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The estimation of gangue content is the main basis for intelligent top coal caving mining by computer vision, and the automatic segmentation of gangue is crucial to computer vision analysis. However, it is still a great challenge due to the degradation of images and the limitation of computing resources. In this paper, a hybrid connected attentional lightweight network (HALNet) with high speed, few parameters and high accuracy is proposed for gangue intelligent segmentation on the conveyor in the top-coal caving face. Firstly, we propose a deep separable dilation convolution block (DSDC) combining deep separable convolution and dilation convolution, which can provide a larger receptive field to learn more information and reduce the size and computational cost of the model. Secondly, a bridging residual learning framework is designed as the basic unit of encoder and decoder to minimize the loss of semantic information in the process of feature extraction. An attention fusion block (AFB) with skip pathway is introduced to capture more representative and distinctive features through the fusion of high-level and low-level features. Finally, the proposed network is trained through the expanded dataset, and the gangue image segmentation results are obtained by pixel-by-pixel classification method. The experimental results show that the proposed HALNet reduces about 57 percentage parameters compared with U-Net, and achieves state-of-the art performance on dataset.

Accurate and rapid prediction of the coal and gas outburst is very significant for preventing accident and protecting environment, the paper presents a novel feature selection and outburst classifier framework which can identify effective candidate features and improve the classification accuracy. First, Apriori is applied for preliminarily extracting the association rules from sample data and attribute features in coal and outburst, and it can present the effective sample data and features for outburst prediction. Second, in order to reduce the redundancy of the strong association rules obtained from Apriori, Boruta is applied for selecting all highly relevant optimal features based on the obtained strong association rules. Third, Random Forest(RF) is used to assign different weights to different features in optimal candidate features considering the importance of different features to outburst, based on the above obtained high-quality sample data and optimal features, the parameters of KNN model optimized by Bayesian Optimization(BO) is used to predict the coal and gas outburst. The experimental results show that the proposed feature selection model Apriori-Boruta can obtain significant sample data, and the proposed RF- KNN optimized classifier model can achieve higher performance in terms of the number of optimal features and prediction accuracy compared with traditional prediction models.

In this paper, the effects of the thermostability, band structure, and the external electric field on the electronic structure of MoSe2/Arsenene heterojunction are calculated based on the density functional theory. The calculation results show that the MoSe2/Arsenene heterojunction is a type-II heterojunction with a band gap of 0.89 eV. When MoSe2 and Arsenene combine by van der Waals force, the electrons are transferred from MoSe2 to Arsenene, the holes are transferred from Arsenene to MoSe2, with the direction of the internal electric field from MoSe2 to Arsenene. The MoSe2/Arsenene heterojunction with valence band top and conduction band bottom contributed by Arsenene and MoSe2, respectively, show excellent thermostability at room temperature. The external electric field can effectively modulate the electronic structure of heterojunction. When the electric field is negative, electrons are transferred from MoSe2 to Arsenene and holes from Arsenene to MoSe2. The Fermi level of Arsenene moves down while that of MoSe2 moves up, with a decreased band gap of the MoSe2/Arsenene heterojunction. When the electric field is positive, the direction of electron and hole transfer are opposite to that when the electric field is negative. The Fermi energy level of Arsenene and MoSe2 also move in the opposite direction, with a first increased then decreased band gap of MoSe2/Arsenene heterojunction. As the electric field increases, the heterojunction eventually transforms from type-II to type-III, achieving the transition from semiconductor to metal.

This research analyzes software vulnerability information from the perspective of software companies. A total of 13019 vulnerabilities from 136 software companies were collected from a public vulnerability database. A latent class model classifies the companies into three classes based on vulnerability information during a five-year period, and then three class-specific models pinpoint the most significant key features of vulnerabilities for each class. A class I company can reduce vulnerability level if it puts focus on "boundary condition errors," "input validation errors," and "exception handling errors." A class II company needs to emphasize "access validation errors" and "race condition errors." Interestingly, a class III company needs to avoid any potential "origin validation errors," "boundary condition errors," "design errors," and "access validation errors." With these significant key features information, software companies can effectively reduce vulnerability by managing related errors throughout the development and testing process.

Introduction:Cordyceps militaris, which has many potential medicinal properties, has rarely been reported to alleviate type 2 diabetes mellitus (T2DM).Methods: The effects of C. militaris extracts (CE) and cordycepin (CCS) on high-fat diet and streptozotocin (STZ) induced T2DM mice were analysed by gut microbiome and metabolomics methods in this study.Results: The results demonstrated that glucose and lipid metabolism parameters, oxidative stress biomarkers and inflammation cytokines were down-regulated in the CCS and CE groups. A comparative analysis of the fecal samples from mice in the model and experimental groups showed that experimental groups resulted in a higher abundance of Firmicutes/Bacteroidetes.Conclusion: This study provides evidence that C. militaris can be used as a food supplement to relieve T2DM, which provides a promising prospect for new functional food in it.

Type 2 diabetes (T2D) constitutes a worldwide health threat, and the underlying mechanism for the development and progression of T2D is complex and multifactorial. During the last decade, gut commensal bacteria have been found to play a crucial role in the regulation of T2D and related metabolic disorders. However, as a considerable component in gut microbiome, the relationship between mycobiota and T2D and related metabolic disorders remains unclear. As a proof-of-concept, we observed that the ablation of the commensal fungi in mice can protect HFD (High fat diet) induced insulin resistance and related metabolic disorders. Both ITS2 (internal transcribed spacer 2) sequencing and culture-dependent analysis show the enrichment of Candida albicans in samples from individuals with T2D (Chinese Clinical Trial Registry, ChiCTR2100042049). Repopulation with C. albicans in HFD mice accelerated insulin resistance and related disorders. Mechanically, we found the β-glucan from C. albicans mirrored the deteriorating effect of C. albicans through the dectin-1 dependent pathway. Our current findings support that gut mycobiota play an important role in the progress of T2D and indicated the preventing of gut mycobiota is a promising strategy to alleviate insulin resistance and related metabolic dysfunctions.

Accurate segmentation of fractures in coal rock CT images is important for the development of coalbed methane. However, due to the large variation of fracture scale and the similarity of gray values between weak fractures and the surrounding matrix, it remains a challenging task. And there is no published dataset of coal rock, which make the task even harder. In this paper, a novel adaptive multi-scale feature fusion method based on U-net (AMSFF-U-net) is proposed for fracture segmentation in coal rock CT images. Specifically, encoder and decoder path consist of residual blocks (ReBlock), respectively. The attention skip concatenation (ASC) module is proposed to capture more representative and distinguishing features by combining the high-level and low-level features of adjacent layers. The adaptive multi-scale feature fusion (AMSFF) module is presented to adaptively fuse different scale feature maps of encoder path; it can effectively capture rich multi-scale features. In response to the lack of coal rock fractures training data, we applied a set of comprehensive data augmentation operations to increase the diversity of training samples. These extensive experiments are conducted via seven state-of-the-art methods (i.e., FCEM, U-net, Res-Unet, Unet++, MSN-Net, WRAU-Net and ours). The experiment results demonstrate that the proposed AMSFF-U-net can achieve better segmentation performance in our works, particularly for weak fractures and tiny scale fractures.

A novel cationic nickel(i) complex exhibiting extremely high efficiency toward the (de)hydrogenative N-alkylation of amines and olefination of alkyl heteroarenes by alcohols is reported.

We have developed a base mediated formamidation of amines with CO under mild conditions, which allows for the synthesis of a wide range of aromatic and aliphatic formamides in high yields and gram amounts in the absence of a transition metal.

Diffusion is a key kinetic factor determining chemical mixing and phase formation in liquids. In multicomponent systems, the presence of different elements makes it experimentally challenging to measure diffusivities and understand their mechanisms. Using a molecular dynamics simulation, we obtain the diffusion constants and the atomic process of a model Cantor alloy liquid made of five equimolar components. We show that the diffusivities conform remarkably well to the Arrhenius law in a wide range of temperature covering both the equilibrium and undercooled liquid regions. The activation energies for all the alloy elements with different bonding energies and atomic sizes are close to each other. The results suggest that the diffusivity in the multicomponent liquid tends to be homogenized by the components with marginal differences. This finding allows us to treat the different elements as a single type of atom, the pseudo-atom, for diffusional and maybe structural and physical properties in multicomponent liquids.

The coal and gas outbursts samples data are affected by all kinds of influencing factors, the accuracy of classification on these sample data is not high, the classification of some samples always have errors, which may be inaccurate annotation. In order to reduce the impact of noise data caused by the labeling errors on classification, this paper proposes a combination of classifier and clustering analysis model, which is used to improve the prediction accuracy of coal and gas outbursts. First, the high-order statistical characteristics of fast independent component analysis(FICA) method are used to extract the features of coal and gas outbursts sample data to obtain the independent nonlinear main metadata; Then, the fuzzy clustering mean (FCM) algorithm is used to cluster the coal and gas outbursts samples. Finally, based on improved clustering analysis results, the support vector machine(SVM) classifier based on quantum particle swarm optimization(QPSO) algorithm is used to compare the classification results with the existing labeled results, and the classification results are corrected to improve the classification accuracy. The results show that the performance of coupling model is significantly better than other exiting classifiers for coal and gas outbursts prediction.

Chronic inflammation and an immunosuppressive microenvironment promote prostate cancer (PCa) progression and diminish the response to immune checkpoint blockade (ICB) therapies. However, it remains unclear how and to what extent these two events are coordinated. Here, we show that ARID1A, a subunit of the SWI/SNF chromatin remodeling complex, functions downstream of inflammation-induced IKKβ activation to shape the immunosuppressive tumor microenvironment (TME). Prostate-specific deletion of Arid1a cooperates with Pten loss to accelerate prostate tumorigenesis. We identify polymorphonuclear myeloid-derived suppressor cells (PMN-MDSCs) as the major infiltrating immune cell type that causes immune evasion and reveal that neutralization of PMN-MDSCs restricts the progression of Arid1a-deficient tumors. Mechanistically, inflammatory cues activate IKKβ to phosphorylate ARID1A, leading to its degradation via β-TRCP. ARID1A downregulation in turn silences the enhancer of A20 deubiquitinase, a critical negative regulator of NF-κB signaling, and thereby unleashes CXCR2 ligand-mediated MDSC chemotaxis. Importantly, our results support the therapeutic strategy of anti-NF-κB antibody or targeting CXCR2 combined with ICB for advanced PCa. Together, our findings highlight that the IKKβ/ARID1A/NF-κB feedback axis integrates inflammation and immunosuppression to promote PCa progression.

The balance of the segmented genome derived from naturally occurring influenza A viruses (IAVs) is delicate and vulnerable to foreign insertions, thus most reporter IAVs up to date are generated using the backbone of the laboratory-adapted strains. In this study, we constructed a reporter influenza A/H3N2 virus (A/NY-HiBiT) which was derived from a clinical isolate, by placing a minimized HiBiT tag to the N-terminus of the viral nuclear-export protein (NEP). Here, we show that this 11-amino acid HiBiT tag did not adversely impact the viral genome balance, and the recombinant A/NY-HiBiT virus maintains its relative stability. Moreover, the replication profile of the HiBiT-tagged virus can be measured by a simple Nano-Glo assay, providing a robust high-throughput screening (THS) platform. We used this platform to evaluate a collection of the pre-purified fractions which were derived from rare Chinese medicinal materials, and we identified three fractions, including wild Trametes robiniophila (50% methanol fraction), Ganoderma (water fraction), and wild Phellinus igniarius (ethyl acetate fraction), as potent anti-IAV actives. Our results demonstrate that this IAV reporter can be used as a powerful HTS platform for antiviral development.

BACKGROUND: We investigated the efficacy and metabolic dose-effect of multi-trace element injection I [MTEI-(I)] for severe pediatric patients via a parallel, randomized control study. METHODS: The inclusion criteria were as follows: (I) patients who required parenteral nutrition (PN) due to various diseases, and were expected to receive PN for >5 days; (II) patients aged

Summary Catalytic reactions that convert green or sustainable starting materials into important classes of chemical compounds, generate hydrogen, and are mediated by Earth-abundant element catalysts are highly desirable. Here, we report on a catalytic dehydrogenative rearrangement of esters into 1,3-diketones. Esters are green or sustainable and inexpensive starting materials that are available in great diversity, and 1,3-diketones are highly attractive building blocks for organic synthesis. The concerted interaction of a base-acid catalyst and a dehydrogenation catalyst mediates our reaction. The dehydrogenation catalyst is based on manganese, and the hydrogen formed can be liberated and is the only by-product formed.

The use of sequence-defined digital polymers for data storage and encryption has received increasing attention due to their precision structures similar to natural biomacromolecules (e.g., DNA) but increased stability. However, the rapid development of sequencing techniques raises the concern of information leakage. Herein, dendritic quaternary-encoded oligourethanes bearing a photoresponsive trigger, self-immolative backbones, and a mass spectrometry tag of PEG dendron have been developed for data encryption. Although the sequence information in linear analogs can be readily deciphered by mass spectrometry, sequencing of dendritic oligourethanes cannot be achieved by either primary MS or tandem MS/MS owing to the unique spatial conformation. Intriguingly, the fragmentation pathways of a quaternary dendrimer under MS/MS conditions can be converted to 2772-bit 2D matrices with ≈1.98×10

With the rapid development of mobile energy storage technology and electric vehicle technology, there are higher requirements on the flexible and convenient interface of mobile energy storage vehicle. This paper presents a gun/seat integrated control system for mobile energy storage vehicle. The integrated system model of the charging gun/charging base is established, the principle block diagram is drawn, the recognition principle and discrimination method of the plug-in state of the double head gun are analyzed, and the vehicle/pile switching function and anti-reverse connection function are designed under the condition of meeting the national standard. The main controller coordinates and controls the charging process of the charging pile and the power supplement process when it is used as a mobile energy storage vehicle. The converter is the hub of the mobile energy storage vehicle and the power grid. Through the real-time sampling of the power grid information and the double loop control strategy, the mobile energy storage vehicle has the power quality control functions such as reactive power compensation, harmonic control and imbalance control while completing its normal functions.

Metabolomics is an essential method to study the dynamic changes of metabolic networks and products using modern analytical techniques, as well as reveal the life phenomena and their inherent laws. Currently, more and more attention has been paid to the development of metabolic histochemistry in the fungus field. This paper reviews the application of metabolomics in fungal research from five aspects: identification, response to stress, metabolite discovery, metabolism engineering, and fungal interactions with plants.

This article has been retracted. Please see the Retraction Notice for more detail: https://doi.org/10.1186/s12885-019-5750-x