Search for: All records

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. 

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. 

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. 

Generative AI (genAI) tools, such as ChatGPT or Copilot, are advertised to improve developer productivity and are being integrated into software development. However, misaligned trust, skepticism, and usability concerns can impede the adoption of such tools. Research also indicates that AI can be exclusionary, failing to support diverse users adequately. One such aspect of diversity is cognitive diversity -- variations in users' cognitive styles -- that leads to divergence in perspectives and interaction styles. When an individual's cognitive style is unsupported, it creates barriers to technology adoption. Therefore, to understand how to effectively integrate genAI tools into software development, it is first important to model what factors affect developers' trust and intentions to adopt genAI tools in practice? We developed a theoretically grounded statistical model to (1) identify factors that influence developers' trust in genAI tools and (2) examine the relationship between developers' trust, cognitive styles, and their intentions to use these tools in their work. We surveyed software developers (N=238) at two major global tech organizations: GitHub Inc. and Microsoft; and employed Partial Least Squares-Structural Equation Modeling (PLS-SEM) to evaluate our model. Our findings reveal that genAI's system/output quality, functional value, and goal maintenance significantly influence developers' trust in these tools. Furthermore, developers' trust and cognitive styles influence their intentions to use these tools in their work. We offer practical suggestions for designing genAI tools for effective use and inclusive user experience. 

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.