Tensor-based analysis of genetic influences on brain integrity using DTI in 100 twins

Information from the full diffusion tensor (DT) was used to compute voxel-wise genetic contributions to brain fiber microstructure. First, we designed a new multivariate intraclass correlation formula in the log-Euclidean framework. We then analyzed used the full multivariate structure of the tensor in a multivariate version of a voxel-wise maximum-likelihood structural equation model (SEM) that computes the variance contributions in the DTs from genetic (A), common environmental (C) and unique environmental (E) factors. Our algorithm was tested on DT images from 25 identical and 25 fraternal twin pairs. After linear and fluid registration to a mean template, we computed the intraclass correlation and Falconer's heritability statistic for several scalar DT-derived measures and for the full multivariate tensors. Covariance matrices were found from the DTs, and inputted into SEM. Analyzing the full DT enhanced the detection of A and C effects. This approach should empower imaging genetics studies that use DTI.

Automatic clustering and population analysis of white matter tracts using maximum density paths

We introduce a framework for population analysis of white matter tracts based on diffusion-weighted images of the brain. The framework enables extraction of fibers from high angular resolution diffusion images (HARDI); clustering of the fibers based partly on prior knowledge from an atlas; representation of the fiber bundles compactly using a path following points of highest density (maximum density path; MDP); and registration of these paths together using geodesic curve matching to find local correspondences across a population. We demonstrate our method on 4-Tesla HARDI scans from 565 young adults to compute localized statistics across 50 white matter tracts based on fractional anisotropy (FA). Experimental results show increased sensitivity in the determination of genetic influences on principal fiber tracts compared to the tract-based spatial statistics (TBSS) method. Our results show that the MDP representation reveals important parts of the white matter structure and considerably reduces the dimensionality over comparable fiber matching approaches.

Identifying candidate gene effects by restricting search space in a multivariate genetic analysis of white matter microstructure

Several genetic variants are thought to influence white matter (WM) integrity, measured with diffusion tensor imaging (DTI). Voxel based methods can test genetic associations, but heavy multiple comparisons corrections are required to adjust for searching the whole brain and for all genetic variants analyzed. Thus, genetic associations are hard to detect even in large studies. Using a recently developed multi-SNP analysis, we examined the joint predictive power of a group of 18 cholesterol-related single nucleotide polymorphisms (SNPs) on WM integrity, measured by fractional anisotropy. To boost power, we limited the analysis to brain voxels that showed significant associations with total serum cholesterol levels. From this space, we identified two genes with effects that replicated in individual voxel-wise analyses of the whole brain. Multivariate analyses of genetic variants on a reduced anatomical search space may help to identify SNPs with strongest effects on the brain from a broad panel of genes.

Wastewater analysis of Census day samples to investigate per capita input of organophosphorus flame retardants and plasticizers into wastewater

The use of organophosphate esters (PFRs) as flame retardants and plasticizers has increased due to the ban of some brominated flame retardants. There is however some concern regarding the toxicity, particularly carcinogenicity and neurotoxicity, of some of the PFRs. In this study we applied wastewater analysis to assess use of PFRs by the Australian population. Influent samples were collected from eleven wastewater treatment plants (STPs) in Australia on Census day and analysed for PFRs using gas chromatography coupled with mass spectrometry (GC-MS). Per capita mass loads of PFRs were calculated using the accurate Census head counts. The results indicate that tris(2-butoxyethyl) phosphate (TBOEP) has the highest per capita input into wastewater followed by tris(2-chloroisopropyl) phosphate (TCIPP), tris(isobutyl) phosphate (TIBP), tris(2-chloroethyl) phosphate (TCEP) and tris(1,3-dichloroisopropyl) phosphate (TDCIPP). Similar PFR profiles were observed across the Australian STPs and a comparison with European and U.S. STPs indicated similar PFR concentrations. We estimate that approximately 2.1 mg person−1 day−1 of PFRs are input into Australian wastewater which equates to 16 tonnes per annum.

A "Candidate-Interactome" Aggregate Analysis of Genome-Wide Association Data in Multiple Sclerosis

Though difficult, the study of gene-environment interactions in multifactorial diseases is crucial for interpreting the relevance of non-heritable factors and prevents from overlooking genetic associations with small but measurable effects. We propose a "candidate interactome" (i.e. a group of genes whose products are known to physically interact with environmental factors that may be relevant for disease pathogenesis) analysis of genome-wide association data in multiple sclerosis. We looked for statistical enrichment of associations among interactomes that, at the current state of knowledge, may be representative of gene-environment interactions of potential, uncertain or unlikely relevance for multiple sclerosis pathogenesis: Epstein-Barr virus, human immunodeficiency virus, hepatitis B virus, hepatitis C virus, cytomegalovirus, HHV8-Kaposi sarcoma, H1N1-influenza, JC virus, human innate immunity interactome for type I interferon, autoimmune regulator, vitamin D receptor, aryl hydrocarbon receptor and a panel of proteins targeted by 70 innate immune-modulating viral open reading frames from 30 viral species. Interactomes were either obtained from the literature or were manually curated. The P values of all single nucleotide polymorphism mapping to a given interactome were obtained from the last genome-wide association study of the International Multiple Sclerosis Genetics Consortium & the Wellcome Trust Case Control Consortium, 2. The interaction between genotype and Epstein Barr virus emerges as relevant for multiple sclerosis etiology. However, in line with recent data on the coexistence of common and unique strategies used by viruses to perturb the human molecular system, also other viruses have a similar potential, though probably less relevant in epidemiological terms. © 2013 Mechelli et al.

Systematic review and meta-analysis of the effects of exercise for those with cancer-related lymphedema

Objective: To evaluate the effects of exercise on cancer-related lymphedema and related symptoms, and to determine the need for those with lymphedema to wear compression during exercise. Data Sources: CINAHL, Cochrane, Ebscohost, MEDLINE, Pubmed, ProQuest Health and Medical Complete, ProQuest Nursing and Allied Health Source, Science Direct and SPORTDiscus databases were searched for trials published prior to 1 January, 2015. Study Selection: Randomised and non-randomised, controlled trials, and single group pre-post studies published in English-language were included. Twenty-one (exercise) and four (compression and exercise) studies met inclusion criteria. Data Extraction: Data was extracted into tabular format using predefined data fields by one reviewer and assessed for accuracy by a second reviewer. Study quality was evaluated using the Effective Public Health Practice Project assessment tool. Data Synthesis: Data was pooled using a random effects model to assess the effects of acute and long-term exercise on lymphedema and lymphedema-associated symptoms, with subgroup analyses for exercise mode and intervention length. There was no effect of exercise (acute or intervention) on lymphedema or associated symptoms with standardised mean differences from all analyses ranging between −0.2 and 0.1 (p-values ≥0.22). Findings from subgroup analyses for exercise mode (aerobic, resistance, mixed, other) and intervention duration (>12 weeks or ≤12 weeks) were consistent with these findings; that is, no effect on lymphedema or associated symptoms. There were too few studies evaluating the effect of compression during regular exercise to conduct a meta-analysis. Conclusions: Individuals with secondary lymphedema can safely participate in progressive, regular exercise without experiencing a worsening of lymphedema or related-symptoms. However, the results also do not suggest any improvements will occur in lymphedema. At present, there is insufficient evidence to support or refute the current clinical recommendation to wear compression garments during regular exercise.

Immunochip analysis identifies multiple susceptibility loci for systemic sclerosis

In this study, 1,833 systemic sclerosis (SSc) cases and 3,466 controls were genotyped with the Immunochip array. Classical alleles, amino acid residues, and SNPs across the human leukocyte antigen (HLA) region were imputed and tested. These analyses resulted in a model composed of six polymorphic amino acid positions and seven SNPs that explained the observed significant associations in the region. In addition, a replication step comprising 4,017 SSc cases and 5,935 controls was carried out for several selected non-HLA variants, reaching a total of 5,850 cases and 9,401 controls of European ancestry. Following this strategy, we identified and validated three SSc risk loci, including DNASE1L3 at 3p14, the SCHIP1-IL12A locus at 3q25, and ATG5 at 6q21, as well as a suggested association of the TREH-DDX6 locus at 11q23. The associations of several previously reported SSc risk loci were validated and further refined, and the observed peak of association in PXK was related to DNASE1L3. Our study has increased the number of known genetic associations with SSc, provided further insight into the pleiotropic effects of shared autoimmune risk factors, and highlighted the power of dense mapping for detecting previously overlooked susceptibility loci.

Prospective meta-analysis of interleukin 1 gene complex polymorphisms confirms associations with ankylosing spondylitis

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.

Whole-genome linkage analysis of rheumatoid arthritis susceptibility loci in 252 affected sibling pairs in the United Kingdom

Objective. To undertake a systematic wholegenome screen to identify regions exhibiting genetic linkage to rheumatoid arthritis (RA). Methods. Two hundred fifty-two RA-affected sibling pairs from 182 UK families were genotyped using 365 highly informative microsatellite markers. Microsatellite genotyping was performed using fluorescent polymerase chain reaction primers and semiautomated DNA sequencing technology. Linkage analysis was undertaken using MAPMAKER/SIBS for single-point and multipoint analysis. Results. Significant linkage (maximum logarithm of odds score 4.7 [P = 0.000003] at marker D6S276, 1 cM from HLA-DRB1) was identified around the major histocompatibility complex (MHC) region on chromosome 6. Suggestive linkage (P < 7.4 × 10-4) was identified on chromosome 6q by single- and multipoint analysis. Ten other sites of nominal linkage (P < 0.05) were identified on chromosomes 3p, 4q, 7p, 2 regions of 10q, 2 regions of 14q, 16p, 21q, and Xq by single-point analysis and on 3 sites (1q, 14q, and 14q) by multipoint analysis. Conclusion. Linkage to the MHC region was confirmed. Eleven non-HLA regions demonstrated evidence of suggestive or nominal linkage, but none reached the genome-wide threshold for significant linkage (P = 2.2 × 10-5). Results of previous genome screens have suggested that 6 of these regions may be involved in RA susceptibility.

Robust principal component analysis in water quality index development

Some statistical procedures already available in literature are employed in developing the water quality index, WQI. The nature of complexity and interdependency that occur in physical and chemical processes of water could be easier explained if statistical approaches were applied to water quality indexing. The most popular statistical method used in developing WQI is the principal component analysis (PCA). In literature, the WQI development based on the classical PCA mostly used water quality data that have been transformed and normalized. Outliers may be considered in or eliminated from the analysis. However, the classical mean and sample covariance matrix used in classical PCA methodology is not reliable if the outliers exist in the data. Since the presence of outliers may affect the computation of the principal component, robust principal component analysis, RPCA should be used. Focusing in Langat River, the RPCA-WQI was introduced for the first time in this study to re-calculate the DOE-WQI. Results show that the RPCA-WQI is capable to capture similar distribution in the existing DOE-WQI.

Analysis of effects of axial shortening of steel columns in frame structure

Steel columns in frame structure always carry heavy upcoming compressive forces. As a consequence, axial shortening becomes a common phenomenon in a multistoried steel structure. A 100 storied steel structure is analyzed in SAP2000 to study the magnitude overall effects of column shortening. It was found from the study that the maximum axial shortening occurs at the columns of top storey of the steel structure and at the columns of bottom storey, the axial deformation is negligible. The increasing rate of axial shortening is significant at the initial levels. However, at the upper levels, the amount of axial shortening in steel columns differs insignificantly. In the selected rigid frame structure, the axial shortening of adjacent steel columns is found to influence significantly the differential shortening of the structure. The consequent effect of differential shortening leads to develop excessive stress in the corner joints which ultimately hamper the normal behavior of the structural systems. The results are discussed elaborately in the paper.

A CCG virtual system for big data application communication costs analysis

Network topology and routing are two important factors in determining the communication costs of big data applications at large scale. As for a given Cluster, Cloud, or Grid system, the network topology is fixed and static or dynamic routing protocols are preinstalled to direct the network traffic. Users cannot change them once the system is deployed. Hence, it is hard for application developers to identify the optimal network topology and routing algorithm for their applications with distinct communication patterns. In this study, we design a CCG virtual system (CCGVS), which first uses container-based virtualization to allow users to create a farm of lightweight virtual machines on a single host. Then, it uses software-defined networking (SDN) technique to control the network traffic among these virtual machines. Users can change the network topology and control the network traffic programmingly, thereby enabling application developers to evaluate their applications on the same system with different network topologies and routing algorithms. The preliminary experimental results through both synthetic big data programs and NPB benchmarks have shown that CCGVS can represent application performance variations caused by network topology and routing algorithm.

An analysis of ethical issues in using wastewater analysis to monitor illicit drug use

Aims To discuss ethical issues that may arise in using WWA to monitor illicit drug use in the general population and in entertainment precincts, prisons, schools and work-places. Method Review current applications of WWA and identify ethical and social issues that may be raised with current and projected future uses of this method. Results Wastewater analysis (WWA) of drug residues is a promising method of monitoring illicit drug use that may overcome some limitations of other monitoring methods. When used for monitoring purposes in large populations, WWA does not raise major ethical concerns because individuals are not identified and the prospects of harming residents of catchment areas are remote. When WWA is used in smaller catchment areas (entertainment venues, prisons, schools or work-places) their results could, possibly, indirectly affect the occupants adversely. Researchers will need to take care in reporting their results to reduce media misreporting. Fears about possible use of WWA for mass individual surveillance by drug law enforcement officials are unlikely to be realized, but will need to be addressed because they may affect public support adversely for this type of research. Conclusions Using wastewater analysis to monitor illicit drug use in large populations does not raise major ethical concerns, but researchers need to minimize possible adverse consequences in studying smaller populations, such as workers, prisoners and students.

Monitoring illicit drug use through wastewater analysis in south east Queensland, Australia

Introduction and Aims: Wastewater analysis has become a useful technique for monitoring illicit drug use in communities. Findings have been reported from different countries in Europe and North America. We applied this technique to gauge the illicit drug consumption in an urban catchment from South East Queensland, Australia. Design and Methods: The sampling campaigns were conducted in 2009 (21st November – 2nd December) and 2010 (19th – 25th November). We collected daily composite wastewater samples from the inlet of the sewage treatment plant using continuous flow-proportional sampling. Ten illicit drug residues (parent compounds and key metabolites) in the samples were measured using liquid chromatography coupled to tandem mass spectrometer. Results: Seven compounds were quantified in all the samples. Our data indicated higher drug consumption on weekends. Cannabis was the highest used drug in both sampling periods. Compared to the first sampling campaign which indicated that cocaine and methamphetamine use exceeded ecstasy usage, the second sampling campaign suggested the use of methamphetamine exceeded that of ecstasy which in turn exceeded cocaine use. Discussion and Conclusions: The observed weekly trend of drug use in our study is in agreement with findings in other studies. The variation between two sampling periods in the prevalence of drug use may relate to the availability and prices of the drugs on markets. The cocaine use we estimated in 2009 was much greater than estimations obtained through the national household survey [1], implying under- reporting of cocaine use in surveys. Future work is underway to tackle methodological challenges for more accurate estimation.

Illicit drug use between an annual key holiday and a control period: Wastewater analysis in a semi-rural, an urban and a vacation area

Introduction and Aims: Holiday periods are potentially a time for increased substance use as social events and private parties are more common. Data on community illicit drug consumption during holiday periods are limited. Besides existing methods for determining drug use, such as population surveys, one emerging method is to measure illicit drugs and/or their metabolites in wastewater samples. This study examined the change in consumption of cannabis, methamphetamine, cocaine and 3,4- methylenedioxymethamphetamine in three different types of areas (an inland semi-rural area, a coastal urban area and a vacation island) with respect to holiday times. Design and Methods: Samples were collected at the inlet of the major wastewater treatment plant in each area during a key annual holiday (i.e. the summer holiday including Christmas and New Year) and control period. Illicit drug residues in the daily composited samples were measured by liquid chromatography coupled with tandem mass spectrometry. Results: Drug use varied substantially among the three areas within each monitoring period as well as between the holiday and control period within each area. Use consistently increased and peaked over New Year particularly for cocaine and 3,4-methylenedioxymethamphetamine whereas cannabis and methamphetamine were relatively less subjected to holiday times in all the areas. Discussion and Conclusions: Wastewater sampling and analysis provides higher spatio-temporal resolution than national surveys and supplements drug epidemiology studies originating primary in metropolitan locations. Such data is essential for policy makers to plan potential intervention strategies associated with these illicit substances in regional areas and other settings besides urban areas in the future.