Radical Scavenging Activity of Black Currant (\u3ci\u3eRibes nigrum\u3c/i\u3e L.) Extract and Its Inhibitory Effect on Gastric Cancer Cell Proliferation via Induction of Apoptosis

A black currant extract (BCE) was prepared and its antiproliferative activity against gastric cancer SGC-7901 cells was investigated. Strong 1,1-diphenyl-2-picrylhydrazyl (DPPH) radical and 2,2′-azinobis(3-ethylbenzothiazoline-6-sulphonic acid) diammonium salt (ABTS) radical scavenging activities and a high reducing power were confirmed with BCE. BCE inhibited the proliferation of SGC-7901 cells in a dose- and time-dependent manner, and the IC50 were 12.7, 10.2 and 9.0 mg/mL for 12, 24 and 48 h, respectively. Morphologic observations with inverted and fluorescence microscopes yielded vivid evidence of cell shrinkage, formation of cytoplasmic filaments, condensation of nuclear chromatin, and cell apoptosis in the presence of BCE. Flow cytometric analysis also showed that BCE treatment at concentrations of 10–20 mg/mL resulted in marked reductions of viable cells. The high concentration of phenolic compounds present in the BCE (12.2 mg/mL), including six prominent anthocyanins identified by HPLC–ESI-MS2, appeared to be responsible for BCE’s antiradical activity and anticancer effects. These findings of inhibition of SGC-7901 cells and induction of apoptosis suggest that black currant may contribute to the reduction in gastric cancer risk

Methylmercury uptake and degradation by methanotrophs

Methylmercury (CH3Hg+) is a potent neurotoxin produced by certain anaerobic microorganisms in natural environments. Although numerous studies have characterized the basis of mercury (Hg) methylation, no studies have examined CH3Hg+ degradation by methanotrophs, despite their ubiquitous presence in the environment. We report that some methanotrophs, such as Methylosinus trichosporium OB3b, can take up and degrade CH3Hg+ rapidly, whereas others, such as Methylococcus capsulatus Bath, can take up but not degrade CH3Hg+. Demethylation by M. trichosporium OB3b increases with increasing CH3Hg+ concentrations but was abolished in mutants deficient in the synthesis of methanobactin, a metal-binding compound used by some methanotrophs, such as M. trichosporium OB3b. Furthermore, addition of methanol (>5 mM) as a competing one-carbon (C1) substrate inhibits demethylation, suggesting that CH3Hg+ degradation by methanotrophs may involve an initial bonding of CH3Hg+ by methanobactin followed by cleavage of the C–Hg bond in CH3Hg+ by the methanol dehydrogenase. This new demethylation pathway by methanotrophs indicates possible broader involvement of C1-metabolizing aerobes in the degradation and cycling of toxic CH3Hg+ in the environment

Interfacial Covalent Bonds Regulated Electron-Deficient 2D Black Phosphorus for Electrocatalytic Oxygen Reactions

Developing resource-abundant and sustainable metal-free bifunctional oxygen electrocatalysts is essential for the practical application of zinc–air batteries (ZABs). 2D black phosphorus (BP) with fully exposed atoms and active lone pair electrons can be promising for oxygen electrocatalysts, which, however, suffers from low catalytic activity and poor electrochemical stability. Herein, guided by density functional theory (DFT) calculations, an efficient metal-free electrocatalyst is demonstrated via covalently bonding BP nanosheets with graphitic carbon nitride (denoted BP-CN-c). The polarized P-N covalent bonds in BP-CN-c can efficiently regulate the electron transfer from BP to graphitic carbon nitride and significantly promote the OOH* adsorption on phosphorus atoms. Impressively, the oxygen evolution reaction performance of BP-CN-c (overpotential of 350 mV at 10 mA cm−2, 90% retention after 10 h operation) represents the state-of-the-art among the reported BP-based metal-free catalysts. Additionally, BP-CN-c exhibits a small half-wave overpotential of 390 mV for oxygen reduction reaction, representing the first bifunctional BP-based metal-free oxygen catalyst. Moreover, ZABs are assembled incorporating BP-CN-c cathodes, delivering a substantially higher peak power density (168.3 mW cm−2) than the Pt/C+RuO2-based ZABs (101.3 mW cm−2). The acquired insights into interfacial covalent bonds pave the way for the rational design of new and affordable metal-free catalysts. © 2021 The Authors. Advanced Materials published by Wiley-VCH Gmb

Quantum chemical calculation study on the thermal decomposition of electrolyte during lithium-ion battery thermal runaway

Understanding the behavior of lithium-ion battery electrolytes during thermal runaway is essential for designing safer batteries. However, current reports on electrolyte decomposition behaviors often focus on reactions with electrode materials. Herein we use quantum chemical calculations to develop a model for the thermal decomposition mechanism of electrolytes under both electrolyte and ambient atmosphere conditions. The thermal stability is found to be associated with the dielectric constants of electrolyte constituents. Within the electrolyte, the solvation effects between molecules increase electrolyte stability, making thermal decomposition a more difficult process. Furthermore, Li+ is observed to facilitate electrolyte thermal decomposition, as the energy required for the thermal decomposition reactions of molecules decreases when they are bonded with Li+. It is hoped that this study will offer a theoretical basis for understanding the complex reactions occurring during thermal runaway events

Development and validation of prognostic dynamic nomograms for hepatitis B Virus-related hepatocellular carcinoma with microvascular invasion after curative resection

Background and AimThe prediction models of postoperative survival for hepatitis B virus-related hepatocellular carcinoma (HBV-HCC) with microvascular invasion (MVI) have not been well established. The study objective was the development of nomograms to predict disease recurrence and overall survival (OS) in these patients.MethodsData were obtained from 1046 HBV-related MVI-positive HCC patients who had undergone curative resection from January 2014 to December 2017. The study was approved by the Eastern Hepatobiliary Surgery Hospital and Jinling Hospital ethics committee, and patients provided informed consent for the use of their data. Nomograms for recurrence and OS were created by Cox regression model in the training cohort (n=530). The modes were verified in an internal validation cohort (n= 265) and an external validation cohort (n= 251).ResultsThe nomograms of recurrence and OS based on preoperative serological indicators (HBV-DNA, neutrophil-lymphocyte ratio, a-fetoprotein), tumor clinicopathologic features (diameter, number), surgical margin and postoperative adjuvant TACE achieved high C-indexes of 0.722 (95% confidence interval [CI], 0.711-0.732) and 0.759 (95% CI, 0.747-0.771) in the training cohort, respectively, which were significantly higher than conventional HCC staging systems (BCLC, CNLC, HKLC).The nomograms were validated in the internal validation cohort (0.747 for recurrence, 0.758 for OS) and external validation cohort(0.719 for recurrence, 0.714 for OS) had well-fitted calibration curves. Our nomograms accurately stratified patients with HBV-HCC with MVI into low-, intermediate- and high-risk groups of postsurgical recurrence and mortality. Prediction models for recurrence-free survival (https://baishileiehbh.shinyapps.io/HBV-MVI-HCC-RFS/) and OS (https://baishileiehbh.shinyapps.io/HBV-MVI-HCC-OS/) were constructed.ConclusionsThe two nomograms showed good predictive performance and accurately distinguished different recurrence and OS by the nomograms scores for HBV-HCC patients with MVI after resection

Organic Fluorescent Compounds that Display Efficient Aggregation-Induced Emission Enhancement and Intramolecular Charge Transfer

A series of symmetric sulfone-linked organic fluorescent compounds (1a–c) was synthesized and characterized. V-shaped 1a–c were designed as aggregate of intramolecular charge transfer (ICT) and aggregation-induced emission enhancement (AIEE) processes. The 1a–c emitted intense blue violet lights in normal solvents. A large red shift of the emission wavelength and dramatic decrease of emission efficiency occurred with increasing solvent polarity. The 1a–c will function well as electron transport and blue light-emitting materials through theoretical calculations

Effect of Potato Dietary Fiber on the Quality, Microstructure, and Thermal Stability of Chicken Patty

A total of 150 chicken patties containing different concentrations of potato dietary fiber (PDF) (0.0–4.0%) (30 for every treatment) with three replicates were used to access the influence of PDF on their quality, microstructure, and thermal stability. PDF improved the quality of chicken patty, including significantly inhibiting dimensional change and improving water- and fat-binding properties and textural properties (p p > 0.05). The samples with PDF (<3.0%) did not have a significant negative effect on sensory properties of chicken patty; meanwhile, there were more abundant nutrients and a lower energy value in samples with PDF compared with the control. Therefore, PDF could be a promising ingredient to improve the properties of chicken patties, which was related to the amount of PDF added and performed best at 3.0% level

An Improved CenterNet Model for Insulator Defect Detection Using Aerial Imagery

For the issue of low accuracy and poor real-time performance of insulator and defect detection by an unmanned aerial vehicle (UAV) in the process of power inspection, an insulator detection model MobileNet_CenterNet was proposed in this study. First, the lightweight network MobileNet V1 was used to replace the feature extraction network Resnet-50 of the original model, aiming to ensure the detection accuracy of the model while speeding up its detection speed. Second, a spatial and channel attention mechanism convolutional block attention module (CBAM) was introduced in CenterNet, aiming to improve the prediction accuracy of small target insulator position information. Then, three transposed convolution modules were added for upsampling, aiming to better restore the semantic information and position information of the image. Finally, the insulator dataset (ID) constructed by ourselves and the public dataset (CPLID) were used for model training and validation, aiming to improve the generalization ability of the model. The experimental results showed that compared with the CenterNet model, MobileNet_CenterNet improved the detection accuracy by 12.2%, the inference speed by 1.1 f/s for FPS-CPU and 4.9 f/s for FPS-GPU, and the model size was reduced by 37 MB. Compared with other models, our proposed model improved both detection accuracy and inference speed, indicating that the MobileNet_CenterNet model had better real-time performance and robustness

Estimation of Leaf Nitrogen Content in Wheat Based on Fusion of Spectral Features and Deep Features from Near Infrared Hyperspectral Imagery

Nitrogen is an important indicator for monitoring wheat growth. The rapid development and wide application of non-destructive detection provide many approaches for estimating leaf nitrogen content (LNC) in wheat. Previous studies have shown that better results have been obtained in the estimation of LNC in wheat based on spectral features. However, the lack of automatically extracted features leads to poor universality of the estimation model. Therefore, a feature fusion method for estimating LNC in wheat by combining spectral features with deep features (spatial features) was proposed. The deep features were automatically obtained with a convolutional neural network model based on the PyTorch framework. The spectral features were obtained using spectral information including position features (PFs) and vegetation indices (VIs). Different models based on feature combination for evaluating LNC in wheat were constructed: partial least squares regression (PLS), gradient boosting decision tree (GBDT), and support vector regression (SVR). The results indicate that the model based on the fusion feature from near-ground hyperspectral imagery has good estimation effect. In particular, the estimation accuracy of the GBDT model is the best (R2 = 0.975 for calibration set, R2 = 0.861 for validation set). These findings demonstrate that the approach proposed in this study improved the estimation performance of LNC in wheat, which could provide technical support in wheat growth monitoring

Towards Global Video Scene Segmentation with Context-Aware Transformer

Videos such as movies or TV episodes usually need to divide the long storyline into cohesive units, i.e., scenes, to facilitate the understanding of video semantics. The key challenge lies in finding the boundaries of scenes by comprehensively considering the complex temporal structure and semantic information. To this end, we introduce a novel Context-Aware Transformer (CAT) with a self-supervised learning framework to learn high-quality shot representations, for generating well-bounded scenes. More specifically, we design the CAT with local-global self-attentions, which can effectively consider both the long-term and short-term context to improve the shot encoding. For training the CAT, we adopt the self-supervised learning schema. Firstly, we leverage shot-to-scene level pretext tasks to facilitate the pre-training with pseudo boundary, which guides CAT to learn the discriminative shot representations that maximize intra-scene similarity and inter-scene discrimination in an unsupervised manner. Then, we transfer contextual representations for fine-tuning the CAT with supervised data, which encourages CAT to accurately detect the boundary for scene segmentation. As a result, CAT is able to learn the context-aware shot representations and provides global guidance for scene segmentation. Our empirical analyses show that CAT can achieve state-of-the-art performance when conducting the scene segmentation task on the MovieNet dataset, e.g., offering 2.15 improvements on AP