统计学习的认知机制及其神经基础

本文主要探讨了人类统计学习研究的起源和发展、影响统计学习的因素及统计学习的脑神经基础.人类统计学习的研究起源于20世纪90年代关于婴儿听觉语音流切分的实验,之后,大量研究又考察了对听觉非语言信息及视觉图形的统计学习.这些研究表明,统计学习是一种具有一般性的、涉及发现和提取规则的学习类型,但是不同范畴的统计学习又受到与特定范畴有关因素的制约.例如,语言统计学习受到语言因素的制约,而非语言统计学习与呈现刺激的特征以及呈现的通道有关.近些年,研究者开始采用事件相关电位(event-related potentials,ERPs)技术考察统计学习的时间进程,以及采用功能核磁共振成像(functional magnetic resonance imaging,fMRI)技术考察统计学习的脑激活模式.ERPs研究较一致地发现400 ms左右产生的负波与统计信息的提取有关,而脑成像研究结果表明,与统计学习相关的脑区有大脑左侧颞上回(superiortemporal gyrus)、右侧纹状体(right striatum)和右侧颞叶内侧记忆系统(right medial temporal memory system).     

Persistent Controversy in Statistical Approaches in Wildlife Sciences: A Perspective of Students

ABSTRACT The controversy over the use of null hypothesis statistical testing (NHST) has persisted for decades, yet NHST remains the most widely used statistical approach in wildlife sciences and ecology. A disconnect exists between those opposing NHST and many wildlife scientists and ecologists who conduct and publish research. This disconnect causes confusion and frustration on the part of students. We, as students, offer our perspective on how this issue may be addressed. Our objective is to encourage academic institutions and advisors of undergraduate and graduate students to introduce students to various statistical approaches so we can make well-informed decisions on the appropriate use of statistical tools in wildlife and ecological research projects. We propose an academic course that introduces students to various statistical approaches (e.g., Bayesian, frequentist, Fisherian, information theory) to build a foundation for critical thinking in applying statistics. We encourage academic advisors to become familiar with the statistical approaches available to wildlife scientists and ecologists and thus decrease bias towards one approach. Null hypothesis statistical testing is likely to persist as the most common statistical analysis tool in wildlife science until academic institutions and student advisors change their approach and emphasize a wider range of statistical methods.     

Statistical analysis and study design in plastic and reconstructive surgical research

Appropriate study design and proper statistical analysis are necessary ingredients for improving the quality and reliability of the information in journal articles. General surgery and plastic surgery articles were compared for principal author's academic degree, a Ph.D.'s presence as a coauthor, the study type, the presence of statistical analysis, the analysis' appropriateness, and the types of errors in study design or statistical analysis. Ph. D. authorship was associated with increased percentage of articles using statistical analysis. When compared with general surgery articles, plastic surgery articles performed four times fewer statistical analyses. However, when statistical analyses were performed, there were few differences between these two specialties. Although there were no differences in the types of statistical analysis errors, there were differences in the types of study design errors. The causes of these discrepancies may lie in the nature of plastic surgery; they may be reduced by adherence to Feinstein's principles of study design and result interpretation.     

Pairwise statistical significance of local sequence alignment using sequence-specific and position-specific substitution matrices

Pairwise sequence alignment is a central problem in bioinformatics, which forms the basis of various other applications. Two related sequences are expected to have a high alignment score, but relatedness is usually judged by statistical significance rather than by alignment score. Recently, it was shown that pairwise statistical significance gives promising results as an alternative to database statistical significance for getting individual significance estimates of pairwise alignment scores. The improvement was mainly attributed to making the statistical significance estimation process more sequence-specific and database-independent. In this paper, we use sequence-specific and position-specific substitution matrices to derive the estimates of pairwise statistical significance, which is expected to use more sequence-specific information in estimating pairwise statistical significance. Experiments on a benchmark database with sequence-specific substitution matrices at different levels of sequence-specific contribution were conducted, and results confirm that using sequence-specific substitution matrices for estimating pairwise statistical significance is significantly better than using a standard matrix like BLOSUM62, and than database statistical significance estimates reported by popular database search programs like BLAST, PSI-BLAST (without pretrained PSSMs), and SSEARCH on a benchmark database, but with pretrained PSSMs, PSI-BLAST results are significantly better. Further, using position-specific substitution matrices for estimating pairwise statistical significance gives significantly better results even than PSI-BLAST using pretrained PSSMs.     

Applications of statistical analysis in diagnostic histopathology and cytopathology

Corresponding to the rapid increase in the amount of data available for use in clinical diagnoses, there is an increased need for procedures that can provide the diagnostician with meaningful statistical summaries of data and with statements concerning the statistical significance associated with a diagnostic evaluation. It has been demonstrated that multivariate statistical assessment of clinical material can provide consistent, reliable and highly sensitive diagnostic clues, even in instances in which trained personnel are unable to see any change. Several examples of applications of statistical analyses in diagnostic cytology and histopathology are given in this paper. The examples were chosen to be illustrative of the different types of problems for which statistical analyses have been found useful. These problems differ with respect to the extent of the statistical methods thus far developed and the difficulty involved in developing further analyses. For many problems, appropriate statistical analyses are readily available; other problems require definition of custom-made test statistics and, in some cases, also definition of new statistical distributions. The problems discussed here are only a small sample of the existing problems, but they provide at least an indication of the scope of the role that statistics plays in cytopathologic and histopathologic diagnosis.     

Statistical Confusion Among Graduate Students: Sickness or Symptom?

Abstract: Statistics is one of the most important yet difficult subjects for many ecology and wildlife graduate students to learn. Insufficient knowledge about how to conduct quality science and the ongoing debate about the relative value of competing statistical ideologies contribute to uncertainties among graduate students regarding which statistical tests are most appropriate. Herein, we argue that increased education of the available statistical tests alone is unlikely to ameliorate the problem. Instead, we suggest that statistical uncertainties among graduate students are a secondary symptom of a larger problem. We believe the root cause lies in the lack of education on how to conduct science as an integrated process from hypothesis creation through statistical analysis. We argue that if students are taught to think about how each step of the process will affect all other steps, many statistical uncertainties will be avoided.     

The quest for natural selection in the age of comparative genomics

Continued genome sequencing has fueled progress in statistical methods for understanding the action of natural selection at the molecular level. This article reviews various statistical techniques (and their applicability) for detecting adaptation events and the functional divergence of proteins. As large-scale automated studies become more frequent, they provide a useful resource for generating biological null hypotheses for further experimental and statistical testing. Furthermore, they shed light on typical patterns of lineage-specific evolution of organisms, on the functional and structural evolution of protein families and on the interplay between the two. More complex models are being developed to better reflect the underlying biological and chemical processes and to complement simpler statistical models. Linking molecular processes to their statistical signatures in genomes can be demanding, and the proper application of statistical models is discussed.     

信息化条件下开展医院统计工作的思考

随着医院信息化进程不断推进,网络技术的不断普及与进步,医院的信息系统逐渐完善,许多传统的工作模式与工作流程被打破,作为医院管理核心部分的医院统计工作也发生了根本的变化,如何在信息化条件下积极有效的开展医院统计工作,充分发挥医院统计工作信息咨询、监督和管理的职能,成为医院统计工作者首先考虑的问题。本文通过剖析传统模式下统计工作的弊端,分析信息化条件下对统计工作带来的影响,思考新形势下完善统计工作的新方法。提出作为医院统计工作者应适应时代需求,应不断提高自身素质,开拓创新,将统计工作与现代化医院管理紧密结合,为医院现代化管理和稳步发展提供可靠保证。充分利用信息技术提高数据质量,全方位多层次进行综合信息数据挖掘,提高统计工作自动化程度,实现医院统计高效管理,为现代医院管理发挥应有的作用。     

From quantal counts to mechanisms and systems: the past, present, and future of biometrics in environmental toxicology

As appreciation for human impact on the environment has developed, so have the experimental systems and associated statistical tools that quantify this impact. Toxicological study in particular has grown in its complexity and its need for advanced statistical support. Within this perspective, we describe statistical practice in environmental toxicology and risk assessment. We present two case studies, one from mammalian toxicology and one from aquatic toxicology, that highlight the evolution of statistical practice in environmental toxicology.     

Statistics, costs and rationality in ecological inference

The so-called 'null hypothesis' debate in ecology opened a statistical Pandora's Box. Ecologists were forced to question whether or not decades of pattern analysis had been productive. Over the past few years, the debate has expanded beyond the role of different kinds of statistical hypothesis to include the importance of different types of statistical error. Our objective in this article is to show how trends governing ecological inferences under uncertainty appear to be changing as ecologists become increasingly aware o f the potential importance of statistical errors.     

Reinventing Biostatistics Education for Basic Scientists

Numerous studies demonstrating that statistical errors are common in basic science publications have led to calls to improve statistical training for basic scientists. In this article, we sought to evaluate statistical requirements for PhD training and to identify opportunities for improving biostatistics education in the basic sciences. We provide recommendations for improving statistics training for basic biomedical scientists, including: 1. Encouraging departments to require statistics training, 2. Tailoring coursework to the students’ fields of research, and 3. Developing tools and strategies to promote education and dissemination of statistical knowledge. We also provide a list of statistical considerations that should be addressed in statistics education for basic scientists.     

How important is the statistical approach for analyzing categorical data? A critique using artificial nests

Ecologists rely heavily upon statistics to make inferences concerning ecological phenomena and to make management recommendations. It is therefore important to use statistical tests that are most appropriate for a given data-set. However, inappropriate statistical tests are often used in the analysis of studies with categorical data (i.e. count data or binary data). Since many types of statistical tests have been used in artificial nests studies, a review and comparison of these tests provides an opportunity to demonstrate the importance of choosing the most appropriate statistical approach for conceptual reasons as well as type I and type II errors.
Artificial nests have routinely been used to study the influences of habitat fragmentation, and habitat edges on nest predation. I review the variety of statistical tests used to analyze artificial nest data within the framework of the generalized linear model and argue that logistic regression is the most appropriate and flexible statistical test for analyzing binary data-sets. Using artificial nest data from my own studies and an independent data set from the medical literature as examples, I tested equivalent data using a variety of statistical methods. I then compared the p-values and the statistical power of these tests. Results vary greatly among statistical methods. Methods inappropriate for analyzing binary data often fail to yield significant results even when differences between study groups appear large, while logistic regression finds these differences statistically significant. Statistical power is is 2–3 times higher for logistic regression than for other tests. I recommend that logistic regression be used to analyze artificial nest data and other data-sets with binary data.     

Screening for possible human carcinogens and mutagens. False positives, false negatives: statistical implications

A screening method aimed at identifying potential human carcinogens using either animal cancer bioassays or short-term genotoxic assays has 4 possible results: true positive, true negative, false positive and false negative. Such a categorisation is superficially similar to the results of hypothesis testing in a statistical analysis. In this latter case the false positive rate is determined by the significance level of the test and the false negative rate by the statistical power of the test. Although the two types of categorisation appear somewhat similar, different statistical issues are involved in their interpretation. Statistical methods appropriate for the analysis of the results of a series of assays include the use of Bayes' theorem and multivariate methods such as clustering techniques for the selection of batteries of short-term test capable of a better prediction of potential carcinogens. The conclusions drawn from such studies are dependent upon the estimates of values of sensitivity and specificity used, the choice of statistical method and the nature of the data set. The statistical issues resulting from the analysis of specific genotoxicity experiments involve the choice of suitable experimental designs and appropriate analyses together with the relationship of statistical significance to biological importance. The purpose of statistical analysis should increasingly be to estimate and explore effects rather than for formal hypothesis testing.     

如何写好数据分析和几个相关的统计问题

该文就生态学论文“材料和方法”部分中“数据分析”的写作规范进行讨论, 希望对论文写作有所帮助。还讨论了在生态学论文数据分析部分常遇到的几个统计问题, 如采用更有效的统计方法、相关因变量、方差不齐性、统计显著性与生物学/生态学显著性、伪重复问题。     

Natural and orthogonal interaction framework for modeling gene-environment interactions with application to lung cancer

Objectives: We aimed at extending the Natural and Orthogonal Interaction (NOIA) framework, developed for modeling gene-gene interactions in the analysis of quantitative traits, to allow for reduced genetic models, dichotomous traits, and gene-environment interactions. We evaluate the performance of the NOIA statistical models using simulated data and lung cancer data. Methods: The NOIA statistical models are developed for additive, dominant, and recessive genetic models as well as for a binary environmental exposure. Using the Kronecker product rule, a NOIA statistical model is built to model gene-environment interactions. By treating the genotypic values as the logarithm of odds, the NOIA statistical models are extended to the analysis of case-control data. Results: Our simulations showed that power for testing associations while allowing for interaction using the NOIA statistical model is much higher than using functional models for most of the scenarios we simulated. When applied to lung cancer data, much smaller p values were obtained using the NOIA statistical model for either the main effects or the SNP-smoking interactions for some of the SNPs tested. Conclusion: The NOIA statistical models are usually more powerful than the functional models in detecting main effects and interaction effects for both quantitative traits and binary traits.     

巢式设计的多元分析

本文把经典巢式设计的统计分析方法由单变量推广到多变量,使得试验或调查的多性状数据能综合在一起进行分析,以得出考虑了性状间相关性在内的统计结论并用一烟草栽培试验数据示明具体步骤。     

Reduced C(beta) statistical potentials can outperform all-atom potentials in decoy identification

We developed a series of statistical potentials to recognize the native protein from decoys, particularly when using only a reduced representation in which each side chain is treated as a single C(beta) atom. Beginning with a highly successful all-atom statistical potential, the Discrete Optimized Protein Energy function (DOPE), we considered the implications of including additional information in the all-atom statistical potential and subsequently reducing to the C(beta) representation. One of the potentials includes interaction energies conditional on backbone geometries. A second potential separates sequence local from sequence nonlocal interactions and introduces a novel reference state for the sequence local interactions. The resultant potentials perform better than the original DOPE statistical potential in decoy identification. Moreover, even upon passing to a reduced C(beta) representation, these statistical potentials outscore the original (all-atom) DOPE potential in identifying native states for sets of decoys. Interestingly, the backbone-dependent statistical potential is shown to retain nearly all of the information content of the all-atom representation in the C(beta) representation. In addition, these new statistical potentials are combined with existing potentials to model hydrogen bonding, torsion energies, and solvation energies to produce even better performing potentials. The ability of the C(beta) statistical potentials to accurately represent protein interactions bodes well for computational efficiency in protein folding calculations using reduced backbone representations, while the extensions to DOPE illustrate general principles for improving knowledge-based potentials.     

A Simulation Study of Permutation,Bootstrap, and Gene Dropping for Assessing Statistical Significance in the Case of Unequal Relatedness

We used simulations to evaluate methods for assessing statistical significance in association studies. When the statistical model appropriately accounted for relatedness among individuals, unrestricted permutation tests and a few other simulation-based methods effectively controlled type I error rates; otherwise, only gene dropping controlled type I error but at the expense of statistical power.