Search for: All records

Standardized protocols for absolute quantification of potato virus Y (PVY) from potato tissue is critical for host-virus dynamic studies. Here, we developed a standardized protocol using a cloned viral sequence as standards to detect and quantify PVY. Starting with total RNA, concentrated via column-based kit, this protocol is able to detect approximately 50 viral copies/reaction from multiple PVY strains. Validation of this protocol confirmed linearity across 8 orders of magnitude with high repeatability, reproducibility and statistical robustness across three independent runs. This protocol offers reliable PVY quantification to manage potato crop health and enables comparative studies with other viral systems. 

Sepsis is a dysregulated host response to infection with high mortality and morbidity. Early detection and intervention have been shown to improve patient outcomes, but existing computational models relying on structured electronic health record data often miss contextual information from unstructured clinical notes. This study introduces COMPOSER-LLM, an open-source large language model (LLM) integrated with the COMPOSER model to enhance early sepsis prediction. For high-uncertainty predictions, the LLM extracts additional context to assess sepsis-mimics, improving accuracy. Evaluated on 2500 patient encounters, COMPOSER-LLM achieved a sensitivity of 72.1%, positive predictive value of 52.9%, F-1 score of 61.0%, and 0.0087 false alarms per patient hour, outperforming the standalone COMPOSER model. Prospective validation yielded similar results. Manual chart review found 62% of false positives had bacterial infections, demonstrating potential clinical utility. Our findings suggest that integrating LLMs with traditional models can enhance predictive performance by leveraging unstructured data, representing a significant advance in healthcare analytics. 

The slow rebound of public transit ridership since the pandemic and major upcoming budget shortfalls have created a perfect storm in which American cities and transit agencies must make difficult decisions regarding operations and service design. Among the many challenges, perceived rider safety has emerged as a key concern. However, implementing effective safety interventions is complicated by the mixed rider experiences with, and perceptions of, crime and law enforcement. Transit agencies can design more effective policy interventions if they understand what shapes riders’ reactions to different safety strategies, and how those strategies can promote rider satisfaction. Using a 2023 survey of 2292 transit riders in the Chicago region, we estimate a Bayesian Structural Equation Model to investigate the connections between rider experiences and demographics, receptiveness to safety measures, and overall satisfaction. We find that enforcement-related strategies are most strongly associated with higher overall rider satisfaction, but they also come with the notable downside of 10%–20% of riders feeling less safe. On the other hand, improvements to various facets of service quality are less strongly related to satisfaction, but they come with little to no downside in terms of negative rider perceptions. Rider experience also plays a role, with more severe crime and nuisance experience directly impacting satisfaction. In contrast, indirect knowledge of transit safety issues obtained from media and hearsay primarily affects riders’ support for safety interventions rather than their overall satisfaction. 

Shifts in agricultural land use over the past 200 years have led to a loss of nearly 50% of existing wetlands in the USA, and agricultural activities contribute up to 65% of the nutrients that reach the Mississippi River Basin, directly contributing to biological disasters such as the hypoxic Gulf of Mexico “Dead” Zone. Federal efforts to construct and restore wetland habitats have been employed to mitigate the detrimental effects of eutrophication, with an emphasis on the restoration of ecosystem services such as nutrient cycling and retention. Soil microbial assemblages drive biogeochemical cycles and offer a unique and sensitive framework for the accurate evaluation, restoration, and management of ecosystem services. The purpose of this study was to elucidate patterns of soil bacteria within and among wetlands by developing diversity profiles from high-throughput sequencing data, link functional gene copy number of nitrogen cycling genes to measured nutrient flux rates collected from flow-through incubation cores, and predict nutrient flux using microbial assemblage composition. Soil microbial assemblages showed fine-scale turnover in soil cores collected across the topsoil horizon (0–5 cm; top vs bottom partitions) and were structured by restoration practices on the easements (tree planting, shallow water, remnant forest). Connections between soil assemblage composition, functional gene copy number, and nutrient flux rates show the potential for soil bacterial assemblages to be used as bioindicators for nutrient cycling on the landscape. In addition, the predictive accuracy of flux rates was improved when implementing deep learning models that paired connected samples across time. 

This repository contains synthetic and real datasets capturing memory, CPU, process, network, and file activity on SPHERE research infrastructure during malicious and legitimate use patterns. 

The Deep Space Quantum Link (DSQL) is a space-mission concept that aims to explore the interplay between general relativity and quantum mechanics using quantum optical interferometry. This mission concept was formally presented to the United States National Academy of Science Decadal Survey as a research campaign for Fundamental Physics in 2022. Since then, advances have been made in the space-based quantum optical technologies required to conduct a DSQL-type mission. In addition, other research efforts have defined alternative measurement concepts to explore the same scientific questions motivating the DSQL mission. This paper serves as an update to the community on the status of the DSQL mission concept and related research and technology development efforts.