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1. Fluoride: Good in Toothpaste, Bad for Plants

   https://doi.org/10.3389/frym.2023.853533  

   Tausta, S. Lori; Auslender, Alysha; Strobel, Scott A.; Hiller, David A. (March 2023, Frontiers for Young Minds)

   Fluorine is the 13th-most abundant element on earth, found most often bound to other elements in its negatively charged form, fluoride. Fluoride compounds are used to improve dental health, to make steel, and to make useful materials like Teflon. Fluoride is also emitted into the environment as a byproduct of both natural and industrial processes. Fluoride even contaminates the fertilizer used to help plants grow. In high amounts, fluoride can be toxic. Single-celled organisms like bacteria protect themselves by making a transporter that specifically removes fluoride from the cell. Yeast have a similar transporter called FEX (fluoride exporter). Bacteria and yeast without these transporters die in the presence of the small amount of fluoride found in tap water. Plants are more complicated, but they also use FEX to keep fluoride from building up inside themselves. Plants without FEX can not make new seeds when grown in normal soil. 

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2. Is confidence in one’s knowledge good or bad? An empirical investigation into the behavioral effects of confidence

   Stone, E; Parker, A; Somerville, A; Kemmerly, R; Nixon, B; Bongard, M (February 2023, N/A)
3. Is confidence in one's knowledge good or bad? An empirical investigation into the behavioral effects of confidence

   Stone, E; Parker, A; Somerville, A; Kemmerly, R; Nixon, B; Bongard, M (February 2023, N/A)
4. Bots for pull requests: the good, the bad, and the promising

   https://doi.org/10.1145/3510003.3512765  

   Wessel, Mairieli; Abdellatif, Ahmad; Wiese, Igor; Conte, Tayana; Shihab, Emad; Gerosa, Marco A.; Steinmacher, Igor (May 2022, ICSE '22: Proceedings of the 44th International Conference on Software Engineering)

   Software bots automate tasks within Open Source Software (OSS) projects' pull requests and save reviewing time and effort ("the good"). However, their interactions can be disruptive and noisy and lead to information overload ("the bad"). To identify strategies to overcome such problems, we applied Design Fiction as a participatory method with 32 practitioners. We elicited 22 design strategies for a bot mediator or the pull request user interface ("the promising"). Participants envisioned a separate place in the pull request interface for bot interactions and a bot mediator that can summarize and customize other bots' actions to mitigate noise. We also collected participants' perceptions about a prototype implementing the envisioned strategies. Our design strategies can guide the development of future bots and social coding platforms. 

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5. Computational host range prediction—The good, the bad, and the ugly

   https://doi.org/10.1093/ve/vead083  

   Howell, Abigail A.; Versoza, Cyril J.; Pfeifer, Susanne P. (December 2023, Virus Evolution)

   Abstract The rapid emergence and spread of antimicrobial resistance across the globe have prompted the usage of bacteriophages (i.e. viruses that infect bacteria) in a variety of applications ranging from agriculture to biotechnology and medicine. In order to effectively guide the application of bacteriophages in these multifaceted areas, information about their host ranges—that is the bacterial strains or species that a bacteriophage can successfully infect and kill—is essential. Utilizing sixteen broad-spectrum (polyvalent) bacteriophages with experimentally validated host ranges, we here benchmark the performance of eleven recently developed computational host range prediction tools that provide a promising and highly scalable supplement to traditional, but laborious, experimental procedures. We show that machine- and deep-learning approaches offer the highest levels of accuracy and precision—however, their predominant predictions at the species- or genus-level render them ill-suited for applications outside of an ecosystems metagenomics framework. In contrast, only moderate sensitivity (<80 per cent) could be reached at the strain-level, albeit at low levels of precision (<40 per cent). Taken together, these limitations demonstrate that there remains room for improvement in the active scientific field of in silico host prediction to combat the challenge of guiding experimental designs to identify the most promising bacteriophage candidates for any given application. 

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