38 research outputs found
No or only population-specific effect of PON1 on human longevity: A comprehensive meta-analysis
No full textParaoxonase 1 (PON1) has been suggested as a plausible candidate gene for human longevity due to its modulation of cardiovascular disease risk, by preventing oxidation of atherogenic low-density lipoprotein. The role of the PON1 192 Q/R polymorphism has been analyzed for association with survival at old age in several populations, albeit with controversial results. To reconcile the conflicting evidence, we performed a large association study with two samples of 2357 Germans and 1025 French, respectively. We combined our results with those from seven previous studies in the largest and most comprehensive meta-analysis on PON1 192 Q/R and longevity to-date, to include a total of 9580 individuals. No significant association of PON1 192 Q/R with longevity was observed, for either R allele or carriership. This finding relied on very large sample sizes, is supported by different analysis methods and is therefore considered very robust. Moreover, we have investigated a potential interaction of PON1 192 Q/R with APOE epsilon4 using data from four populations. Whereas a significant result was found in the German sample, this could not be confirmed in the other examined groups. Our large-scale meta-analysis provided no evidence that the PON1 192 Q/R polymorphism is associated with longevity, but this does not exclude the possibility of population-specific effects due to the influence of, and interaction between, different genetic and/or environmental factors (e.g. diet)
Association Between Personality Traits, Academic Stress, And Coping Styles
No full textNeuroticism is characterized by anxiety, fear, and worry. Neuroticism has been associated with passive coping styles (Afsharet al., 2015). Conscientiousness is characterized by being thorough, careful, and vigilant. Those who score high in Conscientiousness are willing to engage in problem solving thinking (Connor-Smith & Flachsbart, 2007)
Relations Between Personality and Coping: A Meta-Analysis
No full textPersonality may directly facilitate or constrain coping, but relations of personality to coping have been inconsistent across studies, suggesting a need for greater attention to methods and samples. This meta-analysis tested moderators of relations between Big Five personality traits and coping using 2,653 effect sizes drawn from 165 samples and 33,094 participants. Personality was weakly related to broad coping (e.g., Engagement or Disengagement), but all 5 traits predicted specific strategies. Extraversion and Conscientiousness predicted more problem-solving and cognitive restructuring, Neuroticism less. Neuroticism predicted problematic strategies like wishful thinking, withdrawal, and emotion-focused coping but, like Extraversion, also predicted support seeking. Personality more strongly predicted coping in young samples, stressed samples, and samples reporting dispositional rather than situation-specific coping. Daily versus retrospective coping reports and self-selected versus researcher-selected stressors also moderated relations between personality and coping. Cross-cultural differences were present, and ethnically diverse samples showed more protective effects of personality. Richer understanding of the role of personality in the coping process requires assessment of personality facets and specific coping strategies, use of laboratory and daily report studies, and multivariate analyses
Über die Mineralsubstanz der Kalkkörper von Squalus acanthias (Dornhai). Zur molekularen Anatomie der Wirbelsäule, I. Mitteilung
No full textPeer Reviewe
Photoelasticity based dynamic tactile sensor
No full textThe paper presents design, construction and testing of a photoelasticity based dynamic sensor which is capable of detecting slip as well as providing normal force information. Starting with investigations into mechanism of slip, an approximate model of the sensor has been developed. This model explains the design improvements necessary to provide continuous signal during slip. The theoretical model also helps identify various sensor parameters to characterize the sensor. The developed sensor has been compared with other existing sensors and the experimental results from the sensor have been discussed for the type of signal the sensor provides. The sensor is also calibrated for normal force. The sensor is novel in the sense that it offers dynamic slip signal as well as the normal force information from a single contact location, it provides continuous signal during slip, and it has small size which can be easily incorporated into robotic fingers. The sensor has an edge over other existing sensors that its design is simple yet it provides strong signals which are largely unaffected by external disturbances. Copyright © 2005 by ASME
EFFECTS OF DOBUTAMINE ON HEMODYNAMICS AND LEFT VENTRICULAR PERFORMANCE IN THE EARLY POST CARDIOPULMONARY BYPASS PERIOD
No full textMULTILAYERED POLYMER MICROFLUIDIC CHIP WITH NANOFLUIDIC INTERCONNECTS FOR MOLECULAR MANIPULATIONS
No full textA feed forward neural network approach for matrix computations
No full textThis thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.A new neural network approach for performing matrix computations is presented. The idea of this approach is to construct a feed-forward neural network (FNN) and then train it by matching a desired set of patterns. The solution of the problem is the converged weight of the FNN. Accordingly, unlike the conventional FNN research that concentrates on external properties (mappings) of the networks, this study concentrates on the internal properties (weights) of the network. The present network is linear and its weights are usually strongly constrained; hence, complicated overlapped network needs to be construct. It should be noticed, however, that the present approach depends highly on the training algorithm of the FNN. Unfortunately, the available training methods; such as, the original Back-propagation (BP) algorithm, encounter many deficiencies when applied to matrix algebra problems; e. g., slow convergence due to improper choice of learning rates (LR). Thus, this study will focus on the development of new efficient and accurate FNN training methods. One improvement suggested to alleviate the problem of LR choice is the use of a line search with steepest descent method; namely, bracketing with golden section method. This provides an optimal LR as training progresses. Another improvement proposed in this study is the use of conjugate gradient (CG) methods to speed up the training process of the neural network. The computational feasibility of these methods is assessed on two matrix problems; namely, the LU-decomposition of both band and square ill-conditioned unsymmetric matrices and the inversion of square ill-conditioned unsymmetric matrices. In this study, two performance indexes have been considered; namely, learning speed and convergence accuracy. Extensive computer simulations have been carried out using the following training methods: steepest descent with line search (SDLS) method, conventional back propagation (BP) algorithm, and conjugate gradient (CG) methods; specifically, Fletcher Reeves conjugate gradient (CGFR) method and Polak Ribiere conjugate gradient (CGPR) method. The performance comparisons between these minimization methods have demonstrated that the CG training methods give better convergence accuracy and are by far the superior with respect to learning time; they offer speed-ups of anything between 3 and 4 over SDLS depending on the severity of the error goal chosen and the size of the problem. Furthermore, when using Powell's restart criteria with the CG methods, the problem of wrong convergence directions usually encountered in pure CG learning methods is alleviated. In general, CG methods with restarts have shown the best performance among all other methods in training the FNN for LU-decomposition and matrix inversion. Consequently, it is concluded that CG methods are good candidates for training FNN of matrix computations, in particular, Polak-Ribidre conjugate gradient method with Powell's restart criteria
