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

A fatigue multi-site cracks model using coalescence, short and long crack growth laws, for anodized aluminum alloys

It has been shown that decrease of the fatigue life of aluminium alloys treated with anodization can be explained by the degradation of surface condition due to pickling. In order to predict fatigue life of anodized aluminium alloys, a multi-site crack growth model is developed by considering the pickling pits sites as initial flaws from which fatigue cracks develop. A map of the pickled surface is built from topography measurement with a contact profilometer. Then the pits are detected and their sizes are defined (depth, length and width). At the beginning of the calculation, a short crack growth law is used for crack having depths less than grain size. Then Paris long crack growth law is used. The coalescence of cracks is considered when their lengths increased by its crack tip plastic zone are large enough to interact with other neighbouring short cracks. The fatigue life is calculated for Kmax achieving 70 % KIC

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

Neurite outgrowth inhibitory levels of organophosphates induce tissue transglutaminase activity in differentiating N2a cells: evidence for covalent adduct formation

Organophosphate compounds (OPs) induce both acute and delayed neurotoxic effects, the latter of which is believed to involve their interaction with proteins other than acetylcholinesterase. However, few OP-binding proteins have been identified that may have a direct role in OP-induced delayed neurotoxicity. Given their ability to disrupt Ca2+ homeostasis, a key aim of the current work was to investigate the effects of sub-lethal neurite outgrowth inhibitory levels of OPs on the Ca2+-dependent enzyme tissue transglutaminase (TG2). At 1–10 µM, the OPs phenyl saligenin phosphate (PSP) and chlorpyrifos oxon (CPO) had no effect cell viability but induced concentration-dependent decreases in neurite outgrowth in differentiating N2a neuroblastoma cells. The activity of TG2 increased in cell lysates of differentiating cells exposed for 24 h to PSP and chlorpyrifos oxon CPO (10 µM), as determined by biotin-cadaverine incorporation assays. Exposure to both OPs (3 and/or 10 µM) also enhanced in situ incorporation of the membrane permeable substrate biotin-X-cadaverine, as indicated by Western blot analysis of treated cell lysates probed with ExtrAvidin peroxidase and fluorescence microscopy of cell monolayers incubated with FITC-streptavidin. Both OPs (10 µM) stimulated the activity of human and mouse recombinant TG2 and covalent labelling of TG2 with dansylamine-labelled PSP was demonstrated by fluorescence imaging following SDS-PAGE. A number of TG2 substrates were tentatively identified by mass spectrometry, including cytoskeletal proteins, chaperones and proteins involved protein synthesis and gene regulation. We propose that the elevated TG2 activity observed is due to the formation of a novel covalent adduct between TG2 and OPs

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

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

Treatment decisions and employment of breast cancer patients: Results of a population‐based survey

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/142258/1/cncr30959.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/142258/2/cncr30959_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/142258/3/cncr30959-sup-0001-suppinfo1.pd