Analysing the patterning of a sequence of discrete behavioural events

Existing techniques for analysing the organization of behavioural events are discussed. Cluster analysis, mutual replaceability, ‘melody’ detection and Markov analyses are examined in order to discover the validity and usefulness of the concepts searched for by each technique, and the mathematical and statistical background of the techniques is scrutinized. Some new techniques, designed to search for and detect possible important sequential organization patterns, are described in the light of the previous criticisms. Of these techniques, the Pre-post-state Histogram (PPSH)—a multi-order Markov graphical analysis—would appear to be the most important for future work, especially when used in conjunction with the other techniques described.     

Detecting conserved interaction patterns in biological networks.

Molecular interaction data plays an important role in understanding biological processes at a modular level by providing a framework for understanding cellular organization, functional hierarchy, and evolutionary conservation. As the quality and quantity of network and interaction data increases rapidly, the problem of effectively analyzing this data becomes significant. Graph theoretic formalisms, commonly used for these analysis tasks, often lead to computationally hard problems due to their relation to subgraph isomorphism. This paper presents an innovative new algorithm, MULE, for detecting frequently occurring patterns and modules in biological networks. Using an innovative graph simplification technique based on ortholog contraction, which is ideally suited to biological networks, our algorithm renders these problems computationally tractable and scalable to large numbers of networks. We show, experimentally, that our algorithm can extract frequently occurring patterns in metabolic pathways and protein interaction networks from the KEGG, DIP, and BIND databases within seconds. When compared to existing approaches, our graph simplification technique can be viewed either as a pruning heuristic, or a closely related, but computationally simpler task. When used as a pruning heuristic, we show that our technique reduces effective graph sizes significantly, accelerating existing techniques by several orders of magnitude! Indeed, for most of the test cases, existing techniques could not even be applied without our pruning step. When used as a stand-alone analysis technique, MULE is shown to convey significant biological insights at near-interactive rates. The software, sample input graphs, and detailed results for comprehensive analysis of nine eukaryotic PPI networks are available at www.cs.purdue.edu/homes/koyuturk/mule.     

Design and analysis issues in quantitative proteomics studies

Quantitative proteomics is the comparison of distinct proteomes which enables the identification of protein species which exhibit changes in expression or post-translational state in response to a given stimulus. Many different quantitative techniques are being utilized and generate large datasets. Independent of the technique used, these large datasets need robust data analysis to ensure valid conclusions are drawn from such studies. Approaches to address the problems that arise with large datasets are discussed to give insight into the types of statistical analyses of data appropriate for the various experimental strategies that can be employed by quantitative proteomic studies. This review also highlights the importance of employing a robust experimental design and highlights various issues surrounding the design of experiments. The concepts and examples discussed within will show how robust design and analysis will lead to confident results that will ensure quantitative proteomics delivers.     

Counting alleles reveals a connection between chromosome 18q loss and vascular invasion

The analysis of loss of heterozygosity (LOH) is perhaps the most widely used technique in cancer genetics. In primary tumors, however, the analysis of LOH is fraught with technical problems that have limited its reproducibility and interpretation. In particular, tumors are mixtures of neoplastic and nonneoplastic cells, and the DNA from the nonneoplastic cells can mask LOH. We here describe a new experimental approach, involving two components, to overcome these problems. First, a form of digital PCR was employed to directly count, one by one, the number of each of the two alleles in tumor samples. Second, Bayesian-type likelihood methods were used to measure the strength of the evidence for the allele distribution being different from normal. This approach imparts a rigorous statistical basis to LOH analyses, and should be able to provide more reliable information than heretofore possible in LOH studies of diverse tumor types.     

Characterization of exopolymers of aquatic bacteria by pyrolysis-mass spectrometry.

Exopolymers from a diverse collection of marine and freshwater bacteria were characterized by pyrolysis-mass spectrometry (Py-MS). Py-MS provides spectra of pyrolysis fragments that are characteristic of the original material. Analysis of the spectra by multivariate statistical techniques (principal component and canonical variate analysis) separated these exopolymers into distinct groups. Py-MS clearly distinguished characteristic fragments, which may be derived from components responsible for functional differences between polymers. The importance of these distinctions and the relevance of pyrolysis information to exopolysaccharide function in aquatic bacteria is discussed.     

Characterization of exopolymers of aquatic bacteria by pyrolysis-mass spectrometry

Exopolymers from a diverse collection of marine and freshwater bacteria were characterized by pyrolysis-mass spectrometry (Py-MS). Py-MS provides spectra of pyrolysis fragments that are characteristic of the original material. Analysis of the spectra by multivariate statistical techniques (principal component and canonical variate analysis) separated these exopolymers into distinct groups. Py-MS clearly distinguished characteristic fragments, which may be derived from components responsible for functional differences between polymers. The importance of these distinctions and the relevance of pyrolysis information to exopolysaccharide function in aquatic bacteria is discussed.     

Agonistic Behavior in Nesting Iguanas: A Stochastic Analysis of Dispute Settlement Dominated by the Minimization of Energy Cost

Competition within biological populations frequently leads to physical disputes. Natural selection requires that the costs of these disputes to the involved individuals are in some sense minimized. We examine a specific situation in detail, disputes between gravid female iguanas for nesting sites, and show that it is energy costs which are minimized and that this minimization is achieved by escalating the energy expended during the individual dispute and by the use of displays. The analysis uses probabilistic and information theoretic techniques.     

Post-normalization quality assessment visualization of microarray data

Post-normalization checking of microarrays rarely occurs, despite the problems that using unreliable data for inference can cause. This paper considers a number of different ways to check microarrays after normalization for a variety of potential problems. Four types of problem with microarray data that these checks can identify are: clerical mistakes, array-wide hybridization problems, problems with normalization and mishandling problems. Any of these can seriously affect the results of any analysis. The three main techniques used to identify these problems are dimension reduction techniques, false array plots and correlograms. None of the techniques are computationally very intensive and all can be carried out in the R statistical package. Once discovered, problems can either be rectified or excluded from the data.     

Integrating the statistical analysis of spatial data in ecology

In many areas of ecology there is an increasing emphasis on spatial relationships. Often ecologists are interested in new ways of analyzing data with the objective of quantifying spatial patterns, and in designing surveys and experiments in light of the recognition that there may be underlying spatial pattern in biotic responses. In doing so, ecologists have adopted a number of widely different techniques and approaches derived from different schools of thought, and from other scientific disciplines. While the adaptation of a diverse array of statistical approaches and methodologies for the analysis of spatial data has yielded considerable insight into various ecological problems, this diversity of approaches has sometimes impeded communication and retarded more rapid progress in this emergent area. Many of these different statistical methods provide similar information about spatial characteristics, but the differences among these methods make it difficult to compare the results of studies that employ contrasting approaches. The papers in this mini-series explore possible areas of agreement and synthesis between a diversity of approaches to spatial analysis in ecology.     

A score matrix to reveal the hidden links in glycans

MOTIVATION: Glycans are the third major class of biomolecules following DNA and proteins. They are extremely vital for the functioning of multicellular organisms. However, comparing the fast development of sequence analysis techniques, informatics work on glycans have a long way to go. Alignment algorithms for glycan tree structures are one of the foremost concerns. In addition, the statistical analysis of these algorithms in terms of biological significance needs to be addressed. RESULTS: We developed a tree-structure alignment algorithm for glycans and performed a statistical analysis of these alignment scores such that biologically interesting features could be captured into a score matrix for glycans. We generated our score matrix in a manner similar to BLOSUM, but with slight variations to accomodate our glycan data, including the incorporation of linkage information. We verified the effectiveness of our new glycan score matrix by illustrating how well the resulting score matrix entries correspond with biological knowledge. Future work for even better improvements with the use of a variety of score matrices for different subclasses of glycans due to their complexity is also discussed. CONTACT: mami@kuicr.kyoto-u.ac.jp SUPPLEMENTARY INFORMATION: The glycan score matrix can be downloaded from http://kanehisa.kuicr.kyoto-u.ac.jp/Paper/kcam/glycanMatrix0.1.txt.     

Vibrational spectroscopy of fossils

Over the last two decades, there has an been increasing interest in applying vibrational spectroscopy in palaeontological research. For example, this chemical analytical technique has been used to elucidate the chemical composition of a wide variety of fossils, including Archaean putative microfossils, stromatolites, chitinozoans, acritarchs, fossil algae, fossil plant cuticles, putative fossil arthropods, conodonts, scolecodonts and dinosaur bones. The insights provided by these data have been equally far ranging: to taxonomically identify a fossil, to determine biogenicity of a putative fossil, to identify preserved biologically synthesized compounds and to elucidate the preservational mechanisms of fossil material. Vibrational spectroscopy has clearly been a useful tool for investigating various palaeontological problems. However, it is also a tool that has been misapplied and misinterpreted, and thus, this review is dedicated to providing a palaeontologist who is new to vibrational spectroscopy with a basic understanding of these techniques, and the types of chemical information that can be obtained. Two example applications of these techniques are discussed in detail, one looking into fossil palynomorph taxonomy and other into the enigmatic Burgess Shale‐type preservation.     

Subtraction-coupled custom microarray analysis for gene discovery and gene expression studies in the CNS

The revolution in our knowledge about the genomes of organisms gives rise to the question, what do we do with this information? The development of techniques allowing high throughput analysis of RNA and protein expression, such as cDNA microarrays, provide for genome-wide analysis of gene expression. These analyses will help bridge the gap between systems and molecular neuroscience. This review discusses the advantages of using a subtractive hybridization technique, such as a representational difference analysis, to generate a custom cDNA microarray enriched for genes relevant to investigating complex, heterogeneous tissues such as those involved in the chemical senses. Real and hypothetical examples of these experiments are discussed. Benefits of this approach over traditional microarray techniques include having a more relevant clone set, the potential for gene discovery and the creation of a new tool to investigate similar systems. Potential pitfalls may include PCR artifacts and the need for sequencing. However, these disadvantages can be overcome so that the coupling of subtraction techniques to microarray screening can be a fruitful approach to a variety of experimental systems.     

Comparison and quantification of carnivore diet by faecal analysis: a critique, with recommendations, based on a study of the Fox Vulpes vulpes

Failure to address many of the difficulties inherent in the analysis of carnivore faeces has hitherto limited the value of this technique in comparing carnivore diets or quantifying carnivore food consumption. In this review, major problems are discussed, and improved procedures designed to overcome them are recommended.
The principal improvements recommended for laboratory procedures address the microscopic fraction of faeces, which cannot be analysed visually. This fraction may be derived from a variety of sources, leading to bias in previous techniques, particularly with regard to the importance of earthworms and birds. Attention to the microscopic fraction is necessary for correct comparison or quantitative determination of diet.
Particular attention is given to statistical procedures which quantify the errors that arise in collection of samples, or during analysis. Clear statement of such errors will lead to greater comparability between studies. The procedures recommended here define the limits to accuracy in relation to sample size, total faecal production, and laboratory methods. In conjunction with adequate pilot studies, these techniques will allow more effective design of ecological studies relating to carnivore diet and food consumption.     

微生物肥料及其生产应用中的问题

微生物肥料和微生物制剂是新世纪实行绿色农业的重要技术保障,关键是因其中含有大量的有效微生物的生命活动产生特定的肥效或其他生理功能,导致增产,微生物肥料包括根瘤菌肥,解磷菌肥、解钾菌肥、5406菌肥,植物根际促生菌,VA菌根等等,有机肥料堆制剂中含有多种降解农业有机废料的菌种,可缩短堆肥周期,提高养份利用率,由于微生物本身的特殊性,相关市场监督机制不健全,微生物肥料的生产,应用领域存在很多问题,甚至出现明显的伪科学,科研、生产、行政各有关部门应加强基础研究,提高全民科普水平,健全市场监督机制,依靠专家设计严格试验,优选可靠技术,保证良好的经济效益和社会效益。     

Automated selection of DAB-labeled tissue for immunohistochemical quantification.

The increased use of immunohistochemistry (IHC) in both clinical and basic research settings has led to the development of techniques for acquiring quantitative information from immunostains. Staining correlates with absolute protein levels and has been investigated as a clinical tool for patient diagnosis and prognosis. For these reasons, automated imaging methods have been developed in an attempt to standardize IHC analysis. We propose a novel imaging technique in which brightfield images of diaminobenzidene (DAB)-labeled antigens are converted to normalized blue images, allowing automated identification of positively stained tissue. A statistical analysis compared our method with seven previously published imaging techniques by measuring each one's agreement with manual analysis by two observers. Eighteen DAB-stained images showing a range of protein levels were used. Accuracy was assessed by calculating the percentage of pixels misclassified using each technique compared with a manual standard. Bland-Altman analysis was then used to show the extent to which misclassification affected staining quantification. Many of the techniques were inconsistent in classifying DAB staining due to background interference, but our method was statistically the most accurate and consistent across all staining levels.     

Incorporating prior parameter uncertainty in the design of sampling schedules for pharmacokinetic parameter estimation experiments

An experiment design procedure is proposed for nonlinear parameter estimation studies that formally incorporates prior parameter uncertainty. The design criterion derives from information theory considerations and involves an asymptotic interpretation of the expected posterior information provided by an experiment. A pharmacokinetic sample schedule design problem is used to illustrate and evaluate this information theoretic design strategy. The model considered is commonly used to describe the plasma concentration of a drug following its oral administration. The limitations and advantages of the proposed design procedure are discussed in relation to other previously reported design techniques for incorporating parameter uncertainty.     

Multivariate Data Analysis for Neuroimaging Data: Overview and Application to Alzheimer’s Disease

As clinical and cognitive neuroscience mature, the need for sophisticated neuroimaging analysis becomes more apparent. Multivariate analysis techniques have recently received increasing attention as they have many attractive features that cannot be easily realized by the more commonly used univariate, voxel-wise, techniques. Multivariate approaches evaluate correlation/covariance of activation across brain regions, rather than proceeding on a voxel-by-voxel basis. Thus, their results can be more easily interpreted as a signature of neural networks. Univariate approaches, on the other hand, cannot directly address functional connectivity in the brain. The covariance approach can also result in greater statistical power when compared with univariate techniques, which are forced to employ very stringent, and often overly conservative, corrections for voxel-wise multiple comparisons. Multivariate techniques also lend themselves much better to prospective application of results from the analysis of one dataset to entirely new datasets. Multivariate techniques are thus well placed to provide information about mean differences and correlations with behavior, similarly to univariate approaches, with potentially greater statistical power and better reproducibility checks. In contrast to these advantages is the high barrier of entry to the use of multivariate approaches, preventing more widespread application in the community. To the neuroscientist becoming familiar with multivariate analysis techniques, an initial survey of the field might present a bewildering variety of approaches that, although algorithmically similar, are presented with different emphases, typically by people with mathematics backgrounds. We believe that multivariate analysis techniques have sufficient potential to warrant better dissemination. Researchers should be able to employ them in an informed and accessible manner. The following article attempts to provide a basic introduction with sample applications to simulated and real-world data sets.     

Phylogenetic meta-analysis

Meta-analysis is a powerful statistical technique that combines the results of independent studies to identify general trends. When the species under examination are not independent however, it is also necessary to incorporate phylogenetic information into the analysis. Unfortunately, current meta-analytic approaches cannot account for lack of independence resulting from shared evolutionary history, so a general solution to this problem is lacking. In this article, I derive a model for phylogenetic meta-analysis, so that data across studies may be summarized with evolutionary history explicitly incorporated. The approach takes advantage of common aspects of linear statistical models used by both meta-analysis and the phylogenetic comparative method, thereby allowing them to be analytically combined. In this manner, the correlation structure generated by phylogenetic history can be incorporated directly into the meta-analytic procedure. I illustrate the approach by examining the prevalence of body size clines in mammals. The approach is general, and can also be used to incorporate correlation structure among studies generated by other factors, such as spatial or temporal proximity, or environmental similarity. Therefore, this procedure provides a general statistical template for meta-analytic techniques that can account for attributes that generate nonindependence among studies. Implications of the phylogenetic meta-analysis are discussed.     

A DIRECT-SPRAY TECHNIQUE FOR THE BIOLOGICAL EVALUATION OF PYRETHRUM-IN-OIL INSECTICIDES FOR USE AGAINST STORED PRODUCT INSECTS IN WAREHOUSES

A direct-spray technique for the biological assay of pyrethrins in Shell oil P31 is described, together with the experiments leading to its adoption. The technique is such that film effects are reduced to the minimum practicable, but it is otherwise as similar as possible to the film technique described by Parkin & Green (1943). Methods for statistical analysis of the results are discussed. For the biological assay of pyrethrins in Shell oil P31, the direct-spray method is slightly superior to Parkin & Green's film method, but both techniques should have their uses in general experimental work.     

Multimetric statistical analysis of behavior in mice selected for high and low levels of isolation-induced male aggression

Behavioral observations as a matrix of probabilistic changes of postures and acts are multiple measurements that could introduce variability to statistical analysis. We propose the multimetric statistical algorithm that supplements the linear analysis of variance by pair correlation, factor and discriminant function analyses. Although these methods were utilized mostly in behavioral studies, the combined use in frame of one behavioral test was not done before. In present study statistical techniques were applied to analyze social behavior in Turku aggressive (TA) and Turku non-aggressive (TNA) mouse lines, bidirectional selected for offensive aggression towards an unknown male. Each statistical technique amplified new details of mouse behavioral profiles that give possibility to describe TA and TNA subjects in terms of Cloninger's model of personality. Also, it was identified that TA mice displayed fighting-biting aggression while TNA mice demonstrated immobile defensive strategy. Hypothetical discriminant formula was found for each mouse behavioral genotype that might be used to identify behavioral profile and line affiliation of unknown subjects.