目的以入血成分为研究对象,基于网络药理学探究衢枳壳对糖尿病起效的物质基础及作用机制。方法采用高效液相色谱串联四极杆-静电场轨道阱高分辨质谱(high-performance liquid chromatography tandem quadrupole-electrostatic field orb...目的以入血成分为研究对象,基于网络药理学探究衢枳壳对糖尿病起效的物质基础及作用机制。方法采用高效液相色谱串联四极杆-静电场轨道阱高分辨质谱(high-performance liquid chromatography tandem quadrupole-electrostatic field orbitrap high resolution mass spectrometry,HPLC-Q-Exactive Orbitrap MS/MS)对衢枳壳入血成分进行鉴定,在此基础上通过Swiss Target Prediction与SuperPred数据库预测入血成分作用靶点,同时在OMIM,GeneCards等数据库获取糖尿病靶点。采用Cytoscape 3.9.1绘制中药衢枳壳“活性成分-靶点-疾病”网络关系图,利用String数据分析平台进行蛋白互作(protein-protein interaction,PPI)网络分析,筛选关键靶点。通过DAVID数据库对关键靶点进行基因本体功能(gene ontology,GO)和京都基因与基因组百科全书(kyoto encyclopedia of genes and genomes,KEGG)通路富集分析。应用Autodock 1.5.7软件进行分子对接验证。结果共鉴定衢枳壳入血成分20个,筛选出潜在靶点170个,核心靶点32个。GO功能富集和KEGG信号通路分析结果显示缺氧诱导因子(hypoxia-inducible factor,HIF)-1信号通路、晚期糖基化终末产物(advanced glycation end products,AGE)-晚期糖基化终产物受体(receptor for advanced glycation end products,RAGE)信号通路、表皮生长因子(epidermal growth factor receptor,EGFR)信号通路、癌症蛋白聚糖通路等为衢枳壳降糖的关键通路,丝氨酸/苏氨酸蛋白激酶(RAC serine/threonine-protein kinase,AKT)1、白蛋白(albumin,ALB)、细胞肿瘤抗原p53(cellular tumor antigenp 53,TP53)、肿瘤坏死因子(tumor necrosis factor,TNF)、EGFR为其中关键靶点,且衢枳壳中5个活性成分与核心靶点经分子对接后的结合活性较好。结论衢枳壳中的芦丁、新橙皮苷、橙皮苷、芸香柚皮苷、川陈皮素等可能为衢枳壳降糖的物质基础,可能是通过调控HIF-1、AGE-RAGE、EGFR等信号通路及AKT1、ALB、TP53等核心基因发挥降糖作用。展开更多
目前主流人体动作识别大部分都是基于卷积神经网络(Convolutional Neural Network,CNN)实现,而CNN容易忽略视频中的空间位置信息,从而降低了视频空间频域中动作识别能力。同时传统CNN不能快速定位到关键的特征位置,并且在训练过程中不...目前主流人体动作识别大部分都是基于卷积神经网络(Convolutional Neural Network,CNN)实现,而CNN容易忽略视频中的空间位置信息,从而降低了视频空间频域中动作识别能力。同时传统CNN不能快速定位到关键的特征位置,并且在训练过程中不能并行计算导致效率低。为了解决传统CNN在处理时间频域和多并行计算问题,提出了基于视觉Transformer(Vision Transformer,ViT)和3D卷积网络学习时空特征(Learning Spatiotemporal Features with 3D Convolutional Network,C3D)的人体动作识别算法。使用C3D提取视频的多维特征图、ViT的特征切片窗口对多维特征进行全局特征分割;使用Transformer的编码-解码模块对视频中人体动作进行预测。实验结果表明,所提的人体动作识别算法在UCF-101、HMDB51数据集上提高了动作识别的准确率。展开更多
In recent years,skeleton-based action recognition has made great achievements in Computer Vision.A graph convolutional network(GCN)is effective for action recognition,modelling the human skeleton as a spatio-temporal ...In recent years,skeleton-based action recognition has made great achievements in Computer Vision.A graph convolutional network(GCN)is effective for action recognition,modelling the human skeleton as a spatio-temporal graph.Most GCNs define the graph topology by physical relations of the human joints.However,this predefined graph ignores the spatial relationship between non-adjacent joint pairs in special actions and the behavior dependence between joint pairs,resulting in a low recognition rate for specific actions with implicit correlation between joint pairs.In addition,existing methods ignore the trend correlation between adjacent frames within an action and context clues,leading to erroneous action recognition with similar poses.Therefore,this study proposes a learnable GCN based on behavior dependence,which considers implicit joint correlation by constructing a dynamic learnable graph with extraction of specific behavior dependence of joint pairs.By using the weight relationship between the joint pairs,an adaptive model is constructed.It also designs a self-attention module to obtain their inter-frame topological relationship for exploring the context of actions.Combining the shared topology and the multi-head self-attention map,the module obtains the context-based clue topology to update the dynamic graph convolution,achieving accurate recognition of different actions with similar poses.Detailed experiments on public datasets demonstrate that the proposed method achieves better results and realizes higher quality representation of actions under various evaluation protocols compared to state-of-the-art methods.展开更多
Regular exercise is a crucial aspect of daily life, as it enables individuals to stay physically active, lowers thelikelihood of developing illnesses, and enhances life expectancy. The recognition of workout actions i...Regular exercise is a crucial aspect of daily life, as it enables individuals to stay physically active, lowers thelikelihood of developing illnesses, and enhances life expectancy. The recognition of workout actions in videostreams holds significant importance in computer vision research, as it aims to enhance exercise adherence, enableinstant recognition, advance fitness tracking technologies, and optimize fitness routines. However, existing actiondatasets often lack diversity and specificity for workout actions, hindering the development of accurate recognitionmodels. To address this gap, the Workout Action Video dataset (WAVd) has been introduced as a significantcontribution. WAVd comprises a diverse collection of labeled workout action videos, meticulously curated toencompass various exercises performed by numerous individuals in different settings. This research proposes aninnovative framework based on the Attention driven Residual Deep Convolutional-Gated Recurrent Unit (ResDCGRU)network for workout action recognition in video streams. Unlike image-based action recognition, videoscontain spatio-temporal information, making the task more complex and challenging. While substantial progresshas been made in this area, challenges persist in detecting subtle and complex actions, handling occlusions,and managing the computational demands of deep learning approaches. The proposed ResDC-GRU Attentionmodel demonstrated exceptional classification performance with 95.81% accuracy in classifying workout actionvideos and also outperformed various state-of-the-art models. The method also yielded 81.6%, 97.2%, 95.6%, and93.2% accuracy on established benchmark datasets, namely HMDB51, Youtube Actions, UCF50, and UCF101,respectively, showcasing its superiority and robustness in action recognition. The findings suggest practicalimplications in real-world scenarios where precise video action recognition is paramount, addressing the persistingchallenges in the field. TheWAVd dataset serves as a catalyst for the development ofmore robust and effective fitnesstracking systems and ultimately promotes healthier lifestyles through improved exercise monitoring and analysis.展开更多
The skeleton is a dynamic organ that is constantly remodeled. Proteins secreted from bone cells, namely osteoblasts, osteocytes,and osteoclasts exert regulation on osteoblastogenesis, osteclastogenesis, and angiogenes...The skeleton is a dynamic organ that is constantly remodeled. Proteins secreted from bone cells, namely osteoblasts, osteocytes,and osteoclasts exert regulation on osteoblastogenesis, osteclastogenesis, and angiogenesis in a paracrine manner. Osteoblasts secrete a range of different molecules including RANKL/OPG, M-CSF, SEMA3A, WNT5A, and WNT16 that regulate osteoclastogenesis. Osteoblasts also produce VEGFA that stimulates osteoblastogenesis and angiogenesis. Osteocytes produce sclerostin（SOST） that inhibits osteoblast differentiation and promotes osteoclast differentiation. Osteoclasts secrete factors including BMP6, CTHRC1, EFNB2, S1P, WNT10B, SEMA4D, and CT-1 that act on osteoblasts and osteocytes, and thereby influencea A osteogenesis. Osteoclast precursors produce the angiogenic factor PDGF-BB to promote the formation of Type H vessels, which then stimulate osteoblastogenesis. Besides, the evidences over the past decades show that at least three hormones or ＂osteokines＂from bone cells have endocrine functions. FGF23 is produced by osteoblasts and osteocytes and can regulate phosphate metabolism. Osteocalcin（OCN） secreted by osteoblasts regulates systemic glucose and energy metabolism, reproduction, and cognition. Lipocalin-2（LCN2） is secreted by osteoblasts and can influence energy metabolism by suppressing appetite in the brain.We review the recent progresses in the paracrine and endocrine functions of the secretory proteins of osteoblasts, osteocytes, and osteoclasts, revealing connections of the skeleton with other tissues and providing added insights into the pathogenesis of degenerative diseases affecting multiple organs and the drug discovery process.展开更多
文摘目的以入血成分为研究对象,基于网络药理学探究衢枳壳对糖尿病起效的物质基础及作用机制。方法采用高效液相色谱串联四极杆-静电场轨道阱高分辨质谱(high-performance liquid chromatography tandem quadrupole-electrostatic field orbitrap high resolution mass spectrometry,HPLC-Q-Exactive Orbitrap MS/MS)对衢枳壳入血成分进行鉴定,在此基础上通过Swiss Target Prediction与SuperPred数据库预测入血成分作用靶点,同时在OMIM,GeneCards等数据库获取糖尿病靶点。采用Cytoscape 3.9.1绘制中药衢枳壳“活性成分-靶点-疾病”网络关系图,利用String数据分析平台进行蛋白互作(protein-protein interaction,PPI)网络分析,筛选关键靶点。通过DAVID数据库对关键靶点进行基因本体功能(gene ontology,GO)和京都基因与基因组百科全书(kyoto encyclopedia of genes and genomes,KEGG)通路富集分析。应用Autodock 1.5.7软件进行分子对接验证。结果共鉴定衢枳壳入血成分20个,筛选出潜在靶点170个,核心靶点32个。GO功能富集和KEGG信号通路分析结果显示缺氧诱导因子(hypoxia-inducible factor,HIF)-1信号通路、晚期糖基化终末产物(advanced glycation end products,AGE)-晚期糖基化终产物受体(receptor for advanced glycation end products,RAGE)信号通路、表皮生长因子(epidermal growth factor receptor,EGFR)信号通路、癌症蛋白聚糖通路等为衢枳壳降糖的关键通路,丝氨酸/苏氨酸蛋白激酶(RAC serine/threonine-protein kinase,AKT)1、白蛋白(albumin,ALB)、细胞肿瘤抗原p53(cellular tumor antigenp 53,TP53)、肿瘤坏死因子(tumor necrosis factor,TNF)、EGFR为其中关键靶点,且衢枳壳中5个活性成分与核心靶点经分子对接后的结合活性较好。结论衢枳壳中的芦丁、新橙皮苷、橙皮苷、芸香柚皮苷、川陈皮素等可能为衢枳壳降糖的物质基础,可能是通过调控HIF-1、AGE-RAGE、EGFR等信号通路及AKT1、ALB、TP53等核心基因发挥降糖作用。
文摘目前主流人体动作识别大部分都是基于卷积神经网络(Convolutional Neural Network,CNN)实现,而CNN容易忽略视频中的空间位置信息,从而降低了视频空间频域中动作识别能力。同时传统CNN不能快速定位到关键的特征位置,并且在训练过程中不能并行计算导致效率低。为了解决传统CNN在处理时间频域和多并行计算问题,提出了基于视觉Transformer(Vision Transformer,ViT)和3D卷积网络学习时空特征(Learning Spatiotemporal Features with 3D Convolutional Network,C3D)的人体动作识别算法。使用C3D提取视频的多维特征图、ViT的特征切片窗口对多维特征进行全局特征分割;使用Transformer的编码-解码模块对视频中人体动作进行预测。实验结果表明,所提的人体动作识别算法在UCF-101、HMDB51数据集上提高了动作识别的准确率。
基金supported in part by the 2023 Key Supported Project of the 14th Five Year Plan for Education and Science in Hunan Province with No.ND230795.
文摘In recent years,skeleton-based action recognition has made great achievements in Computer Vision.A graph convolutional network(GCN)is effective for action recognition,modelling the human skeleton as a spatio-temporal graph.Most GCNs define the graph topology by physical relations of the human joints.However,this predefined graph ignores the spatial relationship between non-adjacent joint pairs in special actions and the behavior dependence between joint pairs,resulting in a low recognition rate for specific actions with implicit correlation between joint pairs.In addition,existing methods ignore the trend correlation between adjacent frames within an action and context clues,leading to erroneous action recognition with similar poses.Therefore,this study proposes a learnable GCN based on behavior dependence,which considers implicit joint correlation by constructing a dynamic learnable graph with extraction of specific behavior dependence of joint pairs.By using the weight relationship between the joint pairs,an adaptive model is constructed.It also designs a self-attention module to obtain their inter-frame topological relationship for exploring the context of actions.Combining the shared topology and the multi-head self-attention map,the module obtains the context-based clue topology to update the dynamic graph convolution,achieving accurate recognition of different actions with similar poses.Detailed experiments on public datasets demonstrate that the proposed method achieves better results and realizes higher quality representation of actions under various evaluation protocols compared to state-of-the-art methods.
文摘Regular exercise is a crucial aspect of daily life, as it enables individuals to stay physically active, lowers thelikelihood of developing illnesses, and enhances life expectancy. The recognition of workout actions in videostreams holds significant importance in computer vision research, as it aims to enhance exercise adherence, enableinstant recognition, advance fitness tracking technologies, and optimize fitness routines. However, existing actiondatasets often lack diversity and specificity for workout actions, hindering the development of accurate recognitionmodels. To address this gap, the Workout Action Video dataset (WAVd) has been introduced as a significantcontribution. WAVd comprises a diverse collection of labeled workout action videos, meticulously curated toencompass various exercises performed by numerous individuals in different settings. This research proposes aninnovative framework based on the Attention driven Residual Deep Convolutional-Gated Recurrent Unit (ResDCGRU)network for workout action recognition in video streams. Unlike image-based action recognition, videoscontain spatio-temporal information, making the task more complex and challenging. While substantial progresshas been made in this area, challenges persist in detecting subtle and complex actions, handling occlusions,and managing the computational demands of deep learning approaches. The proposed ResDC-GRU Attentionmodel demonstrated exceptional classification performance with 95.81% accuracy in classifying workout actionvideos and also outperformed various state-of-the-art models. The method also yielded 81.6%, 97.2%, 95.6%, and93.2% accuracy on established benchmark datasets, namely HMDB51, Youtube Actions, UCF50, and UCF101,respectively, showcasing its superiority and robustness in action recognition. The findings suggest practicalimplications in real-world scenarios where precise video action recognition is paramount, addressing the persistingchallenges in the field. TheWAVd dataset serves as a catalyst for the development ofmore robust and effective fitnesstracking systems and ultimately promotes healthier lifestyles through improved exercise monitoring and analysis.
基金supported in part by grants from 973 Program from the Chinese Ministry of Science and Technology (MOST) (2014CB964704 and 2015CB964503)the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDB19000000)the National Natural Science Foundation of China (NSFC) (31371463, 81672119, and 81725010)
文摘The skeleton is a dynamic organ that is constantly remodeled. Proteins secreted from bone cells, namely osteoblasts, osteocytes,and osteoclasts exert regulation on osteoblastogenesis, osteclastogenesis, and angiogenesis in a paracrine manner. Osteoblasts secrete a range of different molecules including RANKL/OPG, M-CSF, SEMA3A, WNT5A, and WNT16 that regulate osteoclastogenesis. Osteoblasts also produce VEGFA that stimulates osteoblastogenesis and angiogenesis. Osteocytes produce sclerostin（SOST） that inhibits osteoblast differentiation and promotes osteoclast differentiation. Osteoclasts secrete factors including BMP6, CTHRC1, EFNB2, S1P, WNT10B, SEMA4D, and CT-1 that act on osteoblasts and osteocytes, and thereby influencea A osteogenesis. Osteoclast precursors produce the angiogenic factor PDGF-BB to promote the formation of Type H vessels, which then stimulate osteoblastogenesis. Besides, the evidences over the past decades show that at least three hormones or ＂osteokines＂from bone cells have endocrine functions. FGF23 is produced by osteoblasts and osteocytes and can regulate phosphate metabolism. Osteocalcin（OCN） secreted by osteoblasts regulates systemic glucose and energy metabolism, reproduction, and cognition. Lipocalin-2（LCN2） is secreted by osteoblasts and can influence energy metabolism by suppressing appetite in the brain.We review the recent progresses in the paracrine and endocrine functions of the secretory proteins of osteoblasts, osteocytes, and osteoclasts, revealing connections of the skeleton with other tissues and providing added insights into the pathogenesis of degenerative diseases affecting multiple organs and the drug discovery process.
