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Abstract Observational studies of Hiiregion–molecular cloud interactions constrain models of feedback and quantify its impact on the surrounding environment. A recent hypothesis proposes that a characteristic spectral signature in ground state hyperfine lines of hydroxyl (OH)—the OH flip—may trace gas that is dynamically interacting with an expanding Hiiregion, offering a new means of probing such interactions. We explore this hypothesis using dedicated Jansky Very Large Array observations of three Galactic Hiiregions, G049.205−0.343, G034.256+0.145, and G024.471+0.492, in 1–2 GHz continuum emission, all four 18 cm ground-state OH lines, and multiple hydrogen radio recombination lines. A Gaussian decomposition of the molecular gas data reveals complex OH emission and absorption across our targets. We detect the OH flip toward two of our sources, G049.205−0.343 and G034.256+0.145, finding agreement between key predictions of the flip hypothesis and the observed multiwavelength spectra, kinematics, and morphology. Specifically, we demonstrate a strong spatial and kinematic association between the OH flip and the ionized gas of the Hiiregions—the first time this has been demonstrated for resolved sources—and evidence from¹³CO(1–0) data that the expected OH component originates from the nondisturbed gas of the parent cloud. While we detect no flip in G024.471+0.492, we do find evidence of interacting molecular gas traced by OH, providing further support for OH’s ability to trace Hiiregion–molecular cloud interactions. 

Novice programming students frequently engage in help-seeking to find information and learn about programming concepts. Among the available resources, generative AI (GenAI) chatbots appear resourceful, widely accessible, and less intimidating than human tutors. Programming instructors are actively integrating these tools into classrooms. However, our understanding of how novice programming students trust GenAI chatbots-and the factors influencing their usage-remains limited. To address this gap, we investigated the learning resource selection process of 20 novice programming students tasked with studying a programming topic. We split our participants into two groups: one using ChatGPT (n=10) and the other using a human tutor via Discord (n=10). We found that participants held strong positive perceptions of ChatGPT's speed and convenience but were wary of its inconsistent accuracy, making them reluctant to rely on it for learning entirely new topics. Accordingly, they generally preferred more trustworthy resources for learning (e.g., instructors, tutors), preferring ChatGPT for low-stakes situations or more introductory and common topics. We conclude by offering guidance to instructors on integrating LLM-based chatbots into their curricula-emphasizing verification and situational use-and to developers on designing chatbots that better address novices' trust and reliability concerns. 

Abstract This research aims to develop chitosan-zein protein films supplemented withBergenia ciliata(Bc) extract, a traditionally important medical herb of Himalayan origin. The film’s physicochemical, mechanical, antioxidant, and antimicrobial properties were systematically explored. The opacity of chitosan film increased from 2.42 ± 0.97 to 10.32 ± 1.44 upon introducing zein (Z) protein in chitosan (Cs) in a 1:2 ratio (w/w); conferring enhanced UV-blocking attributes. IncorporatingB. ciliataextracts in the chitosan-zein film (Cs-Z-Bc) under optimized conditions further increased the opacity to 16.27 ± 1.03 without compromising the tensile strength. The α-diphenyl-β-picrylhydrazyl scavenging activity of the Cs-Z-Bc film was found to be 97.07 ± 1.09%. Additionally, these optimized films displayed significant antimicrobial efficacy, with zones of inhibition of 11.4 mm measured for gram-positive strains likeC. subtilisandS. aureusand 11.2 mm and 11.1 mm forE. coliandK. pneumoniae(gram-negative) bacterial strains. The film also showed excellent biodegradable properties. The shelf life study of Himalayan cheese was significantly increased when wrapped with the film. These findings suggested thatB. ciliataextract-fortified chitosan-zein films can be an excellent food packaging material. 

Abstract Efficiency is essential to support responsiveness w.r.t. ever-growing datasets, especially for Deep Learning (DL) systems. DL frameworks have traditionally embraceddeferredexecution-style DL code—supporting symbolic, graph-based Deep Neural Network (DNN) computation. While scalable, such development is error-prone, non-intuitive, and difficult to debug. Consequently, more natural, imperative DL frameworks encouragingeagerexecution have emerged but at the expense of run-time performance. Though hybrid approaches aim for the “best of both worlds,” using them effectively requires subtle considerations to make code amenable to safe, accurate, and efficient graph execution—avoiding performance bottlenecks and semantically inequivalent results. We discuss the engineering aspects of a refactoring tool that automatically determines when it is safe and potentially advantageous to migrate imperative DL code to graph execution and vice-versa. 

Generative AI (genAI) tools (e.g., ChatGPT, Copilot) have become ubiquitous in software engineering (SE). As SE educators, it behooves us to understand the consequences of genAI usage among SE students and to create a holistic view of where these tools can be successfully used. Through 16 reflective interviews with SE students, we explored their academic experiences of using genAI tools to complement SE learning and implementations. We uncover the contexts where these tools are helpful and where they pose challenges, along with examining why these challenges arise and how they impact students. We validated our findings through member checking and triangulation with instructors. Our findings provide practical considerations of where and why genAI should (not) be used in the context of supporting SE students. 

Molecular chaperones play a central role in maintaining protein homeostasis. The highly conserved Hsp70 family of chaperones have major functions in folding of nascent peptides, protein refolding, and protein aggregate disassembly. In yeast, loss of two Hsp70 proteins, Ssa1 and Ssa2, is associated with decreased cellular growth and shortened lifespan. While heterologous or mutant temperature-sensitive proteins form anomalous large cytoplasmic inclusions in ssa1Δssa2Δ strains, it is unclear how endogenous wild-type proteins behave and are regulated in the presence of limiting Hsp70s. Using the wild-type yeast Poly A binding protein (Pab1), which is involved in mRNA binding and forms stress granules (SGs) upon heat shock, Pab1 forms large inclusions in approximately half of ssa1Δssa2Δ cells in the absence of stress. Overexpression of Ssa1, Hsp104, and Sis1 almost completely limits the formation of these large inclusions in ssa1Δssa2Δ, suggesting that excess Ssa1, Hsp104, and Sis1 can each compensate for the lower levels of Ssa proteins. Upon heat shock, SGs also form in cells whether large Pab1 inclusions are present or not. Surprisingly, cells containing only SGs disassemble faster than wild type, whereas cells with both large inclusions disassemble slower albeit completely. We suspect that disassembly of these large inclusions is linked to the elevated heat shock response and elevated Hsp104 and Sis1 levels in ssa1Δssa2Δ strains. We also observed that wild-type cultures grown to saturation also form large Pab1-GFP inclusions. These inclusions can be partially rescued by overexpression of Ssa1. Taken together, our data suggest that Hsp70 not only plays a role in limiting unwanted protein aggregation in normal cells, but as cells age, the depletion of active Hsp70 possibly underlies the age-related aggregation of endogenous proteins. 

Local governments around the world face mounting pressures that affect their provision of public services. To prepare for and respond to stressors and shocks, local service providers can choose from among a wide variety of actions. The adaptive actions they choose will influence which risks are addressed, when, and how. Selection of adaptive actions can also have long-term implications, if actions affect future options for adaptation. This research investigates the influence of institutions on selection of adaptive actions by local public service providers as they seek to respond to climatic stressors. Drawing on insights from focus groups with local drinking water utilities across the USA, the research identifies five institutional dependencies that affect the selection of adaptive actions and examines the pathways through which those institutional dependencies influence decision-making. These pathways are then combined to present a conceptual model of factors shaping selection of adaptive actions. Findings indicate that the polycentric institutional environment in which local service providers are embedded limits control over adaptation decisions, can constrain the set of feasible actions, and can add substantial transaction costs. As a result, selection of adaptive actions includes consideration of the effect of institutional dependencies on the feasibility and ease of implementation. 

Abstract Grasslands cover approximately a third of the Earth’s land surface and account for about a third of terrestrial carbon storage. Yet, we lack strong predictive models of grassland plant biomass, the primary source of carbon in grasslands. This lack of predictive ability may arise from the assumption of linear relationships between plant biomass and the environment and an underestimation of interactions of environmental variables. Using data from 116 grasslands on six continents, we show unimodal relationships between plant biomass and ecosystem characteristics, such as mean annual precipitation and soil nitrogen. Further, we found that soil nitrogen and plant diversity interacted in their relationships with plant biomass, such that plant diversity and biomass were positively related at low levels of nitrogen and negatively at elevated levels of nitrogen. Our results show that it is critical to account for the interactive and unimodal relationships between plant biomass and several environmental variables to accurately include plant biomass in global vegetation and carbon models.