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iCREPCP:A deep learning-based web server for identifying base-resolution cis-regulatory elements within plant core promoters

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Plant Communications 2023年第1期4卷 213-216页

作者： Kaixuan Deng Qizhe Zhang Yuxin Hong Jianbing Yan Xuehai Hu College of Informatics Hubei Engineering Technology Research Center of Agricultural Big DataHuazhong Agricultural UniversityWuhanHubeiP.R.China National Key Laboratory of Crop Genetic Improvement Huazhong Agricultural UniversityWuhan 430070P.R.China

Dear Editor,A central question of plant biology is how to specify the temporal and spatial patterns as well as the quantitative levels of gene expression,which are significantly associated with important agronomic *** has been a growing consensus in the past decade that the two key factors determining gene expression level are cis-regulatory modules(CRMs)and trans-acting factors(TAFs)(Schmitz et al.,2022).Common CRMs include gene-proximal promoters and distal enhancers,which are both considered to be complex assemblies of cis-regulatory elements(CREs).It is the binding or interaction between CREs and TAFs(often transcription factors[TFs])in a ubiquitous or cell-specific manner that determines in which cell,at what time,and at what level a gene is ***,the identification of plant CRMs or critical CREs will not only help us understand transcriptional regulatory mechanisms in plants but also serve as an essential prerequisite for plant breeding 4.0—breeding by genome editing(Gao,2021).

关键词： server web breeding

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Predicting gastric cancer response to anti-HER2 therapy or anti-HER2 combined immunotherapy based on multimodal data

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Signal Transduction and Targeted Therapy 2024年第9期9卷 4137-4148页

作者： Zifan Chen Yang Chen Yu Sun Lei Tang Li Zhang Yajie Hu Meng He Zhiwei Li Siyuan Cheng Jiajia Yuan Zhenghang Wang Yakun Wang Jie Zhao Jifang Gong Liying Zhao Baoshan Cao Guoxin Li Xiaotian Zhang Bin Dong Lin Shen Center for Data Science Peking UniversityBeijingChina Department of Gastrointestinal Oncology Key Laboratory of Carcinogenesis and Translational Research(Ministry of Education)Peking University Cancer Hospital and InstituteBeijingChina Department of Pathology Key Laboratory of Carcinogenesis and Translational Research(Ministry of Education)Peking University Cancer Hospital and InstituteBeijingChina Department of Radiology Key Laboratory of Carcinogenesis and Translational Research(Ministry of Education)Peking University Cancer Hospital and InstituteBeijingChina National Biomedical Imaging Center Peking UniversityBeijingChina Department of General Surgery Nanfang HospitalSouthern Medical UniversityGuangzhouChina Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor GuangzhouChina Department of Medical Oncology and Radiation Sickness Peking University Third HospitalBeijingChina National Engineering Laboratory for Big Data Analysis and Applications Peking UniversityBeijingChina Beijing International Center for Mathematical Research(BICMR) Peking UniversityBeijingChina Center for Machine Learning Research Peking UniversityBeijingChina

The sole use of single modality data often fails to capture the complex heterogeneity among patients,including the variability in resistance to anti-HER2 therapy and outcomes of combined treatment regimens,for the treatment of HER2-positive gastric cancer(GC).This modality deficit has not been fully considered in many ***,the application of artificial intelligence in predicting the treatment response,particularly in complex diseases such as GC,is still in its ***,this study aimed to use a comprehensive analytic approach to accurately predict treatment responses to anti-HER2 therapy or anti-HER2 combined immunotherapy in patients with HER2-positive *** collected multi-modal data,comprising radiology,pathology,and clinical information from a cohort of 429 patients:310 treated with anti-HER2 therapy and 119 treated with a combination of anti-HER2 and anti-PD-1/PD-L1 inhibitors *** introduced a deep learning model,called the Multi-Modal model(MuMo),that integrates these data to make precise treatment response *** achieved an area under the curve score of 0.821 for anti-HER2 therapy and 0.914 for combined ***,patients classified as low-risk by MuMo exhibited significantly prolonged progression-free survival and overall survival(log-rank test,P<0.05).These findings not only highlight the significance of multi-modal data analysis in enhancing treatment evaluation and personalized medicine for HER2-positive gastric cancer,but also the potential and clinical value of our model.

关键词： immunotherapy HER2 cancer

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Impact of Soft Magnetic Material on Electromagnetic Force and Vibration of Permanent Magnet Electrical Machines

Impact of Soft Magnetic Material on Electromagnetic Force an...

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2023 IEEE International Magnetic Conference - Short Papers, INTERMAG Short Papers 2023

作者： Li, Bo Zhu, Jianguo Liu, Chengcheng Lei, Gang Li, Yongjian Hebei University of Technology State Key Laboratory of Reliability and Intelligence of Electrical Equipment Tianjin300130 China Hebei University of Technology Province-Ministry Joint Key Laboratory of EFEAR Tianjin300130 China University of Sydney School of Electrical and Information Engineering SydneyNSW2007 Australia University of Technology School of Electrical and Data Engineering SydneyNSW2007 Australia

ISBN: (纸本)9798350338362

Electromagnetic vibration is excited by the radial electromagnetic force generated by the air-gap magnetic field acting on the stator core. In this paper, an analysis and reduction of electromagnetic force in high-speed permanent magnet electrical machines with different stator cores are investigated. A fast and accurate calculation method of air gap flux density and radial force density distribution are derived. The radial force density including amplitude, frequency, and order is obtained by a two-dimension fast Fourier transform. Then, the effect of the different PM segmentation on radial force is summarized. © 2023 IEEE.

关键词： Modal analysis

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Spatial matching and flow in supply and demand of water provision services: A case study in Xiangjiang River Basin

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Journal of Mountain Science 2022年第1期19卷 228-240页

作者： DENG Chu-xiong ZHU Da-mei LIU Yao-jun LI Zhong-wu College of Resources and Environmental Sciences Hunan Normal UniversityChangsha 410081China Hunan Key Laboratory of Geospatial Big Data Mining and Application Changsha 410081China College of Environmental Science and Engineering Hunan UniversityChangsha 410082China

Global climate change and increased human consumption have aggravated the uneven spatiotemporal distribution of watershed water resources, affecting the water provision supply and demand state. However, this problem has often been ignored. The present study used the Xiangjiang River basin(XRB) as the study area, and the Integrated Valuation of Ecosystem Services and Trade-offs(InVEST) model, demand quantification model,supply–demand ratio, and water flow formula were applied to explore the spatial heterogeneity, flow, and equilibrium between water supply and demand. The results demonstrated significant spatial heterogeneity in the upstream, midstream, and downstream *** areas of water shortage were mainly located the downstream of the Changsha–Zhuzhou–Xiangtan urban agglomeration, and the Hengyang basin was the most scarcity area. Affected by terrain gradients and human needs, water flow varied from-16.33 × 10^(8) m^(3) to 13.69 × 10^(8) m^(3)from the upstream to the downstream area, which provided a possibility to reduce spatial heterogeneity. In the future, measures such as strengthening water resource system control,sponge city construction, and dynamic monitoring technology should be taken to balance the supply and demand of water in different river sections of the basin. This study can provide references for regulating water resources allocation in different reaches of the basin.

关键词： Water provision services Supply and demand Spatiotemporal dislocation Water flow Water management and saving policy Xiangjiang River basin

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A Digital Twin-based Framework of Motion Control and State Monitoring for Pneumatic Muscle

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Recent Patents on Mechanical engineering 2023年第3期16卷 203-213页

作者： Xie, Shenglong Liu, Wenyuan Duan, Huiru Chen, Dijian Wan, Yanjian Key Laboratory of Intelligent Manufacturing Quality Big Data Tracking and Analysis of Zhejiang Province China Ji-liang University Zhejiang Hangzhou310018 China Anhui Engineering Research Center for Intelligent Applications and Security of Industrial Internet Anhui University of Technology Anhui Ma'anshan243032 China

Introduction: The current digital twin systems usually have the drawback of high cost and complex technology, and it is necessary to develop a simple solution to reduce the cost and cycle for the development of digital twin systems, especially for small projects or systems with simple structures. Objective: A low-cost patent technology of digital twin system was proposed by taking the motion control and state monitoring system (MCSMS) of pneumatic muscle as an example. Methods: The MCSMS is developed based on the browser/server architecture. The software of 3ds Max and SolidWorks are used to make the virtual model, *** and JavaScript are applied to build the browser side. data of the physical world is collected and processed on the server side firstly, and then is sent to the browser side through HTTP communication protocol to realize data interchange between the browser and server. Results: In the roaming experiment and the experiment of motion control and state monitoring of pneumatic muscle, the MCSMS can work smoothly without obvious delay and has good real-time performance, which can realize the 3D visual monitoring of the pneumatic muscle very well. Conclusion: The experimental results indicate that the proposed method possesses the ability of good feasibility and effectiveness. © 2023 Bentham Science Publishers.

关键词： Muscle

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Remote Sensing Image River Segmentation Method Based on U-Net 8

Remote Sensing Image River Segmentation Method Based on U-Ne...

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8th IEEE International Conference on Cloud Computing and Intelligence Systems, CCIS 2022

作者： Cai, Qiang Wan, Ruyi Li, Haisheng Wang, Chen Chang, Haodong School of Computer Science and Engineering Beijing Technology and Business University Beijing100048 China Beijing Key Laboratory of Big Data Technology for Food Safety China National Engineering Laboratory for Agri-product Quality Traceability

ISBN: (纸本)9781665477352

River segmentation based on remote sensing images plays an important role in water conservancy business work, water wading monitoring work, and flood disaster prevention. In actual remote sensing images of rivers, most of the backgrounds are complex, and there is no public remote sensing image dataset specifically for the study of river segmentation. The traditional river segmentation methods have rough edge information and serious noise. To solve the above problems, this paper firstly preprocesses the Gaofen Image dataset (GID) and Remote Sensing Image Block Segmentation dataset (BDCI), and creates two datasets for river segmentation in high-resolution remote sensing images respectively (GID-river and BDCI-river) and then proposed a river segmentation method based on U-Net. On the basis of the original U-Net, the ResNet34 and VGG16 structures were combined to strengthen the feature extraction ability of the network, so as to achieve more accurate river edge details. The experimental results shows the mIoU of the ResNet34-UNet network on the GID-river dataset reaches 93.6%, and the mPA of the VGG16-UNet network on the BDCI-river dataset reaches 82.1%. © 2022 IEEE.

关键词： Remote sensing

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Hierarchical vectorization for facial images

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Computational Visual Media 2024年第1期10卷 97-118页

作者： Qian Fu Linlin Liu Fei Hou Ying He School of Computer Science and Engineering Nanyang Technological University639798SingaporeSingapore Data61 Commonwealth Scientific and Industrial Research OrganisationSydney2015Australia Interdisciplinary Graduate School Nanyang Technological University and Alibaba Group639798SingaporeSingapore State Key Laboratory of Computer Science Institute of SoftwareChinese Academy of SciencesBeijing100190China University of Chinese Academy of Sciences Beijing100049China

The explosive growth of social media means portrait editing and retouching are in high *** portraits are commonly captured and stored as raster images,editing raster images is non-trivial and requires the user to be highly *** at developing intuitive and easy-to-use portrait editing tools,we propose a novel vectorization method that can automatically convert raster images into a 3-tier hierarchical *** base layer consists of a set of sparse diffusion curves(DCs)which characterize salient geometric features and low-frequency colors,providing a means for semantic color transfer and facial expression *** middle level encodes specular highlights and shadows as large,editable Poisson regions(PRs)and allows the user to directly adjust illumination by tuning the strength and changing the shapes of *** top level contains two types of pixel-sized PRs for high-frequency residuals and fine details such as pimples and *** train a deep generative model that can produce high-frequency residuals *** to the inherent meaning in vector primitives,editing portraits becomes easy and *** particular,our method supports color transfer,facial expression editing,highlight and shadow editing,and automatic *** quantitatively evaluate the results,we extend the commonly used FLIP metric(which measures color and feature differences between two images)to consider *** new metric,illumination-sensitive FLIP,can effectively capture salient changes in color transfer results,and is more consistent with human perception than FLIP and other quality measures for portrait *** evaluate our method on the FFHQR dataset and show it to be effective for common portrait editing tasks,such as retouching,light editing,color transfer,and expression editing.

关键词： face editing vectorization Poisson editing color transfer illumination editing expression editing

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Energy regulation mechanism and medium-filling effect of energy-focusing blast

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International Journal of Mining Science and Technology 2025年第5期35卷 767-781页

作者： Pengfei Guo Xingyu Zhang Kengkeng Ye Xu Wang Man Huang Jinzhu Hu Manchao He School of Civil Engineering Shaoxing UniversityShaoxing 312000China School of Mines China University of Mining and TechnologyXuzhou 221116China Faculty of Geo-Data Science Geodesy and Environmental EngineeringAGH University of KrakowKrakow 30-059Poland Wencheng County Transportation Bureau Wenzhou 325000China State Key Laboratory of Tunnel Engineering China University of Mining and Technology-BeijingBeijing 100083China

The energy-focusing blast is an innovative and ingenious method to achieve directional *** its energy regulation mechanism is critical to enhancing its practical *** study investigates the energy regulation mechanism and explores the medium-filling effects within the energy-focusing blast by employing theoretical analysis,numerical simulations,and model *** findings by theoretical and numerical analysis first reveal that two stages of the fracturing and tensile stage govern the directionally crack propagation,in which the explosion energy in the non-energyfocusing direction is suppressed,compressing the borehole wall,while redirected energy produces tensile stress in the energy-focusing direction,driving the formation of directional *** choice of filling medium significantly affects directional cracking due to its impact on energy distribution and regulation,and key properties such as wave impedance and compressibility of the filling medium are *** comparisons using air,sand,and water as filling media further disclose the distinct effects of the medium on energy regulation and directional crack growth of the energy-focusing *** maximum shaped-energy coefficients for air,sand,and water are 1.30,4.41,and 6.12 in the energy-focusing direction,***,the stress attenuation rate of air,sand,and water increases in that *** higher wave impedance and lower compressibility of water support efficient and uniform energy propagation,which subtly enhances the tensile actions in the focusing direction and intensifies the overall stress impact of the energy-focusing *** addition,the stresses in the non-energyfocusing directions decrease as the angle from the energy-focusing direction increases,while the stresses are relatively uniform for both air and water but noticeably uneven for sand;meanwhile,the fractal dimensions of blasting cracks in the case of air,water,and sand are 1.076,

关键词： Directional blast Medium influence Energy utilization Fracture mechanism

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DR-XGBoost: An XGBoost model for field-road segmentation based on dual feature extraction and recursive feature elimination

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International Journal of Agricultural and Biological engineering 2023年第3期16卷 169-179页

作者： Yuzhen Xiao Guozhao Mo Xiya Xiong Jiawen Pan Bingbing Hu Caicong Wu Weixin Zhai College of Information and Electrical Engineering China Agricultural UniversityBeijing 100083China Key Laboratory of Agricultural Machinery Monitoring and Big Data Application Ministry of Agriculture and Rural AffairsBeijing 100083China Kunlun Beidou Intelligence Technologies Co. Ltd.Beijing 102200China

Field-road segmentation is one of the key tasks in the processing of the trajectory of agricultural *** improve the accuracy of the field-road segmentation,this study proposed an XGBoost model based on dual feature extraction and recursive feature elimination called ***-XGBoost takes only a small amount of agricultural machine trajectory features as ***,the model adopted the dual feature extraction method we designed to rapidly expand the number of features and then adequately extract local trajectory features by the time window and feature extraction ***,the model applies the recursive feature elimination algorithm to eliminate redundant features from the perspective of the model segmentation effect and thus reduce the computational consumption of model ***,it trains XGBoost to complete the trajectory *** evaluate the effectiveness of DR-XGBoost,we conducted a series of experiments on a real trajectory dataset of agricultural *** model achieves a 98.2%Macro-F1 score on the dataset,which is 10.9%higher than the previous *** proposal of DR-XGBoost fills the knowledge gap of trajectory feature extraction for agricultural machinery and provides a reasonable and effective feature selection scheme for the field-road segmentation problem.

关键词： trajectory segmentation feature extraction recursive feature elimination time window XGBoost

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A Cross-Modal Interactive Memory Network Based on Fine-Grained Medical Feature Extraction for Radiology Report Generation

A Cross-Modal Interactive Memory Network Based on Fine-Grain...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Ma, Xitong Kuang, Yuansen Yuan, Lin Tian, Cheng Zeng, Yijie Liu, Song Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9781665410205

Radiology report generation is an essential task in the medical field, which aims to automate the generation of medical terminology descriptions of radiology images. However, this task currently suffers from several problems: 1) existing methods need to manually build knowledge graphs or templates (consuming time and effort) to introduce medical or prior knowledge to assist in report generation;2) previous models cannot handle the problem of data bias well (anomaly reports and anomaly descriptions make up only a tiny portion of the dataset), causing the models to ignore the learning of anomaly descriptions easily;3) existing approaches cannot robustly supervise the model, resulting in incomplete and inconsistent reports being generated. To address these issues, we propose a cross-modal interactive memory network based on fine-grained medical feature extraction. In our model, we design a cross-modal interactive memory network to automatically store and remember the required medical text knowledge and use this medical knowledge to help generate reports. Furthermore, we design an abnormal medical knowledge enhancement module to enhance the learning of abnormal fine-grained knowledge through the interaction of disease topics and their states to interact with text features. In addition, we design a cross-modal joint semantic loss unit to reduce semantic differences between different features and improve the visual representation ability of the model. We experimented and evaluated our model on MIMIC-CXR and IU-Xray datasets to compare with other baseline models. © 2024 IEEE.

关键词： Radiology

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