Genome-wide association study of CNVs in 16,000 cases of eight common diseases and 3,000 shared controls

Copy number variants (CNVs) account for a major proportion of human genetic polymorphism and have been predicted to have an important role in genetic susceptibility to common disease. To address this we undertook a large, direct genome-wide study of association between CNVs and eight common human diseases. Using a purpose-designed array we typed 19,000 individuals into distinct copy-number classes at 3,432 polymorphic CNVs, including an estimated 50% of all common CNVs larger than 500 base pairs. We identified several biological artefacts that lead to false-positive associations, including systematic CNV differences between DNAs derived from blood and cell lines. Association testing and follow-up replication analyses confirmed three loci where CNVs were associated with diseaseIRGM for Crohns disease, HLA for Crohns disease, rheumatoid arthritis and type 1 diabetes, and TSPAN8 for type 2 diabetesalthough in each case the locus had previously been identified in single nucleotide polymorphism (SNP)-based studies, reflecting our observation that most common CNVs that are well-typed on our array are well tagged by SNPs and so have been indirectly explored through SNP studies. We conclude that common CNVs that can be typed on existing platforms are unlikely to contribute greatly to the genetic basis of common human diseases. © 2010 Macmillan Publishers Limited. All rights reserved.

Effects of PTPN22 C1858T polymorphism on susceptibility and clinical characteristics of British Caucasian rheumatoid arthritis patients

Objectives. To confirm the association of a functional single-nucleotide polymorphism (SNP), C1858T (rs2476601), in the PTPN22 gene of British Caucasian rheumatoid arthritis (RA) patients and to evaluate its influence on the RA phenotype. Methods. A total of 686 RA patients and 566 healthy volunteers, all of British Caucasian origin, were genotyped for C1858T polymorphism by PCR-restriction fragment length polymorphism assay. Data were analysed using SPSS software and the χ 2 test as applicable. Results. The PTPN22 1858T risk allele was more prevalent in the RA patients (13.9%) compared with the healthy controls (10.3%) (P = 0.008, odds ratio 1.4, 95% confidence interval 1.09-1.79). The association of the T allele was restricted to those with rheumatoid factor (RF)-positive disease (n = 524, 76.4%) (P = 0.004, odds ratio 1.5, 95% confidence interval 1.1-1.9). We found no association between PTPN22 and the presence of the HLA-DRB1 shared epitope or clinical characteristics. Conclusions. We confirmed the previously reported association of PTPN22 with RF-positive RA, which was independent from the HLA-DRB1 genotype.

Turning gaming EEG peripherals into trainable brain computer interfaces

Companies such as NeuroSky and Emotiv Systems are selling non-medical EEG devices for human computer interaction. These devices are significantly more affordable than their medical counterparts, and are mainly used to measure levels of engagement, focus, relaxation and stress. This information is sought after for marketing research and games. However, these EEG devices have the potential to enable users to interact with their surrounding environment using thoughts only, without activating any muscles. In this paper, we present preliminary results that demonstrate that despite reduced voltage and time sensitivity compared to medical-grade EEG systems, the quality of the signals of the Emotiv EPOC neuroheadset is sufficiently good in allowing discrimina tion between imaging events. We collected streams of EEG raw data and trained different types of classifiers to discriminate between three states (rest and two imaging events). We achieved a generalisation error of less than 2% for two types of non-linear classifiers.

Automating marine mammal detection in aerial images captured during wildlife surveys: A deep learning approach

Aerial surveys conducted using manned or unmanned aircraft with customized camera payloads can generate a large number of images. Manual review of these images to extract data is prohibitive in terms of time and financial resources, thus providing strong incentive to automate this process using computer vision systems. There are potential applications for these automated systems in areas such as surveillance and monitoring, precision agriculture, law enforcement, asset inspection, and wildlife assessment. In this paper, we present an efficient machine learning system for automating the detection of marine species in aerial imagery. The effectiveness of our approach can be credited to the combination of a well-suited region proposal method and the use of Deep Convolutional Neural Networks (DCNNs). In comparison to previous algorithms designed for the same purpose, we have been able to dramatically improve recall to more than 80% and improve precision to 27% by using DCNNs as the core approach.

Monte Carlo analysis of a puzzle game

When a puzzle game is created, its design parameters must be chosen to allow solvable and interesting challenges to be created for the player. We investigate the use of random sampling as a computationally inexpensive means of automated game analysis, to evaluate the BoxOff family of puzzle games. This analysis reveals useful insights into the game, such as the surprising fact that almost 100% of randomly generated challenges have a solution, but less than 10% will be solved using strictly random play, validating the inventor’s design choices. We show the 1D game to be trivial and the 3D game to be viable.

Path algebra for mobile robots

In this paper, we introduce a path algebra well suited for navigation in environments that can be abstracted as topological graphs. From this path algebra, we derive algorithms to reduce routes in such environments. The routes are reduced in the sense that they are shorter (contain fewer edges), but still connect the endpoints of the initial routes. Contrary to planning methods descended from Disjktra’s Shortest Path Algorithm like D , the navigation methods derived from our path algebra do not require any graph representation. We prove that the reduced routes are optimal when the graphs are without cycles. In the case of graphs with cycles, we prove that whatever the length of the initial route, the length of the reduced route is bounded by a constant that only depends on the structure of the environment.

Palliative care problem severity score: Reliability and acceptability in a national study

Background The Palliative Care Problem Severity Score is a clinician-rated tool to assess problem severity in four palliative care domains (pain, other symptoms, psychological/spiritual, family/carer problems) using a 4-point categorical scale (absent, mild, moderate, severe). Aim To test the reliability and acceptability of the Palliative Care Problem Severity Score. Design: Multi-centre, cross-sectional study involving pairs of clinicians independently rating problem severity using the tool. Setting/participants Clinicians from 10 Australian palliative care services: 9 inpatient units and 1 mixed inpatient/community-based service. Results A total of 102 clinicians participated, with almost 600 paired assessments completed for each domain, involving 420 patients. A total of 91% of paired assessments were undertaken within 2 h. Strength of agreement for three of the four domains was moderate: pain (Kappa = 0.42, 95% confidence interval = 0.36 to 0.49); psychological/spiritual (Kappa = 0.48, 95% confidence interval = 0.42 to 0.54); family/carer (Kappa = 0.45, 95% confidence interval = 0.40 to 0.52). Strength of agreement for the remaining domain (other symptoms) was fair (Kappa = 0.38, 95% confidence interval = 0.32 to 0.45). Conclusion The Palliative Care Problem Severity Score is an acceptable measure, with moderate reliability across three domains. Variability in inter-rater reliability across sites and participant feedback indicate that ongoing education is required to ensure that clinicians understand the purpose of the tool and each of its domains. Raters familiar with the patient they were assessing found it easier to assign problem severity, but this did not improve inter-rater reliability.

Use of the FAR guide to present a pedagogical analogical model of gel electrophoresis in year 10 Science

This paper is a qualitative, practice based study describing the use of the Focus-Action-Reflection (FAR) Guide (Harrison and Treagust, 2000) to address the shortcomings of a pedagogical analogical model in Year 10 Science. The aim of this paper is to present my experience of the FAR Guide in relation to an analogical model that gave rise to perceived shortcomings by both teachers and students. This study found the FAR Guide to be a highly valuable tool, transforming the presentation of the analogical model, and enabling students to develop a deeper understanding of the nature of scientific knowledge.

Traffic and nucleation events as main sources of ultrafine particles in high-insolation developed world cities

Road traffic emissions are often considered the main source of ultrafine particles (UFP, diameter smaller than 100 nm) in urban environments. However, recent studies worldwide have shown that - in high-insolation urban regions at least - new particle formation events can also contribute to UFP. In order to quantify such events we systematically studied three cities located in predominantly sunny environments: Barcelona (Spain), Madrid (Spain) and Brisbane (Australia). Three long term datasets (1-2 years) of fine and ultrafine particle number size distributions (measured by SMPS, Scanning Mobility Particle Sizer) were analysed. Compared to total particle number concentrations, aerosol size distributions offer far more information on the type, origin and atmospheric evolution of the particles. By applying k-Means clustering analysis, we categorized the collected aerosol size distributions in three main categories: “Traffic” (prevailing 44-63% of the time), “Nucleation” (14-19%) and “Background pollution and Specific cases” (7-22%). Measurements from Rome (Italy) and Los Angeles (California) were also included to complement the study. The daily variation of the average UFP concentrations for a typical nucleation day at each site revealed a similar pattern for all cities, with three distinct particle bursts. A morning and an evening spike reflected traffic rush hours, whereas a third one at midday showed nucleation events. The photochemically nucleated particles burst lasted 1-4 hours, reaching sizes of 30-40 nm. On average, the occurrence of particle size spectra dominated by nucleation events was 16% of the time, showing the importance of this process as a source of UFP in urban environments exposed to high solar radiation. On average, nucleation events lasting for 2 hours or more occurred on 55% of the days, this extending to >4hrs in 28% of the days, demonstrating that atmospheric conditions in urban environments are not favourable to the growth of photochemically nucleated particles. In summary, although traffic remains the main source of UFP in urban areas, in developed countries with high insolation urban nucleation events are also a main source of UFP. If traffic-related particle concentrations are reduced in the future, nucleation events will likely increase in urban areas, due to the reduced urban condensation sinks.

Organosolv pretreatment of plant biomass for enhanced enzymatic saccharification

The combination of dwindling petroleum reserves and population growth make the development of renewable energy and chemical resources more pressing than ever before. Plant biomass is the most abundant renewable source for energy and chemicals. Enzymes can selectively convert the polysaccharides in plant biomass into simple sugars which can then be upgraded to liquid fuels and platform chemicals using biological and/or chemical processes. Pretreatment is essential for efficient enzymatic saccharification of plant biomass and this article provides an overview of how organic solvent (organosolv) pretreatments affect the structure and chemistry of plant biomass, and how these changes enhance enzymatic saccharification. A comparison between organosolv pretreatments utilizing broadly different classes of solvents (i.e., low boiling point, high boiling point, and biphasic) is presented, with a focus on solvent recovery and formation of by-products. The reaction mechanisms that give rise to these by-products are investigated and strategies to minimize by-product formation are suggested. Finally, process simulations of organosolv pretreatments are compared and contrasted, and discussed in the context of an industrial-scale plant biomass to fermentable sugar process.

Evaluation of the Red Light Camera Program and the Owner Onus Legislation

Red light cameras were introduced in Victoria in August 1983, with the intention of reducing the number of accidents that result from motorists disobeying red traffic signals at signalised intersections. Accident data from 46 treated and 46 control sites from 1981 to 1986 were analysed. The analysis indicated that red light camera use resulted in a reduction in the incidence of right angle accidents, and in the number of accident casualties. Legislation was introduced in March 1986 to place the onus for red light camera offences onto the vehicle owner. This legislation was intended to improve Police efficiency and therefore increase the number of red light cameras in operation. Data supplied by the Police indicated that these aims have beneficial road safety effects.

Bayesian model comparison in cosmology with Population Monte Carlo

We use Bayesian model selection techniques to test extensions of the standard flat LambdaCDM paradigm. Dark-energy and curvature scenarios, and primordial perturbation models are considered. To that end, we calculate the Bayesian evidence in favour of each model using Population Monte Carlo (PMC), a new adaptive sampling technique which was recently applied in a cosmological context. The Bayesian evidence is immediately available from the PMC sample used for parameter estimation without further computational effort, and it comes with an associated error evaluation. Besides, it provides an unbiased estimator of the evidence after any fixed number of iterations and it is naturally parallelizable, in contrast with MCMC and nested sampling methods. By comparison with analytical predictions for simulated data, we show that our results obtained with PMC are reliable and robust. The variability in the evidence evaluation and the stability for various cases are estimated both from simulations and from data. For the cases we consider, the log-evidence is calculated with a precision of better than 0.08. Using a combined set of recent CMB, SNIa and BAO data, we find inconclusive evidence between flat LambdaCDM and simple dark-energy models. A curved Universe is moderately to strongly disfavoured with respect to a flat cosmology. Using physically well-motivated priors within the slow-roll approximation of inflation, we find a weak preference for a running spectral index. A Harrison-Zel'dovich spectrum is weakly disfavoured. With the current data, tensor modes are not detected; the large prior volume on the tensor-to-scalar ratio r results in moderate evidence in favour of r=0.

Effective earth and space science analogies

There are several good reasons why Earth and Space Science should be a part of any science curriculum. Nearly everything we do each day is connected in some way to the Earth: to its land, oceans, atmosphere, plants and animals. By 2025, eight billion people will live on Earth. If we are to continue extracting resources to maintain a high quality of life, then it is important that our children are scientifically literate in a way that allows them to exploit the Earth’s resources in a sustainable way. People who understand how earth systems work can make informed decisions and may be able to help resolve issues surrounding clean water, urban planning and development, global climate change and the use and management of natural resources.

The construction of a taxonomy of early childhood educators’ work

Despite considerable research on the impact of early childhood education (ECE), there is little empirical evidence about what the everyday work of educators entails. This paper reports on the development of a tool to capture generalisable data on the everyday work of educators so as to inform effective workforce policy. This tool—a taxonomy of early childhood educators’ work—was developed by drawing on the expertise of six early years’ experts from Australia and the United States of America (USA) and includes time-use diaries, focus groups and interviews with 21 early childhood educators working in long day care and preschool services. The taxonomy, which we present here, consists of 10 domains, each with a number of sub-classes. We propose that this taxonomy is a useful codification system for ascertaining the everyday work tasks, activities and actions of early childhood educators in diverse early years’ settings. It is anticipated that the taxonomy will prove a valuable tool for subsequent research investigating the early childhood workforce.