To preliminarily investigate the occurrence, spatial distributions, homolog compositions, and ecological risks of chlorinated paraffins (CPs) in Yunnan, China, 110 soil samples were collected from an area part of Yunnan, representative of the whole Yunnan area, where had similar characteristics to most parts of Yunnan and 22 pooled soil samples were analyzed for 50 CP congener groups (C-Cl). The chlorinated nonane paraffin (C-CP), short chain (SCCP), and medium chain chlorinated paraffin (MCCP) concentrations in soil samples were 8-109 ng/g (average 39 ng/g), 79-948 ng/g (average 348 ng/g), and 20-1206 ng/g (average 229 ng/g), respectively. The C-CP homologs contributed 5%-16% of the C-CP concentrations in soils. No significant correlation was found between CP concentrations and the total organic carbon content (P > 0.05). The CP levels in soils from Yunnan were at a medium level compared with those in other areas worldwide. Human activity and atmosphere deposition would influence the levels and spatial distributions of CPs in this area. The concentrations of CPs in east area were higher than those in west area. CCl were the major SCCP congeners and CCl were the major MCCP congeners. Principal component analysis indicated that SCCPs and MCCPs came from different sources. A preliminary risk assessment indicated that these concentrations of CPs in soil from Yunnan do not pose a significant ecological risk for soil organisms.
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http://dx.doi.org/10.1016/j.chemosphere.2020.125855 | DOI Listing |
J Biochem Mol Toxicol
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Medical Experiment Center, Shaanxi University of Chinese Medicine, Xianyang, China.
Bisphenol A (BPA), an environmental endocrine disrupting chemical, is one of the most widely used chemicals in the world and is widely distributed in the external environment, specifically in food, water, dust, and soil. BPA exposure is associated with abnormal cognitive behaviors. However, the underlying mechanism remains unclear.
View Article and Find Full Text PDFBMC Cancer
January 2025
Department of Community & Family Medicine, All India Institute of Medical Sciences, 151001, Bathinda, Punjab, India.
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View Article and Find Full Text PDFBiometrics
January 2025
Department of Biostatistics, Brown University, Providence, RI 02912, United States.
Motivated by the need for computationally tractable spatial methods in neuroimaging studies, we develop a distributed and integrated framework for estimation and inference of Gaussian process model parameters with ultra-high-dimensional likelihoods. We propose a shift in viewpoint from whole to local data perspectives that is rooted in distributed model building and integrated estimation and inference. The framework's backbone is a computationally and statistically efficient integration procedure that simultaneously incorporates dependence within and between spatial resolutions in a recursively partitioned spatial domain.
View Article and Find Full Text PDFJ Hazard Mater
January 2025
School of Figheries, Aquaculture, and Aquatic Sciences, College of Agriculture, Auburn University, AL 36849, USA. Electronic address:
Thirteen elements were measured in 76 surface grab sediment samples and 90 segments of four cores from Lakes Erie and Ontario. By combining the data obtained previously from Lakes Superior, Michigan, and Huron, the spatial distribution, temporal trends, major influencing factors, anthropogenic enrichments, categorization, and ecological risks of target metals in sediment were evaluated for the Great Lakes region. Regionwide, Lake Ontario had the highest median concentrations for Ag, As, Cd, Zn, and Pb, while the highest Cr concentration was found in the Western Basin of Lake Erie.
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Department of AI & Big Data, Honam University, Gwangju 62399, Republic of Korea.
This study proposes an advanced plant disease classification framework leveraging the Attention Score-Based Multi-Vision Transformer (Multi-ViT) model. The framework introduces a novel attention mechanism to dynamically prioritize relevant features from multiple leaf images, overcoming the limitations of single-leaf-based diagnoses. Building on the Vision Transformer (ViT) architecture, the Multi-ViT model aggregates diverse feature representations by combining outputs from multiple ViTs, each capturing unique visual patterns.
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