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Abstract Research indicates that human-caused mortality (HCM) is a key factor limiting numerous large carnivore populations. However, efforts to represent HCM in spatially explicit models have generally been limited in scope—often relying on proxies, such as road or human density. Yet such efforts fail to distinguish different sources of HCM, which can arise from different antecedent processes. We offer a systems-based conceptual framework for understanding the antecedents of HCMs that is grounded in theory from the social and behavioral sciences. Specifically, we first explain how HCMs are usefully distinguished into four types (e.g., accidental, harvest, illicit, control actions), then discuss how these different types tend to be driven by different sets of psychological and sociopolitical processes. We contend that improvements in understanding the spatial variation in HCMs would rise from more explicit attention to the various antecedent processes that precede each mortality type. 

Salmonella enterica serovar Typhimurium must adapt to rapid environmental shifts, including those encountered upon entry and during replication to survive within macrophages during pathogenesis. Despite extensive RNA-seq-based investigations, questions remain regarding the range, timing and magnitude of response dynamics. Here we constructed a comprehensive GFP-reporter strain library representing 2,901 computationally identified Salmonella promoter regions to study time-resolved Salmonella transcriptional responses. Promoter activity was measured during in vitro growth and during intracellular infection of RAW 264.7 macrophages. Using bulk measurements and single-cell imaging, we uncovered condition-specific transcriptional regulation and population-level heterogeneity in SPI2-related promoter activity. We also discovered previously unidentified transcriptional activity from 234 promoters. These analyses revealed metabolic shifts including requirements for mntS expression to support manganese homeostasis and expression of Entner–Doudoroff pathway-associated genes to support growth within macrophages. Our library and datasets, made available through the online tool SalComKinetics, provide resources for systems-level interrogation of Salmonella transcriptional dynamics. 

AI has entered the public consciousness through generative AI’s impact on work—enhancing efficiency and automating tasks—but it has also driven innovation in education and personalized learning. Still, while AI promises benefits, it also poses risks—from hallucinating false outputs to reinforcing biases and diminishing critical thinking. With the AI education market expected to grow substantially, ethical concerns about the technology’s misuse—AI tools have already falsely accused marginalized students of cheating—are mounting, highlighting the need for responsible creation and deployment. Addressing these challenges requires both technical literacy and critical engagement with AI’s societal impact. Expanding AI expertise must begin in K–12 and higher education in order to ensure that students are prepared to be responsible users and developers. AI education cannot exist in isolation—it must align with broader computer science (CS) education efforts. This chapter examines the global state of AI and CS education, access disparities, and policies shaping AI’s role in learning. This chapter was a collaboration prepared by the Kapor Foundation, CSTA, PIT-UN and the AI Index. The Kapor Foundation works at the intersection of racial equity and technology to build equitable and inclusive computing education pathways, advance tech policies that mitigate harms and promote equitable opportunity, and deploy capital to support responsible, ethical, and equitable tech solutions. The CSTA is a global membership organization that unites, supports, and empowers educators to enhance the quality, accessibility, and inclusivity of computer science education. The Public Interest Technology University Network (PIT-UN) fosters collaboration between universities and colleges to build the PIT field and nurture a new generation of civic-minded technologists. 

This mixed-methods study examined the role of belonging and flourishing in the college experiences of undergraduate students from communities historically underrepresented in science, technology, engineering, and mathematics (STEM) majors. Qualitative findings show that students engaged in strategies to find and develop peer relationships to facilitate their sense of belonging in their STEM major/discipline. Findings from a larger quantitative dataset of undergraduate students reveal an important relationship between sense of belonging in an academic domain and flourishing. Data underscores the critical role of belonging, including feelings of acceptance and membership (e.g. feeling inside the community of one's STEM major), and the potential that students who feel they belong in their majors are more likely to also report thriving in their discipline. 

Exposure to environmental contaminants can result in profound effects on the host immune system. One class of environmental toxicants, known as dioxins, are persistent environmental contaminants termed “forever chemicals”. The archetype toxicant from this group of chemicals is 2, 3, 7, 8-tetrachlorodibenzo-p-dioxin (TCDD), an immunotoxicant that activates the aryl-hydrocarbon receptor pathway leading to a variety of changes in immune cell responses. Immune cell functions are crucial to the development and maintenance of healthy reproduction. Immune cells facilitate tolerance between at the maternal-fetal interface between the parent and the semi-allogenic fetus and help defend the gravid reproductive tract from infectious assault. Epidemiological studies reveal that exposure to environmental contaminants (such as TCDD) are linked to adverse reproductive health outcomes including endometriosis, placental inflammation, and preterm birth. However, little is known about the molecular mechanisms that underpin h 

Abstract Metagenomics provides insights into the potential of microorganisms to mediate key biogeochemical processes encoded in ecosystem models. Efforts have been made to model gene abundance changes, but it is unclear how much gene abundance variation can be explained by modeled biogeochemical rates alone. We compare the relative abundance of 32 genes having the potential for photosynthesis, nitrification, denitrification, and sulfur cycling with rates predicted by a model in the Chesapeake Bay. Modeled rates explained a significant amount of gene abundance variation for half of the genes examined and at least one gene involved in four of five processes examined. An average of 21.3% of gene abundance variability is explained by the modeled rates, which increases to 31.8% when considering the 16 genes with significant relationships. For photosynthesis and denitrification, rates represent the behavior of some taxonomic groups (cyanobacteria and gammaproteobacteria) better than others (eukaryotic algae and Bacteroidetes). Significant correlations between sulfur cycling rates and genes appear for oxidative but not reductive forms of the relevant genes. The marker genesamoABwere not significantly correlated with nitrification rates. However, another gene involved in nitrification but not considered a marker gene (hao) was significantly correlated. This work demonstrates modeled rates often but not always and capture a significant amount variation of genes encoding enzymes involved in the modeled processes. Other factors, like temperature‐dependent rates or cell transport, may need to be incorporated into models to explain more variation in gene abundance. Doing so could be a useful quality control for microbial processes encoded in ecosystem‐level biogeochemical models.