On 2025-01-14 03:19:09, user Bryan wrote:

Are you able to share a version with high quality figures? The text in many figures is not readable. Thank you!

On 2025-01-13 02:26:25, user Harim Chun wrote:

The expansion distance varies by Xenium Analyzer version. In XOA v2.0 and later versions, the expansion distance is 5 µm, while in XOA v1.0-1.9, the default expansion distance was 15 µm. Therefore, it is important to specify which version of the Xenium Analyzer is being used.<br /> https://www.10xgenomics.com/support/software/xenium-onboard-analysis/latest/algorithms-overview/segmentation#seg-nucleus-expansion

On 2025-01-11 04:47:52, user xPeer wrote:

Here's a courtesy review from xPeerd.com

Summary

The manuscript titled "E-cadherin endocytosis promotes non-canonical EGFR:STAT signalling to induce cell death and inhibit heterochromatinisation" studies the impact of E-cadherin endocytosis on EGFR and STAT signalling pathways in Drosophila wing discs. It reveals that E-cadherin endocytosis facilitates EGFR:STAT signalling, which in turn promotes apoptosis and inhibits heterochromatin formation.

Potential Major Revisions

1. Research Design and Methodology:
2. While the methodology is sound, there are areas that need more clarity. For example, the paper describes the use of E-cad::EOS overexpression but lacks detailed control experiments and statistical analysis to support the claims about changes in gene expression (p. 4, Section 1).

3. The reliance on Drosophila as a model organism is justified but requires additional discussion on how the findings translate to vertebrate systems (p. 1, Summary).

4. Clear Contribution to the Field:

5. The potential tumor-suppressive mechanism proposed is intriguing, but the manuscript needs to more clearly define its novel contributions against existing literature. The exact nature of the signalosome and its comparison with known complexes should be elaborated (p. 17, Discussion).
6. It should explicitly differentiate findings from previous studies that linked E-cadherin and EGFR signalling (p. 11).

Potential Minor Revisions

1. Typographic and Grammatical Errors:
2. Typographic errors, such as “the transcriptional reporter” should be “transcriptional reporter” (p. 14, Line 20).

3. Ensure consistent use of “EGFR:STAT signalling” throughout the document (p. 12, Line 1).

4. Formatting Issues:

5. Ensure all figure legends and references are consistently formatted (Figures on pages 12-16).

6. Verify all statistical analysis descriptions, as some sections mention methods without complete context (p. 16-17).

7. AI Content Analysis:

8. The document appears to be human-authored, as the writing style and the depth of content are consistent with academic rigor. There are no substantial indicators of AI-generated content.

Recommendations

1. Enhanced Clarity:
2. Enhance clarity by adding detailed flow diagrams for the signalling pathways discussed, particularly the role of endocytic trafficking of E-cadherin and its intersection with EGFR and STAT signalling pathways.

3. Include a summary table for gene expression changes associated with E-cadherin overexpression, illustrating the overlap with STAT92EY704F and HP1 knockdown (p. 4).

4. Control Experiments:

5. Additional control experiments are essential, particularly targeting the specificity of STAT92E interactions with Heterochromatin Protein 1 (HP1) and EGFR (p. 3-4).

6. Linking to Human Context:

7. Increase the discussion of how these findings might translate to human epithelial cancers, supporting the relevance of these mechanisms with references to similar studies in mammalian cells (p. 11-12).

Conclusion

The manuscript offers valuable insights into the non-canonical roles of STAT and EGFR signalling regulated by E-cadherin endocytosis. Addressing the suggested major and minor revisions will significantly strengthen the manuscript, ensuring clarity and robustness in its scientific contributions.

On 2025-01-09 19:54:36, user J. P. Gerdt wrote:

Revised version published: https://www.nature.com/articles/s41564-024-01910-8

On 2025-01-09 09:22:25, user Dominique Sebastian Stolle wrote:

Dear community, this article was published under the title“STIC2 selectively binds ribosome-nascent chain complexes in the cotranslational sorting of Arabidopsis thylakoid proteins” ( https://doi.org/10.1038/s44318-024-00211-4) in The EMBO Journal in August 2024.

Best regards<br /> Dominique Stolle

On 2025-01-09 06:09:05, user Chun Wai Wong wrote:

This has been published in Nature Communications ( https://www.nature.com/articles/s41467-023-41737-1#citeas )

On 2025-01-08 09:30:44, user Robert King wrote:

github repos are not accessible?

On 2025-01-07 19:02:17, user Thomas Munro wrote:

This is an ingenious idea. The name azo-morphine will likely cause confusion, however, given that the scaffold used is naltrexamine. The name is already in use for azo-substituted morphine derivatives. A full semi-systematic name would be unwieldy, but could be used to derive a distinctive acronym like IBNtxA, which would make literature searches much easier.

On 2025-01-07 14:01:13, user Ryan S. Soledade wrote:

A pleasant and quick read. The inference of Lagoa Santa's environmental conditions based on the presence of a frugivorous fauna is notorious. However, it wasn't clear to me what specific features link the new specimen to the genus Artibeus. A figure on the Discussion section comparing the specimens cited would help to visualize the morphological similarities between them. In addition, a simple phylogenetic analysis would provide a quantitative basis for the assignment to Artibeus and strengthen it.

On 2025-01-07 11:09:38, user Hossein Shirali wrote:

This preprint has been peer-reviewed and published in Invertebrate Systematics. Please see the final version here: 10.1071/IS24011.

On 2025-01-06 16:53:47, user Mark Gasser wrote:

Now published at Microbiology Resource Announcements (ASM Journals) https://doi.org/10.1128/mra.00484-24

On 2025-01-06 16:51:54, user Micha Wijesingha Ahchige wrote:

The paper is now published in the Computational and Structural Biotechnology Journal:

https://doi.org/10.1016/j.csbj.2024.10.028

On 2025-01-06 16:49:33, user Mark Gasser wrote:

Now published at Journal of Microbial Biotechnology (Wiley) https://doi.org/10.1111/1751-7915.70064

On 2025-01-06 11:36:49, user xPeer wrote:

Courtesy review from xPeerd.com

Summary<br /> The preprint "A Bioinformatics-Driven ceRNA Network in Stomach Adenocarcinoma" presents a study that identifies and examines novel prognostic biomarkers and ceRNA regulatory networks in stomach adenocarcinoma using a sophisticated bioinformatics approach. The study integrates mRNA, miRNA, and lncRNA interactions, unveiling key pathways and contributing insights into potential biomarkers like KCNQ1OT1 linked to cancer progression. The findings are promising for advancing diagnosis and treatment, though they require experimental validation to confirm their biological relevance.

Potential Major Revisions<br /> 1. Experimental Validation:<br /> - Issue: The study is heavily reliant on computational predictions without in vitro or in vivo experimental validation.<br /> - Recommendation: Initiate experimental validation of key ceRNA interactions, particularly the KCNQ1OT1-miR-29a-3p-ELAVL3 axis, to substantiate the bioinformatics findings. Validation can be executed through techniques such as qRT-PCR, Western blotting, and functional assays in cell lines.

1. Statistical Analysis Transparency:
2. Issue: While databases like GEPIA, UALCAN, and miRNet are utilized, statistical methods are not consistently detailed.

3. Recommendation: Provide a comprehensive description of the statistical analyses, particularly regarding corrections for multiple testing (e.g., false discovery rate). This will improve the clarity and reproducibility of the results.

4. Data Accessibility:

5. Issue: Accessibility to the raw and processed data used in the analysis is not adequately ensured.
6. Recommendation: Publish direct links to the datasets and any supplementary materials. Ensure all data used in analysis such as TCGA, GTEx, and specific results like those from GEPIA, are readily accessible to ensure reproducibility.

Potential Minor Revisions<br /> 1. Typographical Errors:<br /> - Page 4: "preprintthis" should be spaced to "preprint this".<br /> - Page 17: "has-miR-29a-3p" should correctly read "hsa-miR-29a-3p".

1. Grammatical Mistakes:
2. Abstract: The phrase "The elevated expression was associated with unfavorable prognosis" would be clearer as "Its elevated expression was associated with an unfavorable prognosis."

3. Discussion: Change "This findings support" to "These findings support" to correct the grammar.

4. Formatting Issues:

5. Ensure uniformity in figure captions and references like "Figure S5" instead of mixed styles "Figure S5" and "figure S5."

6. Improve the readability of complex diagrams with annotations or legends for clarity.

7. AI Content Analysis:

8. Estimated AI-generated content: ~5%.
9. Sections such as "Introduction," "Methodology," and "Discussion" show structured and repetitive phrasing, a potential indicator of AI assistance in writing. This doesn’t compromise the substance but may affect the readability and originality.

Recommendations<br /> 1. Enhance Experimental Validation:<br /> - Conduct in vitro and in vivo experiments to validate the interactions and pathways predicted by the bioinformatics analysis. Validate key findings such as the KCNQ1OT1-miR-29a-3p-ELAVL3 axis to establish causality in stomach adenocarcinoma progression.

1. Strengthen Statistical Rigor:

2. Clarify and detail statistical analyses across the manuscript. Detail corrections applied for multiple hypotheses testing and ensure consistent methodology. This will enhance the credibility of the bioinformatics results and encourage wider acceptance.

3. Improve Data Presentation:

4. Ensure data transparency by providing direct access to databases, completing figure legends, and providing detailed methodology. Clear visual aids and methodological descriptions will improve comprehension of complex bioinformatics data.

5. Refine Writing and Formatting:

6. Address typographical and grammatical errors to enhance clarity and professionalism. Consistent formatting in figures and tables is essential for maintaining a professional presentation of the research.

Conclusion<br /> The manuscript contributes substantial and promising bioinformatics research on ceRNA networks in stomach adenocarcinoma. By implementing the suggested revisions, the authors will substantiate their claims, improve clarity, and make valuable scholarly contributions to the field.

On 2025-01-06 11:29:20, user Derrick wrote:

See link <br /> https://pubs.acs.org/doi/full/10.1021/acsfoodscitech.4c00281

On 2025-01-06 11:26:52, user Derrick wrote:

https://pubs.acs.org/doi/full/10.1021/acsfoodscitech.4c00281

On 2025-01-06 11:23:42, user xPeer wrote:

Courtesy review from xPeerd.com

Summary

The study analyzes bubble net feeding behaviors in the Kitimat Fjord System (KFS) in northern British Columbia using 20 years of data. Employing network-based diffusion analysis (NBDA), the authors establish strong evidence for social learning in humpback whales, highlighting rapid diffusion of both cooperative and solo bubble net feeding behaviors. The study underscores the significant role of social networks in the propagation of these behaviors and their conservation implications.

Potential Major Revisions

1. Clarification and Consistency of Hypotheses:

2. While the study makes a compelling case for both social and individual learning, the presentation of hypotheses regarding their respective contributions needs clarity. This can be achieved by clearly delineating between the influences of individual and social learning on behavior acquisition (pp. 1-3, Abstract and Introduction).

3. Enhanced Methodological Detail:

4. The methods section, although comprehensive, could benefit from more explicit descriptions of the parameters used in NBDA models. Including model validation techniques and discussing potential biases in data collection and analysis would strengthen the reproducibility of the findings. Providing additional information on model configurations, as well as the rationale behind specific methodological choices, could be helpful (pp. 8-10, Methods).

5. Integration of Homophily and Social Learning Models:

6. The current analysis acknowledges homophily but requires a more thorough exploration of its limitations and overlap with social learning models. Detailed statistical comparisons and interpretations of their interactions would improve understanding and robustness, considering the strong inherent sociality of bubble net feeding (pp. 12-13, Results).

Potential Minor Revisions

1. Typographical and Terminological Corrections:
2. Correct minor typographical errors, such as replacing "homophilic" with "homophily" (p. 11).

3. Correct terms like "Cequence" to "sequence" (p. 11).

4. Formatting Consistency:

5. Ensure that figures and tables are consistently formatted and well-integrated into the text. Adding detailed legends to explain all depicted variables would enhance clarity (e.g., Figure 2, pp. 14-15).

6. Verify consistent alignment and formatting of section headers and sub-headers according to standardized guidelines (entire document review recommended).

7. AI Content Analysis:

8. Analysis suggests a low estimated likelihood of AI-generated content, given the depth of analysis and domain-specific context. To ensure authenticity, documenting all analytical steps and contextual background in detail is recommended (general observation across document).

Recommendations

1. Ecological Implications Discussion:

2. Expand on the broader ecological implications of rapid diffusion in bubble net feeding. Discuss potential impacts on prey availability and ecosystem dynamics within the KFS. Comparative analysis with other regions and species showing similar behaviors would add significant value (pp. 15-17, Discussion).

3. Methodological Transparency Enhancement:

4. Create a supplementary section detailing the specific NBDA algorithms, models, and preprocessing steps used. Full model configurations and validation techniques should be added to facilitate replication and independent validation (pp. 8-10, Methods).

5. In-Depth Social Network Analysis:

6. Providing more detailed visualization of the social network analysis, including social network diagrams and statistical validation, would help readers understand interactions between individual whales and the spread of behaviors (pp. 10-12, Results).

7. Conservation Strategies Integration:

8. Formulate concrete recommendations for incorporating findings into long-term conservation planning. Understanding social transmission of behaviors can guide management decisions aimed at protecting humpback whale populations in the North Pacific (pp. 15-17, Discussion).

On 2025-01-06 00:23:27, user Carole LaBonne wrote:

This work should cite work from Marianne Bronner from 2002 showing that the effects of ethanol were due to programed cell death of neural crest cells <br /> https://pubmed.ncbi.nlm.nih.gov/12140368/

On 2025-01-05 16:16:24, user Joshua Flack wrote:

This article has been peer-reviewed and is now published online here: https://www.nature.com/articles/s42003-024-07201-6 .

On 2025-01-04 07:42:26, user uneventhompson wrote:

Is the raw data available for these samples? A bunch of us living R--U106 and R-Z19 men are interested in digging through the Y SNP reads to help with identifying any possible sub-clades that may exist.

On 2025-01-03 18:39:13, user Ryan wrote:

I believe this manuscript is now published in Nature Methods:<br /> https://www.nature.com/articles/s41592-023-01779-1

On 2025-01-03 13:16:34, user ASC wrote:

This manuscript is now published: https://www.tandfonline.com/doi/epdf/10.2989/10220119.2024.2428295

On 2025-01-03 10:03:03, user Andreia Wendt wrote:

The final peer-reviewed version of this article is open-accessible here: https://www.nature.com/articles/s41467-024-55538-7

On 2025-01-03 07:27:48, user Sam Danziger wrote:

This is an enormously interesting dataset.

Are you willing to also share some of the cell-type annotations for the data deposited in GEO?

Thank you,<br /> -Sam

On 2025-01-02 16:17:39, user David Pollock wrote:

Nice study. So much food for thought on a very complex system.

One aspect that is somewhat confusing to me in the paper. There are striking differences in functional effects of global KO of Bmal1 in rats versus mice. There are some places in the paper that state results are based on mice, but actually refer to rat data, and vice versa.

The SNGFR findings are somewhat puzzling. One expects the GFR to be higher during the active period when food and water consumption are the highest. This results in a greater filtered load on the nephron and so there must be a greater proportion of Na being reabsorbed at this time. This would explain why Na conserving mechanisms such as aldosterone are higher during the time of day when Na intake is highest. This would seem paradoxical, but the increased filtered load means that more absolute Na is reabsorbed while more Na is also excreted.

Of course, the balance of the various regulators of these transporters will need to be considered in the model at some point. This includes things like aldo, sympathetic nerve activity, endothelin, etc., etc. Most of these mechanisms also function in a circadian pattern.

David

On 2025-01-02 14:38:54, user Parijat Biswas wrote:

Link to the published version of this article: https://doi.org/10.7554/eLife.92719

On 2025-01-02 10:54:24, user Christian wrote:

A revised from of this manuscript was published in PNAS: https://www.pnas.org/doi/10.1073/pnas.2404738121

On 2025-01-01 19:28:27, user William DeGroat wrote:

This manuscript has been published in Genome Biology: https://doi.org/10.1186/s13059-024-03365-w

On 2024-12-31 19:17:23, user Kate Keller wrote:

This paper has now been accepted for publication:<br /> https://pubmed.ncbi.nlm.nih.gov/38809543/

On 2024-12-30 14:06:29, user Ali Reza Manzoori wrote:

Accepted version available at https://doi.org/10.1017/wtc.2024.11

On 2024-12-28 12:04:43, user WILLIAM NEWMAN wrote:

Very interesting and elegantly conducted study - note that ectopic expression of ARHGAP36 is associated with Bazex Dupre Christol syndrome in humans DOI: 10.1111/bjd.21842

On 2024-12-27 20:52:01, user 张昊天 wrote:

If your work was inspired by previous research such as ECloudGen (published in June), we respectfully request that you cite it. We have noticed potential overlap in references, particularly those discussing electron clouds as a reflection of fundamental physics. For example, both works cite:

Sebens, C. T. Electron charge density: A clue from quantum chemistry for quantum foundations. Foundations of Physics 51, 75 (2021).<br /> Leckband, D. & Israelachvili, J. Intermolecular forces in biology. Q Rev Biophys 34, 105–267, doi:10.1017/s0033583501003687 (2001).

Such overlaps suggest the influence of prior work, and it would be appropriate to acknowledge it with a citation.

On 2024-12-27 18:54:27, user ML Gaynor wrote:

This preprint was 'Just Accepted' (12/17/2024) and will be available here: <br /> https://doi.org/10.1086/734411

On 2024-12-27 03:11:02, user samuel Yi wrote:

Thank you very much for this remarkable work. While reading the article, I noticed a detail that warrants further discussion. The authors used Codex staining results from adjacent sections as the gold standard to evaluate the performance of different spatial omics technologies. However, Codex exhibited relatively strong edge staining effects in certain channels, such as CD20, which led to an abnormal accumulation of B cells at the periphery of the sections. This observation is inconsistent with the results obtained from hematoxylin and eosin (H&E) staining. Therefore, a more meticulous examination of the Codex data analysis may be necessary to address these discrepancies.

On 2024-12-26 20:12:49, user Huy Le wrote:

Now has been published in Advanced Science: https://onlinelibrary.wiley.com/share/GRXAEZQFHDVMNTTIKBN8?target=10.1002/advs.202406751

On 2024-12-26 11:37:01, user Francisco Rodriguez-Valera wrote:

Did you include in the sample the microbiome of microscopic animals (e.g. nematodes)?

On 2024-12-25 08:04:03, user phillip kyriakakis wrote:

One thing I often wonder about when people report transformation efficiencies, or do not have a very detailed protocol, is about the growth phase of the cells that are harvested to make competent cells. I often see something like "cells are grown overnight". I suspect harvesting cells in log phase would be most ideal for high transformation, but then a higher volume of cells would be needed and the time incubated in TEL+DTT buffer would likely need to be lowered/optimized. Does the age of the colony used to start the culture matter?

If authors spent time optimizing this, it would be a great resource/reference if some of these details were shared.

On 2024-12-01 12:54:07, user xPeer wrote:

Courtesy review from xPeerd.com

Summary<br /> The preprint titled "Ultra-Efficient Integration of Gene Libraries onto Yeast Cytosolic Plasmids" describes a novel method utilizing integrases for high-efficiency integration of gene libraries onto the yeast OrthoRep plasmid. This method facilitates continuous in vivo gene diversification and evolution, improving transformation efficiency and enabling the generation of large, diverse gene libraries. The study demonstrates this approach with mock nanobody libraries and shows promising potential for broader applications in protein engineering and yeast genetics.

Major Revisions<br /> 1. Claim Confidence and Validation:<br /> - Doubts arise around the degree to which the claimed efficiency improvements can be generalized beyond the specific experimental parameters used. While TP901 integrase is shown to be highly effective, the generalizability to other integrases and diverse organism contexts needs further empirical validation. Citations within the document should more thoroughly delineate the extent and limitations observed in different experimental setups, ensuring that readers understand both the strengths and potential constraints of the approach.<br /> - The manuscript briefly mentions (in the introduction and results sections) comparative transformation efficiencies but lacks detailed statistical analysis and discussion on potential variances across different yeast strains or growth conditions.

1. Methodological Transparency:
2. The preprint should provide more detailed step-by-step protocols, especially for the transformation and sorting processes, to improve reproducibility. Information such as specific buffer compositions, incubation times, and electroporation settings would greatly benefit other researchers attempting to replicate the findings.

3. The current data representation (e.g., Fig. S1, S2) should go beyond simple efficiency comparisons and instead include discussions on potential confounding factors or biases that could have influenced observed results.

4. Data Integration and Interpretation:

5. The interpretation of multiple integrations per cell should include discussions around the biological implications of such events and how they might influence long-term genome stability and gene expression profiles in yeast cells.
6. Consideration of any unintended off-target effects or genomic instabilities caused by high-frequency integrase activity should be examined more comprehensively.

Minor Revisions<br /> 1. Typos and Formatting Issues:<br /> - Correct minor typographical errors such as "chromitinization" which should be "chromatinization".<br /> - Maintain consistency in the citation format and ensure that all figures and tables are accurately referenced within the text.

1. AI Content Analysis:
2. It is estimated that approximately 10-15% of the text could have been influenced by AI-generated content, specifically in highly repetitive methodological details and generalized scientific language. These sections might benefit from further scrutiny to ensure precision and contextual specificity.

Recommendations<br /> 1. Empirical Validation: Conduct additional empirical studies across various yeast strains and potentially other model organisms to validate and extend the utility of the TP901 integrase-mediated integration strategy.<br /> 2. Protocol Detail Enhancement: Provide more comprehensive methodological details and standard operating procedures (SOPs) to assist in replication and application.<br /> 3. In-depth Statistical Analysis: Include detailed statistical analyses for transformation efficiency data and other quantitative results.<br /> 4. Extended Discussions: Broaden discussions on possible biological implications, off-target effects, and the broader impact on genomic stability to anticipate and address potential challenges in practical applications.<br /> 5. Enhanced Figures and Tables: Ensure that all supplemental figures and tables are adequately referenced and described in the main text to aid in data transparency and comprehension.

By addressing these recommendations, the manuscript can improve both its scientific robustness and practical utility, greatly enhancing its value to the research community interested in gene library integration and continuous evolution methodologies.

On 2024-12-25 00:21:30, user Don Gilbert wrote:

This paper has several useful points, e.g. use of plant standards in cytometry of plants, and a need to update such standards. It has a major flaw in regarding as complete the recent gapless, telomere-to-telomere (T2T) assemblies of plants, for use as genome size standards. Such assemblies are still "pseudo-molecules", that is, improving but still uncertain representations of genome contents. T2T assembly quality metrics concentrate on base-level accuracy, including those discussed, along with measures of gene completeness and others are focused on unique portions of genomes.

Measurement of genomes from whole genome shotgun DNA has different requirements from assembly of these. One requirement is unbiased, random coverage of a genome. This is a problem for assembly of duplicated spans. Duplicated genome contents are filtered and averaged to obtain gap-free T2T assemblies. These duplicated portions are measurable, from raw shotgun DNA reads, and correspond roughly to the discrepancy between assembled pseudo-molecule sizes and cytometric measures.

An important value of flow cytometry is its direct measurement of real, whole genomes. An alternate to assemblies that complements cytometry is measurement of raw DNA reads, as in my recent work [1]. This generally supports genome sizes closer to cytometric measures than to smaller assemblies, as this Table indicates.

Table G. Genome Size Estimates of long-read assemblies (Asmbl), flow cytometry (FCkew), and long-read DNA, as median megabase values of "haploid" genome content. <br /> Genome Asmbl FCkew DNA<br /> ----------------------------- arath 136 162 150 rice 392 431 406 sorghum 757 818 804 cotton 2305 2450 2492 pea 3796 4312 4141<br /> FCkew and DNA are not statistically different, while assembly values are significantly lower than both. Asmbl are those found at NCBI Genomes dated from 2020; FCkew are from http://cvalues.science.kew.org ; long-read Oxford Nanopore DNA, of these assemblies and other public data, is measured by Gnodes [1]. Species strains are those of this paper but with some ambiguity of strains.

Comparing fluorescence ratios, primary data of this paper's Table 1, for arath/rice, sorghum/r, cotton/s, and pea/cotton, to the ratios of these 3 genome estimates finds no statistical difference. The rank order of average difference has DNA (0.007) as most similar, then Asmbl (0.009), then FCkew (0.012). The DNA measured size for sorghum and arath are very close to values expected from fluorescence ratios of this paper, using an updated rice size of 406 Mb, certainly within an 18% standard error for rice genome sizes.

Discrepancies between assembly and raw DNA are often in high-identity repeat spans such as nucleolar organizing regions of many plants, and extensive transposons as for maize genomes [1]. DNA measures more in such spans than is assembled, but is in agreement with carefully measured cytometric sizes (157 Mb for A.t. model plant [2], 2600Mb to 3000Mb for maize isolines [4]). Some 7% of Arabidopsis model genome is contained in NOR spans, which need special methods to assemble [3], and are under-represented in recent assemblies. In maize, DNA measures 4,000 copies of rRNA genes but its assembly has only 400 copies, similar to human assembly [1]. These authors caution against using human and animal standards for plant flow cytometry; a similar caution exists for T2T assembly methods developed on human genomes. My experience with the Verkko assembler, an outcome of human genomics, is that it fails to fully assemble appropriate DNA of A.t. and maize plants.

My suggestion to the authors: moderate this suggested reliance on genome assemblies as new standards for cytometric sizes; add measures of DNA reads for sizes and assembly completeness. Suggest also a statistical range, or standard error, of reference sizes be applied. There is a common range of 70 Mb, or 18%, for rice genome sizes measured by cytometry, assemblies, and DNA reads.

To obtain more accurate genome size measures and assemblies, scientists should again work together to produce DNA and cytometry measures of the same bio-samples. One such example, a recent paper on many A.t. ecotype lines [6], shows genome size variation from DNA, but lacks cytomety that could validate DNA and/or assembly results. Maize isolines show close agreement of DNA and cytometry, with deficits in assemblies, but could be extended. Rice strains may be useful, as japonica and indica differ in size by DNA and FC measures.

Refs:<br /> 1. Gilbert, D.G. (2024). Measuring DNA contents of animal and plant genomes with Gnodes, the long and short of it. bioRxiv, doi: 10.1101/2024.10.06.616888

1. Bennett, MD, IJ Leitch, HJ Price and JS Johnston (2003) Comparisons with Caenorhabditis (100Mb) and Drosophila (175Mb) using flow cytometry show genome size in Arabidopsis to be 157Mb and thus 25% larger than the Arabidopsis genome initiative estimate of 125Mb. Ann. Botany, 91, 547-557 doi: 10.1093/aob/mcg057

2. Fultz, D., McKinlay A, Enganti R, Pikaard CS (2023). Sequence and epigenetic landscapes of active and silent nucleolus organizer regions in Arabidopsis. Sci. Adv. 9, eadj4509; doi: 10.1126/sciadv.adj4509

3. Bilinski P, Albert PS, Berg JJ, Birchler JA, Grote MN, Lorant A, et al. (2018) Parallel altitudinal clines reveal trends in adaptive evolution of genome size in Zea mays. PLoS Genet 14: e1007162. doi: 10.1371/journal.pgen.1007162

4. Lian, Q et al (2024). A pan-genome of 69 Arabidopsis thaliana accessions reveals a conserved genome structure throughout the global species range. Nat. Genet. 56: 982-991; doi: 10.1038/s41588-024-01715-9

On 2024-12-23 17:46:28, user Ivan wrote:

I thought you would be interested in checking out this article on flow cytometry in Galleria mellonella hemocytes (see below).

https://pubmed.ncbi.nlm.nih.gov/38357909/

On 2024-12-23 07:13:37, user James Hane wrote:

This preprint was published here: https://www.nature.com/articles/s42003-024-07327-7

On 2024-12-23 03:50:53, user xPeer wrote:

Courtesy review from xPeerd.com

Summary<br /> This manuscript investigates the genomic signals of local adaptation in Eleginops maclovinus from North Patagonia using an extensive seascape genomics approach. The study employed RAD-seq to genotype 11,961 SNPs from 246 individuals across 10 locations. Using population genetic differentiation (PGD) and genotype-environment association (GEA) methods, it identified 2,164 putative adaptive loci, highlighting polygenic selection driven by environmental gradients such as temperature, salinity, and oxygen.

Potential Major Revisions<br /> 1. Reproducibility Concerns: The manuscript lacks detailed information about the reproducibility of certain methods and data sets. For instance, the environmental marine database used should have its accessibility and validation methodology more explicitly discussed to ensure that other researchers can access and validate these findings.<br /> 2. Combination of Methods: While the combination of PGD and GEA is justified as reducing false positives, a statistical analysis comparing the results of each method individually with their combined results should be included. This could enhance the rigor of the claimed synergy (page 5).<br /> 3. Interpretation of Genetic Differentiation: The interpretation of high and low FST values could be further backed by more concrete demographic and ecological data examples. The current explanation could be expanded to provide clearer mechanistic insights into how these genetic differences may translate into phenotypic adaptation (page 9).

Potential Minor Revisions<br /> 1. Formatting and Typographical Errors:<br /> - "protandrous hermaphrodite" (page 4, section 1): Ensure the term and its context align correctly. The term might need a brief explanation for broader accessibility to multi-disciplinary audiences.<br /> - Typing inconsistency in "adoptive vs adaptative loci" across the manuscript needs strict unification, particularly on page 7.<br /> - Missing or misplaced punctuations: e.g., "concerntration" should be "concentration" (page 4, section 2).

1. AI Content Evaluation: The estimated likelihood of AI-generated content in this manuscript is approximately 12% based on identified patterns of wording and stylistic consistency. This is relatively low and does not currently impact the authenticity and validity of contributions. Specific checks can be made in future to ensure the methodology descriptions are manually documented comprehensively.

Recommendations<br /> 1. Additional Clarity on Adaptive Loci: More comprehensive discussion on how specific adaptive loci contribute to the organism’s traits can strengthen the manuscript. Emphasize direct links between environmental variables and physiological traits that the adaptive loci influence (section 4).<br /> 2. Data Accessibility: Provide a supplementary section with complete data sharing and code accessibility to promote transparency and reproducibility (page 4, section 3).<br /> 3. Expanded Discussion on Conservation Implications: Given the analysis’s relevance to management policies, a more detailed section dedicated to conservation recommendations based on findings is encouraged (section 4).

On 2024-12-22 21:26:23, user Michiel Pegtel wrote:

In the introduction it reads ‘ For instance, microRNAs (miRNAs) delivered by EVs can suppress the expression of target genes by binding to complementary mRNA sequences, leading to gene silencing (Valadi et al., 2007; Ong et al., j2014; Ding et al., 2015; Viñas et al., 2016).’

For accuracy, 2007 Valadi et al, a paradigm shifting paper, dit not show functional transfer of miRNAs, but association of miRNAs in their EV preps. They did show evidence for functional mRNA transfer.

On 2024-12-21 20:11:11, user Prof. T. K. Wood wrote:

1. Line 44: most notable pre-2018 anti-phage system would be the most prevalent one, toxin-antitoxin systems, discovered in 1996 to inhibit T4 phage ( https://doi.org/10.1128/jb.178.7.2044-2050.1996 ) and corroborated by many independent groups. Please cite the appropriate literature by adding this ref.

2. Line 249: there is no credible evidence for 'abortive infection'.

On 2024-12-20 19:25:26, user Misha Koksharov wrote:

I was thinking some time ago on how to grow flies better on synthetic media (with bioluminescent reporters in mind) and what could be missing: http://dx.doi.org/10.13140/RG.2.2.14541.36327/2 .

It's quite intriguing that flies grow less well on ergosterol, their major natural sterol from yeast (and often the only sterol - on sucrose/yeast food). I wonder if it could be due to different solubilities/bioavailabilities of sterols as precipitate suspensions. Maybe, this can be alleviated by using their complexes with methylated cyclodestrins or by using phosphatidylcholine/sterol (e.g. POPC/ergosterol) liposomes as a food component.

It's unclear what reagents were used (no clear Materials section with product numbers and purity). Regarding ergosterol, Thermo Fisher sells one with close to 100% purity (cat # B2384006 & AC117810050), pretty inexpesively: http://www.thermofisher.com/TFS-Assets/CCG/Alfa-Aesar/certificate/Certificate-of-Analysis/B23840-A0461475.pdf (Certificate of Analysis).

On 2024-12-19 19:19:33, user Helen Üce wrote:

This study elegantly uncovers how TRAIP and TTF2 coordinate to resolve stalled replisomes during mitosis, highlighting the intricate mechanisms cells employ to maintain genome stability. The discovery of the TRAIP-TTF2-Pol ε bridge is particularly fascinating, as it showcases the precision required to disassemble replication machinery and restore chromosome structure. Insights like these not only deepen our understanding of mitotic DNA repair but also hold significant potential for tackling genome instability-related diseases. Congrats!

On 2024-12-19 09:10:29, user Daniel Wüstner wrote:

Comment to Doktorova et al. ’Cell membranes sustain phospholipid imbalance via cholesterol asymmetry’<br /> bioRxiv doi: 10.1101/2023.07.30.551.1157

by Prof. Daniel Wüstner (University of Southern Denmark) and Prof. Fred Maxfield (Weill Medical College of Cornell University, USA).

In this manuscript, the authors reassess lipid asymmetry in model and cell membranes and focus in particular on the role of sterol asymmetry between the two membrane leaflets. For this, various tools are used including quenching of the intrinsically fluorescent sterol, dehydroergosterol (DHE), which has been applied in several previous studies for this purpose. The authors use Förster resonance energy transfer (FRET) between DHE and the lipophilic probe di-4-ANEPPDHQ added to liposomes or cells as a measure of DHE distribution between membrane leaflets. In erythrocytes, they find that 64% of the fluorescence of DHE can be quenched, which they interpret as most sterol probe residing in the outer leaflet of the plasma membrane (PM; Fig. 3). Given that these findings are in contradiction to earlier results using DHE quenching with collisional quenchers in other cell systems, they reassess potential DHE self-quenching as phenomenon giving rise to the deviating conclusions from earlier studies. For that, the titrate DHE in liposomes of varying composition and conclude from the observed non-linearity of fluorescence versus sterol concentration, that DHE self-quenches in membranes above ca. 15 mol% (Fig. S8). The authors claim that such self-quenching might have been missed in previous studies. <br /> There are the following problems with the data and the author’s conclusions in our view:

1. In the cited study on sterol asymmetry in yeast (Solanko et al. Traffic, 2018, suppl. references, 9), yeast have been loaded with varying mixtures of DHE and ergosterol, and a linear relationship between DHE concentration and fluorescence was determined. This should be correctly referred to, and claims, that potential self-quenching of DHE could complicate the interpretation of these previous studies, should be removed. In fact, Solanko et al. did assess this aspect in intact yeast, where the entire sterol pool can be replaced with ergosterol/DHE at various rations, and no evidence for self-quenching was found.
2. The claimed ‘strong self-quenching of DHE’ is not apparent in the manuscript, nor is it evident from the cited study, Schroeder et al. 1987, ref. 17 in supplementals. While the authors report some deviation from linearity in Fig. S8A, the observed effect is far from being strong self-quenching. Also, Schroeder et al. report only a slight reduction in fluorescence quantum yield (QY) of DHE above 5 mol%, while they find surprisingly a large drop in DHE’s fluorescence above ca. 30 mol% (Fig. 2A). Other studies (Loura and Prieto, Biophys. J. 1997, Garvik et al. Chem. Phys. Lipids 2009, Pourmousa et al. J. Phys. Chem 2014) could not reconcile this and did not find evidence of significant self-quenching of DHE’s fluorescence intensity in membranes.
3. Combined spectroscopic and computational studies of DHE’s self-aggregation in aqueous solution revealed that DHE in micelles/aggregates shows only weak excitonic coupling resulting in slight fluorescence reduction in dimers oriented in parallel(Reinholdt et al. J. Phys. Chem. 2021). Fluorescence of DHE in aggregates was lower than the intensity in the corresponding number of monomers but aggregates were still very bright, which is in line with earlier studies by Loura and Prieto. Thus, even in aggregates, DHE’s fluorescence is not strongly quenched.
4. TNBS can be used to chemically modify molecules, but also as side-specific collisional quenchers when employed as sodium salt. This was shown among others in Solanko et al., and the work should be properly cited in that context. Assessment of side specific quenching in the PM of mammalian cells was done from both sides, i.e., also by injecting the quencher into cells in the study by Mondal and Maxfield. This control should have been added in the current study as well.
5. The claim, that quenching by FRET allows for assessing transbilayer sterol distribution should be substantiated by measuring the Förster radius between both fluorophores. If that radius is larger than 2.5 nm, it is likely that also DHE in the inner leaflet gets lowered in its intensity. In that case, the assumption of side-specificity would not be valid. Also, in contrast to contact quenching by collisions, FRET depends on the mutual orientations of the transition dipoles. Thus, more thorough characterization of the potential FRET system is essential for supporting the claims of the study. An important experiment would also be to inject the FRET acceptor lipid probe into the cells and validate the lower quenching on the inner PM leaflet. Such experiments have been done with TNBS by Mondal and Maxfield (see ref.8 in the Supplementals).

On 2024-12-18 18:47:29, user xPeer wrote:

Courtesy review from xPeerd.com

Summary<br /> The manuscript introduces “semantic mining,” leveraging a 7-billion parameter genomic language model (Evo 1.5) to generate de novo functional proteins guided by genomic context. Applications include toxin-antitoxin systems and anti-CRISPR proteins, with experimental validation demonstrating significant activity. The introduction of SynGenome, an AI-generated database of 120 billion synthetic DNA base pairs, expands accessibility to sequence exploration. While the methodology is innovative, the work requires greater clarity in data validation, reproducibility, and assessment of model biases.

Major Revisions<br /> Validation of Novelty and Utility:

Page 3–6: While Evo-generated proteins are novel, the manuscript lacks detailed benchmarking against state-of-the-art generative tools like ESMFold or AlphaFold for function-guided design. Comparing success rates and biological plausibility of sequences generated by Evo vs. other models would validate its unique contributions.<br /> Recommendation: Include a comparative analysis using publicly available benchmarks or direct competition against contemporary tools.<br /> Model Limitations and Bias:

Page 10–11: The manuscript notes the autoregressive sampling approach's propensity for repetitive or non-functional sequences but does not quantify failure rates. A robust statistical analysis of failures versus successes would strengthen confidence in Evo's predictions.<br /> Recommendation: Provide an error rate analysis for generated sequences, specifying how often Evo outputs unusable or irrelevant results.<br /> Experimental Validation Scope:

Pages 7–8: While experimental validations (e.g., toxin-antitoxin assays) are presented, the sample size of 10–15 per class may not generalize to broader applications.<br /> Recommendation: Increase experimental validations, particularly for diverse protein classes, or clarify why the existing sample size is statistically sufficient.<br /> Ethical Considerations of AI in Genomics:

Page 12–13: The paper touches on the novelty of AI-designed proteins but does not address the ethical implications of releasing 120 billion synthetic sequences, especially regarding misuse.<br /> Recommendation: Include a section discussing potential misuse (e.g., biosecurity risks) and measures to mitigate ethical concerns.<br /> Scalability and Practical Deployment:

Pages 8–10: SynGenome’s database is comprehensive but lacks a discussion on computational resources required for its generation and queries.<br /> Recommendation: Add a performance benchmarking section detailing the hardware and time required for large-scale queries and data generation.<br /> Minor Revisions<br /> Formatting and Presentation:

Figures 3D–G (Page 8): Improve resolution and annotation clarity. Figures lack consistent labeling for protein sequence lengths and functional annotations.<br /> Headings (Throughout): Ensure consistent capitalization for section titles.<br /> AI Content Evaluation:

Estimated AI Contribution: 20–25%.<br /> Identified Sections: Abstract, Methods (Page 13–15), and descriptive portions of Results show repetitive phrasing indicative of AI generation.<br /> Impact: Low epistemic risk as AI-assisted text remains aligned with scientific integrity but lacks nuanced argumentation.<br /> Recommendation: Revise abstract and descriptive content to improve readability and originality.<br /> Methods Reproducibility:

Pages 14–16: The methods section requires clearer step-by-step reproducibility guidelines for Evo training and sampling, including parameter settings and failure case management.<br /> Recommendation: Add a concise reproducibility checklist summarizing critical steps and dependencies.<br /> Recommendations<br /> Enhance comparative benchmarking to validate Evo’s claims of novelty and efficiency.<br /> Provide expanded experimental validation, ensuring sufficient generalizability across diverse protein functions.<br /> Address ethical concerns about AI-generated sequences to preempt potential misuse and establish guidelines for responsible use.<br /> Refine text clarity in AI-generated sections and enhance the formatting of figures for better readability.<br /> Strengthen reproducibility guidelines, particularly for model training and sequence generation, to facilitate adoption by other researchers.

On 2024-12-18 18:17:32, user xPeer wrote:

Courtesy review from xPeerd.com

Summary<br /> The manuscript presents a novel method for producing bioactive peptides (BAPs) in E. coli by encapsulating them within bacteriophage P22 virus-like particles (VLPs). This approach addresses challenges such as host toxicity and proteolytic degradation, enabling biosynthetic production of BAPs that are typically difficult to express. Using this system, the authors successfully produced peptides from three structurally distinct classes and established a downstream processing pipeline for purification. While the study demonstrates a significant advance, it would benefit from more comprehensive validation of scalability and broader applicability.

Major Revisions<br /> Validation of Therapeutic Applications:

The manuscript effectively highlights the potential of the P22 VLP system for producing BAPs, but it lacks robust in vivo or functional validation of these peptides' therapeutic efficacy. For instance, demonstrating antimicrobial or anticancer activities of purified BAPs would strengthen the findings (e.g., Sections 5–7).<br /> Scalability Considerations:

The scalability of the encapsulation and purification process is not adequately addressed. The downstream processing pipeline, while efficient for lab-scale production, may face challenges at industrial scales. Providing pilot data on larger-scale production would be valuable (e.g., Section 8).<br /> Charge Dependency Discussion:

The strong correlation between peptide charge and encapsulation efficiency is noted but not fully explored in the context of broader peptide libraries. A discussion of whether these findings are generalizable to other cationic peptides or specific to the tested classes is needed (e.g., Section 12).<br /> Impact of Toxicity on Host Viability:

While the study mentions host protection through encapsulation, more data on host cell viability under varying peptide expression levels would provide clearer insights into system robustness (e.g., Figures 3–4).<br /> Reproducibility and Transparency:

Include more details on the variability of yields across replicates. For example, reporting standard deviations or confidence intervals for the recombinant yield of encapsulated peptides would enhance data robustness (e.g., Section 13).<br /> Minor Revisions<br /> Formatting and Clarity:

Fix formatting inconsistencies, such as overlapping figure captions and tables, particularly in Figures 2 and 5.<br /> Ensure uniformity in the use of units (e.g., mg/L consistently across text and figures).<br /> Figures and Data Presentation:

Improve the resolution and annotation of figures. For example, the TEM images lack clear scale bar descriptions, and data on cargo loading could benefit from a graphical summary.<br /> AI Content Estimate:

Estimated AI-generated content: ~10-15%, identified mainly in repetitive or formulaic phrasing in sections like the introduction and methods.<br /> Highlighted sections include: Abstract, Sections 2–3, and Methods.<br /> Impact: Low epistemic risk but would benefit from manual stylistic revisions for improved readability.<br /> Referencing and Citations:

Ensure all references are formatted consistently, particularly in-text citations that occasionally deviate from standard formats.<br /> Recommendations<br /> Add experimental data on the bioactivity of purified peptides to strengthen claims of therapeutic potential.<br /> Provide a comparative analysis of yield and cost-efficiency relative to chemical synthesis to highlight economic and environmental benefits.<br /> Expand the discussion on scalability and potential integration into industrial workflows.<br /> Conduct additional studies on encapsulation efficiency with a broader range of peptides to confirm system generalizability.<br /> Enhance figure annotations and standardize formatting for improved readability.

On 2024-12-18 17:12:24, user xPeer wrote:

Courtesy review from xPeerd.com

Summary<br /> This manuscript investigates oxaliplatin resistance in colorectal cancer (CRC), identifying the SERPINE1-based RESIST-M gene signature as a predictive marker for pro-metastatic CMS4/iCMS3-fibrotic CRC subtypes. Employing transcriptomics, in vitro/in vivo experiments, and bioinformatics, the study proposes therapeutic strategies targeting cholesterol biogenesis and SERPINE1 to re-sensitize CRC cells to oxaliplatin. The work is well-structured but needs refinement in statistical models, transparency, and clarity.

Major Revisions<br /> 1. Statistical Models and Reproducibility<br /> Page 6, Lines 95–120: Statistical details for in vivo studies (e.g., metastatic score calculation) are insufficient. Include effect sizes, confidence intervals, and corrections for multiple comparisons.<br /> Recommendation: Present Kaplan-Meier survival curves with hazard ratios (HR) and p-values for different gene signatures (e.g., RESIST-M) in relevant datasets (PETACC-3, TCGA).<br /> Page 11, Line 210: The statistical pipeline for GSEA and pseudotime analyses lacks critical thresholds. Specify adjusted p-values (e.g., FDR-corrected) for hallmark pathways.<br /> 2. Validation of the RESIST-M Signature<br /> Page 14, Lines 275–285: The study compares RESIST-M to other gene signatures but lacks comprehensive head-to-head validation using robust statistical tests.<br /> Recommendation: Provide ROC-AUC scores to quantify predictive accuracy across datasets. Supplement with external validation using independent clinical cohorts.<br /> 3. Mechanistic Insights<br /> Page 8, Lines 150–170: The link between cholesterol biosynthesis, lipid raft dynamics, and TGF-β signaling is compelling but speculative.<br /> Recommendation: Enhance mechanistic validation by including experiments showing cholesterol restoration effects on TGFBRII localization and signaling attenuation.<br /> Page 13, Line 245: Include co-immunoprecipitation or fluorescence resonance energy transfer (FRET) assays to demonstrate direct interactions between SERPINE1, SMAD2/3, and lipid raft components.<br /> 4. Ethical Concerns in In Vivo Studies<br /> Page 23, Lines 495–525: Randomization protocols and blinding measures are not adequately detailed.<br /> Recommendation: Ensure transparency by specifying whether investigators were blinded to treatment arms during tumor and metastasis scoring.<br /> 5. Clinical Utility of SERPINE1 Inhibition<br /> Page 10, Lines 180–200: The therapeutic viability of tiplaxtinin and simvastatin is discussed but lacks detailed pharmacokinetic or toxicity evaluations.<br /> Recommendation: Include dose-response curves and combinatorial therapy data to support clinical translation.<br /> Minor Revisions<br /> 1. Language and Formatting<br /> Page 3, Abstract: Simplify dense phrasing like "RESIST-M signature derived from our models showed that the models can mimic CMS-4/iCMS-fibrotic-like metastatic CRC patients."<br /> Ensure consistent nomenclature for gene/protein names (e.g., "SERPINE1" vs. "PAI-1").<br /> Improve figure legends with more descriptive captions (e.g., axes labels in Figures 4 and 5).<br /> 2. Figure Clarity<br /> Figures 1–6: Use consistent color schemes to distinguish CMS subtypes across datasets. Add error bars to all bar plots and specify statistical tests in figure legends.<br /> 3. Data Accessibility<br /> Page 27, Lines 595–605: Make raw and processed data from in-house RNA-seq experiments publicly available. Provide repository links and accession codes.<br /> AI-Generated Content Analysis<br /> Indicators:

Stylistic Repetition: Frequent repetition of phrases like "RESIST-M signature predicts poor prognosis" and "CMS4/iCMS3-fibrotic subtypes" suggests templated assembly.<br /> Simplistic Explanations: Complex mechanisms (e.g., lipid raft dynamics) are summarized without technical depth, consistent with AI-generated sections.<br /> Sentence Structure: Overuse of passive voice in mechanistic descriptions.<br /> Estimate: 10–15% AI-generated content, primarily in introductory and discussion sections.

Impact:

Minimal: Core scientific claims are data-driven and original.<br /> Recommendations:

Reassess and refine introductory sections to ensure technical accuracy and eliminate redundancy.<br /> Provide nuanced discussions of limitations in the final paragraphs.<br /> Recommendations<br /> Statistical Rigor: Refine statistical models, especially for pathway enrichment and survival analyses.<br /> Mechanistic Validation: Conduct additional experiments to confirm hypothesized pathways.<br /> Data Transparency: Enhance reproducibility by releasing data/code under FAIR principles.<br /> Therapeutic Context: Expand discussion on potential side effects and combinatorial strategies for proposed therapies.

On 2024-12-18 17:08:51, user xPeer wrote:

Courtesy review from xPeerd.com

Summary<br /> The manuscript explores functional connectivity (FC) changes associated with rapid remission from treatment-resistant major depressive disorder (MDD) using Stanford Accelerated Intelligent Neuromodulation Therapy (SAINT). It presents compelling evidence of FC reductions between key brain regions involved in emotion regulation and correlates these changes with clinical improvement. While the results are promising, the manuscript requires revisions for enhanced clarity, rigor, and generalizability.

Major Revisions<br /> Clinical Trial Design and Transparency:

Page 13, Lines 4-18: The open-label design raises concerns about placebo effects and biases. Incorporate a discussion on these limitations and emphasize the ongoing sham-controlled trials as critical next steps.<br /> Recommendation: Clearly articulate (a) participant inclusion criteria (e.g., baseline severity threshold) and (b) statistical rationale for the sample size. Include a CONSORT-style flow diagram for improved transparency.<br /> Interpretation of Functional Connectivity Results:

Page 10, Lines 12-20: The claim that DMN hyper-connectivity underpins MDD remission warrants caution. Highlight alternative interpretations (e.g., compensatory mechanisms) and the variability in individual FC changes.<br /> Recommendation: Contextualize findings with respect to the heterogeneity of depression subtypes and potential outliers in FC changes. Include additional statistical metrics (e.g., effect size for FC changes across participants).<br /> Mechanistic Insights:

Page 8, Lines 7-15: The manuscript lacks direct mechanistic evidence linking SAINT-induced FC changes to emotion regulation improvements. For example, the role of sgACC-DMN decoupling remains speculative.<br /> Recommendation: Discuss whether other circuits, such as hippocampus-related networks, might play a role. Acknowledge gaps in mechanistic understanding due to limited resolution of imaging data.<br /> Ethics and Intellectual Property Disclosure:

Page 2, Footnote: The intellectual property disclosures (methodology patents) should be expanded. Clarify how this might influence interpretation or replication of findings.<br /> Recommendation: Include a conflict-of-interest statement aligned with journal ethics.<br /> Minor Revisions<br /> Language Precision:

Page 3, Abstract: Avoid overgeneralized claims such as "provides a significantly clearer picture." Rephrase to reflect study-specific findings.<br /> Throughout: Replace speculative terms (e.g., "may reflect") with precise qualifiers ("likely reflects based on X evidence").<br /> Figures and Tables:

Figures 1-3: Enhance figure legends to explain axes and statistical thresholds. Add asterisks or annotations to highlight significant FC changes.<br /> Page 6, Line 30: Provide a visual representation of clinical score improvements (e.g., histogram or boxplot for MADRS reductions).<br /> Data Accessibility:

Page 12, Data Analysis: Include a link to de-identified datasets and code used for FC analysis to support reproducibility. Explicitly state if there are restrictions.<br /> Formatting and Style:

Standardize abbreviation usage (e.g., "lDLPFC" inconsistently capitalized).<br /> Ensure all references conform to journal guidelines (e.g., consistent DOI inclusion).<br /> Recommendations<br /> Expand Clinical Impact: Discuss how SAINT might complement existing treatments, particularly in comparison to electroconvulsive therapy and ketamine-based interventions.<br /> Address Generalizability: Highlight limitations in applying SAINT to diverse populations, given the small and homogeneous sample.<br /> Provide Supplementary Details: Include a supplementary table summarizing prior studies on FC changes in MDD for comparative context.

On 2024-12-18 00:44:40, user Lydia Bilinsky wrote:

I’d love to get feedback on whether SFq(D) is invariant with regard to conditions in irradiated cell culture.

On 2024-12-17 23:21:22, user Pedro Ballester wrote:

This issue is not new. It has been studied before in terms of protein sequence similarity:<br /> https://pubs.acs.org/doi/10.1021/ci100264e <br /> https://pubs.acs.org/doi/10.1021/ci200057e

And more recently in terms of protein structure and ligand structure similarity as well:<br /> https://www.mdpi.com/2218-273X/8/1/12

GEMS seems interesting though, thanks for releasing the code, we will try it.

On 2024-12-17 14:36:39, user Susanne Lachmuth wrote:

A peer-reviewed version of this article has now been published here: https://doi.org/10.1002/ecm.1593

On 2024-12-17 12:10:33, user Luc Bussiere wrote:

Note this is now published, and needs a link to the published article: https://academic.oup.com/evolut/article/74/8/1741/6850800

On 2024-12-17 10:07:24, user Preprint Journal Club at MPIPZ wrote:

We recently reviewed this preprint in the PhD preprint journal club in the Department of Plant Microbe Interactions at the MPI for Plant Breeding Research. You can find the PREreview here: https://prereview.org/reviews/14505813

On 2024-12-17 01:46:53, user Cynthia He wrote:

This work is now published in Science Advances, 10, eadq2950 (2024). https://www.science.org/doi/epdf/10.1126/sciadv.adq2950

On 2024-12-16 22:03:01, user Alexander Scheffold wrote:

The specificity of the so called AIM assay is a highly relevant topic since it is now used by many labs. And the manuscript nicely addresses the signals leading to marker upregulation and identifies bystander stimulation to be a major confounding factor after 20 hours of stimulation.<br /> However, I wonder why the authors do not compare with the initial protocol using CD154 upregulation after only 7 hours. There it is convincingly shown that CD154 after 7 hours is absolutely specific for TCR activated T cells (e.g. Frentsch et al Nat Med 2005 PMID: 16186818).<br /> A simple but very reliable confirmation is also AIM combined with single cell sorting, cloning and restimulation. Again for CD154+ T cells after 7 hours for several antigens at least 80-90% specificity has been demonstrated (Bacher et al JI 2013 PMID: 23479226). We have confirmed that for many different antigens since then.<br /> Best<br /> Alex Scheffold

On 2024-12-16 11:16:14, user Monika Opałek wrote:

The published peer-reviewed verison of this article describing R package and shiny-based aplication is avaliable: https://besjournals.onlinelibrary.wiley.com/doi/10.1111/2041-210X.14269

On 2024-12-15 19:46:44, user Leonardo Sepúlveda Durán wrote:

Very useful research. The resolution of some figures is very low, makes them hard to read. Could you update the preprint with higher resolution images?

On 2024-12-14 15:21:00, user Stephanie Wankowicz wrote:

This publication aims to develop a platform of methods to identify small molecule ligand and RNA substrate interactions. Using a library of coumarin derivatives, they discovered a small molecule, C30, with high affinity binding to RNA single G bulges over other bulges (A, U, and C). The authors used Gaussian accelerated Molecular Dynamics (GaMD) simulations to study interactions, NMR to confirm the binding site location, and lasso regression to identify molecular descriptors for structure-activity relationships. The other narrative in this paper argues that this methodology could be used to develop better RNA-small molecule ligands for therapeutic purposes. <br /> Major Revisions:<br /> To strengthen the novel aspects of the authors' methodology, it would be beneficial to add context on why this method is more advantageous than previous workflow paths. <br /> Additionally, to drive this home in the conclusion, it would be beneficial to summarize the novel nature of this workflow and how it was used to discover these new ligands.<br /> We suggest clarifying and justifying the type of RNA substrate used in each assay (ssRNA, dsRNA, etc.). For example, we suggest clarifying if the modeled version of “SL5RNA” used in the Fig 2 in vitro assay is the same as the additional FP simulations. <br /> For Fig. 3, in these FP simulations, please clarify if this “DNA version” of the RNA5 and RNA1 substrains is dsDNA or ssDNA. If it is a helical dsDNA version of the substrate, a justification as to why this was used to probe a minor groove binding mechanism for a seemingly bulged ssRNA substrate would be beneficial.<br /> We would suggest integrating some of the supplementary figures, like Supplementary Figure 6, into the main text. This could enhance the reader's understanding of the electrostatic interactions in RNA-ligand binding and the significance of Ring A's positive charge in identifying binding pockets. <br /> Additionally, we suggest exploring alternative feature selection methods such as SHAP or Markov trees. These could potentially capture more nuanced, nonlinear interactions that might be missed by a linear selection method like LASSO.<br /> Minor Revisions:<br /> In the introduction, we suggest that the authors clarify the importance of preventing deep hydrophobic binding pockets in a pharmaceutical context, including a more streamlined discussion of coumarin derivatives and their therapeutic potential for SARS-CoV-2.<br /> We suggest in Fig. 2D that the graphs’ y-axes should be scaled the same and these curves should be labeled “GA-rich” and “G” for clarity. <br /> For Fig. 3A, we suggest the legend be enhanced and a scale bar added for clarity. <br /> For Fig 3D, it is difficult to identify the yellow, grey, and orange dashed lines. Please make this more obvious to highlight.

On 2024-12-13 12:34:06, user Alexis Verger wrote:

It's a very interesting paper that I really enjoyed. Below are a few comments:

• I don't understand figure S1A. The TAD for VP16 is shown as 304-382. I assume this is a mistake as it is 410-490.

• For the Med7-HA-SNAP549 construction, I assume controls have been made such as the ability to be correctly integrated into the Mediator complex. It would be nice to show this.

• All the experiments were carried out using nuclear extracts and therefore potentially ‘contaminants’ (such as transcription factors interacting with the Mediator and competing with Gal4-VP16) which could interfere with recruitment. Did the authors attempt to directly purify the Mediator and/or Pol II in their system?

• The multiple bridging model at the end is interesting. But if I've understood correctly, it can only work if 2 or more activator molecules interact simultaneously with the same Mediator complex. This model is possible if the activator targets different Mediator subunits. This is the case for VP16 in yeast (Med15 and Med17). But how does the model apply to an activator that targets a single subunit? Did the authors try another activator? A Gal4DBD-Gcn4 TAD for example?

• -VP16 is also known to interact with TBP, TFIIB and p62 TFIIH. How can this be reconciled with the authors' model which suggests that the Mediator is recruited first and then the PIC?

On 2024-12-13 04:23:00, user Florentine Rutaganira wrote:

This preprint has been published at Cryobiology: https://www.sciencedirect.com/science/article/pii/S0011224024003389

On 2024-12-12 18:59:34, user Adrien Jolly wrote:

Dear authors,

Thank you very much for this great study. Many smart ideas, much food for thought.<br /> I have a specific concern regarding figure 6. If I understand correctly, you make the assumption that the frequency of Ki67 positive cells directly correlates with proliferation speed. I find this assumption problematic, Ki67 protein accumulates in S-G2M and is degraded in G0/G1 (see for instance PMID: 30067968). One can find that, at steady growth, a shortening of SG2M will actually reduce the proportion of Ki67+ cells, (a shortening of G1 will however increase it), depending on these parameters, the growth rate of the population could be significantly larger for big clones with no change to the proportion of Ki67+ cells. With the Fucci markers combined with ki67 you might be able to resolve this question, but I can imagine it would be very difficult to also identify different clones in this case.

Cheers,

Adrien

On 2024-12-12 11:47:04, user FKA Arebolas wrote:

Interesting work, that allows of all us to understand a little bit more about 'project-based science'. I do hope that a substantially revised version of this paper would eventually be published—in an internationally renowned journal. The authors have done a great job and deserve such a form of recognition.

Still, the paper also suffers from several shortcomings that have less to do with its 'pre-print' status than with its theoretical and methodological foundations. The following is a brief exposition of its weaknesses, as I see them.

The paper is beset with inconsistencies. To begin with, the contrast between 'ERC science' and 'Consortia science' is a non-starter. Not only do some ERC grants require grantees to set up international consortia (i.e., Synergy Grants), but, also, 'Consortia science' in this context aims to group together types of EU-awarded grants that are internally diverse, including Research and Innovation Actions (that make provision for fundamental research) and Innovation Actions (that do not). 'Consortia science', put it simply, do not exist—not even as an analytical category.

Along these lines, at some point in the 'Introduction' section, the paper hints at an important aspect of grant funding: the constant need to look for new funding, or 'strategic anticipation'. This has to do neither with 'consortia science' nor with 'ERC science', because, in both cases, recipients are expected and must search for funding once the project is finished. Unfortunately, this rather important aspect of grant funding (or project-based funding) is nowhere considered in the presentation and discussion of results. Pursuing this line of research further would be rather illuminating and would help us disclose the real problems connected to grant funding (a topic that Merimans is concerned with in another paper, already published).

Also critical, by this commentator's point of view, is the assimilation of 'international collaboration' to 'knowledge co-creation' and 'knowledge co-production'. This usage is clearly confusing and misguiding. 'Co-production' has a very specific meaning in Science and Technology Studies, and it has nothing whatsoever to do with getting industry involved in the project. Furthermore, the reality of RIAs and IAs do not stand to the implied meaning of such moniker. Most of the time, such involvement truly amounts to a division of labour, according to which the companies in question act as 'demonstration cases' that implement and test (or demonstrate) the technology being proposed. Nowhere can one find an instance of 'co-production'.

I am pretty sure that the authors know a great deal about grant funding, and about EC's Framework Programmes in particular. Yet, the previous critical remarks are a glaring illustration that additional research is needed to get fully acquainted with the subtleties of such funding scheme—knowledge necessary to fully understand what is at stake in the interviews.

To conclude with this informal review, two methodological remarks: 1) it is by no means clear which the selection of participants have been. I believe that only a minority of them have experience in ERC grants (either applying for them or, crucially, gaining them), so that framing the whole paper in terms of 'how well ERC grants are' might be, once again, distorting and misleading; 2) nowhere are details about the coding process being offered. As the research work, despite the pre-print format of the paper, seems to have concluded, this is not an absence that can be attributed to the preliminary stage at which the results have been published.

Hoping that these indications help the authors improve the manuscript.

With kind regards,

L.

On 2024-12-12 11:32:12, user eggersii wrote:

I would like to suggest a few experiments / changes in information that will increase the value of the manuscript:

• Hemoglobin content in the Hb-NPs. In the field of blood substitutes, the Hb content is an important factor in NP synthesis, since the majority of the particle’s weight should consist of Hb to be able to deliver somewhat sufficient oxygen. (in RBCs, Hb is 96% of its dry weight). Could the authors also calculate and add the Hb content within the NP weight (the weight% Hb)?

Going off the information given in Section 4.3: ‘Hemoglobin is passively encapsulated by combining the desired concentration of hemoglobin with silk solution prior to combining with PVA.’ Does that mean 250 µg/mL Hb was added into a 5% w/v silk solution (0.25 mg/mL / 50 mg/mL = 0.005 = 0.5 %). With Silk:PVA = 1:4, thus a 4x dilution (=1.25%). Thus, the Hb content is either 1.25% (for 250 Hb formulations) or 0.625% (for 125 Hb formulations)?

Whatever the Hb content is, it will give valuable information in their potential, and worth reporting in the manuscript.<br /> - UV-Vis Spectroscopy. The authors determine the mHb% using the cyanmethemoglobin method, which is based on the UV-Vis spectra of Hb. Would it be possible to add the full UV-Vis spectra of the tested formulation in the SI? Often, NPs create an absorbance signal itself, so it would be nice to see how this affects the measurements, if at all.<br /> - In vitro studies. Section 4.10: ‘The wells stimulated with silk nanoparticles received 250 μl of M0 media supplemented with 50 μg of silk particles.’ How did this concentration come about? Would it be possible to add a prior cell viability assay that determined the optimal particle concentration to use, or even show a IC50?

On 2024-12-11 14:59:37, user mohit panghal wrote:

We are pleased to share that the NPBD code ( associated with this study ) is available on GitHub for public access and use. You can find it at the following link - https://github.com/cbl-nabi/NPBDetect/

On 2024-12-11 13:13:52, user Kostas Konstantinidis wrote:

Dear Cameron<br /> Thank you for bringing these papers to our attention. We are familiar with most of them and indeed, some of the results reported in these papers are consistent with some of the results of our study. I would like to point out, however, a key point: these papers are not relevant for the main topic of our study because they do not link recombination to the ANI units (clusters) and/or have not shown that recombination is random (unbiased) across the genome (as opposed to selection-driven, and thus spatially and functionally biased) and frequent enough to serve as cohesive force for the unit.<br /> kostas

On 2024-11-01 14:02:04, user Cameron Thrash wrote:

Hello. May I respectfully recommend a few relevant references for the discussion of SAR11 recombination and speciation?

Natural variation in SAR11 marine bacterioplankton genomes inferred from metagenomic data<br /> https://biologydirect.biomedcentral.com/articles/10.1186/1745-6150-2-27

High intraspecific recombination rate in a native population of Candidatus Pelagibacter ubique (SAR11)<br /> https://enviromicro-journals.onlinelibrary.wiley.com/doi/abs/10.1111/j.1462-2920.2007.01361.x

The Evolutionary Success of the Marine Bacterium SAR11 Analyzed through a Metagenomic Perspective<br /> https://journals.asm.org/do...

A comparison of homologous recombination rates in bacteria and archaea<br /> https://academic.oup.com/ismej/article/3/2/199/7588207?login=false

On 2024-12-11 12:53:46, user Sam wrote:

Hi, thank you for this interesting paper. Just a small question for clarification: The 50 kbp or longer uncorrected ONT reads that were used for hifiasm UL input are supplied a second time as hifiasm normal input (but in this case after correction and chopping to 10-30kb), correct?<br /> Thanks!

On 2024-12-10 02:10:47, user Dovini wrote:

The preprint titled "GxE PRS: Genotype-environment interaction in polygenic risk score models for quantitative and binary traits" is published as "Mitigating type 1 error inflation and power loss in GxE PRS: Genotype–environment interaction in polygenic risk score models".

Full text link: https://onlinelibrary.wiley.com/doi/full/10.1002/gepi.22546

On 2024-12-09 17:25:57, user Hannah Moots wrote:

Exciting research!! Just wanted to point you towards some additional research on the appearance of steppe-related ancestries in Italy. You mentioned that previous studies had identified the earliest appearance of these ancestries to be about 3,600 BP in central Italy. We published 4 ancient genomes from the Bronze Age site of Pian Sultano in central Italy and all of these individuals carried steppe-related ancestries, the oldest of which dates back to 3872 - 3719 calBP. https://doi.org/10.1038/s41559-023-02143-4 . Figure S4 has a timeline and admixture plots to visualize this as well.

On 2024-12-09 12:15:47, user Freddy Monteiro wrote:

Published: https://doi.org/10.1016/j.xpro.2020.100020

On 2024-12-09 01:41:58, user avtrader wrote:

Science Discussion

The multiple modern, peer reviewed papers on the field status of Ivory-billed Woodpecker (IBWO) are dominated by visual media depicting putative or actual Ivory-billed Woodpeckers. The most important and impactful data sets determining that the species is extant is the collection of thousands of video and picture frames taken by discreet researchers with various cameras.

Since there is only one other large woodpecker in the USA, which is not a congeneric, using visual evidence is effective as the two species are quite different in plumage, movements, and behavior. Visual evidence of putative or actual IBWOs shows that the subjects are clearly not Pileateds when plumage, movements and behavior are examined.

Elevating the veracity of acoustical evidence firmly indicating IBWO presence, as this paper concludes, is problematic since there are several known and hypothetical avian and other vertebrate sources of kent calls.

A major, glaring omission is that the putative IBWO kent data set is only compared to one Blue Jay kent when it is well known that several species of vertebrates, including birds, amphibians and mammals, are accepted as producing Ivory-billed-like kent sounds. Even if the author establishes or approaches establishing that the putative IBWO kents are not blue jays, which is not accomplished, he has not addressed any other competing species.

Note that the 2024 peer reviewed paper "Echo of extinction: The Ivory-billed Woodpecker's tragic legacy and its impact on scientific integrity", P. Michalak employs a much more sophisticated spectrogram analysis than the prepaper and found the putative IBWO kents from LA did not match with known IBWO kents. (Bio Science, Volume 74, Issue 11, November 2024, Pages 740–746, https://doi.org/10.1093/biosci/biae072

Compounding the impediments to a firm acoustical ID, the only widely accepted kents of IBWOs recorded were of agitated birds (n =2), 90 years ago from the Singer Tract, LA. Alerted or stressed individual birds can have frequencies different than nominal productions of the same song or call type. This prepaper fails to even acknowledge these major and other issues yet somehow has very strong conclusions that are therefore unsupported, metaphysical and proselytizing in nature rather than scientifically anchored.

One well known competing source of IBWO-like kents is the Blue Jay (BLJA). For decades it has been understood that some spectrograms of Blue Jay kents have shown differences with Singer Tract, LA, Ivory-billed kents.

At a minimum any research paper that attempts to address and dispositively conclude this issue will need careful spectrogram analysis of many examples of these Blue Jay kents and then compare them to IBWO kents or putative IBWO kents. The biological context of the putative jay kents must be detailed; not ignored. Many would concur with the prepaper author including Choctawhatchee River, FL kents in the set of putative Ivory-billed kents since these were spatiotemporal to hundreds of other IBWO supporting data points in key data sets such as---suggestive videos, IB sightings some by 2 observers, recorded and heard Campephilus-like double knocks near the kents, IB-like roosts holes. etc.

I have line surveyed the Choctawhatchee River over 120 hours and found Blue Jays to be non-existent to rare since this river bottom corridor has few oaks. Unfortunately the author's standards for most of the non-Choctawhatchee, FL, alleged IBWO kents in his set is minimal and nebulous.

The prepaper has so many biases, omissions, and basic scientific flaws that a rewrite is needed. A terse comment here however would not help the conservation of the few Ivory-bills likely left.

The following observations and recommendations are offered.

The data in this prepaper includes only one Blue Jay kent call spectrogram which was recorded and commented on by many over the decades. The author then inserts an unprecedented, unfounded and unsound comparative method, not found in the ornithological literature. The data includes ~ 136 Blue Jay calls, of which none are kent calls, to claim the establishment of an unabridged frequency capability for all types of Blue Jay calls. The author erroneously takes his incomplete set, in regards to the species extensive repertoire of Blue Jay and any hypothetical spectrograms and concludes that this eliminates all possible frequencies, tonality or partials/harmonics that may be produced when a Blue Jay kents.

The author takes the actually abridged set of Hz and illogically and awkwardly states in the Abstract "Differences are seen such that these two species cannot be mistaken for each other". In the Conclusion sections he supports the hyperbole by stating that interspecific physiological differences make it impossible for a BLJA to produce the Hzs he found in the putative IBWO set of kents he examined. "Because of different morphology and functional anatomy, Blue Jays and Ivory-billed Woodpeckers are going to make different quality sounds."

He continues that all Blue Jay kents cannot be IBWO kents and the reverse but the scientific premise he employs is unprecedented in ornithological research. The text or bibliography does not include one reference that uses body length as a driver or predictor of avian Hz range capabilities. During an on-line comment exchange with the author pivotal parts of his concept are formed by the idea that a larger bird cannot possibly be matched by smaller birds as far as Hz convergence. In this case the birds are of relatively similar size of 20 inches and 12 inches. There is no Ostrich to Hummingbird length disparity involved here. The author may have no field experience with any members of the genus genus Procnias (bellbirds) with a 125 decibel level with a body length of 11 inches. Volume or Hz range may have little to no correlation with body size. I have searched the literature unsuccessful to discover what the author is possibly using as a prerequisite to his sweeping Bergmann's rule-like conclusions that correlate acoustical Hz and moderate changes in avian body size. This is an example of common sense bias that a 20 inch bird cannot produce at least some of the same Hz as a 12 inch bird. These biases and assertions seem unsupported and unlikely hypotheses let alone conclusions by the author.

The author is responsible for doing a literature search, not readers, before prepapers are posted; the text and bibliography portray minimal research was done to support the presented hyperbolic declarations.

Most publication submittals are rejected because the literature search was poor. This prepaper's bibliography verifies that not even one adequate reference let alone comprehensive work on Blue jay ecology, mimicry, relative high intelligence, syrinx, physiology, vocalizations, point surveys for Blue Jays in the alleged IBWO kent areas, etc. was read or done .

Blue Jay' hypothetical acoustical capabilities have as a pretext a species possessing a syrinx with broad capabilities, high avian intelligence, wide repertoire, strong memory and substantial mimicry ability. Years ago I checked the literature on the subject species and at these rather mid value frequencies and harmonics there is no physiologically based reason that IBWO kents and Blue Jay kents must always be at different Hzs. In addition the literature does generalize that there is often sexual intraspecific differences in a species Hz for the same general call, such as a kent. Extrapolating from known ornithological literature, no one knows how a male blue jay would mimic a modern female IBWO it ambiently heard kenting; how a female blue jay would imitate a male IBWO, etc. It is not known how a blue jay would mimic a nuthatches kent vs an IBWO it heard kenting.

A major, glaring omission is that the putative IBWO kent data set is only compared to one Blue Jay kent when it is well known that several species of vertebrates, including birds, amphibians and mammals, are known to produce IBWO-like kent sounds.

Brief info on Hz

Within a single bird species, different calls can have varying frequencies. This variation can depend on several factors:

1. Type of Call: Different calls serve different purposes (e.g., alarm calls, mating calls, contact calls) and can have distinct frequency ranges.

2. Individual Variation: Just like humans have unique voices, individual birds may produce calls with slightly different frequencies due to physiological differences.

3. Context and Situation: The context in which a call is made can influence its frequency. For example, a bird may alter its call if it's trying to signal alarm in a crowded area versus calling to a mate.

4. Environmental Factors: The surrounding environment can affect how calls are produced and perceived. For instance, birds may adjust their calls in dense forests versus open areas.

The Abstract is ambiguous and misleading; also inconsistent with the rest of the paper. The Abstract does not mention many of the strong conclusions and constructs that accumulate as the paper proceeds. The constructs and conclusions, if true, would be quite compelling, with scientific value; the issue of distinguishing recorded Ivory-billed Woodpecker kent calls from other species kent calls is a complex issue. Unfortunately this paper ineffectively addresses the issue with a short, terse incomplete Abstract that is subsequently followed by unsubstantiated and unsupported conclusions and erroneous, sweeping ornithological assertions.

Ambiguity---the paper’s central theme is initially thought to be that IBWO kents can be distinguished from Blue Jay kents. The short Abstract mentions ~ 136 Blue Jay calls have been examined; after quite a bit of reading one finds out that only one of the ~136 Blue Jays calls examined is a jay kent call ( Blue jay calls, n = 137, Blue jay kent call n = 1). At this point some readers will realize the omission in the Abstract is an intended pathway to one of the papers eventual conclusions that Blue Jays are physiologically incapable of producing the Hz found in the putative IBWO putative kent call spectrograms.

Misleading---the Title and Abstract concentrate on Blue Jay kents; the latter highlighting n’s of 136 and n = 200, but these numbers are for data sets that do not include any Blue Jay kents. Actual Blue Jay kents spectrograms examined closely by this paper is n = 1.

Inconsistent --- The Abstract is overstated yet some of the subsequent constructs and conclusions are bold and somewhat hyperbolic. Conclusions presented are unsupported and are more correctly described as hypothesizes or unsupported hypothesizes.

The paper has n = > 200 for putative IBWO kent calls but fails to call them putative. This omission leads to concerns of circular logic. The paper provides no supporting evidence that some of these putative IBWO kent calls were derived from birds that were field IDed as IBWOs or for some reason likely IBWOs. Acceptable reasons could be that the subject kent call were associated with a tempospatially IBWO sighting or double knocks. Field details are needed for all kents that were not from Choctawhatchee River, FL. Kents in that study were spatiotemporal to hundreds of others IBWO supporting data points in data sets.

It is possible that a hypothesis that all IBWO and BLJA kents can be differentiated by spectrograms is correct. I propose it as a hypothesis. However jumping to premature conclusions with so much missing is not the way to proceed. We have delayed the erroneous extinction proposal with solid field techniques that had little if anything to do with Blue Jays and exaggerations but did truthfully present the videos, game cams, sighting notes, etc.

IBWO conservation will not be prodded by examining characteristics such as acoustical Hz with flawed methods. Some will rightfully suspect that these odd assertions are designed to proselytize rather than establish the truth.

On 2024-11-30 19:30:43, user avtrader wrote:

BioRxiv Violation

The author claims no competing interests, yet he and others have said he is the Science Director for Mission Ivorybill. Mission Ivorybill's stated goal--is to save the Ivory-billed Woodpecker. Mission Ivorybill or the related, The Louisiana Wilds do not seem to be federal non-profits via a 501-c3 data base search.

Mission Ivorybill raises money via various channels and seems to be a commercial entity; this violates BioRxiv's publication rules for authors in addition to being a non-disclosed conflict by the author. Mission Ivorybill also has Go Fund Me efforts, publications, etc. and the organization is IBWO-centric. The author participates vigorously in marketing Mission Ivorybill. He controls and/or participates in Mission Ivorybill's media and marketing efforts such as their Facebook page and Zoom public presentations. The author reviews evidence gathered by Mission Ivorybill yet fails to disclose the relationship tainting this prepaper's Abstract and Conclusions.

Mission Ivorybill is mentioned in the subject paper. The author is well known to exaggerate claims of Ivory-bills recently proselytizing a video of a Tufted Titmouse was an Ivory-billed Woodpecker is association with a Mission Ivorybill presentation and on social media. The founder of Mission Ivorybill quickly distanced himself from the false Ivory-billed claims by the author.

The author receives organizational support, professional and informal introductions, recognition, publicity and public face-time from Mission Ivorybill/The Louisiana Wilds perhaps in return for his often aggressive marketing efforts for Mission Ivorybill. His efforts have even included researching and contacting employers, to disparge and econimically damage people who disagree with him on the Ivory-bill's status.

The author may have purposely not disclosed this unambiguous conflict and his association with a possible commercial entity whose only product is the Ivory-billed Woodpecker. Related the subject BioRxiv prepaper has some hyperbolic, marketing-like claims that are not based in science.

On 2024-10-31 14:41:16, user CT wrote:

It’s been known for decades that Blue Jay kents and IBWO kents differ spectrographically… duhhh. And a LOT of other sounds may mimic IBWO kents to the human ear as well (though the paper explores but one single avian species)… all being spectrographically different. This paper adds little to the known literature, except to try to reach further conclusions (actually redundant, re-stated conclusions in previous work) from unsubstantiated assumptions. <br /> I’d be amazed if one can find 2 or 3 (or even one) PhD. level scientists in the entire country who will defend the methods and exaggerated conclusions put forth here.

On 2024-12-07 20:38:59, user Siavash wrote:

Published in https://doi.org/10.1101/gr.279339.124

On 2024-12-07 20:21:15, user Amir Rahmani wrote:

Now published in Optics Express https://doi.org/10.1364/OE.520845

On 2024-12-07 11:02:50, user Thu wrote:

This work is published in American Journal of Botany https://doi.org/10.1002/ajb2.16429

On 2024-12-06 20:40:30, user Laurie Ailles wrote:

A more recent version of this article has now been published in Nature Communications: Nat Commun. 2024 Sep 19;15(1):8232. doi: 10.1038/s41467-024-52507-y

On 2024-12-06 17:54:14, user Malte Elson wrote:

The remarks below are a summary of the points discussed during the Cake Club of the Psychology of Digitalisation lab at University of Bern ( https://www.dig.psy.unibe.ch/studies/cake_club_/index_eng.html ). They do not reflect the opinions of each individual journal club participant. Any responses to these points should be addressed to Malte Elson.

In their preprint, Spiess et al. (2024) illustrate the impact of influential data points on statistical significance in linear regression analyses. The authors reanalyzed data from three high-impact journals by searching for the term "linear regression” and digitizing graphs of the included papers (due to the absence of raw data). Their findings revealed that excluding influential data points often rendered previously significant results non-significant. The simulations included in the study largely confirmed expected outcomes, supporting the overall argument for incorporating leave-one-out analyses in data analyses practices. The authors ultimately advocate for broader adoption of such methods to enhance the robustness of statistical conclusions.

We found the paper to be interesting and an illustrative contribution to statistical education, both in terms of the potential fragility of published claims and as an illustration of an intuitive but underused outlier detection method. We identified points that might allow the authors to strengthen future versions of the manuscript, including some critical points about potential weaknesses or absences in the current version of the manuscript.

1) TERMINOLOGY CONFUSION AND REPORTING ISSUES<br /> * Graphs vs. Papers: There is some confusion regarding the unit of analyses, and probably some reporting errors: On p. 4, l. 115, the paper states that the sample was 24 + 30 + 46 = 100 graphs, whereas on p. 6, l. 170 the authors state they examined 100 publications (going by Table 1, this is a simple clerical error, and should say graphs).

* Similarly, the description of the columns in Table 1 (p. 11) is confusing, and we think has at least one reporting error:

* It is unclear what “Hits” represent: Are these unique papers, or do the search engines of Science/Nature/PNAS return the same paper multiple times for each instance of the search term (“linear regression”)?

* What does "number of graphs that were not shown" mean? We think these are instances of linear regressions that simply were not reported with a corresponding graph in the original publication, but they could also be graphs missing, inaccessible, or excluded <br /> * The “Articles” column is described as “number of Articles in which the analyzable graphs were found” (p. 11, l. 314), but we think these are the 21 articles in which the 29 “influential variables” were found. The number of articles with analyzable graphs is not reported. It thus remains unclear how many papers were included, and how many graphs were analyzed from each paper.

* On p. 6, the authors report having identified 29 graphs in 21 papers in which the removal of one datapoint changes the result of a linear regression (see also Figure 1). On p. 6, l. 179 the “incidence” (should be prevalence instead) of changes in papers is reported as ~20%. However, this puts papers (21) in the numerator and graphs in the denominator (100), which underestimates the prevalence. On the graph-level, it should be 29/100 = 29%. The paper-level prevalence cannot be calculated because the authors do not report the number of papers with analyzable graphs (see above).

* We strongly recommend reporting a Prisma flowchart to clarify the inclusion/exclusion of graphs and papers. In the same vein, the paper lacks basic information about the included studies, such as sample sizes or the distribution of p-values. Other information would also help emphasizing the importance of the present study, e.g. citation metrics.

* The authors refer to “Supplementary Data 1” (p. 4, l. 121) but provide no link.

2) SAMPLING STRATEGY <br /> * The study focuses on digitizable graphs without overlapping data points, inherently excluding studies with (1) larger samples and (2) homogeneous effects, where overlapping data points should be more frequent. This selection skews the included papers towards studies with smaller samples and p-values near 0.05 (due to lower power and publication bias / p-hacking), which are more susceptible to the illustrated effects. This is not a problem per se, but means the findings (including the prevalence rate) are about a narrower population of studies. Either way, the selection effects should be discussed in the paper.

* It is not fully clear how it was decided which graphs are analyzable and which are not. Moreover, on p. 4, l. 127-130 the authors state that the obtained regression parameters match those reported in the paper closely, but they do not further explain what exactly this means, or what happened when they did not match

3) ANALYSES AND CONCLUSIONS <br /> * The analysis does not account for dependencies when multiple graphs from the same paper, which will likely be based on the same data (which are then susceptible to the exclusion effects), are included.

* In a way, the susceptibility of findings to the removal of a single data point is a restatement of issues related to small samples. Small samples are inherently more fragile, and larger sample sizes are more robust to the influence of removing (or adding) single data points and render p-values (and other estimates) more stable. This is not to say that the findings reported are not interesting; however, we were wondering whether a table of all included studies sorted by observed p-value and sample size would have flagged the same fragile papers. This is also not to say that dfstat is redundant, and we absolutely see the pedagogical value in being able to point at individual data points that “cause” a finding to be significant. Rather, we would be interested to what extent dfstat converges with common heuristics.

* Relatedly, the authors decry that influence measures such as dfstat are largely ignored, even by statisticians (p. 4, l. 139). This may well be, but of course, statisticians (and non-statisticians) are obviously aware of issues related to low power and small samples, and one of these issues is the problem of spurious findings (e.g. due to few, extreme data points).

* The authors largely blame frequentist statistics, particularly on p. 10, where e.g. they state that “[a]s long as stating significance or not is still based on the ubiquitous α = 0.05 threshold, these statements can be sensitive to the presence of a single data point.” (l. 282-284). However, it is unclear how this follows from their findings. Any inference (not just α = 0.05) could be susceptible to the influence of single data points when the estimate is close to the criterion. Moreover, particularly when the sample size is low, any metric’s value (e.g. point estimates) will vary as a function of the removal of individual data points, regardless of whether the inference is threshold-based or not. This is simply a property of statistical models fit to a limited amount of data. So again, the issue seems to be with small sample sizes.

4) RECOMMENDATIONS AND FUTURE DIRECTIONS<br /> Things we would have liked to see:

* Additional analyses, such as leave-two-out or leave-k-out methods. The leave-one-out analyses are providing a good intuition of how fragile some small-sample study results are. Additional leave-k-out analyses would provide further information about the fragility of the entire sample.

* So far, the authors are concerned with the fragility of results as an outcome of removing data points. An additional study exploring the reverse scenario would be valuable. Specifically, it could investigate how extreme an additional data point would need to be to alter results, and how adding non-extreme data points could mitigate the relative weight of extreme data points.

* Discussing dfstat as a robustness metric (“How many individual data points would have to be removed/added to render a significant result nonsignificant or vice versa”)

* A discussion of how dfstat could be used for p-hacking by showing researchers which data points they would have to remove to turn a nonsignificant study result into a significant one.

* The authors graciously and immediately shared data and code with one of us who requested it, and we thank them for this. We would like to see this data and code provided in a public repository and linked to in a future version of the manuscript.

* We note that the authors chose to anonymise their data so that the reader cannot tell which original study’s results are robust or not. Personally, we think that meta-scientific interests are best served by making this information public; that is, we would like this data to not merely be used to illustrate the method but also inform the reader about the fragility or robustness of those publications’ results. Of course, not everyone agrees with this practice - perhaps the authors could comment on their perspective on this issue in a future version of the manuscript.

On 2024-12-06 17:32:15, user MJ Muzzatti wrote:

This article is now published here: https://doi.org/10.1098/rsos.241710

On 2024-12-06 16:47:49, user Nick wrote:

Incredible work! I was looking through the supplemental data and it appears that the A549_Compartment tab in Table S2 is duplicated from the HeLa_HPLM_Compartment tab rather than containing the 2,320 compartment hits for this cell line.

On 2024-12-06 16:11:31, user Sanjay Magavi wrote:

Huang et al have developed impressive ELOVL1 inhibitors for the treatment of ALD. Their lead compound effectively reduces the putative pathological substance, VLCFAs, in the CNS. There are a few points to address in this paper that could significantly improve an otherwise strong piece of work.

In figure 2 Huang et al report C26:0/C22:0 levels. These probably slightly underestimate the potency of their compound, as ELOVL1 inhibition reduces not only C26:0, but also C22:0, the denominator. Through most of the paper, they report absolute concentrations, so this is a minor concern.

Huang et al incorrectly report that our studies (Come et al, 2021) "measured only Lysophosphotidylcholine C26:0 levels", and were thus less complete. We report reductions in C26:0 LPCs, acyl carnitines and total VLCFAs in the CNS in our paper.

Huang et al report that "treatment with the ELOVL1 inhibitor unexpectedly led to profound transcriptional changes beyond correction of pathways altered by the loss of ABCD1." Almost all small molecule drugs have off target activities and clinically apparent side effects that are accompanied by transcriptional changes. The most parsimonious explanation for the "off target" transcriptional changes is "off target" activity of the compound. If they were to test multiple structurally distinct ELOVL1 inhibitors and find a shared pattern across scaffolds, that could support the argument that this is somehow a general property of small molecule ELOVL1 inhibition. The conclusion to their abstract that "ELOVL1 inhibition may have broader consequences . . . than the correction of lipid homeostasis" goes beyond the data they have in hand.

In the face of a disease that is fatal in one third of boys who carry mutations, such changes in transcriptional profiles, without any associated findings in traditional in vivo toxicology studies, should not be a reason to discontinue development. Indeed, an absence of "off-target" or unexpected transcriptional changes is a barrier that even most currently prescribed safe and effective drugs could not meet.

This paper presents an impressive chemistry campaign and biological characterization of novel ELOVL1 inhibitors.

On 2024-12-06 15:02:38, user Alex Crits-Christoph wrote:

In examining a few of the top hits from this work, it becomes apparent that most of them are due to contamination during sequencing.

Here are two examples:<br /> https://www.ncbi.nlm.nih.gov/nuccore/NZ_BMOE01000030.1?report=fasta <br /> https://www.ncbi.nlm.nih.gov/nuccore/NZ_WWEN01000019.1?report=fasta

Both of these are small contigs, and therefore may not actually be part of a contiguous bacteria genome. When we BLAST them with BLASTN, they match very closely known synthetic vectors for working with HIV.

Therefore, in these two cases (and all others I've spot checked, although I have not been comprehensive), there is no evidence that these genes are present in the bacterial genome. Rather, these are evidently cases where someone was sequencing an HIV-related sample on the same lane as a bacterial genome, and cross-contamination occurred (either index hopping or well to well). The HIV-vector sequence was then assembled as part of the bacterial genome, and missed in contamination screening (both by the authors and by NCBI!).

Thus I was not able to identify any cases of genomic evidence for the claims of the authors, although I did not look at every hit because the pattern above quickly emerged.

If the authors of this work want to provide sufficient evidence for the claim that there are close homologs of HIV related proteins in bacterial genomes, I would suggest taking a close manual inspection of all hits in their table. They should be able to show that these hits are integrated into bacterial chromosomes, and not always on separate contigs. They should show the raw reads then support those integrations. If the claim is that "some bacteria can acquire HIV-1 genetic material", they should then also do a comparison at the nucleotide level, not at the translated amino acid level. Finally, if that claim was tree, they should construct a phylogenetic tree to identify the nearest HIV relative and estimate time since divergence.

However, it is simply dubious that this is biologically true, because it would be an extraordinary claim if true, with no evidence yet emerged. It is essential to always closely inspect genomic results. Genomic results are not a black box, and manual inspection can quickly shed light on them.

On 2024-12-06 01:19:33, user xPeer wrote:

Courtesy review from xPeerd.com

Summary

The preprint "Cell based dATP delivery as a therapy for chronic heart failure" proposes using genetically modified human pluripotent stem cell-derived cardiomyocytes (hPSC-CMs) to deliver deoxy-ATP (dATP) to improve contractility in chronic heart failure. This strategy involves overexpressing ribonucleotide reductase in hPSC-CMs, enhancing their dATP production. Key outcomes include increased left ventricular function, greater exercise capacity, improved cardiac metabolism, and reduced symptoms of heart failure in animal models. The approach combines remuscularization with enhanced contractility, offering a novel therapeutic direction for chronic heart failure.

Major Revisions

1. Validation of Claims:
2. Doubts: The study's claims about the efficacy of dATP-producing CMs in treating chronic heart failure need stronger validation in larger and diverse animal models and human studies.
3. Critique: The majority of the experimental data involve rodent models. Larger animal studies and eventual human trials are crucial for assessing translative potential and variability in responses (e.g., differences in metabolism and immune reactions).

4. Example: "Our goal was to improve regenerative strategies by genome-editing hPSC to make dATP-donor cells… Our results indicate that dATP donor CMs can… persistently improve the function of the chronically injured heart".

5. Mechanisms of dATP Delivery:

6. Clarification: Detailed mechanisms on how dATP is transported through gap junctions from donor to host cardiomyocytes require further elucidation and quantification.
7. Critique: While the study mentions gap junction-mediated dATP transfer, the precise dynamic and extent of this transfer across intercellular connections in vivo are not fully described.

8. Example: "In vivo, dATP-producing CMs formed new myocardium that transferred dATP to host cardiomyocytes via gap junctions, increasing their dATP levels".

9. Long-Term Safety and Efficacy:

10. Skepticism: Long-term safety data and potential adverse effects of continuous dATP elevation, such as risks of arrhythmogenicity, were not adequately addressed.
11. Critique: Although the study indicates beneficial dATP effects, continuous high levels of dATP need further investigation to rule out chronic side effects including arrhythmias or maladaptive cardiac remodeling.
12. Example: Concerns: "Interventions to increase the contractility of the failing heart have been sought for decades…our novel strategy of cell therapy…".

Minor Revisions

1. Typos and Errors:

2. There are a few minor typographical errors and ambiguities in phrasing that can be corrected for better readability.

3. Figures and Diagrams Consistency:

4. The figures and diagrams should uniformly represent the data and should be referenced consistently within the text (e.g., Fig 6 referenced properly with aligned legends and labels).

5. Formatting and Style:

6. Standardize font sizes and alignments, particularly in figures and tables. Ensure that equation formatting, subscripts, and superscripts are consistently applied.

Recommendations

1. Include Larger Animal Studies:

2. Conduct larger and more diverse animal studies to establish translational efficacy and safety across different species. This would bridge the existing gap between rodent models and potential human applications.

3. Detailed Mechanistic Studies:

4. Expand mechanistic studies on the biophysics of dATP transfer and integration into host cells, detailing the kinetics of dATP movement and concentration gradients across different heart zones.

5. Extended Safety Profiles:

6. Investigate long-term safety profiles of dATP elevation in vivo, focusing on electrical stability of myocardial tissues and potential non-target effects on other tissues/organs跨链接。

7. Human Trials:

8. Initiate phase I clinical trials after thorough preclinical validations to evaluate safety, dosage, efficacy, and delivery mechanisms in human heart failure patients.

9. Data Sharing and Reproducibility:

10. Provide access to raw data and methodological details to enhance reproducibility and allow independent verification of results.

In conclusion, the preprint presents a promising approach to treating chronic heart failure using genetically engineered hPSC-CMs. Nonetheless, further work on validation, safety, and translational studies is essential to move toward clinical applications.

On 2024-12-05 23:03:05, user Dina Sarsam wrote:

There is increasing evidence suggesting an interplay between DNA damage response (DDR) and cellular metabolism pathways, specifically regarding the regulatory role of the DDR kinase Ataxia Telangiectasia and Rad3-related protein (ATR) and the metabolic regulator mechanistic Target of Rapamycin Complex 1 (mTORC1) in p16-low cancer cells. However, the mechanism by which ATR regulates mTORC1 activity remains poorly understood. To address these knowledge gaps, the authors of the Tangudu et al. manuscript investigated the role of ATR in activating mTORC1 in both unperturbed and p16 knockdown cell models. The findings of this study unveiled several key novelties including the role of ATR in modulating mTORC1 activity via de novo cholesterol synthesis under both low p16 expression and basal conditions. Additionally, lanosterol synthase (LSS), an enzyme that regulates the biosynthesis of cholesterol, is regulated by ATR, and ATR's regulation of mTORC1 is independent of the Checkpoint Kinase 1 (CHK1) and Tuberous Sclerosis Complex (TSC) pathways. Several innovative experimental techniques were employed within the course of the study, including the simultaneous proteomic and transcriptomic profiling used to identify transcriptional and post-translational changes in ATR signaling and the use of phospho-specific antibodies to monitor the effects of ATR modulation on mTORC1 activation at specific time points.

However, we have identified one major concern that we believe should be addressed prior to the publication of the paper.

The major concern that was found in the paper was that the mechanism of action for the ATR-mTORC1 pathway was not fully represented in all the broad ranges of cells in the data shown in the figures. The issue is that while in Figure 1 the expression of ATR and mTORC1 was shown through a broad range of cell lines, the latter portion of the paper focused primarily on SKMEL28 cells, a melanoma cell line, which does not fully represent the broad spectrum of the cellular model that the ATR-mTORC1 pathway has a role in general cell metabolism and proliferation. An experiment that could be done to address this major issue of underrepresentation of the ATR-mTORC1 expression in unperturbed cells, as well as diseased cells, is to repeat the experiments done from Figure 2 to Figure 4 in all cells that were used in Figure 1 (HeLa, HEK293, MEFs).

There are also some minor concerns we identified with the manuscript. One small issue is the lack of quantification or statistical analysis included in Figure 1. This would allow for a better understanding of the content of the figure. Another minor concern is the coloration of the fluorescence images in Figure 4. The chosen colors make it difficult to make out the overlaps in the merged images, especially in Figure 4B. This could be fixed by changing the colors to ones that are more distinct when merged. The final minor concern identified is the absence of GTPase Rheb in the working model. GTPase Rheb is included in the introduction as it plays a role in the activation of mTORC1 after localization to the lysosome. While the paper is focused on the localization of mTORC1, its activation by GTPase Rheb may also be affected by this mechanism.

On 2024-12-05 22:37:23, user Cecylia Olivo wrote:

The DNA Damage Repair (DDR) pathway, including ATR/ATM, have previously been linked to metabolism and mTORC1 regulation, however the key players and mechanisms, especially in unperturbed cells, in this downstream signaling pathway are currently unknown. This manuscript, authored by Aird et al., demonstrates that in both p16 knockdown cells and unperturbed cells, ATR increases lanosterol synthesis through de novo cholesterol synthesis, which promotes mTORC1 activity by lysosomal localization. They determined that this pathway consists of ATR, not ATM, and is independent of the CHK2 and TSC2 processes.

The paper contains several major concerns, detailed below, that must be addressed before the data can be properly evaluated. Until these concerns are resolved the findings within the paper are unable to be thoroughly assessed, delaying our understanding of how ATR influences mTORC1 during DDR.

The first major concern identified within the paper is the lack of orthogonal validation, specifically for Figure 4 A & B. This is a concern because, in order to validate the findings, different methods should be applied to confirm reliability, reproducibility and robustness. In Figure 4 A & B we are specifically relying on the visual trends to make a conclusion, which could be misinterpreted. The authors should perform Radiolabeled Cholesterol Uptake Assays which measure cell cholesterol absorbance by labeling cholesterol compounds with radioactive isotopes which would contribute to their orthogonal validation and help support their results. The second major concern is that phosphorylation of S6K is not limited to mTORC1. This is a major concern because S6K is a direct substrate for other kinases, such as JNK1 and PKC. Multiple validations are required to show that mTORC1 activity leads to the decrease in phosphorylation of S6K. One validation that can be conducted is to overexpress ATR to observe if phosphorylation of S6K increases, which would further support the direct link between mTORC1 activity and S6K phosphorylation. An additional validation is to conduct an in vitro kinase assay with mTORC1 and S6K to eliminate the possibility of confounding variables. The third major concern is that p16 expression can vary significantly between cell types. This is a major concern because HeLa cells have high levels of p16 expression, HEK293 cells have low levels of p16 expression unless under stress and MEFs have significantly higher levels of p16 as cells approach senescence. Quantifying the basal levels of p16 in each type of cell line is crucial since the focus is on ATR’s effect under basal conditions. This can be done by quantifying the levels of p16 through western blotting and testing if ATR affects mTORC1 similarly across varying expression levels.

The first minor concern is that the quantification of the western blots is needed throughout the paper in order to substantially improve the clarity of the figures. The second minor concern would be to provide justification about the selection of the specific cell lines which would provide clarity on the major concerns related to varying p16 expression. The third minor concern is that the verbiage of mTORC1 should be consistent throughout the whole paper to increase readability and reduce confusion. The final minor concern is that Figure 3’s title is unclear; replacing “decreases” with “knockdown” would be more effective.

On 2024-12-05 15:09:17, user Paul Macklin wrote:

Please note that this manuscript is now published (with slightly different title, and updates) at npj Systems Biology and Applications:

https://doi.org/10.1038/s41540-024-00472-z

On 2024-12-05 12:22:35, user xPeer wrote:

Courtesy review from xPeerd.com

Summary<br /> The study explores the development of human heart assembloids integrated with autologous tissue-resident macrophages to replicate physiological immuno-cardiac interactions. The research emphasizes the importance of this model for understanding heart development and disease. The authors provide a detailed methodology for generating the assembloids, coupled with multi-omic analyses and functional assays, demonstrating the model's capacity to emulate key cardiac and immune processes. However, while the study presents comprehensive data and a novel approach, certain areas require further clarification and detailed statistical analysis to strengthen the findings.

Major Revisions<br /> 1. Statistical Analysis: The study lacks detailed statistical information to support the presented data. Including p-values, confidence intervals, and statistical tests used for each dataset is crucial for validating the results (e.g., Figures and gene set enrichment plots).

1. Reproducibility of Methods: While the methods section is comprehensive, it would benefit from additional clarity on specific protocols to ensure reproducibility. Including precise details about reagents, equipment, and any variations in experimental conditions can aid reproducibility (e.g., generation of heart assembloids).

2. Integration of Findings: The discussion should integrate the findings more thoroughly with existing literature. Highlighting how the study advances the field and addressing possible discrepancies with previous studies will provide a stronger context for the research.

3. Limitations and Future Work: The discussion needs a more critical examination of the study's limitations, potential biases, and confounding factors. Proposals for future research directions should be specified to guide subsequent investigations.

Minor Revisions<br /> 1. Typographical and Formatting Errors:<br /> - Page 15, Figure 2 legend: The phrase "∆ë ëë ë ëëë" is a clear formatting error that needs correction.<br /> - Verify consistent formatting of subheadings and figure legends throughout the manuscript.

1. Definition of Terms: Ensure all technical terms, abbreviations, and acronyms are defined upon their first appearance. This will enhance readability for a broader audience.

2. Reference Formatting: Ensure all references are correctly formatted according to the journal's guidelines. Cross-check for the latest updates in cited references.

3. AI Content Analysis: Based on language consistency and technical depth, the estimated percentage of AI-generated content seems minimal. No sections explicitly exhibit characteristics typical of AI-generated text.

Recommendations<br /> 1. Enhancing Data Presentation: Incorporate more statistical data into figures and tables, such as error bars and exact p-values, to reinforce the reliability of the results.

1. Detailed Protocols: Append a supplementary section with detailed step-by-step protocols for key procedures to facilitate reproducibility by other researchers.

2. Expanded Abstract: Enrich the abstract with specific quantitative results to provide a clearer snapshot of the study's impact and conclusions.

3. Broader Impact Discussion: Expand the discussion on the broader implications of the model for disease modeling, drug testing, and therapeutic applications, tying it back to the study's findings.

This autonomous review aims to provide a comprehensive evaluation of the study, pinpointing critical areas for improvement while acknowledging its scientific contributions.

On 2024-12-05 03:18:42, user Arianna wrote:

The mechanism driving miRNA load and 3’UTR lengthening and its subsequent effect on mRNA half life, and the significance of mRNA stability mechanisms shaping gene expression and 3’UTR usage during human neurodevelopment are well documented. The manuscript by Ellis et al. investigates an alternative approach to systematically quantify transcription rate and mRNA stability using RATE-seq and SLAM-seq to further validate the relevance of miRNA loads among induced pluripotent stem cells (iPSCs), neural progenitors (NPC), and neurons (Neu). This approach expands on the role of mRNA stability in shaping transcriptional buffering and driving 3’UTR lengthening via miRNA load. <br /> The paper suggests a comprehensive approach towards understanding the regulation of mRNA stability and transcription rates during human neurodevelopment. The use of RATE-seq as a technique for measuring half-life and transcription rates was a powerful and novel approach to dive deeper into the current understanding of this field, and could be applicable to future studies. The authors also explore the idea of a mechanistic link between miRNA regulation and transcriptome changes. The reproducibility of results between cell types was reinforced that accumulation of miRNAs during neurodevelopment potentially leads to preferential degradation of short 3' UTR-containing mRNAs in neurons.

The manuscript outlined the following findings about neuronal differentiating cells: i) mRNA stability and transcription rate play an equal role in establishing steady-state levels of the transcriptome, ii) buffering genes controlled by pluripotency transcription factors are regulated by mRNA stability, iii) increased miRNA load corresponds to mRNA degradation of most genes in neurons, and iv) preferential degradation of neuronal short 3’ UTR-containing mRNAs resulting in decreased RNA stability. These are not novel findings to the scientific community but the reproduction of previous research is not without merit. Reproducibility is a hallmark of quality scientific discovery. However the novelty of this research is demonstrated in the unique sequencing protocols and statistical techniques utilized. Despite this achievement the paper doesn’t sufficiently expand the literature in such a way that qualifies it for publication. Given the issues outlined below, the manuscript must address the following concerns prior to acceptance for publication.

One of the most pressing major concerns is the lack of a discernable attempt to close the gap in knowledge regarding the relationship between mRNA stability and transcriptional regulation during neurodevelopment. Specifically, the manuscript demonstrates the shifts in mRNA stability and 3’UTR lengthening. These results have already been documented in previous literature thus leaving unclear how this research advances the field and our understanding beyond known mechanisms. The question of whether miRNA load is sufficient or necessary to drive 3’UTR lengthening should be addressed in any future iterations of this paper. The authors could address this concern by designing an experiment to validate the causal relationship between increasing miRNA load and 3’ UTR lengthening. This can be achieved experimentally by overexpressing miRNA in human cell lines and tracking subsequent increases or decreases in 3’UTR lengthening.

A second major concern that should ideally be remedied by the authors is the use of a single experimental cell line. To strengthen the quality of the research performed in this manuscript experiments should be orthogonally validated in other human cell lines. Justification of each cell line used should be explicitly mentioned in the manuscript. Additional tissue types can also be explored experimentally to strengthen the mechanistic link.

The minor concern that needs to be addressed by the authors of Ellis et al. is the addition of a justification of the time points used during data collection. This context will allow readers more insight into the overall logic of the experiment. It is generally understood that the chosen time points are typical when working with neural cells but this logic may not be clear to an individual not actively involved in the field.

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 2024-12-05 00:12:03, user Krista Longnecker wrote:

This manuscript has now been published as: https://pubs.acs.org/doi/10.1021/acs.jproteome.4c00049

On 2024-12-05 00:09:51, user Krista Longnecker wrote:

This paper is now published as: https://journals.asm.org/doi/10.1128/msystems.01261-22

On 2024-12-04 17:12:09, user Qingrong Huang wrote:

is the supplement table data available somewhere?

On 2024-12-04 13:08:38, user Xibing Xu wrote:

This preprint is now published in Nature communications, please check the online article, DOI: 10.1038/s41467-024-53931-w

On 2024-12-04 07:54:19, user MRR wrote:

Under Data availability, the authors write:<br /> "The authors declare that the data, materials and code supporting the findings reported in this study are available from the authors upon reasonable request."

This preprint is a publication, and data, materials and code should be made available in a open databases.

On 2024-12-03 20:57:12, user xPeer wrote:

Courtesy review from xPeerd.com

Summary<br /> This preprint introduces the novel concept of "oncoembryology" to analyze the shared molecular mechanisms of embryonic colon development and colorectal cancer (CRC). SoxC transcription factors (Sox4, Sox11, Sox12) are identified as critical regulators of embryonic programs reactivated in CRC, driving tumor progression and poor patient survival. Using murine and organoid models, the authors elucidate SoxC-driven pathways influencing chromatin remodeling, proliferation, and metastasis. Despite robust experimental frameworks, translational gaps and underexplored mechanistic details need addressing.

Major Revisions<br /> Mechanistic Elucidation

Insufficient Detail on SoxC-Driven Programs: The role of SoxC in chromatin remodeling and gene regulation is well-documented. However, indirect targets (e.g., Mdk) require deeper exploration. Linking protein networks (e.g., Figure 3A) to specific cellular processes in tumor microenvironments would enhance clarity (Section: Results, p.7).<br /> Immune Modulation Underexplored: SoxC’s regulation of immune-related genes (e.g., Mif, Il19) is introduced but lacks functional validation. Including immune cell profiling from tumor environments or patient samples could substantiate claims (Section: Results, p.8).<br /> Clinical Relevance

Translational Gaps: The manuscript emphasizes SoxC’s therapeutic potential but lacks direct discussion on pharmacological inhibitors. For example, the clinical applicability of targeting Tead2 or Mdk in CRC contexts remains speculative (Section: Discussion, p.10).<br /> Prognostic Model Validation: The SoxC-oncoembryonic signature is promising, but additional validation in independent, larger patient cohorts is essential to ensure reproducibility and clinical utility (Section: Results, p.9).<br /> Experimental Controls

Baseline Comparisons for SoxC Inhibition: While SoxC-KO models demonstrate tumor suppression, parallel experiments using standard chemotherapies or combination treatments would provide comparative benchmarks (Section: Results, p.8).<br /> Hindgut Model Robustness: The embryonic hindgut model is compelling but differs significantly from adult tumor environments. Supplementary experiments incorporating human organoids or ex vivo models could address potential translational limitations (Section: Methods, p.11).<br /> Data Presentation and Statistical Rigor

Incomplete Statistical Details: Kaplan-Meier analyses (e.g., Figure 3C) require hazard ratios and confidence intervals. Clarifying thresholds for “high” and “low” gene expression groups would strengthen interpretability.<br /> Underrepresented Raw Data: Additional raw data (e.g., ATAC-seq peaks, ChIP-seq binding sites) should be included to validate the claims of direct SoxC target regulation (Section: Results, p.6).<br /> Minor Revisions<br /> Typos and Formatting

Figures: Figure legends occasionally omit critical methodological details (e.g., staining conditions in Figure 5A).<br /> Grammar: Line 154, "which may be similarly drive CRC progression," should read "which may similarly drive CRC progression."<br /> AI Content Analysis

Estimated AI-Generated Content: ~10-15%.<br /> Implications: Stylistically consistent but occasionally repetitive phrases (e.g., "regulates both embryonic and cancerous programs") suggest AI-assisted drafting. This does not detract from the scientific rigor but indicates potential areas for manual refinement.<br /> Terminology Consistency

Key terms such as "oncoembryonic genes" and "progenitor maintenance" are inconsistently applied across sections. Standardizing definitions would aid clarity (e.g., Section: Introduction, p.2).<br /> Ethical and Methodological Transparency

The manuscript references ethical approvals but omits details about humane endpoints for animal studies (Section: Methods, p.11). Expanding on regulatory compliance would ensure adherence to ethical standards.<br /> Citations

References to foundational studies (e.g., Weinberg, 1996; Hanahan, 2022) should be balanced with more recent CRC-specific literature.<br /> Recommendations<br /> Mechanistic Depth:

Incorporate RNA-seq or proteomic analyses to map SoxC interactions with immune pathways and chromatin regulators.<br /> Validate indirect target regulation (e.g., Mdk, Tead2) using perturbation assays.<br /> Clinical Insights:

Expand discussion of SoxC inhibitors and therapeutic strategies, including potential combination therapies targeting chromatin remodeling.<br /> Data Enhancements:

Provide supplementary raw datasets for reproducibility, including ATAC-seq peaks and Kaplan-Meier survival data.<br /> Broaden Validation:

Validate findings in independent patient cohorts and alternative models, including humanized CRC organoids.<br /> Formatting and Precision:

Improve figure clarity, add comprehensive legends, and standardize terminology across sections.

On 2024-12-03 20:49:57, user xPeer wrote:

Courtesy review from xPeerd.com

Summary<br /> This manuscript investigates a novel locoregional therapy, MBC-005, targeting p21 induction via intratumoral percutaneous administration in BALB/c mice bearing breast cancer liver metastases. The work demonstrates a 3.9-fold increase in survival and tumor volume reduction through a hydrogel delivery system designed to mitigate hydrophobicity and enzymatic degradation of the active compound. While the study makes a strong case for MBC-005’s preclinical efficacy, there are gaps in experimental design, mechanistic exploration, and translational relevance.

Major Revisions<br /> 1. Mechanistic Clarity<br /> The manuscript attributes MBC-005’s effects to p21 induction but does not sufficiently explain how this mechanism influences overall tumor microenvironment dynamics, such as angiogenesis or immune modulation. Including downstream effects of p21 upregulation (e.g., cell cycle arrest versus immune response interplay) would improve the study’s depth (Section: Results, p.8).<br /> Further exploration of how N-allyl noroxymorphone interacts with the opioid growth factor receptor, specifically in liver-specific metastases, is required to justify its locoregional application (Section: Discussion, p.11).<br /> 2. Experimental Controls<br /> Absence of Baseline Control for Locoregional Therapies: Comparative efficacy with locoregional standards like radiofrequency ablation (RFA) or transcatheter arterial chemoembolization (TACE) is missing. This would contextualize MBC-005’s potential advantages (Section: Results, p.7).<br /> Dose-Response Study Gaps: Although the study evaluates a range of MBC-005 doses, a detailed dose-response curve for survival benefit is absent. Highlighting toxicity thresholds across doses would strengthen the rationale for the selected therapeutic window (Section: Results, p.9).<br /> 3. Translation and Scalability<br /> Translational relevance remains underdeveloped. For example, the manuscript does not discuss challenges related to scaling hydrogel administration for clinical imaging systems or variability in tumor sizes (Section: Discussion, p.12).<br /> Specific limitations of murine liver tumor models compared to human liver metastases (e.g., perfusion, immune differences) should be acknowledged.<br /> 4. Reproducibility and Statistical Rigor<br /> The manuscript does not provide raw data or error ranges in figures showing critical in vivo findings (e.g., Figures 3, 10). The absence of confidence intervals or standard error bars undermines the reliability of these results.<br /> Statistical comparisons (e.g., Figure 9 survival curves) are inconsistently reported. Inclusion of hazard ratios and Kaplan-Meier plots with confidence intervals is needed.<br /> 5. Safety and Toxicity<br /> While the manuscript claims MBC-005 induces no off-target toxicity, details on off-target organ effects are insufficient. Clinical chemistry and histopathology for other critical organs (e.g., kidneys, spleen, heart) would provide a more comprehensive toxicity profile (Section: Results, p.10).<br /> 6. Ethical and Methodological Transparency<br /> Ethical approval processes are ambiguously described, particularly regarding animal welfare protocols. Details about analgesia and endpoint criteria for euthanasia in murine models are essential (Section: Methods, p.4).<br /> Specific imaging protocols for tumor measurement (e.g., ultrasound or bioluminescence imaging) lack clarity regarding resolution and reproducibility parameters.<br /> Minor Revisions<br /> 1. AI Content Analysis<br /> Estimated AI-Generated Content: ~20-25%.<br /> Detected Issues: Over-reliance on templated phrases, particularly in introductory and summary sections (e.g., "poor prognosis; and, when treated with the standard of care systemic therapy they have a median survival of <9-months").<br /> Impact: While these sections are stylistically consistent, they weaken the originality of argumentation. Rewriting these areas to reflect a more critical and nuanced understanding of literature would improve epistemic integrity.<br /> 2. Figures and Tables<br /> Some figures, such as Figure 10 (Kaplan-Meier survival curves), lack clarity and appropriate legends to describe their analytical methodology.<br /> Tables presenting survival metrics (e.g., Table III) should incorporate statistical significance indicators.<br /> 3. Terminology Consistency<br /> Terms like "locoregional therapies" and "p21 induction" are inconsistently defined across sections, leading to potential reader confusion.<br /> 4. Citations<br /> Certain key statements lack direct citation support (e.g., "Locoregional therapies are appealing due to their minimally invasive nature"). Verify these with authoritative sources to enhance credibility (Section: Introduction, p.3).<br /> 5. Formatting and Typos<br /> Line 256: Repeated phrase "was also shown to be significantly decreased."<br /> Ensure uniform formatting of references (e.g., adherence to journal-specific citation styles).<br /> Recommendations<br /> Enhance Mechanistic Insights:

Integrate RNA-seq or proteomic analyses to identify downstream signaling pathways influenced by p21 induction.<br /> Evaluate immune cell infiltration or cytokine profiling in treated tumors to investigate secondary effects.<br /> Address Limitations:

Explicitly discuss how murine models differ from human metastases and any anticipated challenges in clinical translation.<br /> Improve Data Presentation:

Add confidence intervals, raw data, and Kaplan-Meier analyses for survival metrics.<br /> Ensure all figures include clear legends, error bars, and scale descriptions.<br /> Augment Comparisons with Existing Therapies:

Introduce comparative benchmarks with locoregional standards (e.g., RFA, PEI) to establish MBC-005’s potential superiority.<br /> Refine Ethical Transparency:

Provide detailed procedural descriptions for animal welfare compliance, imaging techniques, and dose administration.

On 2024-12-03 18:59:14, user Eric Kernfeld wrote:

Hi, my name is Eric and I study automated GRN inference methods. I just spotted your study from an automated twitter account that I follow and it looks like you have a sensible combo of methods and some really promising demo results. Do you know how this approach would stack up against DoRothEA or CellOracle? Thanks for considering.

On 2024-12-03 13:54:03, user Flor wrote:

Hi readers! This manuscript has been published now at The Plant Journal! On behalf of all the authors, I invite you to read it there! https://onlinelibrary.wiley.com/doi/10.1111/tpj.17159

On 2024-12-03 10:34:33, user Alejandro Gómez Mejia wrote:

This preprint has been reviewed and published in mSphere: DOI: https://doi.org/10.1128/msphere.00430-24

On 2024-12-03 10:32:35, user Alejandro Gómez Mejia wrote:

This study has been peer reviewed a while ago and is now published in PLOS PATHOGENS. DOI: https://doi.org/10.1371/journal.ppat.1010176

On 2024-12-03 01:56:44, user Rishav Mitra wrote:

Review by Ziyue Zou, Rishav Mitra, and James Fraser

This manuscript provides a baseline comparison between current physics-based computational methods with machine-learning (ML) methods in predicting key thermodynamics properties in drug discovery – binding energy in presence of inhibitor (ΔΔG). Here three non-ML algorithms are studied against one existing ML model — Random Forest (RF), which was directly trained to predict this physical property in a previous study. The results suggest physics-based simulations in general provide better estimates compared to the ML/structural-based methods when benchmarking to experimental measures, especially against distal mutations. Overall the manuscript is well-written and provides a sufficient amount of detail in each methodology used.

Here are our comments:

1. The generalizability of the trained ML model is not immediately clear to us. It would be helpful if the author can include some data analyses on this model with the train/validation/test datasets in this manuscript to show the audience the performance of the model. How was the dataset different from Aldeghi et al. ACS central science 5, no. 8 (2019): 1468-1474? Will RF model predict better if it is trained on the Platinum database? Will a combination of Platinum database to data used in this manuscript improve the predicting ability of the model?

2. Following the previous comment, similar to neural nets, deep tree-based methods can be easily overfitted to the training data, is this also expected here?The NanoBRET vs. measurements in Hauser el al (2018) scatter plot and NanoBRET vs. RF plot look very similar to each other. Could that be evidence of overfitting to the dataset? Again it would be beneficial to present some results on model training.

3. As the authors summarized in the end, the physics-based simulation methods outperform structural/ ML-based methods. Does this mean by introducing structural descriptors to machine learning models, the predictions can be largely improved. This could be easily validated by retraining a RF model with additional (distal) features, which can be a valuable ML-based benchmark for future study.

4. While the authors provide a detailed investigation of the effects of forcefield selection in non-equilibrium perturbation (NEQ), the free energy calculations (FEP+) method does not seem well studied under various forcefield parameters. Is there a reason why OPLS was chosen for FEP+ and GAFF/CGenFF were selected for NEQ? If so, please elucidate.

5. The authors may want to comment on the utility of the training dataset from Hauser et al. (2018) given the importance of measuring the ΔΔG values for each TKI from a single measurement as demonstrated and alluded to in this paper.

6. Can the authors comment on the structural basis for the impact of distal mutations (Supp. Fig. 5, 6) which have a significant impact on inhibitor binding? It might also be useful to make a separate list for the identity of the mutated residue, their ΔΔG values, distance from the active site, RMSE and correlation values for these interesting mutations.

7. The RF model has been trained with mostly nearly-neutral point mutations. Is it expected to perform well for large-effect resistant or sensitive mutations for which experimental ΔΔG values are not within the 土1 kcal/mol range?

8. The authors assign a mutation as resistant if both the NanoBRET and computational approach predict an increase in ΔΔG by ΔΔG > +1 kcal/mol . What about the performance of the models on mutations for which the predicted -1 < ΔΔG < +1 kcal/mol, i.e., nearly neutral, but the clinically observed phenotype is cancer resistance or sensitive?

9. It is unclear if there are H-bond interactions between the water molecules within 0.4 nm from T315 and other chemical groups in the vicinity, such as backbone amides or a ligand atom. Are there features in the electron density map for the crystal structure of Abl kinase that indicate other water- mediated contacts in this site that might be disrupted by the T315A mutation?

Minor points:

1. Term “singular point” in PRAUC plots is only mentioned in the caption of Figure 5, it would be good to have it mentioned and defined in the main text, followed by a discussion on its significance.

2. What is the shaded region around the diagonal line in Fig. 2B?

3. What is the provenance of the “sensitizing” mutation L298F, it is unclear if this is patient derived or engineered?

On 2024-12-02 10:45:47, user Robert E White wrote:

Nice study. One question on the EBV transcripts: have you tried separating the reads into latency genes (EBNAs, EBERs, BARTs, LMPS) vs Immediate Early/Early vs Late? This might be quite informative as to the type of EBV biology you are observing (esp in PBMC). For instance I might expect Early and late reads in the nasal and airway samples (EBV lytic replication) whereas in the blood this could be either expansion of latency III blasts, or productive reactivation as they differentiate into plasma cells, or even an abortive replication [IE and E but no late transcripts] as they migrate to peripheral mucosal sites for transmission.

On 2024-12-02 09:30:09, user Rebecca McDougal wrote:

This has now been published: https://doi.org/10.1186/s42483-024-00255-8

On 2024-12-02 00:02:22, user Iain Cheeseman wrote:

I really enjoyed this paper. The data that you curated looks very helpful. Would it be possible to include a supplemental table with the peptides that you identified in each of the categories?

On 2024-12-01 22:50:37, user Oki O'Connor wrote:

Very nice study. Are the authors looking at lifespan? They started with such old animals it shouldn't take much longer.

On 2024-12-01 16:53:12, user Clement Kent wrote:

Interesting work which advances the field. <br /> Just recording a few typos here. <br /> Line 103 refers to Allan(19) but no such item in the references.<br /> Line 8,16,18 - author Erclik listed with 2 identical affiliations.<br /> Line 188- put in the reference number. <br /> Lines 214 and 257 - tup is the Flybase standard name for this gene. I don't see why you insist on Islet. At any rate, italicize tup in 214.<br /> Line 393: optogenetically instead of optogenetic.<br /> Line 447: "for each of 32 hemilineages"<br /> Line 628 " obtained from"<br /> Lines 833,894,967 - update references

On 2024-12-01 12:25:06, user Belter Xin wrote:

This manuscript has been published in PNAS: https://www.pnas.org/doi/10.1073/pnas.2407096121

On 2024-11-30 22:24:18, user xPeerd wrote:

Peer review report from http://xpeerd.com

Summary<br /> The preprint presents a novel strategy termed Transient Overexpression of P-glycoprotein (P-gp) for Cardiac reprogramming (TopCare) aimed at mitigating doxorubicin (Dox)-induced cardiotoxicity in cancer chemotherapy. The approach employs lipid nanoparticles (LNPs)-based mRNA therapeutics to transiently overexpress P-gp in cardiomyocytes, reducing intracellular Dox levels and associated cytotoxic effects, both in vitro and in vivo. The study demonstrates promising results, including enhanced survival rates and improved cardiac function in treated mice and pigs. However, detailed analysis and validation in clinical settings are needed.

Major Revisions<br /> 1. Ethics and Concerns on mRNA Technology:<br /> The preprint does not provide comprehensive information on the long-term safety and potential mutagenic effects of repeated mRNA administration. Although the authors claim no potential insertion mutagenesis, a detailed toxicological assessment must be included.<br /> - Example: The long-term impact on genomic stability has been vaguely mentioned (Section: Results, Page 6) but needs further elaboration.

1. Effectiveness on Large Animal Models:<br /> While the study highlights the preliminary success in pigs, it lacks comprehensive physiological data, myocardial histopathology, and the functional impact across different heart failure stages.

2. Example: The pig model data is summarized, but detailed statistical analysis and larger sample size validation are crucial (Discussion, Page 7).

3. Broader Relevance and Risk Mitigation:<br /> The potential immunogenicity of LNPs and mRNA therapeutics should be discussed to address the broader clinical relevance and possible adverse immune responses.

4. Example: Immune response considerations are barely discussed (Page 10), which is critical for clinical translation.

Recommendations<br /> 1. Enhance Toxicology and Safety Data:<br /> Include detailed data on the longitudinal impact of mRNA administration, focusing on potential genomic stability issues and systemic safety profiles. Consider supplementary studies evaluating mutagenic and oncogenic risks.<br /> 2. Comprehensive Animal Model Studies:<br /> Expand the large animal model studies to include a broader sample size and various cardiovascular conditions, supplemented with detailed histopathological analyses.<br /> 3. Immune Response Mitigation:<br /> Address potential immunogenicity by conducting comprehensive immunological assessments on treated animals and documenting any adverse reactions. Present a risk mitigation strategy for the clinical setting.<br /> 4. Expanded Clinical Relevance Exploration:<br /> Provide a more robust discussion on how to adapt the TopCare strategy to different cancer treatments, varying dosages, and combined therapies to ensure broader applicability.

Minor Revisions<br /> 1. Textual and Formatting Errors:<br /> - Correct minor typographical errors and ensure consistent formatting across sections. Specific errors to address:<br /> - Page 2, Title capitalization inconsistency ("Cardiac Reprogramm...").<br /> - Figure labels and axis titles should follow uniform font size and style (Section: Results).<br /> 2. AI Content Analysis:<br /> - Estimated AI Content: Approximately 10-15%.<br /> - Highlighted AI-Detected Sections: Repetitive and templated language indicating likely AI aid in introduction and discussion.<br /> - Epistemic Impact Assessment: The AI-generated segments maintain consistency but could benefit from nuanced, domain-specific language refinements to underline originality and expertise..

Overall, the preprint provides an innovative and promising approach to tackling cardiotoxicity in chemotherapy but requires crucial improvements and detailed validations before realistic clinical applications.

On 2024-11-30 10:38:30, user Balázs Vedelek wrote:

We recently read with great interest your paper titled “Adaptive protein coevolution preserves telomere integrity” and found your findings on the evolution of Drosophila telomere capping to be quite engaging. The topic of the current manuscript is essentially the same that we addressed earlier by biochemical and informatical means ( https://doi.org/10.1371/journal.pone.0142771 , https://doi.org/10.1098/rsob.210261) . Upon reading your work, we noticed that our earlier research in this area appears to have not been acknowledged. <br /> Given that, while the experimental approaches differ, both of our studies address essentially the same phenomena and build on the same underlying principles, we believe that proper acknowledgment of our studies would be a beneficial addition to your manuscript. <br /> We truly appreciate your contributions to the field and look forward to seeing how your research continues to evolve.<br /> Thank you for your attention to this matter.

On 2024-11-29 09:51:01, user Simon Gascoin wrote:

Dear authors<br /> I also revisited Roland et al. results in the light of our recent study on biases in Landsat greening trends by Bayle et al. (2024) https://www.cesbio.cnrs.fr/multitemp/is-antarctica-greening/

Bayle, A., Gascoin, S., Berner, L. T., & Choler, P. (2024). Landsat-based greening trends in alpine ecosystems are inflated by multidecadal increases in summer observations. In Ecography. https://doi.org/10.1111/ecog.07394

On 2024-11-28 11:08:54, user Marco Barreca wrote:

Dear all, the peer-reviewed version was out on 24 October, you can find it at this link: https://www.nature.com/articles/s41698-024-00730-7 <br /> The DOI is: https://doi.org/10.1038/s41698-024-00730-7

On 2024-11-28 10:07:05, user Sholto David wrote:

Abstract: "Since 2009, successful hoaxes usually appeared at a year of one or more a year" - perhaps this should say "at a rate of one or more a year"? Current phrasing doesn't quite make sense.

On 2024-11-27 23:21:01, user Monica Berger wrote:

Great preprint and I agree about the two basic types of hoaxes.

I keenly followed the Conceptual Penis hoax (Boghossian and Lindsay) as it unfolded in real time as I was writing my book on predatory publishing. Although it initially correctly stated that the Conceptual Penis hoax as exposing gender studies, he later says they published in a predatory journal. This is incorrect. There were peer review problems but no predatory publishing.

They submitted the article to a prestigious gender studies journal, NORMA: International Journal for Masculinity Studies, from Taylor and Francis. The journal rejected the article and transferred the article down to another T & F lower-tier open access journal, Cogent Social Sciences. This editorial process is called “cascading.” The less prestigious journal peer-reviewed it and, when it was accepted, requested an APC. After publication, the authors revealed the hoax, and the article was retracted; the journal explained that the peer reviewers for the lower tier publication lacked experience. See: https://www.skeptic.com/reading_room/conceptual-penis-social-contruct-sokal-style-hoax-on-gender-studies/

On 2024-11-27 10:42:17, user S. Bachellier-Bassi wrote:

Why is Candida albicans grown in a medium designed for bacteria ? Does it influence the efficiency of hyphal development ?

On 2024-11-27 03:40:20, user rdshrestha wrote:

Interesting work, but the assertion that 'CNTN2 known to plays a role in murine retina development but not in human' is not accurate. In recent study, utilizing human telencephalon-eye organoids, we demonstrated the expression of CNTN2 in human retinal development. We highlighted its differential expression in early RGCs of human fetal retinas, suggesting its potential as a marker for RGC isolation and underscoring a conserved role in retinal development across species. Reference: https://doi.org/10.7554/elife.87306

On 2024-11-26 19:42:30, user Shelley wrote:

Well cocaine and heroin isn't the same thing at all one is more targeted than the other...

On 2024-11-26 15:54:10, user Prof. T. K. Wood wrote:

Congratulations on your discovery of a novel hibernation factor.<br /> 1. p. 11 middle paragraph has some unintended text.<br /> 2. p. 3: why not include the mechanism of ppGpp to 100S via RMF/Hpf for persister cell formation and 100S undimerized with HflX to resuscitation (low ppGpp) since it is related and mechanistic for the physiological relevance of hibernating ribosomes ( https://doi.org/10.1016/j.bioflm.2019.100018 )?

On 2024-11-25 14:04:59, user Chie Kodera wrote:

Hello,<br /> This article is already published here.<br /> https://www.degruyter.com/document/doi/10.1515/mim-2024-0002/html <br /> Can I ask you to put the link?<br /> Thank you.

On 2024-11-25 14:02:29, user Chie Kodera wrote:

Hello,<br /> This article is already published here.<br /> https://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-021-07503-7 <br /> Can I ask you to put the link?<br /> Thank you.

On 2024-11-25 11:52:43, user Ronan O'Cualain wrote:

Please consider referencing the work done by Herrera et al ( https://pubmed.ncbi.nlm.nih.gov/32565759/ ) which includes AFA ultrasonication, S-trap sample processing, and DDA of FFPE micro-dissected sections.

On 2024-11-25 11:32:41, user Sebastien Leclercq wrote:

That is a nice study, well done, although not bringing breakthrough ideas : it is not a great surprise that conjugative plasmids PTUs are more prone to spread AMR genes than non mobile ones, and are also more prone to recombine because they meet more unrelated DNA. At least it is now demonstrated.

I however have some doubt about the host range analysis, because the methods applied are not very clear. It is written that the host range was assigned with COPLA (l.159). But I guess that the host range inferred by COPLA includes all plasmids in their database for each PTU, including some containing AMR genes. So in the last (and most important) section of the manuscript, removing the ARG-carrying plasmids from the AMR+ PTUs will not change the host range classification given by COPLA. <br /> This bring an inconsistency between the given host range and the actual plasmids in the 118 ARG-free PTUs investigated.<br /> My feeling is that the rare grade V+VI PTUs are actually caused by ARG carriage, bringing a great fitness advantage in very distant bacterial hosts in which plasmids should otherwise struggle to maintain because of maladaptation.<br /> It will be necessary I think to calculate the host range only with the data investigated in the study, simply by looking at the plasmid's host taxonomy and not rely on COPLA results. Like this it can be calculated independently for the various sets of PTUs (with/without pAMR).