西南民族大学金·史密斯藏学文献馆的建馆历程

著名藏学家金·史密斯先生用一生的时间从事藏文文献的保护工作,在藏文文献的收集、整理、研究、利用等领域做出了巨大的贡献,为后人留下了大量的藏文文献。他在美国纽约建立了藏传佛教文献资料中心（简称：TBRC）,大量的藏文文献在国内、外藏学界利用起来。同时他也是把藏文文献的原件从国外捐赠给中国的第一位外国藏学家,2012年12月,历时5年的藏文纸质文献的捐赠工作顺利完成。本论文对金·史密斯先生建立TBRC的具体情况,捐赠纸质文献给西南民族大学的事件,西南民族大学金·史密斯藏学文献馆建馆历程等内容进行梳理,以此来缅怀为藏文文献的保护工作付出一生的美国藏学家。     

多维理论视角下的合作行为探析

合作行为一直是国内外学者们的重要研究课题,不同的学科、不同的理论给出了不同的解释。学者们以"行为体之间的合作行为的发生和维持"作为研究主题,分别从以基因为核心变量下的合作行为、文化影响下的合作行为、博弈互动影响下的合作行为和认知影响下的合作行为等多维理论视角对合作行为做出阐释,这些理论阐释既有共同点,也有不同点,既有相一致的方面,也有相冲突的方面。通过对这些理论阐释的基本内容和关键变量进行系统梳理和逻辑剖析,展现这些理论阐释的各自优缺点,并在此基础上对这些理论观点加以综合比较,最终揭示行为体之间的合作行为是如何发生并维持下去的。     

文化与基因有无联系?——现代达尔文主义进军社会领域的思想轨迹

文化与基因是否有关系?达尔文的进化论与社会人文科学的关系如何?解决这些问题,就需要从达尔文主义入手,进而研究达尔文主义与"社会达尔文主义"和种族主义的根本差别,显然,达尔文主义进入人性和道德等领域过程中出现的动物行为学、社会生物学和进化心理学等学科,扩展了人们关于人自身的认识,对人文社会科学的发展具有推动作用。但也留下一系列不能简单地用基因与文化的直接关系来解决的难题。为了解决这些问题,需要对人类的行为模式和心理机制作深入科学的、历史的研究。     

氨基酸的开发与应用

氨基酸作为蛋白质的基本构件,自人类认识它以来,人们就一直在挖掘着它的潜力,生产工艺的不断改进,生产技术的不断革新,应用领域的不断拓展,无不昭示着氨基酸工业的长足进步。可当人们越向深处挖掘时,发现宝藏的资源越是丰富。本文即从氨基酸的生产、应用与开发方面,进行一下综述。     

Statistical methods for the analysis of high-throughput data based on functional profiles derived from the Gene Ontology

The increasing availability of high-throughput data, that is, massive quantities of molecular biology data arising from different types of experiments such as gene expression or protein microarrays, leads to the necessity of methods for summarizing the available information. As annotation quality improves it is becoming common to rely on biological annotation databases, such as the Gene Ontology (GO), to build functional profiles which characterize a set of genes or proteins using the distribution of their annotations in the database. In this work we describe a statistical model for such profiles, provide methods to compare profiles and develop inferential procedures to assess this comparison. An R-package implementing the methods will be available at publication time.     

Inference of Genetic Networks from Time Course Expression Data Using Functional Regression with Lasso Penalty

Statistical inference of genetic regulatory networks is essential for understanding temporal interactions of regulatory elements inside the cells. In this work, we propose to infer the parameters of the ordinary differential equations using the techniques from functional data analysis (FDA) by regarding the observed time course expression data as continuous-time curves. For networks with a large number of genes, we take advantage of the sparsity of the networks by penalizing the linear coefficients with a L ₁ norm. The ability of the algorithm to infer network structure is demonstrated using the cell-cycle time course data for Saccharomyces cerevisiae.     

Bimodal skew-symmetric normal distribution

ABSTRACT

We introduce a new parsimonious bimodal distribution, referred to as the bimodal skew-symmetric Normal (BSSN) distribution, which is potentially effective in capturing bimodality, excess kurtosis, and skewness. Explicit expressions for the moment-generating function, mean, variance, skewness, and excess kurtosis were derived. The shape properties of the proposed distribution were investigated in regard to skewness, kurtosis, and bimodality. Maximum likelihood estimation was considered and an expression for the observed information matrix was provided. Illustrative examples using medical and financial data as well as simulated data from a mixture of normal distributions were worked.     

Estimation of Sobol's sensitivity indices under generalized linear models

We derive explicit formulas for Sobol's sensitivity indices (SSIs) under the generalized linear models (GLMs) with independent or multivariate normal inputs. We argue that the main-effect SSIs provide a powerful tool for variable selection under GLMs with identity links under polynomial regressions. We also show via examples that the SSI-based variable selection results are similar to the ones obtained by the random forest algorithm but without the computational burden of data permutation. Finally, applying our results to the problem of gene network discovery, we identify through the SSI analysis of a public microarray dataset several novel higher-order gene–gene interactions missed out by the more standard inference methods. The relevant functions for SSI analysis derived here under GLMs with identity, log, and logit links are implemented and made available in the R package Sobol sensitivity.     

Sparse partial least squares regression for simultaneous dimension reduction and variable selection

Summary.  Partial least squares regression has been an alternative to ordinary least squares for handling multicollinearity in several areas of scientific research since the 1960s. It has recently gained much attention in the analysis of high dimensional genomic data. We show that known asymptotic consistency of the partial least squares estimator for a univariate response does not hold with the very large p and small n paradigm. We derive a similar result for a multivariate response regression with partial least squares. We then propose a sparse partial least squares formulation which aims simultaneously to achieve good predictive performance and variable selection by producing sparse linear combinations of the original predictors. We provide an efficient implementation of sparse partial least squares regression and compare it with well-known variable selection and dimension reduction approaches via simulation experiments. We illustrate the practical utility of sparse partial least squares regression in a joint analysis of gene expression and genomewide binding data.     

探究基因研究引发的伦理问题

基因技术的突飞猛进,使人们对未来充满美好的憧憬和无限的遐想,但它也带来人类无法回避的一系列伦理道德问题。我们不得不在这一对矛盾的冲撞中寻求人类社会发展之路。治疗性克隆并不必然滑向生殖性克隆。胚胎干细胞研究是一项值得支持的人道主义事业,应该得到伦理辩护。严格伦理规范和伦理程序,是支持胚胎干细胞研究的必要条件。