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The artificial structures can influence wetland topology and sediment properties, thereby shaping plant distribution and composition. Macrobenthos composition was correlated with plant cover. Previous studies on the impact of artificial structures on plant distribution are scarce in incorporating time-series data or extended field surveys. In this study, a machine-learning-based species distribution model with decade-long observation was analyzed to investigate the correlation between the shift in the distribution of B. planiculmis, artificial structure-induced elevation changes and the expansion of other plants, as well as their connection to soil properties and crab composition dynamics under plants in Gaomei Wetland. Long short-term memory model (LSTM) with Shapley additive explanations (SHAP) was employed for predicting the distribution of B. planiculmis and explaining feature importance. The results indicated that wetland topology was influenced by both artificial structures and plants. Areas initially colonized by B. planiculmis were replaced by other species. Soil properties showed significant differences among plant patches; however, principal component analysis (PCA) of sediment properties and niche similarity analysis showed that the niche of plants was overlapped. Crab composition was different under different plants. The presence probability of B. planiculmis near woody paths decreased according to LSTM and field survey data. SHAP analysis suggested that the distribution of other plants, historical distribution of B. planiculmis and sediment properties significantly contributed to the presence probability of B. planiculmis. A sharp decrease in SHAP values with increasing NDVI at suitable elevations, overlap in PCA of sediment properties and niche similarity indicated potential competition among plants. This decade-long time-series field survey revealed the joint effects of artificial structure and vegetation on the topology and soil properties dynamics. These changes influenced the plant distribution through potential plant competition. LSTM with SHAP provided valuable insights in the underlying the mechanisms of artificial structure effects on the plant zonation process.
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http://dx.doi.org/10.1016/j.scitotenv.2024.173426 | DOI Listing |
JMIR Hum Factors
September 2025
Seidenberg School of Computer Science and Information Systems, Pace University, New York City, NY, United States.
Background: As information and communication technologies and artificial intelligence (AI) become deeply integrated into daily life, the focus on users' digital well-being has grown across academic and industrial fields. However, fragmented perspectives and approaches to digital well-being in AI-powered systems hinder a holistic understanding, leaving researchers and practitioners struggling to design truly human-centered AI systems.
Objective: This paper aims to address the fragmentation by synthesizing diverse perspectives and approaches to digital well-being through a systematic literature review.
J Med Microbiol
September 2025
Alberta Precision Laboratories Public Health Lab, Edmonton, Alberta, Canada.
For thousands of years, parasitic infections have represented a constant challenge to human health. Despite constant progress in science and medicine, the challenge has remained mostly unchanged over the years, partly due to the vast complexity of the host-parasite-environment relationships. Over the last century, our approaches to these challenges have evolved through considerable advances in science and technology, offering new and better solutions.
View Article and Find Full Text PDFDiabetologia
September 2025
Department of Diabetology and Internal Medicine, Medical University of Warsaw, Warsaw, Poland.
This review article, developed by the EASD Global Council, addresses the growing global challenges in diabetes research and care, highlighting the rising prevalence of diabetes, the increasing complexity of its management and the need for a coordinated international response. With regard to research, disparities in funding and infrastructure between high-income countries and low- and middle-income countries (LMICs) are discussed. The under-representation of LMIC populations in clinical trials, challenges in conducting large-scale research projects, and the ethical and legal complexities of artificial intelligence integration are also considered as specific issues.
View Article and Find Full Text PDFChaos
September 2025
The Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, School of Internet, Anhui University, Hefei 230601, China.
A captivating challenge in network research is the reconstruction of complex network structures from limited binary-state time series data. Although some reconstruction approaches based on dynamical rules or sparse system of linear equations have been proposed, these approaches either rely on known dynamical rules, limiting their generality, or the system of linear equations is often empirically determined, with weak interpretability and the performance being sensitive to parameter settings. To address these limitations, we propose a network reconstruction method based on linearization grounded in mean-field approximation.
View Article and Find Full Text PDFACS Nano
September 2025
State Key Laboratory of Chemo and Biosensing, College of Chemistry and Chemical Engineering, Hunan University, Changsha 410082, China.
Optical imaging offers high sensitivity and specificity for noninvasive cancer detection, but conventional techniques suffer from limited probe accumulation, tissue autofluorescence, and poor depth resolution. Afterglow luminescence overcomes autofluorescence by emitting persistent light after excitation, yet its utility in vivo remains hindered by weak tumor enrichment and two-dimensional readouts lacking spatial context. Here, we report luminescent-magnetic nanoparticles (LM-NPs) coencapsulating luminescent trianthracene (TA) molecules and iron oxide cores within the amphiphilic polymer pluronic-F127.
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