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Insects damaging and penetrating plastic packaged materials has been reported since the 1950s. Radical innovation breakthroughs of plastic biodegradation have been initiated since the discovery of biodegradation of plastics by Tenebrio molitor larvae in 2015 followed by Galleria mellonella in 2017. Here we review updated studies on the insect-mediated biodegradation of plastics. Plastic biodegradation by insect larvae, mainly by some species of darkling beetles (Tenebrionidae) and pyralid moths (Pyralidae) is currently a highly active and potentially transformative area of research. Over the past eight years, publications have increased explosively, including discoveries of the ability of different insect species to biodegrade plastics, biodegradation performance, and the contribution of host and microbiomes, impacts of polymer types and their physic-chemical properties, and responsible enzymes secreted by the host and gut microbes. To date, almost all major plastics including polyethylene (PE), polypropylene (PP), polyvinyl chloride (PVC), polyethylene terephthalate (PET), polyurethane (PUR), and polystyrene (PS) can be biodegraded by T. molitor and ten other insect species representing the Tenebrionidae and Pyralidae families. The biodegradation processes are symbiotic reactions or performed by synergistic efforts of both host and gut-microbes to rapidly depolymerize and biodegrade plastics with hourly half-lives. The digestive ezymens and bioreagents screted by the insects play an essential role in plasatic biodegradation in certain species of Tenebrionidae and Pyralidae families. New research on the insect itself, gut microbiomes, transcriptomes, proteomes and metabolomes has evaluated the mechanisms of plastic biodegradation in insects. We conclude this review by discussing future research perspectives on insect-mediated biodegradation of plastics. [ABSTRACT FROM AUTHOR]

Semantic image segmentation in computer networks is designed to determine the category to which each pixel in an image belongs. It is a basic computer vision task and has a very wide range of applications in practice. In recent years, semantic image segmentation algorithms in computer networks based on deep learning have attracted widespread attention due to their fast speed and high accuracy. However, due to the large number of downsampling layers in a deep learning model, the segmentation results are usually poor at the edge of an object, and there is currently no universal quantitative evaluation index to measure the performance of segmentation at the edge of an object. Solving these two problems is of great significance to semantic image segmentation algorithms in China. Based on traditional evaluation indicators, this paper proposes a region-based evaluation index to quantitatively measure the performance of segmentation at the edge of an object and proposes an improved loss function to improve model performance. The existing semantic image segmentation methods are summarized. This paper proposes regional-based evaluation indicators. Taking advantage of the particularity of semantic image segmentation tasks, this paper presents an efficient and accurate method for extracting the edges of objects. By defining the distance from pixels to the edges of objects, this paper proposes a fast algorithm for calculating the edge area. Based on this, three methods are proposed as well as an area-based evaluation indicator. The experimental results show that the accuracy of the loss function proposed in this paper, compared with that of the current mainstream cross-entropy loss function, is improved by 1% on the DeepLab model. For area-based evaluation indicators, a 4% accuracy improvement can be achieved, and on other segmentation models, there is also a significant improvement. [ABSTRACT FROM AUTHOR]

The design and fabrication of a surface-enhanced Raman scattering (SERS) aptasensor for simultaneous detection of zearalenone (ZEN) and ochratoxin A (OTA) in wheat and corn samples is described. The capture and reporter probes were SH-cDNA-modified gold nanorods and SH-Apt-modified Au@Ag core-shell nanoparticles, respectively. After recognizing OTA and ZEN aptamers and complementary strands (SH-cDNA), the reporter probe generated a strong SERS signal. The preferred binding of OTA and ZEN aptamers to OTA and ZEN, respectively, caused reporter probes to release the capture probes, resulting in a linear decrease in SERS intensity. The detection of OTA showed good linearity with an R2 value of 0.986, which could be maintained across a wide concentration range (0.01 to 100 ng/mL), with the limit of detection of 0.018 ng/mL. For detection of ZEN, good linearity with an R2 value of 0.987 could be maintained across a wide concentration range (0.05 to 500 ng/mL), with 0.054 ng/mL as the limit of detection. Good accuracy (relative standard deviation < 4.2%) during mycotoxin determination as well as excellent quantitative recoveries (96.0–110.7%) during the analysis of spiked real samples was achieved. The proposed SERS aptasensor exhibited excellent performance in the detection of OTA and ZEN in real food samples. Hence, by simply changing the aptamer, this new model can be applied to the detection of multiple mycotoxins in the food industry. [ABSTRACT FROM AUTHOR]

Perfluorooctane sulfonate (PFOS) is a persistent organic pollutant. Although multiple adverse effects of PFOS have been demonstrated, whether PFOS can accelerate aging and affect animal longevity remains unknown. In Caenorhabditis elegans, we found that a 50 h exposure to 0.2-200 µM PFOS reduced lifespan in a concentration dependent manner. In transgenic nematodes, lifespans are affected by mutations of daf- 16, daf- 2 or age- 1 genes, which are related to the Insulin/IGF-1 Signaling pathway (IIS). PFOS exposure caused an additional reduction in average lifespan in daf- 2( e1370) and daf- 16b( KO) nematodes. In contrast, daf- 16( mu86) nematodes showed no additional reduction with PFOS exposure and age- 1( hx546) mutants did not exhibit a reduction in lifespan with PFOS exposure, compared with wildtype nematodes. Overall, our findings demonstrate that PFOS exposure accelerates aging and shortens longevity of animals. The PFOS-induced effect may involve genes of the IIS pathway, particularly daf- 16 and age- 1. [ABSTRACT FROM AUTHOR]

A mathematical modeling method, theory and process based on measured data from the dropping external characteristic of a thyristor rectifier for arc welding power source have been introduced in this paper. The instance shows the model structure obtained with this method is simple and it can accurately denote the U–I characteristic for welding power. This model can also predict the self-adjusting performance of electric current and short circuit current parameters on different given conditions. This method can not only handle the post-process problem in measuring data, but it can make the limited measuring message be a more effective application. [ABSTRACT FROM AUTHOR]