LOWER LEG MORPHOLOGY AND STRETCH-SHORTENING CYCLE PERFORMANCE IN YOUNG AND ELDERLY MALES

The purpose of this investigation was to examine bone and muscle characteristics of the lower leg and stretch-shortening cycle capabilities of the ankle in young (22.3 ± 1.3 yrs) and elderly (67.5 ± 3.3 yrs) males. Peripheral quantitiative computed tomography (pQCT) was utilized to assess bone stress-strain index, bone ultimate fracture load, muscle density, muscle cross-sectional area (CSA), fat CSA and muscle+bone CSA. Maximal voluntary isometric plantarflexion (MVIP) force and force-velocity measurments during a countermovement hop (CMH) and drop hops from 20, 30 and 40 cm (DH20, DH30, DH40) were also measured. Bone stress-strain index was significantly higher in young males as well as muscle density, muscle CSA and muscle+bone CSA in comparison to elderly males. MVIP peak force and rate of force development was significantly higher in young males in comparsion to elderly males as well. An analysis of the force-velocity curves indicated that young males had significanlty higher levels of force and velocity in both the eccentric and concentric phase during the CMH, DH20, DH30 and DH40 in comparsion to elderly males. The data from this investigation indicate that aging potentially negatively influences lower leg bone and muscle strength and this may be reflected in lower stretch-shortening cycle capabilities of the ankle

Bone health, muscle properties and stretch-shortening cycle function of young and elderly males

The aim of this study was to examine bone, muscle, strength and stretch-shortening cycle (SSC) performance in young and elderly individuals with an ankle model to elucidate potential effects of ageing that have been suggested to influence fall risk. Moderately active young (n=10; age=22.3±1.3 yrs) and elderly (n=8; age=67.5±3.3 yrs) males completed a peripheral quantitative computed tomography scan on the dominant lower leg, maximal voluntary isometric plantarflexions (MVIP) and SSC tasks: a countermovement hop and drop hops from three different heights. Bone stress-strain index at 14% of the lower leg and muscle density, muscle cross-sectional area and muscle+bone cross-sectional area at 66% of the lower leg were all significantly greater (p≤0.05) in younger males than elderly males. Younger males also had significantly greater rate of force development and peak force during the MVIP when compared to the elderly. Younger males achieved significantly higher forces, velocities and hop heights during all SSC tasks than elderly males. Such information provides support for greater specificity in exercise interventions that prevent lower leg morphological and functional decrements in the ageing population

Improved Louvain Method for Directed Networks

Part 5: Natural Language ProcessingInternational audienceExisting studies about community detection mainly focus on undirected networks. However, research results on detecting community structure in directed networks are less extensive and less systematic. The Louvain Method is one of the best algorithms for community detection in undirected networks. In this study, an algorithm was proposed to detect community structure in mass directed networks. First, the definition for modularity of directed networks based on the community connection matrix was proposed. Second, equations to calculate modularity gain in directed networks were derived. Finally, based on the idea of Louvain Method, an algorithm to detect community in directed networks was proposed. Relevant experiments show that not only does the algorithm have obvious advantages both in run-time and accuracy of community discovery results, but it can also obtain multi-granularity community structure that could reflect the self-similarity characteristics and hierarchical characteristics of complex networks. Experimental results indicate the algorithm is excellent in detecting community structure in mass directed networks

Using Graph Components Derived from an Associative Concept Dictionary to Predict fMRI Neural Activation Patterns that Represent the Meaning of Nouns

In this study, we introduce an original distance definition for graphs, called the Markov-inverse-F measure (MiF). This measure enables the integration of classical graph theory indices with new knowledge pertaining to structural feature extraction from semantic networks. MiF improves the conventional Jaccard and/or Simpson indices, and reconciles both the geodesic information (random walk) and co-occurrence adjustment (degree balance and distribution). We measure the effectiveness of graph-based coefficients through the application of linguistic graph information for a neural activity recorded during conceptual processing in the human brain. Specifically, the MiF distance is computed between each of the nouns used in a previous neural experiment and each of the in-between words in a subgraph derived from the Edinburgh Word Association Thesaurus of English. From the MiF-based information matrix, a machine learning model can accurately obtain a scalar parameter that specifies the degree to which each voxel in (the MRI image of) the brain is activated by each word or each principal component of the intermediate semantic features. Furthermore, correlating the voxel information with the MiF-based principal components, a new computational neurolinguistics model with a network connectivity paradigm is created. This allows two dimensions of context space to be incorporated with both semantic and neural distributional representations