Development of an autonomous unmanned aerial system to collect time-stamped samples from the atmosphere and localize potential pathogen sources

This paper presents the hardware development and testing of a new concept for air sampling via the integration of a prototype spore trap onboard an unmanned aerial system (UAS).We propose the integration of a prototype spore trap onboard a UAS to allow multiple capture of spores of pathogens in single remote locations at high or low altitude, otherwise not possible with stationary sampling devices.We also demonstrate the capability of this system for the capture of multiple time-stamped samples during a single mission.Wind tunnel testing was followed by simulation, and flight testing was conducted to measure and quantify the spread during simulated airborne air sampling operations. During autonomous operations, the onboard autopilot commands the servo to rotate the sampling device to a new indexed location once the UAS vehicle reaches the predefined waypoint or set of waypoints (which represents the region of interest). Time-stamped UAS data are continuously logged during the flight to assist with analysis of the particles collected. Testing and validation of the autopilot and spore trap integration, functionality, and performance is described. These tools may enhance the ability to detect new incursions of spores

The role of the multiple banded antigen of ureaplasma parvum in intra-amniotic infection : major virulence factor or decoy?

The multiple banded antigen (MBA) is a predicted virulence factor of Ureaplasma species. Antigenic variation of the MBA is a potential mechanism by which ureaplasmas avoid immune recognition and cause chronic infections of the upper genital tract of pregnant women. We tested whether the MBA is involved in the pathogenesis of intra-amniotic infection and chorioamnionitis by injecting virulent or avirulent-derived ureaplasma clones (expressing single MBA variants) into the amniotic fluid of pregnant sheep. At 55 days of gestation pregnant ewes (n = 20) received intra-amniotic injections of virulent-derived or avirulent-derived U. parvum serovar 6 strains (2×104 CFU), or 10B medium (n = 5). Amniotic fluid was collected every two weeks post-infection and fetal tissues were collected at the time of surgical delivery of the fetus (140 days of gestation). Whilst chronic colonisation was established in the amniotic fluid of animals infected with avirulent-derived and virulent-derived ureaplasmas, the severity of chorioamnionitis and fetal inflammation was not different between these groups (p>0.05). MBA size variants (32–170 kDa) were generated in vivo in amniotic fluid samples from both the avirulent and virulent groups, whereas in vitro antibody selection experiments led to the emergence of MBA-negative escape variants in both strains. Anti-ureaplasma IgG antibodies were detected in the maternal serum of animals from the avirulent (40%) and virulent (55%) groups, and these antibodies correlated with increased IL-1β, IL-6 and IL-8 expression in chorioamnion tissue (p<0.05). We demonstrate that ureaplasmas are capable of MBA phase variation in vitro; however, ureaplasmas undergo MBA size variation in vivo, to potentially prevent eradication by the immune response. Size variation of the MBA did not correlate with the severity of chorioamnionitis. Nonetheless, the correlation between a maternal humoral response and the expression of chorioamnion cytokines is a novel finding. This host response may be important in the pathogenesis of inflammation-mediated adverse pregnancy outcomes.

Sequential fusion of decisions from adaptive and random samples for controlled verification of errors

Fusion techniques have received considerable attention for achieving performance improvement with biometrics. While a multi-sample fusion architecture reduces false rejects, it also increases false accepts. This impact on performance also depends on the nature of subsequent attempts, i.e., random or adaptive. Expressions for error rates are presented and experimentally evaluated in this work by considering the multi-sample fusion architecture for text-dependent speaker verification using HMM based digit dependent speaker models. Analysis incorporating correlation modeling demonstrates that the use of adaptive samples improves overall fusion performance compared to randomly repeated samples. For a text dependent speaker verification system using digit strings, sequential decision fusion of seven instances with three random samples is shown to reduce the overall error of the verification system by 26% which can be further reduced by 6% for adaptive samples. This analysis novel in its treatment of random and adaptive multiple presentations within a sequential fused decision architecture, is also applicable to other biometric modalities such as finger prints and handwriting samples.

A comparison of methods for classifying clinical samples based on proteomics data : a case study for statistical and machine learning approaches

The discovery of protein variation is an important strategy in disease diagnosis within the biological sciences. The current benchmark for elucidating information from multiple biological variables is the so called “omics” disciplines of the biological sciences. Such variability is uncovered by implementation of multivariable data mining techniques which come under two primary categories, machine learning strategies and statistical based approaches. Typically proteomic studies can produce hundreds or thousands of variables, p, per observation, n, depending on the analytical platform or method employed to generate the data. Many classification methods are limited by an n≪p constraint, and as such, require pre-treatment to reduce the dimensionality prior to classification. Recently machine learning techniques have gained popularity in the field for their ability to successfully classify unknown samples. One limitation of such methods is the lack of a functional model allowing meaningful interpretation of results in terms of the features used for classification. This is a problem that might be solved using a statistical model-based approach where not only is the importance of the individual protein explicit, they are combined into a readily interpretable classification rule without relying on a black box approach. Here we incorporate statistical dimension reduction techniques Partial Least Squares (PLS) and Principal Components Analysis (PCA) followed by both statistical and machine learning classification methods, and compared them to a popular machine learning technique, Support Vector Machines (SVM). Both PLS and SVM demonstrate strong utility for proteomic classification problems.

Persistence of multiple genetic lineages within intra-host populations of Ross River virus

We examined the structure and extent of genetic diversity in intrahost populations of Ross River virus (RRV) in samples from six human patients, focusing on the nonstructural (nsP3) and structural (E2) protein genes. Strikingly, although the samples were collected from contrasting ecological settings 3,000 kilometers apart in Australia, we observed multiple viral lineages in four of the six individuals, which is indicative of widespread mixed infections. In addition, a comparison with previously published RRV sequences revealed that these distinct lineages have been in circulation for at least 5 years, and we were able to document their long-term persistence over extensive geographical distances

Ureaplasma parvum serovar 3 multiple banded antigen size variation after chronic intra-amniotic infection/colonization

Ureaplasma species are the microorganisms most frequently associated with adverse pregnancy outcomes. The multiple banded antigen (MBA), a surface-exposed lipoprotein, is a key virulence factor of ureaplasmas. The MBA demonstrates size variation, which we have shown previously to be correlated with the severity of chorioamnion inflammation. We aimed to investigate U. parvum serovar 3 pathogenesis in vivo, using a sheep model, by investigating: MBA variation after long term (chronic) and short term (acute) durations of in utero ureaplasma infections, and the severity of chorioamnionitis and inflammation in other fetal tissues. Inocula of 2x107 colony-forming-units (CFU) of U. parvum serovar 3 (Up) or media controls (C) were injected intra-amniotically into pregnant ewes at one of three time points: day 55 (69d Up, n=8; C69, n=4); day 117 (7d Up, n=8; C7, n=2); and day 121 (3d Up, n=8; C3, n=2) of gestation (term=145-150d). At day 124, preterm fetuses were delivered surgically. Samples of chorioamnion, fetal lung, and umbilical cord were: (i) snap frozen for subsequent ureaplasma culture, and (ii) fixed, embedded, sectioned and stained by haematoxylin and eosin stain for histological analysis. Selected fetal lung clinical ureaplasma isolates were cloned and filtered to obtain cultures from a single CFU. Passage 1 and clone 2 ureaplasma cultures were tested by western blot to demonstrate MBA variation. In acute durations of ureaplasma infection no MBA variants (3d Up) or very few MBA variants (7d Up) were present when compared to the original inoculum. However, numerous MBA size variants were generated in vivo (alike within contiguous tissues, amniotic fluid and fetal lung, but different variants were present within chorioamnion), during chronic, 69d exposure to ureaplasma infection. For the first time we have shown that the degree of ureaplasma MBA variation in vivo increased with the duration of gestation.

Ureaplasma species multiple banded antigen (MBA) variation is associated with the severity of chorioamnionitis in late preterm placentas

Background: Intra-amniotic infection accounts for 30% of all preterm births (PTB), with the human Ureaplasma species being the most frequently identified microorganism from the placentas of women who deliver preterm. The highest prevalence of PTB occurs late preterm (32-36 weeks) but no studies have investigated the role of infectious aetiologies associated with late preterm birth. Method: Placentas from women with late PTB were dissected aseptically and samples of chorioamnion tissue and membrane swabs were collected. These were tested for Ureaplasma spp. and aerobic/anaerobic bacteria by culture and real-time PCR. Western blot was used to assess MBA variation in ureaplasma clinical isolates. The presence of microorganisms was correlated with histological chorioamnionitis. Results: Ureaplasma spp. were isolated from 33/466 (7%) of placentas by culture or PCR. The presence of ureaplasmas, but not other microorganisms, was associated with histological chorioamnionitis (21/33 ureaplasma-positive vs. 8/42 other bacteria; p= 0.001). Ureaplasma clinical isolates demonstrating no MBA variation were associated with histological chorioamnionitis. By contrast, ureaplasmas displaying MBA variation were isolated from placentas with no significant histological chorioamnionitis (p= 0.001). Conclusion: Ureaplasma spp. within placentas delivered late preterm (7%) is associated with histological chorioamnionitis (p = 0.001). Decreased inflammation within chorioamnion was observed when the clinical ureaplasma isolates demonstrated variation of their surface-exposed lipoproteins (MBA). This variation may be a mechanism by which ureaplasmas modulate and evade the host immune response. So whilst ureaplasmas are present intra-amniotically they are not suspected because of the normal macroscopic appearance of the placentas and the amniotic fluid.

Multiple scattering in deep inelastic neutron scattering : Monte Carlo simulations and experiments at the ISIS eVS inverse geometry spectrometer

A procedure for the evaluation of multiple scattering contributions is described, for deep inelastic neutron scattering (DINS) studies using an inverse geometry time-of-flight spectrometer. The accuracy of a Monte Carlo code DINSMS, used to calculate the multiple scattering, is tested by comparison with analytic expressions and with experimental data collected from polythene, polycrystalline graphite and tin samples. It is shown that the Monte Carlo code gives an accurate representation of the measured data and can therefore be used to reliably correct DINS data.

Ingenuity pathways analysis of urine metabonomics phenotypes toxicity of gentamicin in multiple organs

We introduce the use of Ingenuity Pathway Analysis to analyzing global metabonomics in order to characterize phenotypically biochemical perturbations and the potential mechanisms of the gentamicin-induced toxicity in multiple organs. A single dose of gentamicin was administered to Sprague Dawley rats (200 mg/kg, n = 6) and urine samples were collected at -24-0 h pre-dosage, 0-24, 24-48, 48-72 and 72-96 h post-dosage of gentamicin. The urine metabonomics analysis was performed by UPLC/MS, and the mass spectra signals of the detected metabolites were systematically deconvoluted and analyzed by pattern recognition analyses (Heatmap, PCA and PLS-DA), revealing a time-dependency of the biochemical perturbations induced by gentamicin toxicity. As result, the holistic metabolome change induced by gentamicin toxicity in the animal's organisms was characterized. Several metabolites involved in amino acid metabolism were identified in urine, and it was confirmed that gentamicin biochemical perturbations can be foreseen from these biomarkers. Notoriously, it was found that gentamicin induced toxicity in multiple organs system in the laboratory rats. The proof-of-knowledge based Ingenuity Pathway Analysis revealed gentamicin induced liver and heart toxicity, along with the previously known toxicity in kidney. The metabolites creatine, nicotinic acid, prostaglandin E2, and cholic acid were identified and validated as phenotypic biomarkers of gentamicin induced toxicity. Altogether, the significance of the use of metabonomics analyses in the assessment of drug toxicity is highlighted once more; furthermore, this work demonstrated the powerful predictive potential of the Ingenuity Pathway Analysis to study of drug toxicity and its valuable complementation for metabonomics based assessment of the drug toxicity.

Resequencing and fine-mapping of the chromosome 12q13-14 locus associated with multiple sclerosis refines the number of implicated genes

Multiple sclerosis (MS) is a common chronic inflammatory disease of the central nervous system. Susceptibility to the disease is affected by both environmental and genetic factors. Genetic factors include haplotypes in the histocompatibility complex (MHC) and over 50 non-MHC loci reported by genome-wide association studies. Amongst these, we previously reported polymorphisms in chromosome 12q13-14 with a protective effect in individuals of European descent. This locus spans 288 kb and contains 17 genes, including several candidate genes which have potentially significant pathogenic and therapeutic implications. In this study, we aimed to fine-map this locus. We have implemented a two-phase study: a variant discovery phase where we have used next-generation sequencing and two target-enrichment strategies [long-range polymerase chain reaction (PCR) and Nimblegen's solution phase hybridization capture] in pools of 25 samples; and a genotyping phase where we genotyped 712 variants in 3577 healthy controls and 3269 MS patients. This study confirmed the association (rs2069502, P = 9.9 × 10−11, OR = 0.787) and narrowed down the locus of association to an 86.5 kb region. Although the study was unable to pinpoint the key-associated variant, we have identified a 42 (genotyped and imputed) single-nucleotide polymorphism haplotype block likely to harbour the causal variant. No evidence of association at previously reported low-frequency variants in CYP27B1 was observed. As part of the study we compared variant discovery performance using two target-enrichment strategies. We concluded that our pools enriched with Nimblegen's solution phase hybridization capture had better sensitivity to detect true variants than the pools enriched with long-range PCR, whilst specificity was better in the long-range PCR-enriched pools compared with solution phase hybridization capture enriched pools; this result has important implications for the design of future fine-mapping studies.

Circulating microRNAs involved in multiple sclerosis

Multiple sclerosis (MS) is an immune-mediated, demyelinating and neurodegenerative disease of the central nervous system. After traumatic brain injury, it is the leading cause of neurology disability in young adults. Considerable advances have been made in identifying genes involved in MS but the genetic and phenotypic complexity associated with this disease significantly hinders any progress. A novel class of small RNA molecules, microRNAs (miRNAs) has acquired much attention because they regulate the expression of up to 30% of protein-coding genes and may play a pivotal role in the development of many, if not all, complex diseases. Seven published studies investigated miRNAs from peripheral blood mononuclear cells, CD4+, CD8+ T cell, B lymphocytes, peripheral blood leukocytes, whole blood and brain astrocytes with MS risk. The absence of MS studies investigating plasma miRNA prompted the current investigation of identifying a circulating miRNA signature in MS. We conducted a microarray analysis of over 900 known miRNA transcripts from plasma samples collected from four MS individuals and four sex-aged and ethnicity matched healthy controls. We identified six plasma miRNA (miR-614, miR-572, miR-648, miR-1826, miR-422a and miR-22) that were significantly up-regulated and one plasma miRNA (miR-1979) that was significantly down-regulated in MS individuals. Both miR-422a and miR-22 have previously been implicated in MS. The present study is the first to show a circulating miRNA signature involved in MS that could serve as a potential prognostic and diagnostic biomarker for MS.

Modeling the cumulative genetic risk for multiple sclerosis from genome-wide association data

Background: Multiple sclerosis (MS) is the most common cause of chronic neurologic disability beginning in early to middle adult life. Results from recent genome-wide association studies (GWAS) have substantially lengthened the list of disease loci and provide convincing evidence supporting a multifactorial and polygenic model of inheritance. Nevertheless, the knowledge of MS genetics remains incomplete, with many risk alleles still to be revealed. Methods: We used a discovery GWAS dataset (8,844 samples, 2,124 cases and 6,720 controls) and a multi-step logistic regression protocol to identify novel genetic associations. The emerging genetic profile included 350 independent markers and was used to calculate and estimate the cumulative genetic risk in an independent validation dataset (3,606 samples). Analysis of covariance (ANCOVA) was implemented to compare clinical characteristics of individuals with various degrees of genetic risk. Gene ontology and pathway enrichment analysis was done using the DAVID functional annotation tool, the GO Tree Machine, and the Pathway-Express profiling tool. Results: In the discovery dataset, the median cumulative genetic risk (P-Hat) was 0.903 and 0.007 in the case and control groups, respectively, together with 79.9% classification sensitivity and 95.8% specificity. The identified profile shows a significant enrichment of genes involved in the immune response, cell adhesion, cell communication/ signaling, nervous system development, and neuronal signaling, including ionotropic glutamate receptors, which have been implicated in the pathological mechanism driving neurodegeneration. In the validation dataset, the median cumulative genetic risk was 0.59 and 0.32 in the case and control groups, respectively, with classification sensitivity 62.3% and specificity 75.9%. No differences in disease progression or T2-lesion volumes were observed among four levels of predicted genetic risk groups (high, medium, low, misclassified). On the other hand, a significant difference (F = 2.75, P = 0.04) was detected for age of disease onset between the affected misclassified as controls (mean = 36 years) and the other three groups (high, 33.5 years; medium, 33.4 years; low, 33.1 years). Conclusions: The results are consistent with the polygenic model of inheritance. The cumulative genetic risk established using currently available genome-wide association data provides important insights into disease heterogeneity and completeness of current knowledge in MS genetics.

Investigation of a neuronal nitric oxide synthase gene (NOS1) polymorphism in a multiple sclerosis population

Multiple Sclerosis (MS) is a chronic neurological disease characterized by demyelination associated with infiltrating white blood cells in the central nervous system (CNS). Nitric oxide synthases (NOS) are a family of enzymes that control the production of nitric oxide. It is possible that neuronal NOS could be involved in MS pathophysiology and hence the nNOS gene is a potential candidate for involvement in disease susceptibility. The aim of this study was to determine whether allelic variation at the nNOS gene locus is associated with MS in an Australian cohort. DNA samples obtained from a Caucasian Australian population affected with MS and an unaffected control population, matched for gender, age and ethnicity, were genotyped for a microsatellite polymorphism in the promoter region of the nNOS gene. Allele frequencies were compared using chi-squared based statistical analyses with significance tested by Monte Carlo simulation. Allelic analysis of MS cases and controls produced a chi-squared value of 5.63 with simulated P = 0.96 (OR(max) = 1.41, 95% CI: 0.926-2.15). Similarly, a Mann-Whitney U analysis gave a non-significant P-value of 0.377 for allele distribution. No differences in allele frequencies were observed for gender or clinical course subtype (P > 0.05). Statistical analysis indicated that there is no association of this nNOS variant and MS and hence the gene does not appear to play a genetically significant role in disease susceptibility.

Volcanic architecture and chemostratigraphy within the Kalkarindji Continental Flood Basalt Province, Northern Territory : implications for eruption style from multiple fissure eruptions

This thesis was the first to define individual lava flow chemical variation and a detailed definition of the Kalkarindji Continental Flood Basalt Province, a lesser known province of the Phanerozoic eon. This thesis conducted an intensive field study that yielded numerous samples for petrography and chemical analyses as well as the generation of a detailed map of a portion of the Kalkarindji province.

Population variation in biomonitoring data for persistent organic pollutants (POPs) : an examination of multiple population-based datasets for application to Australian pooled biomonitoring data

Background Australian national biomonitoring for persistent organic pollutants (POPs) relies upon age-specific pooled serum samples to characterize central tendencies of concentrations but does not provide estimates of upper bound concentrations. This analysis compares population variation from biomonitoring datasets from the US, Canada, Germany, Spain, and Belgium to identify and test patterns potentially useful for estimating population upper bound reference values for the Australian population. Methods Arithmetic means and the ratio of the 95th percentile to the arithmetic mean (P95:mean) were assessed by survey for defined age subgroups for three polychlorinated biphenyls (PCBs 138, 153, and 180), hexachlorobenzene (HCB), p,p-dichlorodiphenyldichloroethylene (DDE), 2,2′,4,4′ tetrabrominated diphenylether (PBDE 47), perfluorooctanoic acid (PFOA) and perfluorooctane sulfonate (PFOS). Results Arithmetic mean concentrations of each analyte varied widely across surveys and age groups. However, P95:mean ratios differed to a limited extent, with no systematic variation across ages. The average P95:mean ratios were 2.2 for the three PCBs and HCB; 3.0 for DDE; 2.0 and 2.3 for PFOA and PFOS, respectively. The P95:mean ratio for PBDE 47 was more variable among age groups, ranging from 2.7 to 4.8. The average P95:mean ratios accurately estimated age group-specific P95s in the Flemish Environmental Health Survey II and were used to estimate the P95s for the Australian population by age group from the pooled biomonitoring data. Conclusions Similar population variation patterns for POPs were observed across multiple surveys, even when absolute concentrations differed widely. These patterns can be used to estimate population upper bounds when only pooled sampling data are available.