Food production environments in low- and middle-income countries (LMICs) are recognized as posing significant and increasing risks to antimicrobial resistance (AMR), one of the greatest threats to global public health and food security systems. In order to maximize and expedite action in mitigating AMR, the World Bank and AMR Global Leaders Group have recommended that AMR is integrated into wider sustainable development strategies. Thus, there is an urgent need for tools to support decision makers in unravelling the complex social and environmental factors driving AMR in LMIC food-producing environments and in demonstrating meaningful connectivity with other sustainable development issues. Here, we applied the Driver-Pressure-State-Impact-Response (DPSIR) conceptual framework to an aquaculture case study site in rural Bangladesh, through the analysis of distinct social, microbiological, and metagenomic data sets. We show how the DPSIR framework supports the integration of these diverse data sets, first to systematically characterize the complex network of societal drivers of AMR in these environments and second to delineate the connectivity between AMR and wider sustainable development issues. Our study illustrates the complexity and challenges of addressing AMR in rural aquaculture environments and supports efforts to implement global policy aimed at mitigating AMR in aquaculture and other rural LMIC food-producing environments.
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http://dx.doi.org/10.1021/acs.est.2c00799 | DOI Listing |
Sci Rep
December 2024
Department of Architecture, Rafsanjan Branch, Islamic Azad University, Rafsanjan, Iran.
The advent of smart cities has brought about a paradigm shift in urban management and citizen engagement. By leveraging technological advancements, cities are now able to collect and analyze extensive data to optimize service delivery, allocate resources efficiently, and enhance the overall well-being of residents. However, as cities become increasingly interconnected and data-dependent, concerns related to data privacy and security, as well as citizen participation and representation, have surfaced.
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December 2024
School of Civil & Environmental Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA.
Per- and polyfluoroalkyl substances (PFASs) have recently garnered considerable concerns regarding their impacts on human and ecological health. Despite the important roles of polyamide membranes in remediating PFASs-contaminated water, the governing factors influencing PFAS transport across these membranes remain elusive. In this study, we investigate PFAS rejection by polyamide membranes using two machine learning (ML) models, namely XGBoost and multimodal transformer models.
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December 2024
Bioinformatics Laboratory, College of Computing, University Mohammed VI Polytechnic, Ben Guerir, Morocco.
Hepatitis C virus (HCV) presents a significant global health issue due to its widespread prevalence and the absence of a reliable vaccine for prevention. While significant progress has been achieved in therapeutic interventions since the disease was first identified, its resurgence underscores the need for innovative strategies to combat it. The nonstructural protein NS5A is crucial in the life cycle of the HCV, serving as a significant factor in both viral replication and assembly processes.
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December 2024
Department of Movement and Sport Sciences, Faculty of Physical Education and Physiotherapy, Vrije Universiteit Brussel, Pleinlaan 2, 1050, Brussels, Belgium.
The transition from secondary school to college or university is a well-known and well-studied risk period for weight and/or fat gain and not meeting the dietary recommendations. Higher education acts as a promising setting to implement nutrition interventions. An important condition for intervention success is that interventions are implemented as intended by the protocol and integrated in the institutional policy.
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December 2024
Institute of Soil and Water Conservation, Northwest A&F University, Yangling, 712100, Shaanxi, China.
Reservoir-operation optimisation is a crucial aspect of water-resource development and sustainable water process management. This study addresses bi-objective optimisation problems by proposing a novel crossover evolution operator, known as the hybrid simulated binary and improved arithmetic crossover (SBAX) operator, based on the simulated binary cross (SBX) and arithmetic crossover operators, and applies it to the Non-dominated Sorting Genetic Algorithms-II (NSGA-II) algorithm to improve the algorithm. In particular, the arithmetic crossover operator can obtain an optimal solution more precisely within the solution space, whereas the SBX operator can explore a broader range of potential high-quality solutions.
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