Reducing the number of miscreant tasks executions in a multi-use cluster.

Exploiting computational resources within an organisation for more than their primary task offers great benefits – making better use of capital expenditure and provides a pool of computational power. This can be achieved through the deployment of a cycle stealing distributed system, where tasks execute during the idle time on computers. However, if a task has not completed when a computer returns to its primary function the task will be preempted, wasting time (and energy), and is often reallocated to a new resource in an attempt to complete. This becomes exacerbated when tasks are incapable of completing due to excessive execution time or faulty hardware / software, leading to a situation where tasks are perpetually reallocated between computers – wasting time and energy. In this work we investigate techniques to increase the chance of ‘good’ tasks completing whilst curtailing the execution of ‘bad’ tasks. We demonstrate, through simulation, that we could have reduce the energy consumption of our cycle stealing system by approximately 50%

Measuring Extinction Curves of Lensing Galaxies

We critique the method of constructing extinction curves of lensing galaxies using multiply imaged QSOs. If one of the two QSO images is lightly reddened or if the dust along both sightlines has the same properties then the method works well and produces an extinction curve for the lensing galaxy. These cases are likely rare and hard to confirm. However, if the dust along each sightline has different properties then the resulting curve is no longer a measurement of extinction. Instead, it is a measurement of the difference between two extinction curves. This "lens difference curve'' does contain information about the dust properties, but extracting a meaningful extinction curve is not possible without additional, currently unknown information. As a quantitative example, we show that the combination of two Cardelli, Clayton, & Mathis (CCM) type extinction curves having different values of R(V) will produce a CCM extinction curve with a value of R(V) which is dependent on the individual R(V) values and the ratio of V band extinctions. The resulting lens difference curve is not an average of the dust along the two sightlines. We find that lens difference curves with any value of R(V), even negative values, can be produced by a combination of two reddened sightlines with different CCM extinction curves with R(V) values consistent with Milky Way dust (2.1 < R(V) < 5.6). This may explain extreme values of R(V) inferred by this method in previous studies. But lens difference curves with more normal values of R(V) are just as likely to be composed of two dust extinction curves with R(V) values different than that of the lens difference curve. While it is not possible to determine the individual extinction curves making up a lens difference curve, there is information about a galaxy's dust contained in the lens difference curves.Comment: 15 pages, 4 figues, ApJ in pres

Flood modelling for cities using Cloud computing

Urban flood risk modelling is a highly topical example of intensive computational processing. Such processing is increasingly required by a range of organisations including local government, engineering consultancies and the insurance industry to fulfil statutory requirements and provide professional services. As the demands for this type of work become more common, then ownership of high-end computational resources is warranted but if use is more sporadic and with tight deadlines then the use of Cloud computing could provide a cost-effective alternative. However, uptake of the Cloud by such organisations is often thwarted by the perceived technical barriers to entry. In this paper we present an architecture that helps to simplify the process of performing parameter sweep work on an Infrastructure as a Service Cloud. A parameter sweep version of the urban flood modelling, analysis and visualisation software “CityCat” was developed and deployed to estimate spatial and temporal flood risk at a whole city scale – far larger than had previously been possible. Performing this work on the Cloud allowed us access to more computing power than we would have been able to purchase locally for such a short time-frame (∼21 months of processing in a single calendar month). We go further to illustrate the considerations, both functional and non-functional, which need to be addressed if such an endeavour is to be successfully achieved

Reducing the number of miscreant tasks executions in a multi-use cluster

Exploiting computational resources within an organisation for more than their primary task offers great benefits – making better use of capital expenditure and provides a pool of computational power. This can be achieved through the deployment of a cycle stealing distributed system, where tasks execute during the idle time on computers. However, if a task has not completed when a computer returns to its primary function the task will be preempted, wasting time (and energy), and is often reallocated to a new resource in an attempt to complete. This becomes exacerbated when tasks are incapable of completing due to excessive execution time or faulty hardware / software, leading to a situation where tasks are perpetually reallocated between computers – wasting time and energy. In this work we investigate techniques to increase the chance of ‘good’ tasks completing whilst curtailing the execution of ‘bad’ tasks. We demonstrate, through simulation, that we could have reduce the energy consumption of our cycle stealing system by approximately 50%

Severity scoring of manganese health effects for categorical regression

Characterizing the U-shaped exposure response relationship for manganese (Mn) is necessary for estimating the risk of adverse health from Mn toxicity due to excess or deficiency. Categorical regression has emerged as a powerful tool for exposure-response analysis because of its ability to synthesize relevant information across multiple studies and species into a single integrated analysis of all relevant data. This paper documents the development of a database on Mn toxicity designed to support the application of categorical regression techniques. Specifically, we describe (i) the conduct of a systematic search of the literature on Mn toxicity to gather data appropriate for dose-response assessment; (ii) the establishment of inclusion/exclusion criteria for data to be included in the categorical regression modeling database; (iii) the development of a categorical severity scoring matrix for Mn health effects to permit the inclusion of diverse health outcomes in a single categorical regression analysis using the severity score as the outcome variable; and (iv) the convening of an international expert panel to both review the severity scoring matrix and assign severity scores to health outcomes observed in studies (including case reports, epidemiological investigations, and in vivo experimental studies) selected for inclusion in the categorical regression database. Exposure information including route, concentration, duration, health endpoint(s), and characteristics of the exposed population was abstracted from included studies and stored in a computerized manganese database (MnDB), providing a comprehensive repository of exposure-response information with the ability to support categorical regression modeling of oral exposure data

A genomewide scan for Attention-Deficit/Hyperactivity Disorder in an extended sample: Suggestive linkage on 17p11

Attention-deficit/hyperactivity disorder (ADHD [MIM 143465]) is a common, highly heritable neurobehavioral disorder of childhood onset, characterized by hyperactivity, impulsivity, and/or inattention. As part of an ongoing study of the genetic etiology of ADHD, we have performed a genomewide linkage scan in 204 nuclear families comprising 853 individuals and 270 affected sibling pairs (ASPs). Previously, we reported genomewide linkage analysis of a “first wave” of these families composed of 126 ASPs. A follow-up investigation of one region on 16p yielded significant linkage in an extended sample. The current study extends the original sample of 126 ASPs to 270 ASPs and provides linkage analyses of the entire sample, using polymorphic microsatellite markers that define an ∼10-cM map across the genome. Maximum LOD score (MLS) analysis identified suggestive linkage for 17p11 (MLS=2.98) and four nominal regions with MLS values >1.0, including 5p13, 6q14, 11q25, and 20q13. These data, taken together with the fine mapping on 16p13, suggest two regions as highly likely to harbor risk genes for ADHD: 16p13 and 17p11. Interestingly, both regions, as well as 5p13, have been highlighted in genomewide scans for autism

Atypical parkinsonism-associated retromer mutant alters endosomal sorting of specific cargo proteins

The retromer complex acts as a scaffold for endosomal protein complexes that sort integral membrane proteins to various cellular destinations. The retromer complex is a heterotrimer of VPS29, VPS35, and VPS26. Two of these paralogues, VPS26A and VPS26B, are expressed in humans. Retromer dysfunction is associated with neurodegenerative disease, and recently, three VPS26A mutations (p.K93E, p.M112V, and p.K297X) were discovered to be associated with atypical parkinsonism. Here, we apply quantitative proteomics to provide a detailed description of the retromer interactome. By establishing a comparative proteomic methodology, we identify how this interactome is perturbed in atypical parkinsonism-associated VPS26A mutants. In particular, we describe a selective defect in the association of VPS26A (p.K297X) with the SNX27 cargo adaptor. By showing how a retromer mutant leads to altered endosomal sorting of specific PDZ ligand–containing cargo proteins, we reveal a new mechanism for perturbed endosomal cargo sorting in atypical parkinsonism

High loading of polygenic risk for ADHD in children with comorbid aggression

Objective: Although attention deficit hyperactivity disorder (ADHD) is highly heritable, genome-wide association studies (GWAS) have not yet identified any common genetic variants that contribute to risk. There is evidence that aggression or conduct disorder in children with ADHD indexes higher genetic loading and clinical severity. The authors examine whether common genetic variants considered en masse as polygenic scores for ADHD are especially enriched in children with comorbid conduct disorder. Method: Polygenic scores derived from an ADHD GWAS meta-analysis were calculated in an independent ADHD sample (452 case subjects, 5,081 comparison subjects). Multivariate logistic regression analyses were employed to compare polygenic scores in the ADHD and comparison groups and test for higher scores in ADHD case subjects with comorbid conduct disorder relative to comparison subjects and relative to those without comorbid conduct disorder. Association with symptom scores was tested using linear regression. Results: Polygenic risk for ADD, derived from the meta-analysis, was higher in the independent ADHD group than in the comparison group. Polygenic score was significantly higher in ADHD case subjects with conduct disorder relative to ADHD case subjects without conduct disorder. ADHD polygenic score showed significant association with comorbid conduct disorder symptoms. This relationship was explained by,the aggression items. Conclusions: Common genetic variation is relevant to ADHD, especially in individuals with comorbid aggression. The findings suggest that the previously published ADHD GWAS meta-analysis contains weak but true associations with common variants, support for which falls below genome-wide significance levels. The findings also highlight the fact that aggression in ADHD indexes genetic as well as clinical severity

Interstellar Silicate Dust in the z=0.89 Absorber Towards PKS 1830-211: Crystalline Silicates at High Redshift?

We present evidence of a >10-sigma detection of the 10 micron silicate dust absorption feature in the spectrum of the gravitationally lensed quasar PKS 1830-211, produced by a foreground absorption system at redshift 0.886. We have examined more than 100 optical depth templates, derived from both observations of Galactic and extragalactic sources and laboratory measurements, in order to constrain the chemical structure of the silicate dust. We find that the best fit to the observed absorption profile is produced by laboratory crystalline olivine, with a corresponding peak optical depth of tau_10=0.27+/-0.05. The fit is slightly improved upon by including small contributions from additional materials such as silica, enstatite, or serpentine, which suggests that the dust composition may consist of a blend of crystalline silicates. Combining templates for amorphous and crystalline silicates, we find that the fraction of crystalline silicates needs to be at least 95%. Given the rarity of extragalactic sources with such a high degree of silicate crystallinity, we also explore the possibility that the observed spectral features are produced by amorphous silicates in combination with other molecular or atomic transitions, or by foreground source contamination. While we cannot rule out these latter possibilities, they lead to much poorer profile fits than for the crystalline olivine templates. If the presence of crystalline interstellar silicates in this distant galaxy is real, it would be highly unusual, given that the Milky Way interstellar matter contains essentially only amorphous silicates. It is possible that the z=0.886 absorber towards PKS 1830-211, well known for its high molecular content, has a unique star-forming environment that enables crystalline silicates to form and prevail.Comment: 67 pages, 21 figures, accepted for publication in the Astrophysical Journa

Monitoring internet trade to inform species conservation actions

Specimens, parts and products of threatened species are now commonly traded on the internet. This could threaten the survival of some wild populations if inadequately regulated. We outline two methods to monitor internet sales of threatened species in order to assess potential threats and inform conservation actions. Our first method combines systematic monitoring of online offers of plants for sale over the internet with consultation by experts experienced in identifying plants collected from the wild based on images of the specimens, species identity and details of the trade. Our second method utilises a computational model, trained using Bayesian techniques to records that have been classified by an expert as wild collected or artificially propagated, to predict unknown properties of the traded taxa, such as whether a species being sold is collected from the wild or the identity of an unknown wild collected species. We used these methods to monitor internet trade in five genera of succulent plant species endemic to Madagascar, for which some have recently been listed for trade regulation under the Convention on International Trade in Endangered Species (CITES). This revealed potential threats to wild populations: for instance, almost all species recorded were of high conservation concern yet most offers for live plants were of apparently wild collected specimens (85%). Moreover, no records of international trade in the official CITES database were from the countries featured in our survey. Our model predicted with 89% accuracy whether the live plants were classified as propagated or wild collected by an expert, although accuracy dropped for data collected in the following summer due to a change in the patterns of sales. Our results highlight potential threats by internet trade to the survival of some CITES and non-CITES listed plant species from Madagascar. These should be addressed by further conservation actions and policy. More generally, our results reveal how standardised internet surveys can provide information on levels of trade in wild collected threatened species that could impact on natural populations and can provide data that can be incorporated into models to facilitate future monitoring and enforcement