The impact of secondary tasks on multitasking in a virtual environment

One experiment is described that examined the possible involvement of working memory in the Virtual Errands Test (McGeorge et al., 2000), which requires participants to complete errands within a virtual environment, presented on a computer screen. Time was limited, therefore participants had to swap between tasks (multitask) efficiently to complete the errands. Forty-two undergraduates participated, all attempting the test twice. On one of these occasions they were asked to perform a concurrent task throughout (order of single and dual task conditions was counterbalanced). The type of secondary task was manipulated between-groups. Twenty-one participants were asked to randomly generate months of the year aloud in the dual-task condition, while another twenty-one were asked to suppress articulation by repeating the word “December”. An overall dual-task effect on the virtual errands test was observed, although this was qualified by an interaction with the order of single and dual task conditions. Analysis of the secondary task data showed a drop in performance (relative to baseline) under dual-task conditions, and that drop was greater for the random generation group and the articulatory suppression group. These data are interpreted as suggesting that the central executive and phonological loop components of working memory are implicated in this test of multitasking

Towards Emotion Recognition: A Persistent Entropy Application

Emotion recognition and classification is a very active area of research. In this paper, we present a first approach to emotion classification using persistent entropy and support vector machines. A topology-based model is applied to obtain a single real number from each raw signal. These data are used as input of a support vector machine to classify signals into 8 different emotions (calm, happy, sad, angry, fearful, disgust and surprised)

Combination of linear classifiers using score function -- analysis of possible combination strategies

In this work, we addressed the issue of combining linear classifiers using their score functions. The value of the scoring function depends on the distance from the decision boundary. Two score functions have been tested and four different combination strategies were investigated. During the experimental study, the proposed approach was applied to the heterogeneous ensemble and it was compared to two reference methods -- majority voting and model averaging respectively. The comparison was made in terms of seven different quality criteria. The result shows that combination strategies based on simple average, and trimmed average are the best combination strategies of the geometrical combination

Predicting Distribution of Aedes Aegypti and Culex Pipiens Complex, Potential Vectors of Rift Valley Fever Virus in Relation to Disease Epidemics in East Africa.

The East African region has experienced several Rift Valley fever (RVF) outbreaks since the 1930s. The objective of this study was to identify distributions of potential disease vectors in relation to disease epidemics. Understanding disease vector potential distributions is a major concern for disease transmission dynamics. DIVERSE ECOLOGICAL NICHE MODELLING TECHNIQUES HAVE BEEN DEVELOPED FOR THIS PURPOSE: we present a maximum entropy (Maxent) approach for estimating distributions of potential RVF vectors in un-sampled areas in East Africa. We modelled the distribution of two species of mosquitoes (Aedes aegypti and Culex pipiens complex) responsible for potential maintenance and amplification of the virus, respectively. Predicted distributions of environmentally suitable areas in East Africa were based on the presence-only occurrence data derived from our entomological study in Ngorongoro District in northern Tanzania. Our model predicted potential suitable areas with high success rates of 90.9% for A. aegypti and 91.6% for C. pipiens complex. Model performance was statistically significantly better than random for both species. Most suitable sites for the two vectors were predicted in central and northwestern Tanzania with previous disease epidemics. Other important risk areas include western Lake Victoria, northern parts of Lake Malawi, and the Rift Valley region of Kenya. Findings from this study show distributions of vectors had biological and epidemiological significance in relation to disease outbreak hotspots, and hence provide guidance for the selection of sampling areas for RVF vectors during inter-epidemic periods

Gene Function Classification Using Bayesian Models with Hierarchy-Based Priors

We investigate the application of hierarchical classification schemes to the annotation of gene function based on several characteristics of protein sequences including phylogenic descriptors, sequence based attributes, and predicted secondary structure. We discuss three Bayesian models and compare their performance in terms of predictive accuracy. These models are the ordinary multinomial logit (MNL) model, a hierarchical model based on a set of nested MNL models, and a MNL model with a prior that introduces correlations between the parameters for classes that are nearby in the hierarchy. We also provide a new scheme for combining different sources of information. We use these models to predict the functional class of Open Reading Frames (ORFs) from the E. coli genome. The results from all three models show substantial improvement over previous methods, which were based on the C5 algorithm. The MNL model using a prior based on the hierarchy outperforms both the non-hierarchical MNL model and the nested MNL model. In contrast to previous attempts at combining these sources of information, our approach results in a higher accuracy rate when compared to models that use each data source alone. Together, these results show that gene function can be predicted with higher accuracy than previously achieved, using Bayesian models that incorporate suitable prior information

Rates of glycaemic deterioration in a real-world population with type 2 diabetes

Aims/hypothesis: There is considerable variability in how diabetes progresses after diagnosis. Progression modelling has largely focused on 'time to failure' methods, yet determining a 'coefficient of failure' has many advantages. We derived a rate of glycaemic deterioration in type 2 diabetes, using a large real-world cohort, and aimed to investigate the clinical, biochemical, pharmacological and immunological variables associated with fast and slow rates of glycaemic deterioration. Methods: An observational cohort study was performed using the electronic medical records from participants in the Genetics of Diabetes Audit and Research in Tayside Study (GoDARTS). A model was derived based on an individual's observed HbA(1c) measures from the first eligible HbA(1c) after the diagnosis of diabetes through to the study end (defined as insulin initiation, death, leaving the area or end of follow-up). Each HbA(1c) measure was time-dependently adjusted for the effects of non-insulin glucose-lowering drugs, changes in BMI and corticosteroid use. GAD antibody (GADA) positivity was defined as GAD titres above the 97.5th centile of the population distribution. Results: The mean (95% CI) glycaemic deterioration for type 2 diabetes and GADA-positive individuals was 1.4 (1.3, 1.4) and 2.8 (2.4, 3.3) mmol/mol HbA(1c) per year, respectively. A younger age of diagnosis, lower HDL-cholesterol concentration, higher BMI and earlier calendar year of diabetes diagnosis were independently associated with higher rates of glycaemic deterioration in individuals with type 2 diabetes. The rate of deterioration in those diagnosed at over 70 years of age was very low, with 66% having a rate of deterioration of less than 1.1 mmol/mol HbA(1c) per year, and only 1.5% progressing more rapidly than 4.4 mmol/mol HbA(1c) per year. Conclusions/interpretation: We have developed a novel approach for modelling the progression of diabetes in observational data across multiple drug combinations. This approach highlights how glycaemic deterioration in those diagnosed at over 70 years of age is minimal, supporting a stratified approach to diabetes management

Search for new phenomena in final states with an energetic jet and large missing transverse momentum in pp collisions at √ s = 8 TeV with the ATLAS detector

Results of a search for new phenomena in final states with an energetic jet and large missing transverse momentum are reported. The search uses 20.3 fb−1 of √ s = 8 TeV data collected in 2012 with the ATLAS detector at the LHC. Events are required to have at least one jet with pT > 120 GeV and no leptons. Nine signal regions are considered with increasing missing transverse momentum requirements between Emiss T > 150 GeV and Emiss T > 700 GeV. Good agreement is observed between the number of events in data and Standard Model expectations. The results are translated into exclusion limits on models with either large extra spatial dimensions, pair production of weakly interacting dark matter candidates, or production of very light gravitinos in a gauge-mediated supersymmetric model. In addition, limits on the production of an invisibly decaying Higgs-like boson leading to similar topologies in the final state are presente

Identification of novel Coxiella burnetii genotypes from Ethiopian ticks

Background: Coxiella burnetii , the etiologic agent of Q fever, is a highly infectious zoonotic bacterium. Genetic information about the strains of this worldwide distributed agent circulating on the African continent is limited. The aim of the present study was the genetic characterization of C. burnetii DNA samples detected in ticks collected from Ethiopian cattle and their comparison with other genotypes found previously in other parts of the world. Methodology/Principal Findings: A total of 296 tick samples were screened by real-time PCR targeting the IS 1111 region of C. burnetii genome and from the 32 positive samples, 8 cases with sufficient C. burnetii DNA load ( Amblyomma cohaerens ,n 5 6; A. variegatum ,n 5 2) were characterized by multispacer sequence typing (MST) and multiple-locus variable-number tandem repeat analysis (MLVA). One novel sequence type (ST), the proposed ST52, was identified by MST. The MLVA-6 discriminated the proposed ST52 into two newly identified MLVA genotypes: type 24 or AH was detected in both Amblyomma species while type 26 or AI was found only in A. cohaerens . Conclusions/Significance: Both the MST and MLVA genotypes of the present work are closely related to previously described genotypes found primarily in cattle samples from different parts of the globe. This finding is congruent with the source hosts of the analyzed Ethiopian ticks, as these were also collected from cattle. The present study provides genotype information of C. burnetii from this seldom studied East-African region as well as further evidence for the presumed host-specific adaptation of this agent