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As generative AI technologies proliferate across higher education, many U.S. universities are still developing institutional policies to address their ethical, pedagogical, and accessibility implications. This posIT column critically examines AI policies and resources at 50 four year universities—one from each U.S. state—to assess alignment with the Association of Research Libraries’ (ARL) Guiding Principles for Artificial Intelligence. Through content analysis of LibGuides, AI taskforce membership, campus events, and public-facing policies, the study reveals widespread adoption of AI resources but a significant lack of clarity, consistency, and librarian involvement in policy development. While most institutions meet baseline criteria related to privacy, plagiarism, and algorithmic transparency, fewer address AI’s potential harms to marginalized communities or its impact on accessibility for students with disabilities. Notably, fewer than half of the AI taskforces surveyed included library staff, despite librarians’ expertise in digital literacy and ethical information use. This column urges academic librarians to actively seek leadership roles in institutional AI governance to help shape inclusive, responsible, and human-centered AI policy frameworks. 

Abstract Among the animals on this planet, dogs are uniquely adapted for life with humans, a status that exposes them to risks of human-mediated traumatic experiences. At the same time, some lineages of dogs have undergone artificial selection for behavioral phenotypes that might increase risk or resilience to stress exposure, providing an opportunity to examine interactions between heritable and acquired traits. In a large-scale study (N = 4,497), English-speaking dog guardians reported on their dogs’ life histories, current living environments, and provided observer ratings of dog behavior using the Canine Behavior Assessment and Research Questionnaire (C-BARQ). Our analysis revealed that adverse experiences in the first six months of life, such as abuse and relinquishment, were significantly associated with increased aggression and fearfulness in adulthood, even when accounting for factors such as acquisition source, sex, and neuter status. Additionally, effects of adversity on fearful and aggressive behavior systematically varied at the breed level, suggesting heritable factors for risk and resilience for developing particular phenotypes. Our findings establish that breed ancestry and individual experience interact to show fear and aggressive behavior in pet dogs, confirming that socioemotional behavior is shaped by gene-environment interactions. 

Background:Minimum exam averages are an essential component to ensuring academic rigor and subsequent licensure in nursing education, yet there is scant evidence to support such practices. Method:Using a descriptive correlational design, nursing faculty at a medium-sized program in the Northeast explored the relationship between establishing a 77% (C+) minimum exam average requirement for the program and licensure exam passage rates between the 2023 cohort intervention group and the 2022 cohort control group. Results:The implementation of an exam average threshold per course produced a statistically significant effect on the National Council Licensure Examination (NCLEX-RN) pass rate (z = −3.481,p< .001) and provided support for the 77% (C+) examination threshold. Conclusion:A minimum exam average policy may relieve faculty of the moral distress associated with course failures, while also safeguarding academic rigor within the undergraduate program and promoting NCLEX-RN readiness and success. 

Root lodging, the agronomic term for plant mechanical failure, causes yield loss in crops, including maize. Brace roots can provide structural support and assist in preventing root lodging. While the mechanics of brace roots (e.g., stiffness and strength) can play a role in their ability to prevent root lodging, there has been limited characterization of individual brace root mechanical properties. Methods to quantify root mechanics can thus be useful for characterizing maize mechanical traits and breeding new varieties with improved root anchorage and lodging resistance. Here, we describe a protocol for evaluating mechanical properties of maize brace roots. Specifically, we outline the steps necessary to perform three-point bend mechanical testing of maize brace roots using an Instron Universal Testing Stand. We describe root preparation, instrument setup, method establishment, testing, and data analysis. While we exemplify the protocol using maize brace roots, the approach can be adapted for assessing the mechanics of other plants or root types. 

We study algebraic varieties associated with the camera resectioning problem. We characterize these resectioning varieties’ multigraded vanishing ideals using Gröbner basis techniques. As an application, we derive and re-interpret celebrated results in geometric computer vision related to camera-point duality. We also clarify some relationships between the classical problems of optimal resectioning and triangulation, state a conjectural formula for the Euclidean distance degree of the resectioning variety, and discuss how this conjecture relates to the recently-resolved multiview conjecture. 

The mechanical properties of individual roots and entire root systems play key roles in essential root functions such as water and nutrient acquisition, defense against soil microorganisms, and plant anchorage. However, relatively few studies have quantified the mechanics (e.g., stiffness and strength) of individual and entire root systems, or explored the link between root mechanics and root functions. This limitation is likely due to a lack of standardized methods for quantifying root mechanical properties, and has created a gap in our understanding of how root mechanical traits contribute to root functions. To date, most of our knowledge comes from studies in maize, where mechanical failure (i.e., root lodging) has detrimental impacts on crop yield. Here, we review the importance of root mechanics for maize production and discuss methods used to measure individual and entire root system mechanics. 

Abstract Spatial voting models are widely used in political science to analyze legislators’ preferences and voting behavior. Traditional models assume that legislators’ ideal points are static across different types of votes. This article extends the Bayesian spatial voting model to incorporate hierarchical Bayesian methods, allowing for the identification of covariates that explain differences in legislators’ ideal points across voting domains. We apply this model to procedural and final passage votes in the U.S. House of Representatives from the 93rd through 113th Congresses. Our findings indicate that legislators in the minority party and those representing moderate constituencies are more likely to exhibit different ideal points between procedural and final passage votes. This research advances the methodology of ideal point estimation by simultaneously scaling ideal points and explaining variation in these points, providing a more nuanced understanding of legislative voting behavior.