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Objective To observe the change of insulin resistance in the impaired fasting glucose patients with hypertension along with time elapse, and to investigate the relationship between hypertension and insulin resistance. Methods A total of 180 patients with impaired fasting glucose admitted in our outpatient department of endocrinology and physical examination center of our hospital during October 2012 and August 2013 were recruited in this study. All of them were given long-term lifestyle intervention, and were followed up for 5 years. Till the end of the follow-up, there were 114 patients included, and they were divided into the hypertension group (n=56) and the normal blood pressure group (n=58). Routine antihypertensive drugs were given to the hypertension group. Clinical data, such as fasting blood glucose, 2 h postprandial blood glucose, fasting insulin, 2 h postprandial insulin, and insulin resistance index (HOMA-IR) and insulin sensitivity index (ISI) from the third to the fifth year of follow-up were collected and analyzed. Results In the 5 years' follow-up, the prevalence of diabetes in the hypertension group was 22.83%, significantly higher than that of the normal blood pressure group (11.36%, P=0.042). The hypertension group had obviously higher HOMA-IR but statistically lower ISI when compared with the normal blood pressure group (both P < 0.001). Conclusion In the patients with impaired fasting glucose, elevated blood pressure is closely associated with insulin resistance. As time goes by, those impaired fasting glucose accompanied with hypertension are more prone to severe insulin resistance, and are more likely to progress to diabetes.

Introduction: Diabetic patients are often accompanied by complications of diabetic vascular disease, which could lead to heart failure or stroke. In this work, we explored the role of miR-503/Apelin-12 in diabetic angiopathy (DA) in vitro. Methods: ELISA and qPCR were applied to assess the expression of miR-503 and Apelin-12 in high glucose (HG)-treated microvascular endothelial cells (HMEC-1). The effects of miR-503 on apoptosis, inflammation and oxidative stress were assessed by flow cytometry, western blotting, qPCR, and ELISA. The interaction between miR-503 and Apelin-12 was evaluated by dual-luciferase reporter assay, qPCR and ELISA, respectively. Western blotting was performed to examine the function of miR-503/Apelin-12 on JNK and p38MAPK activation. Results: MiR-503 was markedly increased and Apelin-12 was decreased in HG-treated HMEC-1 cells. MiR-503 inhibitor significantly assuaged apoptosis, inflammation and oxidative stress in HMEC-1 cells. MiR-503 could specifically bind to the 3′UTR of Apelin and inversely downregulate Apelin-12 expression. Furthermore, Apelin-12 suppressed apoptosis, inflammation and oxidative stress. Inhibition of Apelin-12 could partially reverse the decrease of p-JNK and p-p38 expression levels induced by miR-503 suppression. Conclusion: In HG-induced microvascular cells injury, miR-503/Apelin-12 enhances inflammation and oxidative stress by regulating JNK and p38MAPK pathway, suggesting a potential therapeutic target for DA.

Trichoderma, widely distributed all over the world, is commonly found in soil and root ecosystems. It is a group comprising beneficial fungi that improve plant disease resistance and promote plant growth. Studies have shown that Trichoderma cellulases can also improve plant disease resistance. Based on previous studies, we reported that a C6 zinc finger protein (Thc6) regulates two cellulase genes, thph1 and thph2, to induce ISR responses in plants. Therefore, in this study, we focused on the role of thph2 in the colonization of maize roots by T. harzianum and the induction of systemic resistance against southern leaf blight. The results showed that thph2 had a positive regulatory effect on the Trichoderma colonization of maize roots. After the root was treated with Trichoderma, the leaf defense genes AOS, LOX5, HPL, and OPR1 were expressed to resist the attack of Cochliobolus heterostrophus. The pure Thph2 protein also resulted in a similar induction activity of the AOS, LOX5, HPL, and OPR1 expression in maize roots, further demonstrating that thph2 can induce plant defense responses and that signal transduction occurs mainly through the JA signaling pathway. [ABSTRACT FROM AUTHOR]

Transformer-based graph neural networks have accomplished notable achievements by utilizing the self-attention mechanism for message passing in various domains. However, traditional methods overlook the diverse significance of intra-node representations, focusing solely on internode interactions. To overcome this limitation, we propose a DAG (Dual Attention Graph), a novel approach that integrates both intra-node and internode dynamics for node classification tasks. By considering the information exchange process between nodes from dual branches, DAG provides a holistic understanding of information propagation within graphs, enhancing the interpretability of graph-based machine learning applications. The experimental evaluations demonstrate that DAG excels in node classification tasks, outperforming current benchmark models across ten datasets. [ABSTRACT FROM AUTHOR]

Objects in a real scene often occlude each other and inferring a complete appearance from the visible part is an important and challenging task. In this paper, the authors propose a self‐supervised generative adversarial network GIGAN (GAN for generating the invisible), which can generate the complete appearance of objects without labelled invisible part information. The authors build two cycle transformation networks CycleIncomplete (CycleI) and CycleComplete (CycleC) that share parameters to improve the accuracy of mask completion. This design does not require well‐matched training images and can make better use of the limited labelled samples. In addition, the authors propose a conditional normalization module and combine it with the inferred complete mask output. The combination not only enhances the content recovery ability and obtains more realistic outputs, but also improves the efficiency of the generation process. Experimental results show that compared with existing self‐supervised learning models, our method achieves l1 error, mean intersection‐over‐union (mIOU), and Fréchet inception distance (FID) improvements on the COCOA and KINS datasets. [ABSTRACT FROM AUTHOR]

Trichoderma spp. are widely used beneficial microbes in agricultural production; however, the improper carrier choice for Trichoderma agent preparation can alter the effectiveness of Trichoderma fungicides. In this study, the co-culture of four Trichoderma strains produced a large amount of free amino acids, with a content of 392.8414 ug/mL, and significantly improved the production level of γ-aminobutyric acid. A greenhouse experiment further showed that the co-culture of Trichoderma synergistically improved the female flower development and bacterial angular leaf spot resistance. The effects of ten kinds of carriers were compared in terms of water absorption and heat generation, as well as their effects on the seedling emergence rate and the plant growth promotion of maize, cucumber, and pakchoi cabbage. Each carrier was screened to mix with four strains of co-culture metabolites to prepare highly soluble and quality powders. The results showed that there were different effects of the carriers themselves and Trichoderma strain co-culture metabolite powder prepared with the carriers on seedling emergence rate and seedling growth. Β-cyclodextrin performed best in high solubility and low heat generation upon absorbing water and in easy drying in processing operations. Trichoderma strains co-culture metabolite powder with β-cyclodextrin as a carrier provided the most obvious promotion effects on seedling emergence rate and seedling growth. Therefore, β-cyclodextrin was determined to be an ideal carrier to prepare a highly water-soluble Trichoderma agent. Taken together, the study successfully developed a new type of highly soluble powder containing Trichoderma co-culture metabolites that is expected to benefit farming drip irrigation and spraying systems for the promotion of crop growth and disease control. [ABSTRACT FROM AUTHOR]

Domain generation algorithms (DGAs) play an important role in network attacks and can be mainly divided into two types: dictionary-based and character-based. Dictionary-based algorithmically generated domains (AGDs) are similar in composition to normal domains and are harder to detect. Although methods based on meaningful word segmentation and n-gram sequence features exhibit good detection performance for AGDs, they are inadequate for mining meaningful word features of domain names, and the performance of hybrid detection of character-based and dictionary-based AGDs needs to be further improved. Therefore, in this paper, we first describe the composition of dictionary-based AGDs using meaningful word segmentation, introduce the standard deviation to better measure the word distribution features, and construct additional 11-dimensional statistical features for word segmentation results as a supplement. Then, by combining 3-gram and 1-gram sequence features, we improve the detection performance for both character-based and dictionary-based AGDs. Finally, we perform feature fusion of the above four kinds of features to achieve an end-to-end detection method for both kinds of AGDs. Experimental results showed that our method achieved an accuracy of 97.24% on the full dataset and better accuracy and F1 values than existing methods on both dictionary-based and character-based AGD datasets. [ABSTRACT FROM AUTHOR]

Since the mechanisms of methane-mudstone interactions are important for estimating shale gas reserves, methane adsorption under supercritical conditions of 30 MPa pressure and 303.15, 333.15, 363.15 K temperatures was studied to measure the excess methane adsorption in two mudstone samples from Yanchang Formation, Ordos Basin. Excess adsorption features inflection points where the amount of adsorbed gas changes from increasing to decreasing concentrations. Three methods (fixed, slope, and freely fitted density) were applied to calculate the adsorbed-phase density (rad), which was then used to fit the measured excess adsorption. Two criteria, the goodness-of-fit and whether the fitting can obtain reasonable absolute adsorption, were applied to determine the most accurate model. Results indicated that the supercritical Dubinin-Radushkevich (SDR) model with freely fitted rad was the most reasonable model. The volume of adsorbed methane at 363.15 K is close to the micropore (d < 2 nm) volume of the corresponding mudstone. Considering the actual geological conditions, the adsorbed gas should be predominantly stored in micropores. Thermodynamic parameters reveal that the methane adsorption on mudstone is a physisorption process that is jointly controlled by the heterogeneity of, and interaction forces between the methane molecule and, the rock surface. [ABSTRACT FROM AUTHOR]

Accordingly, the mass unbalance of the rotors is usually the major cause of excessive vibration. The information extracted at the fundamental frequency is often employed to fix the unbalance issue. However, other rotor faults like rotor bending and bearing-failure effect also generate additional components to the characteristics. Thus, it is necessary to isolate the corresponding features and obtain the intrinsic causes of the multiple failures. In this paper, a productive hybrid method is successfully developed to deal with the root mass unbalance problem with additional force interference by integrating the superiority of different methods, including Ensemble Empirical Mode Decomposition (EEMD) and Nonnegative Matrix Factorization (NMF), where EEMD is used to obtain sensitive IMFs and NMF is employed to acquire the inherent source signal, respectively. Meanwhile, a root dynamic balancing and implemental framework is also developed to accomplish the task of vibration reduction. For verification, a serial of simulations and experimental investigations have been analysed to demonstrate the preferable potentialities of the proposed method. In particular, a standard Bently Nevada rotor rig with a specifically designed device was employed to simulate appended faults by adjusting the additional forces during the experimental steps. The analysis results show that the proposed method can isolate and extract the unbalance faults from the raw vibration signals and achieve accurate correction balancers, where a nearly identical correction angle has been achieved, which indicates that the optimal installation position has been successfully figured out. [ABSTRACT FROM AUTHOR]

Recent years have witnessed the rapid development of car-hailing services, which provide a convenient approach for connecting passengers and local drivers using their personal vehicles. At the same time, the concern on passenger safety has gradually emerged and attracted more and more attention. While car-hailing service providers have made considerable efforts on developing real-time trajectory tracking systems and alarm mechanisms, most of them only focus on providing rescue-supporting information rather than preventing potential crimes. Recently, the newly available large-scale car-hailing order data have provided an unparalleled chance for researchers to explore the risky travel area and behavior of car-hailing services, which can be used for building an intelligent crime early warning system. To this end, in this article, we propose a Risky Area and Risky Behavior Evaluation System (RARBEs) based on the real-world car-hailing order data. In RARBEs, we first mine massive multi-source urban data and train an effective area risk prediction model, which estimates area risk at the urban block level. Then, we propose a transverse and longitudinal double detection method, which estimates behavior risk based on two aspects, including fraud trajectory recognition and fraud patterns mining. In particular, we creatively propose a bipartite graph-based algorithm to model the implicit relationship between areas and behaviors, which collaboratively adjusts area risk and behavior risk estimation based on random walk regularization. Finally, extensive experiments on multi-source real-world urban data clearly validate the effectiveness and efficiency of our system. [ABSTRACT FROM AUTHOR]

Currently, Android malware detection methods always focus on one kind of app feature, such as structural, semantic, or other statistical features. This paper proposes a novel Android malware detection method that integrates multiple features of Android applications. First, to effectively extract the structural and semantic features, we propose a new type of call graph named the class-set call graph (CSCG) that uses the sets of Java classes as nodes and the call relationships between class sets as edges, and we design a dynamic adaptive CSCG construction method that can automatically determine the node size for applications with different scales. The topic model is used to mine the source code semantics from the class sets as the node features. Then, we use a graph attention network (GAT) with max pooling to extract the CSCG feature that encompass both the semantic and structural features of the Android application. Furthermore, we construct a deep multimodal feature fusion network to fuse the CSCG features with permission features. Experimental results show that our method achieves a detection accuracy of 97.28%–99.54% on the three constructed datasets, which is better than the existing methods. • Proposing multimodal features deep fusion method for Android malware detection. • Designing class-set call graph to integrates structural and semantic features. • Adaptive class merging method for class-set call graph construction. • Introducing GAT and max pooling to extract salient graph features. • Proposing fusion network for graph features and permission features. [ABSTRACT FROM AUTHOR]

Networks play important roles in modern life, and cyber security has become a vital research area. An intrusion detection system (IDS) which is an important cyber security technique, monitors the state of software and hardware running in the network. Despite decades of development, existing IDSs still face challenges in improving the detection accuracy, reducing the false alarm rate and detecting unknown attacks. To solve the above problems, many researchers have focused on developing IDSs that capitalize on machine learning methods. Machine learning methods can automatically discover the essential differences between normal data and abnormal data with high accuracy. In addition, machine learning methods have strong generalizability, so they are also able to detect unknown attacks. Deep learning is a branch of machine learning, whose performance is remarkable and has become a research hotspot. This survey proposes a taxonomy of IDS that takes data objects as the main dimension to classify and summarize machine learning-based and deep learning-based IDS literature. We believe that this type of taxonomy framework is fit for cyber security researchers. The survey first clarifies the concept and taxonomy of IDSs. Then, the machine learning algorithms frequently used in IDSs, metrics, and benchmark datasets are introduced. Next, combined with the representative literature, we take the proposed taxonomic system as a baseline and explain how to solve key IDS issues with machine learning and deep learning techniques. Finally, challenges and future developments are discussed by reviewing recent representative studies. [ABSTRACT FROM AUTHOR]

Abstract A novel and facile approach to 3-allyl-2-aminoindoles via the copper-catalyzed reaction of 3-diazoindolin-2-imines with allyltrimethylsilane is described. The domino reaction proceeds efficiently in one-pot and shows broad substrate scope with respect to the diazo substrates. Furthermore, the synthesized 3-allyl-2-aminoindoles can be conveniently transformed into 3-allyl-3-hydroxylindolin-2-imines and 3-allyl-3-fluoroindolin-2-imines. Graphical abstract Image 1 [ABSTRACT FROM AUTHOR]

Abstract In recent years, machine learning has been widely applied to problems in detecting network attacks, particularly novel attacks. However, traditional machine learning methods depend heavily on feature engineering, and extracting features is often time-consuming and complex. Thus, it is impractical to detect attacks with traditional machine learning methods in real-time applications. To discover network attacks efficiently, we propose an end-to-end detection approach. We implement deep learning models to analyze payloads and propose a convolutional neural network-based payload classification approach (PL-CNN) and a recurrent neural network-based payload classification approach (PL-RNN) for use in attack detection. Our two approaches learn feature representations from original payloads without feature engineering and support end-to-end detection. These approaches achieve accuracies of 99.36% and 99.98% when applied to the DARPA1998 dataset, respectively; these accuracies are comparable to or better than those of state-of-the-art methods. In addition, our methods are efficient and practical. [ABSTRACT FROM AUTHOR]