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The online calibration method for current transformers is an important research direction in the field of smart grids. This article constructs a transformer error prediction model based on spatiotemporal integration. This model draws inspiration from the structure of forgetting gates in gated loop units and combines it with a graph convolutional network (GCN) that is good at capturing the spatial relationships within the graph attention network to construct an adaptive GCN. The spatial module formed by this adaptive GCN is used to model the spatial relationships in the circuit network, and the attention mechanism and gated time convolutional network are combined to form a time module to learn the temporal relationships in the circuit network. The layer that combines the time and space modules is used, which consists of a gating mechanism for spatiotemporal fusion, and a transformer error prediction model based on a spatiotemporal correlation analysis is constructed. Finally, it is verified on a real power grid operation dataset, and compared with the existing prediction methods to analyze its performance.

Abstract In recent years, cannabidiol (CBD), a non‐psychotropic cannabinoid, has garnered substantial interest in drug development due to its broad pharmacological activity and multi‐target effects. Diabetes is a chronic metabolic disease that can damage multiple organs in the body, leading to the development of complications such as abnormal kidney function, vision loss, neuropathy, and cardiovascular disease. CBD has demonstrated significant therapeutic potential in treating diabetes mellitus and its complications owing to its various pharmacological effects. This work summarizes the role of CBD in diabetes and its impact on complications such as cardiovascular dysfunction, nephropathy, retinopathy, and neuropathy. Strategies for discovering molecular targets for CBD in the treatment of diabetes and its complications are also proposed. Moreover, ways to optimize the structure of CBD based on known targets to generate new CBD analogues are explored.

Abstract The noise and redundant information are the main reasons for the performance bottleneck of medical image segmentation algorithms based on the deep learning. To this end, we propose a deep network embedded with rough fuzzy discretization (RFDDN) for OCT fundus image segmentation. Firstly, we establish the information decision table of OCT fundus image segmentation, and regard each category of segmentation region as a fuzzy set. Then, we use the fuzzy c-means clustering to get the membership degrees of pixels to each segmentation region. According to membership functions and the equivalence relation generated by the brightness attribute, we design the individual fitness function based on the rough fuzzy set, and use a genetic algorithm to search for the best breakpoints to discretize the features of OCT fundus images. Finally, we take the feature discretization based on the rough fuzzy set as the pre-module of the deep neural network, and introduce the deep supervised attention mechanism to obtain the important multi-scale information. We compare RFDDN with U-Net, ReLayNet, CE-Net, MultiResUNet, and ISCLNet on the two groups of 3D retinal OCT data. RFDDN is superior to the other five methods on all evaluation indicators. The results obtained by ISCLNet are the second only inferior to those obtained by RFDDN. DSC, sensitivity, and specificity of RFDDN are evenly 3.3%, 2.6%, and 7.1% higher than those of ISCLNet, respectively. HD95 and ASD of RFDDN are evenly 6.6% and 19.7% lower than those of ISCLNet, respectively. The experimental results show that our method can effectively eliminate the noise and redundant information in Oct fundus images, and greatly improve the accuracy of OCT fundus image segmentation while taking into account the interpretability and computational efficiency.

Most of the research on disease recognition in chest X-rays is limited to segmentation and classification, but the problem of inaccurate recognition in edges and small parts makes doctors spend more time making judgments. In this paper, we propose a lesion detection method based on a scalable attention residual CNN (SAR-CNN), which uses target detection to identify and locate diseases in chest X-rays and greatly improves work efficiency. We designed a multi-convolution feature fusion block (MFFB), tree-structured aggregation module (TSAM), and scalable channel and spatial attention (SCSA), which can effectively alleviate the difficulties in chest X-ray recognition caused by single resolution, weak communication of features of different layers, and lack of attention fusion, respectively. These three modules are embeddable and can be easily combined with other networks. Through a large number of experiments on the largest public lung chest radiograph detection dataset, VinDr-CXR, the mean average precision (mAP) of the proposed method was improved from 12.83% to 15.75% in the case of the PASCAL VOC 2010 standard, with IoU > 0.4, which exceeds the existing mainstream deep learning model. In addition, the proposed model has a lower complexity and faster reasoning speed, which is conducive to the implementation of computer-aided systems and provides referential solutions for relevant communities.

Abstract Zeroing neural networks (ZNN) have shown their state-of-the-art performance on dynamic problems. However, ZNNs are vulnerable to perturbations, which causes reliability concerns in these models owing to the potentially severe consequences. Although it has been reported that some models possess enhanced robustness but cost worse convergence speed. In order to address these problems, a robust neural dynamic with an adaptive coefficient (RNDAC) model is proposed, aided by the novel adaptive activation function and robust evolution formula to boost convergence speed and preserve robustness accuracy. In order to validate and analyze the performance of the RNDAC model, it is applied to solve the dynamic matrix square root (DMSR) problem. Related experiment results show that the RNDAC model reliably solves the DMSR question perturbed by various noises. Using the RNDAC model, we are able to reduce the residual error from 10 $$^1$$ 1 to 10 $$^{-4}$$ - 4 with noise perturbed and reached a satisfying and competitive convergence speed, which converges within 3 s.

Interleukin-18 (IL18) has a pivotal role in interferon signalling and T cell development, but increasingly recognized as an adipokine that regulates energy metabolism in fat tissue. Authors here dissect the function of IL18 signalling in the adipose compartment by targeted genomic deletion of its two receptors individually and in combination in brown and white adipose tissues.

Bayesian Hierarchical models has been widely used in modern statistical application. To deal with the data having complex structures, we propose a generalized hierarchical normal linear (GHNL) model which accommodates arbitrarily many levels, usual design matrices and ‘vanilla’ covariance matrices. Objective hyperpriors can be employed for the GHNL model to express ignorance or match frequentist properties, yet the common objective Bayesian approaches are infeasible or fraught with danger in hierarchical modelling. To tackle this issue, [Berger, J., Sun, D., & Song, C. (2020b). An objective prior for hyperparameters in normal hierarchical models. Journal of Multivariate Analysis, 178, 104606. https://doi.org/10.1016/j.jmva.2020.104606] proposed a particular objective prior and investigated its properties comprehensively. Posterior propriety is important for the choice of priors to guarantee the convergence of MCMC samplers. James Berger conjectured that the resulting posterior is proper for a hierarchical normal model with arbitrarily many levels, a rigorous proof of which was not given, however. In this paper, we complete this story and provide an user-friendly guidance. One main contribution of this paper is to propose a new technique for deriving an elaborate upper bound on the integrated likelihood, but also one unified approach to checking the posterior propriety for linear models. An efficient Gibbs sampling method is also introduced and outperforms other sampling approaches considerably.

Abstract Background Systemic sclerosis (SSc) is an incurable autoimmune disease characterized by progressive skin fibrosis and organ failure. Tenosynovitis is a common musculoskeletal manifestation, but tendon rupture has seldom reported in SSc. Case presentation We present a rare case of a 49-year-old female with SSc who has suffered from bilateral tendon rupture of the fourth and fifth digits with positive antinuclear antibody (ANA) and anti-centromere B antibody, but negative rheumatoid factor in serum. In the extensor tendons of the patient’s hands, inflammation, edema, hypertrophy and tendon interruption were detected with ultrasound and magnetic resonance imaging(MRI). Tendon transfer repair surgery was performed and 10 mg/week methotrexate was then used in this patient. Her hand function was improved well with methotrexate and rehabilitation treatment postoperatively. Conclusions Early detection of tenosynovitis is necessary to prevent tendon rupture in SSc patients. Ultrasound and Magnetic Resonance Imaging appear to be useful examinations for evaluating tendon pathology for early detection.

Abstract Systemic sclerosis (SSc) is an autoimmune rheumatic disease with high mortality, which is featured by inflammation, vascular damage, and aggressive fibrosis. To date, the pathogenesis of SSc remains unclear and effective treatments are still under research. Active NLRP3 recruits downstream proteins such as ASC and caspase-1 and assembles into inflammasome, resulting in excretion of inflammatory cytokines including IL-1β and IL-18, as well as in pyroptosis mediated by gasdermin D. Various studies demonstrated that NLRP3 inflammasome might be involved in the mechanism of tenosynovitis, arthritis, fibrosis, and vascular damage. The pathophysiological changes might be due to the activation of proinflammatory Th2 cells, profibrotic M2 macrophages, B cells, fibroblasts, and endothelial cells. Here, we review the studies focused on NLRP3 inflammasome activation, its association with innate and adaptive immune cells, endothelium injury, and differentiation of fibroblasts in SSc. Furthermore, we summarize the prospect of therapy targeting NLRP3 pathway.

Background: Whole-genome bisulfite sequencing (WGBS) technology can provide comprehensive DNA methylation at a single-base resolution on a genome-wide scale, and is considered to be the gold standard for the detection of 5-methylcytosine (5 mC). However, the International Human Epigenome Consortium propose a full DNA methylome should have at least 30 fold redundant coverage of the reference genome from a single biological replicate. Therefore, it remains cost prohibitive for large-scale studies. To find a solution, the DNBSEQ-Tx sequencing was developed that can generate up to 6 Tb data in a single run for projects involving large-scale sequencing. Results: In this study, we provided two WGBS library construction methods DNB_PREBSseq and DNB_SPLATseq optimized for the DNBSEQ-Tx sequencer, and demonstrated the performance of these two methods on the DNBSEQ-Tx platform, using the DNA extracted from four different cell lines. We also compared the sequencing data from these two WGBS library construction methods with HeLa cell line data from ENCODE sequenced on Illumina HiSeq X Ten and WGBS data of two other cell lines sequenced on HiSeq2500. Various quality control (QC) analyses such as the base quality scores, methylation-bias (m-bias), and conversion efficiency indicated that the data sequenced on the DNBSEQ-Tx platform met the WGBS-required quality controls. Meanwhile, our data closely resembled the coverage shown by the data generated by the Illumina platform. Conclusions: Our study showed that with our optimized methods, DNBSEQ-Tx could generate high-quality WGBS data with relatively good stability for large-scale WGBS sequencing applications. Thus, we conclude that DNBSEQ-Tx can be used for a wide range of WGBS research.

The widespread use of pharmaceuticals and personal care products (PPCPs) in many fields has brought convenience to human lives but has also caused unavoidable environmental pollution issues. In particular, the resistance gene problem resulting from accumulating antibiotics that cannot be fully absorbed by biological individuals has been a concern; thus, it is urgent to find efficient technologies to boost the degradation efficiency of antibiotics in the environment. Here, an ε-MnO2 catalyst was prepared by a novel droplet-interface-drying method and utilized as a Fenton-like catalyst for efficiently degrading ciprofloxacin (CIP). The ε-MnO2 shell was formed preferentially at the gas–liquid interface and then continued to decompose into ε-MnO2 with abundant O vacancies in the air-insulated microcavity. The XPS result confirms that this particular preparation method can regulate the content of O vacancies in the material. Compared with ε-MnO2 samples obtained by the direct drying method (ε-MnO2-B), the catalytic performance of ε-MnO2 prepared by the droplet-interface-drying method (ε-MnO2-P) is significantly improved. By activating peroxymonosulfate (PMS) with the ε-MnO2-P catalyst, the CIP degradation efficiency can reach 84.1%. The detection and analysis of reactive oxygen species (ROS) in the ε-MnO2-P/PMS oxidation system confirms that ·OH, SO4·− and 1O2 are the main ROS for CIP degradation. This study highlights the creation of miniature hypoxic space to regulate the content of O vacancies in ε-MnO2, providing a new idea for the synthesis of other O-vacancy-rich materials.

Structure determination of covalent organic frameworks (COFs) is the key to pushing the development of COF-based materials further but precise determination of the structure of COFs is challenging. Here, the authors develop a universal ab initio structure determination method for polycrystalline 3D COFs using cryo-cRED by combining hierarchical cluster analysis with cryo-EM technique and demonstrate COF structures with atomic preciscion and up to 0.79-angstrom resolution.

For the unmet need to choose the suitable doublet detection method, an ensemble machine learning algorithm called Chord was developed, which integrates multiple methods and achieves higher accuracy and stability on different scRNA-seq datasets.

External stimuli induced effective regulations of luminescent material are of significant interest in the development of smart optical devices. Here, by simply doping with Er3+ in the 0.94Bi0.5Na0.5TiO3-0.06BaTiO3 (BNTBT) ferroelectric host, using the bendable mica substrate, and exerting mechanical strain (bending) or light illumination (via photochromic reaction), the all-inorganic, highly-transparent and flexible Er-doped BNTBT/Mica luminescent-ferroelectric thin films were designed and fabricated, displaying strain-induced dramatically elevation of up-conversion photoluminescence (PL) intensity, suppression of PL concentration quenching, outstanding endurance and durability, convenient illumination-mediated PL quenching. And the strain-induced structural changes and local lattice distortions of the thin films were further explored through theoretical calculations and Raman measurement. Our results can supply the guidance of designing other luminescent-ferroelectric materials with controlled PL properties via easy mechanical/photo stimuli for expanding the application of multifunctional wearable memory devices.