Effects of perfluorooctanoic acid on the nutritional quality of Mytilus edulis.

Perfluorooctanoic acid (PFOA), which widely presents in marine environment, may produce some adverse effects to aquatic organism. Mytilus edulis are popular due to their high protein and low fat content in China. However, few studies have investigated the effects of PFOA on the quality of aquatic products. Here, PFOA effects on basic nutritional indices in M. edulis were measured, and possible mechanisms were explored. PFOA caused clear variation in physiological and biochemical indices of M. edulis. The contents of some important proteins, nutrients, and amino acids etc. dropped. Integrating metabolomics data, we speculate PFOA exposure triggered inflammation and oxidative stress in mussels, interfered with the metabolic pathways related to the quality and the transport and absorption pathways of metal ions, and affected the levels of some important elements and metabolites, thus decreasing the nutritional quality of M. edulis. The study provides new insights into PFOA adverse effects to marine organism, and may offer some references for some researchers to assess food quality and ecological risk to pollutants.

A Novel Feature Extraction Algorithm and System for Flexible Integrated Circuit Packaging Substrate.

Aiming at the line defect detection of a flexible integrated circuit substrate (FICS) without reference template, there are some problems such as line discontinuity or inaccurate line defect location in the detection results. In order to address these problems, a line feature detection algorithm for extracting an FICS image is proposed. Firstly, FICS image acquisition is carried out by using the appearance defect intelligent detection system independently developed in our lab. Secondly, in the algorithm design of the software system, the binary image of the line image to be segmented is obtained after the color FICS image is classified by K-means, median filtering, morphological filling and closed operation. Finally, for an FICS binary image, an image segmentation model with convexity-preserving indirect regular level set is proposed, which is applied to extract the line features of an FICS image. Experiment results show that, compared with the CV model, LBF model, LCV model, LGIF model, Order-LBF model and RSF model, the proposed model can extract line features with high accuracy, and the line boundary is smooth, which lays an important foundation for high-precision measurement of line width and line distance and high-precision location of defects.

Immunotoxicity of Perfluorooctanoic Acid to the Marine Bivalve Species Ruditapes philippinarum.

Polyfluorinated alkylated substances are recognized as an important class of pollutants in marine environments. Bivalves are good model organisms for evaluating the toxicity of pollutants and monitoring marine environments. In the present study, immunotoxicity of perfluorooctanoic acid (PFOA) was investigated by measuring biomarkers of the immune profile of Ruditapes philippinarum. In bivalves, hemocytes are an important component of the immune system. Thus, hemocyte proliferation, phagocytosis, cell viability, and immune enzyme activities, which have been applied as marine pollution bioindicators, were identified and observed for changes after exposure to PFOA in R. philippinarum. Based on the integrated biomarker responses method, we selected five biomarkers to evaluate PFOA risk at the multibiomarker level. In addition, the histopathological alterations of hemocytes in bivalves were used as indexes of the response to environmental stress. The subcellular structure of the hemocytes in R. philippinarum changed significantly with PFOA exposure, including hemocyte and nucleus morphological changes, organelle dissolution, cytomembrane and karyotheca swelling, and cytoplasm vacuolization. The present study verifies PFOA immunotoxicity to R. philippinarum at different levels and the integrated assessment of stress levels caused by PFOA in marine environment. Our results will provide new insights into evaluating adverse effects of PFOA and monitoring marine ecosystem. Environ Toxicol Chem 2022;41:426-436. © 2021 SETAC.

Integrated analysis of physiological, transcriptomics and metabolomics provides insights into detoxication disruption of PFOA exposure in Mytilus edulis.

Perfluorooctanoic acid (PFOA), a persistent environmental contaminant, resists environmental degradation and bioaccumulates in food chains. Lots of literatures have proved that PFOA exposure could disrupt detoxifying function in a variety of organisms, however, it still remained poorly known about this in mollusk. Here, we examined physiological, transcriptomic, and metabolomic responses to PFOA in Mytilus edulis, a model organism frequently used in studies of aquatic pollution. We aimed to characterize PFOA-induced stress responses and detoxification mechanisms. PFOA exposure significantly altered antioxidant enzyme activity levels and the abundances of lipid peroxidation products. In addition, transcriptomic analysis indicated that several genes associated with oxidative stress and detoxication were differentially expressed after PFOA exposure. In combination, transcriptomic and metabolomic analyses showed that PFOA exposure disturbed several metabolic processes in M. edulis, including the lipid metabolism, amino acid metabolism, and carbohydrate metabolism etc. Molecular examination and enzymes assay of PFOA-exposed M. edulis after a 7-day depuration period still did not recover to control levels. The Pathway enrichment analysis proved that several pathways related to detoxification, such as c-Jun N-terminal kinase (JNK) and p38-dependent mitogen-activated protein kinase (MAPK) pathway, Peroxisome proliferator-activated receptor γ (PPARγ) pathway etc, were obviously affected. The present work verifies firstly PFOA disruption to molluscan detoxification and identifies the key pathways to understand the molecular mechanisms thereof. This study provides new insights into the detoxication disruption invoked in response to PFOA exposure in M. edulis.

Customized 2D Barcode Sensing for Anti-Counterfeiting Application in Smart IoT with Fast Encoding and Information Hiding.

With the development of commodity economy, the emergence of fake and shoddy products has seriously harmed the interests of consumers and enterprises. To tackle this challenge, customized 2D barcode is proposed to satisfy the requirements of the enterprise anti-counterfeiting certification. Based on information hiding technology, the proposed approach can solve these challenging problems and provide a low-cost, difficult to forge, and easy to identify solution, while achieving the function of conventional 2D barcodes. By weighting between the perceptual quality and decoding robustness in sensing recognition, the customized 2D barcode can maintain a better aesthetic appearance for anti-counterfeiting and achieve fast encoding. A new picture-embedding scheme was designed to consider 2D barcode, within a unit image block as a basic encoding unit, where the 2D barcode finder patterns were embedded after encoding. Experimental results demonstrated that the proposed customized barcode could provide better encoding characteristics, while maintaining better decoding robustness than several state-of-the-art methods. Additionally, as a closed source 2D barcode that could be visually anti-counterfeit, the customized 2D barcode could effectively prevent counterfeiting that replicate physical labels. Benefitting from the high-security, high information capacity, and low-cost, the proposed customized 2D barcode with sensing recognition scheme provide a highly practical, valuable in terms of marketing, and anti-counterfeiting traceable solution for future smart IoT applications.

Adaptive Binarization of QR Code Images for FastAutomatic Sorting in Warehouse Systems.

As the fundamental element of the Internet of Things, the QR code has become increasingly crucial for connecting online and offline services. Concerning e-commerce and logistics, we mainly focus on how to identify QR codes quickly and accurately. An adaptive binarization approach is proposed to solve the problem of uneven illumination in warehouse automatic sorting systems. Guided by cognitive modeling, we adaptively select the block window of the QR code for robust binarization under uneven illumination. The proposed method can eliminate the impact of uneven illumination of QR codes effectively whilst meeting the real-time needs in the automatic warehouse sorting. Experimental results have demonstrated the superiority of the proposed approach when benchmarked with several state-of-the-art methods.