基于时间聚类分析和独立成分分析的癫痫fMRI盲分析方法

提出了一种基于时间聚类分析和独立成分分析的癫痫fMRI数据盲分析方法,并将两种方法有效联合,提取发作间期的癫痫fMRI激活时空信息.该方法首先由时间聚类分析得到与激活相关的时间峰度特征曲线,以此特征作为时间参考信息;再由空间独立成分分析分解fMRI信号得到空间独立成分;最后将每个独立成分所对应的时间曲线与参考曲线做相关分析提取相应脑激活图.提出的方法无需任何关于癫痫fMRI的先验假设信息,有效解决了独立成分的排序问题,实现了对数据的盲分析.仿真试验结果阐明了这一方法的有效性及可靠性,对癫痫数据的试验结果显示空间定位准确性优于统计参数图方法.     

Partition-free congruence analysis: implications for sensitivity analysis

A criterion is proposed to compare systematic hypotheses based on multiple sources of information under a diverse set of interpretive assumptions (i.e., sensitivity analysis of Wheeler, 1995 ). This metric, the Meta‐Retention Index (MRI), is the retention index (RI) of Farris calculated over the set of conventional homologous qualitative characters (ordered, unordered, Sankoff, etc.) and molecular fragment characters sensu Wheeler (1996, 1999 ). The superiority of this measure to other similar measures (e.g., incongruence length difference test) comes from its independence from partition information. The only values that participate in its calculation are the minimum, maximum and observed cost (= cladogram cost) of each character. The partition (morphology, gene locus) from which the variant may have come is irrelevant. In the special cases where there is only a single data partition, this measure is equivalent to the conventional RI; and in the case where there are single fragment characters per partition (contiguous molecular loci as data sets) the measure is identical to the complement of the Rescaled Incongruence Length Difference (RILD) of Wheeler and Hayashi (1998 ). The MRI can serve as an optimality criterion for deciding among systematic hypotheses based on the same data, but different sets of analysis assumptions (e.g., character weights, indel costs). The MRI may lose discriminatory power in situations where a minority of highly congruent characters is given high weight. This situation can be detected and seems unlikely to occur frequently in real data sets. © The Willi Hennig Society 2006.     

Proteomic analysis

The field of proteomics is becoming increasingly important as genome sequences are being completed and annotated. Recent advances in proteomics include experimental and mathematical proofs of the need to complement microarray analysis with protein analysis, improved sensitivity for mass spectrometric analysis of separated proteins, better informatic tools for gel analysis and protein spot annotation, first steps towards automated experimental procedures, and new technology for quantitation of protein changes.     

Diagnostic analysis

This paper presents an application of sensitivity analysis to the problem of diagnosing malfunctions in a class of continuous physiological systems. The technique leads to a computational algorithm for determining which parameters in a system have deviated from their nominal values and to what extent.     

Genomic analysis

Advances in genomic analysis include improved technology for DNA sequencing, routine use of DNA microarray technology for the analysis of gene expression profiles at the mRNA level and improved informatic tools to organize and analyze such data. At the same time, new developments in chip-based analysis of samples and the emergence of models of gene networks hold promise for the future of the 'Genomic Era'.     

Paleoenvironmental analysis

New analysis has been carried out concerning the palaeoenvironmental reconstruction of some Italian sites dating from the Middle Pleistocene to the Bronze Age. Different aspects have been investigated on each site considering the data collected. The following sites have been analyzed: Isernia La Pineta (Molise); Visogliano and Caverna degli Orsi (Tieste); Toirano Caves (Liguria); Grotta Paglicci (Gargano); Riparo del Molare (Salerno); Grotta del Cavallo (Lecce); Castellaro Lagusello (Monzambano, Mantova).     

Fingerprint analysis

This article describes the identification and analysis of fingerprint patterns within population samples. Procedures are illustrated by reference to a specific project involving 73 pupils, divided into four subgroups on the basis of examination (GCE) success or failure.

Results suggest a positive relationship may exist between the frequency of certain pattern types on the fingerbulbs of individuals and their academic attainments.     

Y-chromosome analysis

After initially slow progress in identifying DNA polymorphisms on the non recombining part of the human Y chromosome, a large number of polymorphic markers are now available to define individual or population-specific haplotypes or haplogroups. Among them the Y-chromosomal STRs (Short Tandem Repeats) have been increasingly used over the past few years for the study of human evolution as well as for human identification in forensic case-work and paternity testing. After a brief summary of the features of such markers, this paper deals with some applications of the Y-STR haplotyping.     

Microfluorometry analysis

A mathematical model for decomposing an FMF histogram into its G₁S and G₂ + M components is developed. Under certain restrictions, the model applies to both asynchronous and synchronous populations. Two numerical techniques for estimating the percentage of cells in each component are outlined. Using the assumption of exponential growth, theoretical expressions for the percentage of cells in each state and for the S density are derived. This leads to a rapid method for determining the mean time spent in each state by a cell.     

Gait analysis

Gait analysis, or the study of locomotion, has changed dramatically over the last few decades. Advances in computer technology and data analysis techniques have contributed greatly to the progress of this field. Gait analysis has become a valuable tool in the clinical setting. The ability to objectively quantify motion is essential to our understanding of normal and abnormal movement patterns and the evaluation of treatment effectiveness. This paper will discuss the various experimental and analytical techniques currently used for performing clinical gait analyses at the University of New Brunswick, Fredericton, New Brunswick, Canada.     

An analysis of the analysis of herbivore population dynamics

Time series analysis of herbivore data with weather included as covariate is commonly used as a mean to shed light on the state and ecology of the studied population. Conclusions about the herbivore population are drawn from statistical parameter values and presence/absence in the most parsimonious model. However, this procedure is only reliable if the statistical parameters have general interpretations regardless of system characteristics. Here we investigated the extent to which this is true by deriving six different vegetation–herbivore-systems and analyzing their respective statistical parameters. The analysis was done in both continuous and discrete time. It turned out that both density parameters (a₁ and a₂) and rainfall coefficients change with biological interactions and amount of average rainfall, and they do so in different ways in different systems. This means that there is no valid general interpretation of them and, most important, the probability of detecting density dependence and effects of rainfall vary between systems. Hence, you can not make inference about the biological processes from statistical analysis without knowing the system that you study and what model best describes the interactions within it.     

Model-free functional MRI analysis using topographic independent component analysis

Data-driven fMRI analysis techniques include independent component analysis (ICA) and different types of clustering in the temporal domain. Since each of these methods has its particular strengths, it is natural to look for an approach that unifies Kohonen's self-organizing map and ICA. This is given by the topographic independent component analysis. While achieved by a slight modification of the ICA model, it can be at the same time used to define a topographic order (clusters) between the components, and thus has the usual computational advantages associated with topographic maps. In this contribution, we can show that when applied to fMRI analysis it outperforms FastICA.     

多元统计方法在节肢动物群落分析中的应用

近年来有关节肢动物群落的研究报道较多,而传统的、简单的多样性指数分析远远不能满足对高通量群落数据的挖掘。采用多元统计方法可以较好地分析群落数据。其中,在研究某一处理的长期效应时,主响应曲线是一种经典的多元分析方法;而非度量多维尺度分析具有能处理较大量的数据且常能以低维排序图反映结果的优点,是一种具有广泛应用前景的排序技术。本文以Bt棉与非Bt棉不同比例混作棉田的节肢动物群落为例,详尽地介绍了主响应曲线和非度量多维尺度在节肢动物群落分析中的应用。     

Nonlinear analysis of EEG signals: Surrogate data analysis

ObjectivesThe electroencephalogram (EEG) signal contains information about the state and condition of the brain. The aim of the study is to conduct a nonlinear analysis of the EEG signals and to compare the differences in the nonlinear characteristics of the EEG during normal state and the epileptic state.DataThe EEG data used for this study – which consisted of epileptic EEG and normal EEG – were obtained from the EEG database available with the Bonn University, Germany.ResultsThe attractors seen in normal and epileptic human brain dynamics were studied and compared. Surrogate data analyses were conducted on two nonlinear measures, namely the largest Lyapunov exponent and the correlation dimension, to test the hypothesis whether EEG signals were in accordance with linear stochastic models.DiscussionsThe existence of deterministic chaos in brain activity is confirmed by the existence of a chaotic attractor; also, saturation of the correlation dimension towards a definite value is the manifestation of a deterministic dynamics. Also a reduction is observed between the dimensionalities of the brain attractors from normal state to the epileptic state. The evaluation of the largest Lyapunov exponent also confirms the lowering of complexity during an episode of seizure.ConclusionIn case of Lyapunov exponent of EEG data, the change due to surrogating is small suggesting that it is not representing the system complexity properly but there is a marked change in the case of correlation dimension value due to surrogating.     

Revolution in food web analysis and trophic ecology: diet analysis by DNA and stable isotope analysis

Characterization of energy flow in ecosystems is one of the primary goals of ecology, and the analysis of trophic interactions and food web dynamics is key to quantifying energy flow. Predator‐prey interactions define the majority of trophic interactions and food web dynamics, and visual analysis of stomach, gut or fecal content composition is the technique traditionally used to quantify predator‐prey interactions. Unfortunately such techniques may be biased and inaccurate due to variation in digestion rates ( Sheppard & Hardwood 2005 ); however, those limitations can be largely overcome with new technology. In the last 20 years, the use of molecular genetic techniques in ecology has exploded ( King et al. 2008 ). The growing availability of molecular genetic methods and data has fostered the use of PCR‐based techniques to accurately distinguish and identify prey items in stomach, gut and fecal samples. In this month’s issue of Molecular Ecology Resources, Corse et al. (2010) describe and apply a new approach to quantifying predator‐prey relationships using an ecosystem‐level genetic characterization of available and consumed prey in European freshwater habitats ( Fig. 1a ). In this issue of Molecular Ecology, Hardy et al. (2010) marry the molecular genetic analysis of prey with a stable isotope (SI) analysis of trophic interactions in an Australian reservoir community ( Fig. 1b ). Both papers demonstrate novel and innovative approaches to an old problem – how do we effectively explore food webs and energy movement in ecosystems?

Figure 1 Open in figure viewer PowerPoint The aquatic habitats used for two studies of diet and trophic interactions that employed molecular genetic and stable isotope analyses. Panel a: Example of Rhone basin habitat (France) where fish diet was determined using PCR to classify prey to a series of ecological clades (photo by Emmanuel Corse). Panel b: A weir pool on the lower Murray River (Australia) where food web and prey use was evaluated using a combination of advanced molecular genetic and stable isotope analyses (photo credit: CSIRO).