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By training the deep neural network model, the hidden features in Surface Electromyography(sEMG) signals can be extracted. The motion intention of the human can be predicted by analysis of sEMG. However, the models recently proposed by researchers often have a large number of parameters. Therefore, we designed a compact Convolution Neural Network (CNN) model, which not only improves the classification accuracy but also reduces the number of parameters in the model. Our proposed model was validated on the Ninapro DB5 Dataset and the Myo Dataset. The classification accuracy of gesture recognition achieved good results.

Ubiquitination, a pivotal posttranslational modification of proteins, plays a fundamental role in regulating protein stability. The dysregulation of ubiquitinating and deubiquitinating enzymes is a common feature in various cancers, underscoring the imperative to investigate ubiquitin ligases and deubiquitinases (DUBs) for insights into oncogenic processes and the development of therapeutic interventions. In this review, we discuss the contributions of the ubiquitin–proteasome system (UPS) in all hallmarks of cancer and progress in drug discovery. We delve into the multiple functions of the UPS in oncology, including its regulation of multiple cancer-associated pathways, its role in metabolic reprogramming, its engagement with tumor immune responses, its function in phenotypic plasticity and polymorphic microbiomes, and other essential cellular functions. Furthermore, we provide a comprehensive overview of novel anticancer strategies that leverage the UPS, including the development and application of proteolysis targeting chimeras (PROTACs) and molecular glues.

Background Drug resistance is a critical obstacle in cancer therapy. Discovering cancer drug response is important to improve anti-cancer drug treatment and guide anti-cancer drug design. Abundant genomic and drug response resources of cancer cell lines provide unprecedented opportunities for such study. However, cancer cell lines cannot fully reflect heterogeneous tumor microenvironments. Transferring knowledge studied from in vitro cell lines to single-cell and clinical data will be a promising direction to better understand drug resistance. Most current studies include single nucleotide variants (SNV) as features and focus on improving predictive ability of cancer drug response on cell lines. However, obtaining accurate SNVs from clinical tumor samples and single-cell data is not reliable. This makes it difficult to generalize such SNV-based models to clinical tumor data or single-cell level studies in the future. Results We present a new method, DualGCN, a unified Dual Graph Convolutional Network model to predict cancer drug response. DualGCN encodes both chemical structures of drugs and omics data of biological samples using graph convolutional networks. Then the two embeddings are fed into a multilayer perceptron to predict drug response. DualGCN incorporates prior knowledge on cancer-related genes and protein–protein interactions, and outperforms most state-of-the-art methods while avoiding using large-scale SNV data. Conclusions The proposed method outperforms most state-of-the-art methods in predicting cancer drug response without the use of large-scale SNV data. These favorable results indicate its potential to be extended to clinical and single-cell tumor samples and advancements in precision medicine.

The researchers examined the associations between thinking styles and grit. A cross-sectional design was adopted, with two weeks of data collection. The Thinking Styles Inventory-Revised II and the Grit Scale were administered to 365 signing deaf or hard-of-hearing (DHH) Arts and Design students and 443 hearing university students in mainland China. CFA, MANOVA, hierarchical multiple regression analyses, and a multi-group analysis were executed for data analysis. DHH and hearing students with Type I styles (i.e., more creativity-generating, less structured, and cognitively more complex) had higher grit levels, with large effect sizes for the identified relationships. There were no differences in the relations for either group. The associations between thinking styles and grit may protect against psychological pressure and rehabilitation problems and enable university/school administrators, counselors, social workers, teachers, parents, and students to enhance the grit of students who are deaf or hard of hearing.

Prediction of turbulent flow is required for design and assessment of clarifier systems that have been implemented throughout history to treat water in the urban water cycle through physical clarification. Yet, turbulent flow modeling is a relatively new tool that has not existed until the last half-century and can be, and often is, a tenuous component in a computational fluid dynamics simulation of unit operations and processes. Common Reynolds-averaged Navier–Stokes equation (RANS) approaches can be inadequate to obtain consistent and accurate flow solutions. In contrast, this study presents an application of large-eddy simulations (LES) for a clarification system with a high-order spectral element method employing 48 million degrees of freedom. Turbulent and unsteady flow characteristics are investigated, and statistics are examined for such a system. Simulation results are compared with laser Doppler anemometry measurements for mean flow velocity, turbulence kinetic energy, and Reynolds shear stress. LES results agree well with measurements, and the differences between LES and measurements are generally less than the reported measurement error. LES results capture the transition behavior from a jet-like flow at the near-inlet region to an open-channel flow at the downstream end of the system. Furthermore, LES results reveal that the widely adopted log-law of a classical turbulent boundary layer is not established in the system even at the most downstream location. Preliminary examination of commonly used RANS models identifies the challenges in application of RANS to such systems. The results from this study provide a benchmark for turbulence modeling of common water clarification systems.

Abstract Over the past decade, high-throughput RNA sequencing (RNA-seq) has vastly expanded our understanding of transcriptome dynamics in human physiology and disease. As a powerful tool for investigating systematic changes in RNA biology, RNA-seq has facilitated the discovery of novel functional RNA species. Mature RNA transcripts, which transmit genetic information from DNA to proteins, undergo intricate transcriptional and post-transcriptional regulation. This process allows a single gene to produce multiple RNA transcripts, each performing specific functions depending on the physiological or pathological context. Specific RNA transcripts (SRTs) are uniquely expressed in particular tissues or tumors and are closely associated with tissue-specific functions or disease states, particularly cancer. This review explores the generation of SRTs through key mechanisms, such as alternative splicing (AS), transcriptional regulation, polyadenylation (polyA), and the influence of transposable elements (TEs). We also examine their critical roles in normal tissue development and diseases, with an emphasis on their relevance to cancer. Furthermore, the potential applications of SRTs in diagnosing and treating diseases, especially malignancies, are discussed. By serving as diagnostic markers and therapeutic targets, SRTs hold significant promise in the development of personalized medicine and precision therapies. This review aims to provide new insights into the importance of SRTs in advancing the understanding and treatment of human diseases.

In this paper, a modified Green-Naghdi system with the effect of the Coriolis force is derived, which is a model in the equatorial oceanography to describe the propagation of large amplitude surface waves. The effects of the Coriolis force caused by the Earth’s rotation and nonlinearities on local well-posedness and traveling wave solutions are then investigated. Employing Kato’s theory, the local well-posedness in Sobolev space H s with s > 5 2 is established. Based on the qualitative method combined with the bifurcation method of dynamical systems, the classification of all traveling wave solutions, all possible phase portraits of bifurcations and exact traveling wave solutions to this system are obtained under various conditions about the parameters depending on the value of the rotation Ω

Conformation capture technologies (e.g., Hi-C) chart physical interactions between chromatin regions on a genome-wide scale. However, the structural variability of the genome between cells poses a great challenge to interpreting ensemble-averaged Hi-C data, particularly for long-range and interchromosomal interactions. Here, we present a probabilistic approach for deconvoluting Hi-C data into a model population of distinct diploid 3D genome structures, which facilitates the detection of chromatin interactions likely to co-occur in individual cells. Our approach incorporates the stochastic nature of chromosome conformations and allows a detailed analysis of alternative chromatin structure states. For example, we predict and experimentally confirm the presence of large centromere clusters with distinct chromosome compositions varying between individual cells. The stability of these clusters varies greatly with their chromosome identities. We show that these chromosome-specific clusters can play a key role in the overall chromosome positioning in the nucleus and stabilizing specific chromatin interactions. By explicitly considering genome structural variability, our population-based method provides an important tool for revealing novel insights into the key factors shaping the spatial genome organization.

Distributed Energy System (DES), a comprehensive energy utilization system distributed on user side, has been recognized as a promising energy utilization method that can improve energy efficiency, reduce greenhouse gas emissions, and achieve sustainable development. However, the DES is usually driven by various energy sources, and it is a complex issue to decide the composition of the system. To improve the incompleteness of a single subjective or objective assessment. So, it is urgent to find a comprehensive and efficient decision-making method for different systems. This paper states a total of 23 indicators in 4 criterion group: technology, economy, environment, and society. Based on the combination of the order relation analysis method (G1) and the anti-entropy weighting method (a-EWM), a comprehensive evaluation model, order relation-anti-entropy weight model (G1-aEWM), of distributed energy is established. This comprehensive evaluation model is used to analyze a hospital in Henan and find the final solution for the distributed energy system of the hospital. The empirical analysis results verify the rationality of the comprehensive evaluation model and provide an evaluation basis for the establishment of distributed energy systems in the future.

Background The Persian walnut ( Juglans regia ), an economically vital species within the Juglandaceae family, has seen its mitochondrial genome sequenced and assembled in the current study using advanced Illumina and Nanopore sequencing technology. Results The 1,007,576 bp mitogenome of J. regia consisted of three circular chromosomes with a 44.52% GC content encoding 39 PCGs, 47 tRNA, and five rRNA genes. Extensive repetitive sequences, including 320 SSRs, 512 interspersed, and 83 tandem repeats, were identified, contributing to genomic complexity. The protein-coding sequences (PCGs) favored A/T-ending codons, and the codon usage bias was primarily shaped by selective pressure. Intracellular gene transfer occurred among the mitogenome, chloroplast, and nuclear genomes. Comparative genomic analysis unveiled abundant structure and sequence variation among J. regia and related species. The results of selective pressure analysis indicated that most PCGs underwent purifying selection, whereas the atp4 and ccmB genes had experienced positive selection between many species pairs. In addition, the phylogenetic examination, grounded in mitochondrial genome data, precisely delineated the evolutionary and taxonomic relationships of J. regia and its relatives. We identified a total of 539 RNA editing sites, among which 288 were corroborated by transcriptome sequencing data. Furthermore, expression profiling under temperature stress highlighted the complex regulation pattern of 28 differently expressed PCGs, wherein NADH dehydrogenase and ATP synthase genes might be critical in the mitochondria response to cold stress. Conclusions Our results provided valuable molecular resources for understanding the genetic characteristics of J. regia and offered novel perspectives for population genetics and evolutionary studies in Juglans and related woody species.