 2, the authors recorded IPSCs evoked by single and paired electrical stimulation (Figure 2A). mPFC pyramidal neurons in 2h-LPS mice exhibited an increased eIPSC amplitude in response to a single stimulation and had significantly lower paired-pulse ratios, suggesting that treatment with LPS leads to GABAergic changes at the pre- and post-synaptic level (Figure 2B-C). The authors also wanted to see what other presynaptic alterations underlie the increased mIPSC frequency in the mPFC of 2h-LPS mice, so they evaluated GABA levels in the mPFC using a brain-homogenate puff assay. After inducing IPSCs in the mPFC pyramidal neurons of normal C57 mice by puffing with mPFC lysates (the supernatants of mPFC tissue from 2h-LPS mice or controls) in whole-cell patch-clamp experiments, Tang et al. found that the amplitude of puff-evoked IPSC was higher in response to LPS-mPFC supernatant than control supernatant (Figure 2D-F). This suggests that there was an increased amount of GABA in the mPFC of LPS mice, partly contributing to the increased mIPSC frequency of pyramidal neurons.

In Figure 3, the authors investigate whether LPS treatment affects synaptic transmission of mPFC GABAergic interneurons using a GAD67+/GFP knock-in mouse line to conduct whole-cell patch recordings in mPFC GABAergic interneurons (Figure 3A). However, there were no significant differences in the amplitude and frequency of mEPSCs and mIPSCs in the GABAergic interneurons. Overall, this suggests that LPS potentiates IPSCs in the mPFC only when the postsynaptic cells are glutamatergic pyramidal neurons (inhibitory-excitatory synapse), not when the postsynaptic cells are GABAergic interneurons (inhibitory-inhibitory synapse).

In Figure 4, the authors investigate whether there was greater involvement and activation of postsynaptic GABAaRs in the mPFC of LPS mice, how LPS affects other factors related to GABA signaling and degradation, and if LPS affects levels of BDNF. The researchers performed a Western blot to determine the levels of GABAAR?1, GABAAR?2, GABAAR?5, GS, vGAT, GABA-T, SSADH, KCC2, BDNF, TrkB, and pTrkB. They also performed an RT-PCR to look at mRNA levels of GABAAR?1 and GABAAR?2. The mPFC of LPS mice showed increased levels of GABAAR?1, GABAAR?2, GS, and vGAT protein, significantly reduced levels of BDNF and pTrkB, and no significant differences between controls and LPS mice for the other compounds (Figure 4A, C-D). The decrease in GS and vGAT protein suggests that there is an increase in GABA synthesis and vesicle loading. The RT-PCR showed that there were increased levels of mRNA expression of GABAAR?1 and GABAAR?2, suggesting that those two subunits are transcriptionally regulated by LPS (Figure 4B). Next, the authors wanted to see if microglial activation could be the cause of the observed LPS-induced alterations, so the aforementioned Western blot analyses also looked at the protein levels in the mPFC of mice who were pretreated with minocycline or PBS once a day for 3 days, with an LPS injection on the third day. In these mice, the levels of GABAAR?1 and GABAAR?2 proteins and mRNA were not significantly different from those in controls (Figure 4A-B). Western blotting also showed that the expression vGAT, BDNF, and pTrkB were also brought to control levels in LPS mice with minocycline pretreatment (Figure 4C-D). These results indicated that LPS-induced microglial activation played a role in the abnormal expression of the mentioned proteins and the subsequent changes in GABAergic synaptic transmission. To visualize whether the 2h-LPS treatment activated microglia in the mPFC, Tang et al. conducted an immunofluorescent staining with anti-Iba1 antibody. They found that the amount of Iba1 signal was significantly higher and the soma sizes of Iba1+ cells were significantly larger in the mPFC of LPS mice but not in the control mice or the LPS+minocycline pre-treatment mice (Figure 4E-F). This suggests that there was more microglia activation in the LPS and that minocycline was sufficient to prevent that effect in LPS mice. Additionally, this shows that microglia could play a role in the transcriptional regulation of GABAergic receptors. On the other hand, Supplementary Figure 1 looks at whether LPS affects the expression of glutamatergic excitation-related proteins, but the Western blot found that the expression of NMDAR1, NMDAR2B, and GluR1 were not significantly different between the control and LPS mice.

Figure 5 looks at whether minocycline pretreatment also affects the mIPSC amplitude in the mPFC pyramidal neurons of LPS-treated mice. After giving mice the same minocycline treatment described previously, whole-cell patch clamp recordings were used to capture mIPSC data. The authors found that a minocycline pretreatment reduced mIPSC amplitude and frequency in the mPFC of LPS mice, actually normalizing the differences between control and LPS mice, but had no effect on controls (Figure 5A-D). Additionally, LPS increased the mIPSC half-width, but pretreatment with minocycline inhibited that effect (Figure 5E). All in all, these results suggest that LPS-induced microglial activation leads to the LPS potentiation of mPFC inhibitory synaptic activity in mPFC pyramidal neurons.

The authors postulated that BDNF downregulation may have resulted in the enhanced IPSC in the mPFC of 2h-LPS mice, so they studied this in Figure 6. Tang et al. first looked at the dose-response effect of LPS on the mIPSCs of mPFC pyramidal neurons. They then recorded mIPSCs after preincubation with aCSF containing 0, 20, or 50 ng/ml of BDNF followed by a perfusion with 0 or 50ng/ml LPS. Any dosage of LPS incubation significantly increased the amplitude and frequency of mIPSCs, and this was visually shown by representative traces at different doses of LPS incubation (Figure 6A-C). However, preincubation with 20 ng/ml of BDNF was sufficient to significantly reduce the amplitude but not frequency of mIPSCs in the mPFC mice with the LPS treatment (Figure 6F-G). They show that a higher dose, 50 ng/ml, of BDNF was needed to prevent the increase in both mIPSC amplitude and frequency in mPFC pyramidal neurons.

In Figure 7, the authors investigated whether mice treated with LPS would show abnormal behavioral phenotypes. In other words, would they show different levels of anxiety and depression? To test this, mice given 2h-LPS treatment were subject to the open field test, elevated plus maze, tail suspension test, and sucrose consumption observations. 2h-LPS showed a significantly lower total distance and center time in the OFT (Figure 7A, C-D), decreased open arm entries in EPM (Figure 7B, E), and increased immobility in the OFT (Figure 7F). They also consumed significantly less sucrose (Figure 7G). Minocycline pretreatment, however, was able to partially rescue these deficits in the 2h-LPS mice. These results indicate that microglia activated during early inflammation may have important pathological effects and that blocking microglial activation could completely or partly reduce inflammation-induced abnormal behavior.

The authors wrap up with Figure 8, a schematic depicting the proposed underlying mechanisms of abnormal GABAergic synaptic transmission and behavior after microglial activation. They describe their results in the context of related literature about minocycline, BDNF, neurotoxic factors, and LPS challenge protocols. Lastly, they conclude by postulating that components of the investigated GABAergic system may be studied in the future as possible therapeutic targets for inflammatory diseases.

To supplement their conclusions, Figure S1 shows that the levels of excitatory receptors in the mPFCs of control and LPS-treatment mice were not significantly different. Figure S2 looks at whether astrocytes were also activated by LPS treatment; the immunofluorescent staining image suggests that levels of astrocyte activation were comparable between controls and LPS mice. To see if levels of pro-inflammatory cytokines were increased in LPS mice, the authors performed an RT-PCR of TNF-?, IL-1? and IL-6 for Figure S3, showing that mRNA levels for these cytokines were elevated.

Major Criticisms<br /> In Figure 1 we thought that there were a few places that could use improvement or clarification. To go into detail, we would like you to comment on why LPS was used instead of poly I:C. Poly I:C is a common molecule that is also used to generate an immune response, thus can you please comment on what was the logic behind using LPS instead. In addition, we would also like to know if you believe that injecting poly I:C in place of LPS would result in a similar phenotype. We would also like to know why the specific concentration of LPS was chosen and if you have any literature supporting that choice. In terms of the results relating to the mEPSCs, the paper indicated that there were not many differences documented in amplitude and frequency in between groups. However, we believe that the results shown in panel C and D could due to recording from a heterogeneous population. That is, it could be that the mEPSC is affecting certain layers of the cortex differently. Thus, we recommend that you sort through the layers and record from each layer respectively or that there should be a certain layer that you focus on. However, if you decide to focus on a certain layer there should be supporting literature given that cites why this layer is optimal. There also should be a comparative measure between VIP and SST interneurons, as that would help with the understanding of mEPSCs results. We would also like to see you comment on whey the mPFC was chosen specifically as it is highly variable. Would you expect to see the same result from the mPFC as the somatosensory cortex and what would those results entail?

The last major criticism we have for Figure 1 pertains to the age chosen for this experiment. Epidemiological studies have indicated that during gestation of the mice is when you would see the biggest impact due to infection on the development of schizophrenia (Waterhouse et al. 2016). We believe that the experiment would have been best conducted during MIA since the model indicates that infection in this time period Is what increases the probability of schizophrenia. We would like you to comment on why the age of 1 month chosen in specific if the MIA model is so well established and supports a different age? In addition, why was the experiment conducted 2 hours after LPS injection? One of the hallmarks of schizophrenia is that it leads to long lasting effects, because of that we believe that LPS and its effect should be recorded for additional days, weeks, and even months in order to emulate a true schizophrenic model. The receptors that react to LPS at E12.5 could be very different from 1 month in life.

In Figure 2 it was noted the experiment was done in the mPFC, again we would like a bit more information on why that specific location was chosen. The mPFC is a very disorganized area and it seems like the hippocampus may have been a more appropriate area since neuronal pathways can be easily isolated for stimulation. Likewise, we also wondered how you were able to stimulate the same pathways in the control and LPS condition. If the case was that the electrodes were recorded in different pathways in the LPS and control condition, we would expect that it may have led to variability and changed the results. For panel A we would have liked to see an output-input curve (current vs. AP)that attributed how much LPS was injected and how much different cell types are engaged.There is still a possibility that some of the effects in release probability are due to differences in excitability between control and LPS condition.

The last major criticism relating to Figure 2 is pertaining to panel E. In this panel we see that the timescale is almost 5 times longer and the amplitude is two times higher than in panel B and C. We wanted you to comment on why that is and how you can attribute the response to evoke GABAA. That is, we believe that it is possible that more than GABAA was evoked by the large response and recommend blocking other GABA receptors if it is not done so already. We would also like to see a positive and negative control. A possible negative control is using Puffs of aCSF. Likewise a positive control can be looking at different concentration of puff aCSF to evaluate how much GABA needs to be release to get the response that is presented in panel E. A curve would aid in supplementing the findings found using puff aCSF and in indicating the results in panel E are physiologically possible.

In Figure 3 we believe that the analysis is too general in referring to the mPFC and that the experiment should refer to a certain cell type. The interneurons recorded in Figure 3 are too heterogeneous which again may there be no indicated changes in mIPSC and mEPSC. Referring to a criticism made before for Figure 1,it is imperative that the experiment documents changes in the mIPSCs and mEPSC at different time scales The crux of Figure 4 it would seem is minocycline and its ability to ameliorate the effects of LPS by specific inhibition of microglia. While this effect may be possible it is far from the only documented role minocycline plays. Its inhibitory effects in particular are widespread pertaining to myriad enzymes including, iNOS, caspase-3, and p38 MAPK. In C6 glioma cells apoptosis could be induced by minocycline’s inhibition of caspase-3 under the condition that autophagy was also inhibited (Liu et al., 2011). In cerebellar granule neurons (CGN) and glia minocycline has also been shown to inhibit iNOS expression and is related to similar reduced phosphorylation of p38 MAPK in CGN, the consolidated effects of which are believed to block MPTP neurotoxicity (Diguet et al., 2003). Even in microglia the effects are diverse such as reduced production of IL-1?, IL-6, TNF and nerve growth factor in amyloid precursor protein in models of Alzheimer's disease (Seabrook et al., 2006). Thus it seems unbefitting of minocycline to be labeled a microglial inhibitor or to consider the effects it has on the brain as such unless such claims can be further substantiated with more experimentation. The data from the Iba stain was good but could be quantified to better accentuate the figure, elements such as the branching and intensity of the Iba signal for each microglia could be quantified and the microglia could also have been co-marked with common activation markers such as LN3 and Gal-3. Further elaboration on connection of assayed elements like BDNF and the alpha-5 subunit would be helpful as well. BDNF is known to relate to GABA through its effect to regulate the KCC2 symporter which facilitates the inhibitory effect of GABA particularly during the developmental phase, and its ability to phosphorylate TrkB which has an effect on inhibitory drive though its effect on this circuit isn’t clearly defined.

In Figure 6, mIPSCs were recorded in mPFC slices under different concentrations of LPS, so we were wondering if you also tried the same concentration ranges on mEPSCs. If not, we can speculate that the absent effect of LPS on mEPSCs might be due to too low concentration. Or LPS may need longer time to take an effect on the mEPSCs of mPFC than mIPSCs, which also links back to the reason of selecting 2-h LPS treatment duration. So, we suggest you do the same different concentrations and time points of LPS treatment.

In Figure 8, the TrkB receptor and the conversion of glutamate to glutamine by GS are depicted in a microglia, not an astrocyte. Although the authors state that there were unchanged levels of astrocytic activation in the study and provide an immunofluorescent staining showing levels of astrocyte activation in control and LPS mice, this claim does not have quantitative data to support it. We suggest that the authors provide a quantification of astrocyte activation, especially given that a study of mice hippocampi found that postnatal LPS exposure inhibited GABAAR expression through astrocyte activation and the subsequent downregulation of the BDNF-TrkB signaling pathway (Liang et al., 2019).

On 2017-10-28 16:51:14, user Lionel Christiaen wrote:

Student #4<br /> 1. Genetic design: Homie-dependent long-distance regulation<br /> a. What is the background knowledge?<br /> Homie-homie self-pairing interactions can orchestrate enhancer activation of a reporter<br /> b. What is the question or hypothesis addressed?<br /> Is physical proximity central to enhancer-promoter communication?<br /> c. What is the approach? Which methods does it employ?<br /> A transgene consisting of the eve promoter and the lacZ coding sequence is inserted at an attP site located 142 kb upstream of the eve gene.<br /> d. What were the observations and analysis? (i.e the raw data and analyses)<br /> Sporadic expression of LacZ mRNA is observed solely within the limits of the endogenous eve stripes.<br /> e. What are the interpretations?<br /> Activation of the lacZ reporter depends on the enhancers in the eve locus 142 kb away.

1. Visualization of transcription and enhancer-promoter dynamics<br /> a. What is the background knowledge?<br /> b. What is the question or hypothesis addressed?<br /> What is the connection between enhancer action and physical enhancer-promoter proximity?<br /> c. What is the approach? Which methods does it employ?<br /> Insertion of tags. An MS2 stem loop cassette and MCP fused to a blue fluorescent protein to visualize nascent eve transcripts. A PP7 stem loop and PCP fused to a red fluorescent protein to visualize nascent transcripts of lacZ and ParS/ParB DNA labeling system to mark the position of the lacZ reporter whether it's active or not.<br /> d. What were the observations and analysis? (i.e the raw data and analyses)<br /> In the blue channel it can be observed the transcriptional dynamics of the eve gene in the characteristic seven-striped pattern. The green channel trace the movement of the lacZ reporter within the nucleus and in the red channel lacZ expression is observed as a subset of nuclei in the eve stripes<br /> e. What are the interpretations?<br /> LacZ expression is restricted to nuclei that reside within one of the seven eve stripes.<br /> f. What are the conclusions about the biological processes being studied?<br /> There is a close connection between transcription and physical proximity

2. Spatial proximity is necessary for enhancer action<br /> a. What is the background knowledge?<br /> b. What is the question or hypothesis addressed?<br /> How enhancer action is related to spatial proximity?<br /> c. What is the approach? Which methods does it employ?<br /> Analysis of live images and replacing the homie sequence with lambda DNA of the same length<br /> d. What were the observations and analysis? (i.e the raw data and analyses)<br /> For the parS-lambda-lacZ they observe a bimodal distribution for the time-averaged physical distance but when the homie sequenced is replaced with lambda the distribution of the RMS distance is unimodal. None of the nuclei in control parS-lambda-lacZ embryos express lacZ.<br /> e. What are the interpretations?<br /> Homie pairing creates a local chromatin conformation that is permissive to transcription events by ensuring physical proximity between the eve enhancers and the promoter of lacZ<br /> f. What are the conclusions about the biological processes being studied?<br /> Eve enhancers must be in close proximity to the lacZ promoter in order to activate transcription.

3. Necessity for sustained physical association<br /> a. What is the background knowledge?<br /> b. What is the question or hypothesis addressed?<br /> What is the temporal relationship between enhancer-promoter proximity and transcriptional activation?<br /> c. What is the approach? Which methods does it employ?<br /> Measure of the mean distance between the green parS tag and the eve gene as a function of time and alignment of the nuclei with respect of time point when nascent transcripts could first be detected.<br /> d. What were the observations and analysis? (i.e the raw data and analyses)<br /> There is a convergence until the onset of transcription at which point the mean distance corresponds to an average separation of about 340 nm. Also, a drop in transcriptional activity of the lacZ reporter is accompanied by an increase in the mean distance between the ParS transgene and the eve gene.<br /> e. What are the interpretations?<br /> There is a close connection between the establishment of enhancer-promoter proximity and enhancer activation of transcription. <br /> f. What are the conclusions about the biological processes being studied?

4. Physical enhancer-promoter engagement leads to distinct topological conformation<br /> a. What is the background knowledge?<br /> Independent eve enhancers regulate individual stripes of the eve pattern along the embryo<br /> b. What is the question or hypothesis addressed?<br /> Is transcriptional activation associated with an additional step that promotes physical enhancer-promoter engagement?<br /> c. What is the approach? Which methods does it employ?<br /> Examination of nuclei from different stripes separately to explore the topology of the locus under different activating enhancers.<br /> d. What were the observations and analysis? (i.e the raw data and analyses)<br /> Different distances in nuclei belonging to different stripes are observed. The distance between the eve gene and the parS tag of the inactive lacZ reporter in stripe 5 is shorter than the observed for nuclei in stripes 4/6 and 3/7 for which the enhancers are located farther away from the parS tag<br /> e. What are the interpretations?<br /> Eve enhancers directly engage the endogenous eve promoter to activate transcription and that in each eve stripe a distinct topological conformation is adopted<br /> f. What are the conclusions about the biological processes being studied?

5. Promoter compositions has phenotypic consequences<br /> a. What is the background knowledge?<br /> The eve stripe enhancer drives expression from two different eve promoters, one for the endogenous eve gene and the other for the lacZ reporter.<br /> b. What is the question or hypothesis addressed?<br /> Is promoter competition occurring in the genomic setup?<br /> Does the reduction in eve transcription have any phenotypic consequences?<br /> c. What is the approach? Which methods does it employ?<br /> Comparison of eve transcription in individual nuclei in which lacZ is active ann nuclei in which lacZ is silent<br /> Crossing males carrying a homie-lacZ transgene at -142 kb to females heterozygous for a wt eve gene and an eve deficiency.<br /> d. What were the observations and analysis? (i.e the raw data and analyses)<br /> When lacZ is also transcribed there is a 5-25% reduction in endogenous eve transcription <br /> The presence of the homie-LacZ transgene exacerbates eve haploinsufficiency<br /> e. What are the interpretations?<br /> Competition between two promoters at the transcriptional level in the early embryo has phenotypic consequences for patterning in the adult<br /> f. What are the conclusions about the biological processes being studied?<br /> Manipulating topological chromatin structures can interfere with developmental programs.

Review.<br /> By designing a transgene consisting of the eve promoter and the lacZ coding sequence located 142 kb upstream of the eve gene and taking advantage of the homie insulator, which self-pairing interactions can orchestrate enhancer activation of a reporter, the authors created a system where the activation of the lacZ reporter depend on the enhancer in the eve locus 142 kb away. By introducing tags using the MS2-MCP, PP7-PCP and the parS-parB systems and by measuring the mean distance between the green parS tag and the eve gene as a function of time and they found that there is a convergence until the onset of transcription at which point the mean distance corresponds to an average separation of about 340 nm. By modifying their construct (eliminating homie or reversing the homie sequence) they conclude that the eve enhancers must be in close proximity to the lacZ promoter to activate transcription; however, a possible explanation on why reversing the orientation of homie prevents lacZ expression while there is still close proximity between enhancer and promoter. Also, a drop in transcriptional activity of the lacZ reporter is accompanied by an increase in the mean distance between the ParS transgene and the eve gene, suggesting that there is a close connection between the establishment of enhancer-promoter proximity and enhancer activation of transcription. The authors provide evidence that suggests that manipulating topological structures can interfere with developmental programs; for this part (Figure 6, panel A) I would suggest to make a separate figure for measure of % eve activity reduction for each stripe since the way it is shown makes it harder to appreciate the figure.

On 2020-09-04 18:10:17, user David Leung wrote:

Have you tried other photocatalysts?

On 2021-04-07 19:09:52, user Dietmar Lohmann wrote:

Another point: Most children with isolated unilateral rb do not show pathogenic RB1 variants in DNA from blood. However, DNA from retinoblastomas often show pathogenic RB1 variants. I wonder if it was possible to perform genetic testing on DNA from retinoblastomas first and then, as a next step, test DNA from blood for the specific alterations identified in the patient's tumor?

On 2020-07-28 13:46:14, user Dallan McMahon wrote:

Terrific work! This is a very important area of investigation that deserves more focus and I found the results to be of great interest. Perhaps you may be able to comment on a couple of points: (1) were there any clear effects of the magnitude of BBB permeability enhancement or harmonic/wideband emissions on the transcription of specific genes or activation/suppression of pathways (perhaps sample size is too small to make any conclusions for expression of individual genes)?; (2) Would you expect the estrus cycle to play any role in the differential transcription of any of the genes found to be influenced by anaesthetic or FUS+MB exposure? Thank you.

On 2023-11-14 21:36:11, user Sahar Melamed wrote:

Please note that the figures are incorrectly displayed in the preprint (some technical issue on BioRxiv). Please refer to the published version for the updated version and correct display of the figures.

On 2024-10-03 21:10:43, user Gino wrote:

if I may ask, will this paper ever get out of the preprint stage?

On 2020-05-25 11:56:13, user Crayfarmer wrote:

My arguments are not unfounded, but ok, I should go into more detail. The marbled crayfish is not suitable for aquaculture, neither for the production of meat nor chitin and chitosan for the following reasons:<br /> 1. Marbled crayfish is too small a species for aquaculture. It is much smaller than the well established Astacus, Pacifastacus and Cherax species and considerably lighter than Procambarus clarkii, the most intensely cultured freshwater crayfish. Your figures 1A and B depict the by far largest marbled crayfish ever collected or raised. By the way there is no credit given in the figure legend to the collector and photographer Frank Lenich, who is not a co-author of the paper. Figure 1C shows that of 1537 collected and analyzed specimens only 11 (0.7%) were heavier than 30 g and 30 (2%) were heavier than 20 g. Specimens <20 g are of litte or no value for the market. In contrast, in a Procambarus clarkii population harvested by Wang et al. (J. Freshwater Ecol. 26, 287-294, 2011) from a pond more than 50% of the 678 specimens had weights >30 g and the heaviest specimen had a weight of 83 g (compared to 49 g in marbled crayfish, your figure 1B). All of your marketable marbled crayfish sampled with traps from different sites in Madagascar and Germany together had a total weight of about 1 kg (figure 1C). In Spain, you would get a market price of less than 5 US$ for this harvest. How many minutes of the salary of a single person could be payed by that amount of money?

1. The meat of marbled crayfish is rich in valuable Proteins as in any other crayfish. Therefore, it is principally a valuable Food commodity, particularly in developing countries like Madagascar, where these animals are collected from natural water bodies and rice fields without having any production cost. Hope we agree that this highly invasive species must not be transferred to natural waterbodies of other developing countries because of ist high invasiveness and negative effects on the native fauna and ecosytems. The introduction of Procambarus clarkii into many countries around the world for aquaculture and fisheries is an illustrative example for a seemingly good idea that has turned into the opposite (e.g., devastating effects in Spain and Kenia).
2. Freshwater crayfish including the high-priced species are cultured in extensive and semi-intensive (additional feeding of pellets) outdoor facilities, mostly in earthern ponds. This mode of culture is not possible for marbled crayfish, because they can easily escape from such facilities and establish natural populations with all the negative effects. I am not aware of any intense crayfish indoor culture with closed water circulation with the exception of research facilities which don´t have to be profitable. Occassionally, the<br /> vulnerable early life stages of the high-priced species are raised in intensive<br /> indoor facilities until stocking of outdoor grow-out ponds. The costs for<br /> buildings, artificial feeds and labor would be much too high for an intensive<br /> closed indoor system of marbled crayfish to be profitable, even in developing<br /> countries with low labor cost.
3. I am not familiar with the market of chitin and chitosan but actually these products are mainly obtained from crustacean waste, the shells. Culture of the marbled crayfish just for the production of these materials might be no good idea. Large insects like migratory locusts are probably much cheaper resources for bioplastics than marbled crayfish from intense indoor cultures.
4. EU Regulation No. 1143/2014 prohibits keeping and breeding of marbled crayfish in the European Union, placing on the market and releasing into the environment. Keeping for scientific purposes is allowed. In the United States there are similar regulations in several states. I am quite convinced that in the EU, Northern America and Australia nobody would get a licence for aquaculture of marbled crayfish, not even for closed indoor systems. <br /> Therefore, the marbled crayfish Procambarus virginalis Lyko, which in a recent podcast was nicknamed the Lyko-monster because of its high invasiveness and devastating effects on native fauna and flora, is unsuitable for any type of aquaculture in any country.

On 2019-12-25 01:02:02, user Janarth wrote:

This article has been peer reviewed and published in BBREP:

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

On 2021-06-14 16:19:56, user Alyssa Long wrote:

Hello colleagues and interested readers! During my "final" review of this manuscript, I noticed that I had copy-pasted a primer sequence incorrectly in the Methods section. The error occurs in line 168 of the currently posted preprint (I forgot to properly reverse-complement my reverse primer sequence). The correct sequence is 5'-TCCTCGGGTGTCTTAGCACT-3'.

On 2018-02-16 09:42:47, user Guillaume Rousselet wrote:

The ERP figures could be improved by showing the time-course of the pairwise differences, with all participants superimposed, so that readers can assess the time-course of the effects and individual differences. The analyses would be better conducted at all time points and electrodes, with cluster correction for multiple comparisons. It is also important to consider whether all participants (from the two groups) show their maximum effects at the same electrodes, as the group analysis currently assumes. Power could be increased by selecting, per participant, one electrode showing maximum effect/best model fit, or by working in the source space. Whether the additive model is appropriate when comparing groups and hemispheres should also be considered:<br /> [[http://onlinelibrary.wiley....]]

On 2022-03-09 10:30:55, user Marc Joiret wrote:

This paper has been accepted for publication in the peer-reviewed journal 'Physical Review E'. There were three rounds of reviewing. You can find the reviewed published version at https://journals.aps.org/pr...

On 2019-03-28 22:49:04, user vered Raz wrote:

nice work. I suggest reading our recent paper in iSCENCE - comparable results in a model for aged skeletal muscles. Deacetylation Inhibition Reverses PABPN1-Dependent Muscle Wasting

On 2020-04-06 20:42:20, user Frank Aylward wrote:

Thanks very much for your kind words and interest in the preprint! And apologies for the slow reply. You have some great suggestions that are definitely worth following up on. We did not find any putative PRK homologs in the MAGs, so we could not identify the pathway that has been described in some methanogens. With regards to the possible nucleotide salvage metabolism, we did find a ribose 1-5 bisphosphate isomerase (e-value 9e-100 compared to the T. kodakarensis copy), but no clear homolog to an AMP-phosphorylase.

On 2025-11-04 17:39:54, user Kenji Sugioka ~ wrote:

The final version of this manuscript is DOI: 10.1016/j.cub.2025.09.052

On 2024-02-05 05:25:47, user R. wrote:

This paper is now published in New Phytologist: https://nph.onlinelibrary.w...

On 2020-06-16 04:11:28, user Elizabeth Molnar wrote:

An excellent paper, in which I am interested because of the homology of the developing spermatozoa and nervous tissues, with microtubule and mitochondria interacting with laminins and nuclear membrane receptors, axons resembling spermatid tails. I found an article which describes the importance of genes such as that studies here has in <br /> mammalian metabolism and nervous system and formation of collagen arrays, I recall work from the Blackshaw laboratory at Queensland University in the 60's and 70's on the role Luteinizing Hormone in release of spermatids from Sertoli cells, as well as Leydid steroidogenesis. Thus:

A Regulatory Loop between the Retinoid-Related Orphan Nuclear Receptor NHR-23 and let-7 family microRNAs Modulates the C. elegans Molting Cycle

Ruhi Patel, Alison R. Frand

doi: https://doi.org/10.1101/506261

Beth Molnar,

Psychiatrist,

Brisbane

On 2025-05-13 22:37:57, user Chris C. wrote:

Summary<br /> The main objective of this paper is to test the konjac glucomannan (KGM), exercise, and both together on body weight and lipid metabolism in obese rats versus their control. Their idea for testing KGM is based on the findings from Chen et al. 2019 where they tested glucomannan on type 2 diabetes in rats. The added benefit with this paper is the addition of exercise with KGM. The groups that they used were high-fat diet (HFD), normal control group (CON), aerobic exercise group (HAE), KMG group (HKM), and combined treatment (HKE).

I believe that the authors were successful in establishing that KGM and exercise likely reduce body weight, improve lipid metabolism and oxidative status in obese rats. They validated their findings through their rigorous testing of liver lipids, liver enzymes, lipid metabolism, insulin sensitivity, and antioxidants. Their statistical analyses are good and are held to a low p-value with high significance. The authors included a figure for the underlying mechanisms of konjac and exercise on lipid metabolism, which makes it easy to understand.

Overall, the study adds in the benefits of both konjac and exercise. This study also repeats how konjac and exercise can separately improve obesity as well as together. Previous literature has established that konjac and exercise improve obesity through multiple mechanisms, as mentioned in your introduction. While the authors' findings are not particularly surprising, they have provided rigorous and sufficient data to support their hypothesis. The overall impact of the paper is modest.

Major Points

The figure captions could be more descriptive.<br /> For example, figure 5 would benefit from a more detailed explanation of the role and relevance of each antioxidant and a clear indication of which groups showed the most favorable results in the figure caption.

I am unsure if this statement, “Exercise is reported to enhance the ability of skeletal muscles to utilise lipids as opposed to glycogen, thus reducing lipid levels.” is completely true. <br /> It appears to be an oversimplification of the findings from Mann et al. (2014). Current literature suggests that utilization depends on exercise intensity and other factors. Clarifying that the statement applies to low-intensity steady-state cardio would improve its accuracy. Please let me know if you think otherwise.

Minor Points

If you could please write out the groups in the abstract or introduction that would be helpful for me to understand what the groups are from the beginning.

Figure 2 may be more interpretable as a bar graph, which could better illustrate group differences. It would help me see the differences easily.

I am confused about Figure 4. Please clarify the labeling—specifically, whether Figure 4A represents a control or a healthy adipose tissue sample. This will help me understand it easily.

Ensure all figure panels are clearly labeled and legible. Enlarging Figure 4 slightly might improve readability as well as getting a better resolution for figure 3.

Recommendation<br /> I recommend publishing this paper with intermediate/minor revisions. Here are the key revisions required:

More comprehensive figure captions

Clarification in the introduction regarding lipid vs. glycogen utilization during exercise.

Improved labeling for Figure 4.

Optional suggestions:<br /> Reformatting Figure 2 as a bar graph for clarity.

Slight enlargement of figures to improve visual accessibility.

Write out full group names at the beginning of the preprint for easier readability

On 2022-01-18 17:40:10, user Titus Brown wrote:

Here's a blog post for y'all: http://ivory.idyll.org/blog...

On 2020-11-26 04:27:34, user Michael To wrote:

I found your paper enjoyable, thought-provoking, and essential. Certainly, you’ve highlighted the role that LPO NMDA receptors play in sleep and sleep homeostasis. Your paper flowed well and I was able to follow your progression from the identification of NMDA receptor mediation of homeostatic sleep drive in the PO hypothalamus, to your subsequent deletion of the GluN1 NMDA subunit to determine the role it played in the LPO hypothalamus, and the subsequent novel “insomnia” phenotype presenting in mice who undergo this deletion. Nice work. I have a clarifying question: In Figure 2C, did you use any vectors/protocols to prevent recombination events when using AAV-flex-GCaMP6s? Since the lox p sites could cause further, unwanted recombination, was this accounted for…?

It is evident that you are passionate about Neuropathology, and you have presented an incredible array of detailed data. This data might be made more accessible with some clarifying edits. For example, in Figure 2D, I found it difficult to interpret what is a substantial reduction in calcium activity in ?GluN1-LPO neurons. This can be clarified by adding a transition state tracing, like Figure 1D, to emphasize the reduction in calcium activity (which had spiked previously in naïve mice when moving from NREM -> REM). Additionally, for figure 1E, it would be helpful add a legend for ? F/F – the simple addition of ?F/F = (F-F0)/F0 on the graph would add to its interpretability. All-in-all, you have presented a fine paper which can be made even better with some light revisions.

On 2020-04-06 06:55:50, user Bernhard Englitz wrote:

Very interesting analysis! Maybe also of interest to you is our recent work on this topic: https://doi.org/10.1162/nec...

On 2021-04-06 17:11:58, user YSR wrote:

In the sentence below

"Moreover, these conditioning regimens were used therapeutically in mouse models of sickle cell disease, hemophilia, Fanconi anemia, and recombinase-activating gene (RAG) deficiency" The citation numbers needs to be switched ( 20 and 21).

On 2020-02-23 15:31:43, user Noémie Aubert Bonn wrote:

See the associated preprint at https://www.biorxiv.org/con...

On 2023-11-14 06:19:25, user Malgorzata Biczysko wrote:

The article is now published https://link.springer.com/a...

On 2020-02-28 10:36:45, user István Zachar wrote:

I have 3 questions to the authors: 1) The paper refers to MAG-s of high quality of ">50% completeness, <10% contamination". Could this actually mean a HUGE contamination (e.g. 9.9%) of bacterial or protist sources? How did the authors exclude this possibility? 2) Why did the authors ignore the possibility that photosynthesis-related proteins in archaea were acquired horizontally from e.g. bacteria? 3) The monophyly of photosynthesis within Eukarya is well supported via the monophyly of all plastids (plus Paulinella), coming from cyanobacteria. How could then eukaryotic photosynthesis stem from (putative) archaeal photosynthesis? Line 91 is rather misleading in this regard.

On 2022-10-24 04:59:52, user Sarah O'Malley wrote:

Hello, my name is Sarah O’Malley, and I am a student of the Biomedical Research minor at UCLA. I recently read this paper with my program’s journal club, and I want to thank you for your work on mEVs and early biomarkers of tobacco smoking-induced disease. My class learned a great deal of information while reading and discussing this paper, and I would like to present some suggestions and comments:

The variety of techniques utilized to isolate and characterize mEVs here were impressive. However, I suggest including percentage breakdowns of the different populations studied on the flow cytometry plots (Figure 2A, 2B, 2E). This data may have already been calculated through FlowJo or could easily be calculated with this software, and it would be valuable to display these percentages to provide more precise quantifications of EV populations. In addition, I believe that Figure 2D may have been incorrectly referred to as Figure 5D in the results section titled “Extracellular vesicle concentration increases in circulation 1 hour after smoking in never-smokers”.

Also, in the results or discussion section, I would suggest including a description of why there are four post-smoking samples in Figure 2F compared to the 20 non-smoking participants or the nine pre-smoking samples shown in Figure 2F. Next, if possible, I would also suggest conducting the tests performed on nonsmokers in Figure 1 and 2 on smokers as well, which could provide additional data on the acute effects of smoking and if these effects change with the chronic smoking of tobacco. I understand that this data may be difficult to collect, but I believe that it could bolster the content of this paper.

Lastly, I was wondering what specific statistical test you conducted for this figure. The figure legend states that a non-parametric unpaired t-test was performed. However, I wonder if a paired test should have been used, as this data consists of blood from the same individuals pre- and post-smoking. Thus, I do not know if the groups can be considered independent. Also, t-tests are parametric tests, so I am unsure of what a nonparametric t-test refers to. This pattern of referring to a nonparametric t-test was also maintained throughout the paper. Was a Wilcoxon signed-rank test performed? If not, then I would suggest implementing this statistical test here, as it serves a similar purpose to a t-test but is applicable to paired, nonparametric data. For the other instances of unpaired nonparametric t-tests, I would suggest using a Mann-Whitney U test, which also serves a similar purpose to a t-test but is applicable to unpaired, nonparametric data.

In Figure 4B, I would suggest expanding the heatmap to display MFI levels for each sample analyzed instead of condensing the data as shown. In this condensed form, the data is a bit difficult to interpret. Alternatively, I would suggest displaying some of the quantifications of activation marker levels described in the results section, as these quantifications would convey the same message but through a more easily interpretable form.

The discussion around Figure 5 depends on a trend shown in sTREM2 expression and a statistical decrease in BDNF expression. In the results and discussion sections, the following conclusions made about the smoking-linked mechanisms of neurodegeneration may be a bit strong based on this data. I would suggest performing follow-up experiments on other neurodegeneration markers to strengthen this evidence or perhaps test BBB functionality, as this was a concept linked to neurodegeneration throughout this paper.

I have a quick general note on the references section. I had some trouble finding a few of the papers cited in-text in the references section (e.g. Zalba et al. 2007, Sophocles Chrissobolis et al. 2011). My class had similar difficulties navigating the references section, so I would suggest following up on the consistency of citations in-text and within this section.

Overall, thank you for posting this paper! It was a highly educational read.

On 2017-11-10 13:46:03, user Joram Soch wrote:

Dear readers,

weirdly, Frontiers in Neuroinformatics has returned the paper, because "no handling editor [could be] found" [1]. We have now submitted it to the Journal of Neuroscience Methods where it is in review at the moment. I have also updated the preprint here at bioRxiv [2]. The text is virtually unchanged except from the abstract and section headings which had to be adapted to meet journal guidelines.

Cheers<br /> Joram

[1] https://twitter.com/JoramSo...<br /> [2] https://www.biorxiv.org/con...

On 2016-04-07 18:14:42, user Sergio Vargas wrote:

I don't know if I missed it, but how "low coverage" is defined?

On 2018-03-01 18:59:08, user Emily wrote:

Note: This paper was published in the European Conference on Artificial Life, a peer-reviewed conference (they are common in computer science), and can be cited as: Emily L. Dolson and Charles A. Ofria. Spatial resource heterogeneity creates local hotspots of evolutionary potential. In Proceedings of the 14th European Conference on Artificial Life 2017. Edited by Carole Knibbe, Guillaume Beslon, David Parsons, Dusan Misevic, Jonathan Rouzaud-Cornabas, Nicolas Bredèche, Salima Hassas, Olivier Simonin, and Hédi Soula. Vol. 14. pp. 122 – 129. DOI: 10.7551/ecal_a_023. MIT Press. 2017.

The final published version of the paper is here: http://cognet.mit.edu/proce...

On 2019-11-12 08:10:48, user Biorobothuman wrote:

This is awesome! Have you tried any other proteins in the place of Smt3, and do you think this method could be used to probe structural stability or confirmation changes of the "pore blocking" protein?

On 2020-06-30 06:20:44, user Ivan van Ogre wrote:

Should we be worrying about mosquitos?

On 2022-09-14 20:58:14, user CK wrote:

It's mentioned that Replacing Cs with Ch results in a 133-fold difference in the ratio of favorable (hippurate and cinnamoylglycine) to unfavorable (phenylacetylglycine) Phe metabolites. Given that it is known Phenylacetylglycine plays a causative role in cardiovascular disease, did you see any phenotypic differences in ?Ch vs ?Cs mice in terms of cardiovascular disease/mouse growth?

On 2020-09-07 09:24:30, user ELININGAYA JOHN KWEKA wrote:

This is great ecological study coordinated in laboratory to get the findings of what occurs in nature! Congrants

On 2020-10-01 13:03:28, user Adam Smith wrote:

Major things are missing from this like the number count of animals or insects testing.

On 2021-01-26 19:39:42, user Matt wrote:

Very interesting paper! Authors, have you considered running your qpAdm result in a 3-way model with (East Asian)+Maniq+(South Asian)? Another reader of the paper has suggested to me that the time depth of divergence of the Onge from the true admixing source into South East Asian populations may lead the qpAdm results to be biased towards higher probability values for South Asian populations with high levels of ASI (ancestral South Indian) ancestry. Testing the same models with the Maniq population in place of Onge (although this population has some East Asian ancestry) might allow to test this idea. Also it would allow the use of the Onge population in the outgroup set.

On 2024-04-12 19:22:42, user Xiaohui Zha wrote:

It is now published by eLife:

https://elifesciences.org/a...

Xiaohui Zha

On 2017-06-22 16:26:27, user Vincent Calcagno wrote:

Interesting! But where are the figures and tables? I cannot see any in the pdf.

On 2017-05-15 12:07:18, user Nick Schurch wrote:

I really hope this gets a good reviewer because there are some real red-flag problems here. For example, the unexpected fig2 properties, p11. These are a direct result of the way the authors are defining dominant isoforms. Currently they define dominant isoforms as any isoform with an expression level above the median isoform expression for that gene. for 2 isoform genes this means one is always dominant, one isn't. for three isoforms, it means either a 2:1 or 1:2 split - and this is the reason they see this stepwise behaviour

In my opinion, the authors really should do a proper statistical test to see whether the distribution of isoform expressions changes, rather than just pick a per-gene-median threshold. (if I might humbly recommend: http://biorxiv.org/content/... "http://biorxiv.org/content/early/2017/05/02/132761)")

On 2019-12-06 00:57:33, user Vahe Demirjian wrote:

The abstract by Campbell et al. (2013) that you cite when discussing the Chasmosaurus russelli holotype has already been published (Campbell et al. 2016). Also, some Agujaceratops specimens have been assigned to a new species, A. mavericus (Lehman et al. 2016).

Campbell JA, Ryan MJ, Holmes RB, Schröder-Adams CJ (2016) A Re-Evaluation of the Chasmosaurine Ceratopsid Genus Chasmosaurus (Dinosauria: Ornithischia) from the Upper Cretaceous (Campanian) Dinosaur Park Formation of Western Canada. PLoS ONE 11(1): e0145805. https://doi.org/10.1371/jou... "https://doi.org/10.1371/journal.pone.0145805)")

Lehman, T.M.; Wick, S.L.; Barnes, K.R. (2016). New specimens of horned dinosaurs from the Aguja Formation of West Texas, and a revision of Agujaceratops. Journal of Systematic Palaeontology. Online edition. doi:10.1080/14772019.2016.1210683.

On 2023-08-17 16:59:27, user Oliver Hahn wrote:

The study has now been published at Cell<br /> "Atlas of the aging mouse brain reveals white matter as vulnerable foci"<br /> https://www.cell.com/cell/f...

On 2022-10-24 11:08:05, user Anne Urai wrote:

In this paper, Weilnhammer et al. tackle an intruiging question at the heart of decision-making theory: do observers use stable strategies, or do their strategies ('modes') change within a session? This papers analyses the autocorrelation in perceptual decision-making, and finds that it fluctuates between externally- and internally-driven strategies in ways that are strikingly similar between humans and mice.

I very much enjoyed reading this work. The topic is exciting, and together with the Ashwood paper (which I see as a companion) this work will undoubtedly spur many future developments across psychology and neuroscience. The author's commitment to open science is laudable, and I am glad to see them making such good use of open, large-scale, community-curated datasets. I do have a few questions that are important to fully understand and validate the author's conclusions, and several minor suggestions.

My expertise lies in the analysis of behavioral data, so I will mostly comment on main figures 1-4 rather than the model in figure 5.

I thank the authors for also sharing this work as a preprint. As a signatory of Publish Your Reviews, I have committed to publish my peer reviews alongside the preprint version of an article. Specfically, this review will also be posted on the bioRxiv comments section. For more information, see publishyourreviews.org.

Major questions<br /> 1. Do mode fluctuations have a characteristic timescale? I find terminology like 'oscillated as 1/f noise' (l. 985) a bit confusing - there may be frequency-specific oscillations, *or* a 1/f spectrum, but these are not the same (https://www.nature.com/arti... "https://www.nature.com/articles/s41593-020-00744-x)"). While the 1/f characteristic is clearly shown in Figures 2D, 3D, 4D (and the authors discuss self-organized criticality), I could not reconcile this with some other aspects of the analyses and writing. First, are phase and coherence not usually computed as a function of frequency? If so, which frequency is used in Figure 2E,F? Methods section 7.3.4 and l. 148 could be elaborated for readers unfamiliar with spectral analyses of behavior (how should the units of coherence be intrepreted?). Second, how can the simulation with a phasic mode switch at a single frequency (Figure 5A) give rise to a 1/f spectrum (Figure 4D), rather than a spectrum with an oscillatory peak (l. 425)? Third, there is a long history of investigating oscillations in perception (work by Fries, VanRullen, Kastner and many others, as the authors cite) albeit at a much faster timescale than shown here. Since the unit in this manuscript is trials (not time), are these two lines of work inherently incomparable, or can something be said about the typical trial length and therefore the interpretation of the best-fitting f = 0.11 in their model fits (l. 890/891)? Throughout the manuscript, it would help to distinguish oscillatory vs. 1/f. Alternatively, I may fundamentally misunderstand the results, in which case further elaboration and clarification would be great.<br /> 4. It would be very helpful if the full set of figure panels (as in Figures 2, 3, 4) was reproduced for each of their control simulations (S4, S6, S7), to better compare these models' behavior against human and mouse data. This would increase confidence in the main findings, and further pinpoint what exact behavioral signatures are unique to bimodal inference (rather than arousal fluctuations or decision bouds). I would like to suggest further control analyses to strengthen the existing ones. First, to conclude that internal-external mode fluctuations do not reflect periods of 'limited capacity', 'energy budget' or 'unstructured neuro-cognitive noise' (section 5.3), can you simulate and fit data from a process that additionally (or only) has periods of low/high perceptual sensitivity or task engagement (for instance, simulating high lapses)? Especially in the mouse data (and shown by Ashwood, there are likely periods of disengagement from the task, e.g. when mice become satieted towards the end of the session. One prediction may be that this would lead to more errors in history-congruent modes (as in 3A), Second, recent work (reviewed in https://doi.org/10.51628/00... "https://doi.org/10.51628/001c.35908)") has shown that slow drifts in decision boundary, without any strategic history-dependent updating, may give rise to statistical confounds and apparent history-dependence. It is difficult to intuit how such a process may affect the analyses presented here: could the authors simulate a process with only a slowly drifting bound (beyond the static response bias in section 5.4)?

Minor suggestions<br /> 1. I applaud the authors for sharing their full workflow on OSF. However, I did find the format (all files in a zip) a bit difficult to work with: for instance, it's not possible to view the code in-line without downloading. To further increase the usefulness of the codebase to others, consider exploring ways to present the code in a way that allows easy re-running, inspection and versioning (e.g. in a notebook form, or at least with the scripts on GitHub). Also, comments on how to use the files (where to start? how to install/run? what are the dependencies? what version of R?) would be of great help to others who want to implement the same method.<br /> 3. Could the authors show standard (and history-conditioned) psychometric curves in both modes? This would show if there are considerable lapses, which can bias the estimation of history-dependent logistic regression models (see http://www.journalofvision.... "http://www.journalofvision.org/lookup/doi/10.1167/14.7.9)"). If lapses are considerable, this may need to be taken into account in the model (or at least simulated to check that it doesn't introduce confounds). Related to this, how many human studies did not have stimulus strength information (and thus presumably only one level of task difficulty), and do the results look the same without studies with these missing data?<br /> 17. The authors recognize that trial-to-trial variations in stimulus strength (i.e. task difficulty) are a major driver of choices, and account for this in their control analysis. However, when defining stimulus-congruence, this is (as far as I can tell) only done based on the sign (i.e. a binary indicator), thus removing the _degree_ of stimulus congruence. Would the results look the same is stimulus-congruence was instead coded as a continuous variable, i.e. being more congruent when stimulus strength is high?<br /> 18. Why not show the logistic regression results in Figure 2B, which takes into account several confounds that are now hidden in the supplement?<br /> 19. Why do the mixed effects models only have random intercepts, and not random slopes? It seems that sensory and history dependence vary substantially across observers, which random slopes could capture.<br /> 20. Consider visualizing the phase information (Figure 2E) on a circular plot. To better interpret the coherence and phase information (see my first main question), it would be very helpful to discuss whether the two modes are anti-correlated/alternate at a specific frequency.<br /> 21. Please define 'infra-slow' when first used (see first main question). Is this characterized by a specific frequency range?<br /> 22. I am unsure if the selection of IBL mouse sessions may affect these results. Specifically, the authors here use a simple performance criterion of 80% in easy trials. However, in this task contrasts were introduced gradually, meaning that 80% correct on easy trials may happen early on in training as well as very late (with very different contrast sets). In Figure S5, could it be indicated which sessions were incorporated into the main analysis? Would the results hold if using a more stringent criterion to consider animals 'trained', as proposed in the original IBL paper (https://github.com/int-brai..., which also incorporates bias and lapses)? A related point is that TDs are a lot larger (Figure 3H) than in the original paper (IBL et al. 2021, Figure 3 - supplement 2A), which may be remarked upon.<br /> 23. Also Figure S5: it would be of great interest to also see the within-session changes in mode, for mice as well as humans (see also my main question above on satiety).<br /> 24. The green plots in Supplementary Figure 2 are interesting but also a bit worrying, showing all sorts of autocorrelation in the stimulus sequences that make the paper's conclusions trickier to assess. The authors already discuss some features of the IBL task design that introduce specific autocorrelation patterns (i.e. post-error bias correction). Is such information, describing the specific algorithms for sequence generation, available for the studies from the confidence database? And could the authors relate specific stimulus sequences to the behavioral modes they observe?<br /> 25. In section 5.7, it should be noted that mice did receive single-trial feedback. How about humans? Splitting the confidence database into those studies with and without single-trial feedback could be used to nicely test the predictions in line 549 / Figure 5.<br /> 26. RT distributions are characteristically long-tailed, which can strongly affect scaling them. I am a bit confused that all values in Figure 2H lie below zero, should this not be zero-meaned? Was a transform (e.g. log) used before zscore? If not, could the authors show the RT distributions per study before and after outlier removal and scaling, to give a better sense of the distributions that were used in the analyses (or replicate 2H without normalization so that the real RT units are visible, as in 3H)? It would also be nice to add the range of individual cutoff values used for exclusion criteria (l. 776).<br /> 27. To make the magnitude of weights in figure 2B, 3B and 4B easier to interpret, consider adding the weight for sensory stimuli (see Abrahamyan figure 3 - may need to be on a differen y-scale). <br /> 28. lines 436-445: are these correlations linear, or may there be quadratic relationships between posterior certainty and RT, confidence, TDs? A supplementary figure would be nice.<br /> 29. Very minor: I had some issues printing the figures, likely due to many transparent datapoints in the pdf. For the final version, consider exporting the figures to a high-resolution bitmap format to reduce the size.<br /> 30. The work contains a couple of remaining typos (e.g. ressource'), duplicate words, etc.<br /> 31. l. 148, S2F -> 2F.<br /> 32. l. 653, where in the Ashwood paper is this number of >100 trials mentioned? As far as I can tell, they only analyze the first 90 trials of each session (see their figure 3E).<br /> 33. Some references should be updated: for instance, 21 is now published in eLife, and 12 & 66 point to the same paper (published and preprint). Please check all the references to make sure that they point to the most recent versions.<br /> 34. Consider adding author contribution statement and/or displaying this visually (see e.g. https://twitter.com/Steinme..., https://elifesciences.org/a... figure 6). Also, is there a reason why one author is now omitted, compared to the first bioRxiv version?

Further questionsAs with all fresh ideas, this work raises many more questions that it can answer: while beyond the scope of this manuscript, I list some here so that they may be of use to the community.

• Especially together with the paper by Ashwood et al., the obvious next question is the structure of mode fluctuations: are there two modes (as suggested here) or more (as suggested by Ashwood)? Do these switch in a discrete vs. continuous way?
• At what timescale do states/modes change? How does this relate to even slower timescales in biology, e.g. at the level of circadian rhythms?
• It is fantastic to see that large-scale databases are increasingly being used for cross-species comparison. In a way, it's a shame that these exist only for humans and mice. Are there plans or efforts to collect and publish similar databases from non-human primates (where many, many trials of perceptual decision-making tasks have been collected over the years)?

On 2024-08-26 21:21:30, user markus wrote:

Hi Andre:<br /> The text mentions various supplement figures. Where are those located?<br /> Thanks!<br /> - Markus

On 2022-10-31 15:53:13, user Daniel Lüdke wrote:

Figure 3a, b and c: Are the same gene expression patterns and SA levels observed for the different light settings when treatment with Pst instead of flg22 is performed?

On 2022-10-31 15:55:47, user Daniel Lüdke wrote:

Line 442: “We are still in the dark about” …. Nice one :)

Regarding the specific wavelength that could lead to induction of defense responses: SA and other immunity associated metabolites can also be induced via UV-C treatment eg:<br /> Yalpani et al., 1994 https://link.springer.com/a...<br /> Rekhter et al., 2019 https://www.biorxiv.org/con...<br /> Mohnike et al., 2021 https://doi.org/10.1093/plc...

On 2016-08-28 12:46:25, user David Roberts wrote:

May I suggest to the authors that they do no need to post their article here in a format demanded by publishers for submission. Indeed, this reader would have found it much more useful to have figures actually placed where they were intended to go. We are in the 21st century, not the mid-20th.

On 2019-09-20 22:42:50, user Mikhail V Matz wrote:

Nice one! One concern about Fig. 6: comparison of distance matrices must be based on Mantel test, not regular correlation, since data points are not independent. Should not draw trendline and especially the shaded credible interval.

On 2021-06-22 04:41:27, user Hamid Gaikani wrote:

This preprint was peer-reviewed in "Frontiers in Fungal Biology" and is currently available under the title of "Systematic prediction of antifungal drug synergy by chemogenomic screening in Saccharomyces cerevisiae"<br /> doi: 10.3389/ffunb.2021.683414

On 2020-10-20 10:42:04, user Stuart J.E. Baird wrote:

Amazing. Exactly the tool we need. Thank you to all involved.

On 2021-07-23 10:24:10, user Lore Pottie wrote:

Our preprint has been published (https://doi.org/10.1371/jou... "https://doi.org/10.1371/journal.pgen.1009603)"). The link will follow forthcoming.

On 2023-06-20 09:58:53, user Laboratory for Cell, Tissue & wrote:

Thanks a lot for sharing this very interesting piece of work! I have a question on the recovery of cells with/without fixation; after digestion do you obtain the same cell number? This is especially interesting when it comes to tissue digestion. <br /> best,