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Severe convection is a disastrous mesoscale weather system. The early detection of such systems is very important for saving peoples’ lives and properties. Previous studies address the issue mainly based on thresholding methods, which are not robust and accurate enough. In this paper, we propose a novel semantic segmentation method (Dugs-UNet) to solve the problem. Our method is based on the well-known U-Net framework. As convective clouds mimic fluids, its detection faces two important challenges. First, the shape and boundary features of clouds need to be carefully exploited. Second, the positive and negative samples for convection detection are very imbalanced. To address the two challenges, our method was carefully developed. Regarding the importance of the shape and boundary features for convective target detection, we introduce a shape stream module to extract these features. Also, a data-dependent upsample operation is adopted in the decoder of U-Net to effectively utilize the features. This is one of our contributions. To address the imbalance issue for convective target detection, the a focal loss function is employed to train our method, which is another contribution. Experimental results of 2018 Fengyun-4A satellite observations in China demonstrate the effectiveness of the proposed method. Compared to conventional thresholding-based methods and deep semantic segmentation algorithms such as SegNet, PSPNet, DeepLav-v3+ and U-Net, the proposed approach performs the best.

In recent years, silica nanomaterials have been widely studied as carriers in the field of antibacterial activity in food. Therefore, it is a promising but challenging proposition to construct responsive antibacterial materials with food safety and controllable release capabilities using silica nanomaterials. In this paper, a pH-responsive self-gated antibacterial material is reported, which uses mesoporous silica nanomaterials as a carrier and achieves self-gating of the antibacterial agent through pH-sensitive imine bonds. This is the first study in the field of food antibacterial materials to achieve self-gating through the chemical bond of the antibacterial material itself. The prepared antibacterial material can effectively sense changes in pH values caused by the growth of foodborne pathogens and choose whether to release antibacterial substances and at what rate. The development of this antibacterial material does not introduce other components, ensuring food safety. In addition, carrying mesoporous silica nanomaterials can also effectively enhance the inhibitory ability of the active substance.

Concurrent use of multiple drugs can lead to unexpected adverse drug reactions. The interaction between drugs can be confirmed by routine in vitro and clinical trials. However, it is difficult to test the drug–drug interactions widely and effectively before the drugs enter the market. Therefore, the prediction of drug–drug interactions has become one of the research priorities in the biomedical field. In recent years, researchers have been using deep learning to predict drug–drug interactions by exploiting drug structural features and graph theory, and have achieved a series of achievements. A drug–drug interaction prediction model SmileGNN is proposed in this paper, which can be characterized by aggregating the structural features of drugs constructed by SMILES data and the topological features of drugs in knowledge graphs obtained by graph neural networks. The experimental results show that the model proposed in this paper combines a variety of data sources and has a better prediction performance compared with existing prediction models of drug–drug interactions. Five out of the top ten predicted new drug–drug interactions are verified from the latest database, which proves the credibility of SmileGNN.

The task of precipitation nowcasting is significant in the operational weather forecast. The radar echo map extrapolation plays a vital role in this task. Recently, deep learning techniques such as Convolutional Recurrent Neural Network (ConvRNN) models have been designed to solve the task. These models, albeit performing much better than conventional optical flow based approaches, suffer from a common problem of underestimating the high echo value parts. The drawback is fatal to precipitation nowcasting, as the parts often lead to heavy rains that may cause natural disasters. In this paper, we propose a novel interaction dual attention long short-term memory (IDA-LSTM) model to address the drawback. In the method, an interaction framework is developed for the ConvRNN unit to fully exploit the short-term context information by constructing a serial of coupled convolutions on the input and hidden states. Moreover, a dual attention mechanism on channels and positions is developed to recall the forgotten information in the long term. Comprehensive experiments have been conducted on CIKM AnalytiCup 2017 data sets, and the results show the effectiveness of the IDA-LSTM in addressing the underestimation drawback. The extrapolation performance of IDA-LSTM is superior to that of the state-of-the-art methods.

Cable termination is a weak point in an underground cable system. The transient earth voltage (TEV) method is an effective and nonintrusive method for estimating the insulation condition of cable termination. However, the practical application of TEV detection is mainly focused on switchgears, generators, and transformers with a flat and conductive shell. A flexible sensor array based on the TEV method is presented for online partial discharge (OLPD) monitoring of the cable termination. Each sensing element is designed with a dual-capacitor structure made of flexible polymer material to obtain better and more stable sensitivity. Based on the electromagnetic (EM) wave propagation theory, the partial discharge (PD) propagation model in the cable termination is built to analyze and verify the rationality and validity of the sensor unit. Some influencing factors are discussed regarding the response characteristics of sensors. Finally, the performance of the sensor array is verified by simulations and experiments. Besides, an OLPD monitoring system is introduced. The monitoring system is composed of the on-site monitoring device and the remote monitoring host. The two parts of the system exchange the data through wireless networks using a wireless communication module. The experiment results show that the monitoring device could supply the PD condition monitoring demand for cable termination.

Accurate hyperspectral image classification has been an important yet challenging task for years. With the recent success of deep learning in various tasks, 2-dimensional (2D)/3-dimensional (3D) convolutional neural networks (CNNs) have been exploited to capture spectral or spatial information in hyperspectral images. On the other hand, few approaches make use of both spectral and spatial information simultaneously, which is critical to accurate hyperspectral image classification. This paper presents a novel Synergistic Convolutional Neural Network (SyCNN) for accurate hyperspectral image classification. The SyCNN consists of a hybrid module that combines 2D and 3D CNNs in feature learning and a data interaction module that fuses spectral and spatial hyperspectral information. Additionally, it introduces a 3D attention mechanism before the fully-connected layer which helps filter out interfering features and information effectively. Extensive experiments over three public benchmarking datasets show that our proposed SyCNNs clearly outperform state-of-the-art techniques that use 2D/3D CNNs.

Presently, many users are involved in multiple social networks. Identifying the same user in different networks, also known as anchor link prediction, becomes an important problem, which can serve numerous applications, e.g., cross-network recommendation, user profiling, etc. Previous studies mainly use hand-crafted structure features, which, if not carefully designed, may fail to reflect the intrinsic structure regularities. Moreover, most of the methods neglect the attribute information of social networks. In this paper, we propose a novel semi-supervised network-embedding model to address the problem. In the model, each node of the multiple networks is represented by a vector for anchor link prediction, which is learnt with awareness of observed anchor links as semi-supervised information, and topology structure and attributes as input. Experimental results on the real-world data sets demonstrate the superiority of the proposed model compared to state-of-the-art techniques.

Chilling causes a significant decline in photosynthesis in tomato plants. Tomato tryptophan decarboxylase gene 1 (SlTDC1) is the first rate-limiting gene for melatonin (MT) biosynthesis and is involved in the regulation of photosynthesis under various abiotic stresses. However, it is not clear whether SlTDC1 participates in the photosynthesis of tomato under chilling stress. Here, we obtained SlTDC1 overexpression transgenic tomato seedlings, which showed higher SlTDC1 mRNA abundance and MT content compared with the wild type (WT). The results showed that the overexpression of SlTDC1 obviously alleviated the chilling damage to seedlings in terms of the lower electrolyte leakage rate and hydrogen peroxide content, compared with the WT after 2 d of chilling stress. Moreover, the overexpression of SlTDC1 notably increased photosynthesis under chilling stress, which was related to the higher chlorophyll content, normal chloroplast structure, and higher mRNA abundance and protein level of Rubisco and RCA, as well as the higher carbon metabolic capacity, compared to the WT. In addition, we found that SlTDC1-overexpressing seedlings showed higher Wk (damage degree of OEC on the PSII donor side), φEo (quantum yield for electron transport in the PSII reaction center), and PIABS (photosynthetic performance index) than WT seedlings after low-temperature stress, implying that the overexpression of SlTDC1 decreased the damage to the reaction center and donor-side and receptor-side electron transport of PSII and promoted PSI activity, as well as energy absorption and distribution, to relieve the photoinhibition induced by chilling stress. Our results support the notion that SlTDC1 plays a vital role in the regulation of photosynthesis under chilling stress.

Insulin sensitizers targeting insulin receptors (IR) are a potential drug for the treatment of diabetes. Serpentine is an alkaloid component in the root of Catharanthus roseus (L.) G. Don. Serpentine screened by surface plasmon resonance (SPR) technology has the ability to target IR. The objective of this study was to investigate whether serpentine could modulate the role of insulin in regulating blood glucose through insulin receptors in cells and in animal models of diabetes. SPR technology was used to detect the affinity of different concentrations of serpentine with insulin receptors. The Western blotting method was used to detect the expression levels of key proteins of the insulin signaling pathway in C2C12 cells and 3T3-L1 cells as well as in muscle and subcutaneous adipose tissue of diabetic mice after serpentine and insulin treatment. Diabetic mice were divided into four groups and simultaneously injected with insulin or serpentine, and the blood glucose concentration and serum levels of insulin, glucagon, and C-peptide were measured 150 min later. mRNA levels of genes related to lipid metabolism and glucose metabolism in liver, muscle, and subcutaneous adipose tissue were detected by RT-PCR. Serpentine was able to bind to the extracellular domain of IR with an affinity of 2.883 × 10−6 M. Serpentine combined with insulin significantly enhanced the ability of insulin to activate the insulin signaling pathway and significantly enhanced the glucose uptake capacity of C2C12 cells. Serpentine enhanced the ability of low-dose insulin (1 nM) and normal-dose insulin (100 nM) to activate the insulin signaling pathway. Serpentine also independently activated AMPK phosphorylation, thus stimulating glucose uptake by C2C12 cells. In high-fat-diet/streptozotocin (HFD/STZ)-induced diabetic mice, serpentine significantly prolonged the hypoglycemic time of insulin, significantly reduced the use of exogenous insulin, and inhibited endogenous insulin secretion. In addition, serpentine alone significantly increased the expression of GSK-3β mRNA in muscle tissue, thus enhancing glucose uptake, and at the same time, serpentine significantly increased glucagon secretion and liver gluconeogenesis. Serpentine enhances the ability of insulin to regulate blood glucose through the insulin receptor, and can also regulate blood glucose alone, but it has a negative regulation mechanism and cannot produce a hypoglycemic effect. Therefore, serpentine may be useful as an insulin sensitizer to assist insulin to lower blood glucose.

Mammalian adipose tissue can be divided into white and brown adipose tissue based on its colour, location, and cellular structure. Certain conditions, such as sympathetic nerve excitement, can induce the white adipose adipocytes into a new type of adipocytes, known as beige adipocytes. The process, leading to the conversion of white adipocytes into beige adipocytes, is called white fat browning. The dynamic balance between white and beige adipocytes is closely related to the body’s metabolic homeostasis. Studying the signal transduction pathways of the white fat browning might provide novel ideas for the treatment of obesity and alleviation of obesity-related glucose and lipid metabolism disorders. This article aimed to provide an overview of recent advances in understanding white fat browning and the role of BAT in lipid metabolism.

Long-chain noncoding RNAs (lncRNAs) are RNAs that do not code for proteins, widely present in eukaryotes. They regulate gene expression at multiple levels through different mechanisms at epigenetic, transcription, translation, and the maturation of mRNA transcripts or regulation of the chromatin structure, and compete with microRNAs for binding to endogenous RNA. Adipose tissue is a large and endocrine-rich functional tissue in mammals. Excessive accumulation of white adipose tissue in mammals can cause metabolic diseases. However, unlike white fat, brown and beige fats release energy as heat. In recent years, many lncRNAs associated with adipogenesis have been reported. The molecular mechanisms of how lncRNAs regulate adipogenesis are continually investigated. In this review, we discuss the classification of lncRNAs according to their transcriptional location. lncRNAs that participate in the adipogenesis of white or brown fats are also discussed. The function of lncRNAs as decoy molecules and RNA double-stranded complexes, among other functions, is also discussed.

Excessive fat accumulation can lead to obesity, diabetes, hyperlipidemia, atherosclerosis, and other diseases. MicroRNAs are a class of microRNAs that regulate gene expression and are highly conserved in function among species. microRNAs have been shown to act as regulatory factors to inhibit fat accumulation in the body. We found that miR-370-3p was expressed at lower levels in the fat mass of mice on a high-fat diet than in mice on a normal control diet. Furthermore, our data showed that the overexpression of miR-370-3p significantly suppressed the mRNA expression levels of adipogenic markers. Thus, miR-370-3p overexpression reduced lipid accumulation. Conversely, the inhibition of miR-370-3p suppressed 3T3-L1 preadipocyte proliferation and promoted preadipocyte differentiation. In addition, Mknk1, a target gene of miR-370-3p, plays an opposing role in preadipocyte proliferation and differentiation. Moreover, consistent results from in vitro as well as in vivo experiments suggest that the inhibition of fat accumulation by miR-370-3p may result from the inhibition of saturated fatty acids that promote the accumulation of polyunsaturated fatty acids. In conclusion, these results suggest that miR-370-3p plays an important role in adipogenesis and fatty acid metabolism through the regulation of Mknk1.