Genome-wide linkage analysis of multiple measures of neuroticism of 2 large cohorts from Australia and the Netherlands

CONTEXT People meeting diagnostic criteria for anxiety or depressive disorders tend to score high on the personality scale of neuroticism. Studying this personality dimension can give insights into the etiology of these important psychiatric disorders. OBJECTIVES To undertake a comprehensive genome-wide linkage study of neuroticism using large study samples that have been measured multiple times and to compare the results between countries for replication and across time within countries for consistency. DESIGN Genome-wide linkage scan. SETTING Twin individuals and their family members from Australia and the Netherlands. PARTICIPANTS Nineteen thousand six hundred thirty-five sibling pairs completed self-report questionnaires for neuroticism up to 5 times over a period of up to 22 years. Five thousand sixty-nine sibling pairs were genotyped with microsatellite markers. METHODS Nonparametric linkage analyses were conducted in MERLIN-REGRESS for the mean neuroticism scores averaged across time. Additional analyses were conducted for the time-specific measures of neuroticism from each country to investigate consistency of linkage results. RESULTS Three chromosomal regions exceeded empirically derived thresholds for suggestive linkage using mean neuroticism scores: 10p 5 Kosambi cM (cM) (Dutch study sample), 14q 103 cM (Dutch study sample), and 18q 117 cM (combined Australian and Dutch study sample), but only 14q retained significance after correction for multiple testing. These regions all showed evidence for linkage in individual time-specific measures of neuroticism and 1 (18q) showed some evidence for replication between countries. Linkage intervals for these regions all overlap with regions identified in other studies of neuroticism or related traits and/or in studies of anxiety in mice. CONCLUSIONS Our results demonstrate the value of the availability of multiple measures over time and add to the optimism reported in recent reviews for replication of linkage regions for neuroticism. These regions are likely to harbor causal variants for neuroticism and its related psychiatric disorders and can inform prioritization of results from genome-wide association studies.

On the probability of dizygotic twins being concordant for two alleles at multiple polymorphic loci

Accurate determination of same-sex twin zygosity is important for medical, scientific and personal reasons. Determination may be based upon questionnaire data, blood group, enzyme isoforms and fetal membrane examination, but assignment of zygosity must ultimately be confirmed by genotypic data. Here methods are reviewed for calculating average probabilities of correctly concluding a twin pair is monozygotic, given they share the same genotypes across all loci for commonly utilized multiplex short tandem repeat (STR) kits.

A simple correction for multiple testing for single-nucleotide polymorphisms in linkage disequilibrium with each other

In this report, we describe a simple correction for multiple testing of single-nucleotide polymorphisms (SNPs) in linkage disequilibrium (LD) with each other, on the basis of the spectral decomposition (SpD) of matrices of pairwise LD between SNPs. This method provides a useful alternative to more computationally intensive permutation tests. Additionally, output from SNPSpD includes eigenvalues, principal-component coefficients, and factor "loadings" after varimax rotation, enabling the selection of a subset of SNPs that optimize the information in a genomic region.

Commentary: Chronic SSRI stimulation of astrocytic 5-HT2B receptors change multiple gene expressions/editings and metabolism of glutamate, glucose and glycogen: A potential paradigm shift

Working on the serotonin (5-hydroxytryptamine, 5-HT) 5-HT2B receptor since several years, we have read with high interest the review by Hertz et al. (2015). Previous studies from our group demonstrated that a direct injection in mouse raphe nucleus of the 5-HT2B agonist BW723C86 has the ability to increase extracellular levels of serotonin, which can be blocked by the selective 5-HT2B receptor antagonist RS127445 (Doly et al., 2008, 2009). We also reported that an acute injection of paroxetine 2 mg/kg in mice knocked out for the 5-HT2B receptor gene or in wild type mice injected with RS127445 (0.5 mg/kg) triggers a strong reduction in extracellular accumulation of 5-HT in hippocampus (Diaz et al., 2012). Following these observations, we showed that acute and chronic BW723C86 injection (3 mg/kg) can mimic the fluoxetine (3 mg/kg) and paroxetine (1 mg/kg) behavioral and biochemical antidepressant effects in mice (Diaz and Maroteaux, 2011; Diaz et al., 2012)...

A flexible hierarchical approach for facial age estimation based on multiple features

Age estimation from facial images is increasingly receiving attention to solve age-based access control, age-adaptive targeted marketing, amongst other applications. Since even humans can be induced in error due to the complex biological processes involved, finding a robust method remains a research challenge today. In this paper, we propose a new framework for the integration of Active Appearance Models (AAM), Local Binary Patterns (LBP), Gabor wavelets (GW) and Local Phase Quantization (LPQ) in order to obtain a highly discriminative feature representation which is able to model shape, appearance, wrinkles and skin spots. In addition, this paper proposes a novel flexible hierarchical age estimation approach consisting of a multi-class Support Vector Machine (SVM) to classify a subject into an age group followed by a Support Vector Regression (SVR) to estimate a specific age. The errors that may happen in the classification step, caused by the hard boundaries between age classes, are compensated in the specific age estimation by a flexible overlapping of the age ranges. The performance of the proposed approach was evaluated on FG-NET Aging and MORPH Album 2 datasets and a mean absolute error (MAE) of 4.50 and 5.86 years was achieved respectively. The robustness of the proposed approach was also evaluated on a merge of both datasets and a MAE of 5.20 years was achieved. Furthermore, we have also compared the age estimation made by humans with the proposed approach and it has shown that the machine outperforms humans. The proposed approach is competitive with current state-of-the-art and it provides an additional robustness to blur, lighting and expression variance brought about by the local phase features.

The puzzle of falling enrolments in physics and chemistry courses: Putting some pieces together

This paper reports and discusses the principal findings of an Australian study exploring the decisions of high achieving Year 10 students about taking physics and chemistry courses (Lyons, 2003). The study used a ‘multiple worlds’ framework to explore the diverse background characteristics that previous quantitative research had shown were implicated in these decisions. Based on analyses of questionnaire and interview data, the study found that the students’ decisions involved the complex negotiation of a number of cultural characteristics within their school science and family worlds. Many of the students regarded junior high school science as irrelevant, uninteresting and difficult, leaving them with few intrinsic reasons for enrolling in senior science courses. The study found that decisions about taking physical science courses were associated with the resources of cultural and social capital within their families, and the degree to which these resources were congruent with the advantages of choosing these courses. The paper concludes that the low intrinsic value of school science and the erosion of its strategic value contribute to the reluctance of students to choose physical science courses in the senior school.

Recipe for success — Lessons learnt from using xAPI within the Connected Learning Analytics toolkit

An ongoing challenge for Learning Analytics research has been the scalable derivation of user interaction data from multiple technologies. The complexities associated with this challenge are increasing as educators embrace an ever growing number of social and content related technologies. The Experience API (xAPI) alongside the development of user specific record stores has been touted as a means to address this challenge, but a number of subtle considerations must be made when using xAPI in Learning Analytics. This paper provides a general overview to the complexities and challenges of using xAPI in a general systemic analytics solution - called the Connected Learning Analytics (CLA) toolkit. The importance of design is emphasised, as is the notion of common vocabularies and xAPI Recipes. Early decisions about vocabularies and structural relationships between statements can serve to either facilitate or handicap later analytics solutions. The CLA toolkit case study provides us with a way of examining both the strengths and the weaknesses of the current xAPI specification, and we conclude with a proposal for how xAPI might be improved by using JSON-LD to formalise Recipes in a machine readable form.

Multiple instance dictionary learning for activity representation

This paper presents an effective feature representation method in the context of activity recognition. Efficient and effective feature representation plays a crucial role not only in activity recognition, but also in a wide range of applications such as motion analysis, tracking, 3D scene understanding etc. In the context of activity recognition, local features are increasingly popular for representing videos because of their simplicity and efficiency. While they achieve state-of-the-art performance with low computational requirements, their performance is still limited for real world applications due to a lack of contextual information and models not being tailored to specific activities. We propose a new activity representation framework to address the shortcomings of the popular, but simple bag-of-words approach. In our framework, first multiple instance SVM (mi-SVM) is used to identify positive features for each action category and the k-means algorithm is used to generate a codebook. Then locality-constrained linear coding is used to encode the features into the generated codebook, followed by spatio-temporal pyramid pooling to convey the spatio-temporal statistics. Finally, an SVM is used to classify the videos. Experiments carried out on two popular datasets with varying complexity demonstrate significant performance improvement over the base-line bag-of-feature method.

Choosing physical science courses: The importance of cultural and social capital in the enrolment decisions of high achieving students

This paper reports and discusses findings from a recent study which explored the science enrolment decisions of high achieving, or ‘science proficient’ secondary level students in Australia (Lyons 2003). The research was prompted by the increasing reluctance of such students to enrol in postcompulsory science courses, particularly in physics and chemistry. The study investigated the influences on students’ deliberations about taking a range of science courses. However, this report confines itself to decisions about enrolling in the physical sciences. The paper summarises the students’ experiences and conceptions of school science, as well as the characteristics of their ‘family worlds’ found to be influential in their decisions1. The paper discusses the important roles of cultural and social capital in these decisions, and concludes that enrolment in physical science courses was associated with congruence between the students’ conceptions of school science, and characteristics of their family backgrounds.

Moving from evaluation to trial: How do SMEs start adopting Cloud ERP?

The advent of cloud technology involving low subscription overheads cost has provided small and medium-sized enterprises (SMEs) with the opportunity to adopt new cloud-based corporate-wide systems (i.e., cloud ERP). This technology, operating through subscription-based services, has now provided SMEs with a complete range of IT applications that were once restricted to larger organisations. As anecdotal evidences suggest, SMEs are increasingly adopting cloud-based ERP software. The selection of an ERP is a complex process involving multiple stages and stakeholders, suggesting the importance of closer examination of cloud ERP adoption in SMEs. Yet, prior studies have predominantly treated technology adoption as a single activity and largely ignored the issue of ERP adoption in SMEs. Understanding of the process nature of the adoption and the factors that are important in each stage of the adoption potentially may result in guiding SMEs to make well-informed decisions throughout the ERP selection process. Thus, our study proposes that the adoption of cloud ERP should be examined as a multi-stage process. Using the Theory of Planned Behaviour (TPB) and Ettlie’s adoption stages, as well as employing data gathered from 162 owners of SMEs, our findings show that the factors that influence the intention to adopt cloud ERP vary significantly across adoptive stages.

A data fusion approach of multiple maintenance data sources for real-world reliability modelling

A central tenet in the theory of reliability modelling is the quantification of the probability of asset failure. In general, reliability depends on asset age and the maintenance policy applied. Usually, failure and maintenance times are the primary inputs to reliability models. However, for many organisations, different aspects of these data are often recorded in different databases (e.g. work order notifications, event logs, condition monitoring data, and process control data). These recorded data cannot be interpreted individually, since they typically do not have all the information necessary to ascertain failure and preventive maintenance times. This paper presents a methodology for the extraction of failure and preventive maintenance times using commonly-available, real-world data sources. A text-mining approach is employed to extract keywords indicative of the source of the maintenance event. Using these keywords, a Naïve Bayes classifier is then applied to attribute each machine stoppage to one of two classes: failure or preventive. The accuracy of the algorithm is assessed and the classified failure time data are then presented. The applicability of the methodology is demonstrated on a maintenance data set from an Australian electricity company.

Transforming higher education and student engagement through collaborative review to inform educational design

This paper reports on staff perceptions arising from a review process designed to assist staff in making informed decisions regarding educational design, approaches to engage students in learning, and the technology to support engagement in the classroom and across multiple locations and delivery modes. The aim of the review process was to transform the level of student engagement in the business faculty of an Australian university. The process took a collaborative approach through consultation with academic staff involved in the design and delivery of the units under review, and included targeted professional development as necessary. An institutional framework that characterises engagement indicator contexts and their attributes facilitated dialog during the review process. This paper reports on a mixed method study that included a survey of participants, and purposeful interviews to evaluate the effectiveness of the process. Although the study identified factors that hindered implementation and operationalization of review recommendations in some instances, study participants were generally of the view that recommendations would enhance student engagement. It is demonstrated that the bottom-up approach described in this paper is consistent with theoretical frameworks for transformational change in teaching and learning and the adoption of innovations.

Making decisions about antibiotic use in the Australian primary healthcare sector

Background Australia has one of the highest rates of antibiotic use amongst OECD countries. Data from the Australian primary healthcare sector suggests unnecessary antibiotics were prescribed for self-resolving conditions. We need to better understand what drives general practitioners (GPs) to prescribe antibiotics, consumers to seek antibiotics, and pharmacists to fill repeat antibiotic prescriptions. It is also not clear how these individuals trade-off between the possible benefits that antibiotics may provide in the immediate/short term, against the longer term societal risk of antimicrobial resistance. This project investigates what factors drive decisions to use antibiotics for GPs, pharmacists and consumers, and how these individuals discount the future. Methods Factors will be gleaned from published literature and from semi-structured interviews, to inform the development of Discrete Choice Experiments (DCEs). Three DCEs will be constructed – one for each group of interest – to allow investigation of which factors are more important in influencing (a) GPs to prescribe antibiotics, (b) consumers to seek antibiotics, and (c) pharmacists to fill legally valid but old or repeat prescriptions of antibiotics. Regression analysis will be conducted to understand the relative importance of these factors. A Time Trade Off exercise will be developed to investigate how these individuals discount the future. Results Findings from the DCEs will provide an insight into which factors are more important in driving decision making in antibiotic use for GPs, pharmacists and consumers. Findings from the Time Trade Off exercise will show what individuals are willing to trade for preserving the miracle of antibiotics. Conclusion Research findings will contribute to existing national programs to bring about a reduction in inappropriate use of antibiotic in Australia. Specifically, influencing how key messages and public health campaigns are crafted, and clinical education and empowerment of GPs and pharmacists to play a more responsive role as stewards of antibiotic use in the community.