多标签深度森林(Multi-Label Deep Forest,MLDF)是一种基于深度森林的深度集成学习模型。为了限制模型的复杂度以及用户可以根据需求优化评价指标,多标签深度森林使用了两种算法:度量感知特征重用与度量感知层增长。前者重用前一层较好...多标签深度森林(Multi-Label Deep Forest,MLDF)是一种基于深度森林的深度集成学习模型。为了限制模型的复杂度以及用户可以根据需求优化评价指标,多标签深度森林使用了两种算法:度量感知特征重用与度量感知层增长。前者重用前一层较好的特征,后者用于限制模型增长,然而度量感知特征重用算法使用当前层的输出取算术平均值来计算置信度,忽略了各个森林的精度差异造成的影响。因此,提出一种改进的度量感知特征重用算法,为每个森林在每个标签上赋予权重来计算置信度。实验结果表明,改进的算法在低维多标签数据集上有一定的提升。展开更多
To prepare calcium-binding peptides, the flesh residue of Mactra Veneriformis was subjected to enzymatic hydrolysis. By comparing the capability of combining calcium of the hydrolyzates, pepsin was confirmed to be the...To prepare calcium-binding peptides, the flesh residue of Mactra Veneriformis was subjected to enzymatic hydrolysis. By comparing the capability of combining calcium of the hydrolyzates, pepsin was confirmed to be the most suitable enzyme for hydrolyzing the flesh residue to release calcium-binding peptides among the seven tested proteases. The pepsin hydrolyzate (PHM) was divided into three fractions according to the molecule weight of its composition, which ranged from 0.5 to 15 kDa. The low-molecule-weight fraction named PHM-3 had the highest capability in combining calcium. The peptides existing in the PHM-3 fraction consisted of higher contents of Glu, Ala and Leu, and could produce one type of calcium-peptide complex by powerfully chelating calcium ions. PHM-3 products could effectively increase calcium absorption and retention while they decreased the calcium excretion in animal tests. Additionally, symptoms caused by low calcium bioavailability in ovariectomized rats, such as bone mineral density reduction and mechanical strength loss could be significantly ameliorated by the hydrolytic products addition in diet.展开更多
Background： Lower body positive pressure （LBPP） treadmills can be used in rehabilitation programs and/or to supplement tun mileage in healthy runners by reducing the effective body weight and impact associated with...Background： Lower body positive pressure （LBPP） treadmills can be used in rehabilitation programs and/or to supplement tun mileage in healthy runners by reducing the effective body weight and impact associated with running. The purpose of this study is to determine if body weight support influences the stride length （SL）-velocity as well as leg impact acceleration relationship during running. Methods： Subjects （n = 10, 21.4 ± 2.0 years, 72.4 ± 10.3 kg, 1.76 ± 0.09 m） completed 16 run conditions consisting of specific body weight support and velocity combinations. Velocities tested were 100%, 110%, 120%, and 130% of the preferred velocity （2.75± 0.36 m/s）. Body weight support conditions consisted of 0, 60%,5, 70%, and 80% body weight support. SL and leg impact accelerations were determined using a light-weight accelerometer mounted on the surface of the anterior-distal aspect of the tibia. A 4 × 4 （velocity x body weight support） repeated measures ANOVA was used for each dependent variable （a = 0.05）. Results： Neither SL nor leg impact acceleration were influenced by the interaction of body weight support and velocity （p 〉 0.05）. SL was least during no body weight support （p 〈 0.05） but not different between 60%, 70%, and 80% support （p 〉 0.05）. Leg impact acceleration was greatest during no body weight support （p 〈 0.05） but not different between 60%, 70%, and 80% support （p 〉 0.05）. SL and leg impact accelerations increased with velocity regardless of support （p 〈 0.05）. Conclusion： The relationships between SL and leg impact accelerations with velocity were not influenced by body weight support.展开更多
文摘多标签深度森林(Multi-Label Deep Forest,MLDF)是一种基于深度森林的深度集成学习模型。为了限制模型的复杂度以及用户可以根据需求优化评价指标,多标签深度森林使用了两种算法:度量感知特征重用与度量感知层增长。前者重用前一层较好的特征,后者用于限制模型增长,然而度量感知特征重用算法使用当前层的输出取算术平均值来计算置信度,忽略了各个森林的精度差异造成的影响。因此,提出一种改进的度量感知特征重用算法,为每个森林在每个标签上赋予权重来计算置信度。实验结果表明,改进的算法在低维多标签数据集上有一定的提升。
基金supported by the National Natural Science Foundation of China (No.30900293)the Open Project Program of National First-Class Key Discipline for Traditional Chinese Medicine of Nanjing University of Chinese Medicine (No.2011ZYX5-004),which is a project funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions,PAPD(ysxk-2010)
文摘To prepare calcium-binding peptides, the flesh residue of Mactra Veneriformis was subjected to enzymatic hydrolysis. By comparing the capability of combining calcium of the hydrolyzates, pepsin was confirmed to be the most suitable enzyme for hydrolyzing the flesh residue to release calcium-binding peptides among the seven tested proteases. The pepsin hydrolyzate (PHM) was divided into three fractions according to the molecule weight of its composition, which ranged from 0.5 to 15 kDa. The low-molecule-weight fraction named PHM-3 had the highest capability in combining calcium. The peptides existing in the PHM-3 fraction consisted of higher contents of Glu, Ala and Leu, and could produce one type of calcium-peptide complex by powerfully chelating calcium ions. PHM-3 products could effectively increase calcium absorption and retention while they decreased the calcium excretion in animal tests. Additionally, symptoms caused by low calcium bioavailability in ovariectomized rats, such as bone mineral density reduction and mechanical strength loss could be significantly ameliorated by the hydrolytic products addition in diet.
文摘Background： Lower body positive pressure （LBPP） treadmills can be used in rehabilitation programs and/or to supplement tun mileage in healthy runners by reducing the effective body weight and impact associated with running. The purpose of this study is to determine if body weight support influences the stride length （SL）-velocity as well as leg impact acceleration relationship during running. Methods： Subjects （n = 10, 21.4 ± 2.0 years, 72.4 ± 10.3 kg, 1.76 ± 0.09 m） completed 16 run conditions consisting of specific body weight support and velocity combinations. Velocities tested were 100%, 110%, 120%, and 130% of the preferred velocity （2.75± 0.36 m/s）. Body weight support conditions consisted of 0, 60%,5, 70%, and 80% body weight support. SL and leg impact accelerations were determined using a light-weight accelerometer mounted on the surface of the anterior-distal aspect of the tibia. A 4 × 4 （velocity x body weight support） repeated measures ANOVA was used for each dependent variable （a = 0.05）. Results： Neither SL nor leg impact acceleration were influenced by the interaction of body weight support and velocity （p 〉 0.05）. SL was least during no body weight support （p 〈 0.05） but not different between 60%, 70%, and 80% support （p 〉 0.05）. Leg impact acceleration was greatest during no body weight support （p 〈 0.05） but not different between 60%, 70%, and 80% support （p 〉 0.05）. SL and leg impact accelerations increased with velocity regardless of support （p 〈 0.05）. Conclusion： The relationships between SL and leg impact accelerations with velocity were not influenced by body weight support.
