SNPs, haplotypes, and model selection in a candidate gene region: the SIMPle analysis for multilocus data

Modern molecular techniques make discovery of numerous single nucleotide polymorphims (SNPs) in candidate gene regions feasible. Conventional analysis relies on either independent tests with each variant or the use of haplotypes in association analysis. The first technique ignores the dependencies between SNPs. The second, though it may increase power, often introduces uncertainty by estimating haplotypes from population data. Additionally, as the number of loci expands for a haplotype, ambiguity in interpretation increases for determining the underlying genetic components driving a detected association. Here, we present a genotype-level analysis to jointly model the SNPs via a SNP interaction model with phase information (SIMPle) to capture the underlying haplotype structure. This analysis estimates both the risk associated with each variant and the importance of phase between pairwise combinations of SNPs. Thus, rather than selecting between genotype- or haplotype-level approaches, the SIMPle method frames the analysis of multilocus data in a model selection paradigm, the aim to determine which SNPs, phase terms, and linear combinations best describe the relation between genetic variation and a trait of interest. To avoid unstable estimation due to sparse data and to incorporate both the dependencies among terms and the uncertainty in model selection, we propose a Bayes model averaging procedure. This highlights key SNPs and phase terms and yields a set of best representative models. Using simulations, we demonstrate the utility of the SIMPle model to identify crucial SNPs and underlying haplotype structures across a variety of causal models and genetic architectures.     

Applications of population approaches in toxicology

Many experimental or observational studies in toxicology are best analysed in a population framework. Recent examples include investigations of the extent and origin of intra-individual variability in toxicity studies, incorporation of genotypic information to address intra-individual variability, optimal design of experiments, and extension of toxicokinetic modelling to the analysis of biomarker studies. Bayesian statistics provide powerful numerical methods for fitting population models, particularly when complex mechanistic models are involved. Challenges and limitations to the use of population models, in terms of basic structure, computational burden, ease of implementation and data accessibility, are identified and discussed.     

匹配病例对照研究的Bayes方法

目的 探讨匹配的病例对照研究的Bayes方法.方法 构建病例对照研究的Bayes多项式分布模型,利用WinBUGS软件用Gibbs抽样的MCMC模拟技术估计疾病与暴露之间的优势比.结果 通过WinBUGS软件实例分析,所得结果与经典方法相比,认为两者结果相似,但是部分区间较宽.结论 病例对照研究的Bayes模型的解释合理、恰当.     

Are apparent findings of nonlinearity due to structural instability in economic time series?

Many modelling issues and policy debates in macroeconomics depend on whether macroeconomic time series are best characterized as linear or nonlinear. If departures from linearity exist, it is important to know whether these are endogenously generated (as in, e.g. a threshold autoregressive model) or whether they merely reflect changing structure over time. In this paper, we discuss a model comparison methodology which addresses these issues. We advocate a Bayesian approach and show how such an approach can be implemented in practice. An empirical exercise involving several macroeconomic time series shows that apparent findings of threshold type nonlinearities could be due to structural instability. JEL: C22, C11, E30     

Finite-time stabilization control for discontinuous time-delayed networks: New switching design

This paper discusses the finite-time stabilization problem for time-varying delayed neural networks (DNNs) with discontinuous activation functions. By using fixed point theory and set-valued analysis, we establish the existence theorem of equilibrium point. In order to stabilize the states of this class of discontinuous DNNs in finite time, we design two different kinds of switching controllers which are described by discontinuous functions. Under the framework of Filippov solutions, several new and effective criteria are derived to realize finite-time stabilization of discontinuous DNNs based on the famous finite-time stability theory. Besides, the upper bounds of the settling time of stabilization are estimated. Numerical examples are finally provided to illustrate the correctness of the proposed design method and theoretical results.     

A topological approach unveils system invariances and broken symmetries in the brain

Symmetries are widespread invariances underscoring countless systems, including the brain. A symmetry break occurs when the symmetry is present at one level of observation but is hidden at another level. In such a general framework, a concept from algebraic topology, namely, the Borsuk‐Ulam theorem (BUT), comes into play and sheds new light on the general mechanisms of nervous symmetries. The BUT tells us that we can find, on an n‐dimensional sphere, a pair of opposite points that have the same encoding on an n – 1 sphere. This mapping makes it possible to describe both antipodal points with a single real‐valued vector on a lower dimensional sphere. Here we argue that this topological approach is useful for the evaluation of hidden nervous symmetries. This means that symmetries can be found when evaluating the brain in a proper dimension, although they disappear (are hidden or broken) when we evaluate the same brain only one dimension lower. In conclusion, we provide a topological methodology for the evaluation of the most general features of brain activity, i.e., the symmetries, cast in a physical/biological fashion that has the potential to be operationalized. © 2016 Wiley Periodicals, Inc.     

Abnormalities of the executive control network in multiple sclerosis phenotypes: An fMRI effective connectivity study

The Stroop interference task is a cognitively demanding task of executive control, a cognitive ability that is often impaired in patients with multiple sclerosis (MS). The aim of this study was to compare effective connectivity patterns within a network of brain regions involved in the Stroop task performance between MS patients with three disease clinical phenotypes [relapsing‐remitting (RRMS), benign (BMS), and secondary progressive (SPMS)] and healthy subjects. Effective connectivity analysis was performed on Stroop task data using a novel method based on causal Bayes networks. Compared with controls, MS phenotypes were slower at performing the task and had reduced performance accuracy during incongruent trials that required increased cognitive control. MS phenotypes also exhibited connectivity abnormalities reflected as weaker shared connections, presence of extra connections (i.e., connections absent in the HC connectivity pattern), connection reversal, and loss. In SPMS and the BMS groups but not in the RRMS group, extra connections were associated with deficits in the Stroop task performance. In the BMS group, the response time associated with correct responses during the congruent condition showed a positive correlation with the left posterior parietal → dorsal anterior cingulate connection. In the SPMS group, performance accuracy during the congruent condition showed a negative correlation with the right insula → left insula connection. No associations between extra connections and behavioral performance measures were observed in the RRMS group. These results suggest that, depending on the phenotype, patients with MS use different strategies when cognitive control demands are high and rely on different network connections. Hum Brain Mapp, 37:2293–2304, 2016. © 2016 Wiley Periodicals, Inc.     

《内经》中刺灸法则探析

《内经》中有关针灸治疗的原则论述颇多,刺法灸法的正确运用和临床疗效密切相关,通过对清热温寒、补虚泻实及三因论治思想的阐述,明确了《内经》中的刺灸法则对临证针灸运用具有的重要指导意义。