Using Large Language Models to Assist Antimicrobial Resistance Policy Development: Integrating the Environment into Health Protection Planning.

Increasing antimicrobial resistance (AMR) poses a substantial threat to global health and economies, which has led many countries and regions to develop AMR National Action Plans (NAPs). However, inadequate logistical capacity, funding, and essential information can hinder NAP policymaking, especially in low-to-middle-income countries (LMICs). Therefore, major gaps exist between aspirations and actions, such as fully operationalized environmental AMR surveillance programs in NAPs.

Prediction of Bandgap in Lithium-Ion Battery Materials Based on Explainable Boosting Machine Learning Techniques.

The bandgap is a critical factor influencing the energy density of batteries and a key physical quantity that determines the semiconducting behavior of materials. To further improve the prediction accuracy of the bandgap in silicon oxide lithium-ion battery materials, a boosting machine learning model was established to predict the material's bandgap. The optimal model, AdaBoost, was selected, and the SHapley Additive exPlanations (SHAP) method was used to quantitatively analyze the importance of different input features in relation to the model's prediction accuracy.

Fertilization regulates global thresholds in soil bacteria.

Global patterns in soil microbiomes are driven by non-linear environmental thresholds. Fertilization is known to shape the soil microbiome of terrestrial ecosystems worldwide. Yet, whether fertilization influences global thresholds in soil microbiomes remains virtually unknown.

Wastewater Discharge Transports Riverine Microplastics over Long Distances.

Wastewater discharge from wastewater treatment plants continuously pumps microplastics into rivers, yet their transport distances within these waterways remain unknown. Herein, we developed a conceptual framework by synthesizing the microplastic data from the Yangtze River Basin to evaluate its transport distances, quantifying a significant spatial dependence between large-scale wastewater discharge and riverine microplastics ( < 0.05).

Distance-decay equations of antibiotic resistance genes across freshwater reservoirs.

Distance-decay (DD) equations can discern the biogeographical pattern of organisms and genes in a better way with advanced statistical methods. Here, we developed a data Compilation, Arrangement, and Statistics framework to advance quantile regression (QR) into the generation of DD equations for antibiotic resistance genes (ARGs) across various spatial scales using freshwater reservoirs as an illustration. We found that QR is superior at explaining dissemination potential of ARGs to the traditionally used least squares regression (LSR).

Microplastic accumulation in groundwater: Data-scaled insights and future research.

Given that microplastics (MPs) in groundwater have been concerned for risks to humans and ecosystems with increased publications, a Contrasting Analysis of Scales (CAS) approach is developed by this study to synthesize all existing data into a hierarchical understanding of MP accumulation in groundwater. Within the full data of 386 compiled samples, the median abundance of MPs in Open Groundwater (OG) and Closed Groundwater (CG) were 4.4 and 2.

Significant regional disparities in riverine microplastics.

Research on riverine microplastics has gradually increased, highlighting an area for further exploration: the lack of extensive, large-scale regional variations analysis due to methodological and spatiotemporal limitations. Herein, we constructed and applied a comprehensive framework for synthesizing and analyzing literature data on riverine microplastics to enable comparative research on the regional variations on a large scale. Research results showed that in 76 rivers primarily located in Asia, Europe, and North America, the microplastic abundance of surface water in Asian rivers was three times higher than that in Euro-America rivers, while sediment in Euro-American rivers was five times more microplastics than Asia rivers, indicating significant regional variations (p < 0.

Data-driven discoveries on widespread contamination of freshwater reservoirs by dominant antibiotic resistance genes.

The propagation of antibiotic resistance genes (ARGs) in freshwater reservoirs threatens ecosystem security and human health, and has attracted increasing attention. A series of recent research articles on ARGs provides a unique opportunity for data-driven discoveries in this emerging field. Here, we mined data from a total of 290 samples from 60 reservoirs worldwide with a data-driven framework (DD) developed to discover geographical distribution, influencing factors and pollution hotspots of ARGs in freshwater reservoirs.

Widespread occurrence of microplastics in marine bays with diverse drivers and environmental risk.

Microplastic contamination in the sediment of marine bays has attracted widespread attention, whereas the distribution, sedimentation, morphology and risk of microplastics at regional scale remain poorly understood. By introducing a data mining framework into microplastic research, we compiled a microplastic dataset of 649 samples from 24 bays to enhance the understanding of geographical difference and drivers, transfer, composition profile and environmental risk of sedimental microplastics. Microplastic abundance varied from 0.

A Study on the Application of Distributed System Technology-Guided Machine Learning in Malware Detection.

In recent years, with the development of information technology, the Internet has become an essential tool for human daily life. However, as the popularity and scale of the Internet continue to expand, malware has also emerged as an increasingly widespread trend, and its development has brought many negative impacts to the society. As the number of types of malware is getting enormous, the attacks are constantly updated, and at the same time, the spread is very fast, causing more and more damage to the network, the requirements and standards for malware detection are constantly rising.

Global meta-analysis of microplastic contamination in reservoirs with a novel framework.

Microplastic contamination in reservoirs is receiving increasing attention worldwide. However, a holistic understanding of the occurrence, drivers, and potential risks of microplastics in reservoirs is lacking. Building on a systematic review and meta-analysis of 30 existing publications, we construct a global microplastic dataset consisting of 440 collected samples from 43 reservoirs worldwide which we analyze through a framework of Data processing and Multivariate statistics (DM).

Linking reservoir ecosystems research to the sustainable development goals.

Reservoirs account for about 10% of the freshwater stored in lakes worldwide. These reservoirs are home to 'reservoir ecosystems', that is, the aquatic and non-aquatic interactive ecosystems associated with artificial lakes where water is stored, typically behind a dam, for human purposes. While reservoir ecosystems provide various ecosystem services for sustainable development, their significance in research and policy has not been well understood and not well defined in the 2030 United Nation's (UN) Agenda for Sustainable Development.