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The dissipation, uptake, translocation and accumulation of phthalic acid esters (PAEs) including diallyl phthalate (DAP), diisobutyl phthalate (DIBP), dibutyl phthalate (DBP), benzyl butyl phthalate (BBP) and di-(2-ethylhexyl) phthalate (DEHP) in sediment- Zizania latifolia system were investigated by gas chromatography-flame ionization detector after a QuEChERS pretreatment method. The dissipation rates of PAEs in sediment were positively correlated with exposure time, and more than 68.12% of PAEs in sediment were decreased after 28 d even when the spiked contents were extremely high. All the five PAEs could be taken up by roots from contaminated sediment and subsequently be transported into stems and leaves. There were significant linear correlations between the sediment content and the content in each tissue. DEHP was most readily transported from sediment to roots and stems, followed by BBP, DBP, DIBP and DAP, whereas the order of transportation from roots to leaves was reversed. During 28 d of exposure, the average concentration of each PAE in stems was the highest, followed by roots, leaves and edible parts. DEHP and BBP were the major contaminants in edible parts but could not pose a risk to human health. The accumulation of PAEs in edible parts was influenced by the species and concentration of PAEs as well as the survival time and harvest time of edible parts. The differences in uptake and translocation behaviors among PAEs in plant tissues were significantly correlated to their physicochemical properties, such as alkyl chain length and octanol/water partition coefficient (logK ow). The results reveal that Zizania latifolia is not only a kind of safe food, but also a potential plant to remediate contaminated sediment by accumulating and degrading PAEs from the habitats. [Display omitted] • Z. latifolia is a potential plant for sediment remediation. • There were linear correlations between the contents in sediment and each tissue. • The contents of PAEs followed the order of stems > roots > leaves > edible parts. • In general, PAEs could not pose a risk to human health in edible parts. • The accumulation behavior of PAEs was related to physicochemical properties. [ABSTRACT FROM AUTHOR]

Herein, hydroxypropyl chitosan azide (AZ-HPCTS) was synthesized and prepared as a hydrogel coating applied to a polypropylene mesh (PPM) through UV irradiation. This study confirmed the hypothesis that hydrogels with porous three-dimensional network structures exhibited excellent biocompatibility and biodegradability and adhered well to PPM. During the 180-day follow-up period, the AZ-HPCTS-coated PPM (AH-PPM) promoted wound healing by promoting the secretion of transforming growth factor-beta1 (TGF-beta1) in the acute reaction stage, which was reduced to a lower level at 30 d. The PPM exhibited a lower fibrin lysozyme activity based on the expression of tissue plasminogen activator (tPA) compared with that of AH-PPM (P < 0.05). The intraperitoneal adhesion score of AH-PPM decreased to 2.4 at 180 d in contrast with PPM (P < 0.01), which remained at a high level throughout the study. In conclusion, the AZ-HPCTS hydrogel is a potential coating for hernia patches that deserves further study in the biomaterial field. [Display omitted] • AZ-HPCTS hydrogel crosslinking process used ultraviolet with no chemical agent. • AZ-HPCTS hydrogel exhibited adhesion properties on the polypropylene mesh. • AZ-HPCTS hydrogel showed satisfactory cytocompatibility and biodegradability. • AZ-HPCTS hydrogel coating can help reduce intraperitoneal adhesion. • AZ-HPCTS hydrogel can be used as potential hernia mesh coating in clinical practice. [ABSTRACT FROM AUTHOR]

This study was aimed at preparing O -carboxymethyl chitosan (CM-CTS) fabrics, and examining the wound healing effects on partial-thickness burn. The functional polysaccharides were produced from chitosan needle-punched nonwovens reacted with chloroacetic acid. Then the biocompatibility and biological functions were evaluated through fibroblast L-929 and SD rats. CM-CTS fabrics were obtained with elongation at break more than 42%, tensile strength reaching 0.65 N/mm2, and water vapor transmission rate about 2600 g/m2∙24 h. Moreover, CM-CTS fabrics could effectively promote the mouse L-929 migration in vitro. CM-CTS fabrics yielded satisfactory results in angiogenesis, collagen deposition, interleukin-6 content, transforming growth factor level and healing rate, which were superior to the positive control and model groups after rats suffering with partial-thickness burn. In conclusion, CM-CTS fabrics possessed proper mechanical properties, air permeability, favorable biocompatibility, acceleration on fibroblasts migration and healing capacity for partial-thickness burn injury, and owned good potential as high-quality wound dressing. • Carboxymethyl chitosan (CM-CTS) fabrics were prepared from chitosan nonwovens. • CM-CTS fabrics effectively promoted the 2D & 3D migration of fibroblast L-929 cells. • CM-CTS fabric accelerated angiogenesis and new collagen deposition in scalded rats. • Wound healing rate of CM-CTS fabric group was superior to control (95.21% vs 84.27%). [ABSTRACT FROM AUTHOR]

• We propose a new clustering validity index for credit risk assessment problems. • We provide to realize the clustering of credit datasets by a multi-objective evolutionary optimization algorithm. • We introduce a GPU-based parallel computation framework for our proposed. algorithm. • We conduct a comprehensive experiment on our proposed approach and get promising experimental results. The financial liberalization and globalization have increased the need for expert and intelligent systems to deal with credit risk management works. Clustering algorithms are widely used for knowledge acquisition in such systems to assess the credit risk from a data point of view. Conventional clustering algorithms fall short in credit risk assessment because credit datasets are typically high-dimensional, class-imbalanced, and with large sample size. To address these problems, this paper proposes a novel evolutionary multi-objective soft subspace clustering (EMOSSC) algorithm for credit risk assessment. Firstly, we develop a soft subspace clustering validity index for credit risk assessment, by which we can detect the underlying subspace for each cluster from the entire high-dimensional feature space, and we also incorporate the weight of each cluster and the between-cluster separation into the clustering validity index to obtain a comprehensive data structure in the clustering process. Secondly, we propose to optimize the clustering criteria of the new clustering validity index simultaneously by a multi-objective evolutionary algorithm without any predefined weighting coefficients, which guarantees the robustness of the algorithm. We also further provide a local search strategy which significantly accelerates the convergence of the algorithm. Thirdly, we design a GPU-based parallel computation framework for updating the weights of features in our proposed algorithm to improve the computational efficiency on credit datasets with large sample size. Finally, we conduct a comprehensive experiment, with its results demonstrate the superiority of our proposed algorithm in credit risk assessment. [ABSTRACT FROM AUTHOR]