This study utilizes a psychophysical approach to examine the effects on
carrying capacity for bi-manual carrying tasks involving different handle positions and
carrying ranges. A total of 16 female subjects participated in the experiment in groups of
two people, and each group of subjects performed the tasks in a random order with 12
different combinations of carrying task. The independent variables are handle position
(upper, middle, lower) and carrying range (F–F: floor height carried to floor height, F–W:
floor height carried to waist height, W–W: waist height carried to waist height, W–F:
waist height carried to floor height), the dependent variable is the maximum acceptable
carried weight (MAWC), heart rate (HR), and the rating of perceived exertion (RPE). The
results show that the handle position has a significant effect on MAWC and overall RPE but
no significant effect on HR. Carrying range has a significant effect on the MAWC and HR,
but no significant effect on overall HR. The handle position and carrying range have a
significant interaction on the MAWC and HR. The RPE for different body parts shows
significant differences, and the hands feel the most tired. Overall, this study confirms
that the lower handle position with the W–W carrying range is the best combination for a
two-person carrying task. 相似文献
In the area of large-scale graph data representation and semi-supervised learning, deep graph-based convolutional neural networks have been widely applied. However, typical graph convolutional network (GCN) aggregates information of neighbor nodes based on binary neighborhood similarity (adjacency matrix). It treats all neighbor nodes of one node equally, which does not suppress the influence of dissimilar neighbor nodes. In this paper, we investigate GCN based on similarity matrix instead of adjacency matrix of graph nodes. Gaussian heat kernel similarity in Euclidean space is first adopted, which is named EGCN. Then biologically inspired manifold similarity is trained in reproducing kernel Hilbert space (RKHS), based on which a manifold GCN (named MGCN) is proposed for graph data representation and semi-supervised learning with four different kernel types. The proposed method is evaluated with extensive experiments on four benchmark document citation network datasets. The objective function of manifold similarity learning converges very quickly on different datasets using various kernel functions. Compared with state-of-the-art methods, our method is very competitive in terms of graph node recognition accuracy. In particular, the recognition rates of MGCN (Gaussian kernel) and MGCN (Polynomial Kernel) outperform that of typical GCN about 3.8% on Cora dataset, 3.5% on Citeseer dataset, 1.3% on Pubmed dataset and 4% on Cora_ML dataset, respectively. Although the proposed MGCN is relatively simple and easy to implement, it can discover local manifold structure by manifold similarity learning and suppress the influence of dissimilar neighbor nodes, which shows the effectiveness of the proposed MGCN.
目的:基于网络药理学方法探讨生脉散治疗心房纤颤的作用靶点和相关信号通路并探讨其作用机制。方法:运用中药系统药理学成分分析平台(bioinformatics analysis tool for molecular mechanism of TCM,BATMAN-TCM)数据库获取生脉散的化学成分及作用靶标基因,通过GeneCards,OMIM,DisGeNET数据库收集心房纤颤的靶标基因。将两者取交集后得到生脉散-心房纤颤靶基因交集,运用STRING构建蛋白质间相互作用网络,并将结果进行网络可视化展示。将药物-疾病交集基因导入DAVID6.8数据库,进行基因本体(gene ontology,GO)分析和基于京都基因与基因组百科全书(Kyoto Encyclopedia of Genes and Geomes,KEGG)通路富集分析。结果:生脉散干预房颤的有效活性成分159个,药物靶点与疾病靶点交集后获得206个共有靶点,PPI蛋白互作网络分析发现AKT1,TP53,PRKACA,IL-1B,TNF,INS,PPAR,RXR,F2,CACAN1C,PKC等是生脉散治疗房颤的核心靶点。GO富集分析确定了175个条目(P0.05),其中生物过程主要心脏传导调节心率、动作电位时膜去极化等;分子功能主要包括电压门控钙通道、类固醇激素受体活性、肾上腺素结合等,在细胞组成方面,主要包括钠、钾、钙通道复合物等。KEGG通路富集分析确定了100条相关信号通路,主要有cGMP/PKG信号通路,cAMP信号通路,血清素能突触,肾素分泌,钙信号通路等。结论:生脉散治疗心房纤颤具有多途径、多靶点作用的特点。该研究初步探讨了其作用的关键靶点及涉及的生物学过程和信号通路,为生脉散治疗心房纤颤后续的实验研究提供一定的参考。 相似文献