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Riverbed sediments have been identified as temporary and long-term accumulation sites for microplastic particles (MPs), but the relocation and retention mechanisms in riverbeds still need to be better understood. In this study, we investigated the depth-specific occurrence and distribution (abundance, type, and size) of MPs in river sediments down to a depth of 100 cm, which had not been previously investigated in riverbeds. In four sediment freeze cores taken for the Main River (Germany), MPs (≥ 100 µm) were detected using two complementary analytical approaches (spectroscopy and thermoanalytical) over the entire depth with an average of 21.7 ± 21.4 MP/kg or 31.5 ± 28.0 mg/kg. Three vertical trends for MP abundance could be derived, fairly constant in top layers (0-30 cm), a decrease in middle layers (30-60 cm), and a strong increase in deep layers (60-100 cm). The dominant polymer types were polyethylene (PE), polypropylene (PP), and polystyrene (PS). Polyethylene terephthalate (PET) and PP were also found in deep layers, albeit with the youngest age of earliest possible occurrence (EPO age of 1973 and 1954). The fraction of smaller-sized MPs (100-500 µm) increased with depth in shallow layers, but the largest MPs (> 1 mm) were detected in deep layers. Based on these findings, we elucidate the relationship between the depth-specific MP distribution and the prevailing processes of MP retention and sediment dynamics in the riverbed. We propose some implications and offer an initial conceptual approach, suggesting the use of microplastics as a potential environmental process tracer for driving riverbed sediment dynamics.
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http://dx.doi.org/10.1007/s11356-024-34094-z | DOI Listing |
Methods
September 2025
School of Computer and Information Engineering, Henan University, Kaifeng, Henan, China; Henan Key Laboratory of Big Data Analysis and Processing, Henan University, Kaifeng, Henan, China. Electronic address:
Genomic selection (GS) is a breeding technique that utilizes genomic markers to predict the genetic potential of crops and animals. This approach holds significant promise for accelerating the improvement of agronomic traits and addressing food security challenges. While traditional breeding methods based on statistical or machine learning techniques have been useful in predicting traits for some crops, they often fail to capture the complex interactions between genotypes and phenotypes.
View Article and Find Full Text PDFJ Neurosci Methods
September 2025
Department of Computer Science and Engineering, IIT (ISM) Dhanbad, Dhanbad, 826004, Jharkhand, India. Electronic address:
Background: Interpretation of motor imagery (MI) in brain-computer interface (BCI) applications is largely driven by the use of electroencephalography (EEG) signals. However, precise classification in stroke patients remains challenging due to variability, non-stationarity, and abnormal EEG patterns.
New Methods: To address these challenges, an integrated architecture is proposed, combining multi-domain feature extraction with evolutionary optimization for enhanced EEG-based MI classification.
PLoS One
September 2025
School of Computer Science, CHART Laboratory, University of Nottingham, Nottingham, United Kingdom.
Background And Objective: Male fertility assessment through sperm morphology analysis remains a critical component of reproductive health evaluation, as abnormal sperm morphology is strongly correlated with reduced fertility rates and poor assisted reproductive technology outcomes. Traditional manual analysis performed by embryologists is time-intensive, subjective, and prone to significant inter-observer variability, with studies reporting up to 40% disagreement between expert evaluators. This research presents a novel deep learning framework combining Convolutional Block Attention Module (CBAM) with ResNet50 architecture and advanced deep feature engineering (DFE) techniques for automated, objective sperm morphology classification.
View Article and Find Full Text PDFMol Omics
September 2025
Laboratory of Structural Bioinformatics and Computational Biology, Federal University of Rio Grande do Sul, Av. Bento Gonçalves, 9500, Porto Alegre 91501-970, RS, Brazil.
The integration of multimodal single-cell omics data is a state-of-art strategy for deciphering cellular heterogeneity and gene regulatory mechanisms. Recent advances in single-cell technologies have enabled the comprehensive characterization of cellular states and their interactions. However, integrating these high-dimensional and heterogeneous datasets poses significant computational challenges, including batch effects, sparsity, and modality alignment.
View Article and Find Full Text PDFChaos
September 2025
School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, USA.
Although many real-world time series are complex, developing methods that can learn from their behavior effectively enough to enable reliable forecasting remains challenging. Recently, several machine-learning approaches have shown promise in addressing this problem. In particular, the echo state network (ESN) architecture, a type of recurrent neural network where neurons are randomly connected and only the read-out layer is trained, has been proposed as suitable for many-step-ahead forecasting tasks.
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