The Global Omnivore: Identifying Musical Taste Groups in Austria, England, Israel and Serbia

This research offers a unique opportunity to revisit the omnivore hypothesis under a unified method of cross-national analysis. To accomplish this, we interpret omnivourism as a special case of cultural eclecticism (Ollivier, 2008; Ollivier, Gauthier and Truong, 2009). Our methodological approach incorporates the simultaneous analysis of locally produced and globally known musical genres. Its objective is to verify whether cultural omnivourism is a widespread phenomenon, and to determine to what extent any conclusions can be generalised across countries with different social structures and different levels of cultural openness. To truly understand the scope of the omnivourism hypothesis, we argue that it is essential to perform a cross-national comparison to test the hypothesis within a range of social, political and cultural contexts, and a reflection of different historical and cultural repertoires (Lamont, 1992)

Assessing Performance of Orthology Detection Strategies Applied to Eukaryotic Genomes

Orthology detection is critically important for accurate functional annotation, and has been widely used to facilitate studies on comparative and evolutionary genomics. Although various methods are now available, there has been no comprehensive analysis of performance, due to the lack of a genomic-scale ‘gold standard’ orthology dataset. Even in the absence of such datasets, the comparison of results from alternative methodologies contains useful information, as agreement enhances confidence and disagreement indicates possible errors. Latent Class Analysis (LCA) is a statistical technique that can exploit this information to reasonably infer sensitivities and specificities, and is applied here to evaluate the performance of various orthology detection methods on a eukaryotic dataset. Overall, we observe a trade-off between sensitivity and specificity in orthology detection, with BLAST-based methods characterized by high sensitivity, and tree-based methods by high specificity. Two algorithms exhibit the best overall balance, with both sensitivity and specificity>80%: INPARANOID identifies orthologs across two species while OrthoMCL clusters orthologs from multiple species. Among methods that permit clustering of ortholog groups spanning multiple genomes, the (automated) OrthoMCL algorithm exhibits better within-group consistency with respect to protein function and domain architecture than the (manually curated) KOG database, and the homolog clustering algorithm TribeMCL as well. By way of using LCA, we are also able to comprehensively assess similarities and statistical dependence between various strategies, and evaluate the effects of parameter settings on performance. In summary, we present a comprehensive evaluation of orthology detection on a divergent set of eukaryotic genomes, thus providing insights and guides for method selection, tuning and development for different applications. Many biological questions have been addressed by multiple tests yielding binary (yes/no) outcomes but no clear definition of truth, making LCA an attractive approach for computational biology

Latent cluster analysis of ALS phenotypes identifies prognostically differing groups

BACKGROUND Amyotrophic lateral sclerosis (ALS) is a degenerative disease predominantly affecting motor neurons and manifesting as several different phenotypes. Whether these phenotypes correspond to different underlying disease processes is unknown. We used latent cluster analysis to identify groupings of clinical variables in an objective and unbiased way to improve phenotyping for clinical and research purposes. METHODS Latent class cluster analysis was applied to a large database consisting of 1467 records of people with ALS, using discrete variables which can be readily determined at the first clinic appointment. The model was tested for clinical relevance by survival analysis of the phenotypic groupings using the Kaplan-Meier method. RESULTS The best model generated five distinct phenotypic classes that strongly predicted survival (p<0.0001). Eight variables were used for the latent class analysis, but a good estimate of the classification could be obtained using just two variables: site of first symptoms (bulbar or limb) and time from symptom onset to diagnosis (p<0.00001). CONCLUSION The five phenotypic classes identified using latent cluster analysis can predict prognosis. They could be used to stratify patients recruited into clinical trials and generating more homogeneous disease groups for genetic, proteomic and risk factor research

Evidence-based practice profiles of physiotherapists transitioning into the workforce: a study of two cohorts

<p>Abstract</p> <p>Background</p> <p>Training in the five steps of evidence-based practice (EBP) has been recommended for inclusion in entry-level health professional training. The effectiveness of EBP education has been explored predominantly in the medical and nursing professions and more commonly in post-graduate than entry-level students. Few studies have investigated longitudinal changes in EBP attitudes and behaviours. This study aimed to assess the changes in EBP knowledge, attitudes and behaviours in entry-level physiotherapy students transitioning into the workforce.</p> <p>Methods</p> <p>A prospective, observational, longitudinal design was used, with two cohorts. From 2008, 29 participants were tested in their final year in a physiotherapy program, and after the first and second workforce years. From 2009, 76 participants were tested in their final entry-level and first workforce years. Participants completed an Evidence-Based Practice Profile questionnaire (EBP²), which includes self-report EBP domains [Relevance, Terminology (knowledge of EBP concepts), Confidence, Practice (EBP implementation), Sympathy (disposition towards EBP)]. Mixed model analysis with sequential Bonferroni adjustment was used to analyse the matched data. Effect sizes (ES) (95% CI) were calculated for all changes.</p> <p>Results</p> <p>Effect sizes of the changes in EBP domains were small (ES range 0.02 to 0.42). While most changes were not significant there was a consistent pattern of decline in scores for Relevance in the first workforce year (ES -0.42 to -0.29) followed by an improvement in the second year (ES +0.27). Scores in Terminology improved (ES +0.19 to +0.26) in each of the first two workforce years, while Practice scores declined (ES -0.23 to -0.19) in the first year and improved minimally in the second year (ES +0.04). Confidence scores improved during the second workforce year (ES +0.27). Scores for Sympathy showed little change.</p> <p>Conclusions</p> <p>During the first two years in the workforce, there was a transitory decline in the self-reported practice and sense of relevance of EBP, despite increases in confidence and knowledge. The pattern of progression of EBP skills beyond these early professional working years is unknown.</p

An empirical investigation of dance addiction

Although recreational dancing is associated with increased physical and psychological well-being, little is known about the harmful effects of excessive dancing. The aim of the present study was to explore the psychopathological factors associated with dance addiction. The sample comprised 447 salsa and ballroom dancers (68% female, mean age: 32.8 years) who danced recreationally at least once a week. The Exercise Addiction Inventory (Terry, Szabo, & Griffiths, 2004) was adapted for dance (Dance Addiction Inventory, DAI). Motivation, general mental health (BSI-GSI, and Mental Health Continuum), borderline personality disorder, eating disorder symptoms, and dance motives were also assessed. Five latent classes were explored based on addiction symptoms with 11% of participants belonging to the most problematic class. DAI was positively associated with psychiatric distress, borderline personality and eating disorder symptoms. Hierarchical linear regression model indicated that Intensity (ß=0.22), borderline (ß=0.08), eating disorder (ß=0.11) symptoms, as well as Escapism (ß=0.47) and Mood Enhancement (ß=0.15) (as motivational factors) together explained 42% of DAI scores. Dance addiction as assessed with the Dance Addiction Inventory is associated with indicators of mild psychopathology and therefore warrants further research

Language Comprehension in the Balance: The Robustness of the Action-Compatibility Effect (ACE)

How does language comprehension interact with motor activity? We investigated the conditions under which comprehending an action sentence affects people's balance. We performed two experiments to assess whether sentences describing forward or backward movement modulate the lateral movements made by subjects who made sensibility judgments about the sentences. In one experiment subjects were standing on a balance board and in the other they were seated on a balance board that was mounted on a chair. This allowed us to investigate whether the action compatibility effect (ACE) is robust and persists in the face of salient incompatibilities between sentence content and subject movement. Growth-curve analysis of the movement trajectories produced by the subjects in response to the sentences suggests that the ACE is indeed robust. Sentence content influenced movement trajectory despite salient inconsistencies between implied and actual movement. These results are interpreted in the context of the current discussion of embodied, or grounded, language comprehension and meaning representation

The Stroke Outcomes Study 2 (SOS2): a prospective, analytic cohort study of depressive symptoms after stroke

<p>Abstract</p> <p>Background</p> <p>Mood disorder is recognised as an important and common problem after stroke but little is known about the longer term effects of mood on functional outcomes. This protocol paper describes the Stroke Outcomes Study 2 (SOS2), a research study conducted in two large acute NHS Trusts in the North of England, which was designed to investigate the impact of early depressive symptoms on outcomes after an acute stroke.</p> <p>Methods and design</p> <p>SOS2 was a prospective cohort study that aimed to recruit patients in the first few weeks after a stroke, and to follow them up at regular intervals for one year thereafter in order to describe the trajectory of psychological symptoms and study their impact on physical functional recovery. Measures of mood and function were completed at baseline (approximately 3 weeks) and at four follow-up time-points: approximately 9, 13, 26 and 52 weeks after the index stroke.</p> <p>Discussion</p> <p>Recruiting patients to research studies soon after an acute stroke is difficult. Mortality following stroke is approximately 30% and in the region of half the patients that survive the initial event are significantly disabled. Together these factors reduced the number of patients available to participate in SOS2 but once recruited to the study the drop-out rate was relatively low. During the recruitment period over 6000 admissions for stroke or query stroke were screened for eligibility. A cohort of 592 study participants was finally achieved.</p

Distinguishing Asthma Phenotypes Using Machine Learning Approaches.

Asthma is not a single disease, but an umbrella term for a number of distinct diseases, each of which are caused by a distinct underlying pathophysiological mechanism. These discrete disease entities are often labelled as asthma endotypes. The discovery of different asthma subtypes has moved from subjective approaches in which putative phenotypes are assigned by experts to data-driven ones which incorporate machine learning. This review focuses on the methodological developments of one such machine learning technique-latent class analysis-and how it has contributed to distinguishing asthma and wheezing subtypes in childhood. It also gives a clinical perspective, presenting the findings of studies from the past 5 years that used this approach. The identification of true asthma endotypes may be a crucial step towards understanding their distinct pathophysiological mechanisms, which could ultimately lead to more precise prevention strategies, identification of novel therapeutic targets and the development of effective personalized therapies

A comparison of three clustering methods for finding subgroups in MRI, SMS or clinical data: SPSS TwoStep Cluster analysis, Latent Gold and SNOB

Background: There are various methodological approaches to identifying clinically important subgroups and one method is to identify clusters of characteristics that differentiate people in cross-sectional and/or longitudinal data using Cluster Analysis (CA) or Latent Class Analysis (LCA). There is a scarcity of head-to-head comparisons that can inform the choice of which clustering method might be suitable for particular clinical datasets and research questions. Therefore, the aim of this study was to perform a head-to-head comparison of three commonly available methods (SPSS TwoStep CA, Latent Gold LCA and SNOB LCA). Methods. The performance of these three methods was compared: (i) quantitatively using the number of subgroups detected, the classification probability of individuals into subgroups, the reproducibility of results, and (ii) qualitatively using subjective judgments about each program's ease of use and interpretability of the presentation of results.We analysed five real datasets of varying complexity in a secondary analysis of data from other research projects. Three datasets contained only MRI findings (n = 2,060 to 20,810 vertebral disc levels), one dataset contained only pain intensity data collected for 52 weeks by text (SMS) messaging (n = 1,121 people), and the last dataset contained a range of clinical variables measured in low back pain patients (n = 543 people). Four artificial datasets (n = 1,000 each) containing subgroups of varying complexity were also analysed testing the ability of these clustering methods to detect subgroups and correctly classify individuals when subgroup membership was known. Results: The results from the real clinical datasets indicated that the number of subgroups detected varied, the certainty of classifying individuals into those subgroups varied, the findings had perfect reproducibility, some programs were easier to use and the interpretability of the presentation of their findings also varied. The results from the artificial datasets indicated that all three clustering methods showed a near-perfect ability to detect known subgroups and correctly classify individuals into those subgroups. Conclusions: Our subjective judgement was that Latent Gold offered the best balance of sensitivity to subgroups, ease of use and presentation of results with these datasets but we recognise that different clustering methods may suit other types of data and clinical research questions

MI-GWAS: a SAS platform for the analysis of inherited and maternal genetic effects in genome-wide association studies using log-linear models

<p>Abstract</p> <p>Background</p> <p>Several platforms for the analysis of genome-wide association data are available. However, these platforms focus on the evaluation of the genotype inherited by affected (i.e. case) individuals, whereas for some conditions (e.g. birth defects) the genotype of the mothers of affected individuals may also contribute to risk. For such conditions, it is critical to evaluate associations with both the maternal and the inherited (i.e. case) genotype. When genotype data are available for case-parent triads, a likelihood-based approach using log-linear modeling can be used to assess both the maternal and inherited genotypes. However, available software packages for log-linear analyses are not well suited to the analysis of typical genome-wide association data (e.g. including missing data).</p> <p>Results</p> <p>An integrated platform, Maternal and Inherited Analyses for Genome-wide Association Studies <b>(</b>MI-GWAS) for log-linear analyses of maternal and inherited genetic effects in large, genome-wide datasets, is described. MI-GWAS uses SAS and LEM software in combination to appropriately format data, perform the log-linear analyses and summarize the results. This platform was evaluated using existing genome-wide data and was shown to perform accurately and relatively efficiently.</p> <p>Conclusions</p> <p>The MI-GWAS platform provides a valuable tool for the analysis of association of a phenotype or condition with maternal and inherited genotypes using genome-wide data from case-parent triads. The source code for this platform is freely available at <url>http://www.sph.uth.tmc.edu/sbrr/mi-gwas.htm</url>.</p