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Glutathione plays a critical role in plant growth, development and response to stress. It is a major cellular antioxidant and is involved in the detoxification of xenobiotics in many organisms, including plants. However, the role of glutathione-dependent redox homeostasis and associated molecular mechanisms regulating the antioxidant system and pesticide metabolism remains unclear. In this study, endogenous glutathione levels were manipulated by pharmacological treatments with glutathione synthesis inhibitors and oxidized glutathione. The application of oxidized glutathione enriched the cellular oxidation state, reduced the activity and transcript levels of antioxidant enzymes, upregulated the expression level of nitric oxide and Ca2+ related genes and the content, and increased the residue of chlorothalonil in tomato leaves. Further experiments confirmed that glutathione-induced redox homeostasis is critical for the reduction of pesticide residues. RNA sequencing analysis revealed that miRNA156 and miRNA169 that target transcription factor SQUAMOSA-Promoter Binding Proteins (SBP) and NUCLEAR FACTOR Y (NFY) potentially participate in glutathione-mediated pesticide degradation in tomato plants. Our study provides important clues for further dissection of pesticide degradation mechanisms via miRNAs in plants.

Glutathione (GSH) biosynthesis and regeneration play a significant role in the metabolism of chlorothalonil (CHT) in tomatoes. However, the specific regulatory mechanism of GSH in the degradation of CHT remains uncertain. To address this, we investigate the critical regulatory pathways in the degradation of residual CHT in tomatoes. The results revealed that the detoxification of CHT residue in tomatoes was inhibited by buthionine sulfoximine and oxidized glutathione pretreatment, which increased by 26% and 46.12% compared with control, respectively. Gene silencing of γECS, GS, and GR also compromised the CHT detoxification potential of plants, which could be alleviated by GSH application and decreased the CHT accumulation by 33%, 25%, and 21%, respectively. Notably, it was found that the jasmonic acid (JA) pathway participated in the degradation of CHT regulated by GSH. CHT residues reduced by 28% after application of JA. JA played a role downstream of the glutathione pathway by promoting the degradation of CHT residue in tomatoes via nitric oxide signaling and improving the gene expression of antioxidant and detoxification-related enzymes. This study unveiled a crucial regulatory mechanism of GSH via the JA pathway in CHT degradation in tomatoes and offered new insights for understanding residual pesticide degradation.

Due to the increasing number of vehicles in urban areas and the degradation of pavement performance, the failure rate of polyethylene pipeline, across roads, has increased rapidly. Based on relevant data, the traffic loads are regarded as a random variable; also its distribution is determined. First, the nonlinear contact interaction model was established, among traffic loads, soil and buried polyethylene pipeline, using finite element analysis software. Second, considering the decline of the pavement performance, the mechanical characteristics of buried gas pipeline suffering from different traffic loads were analyzed. Based on statistics of vehicle load, the distribution law of its maximum equivalent stress was determined by numerical simulation. Particularly, the strength distribution law of polyethylene pipeline was obtained through experiments. The reliability index of polyethylene pipeline was calculated using central point method and Monte Carlo method. Finally, based on the degradation curve of pavement performance, the remaining life of buried polyethylene pipeline was assessed.

Copyright of Journal of Modern Laboratory Medicine is the property of Journal of Modern Laboratory Medicine Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Modern Laboratory Medicine is the property of Journal of Modern Laboratory Medicine Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Modern Laboratory Medicine is the property of Journal of Modern Laboratory Medicine Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract: In situ carbon dioxide (CO2) foam flooding has proved to be economically feasible in the oil field, but its self‐generation behavior in the bulk scale/porous media is far from understood. In this study, the optimum in situ CO2‐foaming agent was first screened, and then in situ foam was investigated in the bulk. In situ foam flooding was conducted to evaluate the displacement characteristics and enhanced oil recovery of this system. The results showed that the foaming agent comprising 0.5% sodium dodecyl sulfonate (SDS) + 0.5% lauramido propyl hydroxyl sultaine (LHSB) gave the best foam properties and that the in situ CO2 foam with a slow releasing rate is effective both in bulk scale and in porous media, allowing a considerable enhancement of oil recovery in sand packs with different permeabilities. [ABSTRACT FROM AUTHOR]

Single-phase polycrystalline Sr2Ru1− xCo xO4 are successfully synthesized by conventional solid-state method with doping content up to x = 0.25. Ferrimagnetism develops among the incommensurate antiferromagnetic fluctuations in the doped compounds. An abnormal coercivity-temperature relation at a high doping level ( x > 0.10), in which the samples have smaller coercive field at low temperature than that at high temperature, i.e., HC(5 K) < HC(50 K), is observed. The Curie-Weiss temperature shows the negative value for all samples, and the susceptibility increases with Co doping content x. The exchange bias effect is detected in magnetic hysteresis loops. A three-dimensional transport property has been derived based on the variable rang hopping theory, indicating a thermally assisted hopping of electrons between the localized states. [ABSTRACT FROM AUTHOR]

Recently, the "noisy label" problem has become a hot topic in supervised classification of hyperspectral images (HSI). Nonetheless, how to effectively remove noisy labels from a training set with mislabeled samples is a nontrivial task for a multitude of supervised classification methods in HSI processing. This paper is the first to propose a kernel entropy component analysis (KECA)-based method for noisy label detection that can remove noisy labels of a training set with mislabeled samples and improve performance of supervised classification in HSI, which consists of the following steps. First, the kernel matrix of training samples with noisy labels for each class can be achieved by exploiting a nonlinear mapping function to enlarge the sample separability. Then, the eigenvectors and eigenvalues of the kernel matrix can be obtained by employing symmetric matrix decomposition. Next, the entropy corresponding to each training sample in each class is calculated based on entropy component analysis using the eigenvalues arranged in descending order and the corresponding eigenvectors. Finally, the sigmoid function is applied to the entropy of each sample to obtain the probability distribution. Meanwhile, a decision probability threshold is introduced into the above probability distribution to cleanse the noisy labels of training samples with mislabeled samples for each class. The effectiveness of the proposed method is evaluated by support vector machines on several real hyperspectral data sets. The experimental results show that the proposed KECA method is more efficient than other noisy label detection methods in terms of improving performance of the supervised classification of HSI. [ABSTRACT FROM AUTHOR]