FINITE ELEMENT ANALYSIS OF ACHILLES TENDON IN JUMPING PHASE

The prevalence of Achilles tendon(AT) injury is high in various sports, but the tendon internal dynamic variation in the movement is not fully understood.Based on the CT scan and CAD/CAM software processing, the ankle three-dimensional model were created. Achilles tendon’s stress-strain change were acquired through finite element analysis of one-legged jumping. The largest stress was appeared in the push phase and concentrated in the 6 cm above the calcaneal insertion. This model can be applied to research the achilles tendon, and provide mechanics data in jumping trauma

Factorized Q-Learning for Large-Scale Multi-Agent Systems

Deep Q-learning has achieved significant success in single-agent decision making tasks. However, it is challenging to extend Q-learning to large-scale multi-agent scenarios, due to the explosion of action space resulting from the complex dynamics between the environment and the agents. In this paper, we propose to make the computation of multi-agent Q-learning tractable by treating the Q-function (w.r.t. state and joint-action) as a high-order high-dimensional tensor and then approximate it with factorized pairwise interactions. Furthermore, we utilize a composite deep neural network architecture for computing the factorized Q-function, share the model parameters among all the agents within the same group, and estimate the agents' optimal joint actions through a coordinate descent type algorithm. All these simplifications greatly reduce the model complexity and accelerate the learning process. Extensive experiments on two different multi-agent problems demonstrate the performance gain of our proposed approach in comparison with strong baselines, particularly when there are a large number of agents.Comment: 7 pages, 5 figures, DAI 201

Nodules from fynbos legume Virgilia divaricata have high functional plasticity under variable P supply levels

Legumes have the unique ability to fix atmospheric nitrogen (N2) via symbiotic bacteria in their nodules but depend heavily on phosphorus (P), which affects nodulation, and the carbon costs and energy costs of N2 fixation. Consequently, legumes growing in nutrient-poor ecosystems (e.g., sandstone-derived soils) have to enhance P recycling and/or acquisition in order to maintain N2 fixation. In this study, we investigated the flexibility of P recycling and distribution within the nodules and their effect on N nutrition in Virgilia divaricata Adamson, Fabaceae, an indigenous legume in the Cape Floristic Region of South Africa. Specifically, we assessed tissue elemental localization using micro-particle-induced X-ray emission (PIXE), measured N fixation using nutrient concentrations derived from inductively coupled mass-spectrometry (ICP-MS), calculated nutrient costs, and determined P recycling from enzyme activity assays. Morphological and physiological features characteristic of adaptation to P deprivation were observed for V. divaricata. Decreased plant growth and nodule production with parallel increased root:shoot ratios are some of the plastic features exhibited in response to P deficiency. Plants resupplied with P resembled those supplied with optimal P levels in terms of growth and nutrient acquisition. Under low P conditions, plants maintained an increase in N2-fixing efficiency despite lower levels of orthophosphate (Pi) in the nodules. This can be attributed to two factors: (i) an increase in Fe concentration under low P, and (ii) greater APase activity in both the roots and nodules under low P. These findings suggest that V. divaricata is well adapted to acquire N under P deficiency, owing to the plasticity of its nodule physiologyDST-NRF Centre of Excellence in Tree Health Biotechnology(CTHB).http://www.elsevier.com/locate/jplpham201

Foot Loading Characteristics of Chinese Bound Feet Women: A Comparative Analysis

The custom of bound feet among Chinese women has existed for almost a century. This practice has influenced the daily life of Chinese women, especially during everyday locomotion. The primary aim of this study is to analyze the loading patterns of bound feet. Specifically, the plantar pressure and center of pressure were analyzed for peak pressure, contact area, force time integral, center of pressure displacement velocity and trajectory in the anterior- posterior direction via a comparison with normal feet. The key outcomes from this work were that the forefoot and rearfoot of bound feet bear the whole loading during stance phase. The center of pressure displacement velocity of bound feet was also greatly reduced with the shortening of trajectories. This suggests that the proprioceptive system adjusts motor function to adapt to new loading patterns while maintaining locomotive stability. A biomechanical understanding of bound feet may assist with prevention, treatment and rehabilitation of bound feet disorders

Prediction of Plant Height in \u3ci\u3eArabidopsis thaliana\u3c/i\u3e Using DNA Methylation Data

Prediction of complex traits using molecular genetic information is an active area in quantitative genetics research. In the postgenomic era, many types of -omic (e.g., transcriptomic, epigenomic, methylomic, and proteomic) data are becoming increasingly available. Therefore, evaluating the utility of this massive amount of information in prediction of complex traits is of interest. DNA methylation, the covalent change of a DNA molecule without affecting its underlying sequence, is one quantifiable form of epigenetic modification. We used methylation information for predicting plant height (PH) in Arabidopsis thaliana nonparametrically, using reproducing kernel Hilbert spaces (RKHS) regression. Also, we used different criteria for selecting smaller sets of probes, to assess how representative probes could be used in prediction instead of using all probes, which may lessen computational burden and lower experimental costs. Methylation information was used for describing epigenetic similarities between individuals through a kernel matrix, and the performance of predicting PH using this similarity matrix was reasonably good. The predictive correlation reached 0.53 and the same value was attained when only preselected probes were used for prediction. We created a kernel that mimics the genomic relationship matrix in genomic best linear unbiased prediction (G-BLUP) and estimated that, in this particular data set, epigenetic variation accounted for 65% of the phenotypic variance. Our results suggest that methylation information can be useful in whole-genome prediction of complex traits and that it may help to enhance understanding of complex traits when epigenetics is under examination