Examining linguistic shifts between preprints and publications.

Preprints allow researchers to make their findings available to the scientific community before they have undergone peer review. Studies on preprints within bioRxiv have been largely focused on article metadata and how often these preprints are downloaded, cited, published, and discussed online. A missing element that has yet to be examined is the language contained within the bioRxiv preprint repository. We sought to compare and contrast linguistic features within bioRxiv preprints to published biomedical text as a whole as this is an excellent opportunity to examine how peer review changes these documents. The most prevalent features that changed appear to be associated with typesetting and mentions of supporting information sections or additional files. In addition to text comparison, we created document embeddings derived from a preprint-trained word2vec model. We found that these embeddings are able to parse out different scientific approaches and concepts, link unannotated preprint-peer-reviewed article pairs, and identify journals that publish linguistically similar papers to a given preprint. We also used these embeddings to examine factors associated with the time elapsed between the posting of a first preprint and the appearance of a peer-reviewed publication. We found that preprints with more versions posted and more textual changes took longer to publish. Lastly, we constructed a web application (https://greenelab.github.io/preprint-similarity-search/) that allows users to identify which journals and articles that are most linguistically similar to a bioRxiv or medRxiv preprint as well as observe where the preprint would be positioned within a published article landscape.

MyGeneset.info: an interactive and programmatic platform for community-curated and user-created collections of genes.

Gene definitions and identifiers can be painful to manage-more so when trying to include gene function annotations as this can be highly context-dependent. Creating groups of genes or gene sets can help provide such context, but it compounds the issue as each gene within the gene set can map to multiple identifiers and have annotations derived from multiple sources. We developed MyGeneset.info to provide an API for integrated annotations for gene sets suitable for use in analytical pipelines or web servers. Leveraging our previous work with MyGene.info (a server that provides gene-centric annotations and identifiers), MyGeneset.info addresses the challenge of managing gene sets from multiple resources. With our API, users readily have read-only access to gene sets imported from commonly-used resources such as Wikipathways, CTD, Reactome, SMPDB, MSigDB, GO, and DO. In addition to supporting the access and reuse of approximately 180k gene sets from humans, common model organisms (mice, yeast, etc.), and less-common ones (e.g. black cottonwood tree), MyGeneset.info supports user-created gene sets, providing an important means for making gene sets more FAIR. User-created gene sets can serve as a way to store and manage collections for analysis or easy dissemination through a consistent API.

Microscale Corrosion Inhibition Behavior of Four Corrosion Inhibitors (BTA, MBI, MBT, and MBO) on Archeological Silver Artifacts Based on Scanning Electrochemical Cell Microscopy.

The problem of corrosion-induced discoloration and embrittlement in silverware is a significant concern for the long-term preservation of excavated archeological silver artifacts, even after thermal restoration. The key to addressing this issue lies in the meticulous selection and evaluation of corrosion inhibitors that possess targeted corrosion inhibition capabilities. This study focuses on the evaluation of corrosion inhibitors for archeological silver artifacts using scanning electrochemical cell microscopy (SECCM) and X-ray photoelectron spectroscopy (XPS). The researchers aimed to compare the inhibition effects of four corrosion inhibitors [1,2,3-benzotriazole (BTA), 2-mercaptobenzimidazole (MBI), 2-mercaptobenzothiazole (MBT), and 2-mercaptobenzoxazole (MBO)] on a simulated Ag-Cu alloy sample and understand their mechanisms. The results showed that MBT exhibited better corrosion inhibition for microstructural regions with higher silver content due to its ability to form stable chelation structures with Ag(I). MBO exhibited better corrosion inhibition for microstructural regions with higher copper content due to its strong affinity with Cu(I). The targeted corrosion inhibition ability for the ß-phase was ranked as MBO > BTA ≈ MBI > MBT, while for the α-phase the ranking was MBT > MBO > MBI > BTA. The study demonstrated the feasibility and capabilities of SECCM in the targeted screening of corrosion inhibitors for different compositions and microstructural regions in archeological metal artifacts. This study highlights the potential of SECCM in corrosion inhibitor research for archeological metal artifacts and wider applications in metal material corrosion protection.

Open collaborative writing with Manubot.

Open, collaborative research is a powerful paradigm that can immensely strengthen the scientific process by integrating broad and diverse expertise. However, traditional research and multi-author writing processes break down at scale. We present new software named Manubot, available at https://manubot.org, to address the challenges of open scholarly writing. Manubot adopts the contribution workflow used by many large-scale open source software projects to enable collaborative authoring of scholarly manuscripts. With Manubot, manuscripts are written in Markdown and stored in a Git repository to precisely track changes over time. By hosting manuscript repositories publicly, such as on GitHub, multiple authors can simultaneously propose and review changes. A cloud service automatically evaluates proposed changes to catch errors. Publication with Manubot is continuous: When a manuscript's source changes, the rendered outputs are rebuilt and republished to a web page. Manubot automates bibliographic tasks by implementing citation by identifier, where users cite persistent identifiers (e.g. DOIs, PubMed IDs, ISBNs, URLs), whose metadata is then retrieved and converted to a user-specified style. Manubot modernizes publishing to align with the ideals of open science by making it transparent, reproducible, immediate, versioned, collaborative, and free of charge.

ADAGE signature analysis: differential expression analysis with data-defined gene sets.

BACKGROUND: Gene set enrichment analysis and overrepresentation analyses are commonly used methods to determine the biological processes affected by a differential expression experiment. This approach requires biologically relevant gene sets, which are currently curated manually, limiting their availability and accuracy in many organisms without extensively curated resources. New feature learning approaches can now be paired with existing data collections to directly extract functional gene sets from big data. RESULTS: Here we introduce a method to identify perturbed processes. In contrast with methods that use curated gene sets, this approach uses signatures extracted from public expression data. We first extract expression signatures from public data using ADAGE, a neural network-based feature extraction approach. We next identify signatures that are differentially active under a given treatment. Our results demonstrate that these signatures represent biological processes that are perturbed by the experiment. Because these signatures are directly learned from data without supervision, they can identify uncurated or novel biological processes. We implemented ADAGE signature analysis for the bacterial pathogen Pseudomonas aeruginosa. For the convenience of different user groups, we implemented both an R package (ADAGEpath) and a web server ( http://adage.greenelab.com ) to run these analyses. Both are open-source to allow easy expansion to other organisms or signature generation methods. We applied ADAGE signature analysis to an example dataset in which wild-type and ∆anr mutant cells were grown as biofilms on the Cystic Fibrosis genotype bronchial epithelial cells. We mapped active signatures in the dataset to KEGG pathways and compared with pathways identified using GSEA. The two approaches generally return consistent results; however, ADAGE signature analysis also identified a signature that revealed the molecularly supported link between the MexT regulon and Anr. CONCLUSIONS: We designed ADAGE signature analysis to perform gene set analysis using data-defined functional gene signatures. This approach addresses an important gap for biologists studying non-traditional model organisms and those without extensive curated resources available. We built both an R package and web server to provide ADAGE signature analysis to the community.

Construction and Performance of Superhydrophobic Surfaces for Rusted Iron Artifacts.

Ancient iron artifacts need to be protected with a rust layer, often stabilized by tannic acid corrosion inhibition. In humid environments, water vapor could slowly penetrate and trigger galvanic corrosion of metal artefacts. Sealing treatments are generally applied to the artefact surface to isolate water and enhance its corrosion resistance. Superhydrophobic modifications could effectively block the penetration of moisture into the interior of the artefact and provide a nice water barrier. Stearic acid with tannic acid inhibition treatment creates a superhydrophobic protective layer on the surface of rusted iron artifacts and enhances corrosion resistance effectively. Various scientific analyses and testing methods are used in this paper to evaluate the corrosion resistance of rusted surfaces after superhydrophobic modification and investigate the reaction mechanisms. The results indicate that the contact angle of the rusted surface after corrosion inhibition by tannic acid and modified by stearic acid is increased to 152.2°, which means the superhydrophobic protective layer has been successfully constructed. The C/Fe ratio of the rusted surface is increased from 0.21 to 2.10, and the characteristic diffraction peaks of O1s and Fe 2p3/2 shift toward higher binding energy. Stearic acid is combined with the corrosion product layer by chemical bonding. Chelation between rust products, tannic acid, and steric acid is effective, and the chelate is chemically stable. The superhydrophobic surface forms a lamellar wax-like layer as an air barrier to isolate liquid water, resulting in a significant decrease in corrosion current and an increase in Warburg impedance to 217.9 times the original state, with a protection efficiency of 88.3%. Tannic acid corrosion inhibition and stearic acid superhydrophobic modification have an excellent synergistic protective effect on improving the corrosion resistance of iron artifacts, resulting in better corrosion resistance of iron artifact materials. The research provides new ideas and references for the protection of ancient iron artifacts sealing.

Hetnet connectivity search provides rapid insights into how two biomedical entities are related.

Hetnets, short for "heterogeneous networks", contain multiple node and relationship types and offer a way to encode biomedical knowledge. One such example, Hetionet connects 11 types of nodes - including genes, diseases, drugs, pathways, and anatomical structures - with over 2 million edges of 24 types. Previous work has demonstrated that supervised machine learning methods applied to such networks can identify drug repurposing opportunities. However, a training set of known relationships does not exist for many types of node pairs, even when it would be useful to examine how nodes of those types are meaningfully connected. For example, users may be curious not only how metformin is related to breast cancer, but also how the GJA1 gene might be involved in insomnia. We developed a new procedure, termed hetnet connectivity search, that proposes important paths between any two nodes without requiring a supervised gold standard. The algorithm behind connectivity search identifies types of paths that occur more frequently than would be expected by chance (based on node degree alone). We find that predictions are broadly similar to those from previously described supervised approaches for certain node type pairs. Scoring of individual paths is based on the most specific paths of a given type. Several optimizations were required to precompute significant instances of node connectivity at the scale of large knowledge graphs. We implemented the method on Hetionet and provide an online interface at https://het.io/search . We provide an open source implementation of these methods in our new Python package named hetmatpy .

SOPHIE: Generative Neural Networks Separate Common and Specific Transcriptional Responses.

Genome-wide transcriptome profiling identifies genes that are prone to differential expression (DE) across contexts, as well as genes with changes specific to the experimental manipulation. Distinguishing genes that are specifically changed in a context of interest from common differentially expressed genes (DEGs) allows more efficient prediction of which genes are specific to a given biological process under scrutiny. Currently, common DEGs or pathways can only be identified through the laborious manual curation of experiments, an inordinately time-consuming endeavor. Here we pioneer an approach, Specific cOntext Pattern Highlighting In Expression data (SOPHIE), for distinguishing between common and specific transcriptional patterns using a generative neural network to create a background set of experiments from which a null distribution of gene and pathway changes can be generated. We apply SOPHIE to diverse datasets including those from human, human cancer, and bacterial pathogen Pseudomonas aeruginosa. SOPHIE identifies common DEGs in concordance with previously described, manually and systematically determined common DEGs. Further molecular validation indicates that SOPHIE detects highly specific but low-magnitude biologically relevant transcriptional changes. SOPHIE's measure of specificity can complement log2 fold change values generated from traditional DE analyses. For example, by filtering the set of DEGs, one can identify genes that are specifically relevant to the experimental condition of interest. Consequently, these results can inform future research directions. All scripts used in these analyses are available at https://github.com/greenelab/generic-expression-patterns. Users can access https://github.com/greenelab/sophie to run SOPHIE on their own data.

Hetnet connectivity search provides rapid insights into how biomedical entities are related.

BACKGROUND: Hetnets, short for "heterogeneous networks," contain multiple node and relationship types and offer a way to encode biomedical knowledge. One such example, Hetionet, connects 11 types of nodes-including genes, diseases, drugs, pathways, and anatomical structures-with over 2 million edges of 24 types. Previous work has demonstrated that supervised machine learning methods applied to such networks can identify drug repurposing opportunities. However, a training set of known relationships does not exist for many types of node pairs, even when it would be useful to examine how nodes of those types are meaningfully connected. For example, users may be curious about not only how metformin is related to breast cancer but also how a given gene might be involved in insomnia. FINDINGS: We developed a new procedure, termed hetnet connectivity search, that proposes important paths between any 2 nodes without requiring a supervised gold standard. The algorithm behind connectivity search identifies types of paths that occur more frequently than would be expected by chance (based on node degree alone). Several optimizations were required to precompute significant instances of node connectivity at the scale of large knowledge graphs. CONCLUSION: We implemented the method on Hetionet and provide an online interface at https://het.io/search. We provide an open-source implementation of these methods in our new Python package named hetmatpy.

High retreatability and dimensional stability of polymer grafted waterlogged archaeological wood achieved by ARGET ATRP.

To explore new methods to maintain the dimensional stability of waterlogged archaeological wood after drying and keep the natural cell lumens unaltered for future retreatments, activator regenerated by electron transfer (ARGET) atom transfer radical polymerization (ATRP) is employed to consolidate archaeological wood. To prepare the ATRP process, the waterlogged archaeological wood samples (Pinus massoniana with maximum moisture content of around 529%) were first modified by 2-bromoisobutyryl bromide in CH2Cl2 to acquire C-Br bonds as initiators. Then, butyl methacrylate or styrene was polymerized to the remaining cell walls with catalyst (CuBr2), reductant (ascorbic acid) and ligand (PMDETA) in ethanol. After the treatment, the samples were washed and naturally dried. The results characterized by microscopy showed that the polymerization only took place within the remaining cell walls, showing no sign of collapse or distortion after air drying, and all natural cell lumens could be retained for future retreatments. Also, anti-shrinkage efficiencies as high as 87.8% for the wood sample grafted with polystyrene and 98.5% for the wood sample grafted with polybutylmethacrylate were obtained from the treatment described in this paper, indicating modification of grafting polymer through ARGET ATRP can help maintain the dimensional stability of water archaeological wood effectively.