Osteoporosis is a known risk factor for rotator cuff tears(RCTs),but the causal correlation and underlying mechanisms remain unclear.This study aims to evaluate the impact of osteoporosis on RCT risk and investigate t...Osteoporosis is a known risk factor for rotator cuff tears(RCTs),but the causal correlation and underlying mechanisms remain unclear.This study aims to evaluate the impact of osteoporosis on RCT risk and investigate their genetic associations.Using data from the UK Biobank(n=457871),cross-sectional analyses demonstrated that osteoporosis was significantly associated with an increased risk of RCTs(adjusted OR[95%CI]=1.38[1.25–1.52]).A longitudinal analysis of a subset of patients(n=268117)over 11 years revealed that osteoporosis increased the risk of RCTs(adjusted HR[95%CI]=1.56[1.29–1.87]),which is notably varied between sexes in sex-stratified analysis.Causal inference methods,including propensity score matching,inverse probability weighting,causal random forest and survival random forest models further confirmed the causal effect,both from cross-sectional and longitudinal perspectives.展开更多
Instance co-segmentation aims to segment the co-occurrent instances among two images.This task heavily relies on instance-related cues provided by co-peaks,which are generally estimated by exhaustively exploiting all ...Instance co-segmentation aims to segment the co-occurrent instances among two images.This task heavily relies on instance-related cues provided by co-peaks,which are generally estimated by exhaustively exploiting all paired candidates in point-to-point patterns.However,such patterns could yield a high number of false-positive co-peaks,resulting in over-segmentation whenever there are mutual occlusions.To tackle with this issue,this paper proposes an instance co-segmentation method via tensor-based salient co-peak search(TSCPS-ICS).The proposed method explores high-order correlations via triple-to-triple matching among feature maps to find reliable co-peaks with the help of co-saliency detection.The proposed method is shown to capture more accurate intra-peaks and inter-peaks among feature maps,reducing the false-positive rate of co-peak search.Upon having accurate co-peaks,one can efficiently infer responses of the targeted instance.Experiments on four benchmark datasets validate the superior performance of the proposed method.展开更多
The rapid advancement of high-throughput sequencing technologies and the explosive growth of biological data have revolutionized the field of bioinformatics and biomedical computing[1-4].The generation of vast amounts...The rapid advancement of high-throughput sequencing technologies and the explosive growth of biological data have revolutionized the field of bioinformatics and biomedical computing[1-4].The generation of vast amounts of genomic,transcriptomic,proteomic,and metabolomic data has created unprecedented opportunities for understanding the complexities of biological systems and their implications for human health[5-6].Moreover,the emergence of spatial omics technologies,such as spatial transcriptomics and spatial proteomics,has added a new dimension to our understanding of the spatial organization and heterogeneity of biological systems.These cutting-edge technologies enable the mapping of molecular information at a high spatial resolution,providing valuable insights into the tissue microenvironment and the interplay between cells in various physiological and pathological conditions[7-10].展开更多
基金the Scientific Research Innovation Capability Support Project for Young Faculty(ZYGXQNJSKYCXNLZCXM-H8)Fundamental Research Funds for the Central Universities(2024ZYGXZR077)+3 种基金Guangdong Basic and Applied Basic Research Foundation(2023B1515120006)Guangzhou Basic and Applied Basic Research Foundation(2024A04J5776)the Research Fund(2023QN10Y421)Guangzhou Talent Recruitment Team Program(2024D03J0004),all related to this study.
文摘Osteoporosis is a known risk factor for rotator cuff tears(RCTs),but the causal correlation and underlying mechanisms remain unclear.This study aims to evaluate the impact of osteoporosis on RCT risk and investigate their genetic associations.Using data from the UK Biobank(n=457871),cross-sectional analyses demonstrated that osteoporosis was significantly associated with an increased risk of RCTs(adjusted OR[95%CI]=1.38[1.25–1.52]).A longitudinal analysis of a subset of patients(n=268117)over 11 years revealed that osteoporosis increased the risk of RCTs(adjusted HR[95%CI]=1.56[1.29–1.87]),which is notably varied between sexes in sex-stratified analysis.Causal inference methods,including propensity score matching,inverse probability weighting,causal random forest and survival random forest models further confirmed the causal effect,both from cross-sectional and longitudinal perspectives.
基金supported in part by the National Natural Science Foundation of China (Grant Nos.U21A20520,62172112)the Key-Area Research and Development of Guangdong Province (2022A0505050014,2020B1111190001)+1 种基金the National Key Research and Development Program of China (2022YFE0112200)the Key-Area Research and Development Program of Guangzhou City (202206030009).
文摘Instance co-segmentation aims to segment the co-occurrent instances among two images.This task heavily relies on instance-related cues provided by co-peaks,which are generally estimated by exhaustively exploiting all paired candidates in point-to-point patterns.However,such patterns could yield a high number of false-positive co-peaks,resulting in over-segmentation whenever there are mutual occlusions.To tackle with this issue,this paper proposes an instance co-segmentation method via tensor-based salient co-peak search(TSCPS-ICS).The proposed method explores high-order correlations via triple-to-triple matching among feature maps to find reliable co-peaks with the help of co-saliency detection.The proposed method is shown to capture more accurate intra-peaks and inter-peaks among feature maps,reducing the false-positive rate of co-peak search.Upon having accurate co-peaks,one can efficiently infer responses of the targeted instance.Experiments on four benchmark datasets validate the superior performance of the proposed method.
文摘The rapid advancement of high-throughput sequencing technologies and the explosive growth of biological data have revolutionized the field of bioinformatics and biomedical computing[1-4].The generation of vast amounts of genomic,transcriptomic,proteomic,and metabolomic data has created unprecedented opportunities for understanding the complexities of biological systems and their implications for human health[5-6].Moreover,the emergence of spatial omics technologies,such as spatial transcriptomics and spatial proteomics,has added a new dimension to our understanding of the spatial organization and heterogeneity of biological systems.These cutting-edge technologies enable the mapping of molecular information at a high spatial resolution,providing valuable insights into the tissue microenvironment and the interplay between cells in various physiological and pathological conditions[7-10].
