In order to improve the science and rationality of physical education, reduce various sports injury situations and improve students’ physical fitness. The prediction model method proposed in this ...paper organizes college and university students to participate in functional movement screening and selective functional movement assessment experiments to grasp the actual situation of students in physical exercise and to propose corresponding countermeasures. The prediction model takes advantage of the correlation nature of the time series, converts the vector autoregressive model into a linear regression model for research, then considers the graph regularization penalty function, combines this method with the bridge penalty, adds the correlation sign information between the variables, and uses the coordinate descent method for estimation, and finally proposes the vector autoregressive correlation prediction model method based on the bridge and the graph regularization. After the intervention-corrected training, the FMS scores of the college students were all improved, and the total score increased from the previous 12.39 to 17.51. Changes in students’ strength qualities before and after the experiment Except for pull-ups, there were significant interactions between students’ standing long jump scores, vertical long jump scores, grip strength, 1-minute push-ups, and 1-minute sit-ups in terms of time and group. This study led to an improvement in students’ physical functioning and reduced the risk of injury during physical activity.
This letter considers a simultaneous wireless information and power transfer nonorthogonal multiple access network, where the relay is energy constrained and harvests energy from the source radio ...frequency signals using a time-switching protocol. Our analysis accounts for imperfect channel state information (ICSI) and residual hardware impairments (RHIs). To characterize the effects of the two imperfections brought in the considered network, the outage probability (OP) and throughput are investigated. More particularly, we derive a lower bound on the OP in closed-form, as well as the asymptotic OP and diversity order. We also investigate the throughput in the delay-limited transmission mode in terms of deriving corresponding expression. It is observed that the performance of system is limited by RHIs and ICSI, which results in error floor for the OP and zero diversity order. In addition, a key conclusion is that by carefully selecting the ratio factor between the energy harvesting and information transmission phases, the throughput can be maximized.
Abstract
Motivation
Annotation of enzyme function has a broad range of applications, such as metagenomics, industrial biotechnology, and diagnosis of enzyme deficiency-caused diseases. However, the ...time and resource required make it prohibitively expensive to experimentally determine the function of every enzyme. Therefore, computational enzyme function prediction has become increasingly important. In this paper, we develop such an approach, determining the enzyme function by predicting the Enzyme Commission number.
Results
We propose an end-to-end feature selection and classification model training approach, as well as an automatic and robust feature dimensionality uniformization method, DEEPre, in the field of enzyme function prediction. Instead of extracting manually crafted features from enzyme sequences, our model takes the raw sequence encoding as inputs, extracting convolutional and sequential features from the raw encoding based on the classification result to directly improve the prediction performance. The thorough cross-fold validation experiments conducted on two large-scale datasets show that DEEPre improves the prediction performance over the previous state-of-the-art methods. In addition, our server outperforms five other servers in determining the main class of enzymes on a separate low-homology dataset. Two case studies demonstrate DEEPre's ability to capture the functional difference of enzyme isoforms.
Availability and implementation
The server could be accessed freely at http://www.cbrc.kaust.edu.sa/DEEPre.
Supplementary information
Supplementary data are available at Bioinformatics online.
Monolayer graphene exhibits extraordinary properties owing to the unique, regular arrangement of atoms in it. However, graphene is usually modified for specific applications, which introduces ...disorder. This article presents details of graphene structure, including sp
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hybridization, critical parameters of the unit cell, formation of σ and π bonds, electronic band structure, edge orientations, and the number and stacking order of graphene layers. We also discuss topics related to the creation and configuration of disorders in graphene, such as corrugations, topological defects, vacancies, adatoms and sp
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-defects. The effects of these disorders on the electrical, thermal, chemical and mechanical properties of graphene are analyzed subsequently. Finally, we review previous work on the modulation of structural defects in graphene for specific applications.
A novel computer-aided detection method based on deep learning framework was proposed to detect small intestinal ulcer and erosion in wireless capsule endoscopy (WCE) images. To the best of our ...knowledge, this is the first time that deep learning framework has been exploited on automated ulcer and erosion detection in WCE images. Compared with the traditional detection method, deep learning framework can produce image features directly from the data and increase recognition accuracy as well as efficiency, especially for big data. The developed method included image cropping and image compression. The AlexNet convolutional neural network was trained to the database with tens of thousands of WCE images to differentiate lesion and normal tissue. The results of ulcer and erosion detection reached a high accuracy of 95.16% and 95.34%, sensitivity of 96.80% and 93.67%, and specificity of 94.79% and 95.98%, correspondingly. The area under the receiver operating characteristic curve was over 0.98 in both of the networks. The promising results indicate that the proposed method has the potential to work in tandem with doctors to efficiently detect intestinal ulcer and erosion.
The transcription factor EGR1 is a tumor suppressor gene that is downregulated in many cancer types. Clinically, loss of EGR1 translates to increased tumor transformation and subsequent patient ...morbidity and mortality. In synovial sarcoma, the SS18-SSX fusion protein represses EGR1 expression through a direct association with the EGR1 promoter. However, the mechanism through which EGR1 becomes downregulated in other tumor types is unclear. Here, we report that EGR1 is regulated by microRNA (miR)-183 in multiple tumor types including synovial sarcoma, rhabdomyosarcoma (RMS), and colon cancer. Using an integrative network analysis, we identified that miR-183 is significantly overexpressed in these tumor types as well as in corresponding tumor cell lines. Bioinformatic analyses suggested that miR-183 could target EGR1 mRNA and this specific interaction was validated in vitro. miR-183 knockdown in synovial sarcoma, RMS, and colon cancer cell lines revealed deregulation of a miRNA network composed of miR-183-EGR1-PTEN in these tumors. Integrated miRNA- and mRNA-based genomic analyses indicated that miR-183 is an important contributor to cell migration in these tumor types and this result was functionally validated to be occurring via an EGR1-based mechanism. In conclusion, our findings have significant implications in the mechanisms underlying EGR1 regulation in cancers. miR-183 has a potential oncogenic role through the regulation of 2 tumor suppressor genes, EGR1 and PTEN, and the deregulation of this fundamental miRNA regulatory network may be central to many tumor types.
Advanced tumours are often heterogeneous, consisting of subclones with various genetic alterations and functional roles. The precise molecular features that characterize the contributions of ...multiscale intratumour heterogeneity to malignant progression, metastasis, and poor survival are largely unknown. Here, we address these challenges in breast cancer by defining the landscape of heterogeneous tumour subclones and their biological functions using radiogenomic signatures. Molecular heterogeneity is identified by a fully unsupervised deconvolution of gene expression data. Relative prevalence of two subclones associated with cell cycle and primary immunodeficiency pathways identifies patients with significantly different survival outcomes. Radiogenomic signatures of imaging scale heterogeneity are extracted and used to classify patients into groups with distinct subclone compositions. Prognostic value is confirmed by survival analysis accounting for clinical variables. These findings provide insight into how a radiogenomic analysis can identify the biological activities of specific subclones that predict prognosis in a noninvasive and clinically relevant manner.
We herein report a two-step strategy for oxidative cleavage of lignin C–C bond to aromatic acids and phenols with molecular oxygen as oxidant. In the first step, lignin β-O-4 alcohol was oxidized to ...β-O-4 ketone over a VOSO4/TEMPO (2,2,6,6-tetramethylpiperidin-1-yl)oxyl) catalyst. In the second step, the C–C bond of β-O-4 linkages was selectively cleaved to acids and phenols by oxidation over a Cu/1,10-phenanthroline catalyst. Computational investigations suggested a copper-oxo-bridged dimer was the catalytically active site for hydrogen-abstraction from Cβ–H bond, which was the rate-determining step for the C–C bond cleavage.
The purpose of this study was to investigate the role of features derived from breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and to incorporated clinical information to ...predict the molecular subtypes of breast cancer. In particular, 60 breast cancers with the following four molecular subtypes were analyzed: luminal A, luminal B, human epidermal growth factor receptor-2 (HER2)-over-expressing and basal-like. The breast region was segmented and the suspicious tumor was depicted on sequentially scanned MR images from each case. In total, 90 features were obtained, including 88 imaging features related to morphology and texture as well as dynamic features from tumor and background parenchymal enhancement (BPE) and 2 clinical information-based parameters, namely, age and menopausal status. An evolutionary algorithm was used to select an optimal subset of features for classification. Using these features, we trained a multi-class logistic regression classifier that calculated the area under the receiver operating characteristic curve (AUC). The results of a prediction model using 24 selected features showed high overall classification performance, with an AUC value of 0.869. The predictive model discriminated among the luminal A, luminal B, HER2 and basal-like subtypes, with AUC values of 0.867, 0.786, 0.888 and 0.923, respectively. An additional independent dataset with 36 patients was utilized to validate the results. A similar classification analysis of the validation dataset showed an AUC of 0.872 using 15 image features, 10 of which were identical to those from the first cohort. We identified clinical information and 3D imaging features from DCE-MRI as candidate biomarkers for discriminating among four molecular subtypes of breast cancer.
The utilization efficiency of land resources is an essential embodiment of economic development, social development, and ecological development and is a critical core to measure how to maximize the ...efficiency of land resources under limited conditions. The land is an important content and essential carrier of the research of tourism development level. This paper selects Panel Data from 2010 to 2019 to research the Guangxi regional tourism development. The entropy weight method and stochastic frontier production function (SFA) model were used to evaluate the development level of urban-rural tourism and the utilization efficiency of land resources in Guangxi. This paper uses the Panel Vector Autoregression (PVAR) model to analyze the internal relationship between urban-rural tourism development. The results show that: (1) Guangxi has a good level of tourism development and a high land use efficiency. (2) There is a reciprocal causation relationship between the regional tourism development level and land use efficiency in Guangxi, with significant levels of 0.005 and 0.034 respectively, indicating high credibility. This indicates that there is a mutual promotion and interaction between the two, which rely on and drive each other, promoting the joint sustainability of tourism development and land use efficiency. (3) . The tourism development level is greatly influenced by itself, with impact values all above 0.99. At the same time, land use also has a significant self-impact, with impact values all above 0.87. Their internal optimization system is solid and endogenous impetus is robust, which can drive their development. Establishing an effective strategy for developing and protecting land use is beneficial to promote the long-term effectiveness of sustainable tourism development, enhancing high-quality development of the tourism economy and improving people's living standards and quality.
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