A Profile of Pornography Users in Australia: Findings From the Second Australian Study of Health and Relationships

Copyright © The Society for the Scientific Study of Sexuality. There are societal concerns that looking at pornography has adverse consequences among those exposed. However, looking at sexually explicit material could have educative and relationship benefits. This article identifies factors associated with looking at pornography ever or within the past 12 months for men and women in Australia, and the extent to which reporting an “addiction” to pornography is associated with reported bad effects. Data from the Second Australian Study of Health and Relationships (ASHR2) were used: computer-assisted telephone interviews (CASIs) completed by a representative sample of 9,963 men and 10,131 women aged 16 to 69 years from all Australian states and territories, with an overall participation rate of 66%. Most men (84%) and half of the women (54%) had ever looked at pornographic material. Three-quarters of these men (76%) and more than one-third of these women (41%) had looked at pornographic material in the past year. Very few respondents reported that they were addicted to pornography (men 4%, women 1%), and of those who said they were addicted about half also reported that using pornography had had a bad effect on them. Looking at pornographic material appears to be reasonably common in Australia, with adverse effects reported by a small minority

Unsupervised Bayesian linear unmixing of gene expression microarrays

Background: This paper introduces a new constrained model and the corresponding algorithm, called unsupervised Bayesian linear unmixing (uBLU), to identify biological signatures from high dimensional assays like gene expression microarrays. The basis for uBLU is a Bayesian model for the data samples which are represented as an additive mixture of random positive gene signatures, called factors, with random positive mixing coefficients, called factor scores, that specify the relative contribution of each signature to a specific sample. The particularity of the proposed method is that uBLU constrains the factor loadings to be non-negative and the factor scores to be probability distributions over the factors. Furthermore, it also provides estimates of the number of factors. A Gibbs sampling strategy is adopted here to generate random samples according to the posterior distribution of the factors, factor scores, and number of factors. These samples are then used to estimate all the unknown parameters. Results: Firstly, the proposed uBLU method is applied to several simulated datasets with known ground truth and compared with previous factor decomposition methods, such as principal component analysis (PCA), non negative matrix factorization (NMF), Bayesian factor regression modeling (BFRM), and the gradient-based algorithm for general matrix factorization (GB-GMF). Secondly, we illustrate the application of uBLU on a real time-evolving gene expression dataset from a recent viral challenge study in which individuals have been inoculated with influenza A/H3N2/Wisconsin. We show that the uBLU method significantly outperforms the other methods on the simulated and real data sets considered here. Conclusions: The results obtained on synthetic and real data illustrate the accuracy of the proposed uBLU method when compared to other factor decomposition methods from the literature (PCA, NMF, BFRM, and GB-GMF). The uBLU method identifies an inflammatory component closely associated with clinical symptom scores collected during the study. Using a constrained model allows recovery of all the inflammatory genes in a single factor

Approaches to working in high-dimensional data spaces: gene expression microarrays

This review provides a focused summary of the implications of high-dimensional data spaces produced by gene expression microarrays for building better models of cancer diagnosis, prognosis, and therapeutics. We identify the unique challenges posed by high dimensionality to highlight methodological problems and discuss recent methods in predictive classification, unsupervised subclass discovery, and marker identification

Expression profiles of switch-like genes accurately classify tissue and infectious disease phenotypes in model-based classification

<p>Abstract</p> <p>Background</p> <p>Large-scale compilation of gene expression microarray datasets across diverse biological phenotypes provided a means of gathering a priori knowledge in the form of identification and annotation of bimodal genes in the human and mouse genomes. These switch-like genes consist of 15% of known human genes, and are enriched with genes coding for extracellular and membrane proteins. It is of interest to determine the prediction potential of bimodal genes for class discovery in large-scale datasets.</p> <p>Results</p> <p>Use of a model-based clustering algorithm accurately classified more than 400 microarray samples into 19 different tissue types on the basis of bimodal gene expression. Bimodal expression patterns were also highly effective in differentiating between infectious diseases in model-based clustering of microarray data. Supervised classification with feature selection restricted to switch-like genes also recognized tissue specific and infectious disease specific signatures in independent test datasets reserved for validation. Determination of "on" and "off" states of switch-like genes in various tissues and diseases allowed for the identification of activated/deactivated pathways. Activated switch-like genes in neural, skeletal muscle and cardiac muscle tissue tend to have tissue-specific roles. A majority of activated genes in infectious disease are involved in processes related to the immune response.</p> <p>Conclusion</p> <p>Switch-like bimodal gene sets capture genome-wide signatures from microarray data in health and infectious disease. A subset of bimodal genes coding for extracellular and membrane proteins are associated with tissue specificity, indicating a potential role for them as biomarkers provided that expression is altered in the onset of disease. Furthermore, we provide evidence that bimodal genes are involved in temporally and spatially active mechanisms including tissue-specific functions and response of the immune system to invading pathogens.</p

Prescribing practice for malaria following introduction of artemether-lumefantrine in an urban area with declining endemicity in West Africa

<p>Abstract</p> <p>Background</p> <p>The decline in malaria coinciding with the introduction of newer, costly anti-malarials has prompted studies into the overtreatment for malaria mostly in East Africa. The study presented here describes prescribing practices for malaria at health facilities in a West African country.</p> <p>Methods</p> <p>Cross-sectional surveys were carried out in two urban Gambian primary health facilities (PHFs) during and outside the malaria transmission season. Facilities were comparable in terms of the staffing compliment and capability to perform slide microscopy. Patients treated for malaria were enrolled after consultations and blood smears collected and read at a reference laboratory. Slide reading results from the PHFs were compared to the reference readings and the proportion of cases treated but with a negative test result at the reference laboratory was determined.</p> <p>Results</p> <p>Slide requests were made for 33.2% (173) of those enrolled, being more frequent in children (0-15 yrs) than adults during the wet season (p = 0.003). In the same period, requests were commoner in under-fives compared to older children (p = 0.022); however, a positive test result was 4.4 times more likely in the latter group (p = 0.010). Parasitaemia was confirmed for only 4.7% (10/215) and 12.5% (37/297) of patients in the dry and wet seasons, respectively. The negative predictive value of a PHF slide remained above 97% in both seasons.</p> <p>Conclusions</p> <p>The study provides evidence for considerable overtreatment for malaria in a West African setting comparable to reports from areas with similar low malaria transmission in East Africa. The data suggest that laboratory facilities may be under-used, and that adherence to negative PHF slide results could significantly reduce the degree of overtreatment. The "peak prevalence" in 5-15 year olds may reflect successful implementation of malaria control interventions in under-fives, but point out the need to extend such interventions to older children.</p

The impact of perfectionism and anxiety traits on action monitoring in major depressive disorder

Perfectionism and anxiety features are involved in the clinical presentation and neurobiology of major depressive disorder (MDD). In MDD, cognitive control mechanisms such as action monitoring can adequately be investigated applying electrophysiological registrations of the error-related negativity (ERN) and error positivity (Pe). It is also known that traits of perfectionism and anxiety influence ERN amplitudes in healthy subjects. The current study explores the impact of perfectionism and anxiety traits on action monitoring in MDD. A total of 39 MDD patients performed a flankers task during an event-related potential (ERP) session and completed the multidimensional perfectionism scale (MPS) with its concern over mistakes (CM) and doubt about actions (DA) subscales and the trait form of the State Trait Anxiety Inventory. Multiple regression analyses with stepwise backward elimination revealed MPS-DA to be a significant predictor (R2:0.22) for the ERN outcomes, and overall MPS (R2:0.13) and MPS-CM scores (R2:0.18) to have significant predictive value for the Pe amplitudes. Anxiety traits did not have a predictive capacity for the ERPs. MPS-DA clearly affected the ERN, and overall MPS and MPS-CM influenced the Pe, whereas no predictive capacity was found for anxiety traits. The manifest impact of perfectionism on patients’ error-related ERPs may contribute to our understanding of the action-monitoring process and the functional significance of the Pe in MDD. The divergent findings for perfectionism and anxiety features also indicate that the wide range of various affective personality styles might exert a different effect on action monitoring in MDD, awaiting further investigation

Gene selection for classification of microarray data based on the Bayes error

<p>Abstract</p> <p>Background</p> <p>With DNA microarray data, selecting a compact subset of discriminative genes from thousands of genes is a critical step for accurate classification of phenotypes for, e.g., disease diagnosis. Several widely used gene selection methods often select top-ranked genes according to their individual discriminative power in classifying samples into distinct categories, without considering correlations among genes. A limitation of these gene selection methods is that they may result in gene sets with some redundancy and yield an unnecessary large number of candidate genes for classification analyses. Some latest studies show that incorporating gene to gene correlations into gene selection can remove redundant genes and improve classification accuracy.</p> <p>Results</p> <p>In this study, we propose a new method, Based Bayes error Filter (BBF), to select relevant genes and remove redundant genes in classification analyses of microarray data. The effectiveness and accuracy of this method is demonstrated through analyses of five publicly available microarray datasets. The results show that our gene selection method is capable of achieving better accuracies than previous studies, while being able to effectively select relevant genes, remove redundant genes and obtain efficient and small gene sets for sample classification purposes.</p> <p>Conclusion</p> <p>The proposed method can effectively identify a compact set of genes with high classification accuracy. This study also indicates that application of the Bayes error is a feasible and effective wayfor removing redundant genes in gene selection.</p