Statistical mechanics approaches to granular media: between micromechanics and macromechanics

The mechanical behavior of granular materials has been traditionally approached through two theoretical and computational frameworks: macromechanics and micromechanics. Macromechanics focuses on continuum based models. In consequence it is assumed that the matter in the granular material is homogeneous and continuously distributed over its volume so that the smallest element cut from the body possesses the same physical properties as the body. In particular, it has some equivalent mechanical properties, represented by complex and non-linear constitutive relationships. Engineering problems are usually solved using computational methods such as FEM or FDM. On the other hand, micromechanics is the analysis of heterogeneous materials on the level of their individual constituents. In granular materials, if the properties of particles are known, a micromechanical approach can lead to a predictive response of the whole heterogeneous material. Two classes of numerical techniques can be differentiated: computational micromechanics, which consists on applying continuum mechanics on each of the phases of a representative volume element and then solving numerically the equations, and atomistic methods (DEM), which consist on applying rigid body dynamics together with interaction potentials to the particles. Statistical mechanics approaches arise between micro and macromechanics. It tries to state which the expected macroscopic properties of a granular system are, by starting from a micromechanical analysis of the features of the particles and the interactions. The main objective of this paper is to introduce this approach.

Statistical models of accidents on interchange ramps and speed-change lanes.

Federal Highway Administration, Office of Safety and Traffic Operations, Washington, D.C.

Statistical Hurdle Models for Single Cell Gene Expression: Differential Expression and Graphical Modeling

Thesis (Ph.D.)--University of Washington, 2016-06

Modelling pre-clearing vegetation distribution using GIS-integrated statistical, ecological and data models: A case study from the wet tropics of Northeastern Australia

Traditional vegetation mapping methods use high cost, labour-intensive aerial photography interpretation. This approach can be subjective and is limited by factors such as the extent of remnant vegetation, and the differing scale and quality of aerial photography over time. An alternative approach is proposed which integrates a data model, a statistical model and an ecological model using sophisticated Geographic Information Systems (GIS) techniques and rule-based systems to support fine-scale vegetation community modelling. This approach is based on a more realistic representation of vegetation patterns with transitional gradients from one vegetation community to another. Arbitrary, though often unrealistic, sharp boundaries can be imposed on the model by the application of statistical methods. This GIS-integrated multivariate approach is applied to the problem of vegetation mapping in the complex vegetation communities of the Innisfail Lowlands in the Wet Tropics bioregion of Northeastern Australia. The paper presents the full cycle of this vegetation modelling approach including sampling sites, variable selection, model selection, model implementation, internal model assessment, model prediction assessments, models integration of discrete vegetation community models to generate a composite pre-clearing vegetation map, independent data set model validation and model prediction's scale assessments. An accurate pre-clearing vegetation map of the Innisfail Lowlands was generated (0.83r(2)) through GIS integration of 28 separate statistical models. This modelling approach has good potential for wider application, including provision of. vital information for conservation planning and management; a scientific basis for rehabilitation of disturbed and cleared areas; a viable method for the production of adequate vegetation maps for conservation and forestry planning of poorly-studied areas. (c) 2006 Elsevier B.V. All rights reserved.

Advanced mean field methods:theory and practice

A major problem in modern probabilistic modeling is the huge computational complexity involved in typical calculations with multivariate probability distributions when the number of random variables is large. Because exact computations are infeasible in such cases and Monte Carlo sampling techniques may reach their limits, there is a need for methods that allow for efficient approximate computations. One of the simplest approximations is based on the mean field method, which has a long history in statistical physics. The method is widely used, particularly in the growing field of graphical models. Researchers from disciplines such as statistical physics, computer science, and mathematical statistics are studying ways to improve this and related methods and are exploring novel application areas. Leading approaches include the variational approach, which goes beyond factorizable distributions to achieve systematic improvements; the TAP (Thouless-Anderson-Palmer) approach, which incorporates correlations by including effective reaction terms in the mean field theory; and the more general methods of graphical models. Bringing together ideas and techniques from these diverse disciplines, this book covers the theoretical foundations of advanced mean field methods, explores the relation between the different approaches, examines the quality of the approximation obtained, and demonstrates their application to various areas of probabilistic modeling.

The use of multivariate methods in the identification of subtypes of Alzheimer's disease: a comparison of principal components and cluster analysis

Two contrasting multivariate statistical methods, viz., principal components analysis (PCA) and cluster analysis were applied to the study of neuropathological variations between cases of Alzheimer's disease (AD). To compare the two methods, 78 cases of AD were analyzed, each characterised by measurements of 47 neuropathological variables. Both methods of analysis revealed significant variations between AD cases. These variations were related primarily to differences in the distribution and abundance of senile plaques (SP) and neurofibrillary tangles (NFT) in the brain. Cluster analysis classified the majority of AD cases into five groups which could represent subtypes of AD. However, PCA suggested that variation between cases was more continuous with no distinct subtypes. Hence, PCA may be a more appropriate method than cluster analysis in the study of neuropathological variations between AD cases.

The classification of HLA supertypes by GRID/CPCA and hierarchical clustering methods

Biological experiments often produce enormous amount of data, which are usually analyzed by data clustering. Cluster analysis refers to statistical methods that are used to assign data with similar properties into several smaller, more meaningful groups. Two commonly used clustering techniques are introduced in the following section: principal component analysis (PCA) and hierarchical clustering. PCA calculates the variance between variables and groups them into a few uncorrelated groups or principal components (PCs) that are orthogonal to each other. Hierarchical clustering is carried out by separating data into many clusters and merging similar clusters together. Here, we use an example of human leukocyte antigen (HLA) supertype classification to demonstrate the usage of the two methods. Two programs, Generating Optimal Linear Partial Least Square Estimations (GOLPE) and Sybyl, are used for PCA and hierarchical clustering, respectively. However, the reader should bear in mind that the methods have been incorporated into other software as well, such as SIMCA, statistiXL, and R.

Distribution, occurrence and biotoxin composition of the main shellfish toxin producing microalgae within European waters: A comparison of methods of analysis.

Harmful algal blooms (HABs) are a natural global phenomena emerging in severity and extent. Incidents have many economic, ecological and human health impacts. Monitoring and providing early warning of toxic HABs are critical for protecting public health. Current monitoring programmes include measuring the number of toxic phytoplankton cells in the water and biotoxin levels in shellfish tissue. As these efforts are demanding and labour intensive, methods which improve the efficiency are essential. This study compares the utilisation of a multitoxin surface plasmon resonance (multitoxin SPR) biosensor with enzyme-linked immunosorbent assay (ELISA) and analytical methods such as high performance liquid chromatography with fluorescence detection (HPLC-FLD) and liquid chromatography–tandem mass spectrometry (LC–MS/MS) for toxic HAB monitoring efforts in Europe. Seawater samples (n = 256) from European waters, collected 2009–2011, were analysed for biotoxins: saxitoxin and analogues, okadaic acid and dinophysistoxins 1/2 (DTX1/DTX2) and domoic acid responsible for paralytic shellfish poisoning (PSP), diarrheic shellfish poisoning (DSP) and amnesic shellfish poisoning (ASP), respectively. Biotoxins were detected mainly in samples from Spain and Ireland. France and Norway appeared to have the lowest number of toxic samples. Both the multitoxin SPR biosensor and the RNA microarray were more sensitive at detecting toxic HABs than standard light microscopy phytoplankton monitoring. Correlations between each of the detection methods were performed with the overall agreement, based on statistical 2 × 2 comparison tables, between each testing platform ranging between 32% and 74% for all three toxin families illustrating that one individual testing method may not be an ideal solution. An efficient early warning monitoring system for the detection of toxic HABs could therefore be achieved by combining both the multitoxin SPR biosensor and RNA microarray.

Distribution, occurrence and biotoxin composition of the main shellfish toxin producing microalgae within European waters: A comparison of methods of analysis.

Harmful algal blooms (HABs) are a natural global phenomena emerging in severity and extent. Incidents have many economic, ecological and human health impacts. Monitoring and providing early warning of toxic HABs are critical for protecting public health. Current monitoring programmes include measuring the number of toxic phytoplankton cells in the water and biotoxin levels in shellfish tissue. As these efforts are demanding and labour intensive, methods which improve the efficiency are essential. This study compares the utilisation of a multitoxin surface plasmon resonance (multitoxin SPR) biosensor with enzyme-linked immunosorbent assay (ELISA) and analytical methods such as high performance liquid chromatography with fluorescence detection (HPLC-FLD) and liquid chromatography–tandem mass spectrometry (LC–MS/MS) for toxic HAB monitoring efforts in Europe. Seawater samples (n = 256) from European waters, collected 2009–2011, were analysed for biotoxins: saxitoxin and analogues, okadaic acid and dinophysistoxins 1/2 (DTX1/DTX2) and domoic acid responsible for paralytic shellfish poisoning (PSP), diarrheic shellfish poisoning (DSP) and amnesic shellfish poisoning (ASP), respectively. Biotoxins were detected mainly in samples from Spain and Ireland. France and Norway appeared to have the lowest number of toxic samples. Both the multitoxin SPR biosensor and the RNA microarray were more sensitive at detecting toxic HABs than standard light microscopy phytoplankton monitoring. Correlations between each of the detection methods were performed with the overall agreement, based on statistical 2 × 2 comparison tables, between each testing platform ranging between 32% and 74% for all three toxin families illustrating that one individual testing method may not be an ideal solution. An efficient early warning monitoring system for the detection of toxic HABs could therefore be achieved by combining both the multitoxin SPR biosensor and RNA microarray.

Recent advances in explaining hedge fund returns:implicit factors and exposures

We survey articles covering how hedge fund returns are explained, using largely non-linear multifactor models that examine the non-linear pay-offs and exposures of hedge funds. We provide an integrated view of the implicit factor and statistical factor models that are largely able to explain the hedge fund return-generating process. We present their evolution through time by discussing pioneering studies that made a significant contribution to knowledge, and also recent innovative studies that examine hedge fund exposures using advanced econometric methods. This is the first review that analyzes very recent studies that explain a large part of hedge fund variation. We conclude by presenting some gaps for future research.

Effective web service discovery using a combination of a semantic model and a data mining technique

With the advent of Service Oriented Architecture, Web Services have gained tremendous popularity. Due to the availability of a large number of Web services, finding an appropriate Web service according to the requirement of the user is a challenge. This warrants the need to establish an effective and reliable process of Web service discovery. A considerable body of research has emerged to develop methods to improve the accuracy of Web service discovery to match the best service. The process of Web service discovery results in suggesting many individual services that partially fulfil the user’s interest. By considering the semantic relationships of words used in describing the services as well as the use of input and output parameters can lead to accurate Web service discovery. Appropriate linking of individual matched services should fully satisfy the requirements which the user is looking for. This research proposes to integrate a semantic model and a data mining technique to enhance the accuracy of Web service discovery. A novel three-phase Web service discovery methodology has been proposed. The first phase performs match-making to find semantically similar Web services for a user query. In order to perform semantic analysis on the content present in the Web service description language document, the support-based latent semantic kernel is constructed using an innovative concept of binning and merging on the large quantity of text documents covering diverse areas of domain of knowledge. The use of a generic latent semantic kernel constructed with a large number of terms helps to find the hidden meaning of the query terms which otherwise could not be found. Sometimes a single Web service is unable to fully satisfy the requirement of the user. In such cases, a composition of multiple inter-related Web services is presented to the user. The task of checking the possibility of linking multiple Web services is done in the second phase. Once the feasibility of linking Web services is checked, the objective is to provide the user with the best composition of Web services. In the link analysis phase, the Web services are modelled as nodes of a graph and an allpair shortest-path algorithm is applied to find the optimum path at the minimum cost for traversal. The third phase which is the system integration, integrates the results from the preceding two phases by using an original fusion algorithm in the fusion engine. Finally, the recommendation engine which is an integral part of the system integration phase makes the final recommendations including individual and composite Web services to the user. In order to evaluate the performance of the proposed method, extensive experimentation has been performed. Results of the proposed support-based semantic kernel method of Web service discovery are compared with the results of the standard keyword-based information-retrieval method and a clustering-based machine-learning method of Web service discovery. The proposed method outperforms both information-retrieval and machine-learning based methods. Experimental results and statistical analysis also show that the best Web services compositions are obtained by considering 10 to 15 Web services that are found in phase-I for linking. Empirical results also ascertain that the fusion engine boosts the accuracy of Web service discovery by combining the inputs from both the semantic analysis (phase-I) and the link analysis (phase-II) in a systematic fashion. Overall, the accuracy of Web service discovery with the proposed method shows a significant improvement over traditional discovery methods.

Difference-based meta-analytic procedures for between-participant and/or within-participant designs : a tutorial review for sports and exercise scientists

The aim of this paper is to provide a contemporary summary of statistical and non-statistical meta-analytic procedures that have relevance to the type of experimental designs often used by sport scientists when examining differences/change in dependent measure(s) as a result of one or more independent manipulation(s). Using worked examples from studies on observational learning in the motor behaviour literature, we adopt a random effects model and give a detailed explanation of the statistical procedures for the three types of raw score difference-based analyses applicable to between-participant, within-participant, and mixed-participant designs. Major merits and concerns associated with these quantitative procedures are identified and agreed methods are reported for minimizing biased outcomes, such as those for dealing with multiple dependent measures from single studies, design variation across studies, different metrics (i.e. raw scores and difference scores), and variations in sample size. To complement the worked examples, we summarize the general considerations required when conducting and reporting a meta-analysis, including how to deal with publication bias, what information to present regarding the primary studies, and approaches for dealing with outliers. By bringing together these statistical and non-statistical meta-analytic procedures, we provide the tools required to clarify understanding of key concepts and principles.

Development of rapid and highly resolving combinatorial genotyping schemes for Campylobacter jejuni and Campylobacter coli

Campylobacter jejuni followed by Campylobacter coli contribute substantially to the economic and public health burden attributed to food-borne infections in Australia. Genotypic characterisation of isolates has provided new insights into the epidemiology and pathogenesis of C. jejuni and C. coli. However, currently available methods are not conducive to large scale epidemiological investigations that are necessary to elucidate the global epidemiology of these common food-borne pathogens. This research aims to develop high resolution C. jejuni and C. coli genotyping schemes that are convenient for high throughput applications. Real-time PCR and High Resolution Melt (HRM) analysis are fundamental to the genotyping schemes developed in this study and enable rapid, cost effective, interrogation of a range of different polymorphic sites within the Campylobacter genome. While the sources and routes of transmission of campylobacters are unclear, handling and consumption of poultry meat is frequently associated with human campylobacteriosis in Australia. Therefore, chicken derived C. jejuni and C. coli isolates were used to develop and verify the methods described in this study. The first aim of this study describes the application of MLST-SNP (Multi Locus Sequence Typing Single Nucleotide Polymorphisms) + binary typing to 87 chicken C. jejuni isolates using real-time PCR analysis. These typing schemes were developed previously by our research group using isolates from campylobacteriosis patients. This present study showed that SNP + binary typing alone or in combination are effective at detecting epidemiological linkage between chicken derived Campylobacter isolates and enable data comparisons with other MLST based investigations. SNP + binary types obtained from chicken isolates in this study were compared with a previously SNP + binary and MLST typed set of human isolates. Common genotypes between the two collections of isolates were identified and ST-524 represented a clone that could be worth monitoring in the chicken meat industry. In contrast, ST-48, mainly associated with bovine hosts, was abundant in the human isolates. This genotype was, however, absent in the chicken isolates, indicating the role of non-poultry sources in causing human Campylobacter infections. This demonstrates the potential application of SNP + binary typing for epidemiological investigations and source tracing. While MLST SNPs and binary genes comprise the more stable backbone of the Campylobacter genome and are indicative of long term epidemiological linkage of the isolates, the development of a High Resolution Melt (HRM) based curve analysis method to interrogate the hypervariable Campylobacter flagellin encoding gene (flaA) is described in Aim 2 of this study. The flaA gene product appears to be an important pathogenicity determinant of campylobacters and is therefore a popular target for genotyping, especially for short term epidemiological studies such as outbreak investigations. HRM curve analysis based flaA interrogation is a single-step closed-tube method that provides portable data that can be easily shared and accessed. Critical to the development of flaA HRM was the use of flaA specific primers that did not amplify the flaB gene. HRM curve analysis flaA interrogation was successful at discriminating the 47 sequence variants identified within the 87 C. jejuni and 15 C. coli isolates and correlated to the epidemiological background of the isolates. In the combinatorial format, the resolving power of flaA was additive to that of SNP + binary typing and CRISPR (Clustered regularly spaced short Palindromic repeats) HRM and fits the PHRANA (Progressive hierarchical resolving assays using nucleic acids) approach for genotyping. The use of statistical methods to analyse the HRM data enhanced sophistication of the method. Therefore, flaA HRM is a rapid and cost effective alternative to gel- or sequence-based flaA typing schemes. Aim 3 of this study describes the development of a novel bioinformatics driven method to interrogate Campylobacter MLST gene fragments using HRM, and is called ‘SNP Nucleated Minim MLST’ or ‘Minim typing’. The method involves HRM interrogation of MLST fragments that encompass highly informative “Nucleating SNPS” to ensure high resolution. Selection of fragments potentially suited to HRM analysis was conducted in silico using i) “Minimum SNPs” and ii) the new ’HRMtype’ software packages. Species specific sets of six “Nucleating SNPs” and six HRM fragments were identified for both C. jejuni and C. coli to ensure high typeability and resolution relevant to the MLST database. ‘Minim typing’ was tested empirically by typing 15 C. jejuni and five C. coli isolates. The association of clonal complexes (CC) to each isolate by ‘Minim typing’ and SNP + binary typing were used to compare the two MLST interrogation schemes. The CCs linked with each C. jejuni isolate were consistent for both methods. Thus, ‘Minim typing’ is an efficient and cost effective method to interrogate MLST genes. However, it is not expected to be independent, or meet the resolution of, sequence based MLST gene interrogation. ‘Minim typing’ in combination with flaA HRM is envisaged to comprise a highly resolving combinatorial typing scheme developed around the HRM platform and is amenable to automation and multiplexing. The genotyping techniques described in this thesis involve the combinatorial interrogation of differentially evolving genetic markers on the unified real-time PCR and HRM platform. They provide high resolution and are simple, cost effective and ideally suited to rapid and high throughput genotyping for these common food-borne pathogens.

Alternative analytical methods for the identification of cancer-related symptom clusters

Advances in symptom management strategies through a better understanding of cancer symptom clusters depend on the identification of symptom clusters that are valid and reliable. The purpose of this exploratory research was to investigate alternative analytical approaches to identify symptom clusters for patients with cancer, using readily accessible statistical methods, and to justify which methods of identification may be appropriate for this context. Three studies were undertaken: (1) a systematic review of the literature, to identify analytical methods commonly used for symptom cluster identification for cancer patients; (2) a secondary data analysis to identify symptom clusters and compare alternative methods, as a guide to best practice approaches in cross-sectional studies; and (3) a secondary data analysis to investigate the stability of symptom clusters over time. The systematic literature review identified, in 10 years prior to March 2007, 13 cross-sectional studies implementing multivariate methods to identify cancer related symptom clusters. The methods commonly used to group symptoms were exploratory factor analysis, hierarchical cluster analysis and principal components analysis. Common factor analysis methods were recommended as the best practice cross-sectional methods for cancer symptom cluster identification. A comparison of alternative common factor analysis methods was conducted, in a secondary analysis of a sample of 219 ambulatory cancer patients with mixed diagnoses, assessed within one month of commencing chemotherapy treatment. Principal axis factoring, unweighted least squares and image factor analysis identified five consistent symptom clusters, based on patient self-reported distress ratings of 42 physical symptoms. Extraction of an additional cluster was necessary when using alpha factor analysis to determine clinically relevant symptom clusters. The recommended approaches for symptom cluster identification using nonmultivariate normal data were: principal axis factoring or unweighted least squares for factor extraction, followed by oblique rotation; and use of the scree plot and Minimum Average Partial procedure to determine the number of factors. In contrast to other studies which typically interpret pattern coefficients alone, in these studies symptom clusters were determined on the basis of structure coefficients. This approach was adopted for the stability of the results as structure coefficients are correlations between factors and symptoms unaffected by the correlations between factors. Symptoms could be associated with multiple clusters as a foundation for investigating potential interventions. The stability of these five symptom clusters was investigated in separate common factor analyses, 6 and 12 months after chemotherapy commenced. Five qualitatively consistent symptom clusters were identified over time (Musculoskeletal-discomforts/lethargy, Oral-discomforts, Gastrointestinaldiscomforts, Vasomotor-symptoms, Gastrointestinal-toxicities), but at 12 months two additional clusters were determined (Lethargy and Gastrointestinal/digestive symptoms). Future studies should include physical, psychological, and cognitive symptoms. Further investigation of the identified symptom clusters is required for validation, to examine causality, and potentially to suggest interventions for symptom management. Future studies should use longitudinal analyses to investigate change in symptom clusters, the influence of patient related factors, and the impact on outcomes (e.g., daily functioning) over time.