Assessment of causal effects of physical activity on neurodegenerative diseases: A Mendelian randomization study

BackgroundPhysical activity has been hypothesized to play a protective role in neurodegenerative diseases. However, effect estimates previously derived from observational studies were prone to confounding or reverse causation.MethodsWe performed a two-sample Mendelian randomization (MR) analysis to explore the causal association of accelerometer-measured physical activity with 3 common neurodegenerative diseases: Alzheimer''s disease (AD), Parkinson''s disease (PD), and amyotrophic lateral sclerosis (ALS). We selected genetic instrumental variants reaching genome-wide significance (p < 5 × 10⁻⁸) from 2 largest meta-analyses of about 91,100 UK Biobank participants. Summary statistics for AD, PD, and ALS were retrieved from the up-to-date studies in European ancestry led by the international consortia. The random-effect, inverse-variance weighted MR was employed as the primary method, while MR pleiotropy residual sum and outlier (MR-PRESSO), weighted median, and MR-Egger were implemented as sensitivity tests. All statistical analyses were performed using the R programming language (Version 3.6.1; R Foundation for Statistical Computing, Vienna, Austria).ResultsPrimary MR analysis and replication analysis utilized 5 and 8 instrumental variables, which explained 0.2% and 0.4% variance in physical activity, respectively. In each set, one variant at 17q21 was significantly associated with PD, and MR sensitivity analyses indicated them it as an outlier and source of heterogeneity and pleiotropy. Primary results with the removal of outlier variants suggested odds ratios (ORs) of neurodegenerative diseases per unit increase in objectively measured physical activity were 1.52 for AD (95% confidence interval (95%CI): 0.88–2.63, p = 0.13) and 3.35 for PD (95%CI: 1.32–8.48, p = 0.01), while inconsistent results were shown in the replication set for AD (OR = 1.06, 95%CI: 1.01–1.12, p = 0.02) and PD (OR = 0.99, 95%CI: 0.88–0.12, p = 0.97). Similarly, the beneficial effect of physical activity on ALS (OR = 0.51, 95%CI: 0.29–0.91, p = 0.02) was not confirmed in the replication analysis (OR = 0.96, 95%CI: 0.91–1.02, p = 0.22).ConclusionGenetically predicted physical activity was not robustly associated with risk of neurodegenerative disorders. Triangulating evidence across other studies is necessary in order to elucidate whether enhancing physical activity is an effective approach in preventing the onset of AD, PD, or ALS.     

基于可进化硬件的FIR数字滤波器设计

本文介绍了可进化硬件的机理和相关技术,阐述了一种基于进化论中遗传算法的大规模电子电路设计方法,用可进化硬件设计出了有限冲击响应数字滤波FIR系统,利用VHDL语言在QUARTUSII对设计进行仿真,得到了较好的效果关键词:遗传算法;可进化硬件;FIR;EHW;FPGA;VHDL     

霍兰的复杂适应系统理论及其应用

通过解读霍兰(J．H．Holland)的作《自然系统和人工系统中的适应》、《涌现性》及《隐秩序》,从而了解到霍兰提出的复杂适应系统理论和遗传算法、分类器系统等人工适应主体技术为演化经济学理论的计算机模拟模型研究提供了广阔的前景。     

混合遗传算法及其应用

给出一种结合梯度法和正交遗传算法的混合算法。实验表明,它通过对问题的解空间交替进行全局和局部搜索,能更有效地求解函数优化问题。     

泡沙参复合体(桔梗科)的物种生物学研究 Ⅳ. 等位酶水平的变异和分化

 利用聚丙烯酰胺凝胶电泳技术,对采自泡沙参复合体整个分布区的15个天然居群进行了等位酶 分析。7种酶系统共10个等位酶基因位点的检测表明,本复合体植物具有高水平的遗传变异性,居群 水平上的遗传多样性指标分别为:多态位点百分率P=0.40～0.70,等位基因平均数A=1.5～2.5,平 均期望杂合度He=0.102～0.291。3个类群在居群水平上的平均多样性水平由高至低依次为;A． wawreana(A=2.10,P=0.63, He=0.237),A．lobophylla(A=1.85,P=0.60,He=0.204)和A． potaninii(A=1.83,P=O.60,He=0.188)。居群遗传结构和交配系统的分析表明,本复合体植物的大 部分遗传变异存在于居群之内,Fst=0.176(A．wawreana)和0.115(A．potaninii),并以异交为主,异 交率估计值分别为t=0.86(A．wawreana),0.74(A．potaninii)和0.63(A．lobophylla),这是本复合 体植物具有丰富遗传变异的主要原因之一。根据15个居群的遗传一致度(I)值进行聚类分析发现, 15 个居群可分为明显的2支,由A．lobophylla 2个居群构成的一支与由A. wawreana 和A．potaninii 13 个居群构成的一支在遗传上的分化十分明显,而6个A．wawreana 居群和7个A．potaninii 居群间的 遗传差异不明显,是两个分化不充分的类群。因此,本文的等位酶证据说明,A．lobophylla是遗传上独立的好种,同时不支持将A．wawreana 和A. potaninii 分别作为独立种的处理。     

Spontaneous focusing on numerosity and the arithmetic advantage

Children show individual differences in their tendency to focus on the numerical aspects of their environment. These individual differences in ‘Spontaneous Focusing on Numerosity’ (SFON) have been shown to predict both current numerical skills and later mathematics success. Here we investigated possible factors which may explain the positive relationship between SFON and symbolic number development. Children aged 4–5 years (N = 130) completed a battery of tasks designed to assess SFON and a range of mathematical skills. Results showed that SFON was positively associated with children's symbolic numerical processing skills and their performance on a standardised test of arithmetic. Hierarchical regression analyses demonstrated that the relationship between SFON and symbolic mathematics achievement can be explained, in part, by individual differences in children's nonsymbolic numerical processing skills and their ability to map between nonsymbolic and symbolic representations of number.     

模糊数据挖掘及遗传算法在IDS中的应用研究

针对关联规则挖掘中存在的＂尖锐边界问题＂,重点研究了模糊关联规则挖掘及遗传算法在入侵检测中的应用,在隶属度的计算中采用了折中的准则。实验结果表明该方法能有效区分正常状态和异常状态,提高入侵检测准确度。     

Control vector optimization and genetic algorithms for mixed-integer dynamic optimization in the synthesis of rice drying processes

Rice drying synthesis is an essential operation that has to be done carefully and cost-effectively. Rice is harvested at high moisture content and hence must be dried within 24 h for safe storage. However, improper drying can cause fissuring in the rice grain, and thus greatly reduce its market value. Multi-pass drying systems are therefore used to gradually bring moisture content to desired level.The problem of rice synthesis, considered in this study, seeks the configuration of units and their corresponding operating conditions that maximize rice quality. This problem is formulated as a mixed-integer dynamic optimization problem. The integer part of the problem reflects process alternatives while the dynamic part originates from nonlinear differential-algebraic equations describing the drying behavior of a rice grain.Clearly such a formidable problem is not easy to solve. Hence, we propose an approach that makes use of two algorithms: a genetic algorithm to search for the best configuration of units and a control vector parameterization approach that optimizes the operating conditions for each configuration. We demonstrate the effectiveness of the approach on a case study.     

H2 and H∞ optimal model reduction using genetic algorithms

The mathematical modeling of most physical systems, such as aerospace systems, heat processes, telecommunication systems, transmission lines and chemical reactors, results in complex high order models. The complexity of the models imposes a lot of difficulties in analysis, simulation and control designs. Several analytical model reduction techniques have been proposed in literature over the past few decades to reduce these difficulties. However, most of the optimal techniques follow computationally demanding, time consuming, iterative procedures that usually result in non-robustly stable models with poor frequency response resemblance to the original high order model in some frequency ranges. Genetic Algorithm (GA) has proved to be an excellent optimization tool in the past few years. Therefore, the aim of this paper will be to use GA to solve H₂ and H_∞ norm model reduction problems, and help obtain globally optimized nominal models.     

基于改进蚁群遗传算法的排课问题研究

提出了一种以蚁群算法为主,利用遗传算法经过交叉,变异,选择后产生进化的信息素作为蚁群使用的信息素.在遗传算法多次进化效果不明显时,引入模拟退火算法进行跳变的混合算法,使各种算法取长补短,改善了蚁群算法初始阶段运行缓慢和遗传算法局部搜索能力弱的缺点.提高了运行效率,同时运用这种改进的算法对高校排课问题进行仿真,从而比较算法改进的优缺点.