302 resultados para Complex sample analysis

em Queensland University of Technology - ePrints Archive


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Genome-wide association studies (GWASs) have been successful at identifying single-nucleotide polymorphisms (SNPs) highly associated with common traits; however, a great deal of the heritable variation associated with common traits remains unaccounted for within the genome. Genome-wide complex trait analysis (GCTA) is a statistical method that applies a linear mixed model to estimate phenotypic variance of complex traits explained by genome-wide SNPs, including those not associated with the trait in a GWAS. We applied GCTA to 8 cohorts containing 7096 case and 19 455 control individuals of European ancestry in order to examine the missing heritability present in Parkinson's disease (PD). We meta-analyzed our initial results to produce robust heritability estimates for PD types across cohorts. Our results identify 27% (95% CI 17-38, P = 8.08E - 08) phenotypic variance associated with all types of PD, 15% (95% CI -0.2 to 33, P = 0.09) phenotypic variance associated with early-onset PD and 31% (95% CI 17-44, P = 1.34E - 05) phenotypic variance associated with late-onset PD. This is a substantial increase from the genetic variance identified by top GWAS hits alone (between 3 and 5%) and indicates there are substantially more risk loci to be identified. Our results suggest that although GWASs are a useful tool in identifying the most common variants associated with complex disease, a great deal of common variants of small effect remain to be discovered. © Published by Oxford University Press 2012.

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In this paper, we present a finite sample analysis of the sample minimum-variance frontier under the assumption that the returns are independent and multivariate normally distributed. We show that the sample minimum-variance frontier is a highly biased estimator of the population frontier, and we propose an improved estimator of the population frontier. In addition, we provide the exact distribution of the out-of-sample mean and variance of sample minimum-variance portfolios. This allows us to understand the impact of estimation error on the performance of in-sample optimal portfolios. Key Words: minimum-variance frontier; efficiency set constants; finite sample distribution

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Multiple reaction monitoring (MRM) mass spectrometry coupled with stable isotope dilution (SID) and liquid chromatography (LC) is increasingly used in biological and clinical studies for precise and reproducible quantification of peptides and proteins in complex sample matrices. Robust LC-SID-MRM-MS-based assays that can be replicated across laboratories and ultimately in clinical laboratory settings require standardized protocols to demonstrate that the analysis platforms are performing adequately. We developed a system suitability protocol (SSP), which employs a predigested mixture of six proteins, to facilitate performance evaluation of LC-SID-MRM-MS instrument platforms, configured with nanoflow-LC systems interfaced to triple quadrupole mass spectrometers. The SSP was designed for use with low multiplex analyses as well as high multiplex approaches when software-driven scheduling of data acquisition is required. Performance was assessed by monitoring of a range of chromatographic and mass spectrometric metrics including peak width, chromatographic resolution, peak capacity, and the variability in peak area and analyte retention time (RT) stability. The SSP, which was evaluated in 11 laboratories on a total of 15 different instruments, enabled early diagnoses of LC and MS anomalies that indicated suboptimal LC-MRM-MS performance. The observed range in variation of each of the metrics scrutinized serves to define the criteria for optimized LC-SID-MRM-MS platforms for routine use, with pass/fail criteria for system suitability performance measures defined as peak area coefficient of variation <0.15, peak width coefficient of variation <0.15, standard deviation of RT <0.15 min (9 s), and the RT drift <0.5min (30 s). The deleterious effect of a marginally performing LC-SID-MRM-MS system on the limit of quantification (LOQ) in targeted quantitative assays illustrates the use and need for a SSP to establish robust and reliable system performance. Use of a SSP helps to ensure that analyte quantification measurements can be replicated with good precision within and across multiple laboratories and should facilitate more widespread use of MRM-MS technology by the basic biomedical and clinical laboratory research communities.

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Samples of sea water contain phytoplankton taxa in varying amounts, and marine scientists are interested in the relative abundance of each taxa. Their relative biomass can be ascertained indirectly by measuring the quantity of various pigments using high performance liquid chromatography. However, the conversion from pigment to taxa is mathematically non trivial as it is a positive matrix factorisation problem where both matrices are unknown beyond the level of initial estimates. The prior information on the pigment to taxa conversion matrix is used to give the problem a unique solution. An iteration of two non-negative least squares algorithms gives satisfactory results. Some sample analysis of data indicates prospects for this type of analysis. An alternative more computationally intensive approach using Bayesian methods is discussed.

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2,4,6-trinitrotoluene (TNT) is one of the most commonly used nitro aromatic explosives in landmine, military and mining industry. This article demonstrates rapid and selective identification of TNT by surface-enhanced Raman spectroscopy (SERS) using 6-aminohexanethiol (AHT) as a new recognition molecule. First, Meisenheimer complex formation between AHT and TNT is confirmed by the development of pink colour and appearance of new band around 500 nm in UV-visible spectrum. Solution Raman spectroscopy study also supported the AHT:TNT complex formation by demonstrating changes in the vibrational stretching of AHT molecule between 2800-3000 cm−1. For surface enhanced Raman spectroscopy analysis, a self-assembled monolayer (SAM) of AHT is formed over the gold nanostructure (AuNS) SERS substrate in order to selectively capture TNT onto the surface. Electrochemical desorption and X-ray photoelectron studies are performed over AHT SAM modified surface to examine the presence of free amine groups with appropriate orientation for complex formation. Further, AHT and butanethiol (BT) mixed monolayer system is explored to improve the AHT:TNT complex formation efficiency. Using a 9:1 AHT:BT mixed monolayer, a very low detection limit (LOD) of 100 fM TNT was realized. The new method delivers high selectivity towards TNT over 2,4 DNT and picric acid. Finally, real sample analysis is demonstrated by the extraction and SERS detection of 302 pM of TNT from spiked.

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Quantitative analysis is increasingly being used in team sports to better understand performance in these stylized, delineated, complex social systems. Here we provide a first step toward understanding the pattern-forming dynamics that emerge from collective offensive and defensive behavior in team sports. We propose a novel method of analysis that captures how teams occupy sub-areas of the field as the ball changes location. We used the method to analyze a game of association football (soccer) based upon a hypothesis that local player numerical dominance is key to defensive stability and offensive opportunity. We found that the teams consistently allocated more players than their opponents in sub-areas of play closer to their own goal. This is consistent with a predominantly defensive strategy intended to prevent yielding even a single goal. We also find differences between the two teams' strategies: while both adopted the same distribution of defensive, midfield, and attacking players (a 4:3:3 system of play), one team was significantly more effective both in maintaining defensive and offensive numerical dominance for defensive stability and offensive opportunity. That team indeed won the match with an advantage of one goal (2 to 1) but the analysis shows the advantage in play was more pervasive than the single goal victory would indicate. Our focus on the local dynamics of team collective behavior is distinct from the traditional focus on individual player capability. It supports a broader view in which specific player abilities contribute within the context of the dynamics of multiplayer team coordination and coaching strategy. By applying this complex system analysis to association football, we can understand how players' and teams' strategies result in successful and unsuccessful relationships between teammates and opponents in the area of play.

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Objective. To assess the role of genes and the environment in determining the severity of ankylosing spondylitis. Methods: One hundred seventy-three families with >1 case of ankylosing spondylitis were recruited (120 affected sibling pairs, 26 affected parent-child pairs, 20 families with both first- and second-degree relatives affected, and 7 families with only second-degree relatives affected), comprising a total of 384 affected individuals. Disease severity was assessed by the Bath Ankylosing Spondylitis Disease Activity Index (BASDAI) and functional impairment was determined using the Bath Ankylosing Spondylitis Functional Index (BASFI). Disease duration and age at onset were also studied. Variance-components modeling was used to determine the genetic and environmental components Contributing to familiality of the traits examined, and complex segregation analysis was performed to assess different disease models. Results. Both the disease activity and functional capacity as assessed by the BASDAI and the BASFI, respectively, were found to be highly familial (BASDAI familiality 0.51 [P = 10-4], BASFI familiality 0,68 [P = 3 × 10-7]). No significant shared environmental component was demonstrated to be associated with either the BASDAI or the BASFI. Including age at disease onset and duration of disease as covariates made no difference in the heritability assessments. A strong correlation was noted between the BASDAI and the BASFI (genetic correlation 0.9), suggesting the presence of shared determinants of these 2 measures. However, there was significant residual heritability for each measure independent of the other (BASFI residual heritability 0.48, BASDAI 0,36), perhaps indicating that not all genes influencing disease activity influence chronicity. No significant heritability of age at disease onset was found (heritability 0.18; P = 0.2). Segregation studies suggested the presence of a single major gene influencing the BASDAI and the BASFI. Conclusion. This study demonstrates a major genetic contribution to disease severity in ankylosing spondylitis. As with susceptibility to ankylosing spondylitis, shared environmental factors play little role in determining the disease severity.

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Objective To investigate differences in genetic risk factors for rheumatoid arthritis (RA) in Han Chinese as compared with Europeans. Methods A genome-wide association study was conducted in China with 952 patients and 943 controls, and 32 variants were followed up in 2,132 patients and 2,553 controls. A transpopulation meta-analysis with results from a large European RA study was also performed to compare the genetic architecture across the 2 ethnic remote populations. Results Three non-major histocompatibility complex (non-MHC) loci were identified at the genome-wide significance level, the effect sizes of which were larger in anti-citrullinated protein antibody (ACPA)-positive patients than in ACPA-negative patients. These included 2 novel variants, rs12617656, located in an intron of DPP4 (odds ratio [OR] 1.56, P = 1.6 × 10 -21), and rs12379034, located in the coding region of CDK5RAP2 (OR 1.49, P = 1.1 × 10-16), as well as a variant at the known CCR6 locus, rs1854853 (OR 0.71, P = 6.5 × 10-15). The analysis of ACPA-positive patients versus ACPA-negative patients revealed that rs12617656 at the DPP4 locus showed a strong interaction effect with ACPAs (P = 5.3 × 10-18), and such an interaction was also observed for rs7748270 at the MHC locus (P = 5.9 × 10-8). The transpopulation meta-analysis showed genome-wide overlap and enrichment in association signals across the 2 populations, as confirmed by prediction analysis. Conclusion This study has expanded the list of alleles that confer risk of RA, provided new insight into the pathogenesis of RA, and added empirical evidence to the emerging polygenic nature of complex trait variation driven by common genetic variants. Copyright © 2014 by the American College of Rheumatology.

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Differences in genetic control of BMD by skeletal sites and genders were examined by complex segregation analysis in 816 members of 147 families with probands with extreme low BMD. Spine BMD correlated more strongly in male-male comparisons and hip BMD in female-female comparisons, consistent with gender- and site-specificity of BMD heritability. Introduction: Evidence from studies in animals and humans suggests that the genetic control of bone mineral density (BMD) may differ at different skeletal sites and between genders. This question has important implications for the design and interpretation of genetic studies of osteoporosis. Methods: We examined the genetic profile of 147 families with 816 individuals recruited through probands with extreme low BMD (T-score < −2.5, Z-score < −2.0). Complex segregation analysis was performed using the Pedigree Analysis Package. BMD was measured by DXA at both lumbar spine (L1-L4) and femoral neck. Results: Complex segregation analysis excluded purely monogenic and environmental models of segregation of lumbar spine and femoral neck BMD in these families. Pure polygenic models were excluded at the lumbar spine when menopausal status was considered as a covariate, but not at the femoral neck. Mendelian models with a residual polygenic component were not excluded. These models were consistent with the presence of a rare Mendelian genotype of prevalence 3–19 %, causing high BMD at the hip and spine in these families, with additional polygenic effects. Total heritability range at the lumbar spine was 61–67 % and at the femoral neck was 44–67 %. Significant differences in correlation of femoral neck and lumbar spine BMD were observed between male and female relative pairs, with male-male comparisons exhibiting stronger lumbar spine BMD correlation than femoral neck, and female-female comparisons having greater femoral neck BMD correlation than lumbar spine. These findings remained true for parent-offspring correlations when menopausal status was taken into account. The recurrence risk ratio for siblings of probands of a Z-score < −2.0 was 5.4 at the lumbar spine and 5.9 at the femoral neck. Conclusions: These findings support gender- and site-specificity of the inheritance of BMD. These results should be considered in the design and interpretation of genetic studies of osteoporosis.

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This paper considers the boundaries of new public management (NPM) principles in the context of the mandate for a commercial approach within in a New Zealand state-owned enterprise (SOE). Investigating a commercial approach to NPM through an institutional theory lens, the case study highlights complexities and potential conflict between structured NPM principles and the more complex reality. Analysis reveals blurred lines and boundaries have implications for public sector organisations such as SOEs, government and other stakeholders, where managers push the boundaries beyond the point where stakeholders are comfortable. Thus, a key challenge involves developing clearer institutional boundaries to balance freedoms with stakeholder acceptability.

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Complex networks have been studied extensively due to their relevance to many real-world systems such as the world-wide web, the internet, biological and social systems. During the past two decades, studies of such networks in different fields have produced many significant results concerning their structures, topological properties, and dynamics. Three well-known properties of complex networks are scale-free degree distribution, small-world effect and self-similarity. The search for additional meaningful properties and the relationships among these properties is an active area of current research. This thesis investigates a newer aspect of complex networks, namely their multifractality, which is an extension of the concept of selfsimilarity. The first part of the thesis aims to confirm that the study of properties of complex networks can be expanded to a wider field including more complex weighted networks. Those real networks that have been shown to possess the self-similarity property in the existing literature are all unweighted networks. We use the proteinprotein interaction (PPI) networks as a key example to show that their weighted networks inherit the self-similarity from the original unweighted networks. Firstly, we confirm that the random sequential box-covering algorithm is an effective tool to compute the fractal dimension of complex networks. This is demonstrated on the Homo sapiens and E. coli PPI networks as well as their skeletons. Our results verify that the fractal dimension of the skeleton is smaller than that of the original network due to the shortest distance between nodes is larger in the skeleton, hence for a fixed box-size more boxes will be needed to cover the skeleton. Then we adopt the iterative scoring method to generate weighted PPI networks of five species, namely Homo sapiens, E. coli, yeast, C. elegans and Arabidopsis Thaliana. By using the random sequential box-covering algorithm, we calculate the fractal dimensions for both the original unweighted PPI networks and the generated weighted networks. The results show that self-similarity is still present in generated weighted PPI networks. This implication will be useful for our treatment of the networks in the third part of the thesis. The second part of the thesis aims to explore the multifractal behavior of different complex networks. Fractals such as the Cantor set, the Koch curve and the Sierspinski gasket are homogeneous since these fractals consist of a geometrical figure which repeats on an ever-reduced scale. Fractal analysis is a useful method for their study. However, real-world fractals are not homogeneous; there is rarely an identical motif repeated on all scales. Their singularity may vary on different subsets; implying that these objects are multifractal. Multifractal analysis is a useful way to systematically characterize the spatial heterogeneity of both theoretical and experimental fractal patterns. However, the tools for multifractal analysis of objects in Euclidean space are not suitable for complex networks. In this thesis, we propose a new box covering algorithm for multifractal analysis of complex networks. This algorithm is demonstrated in the computation of the generalized fractal dimensions of some theoretical networks, namely scale-free networks, small-world networks, random networks, and a kind of real networks, namely PPI networks of different species. Our main finding is the existence of multifractality in scale-free networks and PPI networks, while the multifractal behaviour is not confirmed for small-world networks and random networks. As another application, we generate gene interactions networks for patients and healthy people using the correlation coefficients between microarrays of different genes. Our results confirm the existence of multifractality in gene interactions networks. This multifractal analysis then provides a potentially useful tool for gene clustering and identification. The third part of the thesis aims to investigate the topological properties of networks constructed from time series. Characterizing complicated dynamics from time series is a fundamental problem of continuing interest in a wide variety of fields. Recent works indicate that complex network theory can be a powerful tool to analyse time series. Many existing methods for transforming time series into complex networks share a common feature: they define the connectivity of a complex network by the mutual proximity of different parts (e.g., individual states, state vectors, or cycles) of a single trajectory. In this thesis, we propose a new method to construct networks of time series: we define nodes by vectors of a certain length in the time series, and weight of edges between any two nodes by the Euclidean distance between the corresponding two vectors. We apply this method to build networks for fractional Brownian motions, whose long-range dependence is characterised by their Hurst exponent. We verify the validity of this method by showing that time series with stronger correlation, hence larger Hurst exponent, tend to have smaller fractal dimension, hence smoother sample paths. We then construct networks via the technique of horizontal visibility graph (HVG), which has been widely used recently. We confirm a known linear relationship between the Hurst exponent of fractional Brownian motion and the fractal dimension of the corresponding HVG network. In the first application, we apply our newly developed box-covering algorithm to calculate the generalized fractal dimensions of the HVG networks of fractional Brownian motions as well as those for binomial cascades and five bacterial genomes. The results confirm the monoscaling of fractional Brownian motion and the multifractality of the rest. As an additional application, we discuss the resilience of networks constructed from time series via two different approaches: visibility graph and horizontal visibility graph. Our finding is that the degree distribution of VG networks of fractional Brownian motions is scale-free (i.e., having a power law) meaning that one needs to destroy a large percentage of nodes before the network collapses into isolated parts; while for HVG networks of fractional Brownian motions, the degree distribution has exponential tails, implying that HVG networks would not survive the same kind of attack.

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Bactrocera dorsalis sensu stricto, B. papayae, B. philippinensis and B. carambolae are serious pest fruit fly species of the B. dorsalis complex that predominantly occur in south-east Asia and the Pacific. Identifying molecular diagnostics has proven problematic for these four taxa, a situation that cofounds biosecurity and quarantine efforts and which may be the result of at least some of these taxa representing the same biological species. We therefore conducted a phylogenetic study of these four species (and closely related outgroup taxa) based on the individuals collected from a wide geographic range; sequencing six loci (cox1, nad4-3′, CAD, period, ITS1, ITS2) for approximately 20 individuals from each of 16 sample sites. Data were analysed within maximum likelihood and Bayesian phylogenetic frameworks for individual loci and concatenated data sets for which we applied multiple monophyly and species delimitation tests. Species monophyly was measured by clade support, posterior probability or bootstrap resampling for Bayesian and likelihood analyses respectively, Rosenberg's reciprocal monophyly measure, P(AB), Rodrigo's (P(RD)) and the genealogical sorting index, gsi. We specifically tested whether there was phylogenetic support for the four 'ingroup' pest species using a data set of multiple individuals sampled from a number of populations. Based on our combined data set, Bactrocera carambolae emerges as a distinct monophyletic clade, whereas B. dorsalis s.s., B. papayae and B. philippinensis are unresolved. These data add to the growing body of evidence that B. dorsalis s.s., B. papayae and B. philippinensis are the same biological species, which poses consequences for quarantine, trade and pest management.

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Objectives: The aim of the current study was to determine the contribution of interleukin (IL) 1 gene cluster polymorphisms previously implicated in susceptibility for ankylosing spondylitis (AS) to AS susceptibility in different populations worldwide. Methods: Nine polymorphisms in the IL1 gene cluster members IL1A (rs2856836, rs17561 and rs1894399), IL1B (rs16944), IL1F10 (rs3811058) and IL1RN (rs419598, the IL1RA VNTR, rs315952 and rs315951) were genotyped in 2675 AS cases and 2592 healthy controls recruited in 12 different centres in 10 countries. Association of variants with AS was tested by Mantel-Haenszel random effects analysis. Results: Strong association was observed with three single nucleotide polymorphisms (SNPs) in the IL1A gene (rs2856836, rs17561, rs1894399, p = 0.0036, 0.000019 and 0.0003, respectively). There was no evidence of significant heterogeneity of effects between centres, and no evidence of non-combinability of findings. The population attributable risk fraction of these variants in Caucasians is estimated at 4-6%. Conclusions: This study confirms that IL1A is associated with susceptibility to AS. Association of the other IL1 gene complex members could not be excluded in specific populations. Prospective meta-analysis is a useful tool in confirmation studies of genes associated with complex genetic disorders such as AS, providing sufficiently large sample sizes to produce robust findings often not achieved in smaller individual cohorts.

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