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AbstractMost bacterial genomes contain integrated bacteriophages—prophages—in various states of decay. Many are active and able to excise from the genome and replicate, while others are cryptic prophages, remnants of their former selves. Over the last two decades, many computational tools have been developed to identify the prophage components of bacterial genomes, and it is a particularly active area for the application of machine learning approaches. However, progress is hindered and comparisons thwarted because there are no manually curated bacterial genomes that can be used to test new prophage prediction algorithms.Here, we present a library of gold-standard bacterial genome annotations that include manually curated prophage annotations, and a computational framework to compare the predictions from different algorithms. We use this suite to compare all extant stand-alone prophage prediction algorithms to identify their strengths and weaknesses.We provide a FAIR dataset for prophage identification, and demonstrate the accuracy, precision, recall, and f1 score from the analysis of seven different algorithms for the prediction of prophages. We discuss caveats and concerns in this analysis and how those concerns may be mitigated.

Early stage technology startups rely critically on talented scientists and engineers to commercialize new technologies. And yet these startups compete with established technology firms to hire the best workers. Theories of ability sorting predict that high-ability workers will choose jobs in established firms that offer greater complementary assets and higher pay, leaving low-ability workers to take lower paying and riskier jobs in startups. We propose an alternative view in which heterogeneity in both worker ability and preferences enable startups to hire talented workers who have a taste for a startup work environment even at lower pay. Using a longitudinal survey that follows 2,394 science and engineering PhDs from graduate school into their first industrial employment, we overcome common empirical challenges by observing ability and stated preferences prior to entry into the labor market. We find that both ability and career preferences strongly predict startup employment with high-ability workers who prefer startup employment being the most likely to work in a startup. We show that this partly reflects dual selection effects whereby worker preferences result in a large pool of startup job applicants and startups make job offers to the most talented workers. Additional analyses confirm that startup employees earn approximately 17% lower pay. This gap is greatest for high-ability workers and persists over workers' early careers, suggesting that they accept a negative compensating differential in exchange for the nonpecuniary benefits of startup employment. Data on job attributes and stated reasons for job choice further support this interpretation. This paper was accepted by Toby Stuart, entrepreneurship and innovation. Funding: This work was supported by the National Science Foundation SciSIP Award [Grant 1262270] and the Ewing Marion Kauffman Foundation (Junior Faculty Fellowship). Supplemental Material: The online appendix and data are available at https://doi.org/10.1287/mnsc.2023.4868. [ABSTRACT FROM AUTHOR]

Titanium has a long history of clinical use, but the naturally forming oxide is not ideal for bacterial resistance. Anodization processes can modify the crystallinity, surface topography, and surface chemistry of titanium oxides. Anatase, rutile, and mixed phase oxides are known to exhibit photocatalytic activity (PCA)-driven bacterial resistance under UVA irradiation. Silver additions are reported to enhance PCA and reduce bacterial attachment. This study investigated the effects of silver-doping additions to three established anodization processes. Silver doping showed no significant influence on oxide crystallinity, surface topography, or surface wettability. Oxides from a sulfuric acid anodization process exhibited significantly enhanced PCA after silver doping, but silver-doped oxides produced from phosphoric-acid-containing electrolytes did not. Staphylococcus aureus attachment was also assessed under dark and UVA-irradiated conditions on each oxide. Each oxide exhibited a photocatalytic antimicrobial effect as indicated by significantly decreased bacterial attachment under UVA irradiation compared to dark conditions. However, only the phosphorus-doped mixed anatase and rutile phase oxide exhibited an additional significant reduction in bacteria attachment under UVA irradiation as a result of silver doping. The antimicrobial success of this oxide was attributed to the combination of the mixed phase oxide and higher silver-doping uptake levels. [ABSTRACT FROM AUTHOR]