Are autistic traits measured equivalently in individuals with and without an Autism Spectrum Disorder?:An invariance analysis of the Autism Spectrum Quotient Short Form

It is common to administer measures of autistic traits to those without autism spectrum disorders (ASDs) with, for example, the aim of understanding autistic personality characteristics in non-autistic individuals. Little research has examined the extent to which measures of autistic traits actually measure the same traits in the same way across those with and without an ASD. We addressed this question using a multi-group confirmatory factor invariance analysis of the Autism Quotient Short Form (AQ-S: Hoekstra et al. in J Autism Dev Disord 41(5):589-596, 2011) across those with (n = 148) and without (n = 168) ASD. Metric variance (equality of factor loadings), but not scalar invariance (equality of thresholds), held suggesting that the AQ-S measures the same latent traits in both groups, but with a bias in the manner in which trait levels are estimated. We, therefore, argue that the AQ-S can be used to investigate possible causes and consequences of autistic traits in both groups separately, but caution is due when combining or comparing levels of autistic traits across the two group

Accurate masses and radii of normal stars: modern results and applications

This paper presents and discusses a critical compilation of accurate, fundamental determinations of stellar masses and radii. We have identified 95 detached binary systems containing 190 stars (94 eclipsing systems, and alpha Centauri) that satisfy our criterion that the mass and radius of both stars be known to 3% or better. To these we add interstellar reddening, effective temperature, metal abundance, rotational velocity and apsidal motion determinations when available, and we compute a number of other physical parameters, notably luminosity and distance. We discuss the use of this information for testing models of stellar evolution. The amount and quality of the data also allow us to analyse the tidal evolution of the systems in considerable depth, testing prescriptions of rotational synchronisation and orbital circularisation in greater detail than possible before. The new data also enable us to derive empirical calibrations of M and R for single (post-) main-sequence stars above 0.6 M(Sun). Simple, polynomial functions of T(eff), log g and [Fe/H] yield M and R with errors of 6% and 3%, respectively. Excellent agreement is found with independent determinations for host stars of transiting extrasolar planets, and good agreement with determinations of M and R from stellar models as constrained by trigonometric parallaxes and spectroscopic values of T(eff) and [Fe/H]. Finally, we list a set of 23 interferometric binaries with masses known to better than 3%, but without fundamental radius determinations (except alpha Aur). We discuss the prospects for improving these and other stellar parameters in the near future.Comment: 56 pages including figures and tables. To appear in The Astronomy and Astrophysics Review. Ascii versions of the tables will appear in the online version of the articl

Epithelial-to-mesenchymal transition supports ovarian carcinosarcoma tumorigenesis and confers sensitivity to microtubule-targeting with eribulin

Ovarian carcinosarcoma (OCS) is an aggressive and rare tumour type with limited treatment options. OCS is hypothesised to develop via the combination theory, with a single progenitor resulting in carcinomatous and sarcomatous components, or alternatively via the conversion theory, with the sarcomatous component developing from the carcinomatous component through epithelial-to-mesenchymal transition (EMT). In this study, we analysed DNA variants from isolated carcinoma and sarcoma components to show that OCS from 18 women is monoclonal. RNA sequencing indicated the carcinoma components were more mesenchymal when compared with pure epithelial ovarian carcinomas, supporting the conversion theory and suggesting that EMT is important in the formation of these tumours. Preclinical OCS models were used to test the efficacy of microtubule-targeting drugs, including eribulin, which has previously been shown to reverse EMT characteristics in breast cancers and induce differentiation in sarcomas. Vinorelbine and eribulin more effectively inhibited OCS growth than standard-of-care platinum-based chemotherapy, and treatment with eribulin reduced mesenchymal characteristics and N-MYC expression in OCS patient-derived xenografts (PDX). Eribulin treatment resulted in an accumulation of intracellular cholesterol in OCS cells, which triggered a down-regulation of the mevalonate pathway and prevented further cholesterol biosynthesis. Finally, eribulin increased expression of genes related to immune activation and increased the intratumoral accumulation of CD8+ T cells, supporting exploration of immunotherapy combinations in the clinic. Together, these data indicate EMT plays a key role in OCS tumourigenesis and support the conversion theory for OCS histogenesis. Targeting EMT using eribulin could help improve OCS patient outcomes

Cross-National Measurement Invariance of the Teacher and Classmate Support Scale

The cross-national measurement invariance of the teacher and classmate support scale was assessed in a study of 23202 Grade 8 and 10 students from Austria, Canada, England, Lithuania, Norway, Poland, and Slovenia, participating in the Health Behaviour in School-aged Children (HBSC) 2001/2002 study. A multi-group means and covariance analysis supported configural and metric invariance across countries, but not full scalar equivalence. The composite reliability was adequate and highly consistent across countries. In all seven countries, teacher support showed stronger associations with school satisfaction than did classmate support, with the results being highly consistent across countries. The results indicate that the teacher and classmate support scale may be used in cross-cultural studies that focus on relationships between teacher and classmate support and other constructs. However, the lack of scalar equivalence indicates that direct comparison of the levels support across countries might not be warranted

Psychometric properties of the IDS-SR30 for the assessment of depressive symptoms in spanish population

<p>Abstract</p> <p>Background</p> <p>Due to the high prevalence of depression, it is clinically relevant to improve the early identification and assessment of depressive episodes. The main objective of the present study was to examine the psychometric properties of the IDS-SR₃₀(Self-rated Inventory of Depressive Symptomatology) in a large Spanish sample of depressive patients.</p> <p>Methods</p> <p>This prospective, naturalistic, multicenter, nationwide epidemiological study conducted in Spain included 1595 adult patients (65.3% females) with a DSM-IV Major Depressive Disorder (MDD. IDS-SR₃₀and the Hamilton Depression Rating Scale (HDRS, 21 items)were administered to the sample. Data was collected during 2 routine visits. The second assessment was carried out after 10 ± 2 weeks after first assessment.</p> <p>Results</p> <p>The IDS-SR₃₀showed good internal consistency (α = 0.94) and high item total correlations (≥ 0.50) were found in 70% of the items. The convergent validity was 0.85. Results of the principal component analysis (PCA) and confirmatory factor analyses (CFA) showed that a three factor model (labelled mood/cognition, anxiety/somatic and sleep) is adequate for the current sample.</p> <p>Conclusions</p> <p>The Spanish version of the IDS-SR₃₀seems a reliable, valid and useful tool for measuring depression symptomatology in Spanish population.</p

Multiway modeling and analysis in stem cell systems biology

<p>Abstract</p> <p>Background</p> <p>Systems biology refers to multidisciplinary approaches designed to uncover emergent properties of biological systems. Stem cells are an attractive target for this analysis, due to their broad therapeutic potential. A central theme of systems biology is the use of computational modeling to reconstruct complex systems from a wealth of reductionist, molecular data (e.g., gene/protein expression, signal transduction activity, metabolic activity, etc.). A number of deterministic, probabilistic, and statistical learning models are used to understand sophisticated cellular behaviors such as protein expression during cellular differentiation and the activity of signaling networks. However, many of these models are bimodal i.e., they only consider row-column relationships. In contrast, multiway modeling techniques (also known as tensor models) can analyze multimodal data, which capture much more information about complex behaviors such as cell differentiation. In particular, tensors can be very powerful tools for modeling the dynamic activity of biological networks over time. Here, we review the application of systems biology to stem cells and illustrate application of tensor analysis to model collagen-induced osteogenic differentiation of human mesenchymal stem cells.</p> <p>Results</p> <p>We applied Tucker1, Tucker3, and Parallel Factor Analysis (PARAFAC) models to identify protein/gene expression patterns during extracellular matrix-induced osteogenic differentiation of human mesenchymal stem cells. In one case, we organized our data into a tensor of type protein/gene locus link × gene ontology category × osteogenic stimulant, and found that our cells expressed two distinct, stimulus-dependent sets of functionally related genes as they underwent osteogenic differentiation. In a second case, we organized DNA microarray data in a three-way tensor of gene IDs × osteogenic stimulus × replicates, and found that application of tensile strain to a collagen I substrate accelerated the osteogenic differentiation induced by a static collagen I substrate.</p> <p>Conclusion</p> <p>Our results suggest gene- and protein-level models whereby stem cells undergo transdifferentiation to osteoblasts, and lay the foundation for mechanistic, hypothesis-driven studies. Our analysis methods are applicable to a wide range of stem cell differentiation models.</p

Hierarchical cluster analysis of labour market regulations and population health: a taxonomy of low- and middle-income countries

<p>Abstract</p> <p>Background</p> <p>An important contribution of the social determinants of health perspective has been to inquire about non-medical determinants of population health. Among these, labour market regulations are of vital significance. In this study, we investigate the labour market regulations among low- and middle-income countries (LMICs) and propose a labour market taxonomy to further understand population health in a global context.</p> <p>Methods</p> <p>Using Gross National Product per capita, we classify 113 countries into either low-income (n = 71) or middle-income (n = 42) strata. Principal component analysis of three standardized indicators of labour market inequality and poverty is used to construct 2 factor scores. Factor score reliability is evaluated with Cronbach's alpha. Using these scores, we conduct a hierarchical cluster analysis to produce a labour market taxonomy, conduct zero-order correlations, and create box plots to test their associations with adult mortality, healthy life expectancy, infant mortality, maternal mortality, neonatal mortality, under-5 mortality, and years of life lost to communicable and non-communicable diseases. Labour market and health data are retrieved from the International Labour Organization's Key Indicators of Labour Markets and World Health Organization's Statistical Information System.</p> <p>Results</p> <p>Six labour market clusters emerged: Residual (n = 16), Emerging (n = 16), Informal (n = 10), Post-Communist (n = 18), Less Successful Informal (n = 22), and Insecure (n = 31). Primary findings indicate: (i) labour market poverty and population health is correlated in both LMICs; (ii) association between labour market inequality and health indicators is significant only in low-income countries; (iii) Emerging (e.g., East Asian and Eastern European countries) and Insecure (e.g., sub-Saharan African nations) clusters are the most advantaged and disadvantaged, respectively, with the remaining clusters experiencing levels of population health consistent with their labour market characteristics.</p> <p>Conclusions</p> <p>The labour market regulations of LMICs appear to be important social determinant of population health. This study demonstrates the heuristic value of understanding the labour markets of LMICs and their health effects using exploratory taxonomy approaches.</p

Endocrine disruptors and obesity

The purpose of this review is to summarise current evidence that some environmental chemicals may be able to interfere in endocrine regulation of energy metabolism and adipose tissue structure. Recent findings demonstrate that such endocrine disrupting chemicals, termed “obesogens”, can promote adipogenesis and cause weight gain. This includes compounds to which the human population is exposed in daily life through their use in pesticides/herbicides, industrial and household products, plastics, detergents, flame retardants and ingredients in personal care products. Animal models and epidemiological studies have shown that an especially sensitive time for exposure is in utero or the neonatal period. In summarising the actions of obesogens, it is noteworthy that as their structures are mainly lipophilic, their ability to increase fat deposition has the added consequence of increasing the capacity for their own retention. This has the potential for a vicious spiral not only of increasing obesity but also increasing retention of other lipophilic pollutant chemicals with an even broader range of adverse actions. This might offer an explanation as to why obesity is an underlying risk factor for so many diseases including cancer