Bayesian hierarchical analysis on crash prediction models

Traditional crash prediction models, such as generalized linear regression models, are incapable of taking into account the multilevel data structure, which extensively exists in crash data. Disregarding the possible within-group correlations can lead to the production of models giving unreliable and biased estimates of unknowns. This study innovatively proposes a -level hierarchy, viz. (Geographic region level – Traffic site level – Traffic crash level – Driver-vehicle unit level – Vehicle-occupant level) Time level, to establish a general form of multilevel data structure in traffic safety analysis. To properly model the potential cross-group heterogeneity due to the multilevel data structure, a framework of Bayesian hierarchical models that explicitly specify multilevel structure and correctly yield parameter estimates is introduced and recommended. The proposed method is illustrated in an individual-severity analysis of intersection crashes using the Singapore crash records. This study proved the importance of accounting for the within-group correlations and demonstrated the flexibilities and effectiveness of the Bayesian hierarchical method in modeling multilevel structure of traffic crash data.

A phylogenetic analysis and taxonomic revision of the oribatid mite family Malaconothridae (Acari: Oribatida), with new species of Tyrphonothrus and Malaconothrus from Australia

Hitherto, the Malaconothridae contained Malaconothrus Berlese, 1904 and Trimalaconothrus Berlese, 1916, defined by the possession of one pre-tarsal claw (monodactyly) or by three claws (tridactyly) respectively. However, monodactyly is a convergent apomorphy within the Oribatida and an unreliable character for a classification. Therefore we undertook a phylogenetic analysis of 102 species as the basis for a taxonomic review of the Malaconothridae. We identified two major clades, equivalent to the genera Tyrphonothrus Knülle, 1957 and Malaconothrus. These genera are redefined. Trimala-conothrus becomes the junior subjective synonym of Malaconothrus. Some 42 species of Trimalaconothrus are recom-bined to Malaconothrus and 15 species to Tyrphonothrus. Homonyms created by the recombinations are rectified. The replacement name M. hammerae nom. nov. is proposed for M. angulatus Hammer, 1958, the junior homonym of M. an-gulatus (Willmann, 1931) and the replacement name M. luxtoni nom. nov. is proposed for M. scutatus Luxton, 1987, the junior homonym of M. scutatus Mihelč ič, 1959. Trimalaconothrus iteratus Subías, 2004 is an unnecessary replacement name and is a junior objective synonym of Malaconothrus longirostrum (Hammer 1966). Malaconothrus praeoccupatus Subías, 2004 is a junior objective synonym of M. machadoi Balogh & Mahunka, 1969. Malaconothrus obsessus (Subías, 2004), an unnecessary replacement name for Trimalaconothrus albulus Hammer 1966 sensu Tseng 1982, becomes an available name for what is in fact a previously-undescribed species of Malaconothrus. We describe four new species of Tyrphonothrus: T. gnammaensis sp. nov. from Western Australia, T. gringai sp. nov. and T. maritimus sp. nov. from New South Wales, and T. taylori sp. nov. from Queensland. We describe six new species of Malaconothrus: M. beecroftensis sp. nov., M. darwini sp. nov. M. gundungurra sp. nov. and M. knuellei sp. nov. from New South Wales, M. jowettae sp. nov. from Norfolk Island, and M. talaitae sp. nov. from Victoria.

Bayesian survival analysis using gene expression

This thesis developed and applied Bayesian models for the analysis of survival data. The gene expression was considered as explanatory variables within the Bayesian survival model which can be considered the new contribution in the analysis of such data. The censoring factor that is inherent of survival data has also been addressed in terms of its impact on the fitting of a finite mixture of Weibull distribution with and without covariates. To investigate this, simulation study were carried out under several censoring percentages. Censoring percentage as high as 80% is acceptable here as the work involved high dimensional data. Lastly the Bayesian model averaging approach was developed to incorporate model uncertainty in the prediction of survival.

Weather variability and influenza A (H7N9) transmission in Shanghai, China: A Bayesian spatial analysis

Abstract Background A novel avian influenza A (H7N9) virus was first found in humans in Shanghai, and infected over 433 patients in China. To date, very little is known about the spatiotemporal variability or environmental drivers of the risk of H7N9 infection. This study explored the spatial and temporal variation of H7N9 infection and assessed the effects of temperature and rainfall on H7N9 incidence. Methods A Bayesian spatial conditional autoregressive (CAR) model was used to assess the spatiotemporal distribution of the risk of H7N9 infection in Shanghai, by district and fortnight for the period 19th February–14th April 2013. Data on daily laboratory-confirmed H7N9 cases, and weather variability including temperature (°C) and rainfall (mm) were obtained from the Chinese Information System for Diseases Control and Prevention and Chinese Meteorological Data Sharing Service System, respectively, and aggregated by fortnight. Results High spatial variations in the H7N9 risk were mainly observed in the east and centre of Shanghai municipality. H7N9 incidence rate was significantly associated with fortnightly mean temperature (Relative Risk (RR): 1.54; 95% credible interval (CI): 1.22–1.94) and fortnightly mean rainfall (RR: 2.86; 95% CI: 1.47–5.56). Conclusion There was a substantial variation in the spatiotemporal distribution of H7N9 infection across different districts in Shanghai. Optimal temperature and rainfall may be one of the driving forces for H7N9.

Socio-environmental drivers and suicide in Australia: Bayesian spatial analysis

Background The impact of socio-environmental factors on suicide has been examined in many studies. Few of them, however, have explored these associations from a spatial perspective, especially in assessing the association between meteorological factors and suicide. This study examined the association of meteorological and socio-demographic factors with suicide across small areas over different time periods. Methods Suicide, population and socio-demographic data (e.g., population of Aboriginal and Torres Strait Islanders (ATSI), and unemployment rate (UNE) at the Local Government Area (LGA) level were obtained from the Australian Bureau of Statistics for the period of 1986 to 2005. Information on meteorological factors (rainfall, temperature and humidity) was supplied by Australian Bureau of Meteorology. A Bayesian Conditional Autoregressive (CAR) Model was applied to explore the association of socio-demographic and meteorological factors with suicide across LGAs. Results In Model I (socio-demographic factors), proportion of ATSI and UNE were positively associated with suicide from 1996 to 2000 (Relative Risk (RR)ATSI = 1.0107, 95% Credible Interval (CI): 1.0062-1.0151; RRUNE = 1.0187, 95% CI: 1.0060-1.0315), and from 2001 to 2005 (RRATSI = 1.0126, 95% CI: 1.0076-1.0176; RRUNE = 1.0198, 95% CI: 1.0041-1.0354). Socio-Economic Index for Area (SEIFA) and IND, however, had negative associations with suicide between 1986 and 1990 (RRSEIFA = 0.9983, 95% CI: 0.9971-0.9995; RRATSI = 0.9914, 95% CI: 0.9848-0.9980). Model II (meteorological factors): a 1°C higher yearly mean temperature across LGAs increased the suicide rate by an average by 2.27% (95% CI: 0.73%, 3.82%) in 1996–2000, and 3.24% (95% CI: 1.26%, 5.21%) in 2001–2005. The associations between socio-demographic factors and suicide in Model III (socio-demographic and meteorological factors) were similar to those in Model I; but, there is no substantive association between climate and suicide in Model III. Conclusions Proportion of Aboriginal and Torres Strait Islanders, unemployment and temperature appeared to be statistically associated with of suicide incidence across LGAs among all selected variables, especially in recent years. The results indicated that socio-demographic factors played more important roles than meteorological factors in the spatial pattern of suicide incidence.

Dissimilarities in alignment-free methods for phylogenetic analysis based on genomes

Whole genome sequences are generally accepted as excellent tools for studying evolutionary relationships. Due to the problems caused by the uncertainty in alignment, existing tools for phylogenetic analysis based on multiple alignments could not be directly applied to the whole-genome comparison and phylogenomic studies. There has been a growing interest in alignment-free methods for phylogenetic analysis using complete genome data. The “distances” used in these alignment-free methods are not proper distance metrics in the strict mathematical sense. In this study, we first review them in a more general frame — dissimilarity. Then we propose some new dissimilarities for phylogenetic analysis. Last three genome datasets are employed to evaluate these dissimilarities from a biological point of view.

Phylogenetic analysis of betanodavirus isolates from Australian finfish

In Australia, disease caused by betanodavirus has been reported in an increasing number of cultured finfish since the first report of mortalities in 1990. Partial coat protein gene sequences from the T2 or T4 regions of 8 betanodaviruses from barramundi Lates calcarifer, sleepy cod Oxyeleotris lineolata, striped trumpeter Latris lineata, barramundi cod Cromileptes altivelis, Australian bass Macquaria novemaculata and gold-spotted rockcod Epinephelus coioides from several Australian states were determined. Analysis of the 606 bp nucleotide sequences of the T2 region of 4 isolates demonstrated the close relationship with isolates from the red-spotted grouper nervous necrosis virus (RGNNV) genotype and the Cluster Ia subtype. Comparison of a smaller 289 bp sequence from the T4 region identified 2 distinct groupings of the Australian isolates within the RGNNV genotype. Isolates from barramundi from the Northern Territory, barramundi, sleepy cod, barramundi cod and gold-spotted rockcod from Queensland, and striped trumpeter from Tasmania shared a 96.2 to 99.7%, nucleotide identity with each other. These isolates were most similar to the RGNNV genotype Cluster Ia. Isolates from Australian bass from New South Wales and from barramundi from South Australia shared a 98.6% sequence identity with each other. However, these isolates only shared an 85.8 to 87.9%, identity with the other Australian isolates and representative RGNNV isolates. The closest nucleotide identity to sequences reported in the literature for the New South Wales and South Australian isolates was to an Australian barramundi isolate (Ba94Aus) from 1994. These 2 Australian isolates formed a new subtype within the RGNNV genotype, which is designated as Cluster Ic.

Refined Global Analysis of Bemisia tabaci (Hemiptera: Sternorrhyncha: Aleyrodoidea: Aleyrodidae) Mitochondrial Cytochrome Oxidase 1 to Identify Species Level Genetic Boundaries.

Identifying species boundaries within morphologically indistinguishable cryptic species complexes is often contentious. For the whitefly Bemisia tabaci (Gennadius) (Hemiptera: Sternorrhyncha: Aleyrodoidea: Aleyrodidae), the lack of a clear understanding about the genetic limits of the numerous genetic groups and biotypes so far identified has resulted in a lack of consistency in the application of the terms, the approaches use to apply them and in our understanding of what genetic structure within B. tabaci means. Our response has been to use mitochondrial gene cytochrome oxidase one to consider how to clearly and consistently define genetic separation. Using Bayesian phylogenetic analysis and analysis of sequence pairwise divergence we found a considerably higher to number of genetic groups than had been previously determined with two breaks in the distribution, one at 11% and another at 3.5%. At >11% divergence, 11 distinct groups were resolved, whereas at >3.5% divergence 24 groups were identified. Consensus sequences for each of these groups were determined and were shown to be useful in the correct assignment of sequences of unknown origin. The 3.5% divergence bound is consistent with species level separations in other insect taxa and Suggests that B. tabaci is it cryptic species composed of at least 24 distinct species. We further show that the placement of Bemesia atriplex (Froggatt) within the B. tabaci in, group adds further weight to the argument for species level separation within B. tabaci. This new analysis, which constructs consensus sequences and uses these its a standard against which unknown sequences call be compared, provides for the first time it consistent means of identifying the genetic hounds of each species with it high degree of certainty.

Molecular phylogenetic analysis reveals six new species of Diaporthe from Australia

Six new species of Diaporthe, D. beilharziae on Indigofera australis, D. fraxini-angustifoliae on Fraxinus angustifolia subsp. oxycarpa, D. litchicola on Litchi chinensis, D. nothofagi on Nothofagus cunninghamii, D. pascoei on Persea americana and D. salicicola on Salix purpurea from Australia are described and illustrated based on morphological characteristics and molecular analyses. Three of the new species no longer produced sporulating structures in culture and two of these were morphologically described from voucher specimens. Phylogenetic relationships of the new species with other Diaporthe species are revealed by DNA sequence analyses based on the internal transcribed spacer (ITS) region, and partial regions of the β-tubulin (BT) and translation elongation factor 1-alpha (TEF). © 2013 Mushroom Research Foundation.

Johnalcornia gen. et. comb. nov., and nine new combinations in Curvularia based on molecular phylogenetic analysis

An examination of ex-type and authentic cultures of 34 species of Bipolaris and Curvularia by phylogenetic analysis of four loci (EF-1α, GAPDH, ITS and LSU) resulted in nine new combinations in Curvularia, as well as new synonymies for some species of Bipolaris and Curvularia. Lectotypes are designated for Bipolaris secalis and Curvularia richardiae, and an epitype is designated for Curvularia crustacea. A new monotypic genus, Johnalcornia, is introduced to accommodate Bipolaris aberrans, which clusters sister to the newly described Porocercospora. Johnalcornia differs morphologically from this taxon by producing distinctive conidia-like chlamydospores as well as comparatively thick-walled, geniculate conidiophores, with conidiogenous cells that have conspicuous scars. Johnalcornia further differs from related genera by forming the second conidial septum in the apical cell.

Bayesian statistical analysis of bacterial diversity

Bacteria play an important role in many ecological systems. The molecular characterization of bacteria using either cultivation-dependent or cultivation-independent methods reveals the large scale of bacterial diversity in natural communities, and the vastness of subpopulations within a species or genus. Understanding how bacterial diversity varies across different environments and also within populations should provide insights into many important questions of bacterial evolution and population dynamics. This thesis presents novel statistical methods for analyzing bacterial diversity using widely employed molecular fingerprinting techniques. The first objective of this thesis was to develop Bayesian clustering models to identify bacterial population structures. Bacterial isolates were identified using multilous sequence typing (MLST), and Bayesian clustering models were used to explore the evolutionary relationships among isolates. Our method involves the inference of genetic population structures via an unsupervised clustering framework where the dependence between loci is represented using graphical models. The population dynamics that generate such a population stratification were investigated using a stochastic model, in which homologous recombination between subpopulations can be quantified within a gene flow network. The second part of the thesis focuses on cluster analysis of community compositional data produced by two different cultivation-independent analyses: terminal restriction fragment length polymorphism (T-RFLP) analysis, and fatty acid methyl ester (FAME) analysis. The cluster analysis aims to group bacterial communities that are similar in composition, which is an important step for understanding the overall influences of environmental and ecological perturbations on bacterial diversity. A common feature of T-RFLP and FAME data is zero-inflation, which indicates that the observation of a zero value is much more frequent than would be expected, for example, from a Poisson distribution in the discrete case, or a Gaussian distribution in the continuous case. We provided two strategies for modeling zero-inflation in the clustering framework, which were validated by both synthetic and empirical complex data sets. We show in the thesis that our model that takes into account dependencies between loci in MLST data can produce better clustering results than those methods which assume independent loci. Furthermore, computer algorithms that are efficient in analyzing large scale data were adopted for meeting the increasing computational need. Our method that detects homologous recombination in subpopulations may provide a theoretical criterion for defining bacterial species. The clustering of bacterial community data include T-RFLP and FAME provides an initial effort for discovering the evolutionary dynamics that structure and maintain bacterial diversity in the natural environment.