Characterization of Comments About bioRxiv and medRxiv Preprints

Importance

Preprints have been increasingly used in biomedical science, and a key feature of many platforms is public commenting. The content of these comments, however, has not been well studied, and it is unclear whether they resemble those found in journal peer review.

Objective

To describe the content of comments on the bioRxiv and medRxiv preprint platforms.

Design, Setting, and Participants

In this cross-sectional study, preprints posted on the bioRxiv and medRxiv platforms in 2020 were accessed through each platform's application programming interface on March 29, 2021, and a random sample of preprints containing between 1 and 20 comments was evaluated independently by 3 evaluators using an instrument to assess their features and general content.

Main Outcome and Measures

The numbers and percentages of comments from authors or nonauthors were assessed, and the comments from nonauthors were assessed for content. These nonauthor comments were assessed to determine whether they included compliments, criticisms, corrections, suggestions, or questions, as well as their topics (eg, relevance, interpretation, and methods). Nonauthor comments were also analyzed to determine whether they included references, provided a summary of the findings, or questioned the preprint's conclusions.

Results

Of 52 736 preprints, 3850 (7.3%) received at least 1 comment (mean [SD] follow-up, 7.5 [3.6] months), and the 1921 assessed comments (from 1037 preprints) had a median length of 43 words (range, 1-3172 words). The criticisms, corrections, or suggestions present in 694 of 1125 comments (61.7%) were the most prevalent content, followed by compliments (n = 428 [38.0%]) and questions (n = 393 [35.0%]). Criticisms usually regarded interpretation (n = 286), methodological design (n = 267), and data collection (n = 238), while compliments were mainly about relevance (n = 111) and implications (n = 72).

Conclusions and Relevance

In this cross-sectional study of preprint comments, topics commonly associated with journal peer review were frequent. However, only a small percentage of preprints posted on the bioRxiv and medRxiv platforms in 2020 received comments on these platforms. A clearer taxonomy of peer review roles would help to describe whether postpublication peer review fulfills them.

Using logical constraints to validate statistical information about disease outbreaks in collaborative knowledge graphs: the case of COVID-19 epidemiology in Wikidata

Urgent global research demands real-time dissemination of precise data. Wikidata, a collaborative and openly licensed knowledge graph available in RDF format, provides an ideal forum for exchanging structured data that can be verified and consolidated using validation schemas and bot edits. In this research article, we catalog an automatable task set necessary to assess and validate the portion of Wikidata relating to the COVID-19 epidemiology. These tasks assess statistical data and are implemented in SPARQL, a query language for semantic databases. We demonstrate the efficiency of our methods for evaluating structured non-relational information on COVID-19 in Wikidata, and its applicability in collaborative ontologies and knowledge graphs more broadly. We show the advantages and limitations of our proposed approach by comparing it to the features of other methods for the validation of linked web data as revealed by previous research.

Complex Portal 2022: new curation frontiers

The Complex Portal (www.ebi.ac.uk/complexportal) is a manually curated, encyclopaedic database of macromolecular complexes with known function from a range of model organisms. It summarizes complex composition, topology and function along with links to a large range of domain-specific resources (i.e. wwPDB, EMDB and Reactome). Since the last update in 2019, we have produced a first draft complexome for Escherichia coli, maintained and updated that of Saccharomyces cerevisiae, added over 40 coronavirus complexes and increased the human complexome to over 1100 complexes that include approximately 200 complexes that act as targets for viral proteins or are part of the immune system. The display of protein features in ComplexViewer has been improved and the participant table is now colour-coordinated with the nodes in ComplexViewer. Community collaboration has expanded, for example by contributing to an analysis of putative transcription cofactors and providing data accessible to semantic web tools through Wikidata which is now populated with manually curated Complex Portal content through a new bot. Our data license is now CC0 to encourage data reuse. Users are encouraged to get in touch, provide us with feedback and send curation requests through the 'Support' link.

OUP accepted manuscript

Motivation

Wikipedia is one of the most important channels for the public communication of science and is frequently accessed as an educational resource in computational biology. Joint efforts between the International Society for Computational Biology (ISCB) and the Computational Biology taskforce of WikiProject Molecular Biology (a group of expert Wikipedia editors) have considerably improved computational biology representation on Wikipedia in recent years. However, there is still an urgent need for further improvement in quality, especially when compared to related scientific fields such as genetics and medicine. Facilitating involvement of members from ISCB Communities of Special Interest (COSIs) would improve a vital open education resource in computational biology, additionally allowing COSIs to provide a quality educational resource highly specific to their subfield.

Results

We generate a list of around 1500 English Wikipedia articles relating to computational biology and describe the development of a binary COSI-Article matrix, linking COSIs to relevant articles and thereby defining domain-specific open educational resources. Our analysis of the COSI-Article matrix data provides a quantitative assessment of computational biology representation on Wikipedia against other fields and at a COSI-specific level. Furthermore, we conducted similarity analysis and subsequent clustering of COSI-Article data to provide insight into potential relationships between COSIs. Finally, based on our analysis, we suggest courses of action to improve the quality of computational biology representation on Wikipedia.

Pathogenesis, Symptomatology, and Transmission of SARS-CoV-2 through Analysis of Viral Genomics and Structure

The novel coronavirus SARS-CoV-2, which emerged in late 2019, has since spread around the world and infected hundreds of millions of people with coronavirus disease 2019 (COVID-19). While this viral species was unknown prior to January 2020, its similarity to other coronaviruses that infect humans has allowed for rapid insight into the mechanisms that it uses to infect human hosts, as well as the ways in which the human immune system can respond. Here, we contextualize SARS-CoV-2 among other coronaviruses and identify what is known and what can be inferred about its behavior once inside a human host. Because the genomic content of coronaviruses, which specifies the virus's structure, is highly conserved, early genomic analysis provided a significant head start in predicting viral pathogenesis and in understanding potential differences among variants. The pathogenesis of the virus offers insights into symptomatology, transmission, and individual susceptibility. Additionally, prior research into interactions between the human immune system and coronaviruses has identified how these viruses can evade the immune system's protective mechanisms. We also explore systems-level research into the regulatory and proteomic effects of SARS-CoV-2 infection and the immune response. Understanding the structure and behavior of the virus serves to contextualize the many facets of the COVID-19 pandemic and can influence efforts to control the virus and treat the disease. IMPORTANCE COVID-19 involves a number of organ systems and can present with a wide range of symptoms. From how the virus infects cells to how it spreads between people, the available research suggests that these patterns are very similar to those seen in the closely related viruses SARS-CoV-1 and possibly Middle East respiratory syndrome-related CoV (MERS-CoV). Understanding the pathogenesis of the SARS-CoV-2 virus also contextualizes how the different biological systems affected by COVID-19 connect. Exploring the structure, phylogeny, and pathogenesis of the virus therefore helps to guide interpretation of the broader impacts of the virus on the human body and on human populations. For this reason, an in-depth exploration of viral mechanisms is critical to a robust understanding of SARS-CoV-2 and, potentially, future emergent human CoVs (HCoVs).

Pathogenesis, Symptomatology, and Transmission of SARS-CoV-2 through Analysis of Viral Genomics and Structure

The novel coronavirus SARS-CoV-2, which emerged in late 2019, has since spread around the world and infected hundreds of millions of people with coronavirus disease 2019 (COVID-19). While this viral species was unknown prior to January 2020, its similarity to other coronaviruses that infect humans has allowed for rapid insight into the mechanisms that it uses to infect human hosts, as well as the ways in which the human immune system can respond. Here, we contextualize SARS-CoV-2 among other coronaviruses and identify what is known and what can be inferred about its behavior once inside a human host. Because the genomic content of coronaviruses, which specifies the virus's structure, is highly conserved, early genomic analysis provided a significant head start in predicting viral pathogenesis and in understanding potential differences among variants. The pathogenesis of the virus offers insights into symptomatology, transmission, and individual susceptibility. Additionally, prior research into interactions between the human immune system and coronaviruses has identified how these viruses can evade the immune system's protective mechanisms. We also explore systems-level research into the regulatory and proteomic effects of SARS-CoV-2 infection and the immune response. Understanding the structure and behavior of the virus serves to contextualize the many facets of the COVID-19 pandemic and can influence efforts to control the virus and treat the disease.

Zebrafish sp7 mutants show tooth cycling independent of attachment, eruption and poor differentiation of teeth

The capacity to fully replace teeth continuously makes zebrafish an attractive model to explore regeneration and tooth development. The requirement of attachment bone for the appearance of replacement teeth has been hypothesized but not yet investigated. The transcription factor sp7 (osterix) is known in mammals to play an important role during odontoblast differentiation and root formation. Here we study tooth replacement in the absence of attachment bone using sp7 zebrafish mutants. We analysed the pattern of tooth replacement at different stages of development and demonstrated that in zebrafish lacking sp7, attachment bone is never present, independent of the stage of tooth development or fish age, yet replacement is not interrupted. Without bone of attachment we observed abnormal orientation of teeth, and abnormal connection of pulp cavities of predecessor and replacement teeth. Mutants lacking sp7 show arrested dentinogenesis, with non-polarization of odontoblasts and only a thin layer of dentin deposited. Osteoclast activity was observed in sp7 mutants; due to the lack of bone of attachment, remodelling was diminished but nevertheless present along the pharyngeal bone. We conclude that tooth replacement is ongoing in the sp7 mutant despite poor differentiation and defective attachment. Without bone of attachment tooth orientation and pulp organization are compromised.