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The COllaborative project of Development of Anthropometrical measures in Twins (CODATwins) project is a large international collaborative effort to analyze individual-level phenotype data from twins in multiple cohorts from different environments. The main objective is to study factors that modify genetic and environmental variation of height, body mass index (BMI, kg/m2) and size at birth, and additionally to address other research questions such as long-term consequences of birth size. The project started in 2013 and is open to all twin projects in the world having height and weight measures on twins with information on zygosity. Thus far, 54 twin projects from 24 countries have provided individual-level data. The CODATwins database includes 489,981 twin individuals (228,635 complete twin pairs). Since many twin cohorts have collected longitudinal data, there is a total of 1,049,785 height and weight observations. For many cohorts, we also have information on birth weight and length, own smoking behavior and own or parental education. We found that the heritability estimates of height and BMI systematically changed from infancy to old age. Remarkably, only minor differences in the heritability estimates were found across cultural–geographic regions, measurement time and birth cohort for height and BMI. In addition to genetic epidemiological studies, we looked at associations of height and BMI with education, birth weight and smoking status. Within-family analyses examined differences within same-sex and opposite-sex dizygotic twins in birth size and later development. The CODATwins project demonstrates the feasibility and value of international collaboration to address gene-by-exposure interactions that require large sample sizes and address the effects of different exposures across time, geographical regions and socioeconomic status.
In search of empirical classifications of depression and anxiety, most subtyping studies focus solely on symptoms and do so within a single disorder. This study aimed to identify and validate cross-diagnostic subtypes by simultaneously considering symptoms of depression and anxiety, and disability measures.
A large cohort of adults (Lifelines, n = 73 403) had a full assessment of 16 symptoms of mood and anxiety disorders, and measurement of physical, social and occupational disability. The best-fitting subtyping model was identified by comparing different hybrid mixture models with and without disability covariates on fit criteria in an independent test sample. The best model's classes were compared across a range of external variables.
The best-fitting Mixed Measurement Item Response Theory model with disability covariates identified five classes. Accounting for disability improved differentiation between people reporting isolated non-specific symptoms [‘Somatic’ (13.0%), and ‘Worried’ (14.0%)] and psychopathological symptoms [‘Subclinical’ (8.8%), and ‘Clinical’ (3.3%)]. Classes showed distinct associations with clinically relevant external variables [e.g. somatization: odds ratio (OR) 8.1–12.3, and chronic stress: OR 3.7–4.4]. The Subclinical class reported symptomatology at subthreshold levels while experiencing disability. No pure depression or anxiety, but only mixed classes were found.
An empirical classification model, incorporating both symptoms and disability identified clearly distinct cross-diagnostic subtypes, indicating that diagnostic nets should be cast wider than current phenomenology-based categorical systems.
We present the results of a pupil masking experiment using the Sun as a source. The goal of the experiment was a first proof of concept validation for Fizeau interferometric beam combination using a source that fills the field of view of the telescope. Phase diversity techniques are employed to record the phasing errors in the mask required to construct the optical transfer function (OTF) and are used to deconvolve the dirty images.
Two high-fidelity computer simulations are used to study low-order adaptive optics systems operating in the near-infrared. We study obtainable system performance using very dim reference sources at three IR wavelengths.
To determine the contribution of forest foods to dietary intake and estimate their association with household food insecurity.
Cross-sectional survey conducted among 279 households. Using a 7 d recall questionnaire, information on household food consumption was collected from women and used to determine the household dietary diversity score, food variety score and forest food consumption score (FFCS). Household Food Insecurity Access Scale (HFIAS) score was determined and Spearman rank correlation was used to establish the relationship between consumption of forest foods and HFIAS score. Women’s dietary intake was estimated from two 24 h recalls. The contribution of forest foods to women’s nutrient intakes was calculated and women’s nutrient intakes were compared with estimated average nutrient requirements.
Rural forest-dependent households in twelve villages in eastern and southern Cameroon.
Household heads and their non-pregnant, non-lactating spouses.
Forty-seven unique forest foods were identified; of these, seventeen were consumed by 98 % of respondents over the course of one week and by 17 % of women during the two 24 h recall periods. Although forest foods contributed approximately half of women’s total daily energy intake, considerably greater contributions were made to vitamin A (93 %), Na (100 %), Fe (85 %), Zn (88 %) and Ca (89 %) intakes. Despite a highly biodiverse pool of foods, most households (83 %) suffered from high food insecurity based on the HFIAS. A significant inverse correlation was observed between the HFIAS score and the FFCS (r2=−0·169, P=0·0006), demonstrating that forest foods play an important role in ensuring food security in these forest-dependent communities.
Forest foods are widely consumed by forest-dependent communities. Given their rich nutrient content, they have potential to contribute to food and nutrition security.
Clinicians need guidance to address the heterogeneity of treatment responses of patients with major depressive disorder (MDD). While prediction schemes based on symptom clustering and biomarkers have so far not yielded results of sufficient strength to inform clinical decision-making, prediction schemes based on big data predictive analytic models might be more practically useful.
We review evidence suggesting that prediction equations based on symptoms and other easily-assessed clinical features found in previous research to predict MDD treatment outcomes might provide a foundation for developing predictive analytic clinical decision support models that could help clinicians select optimal (personalised) MDD treatments. These methods could also be useful in targeting patient subsamples for more expensive biomarker assessments.
Approximately two dozen baseline variables obtained from medical records or patient reports have been found repeatedly in MDD treatment trials to predict overall treatment outcomes (i.e., intervention v. control) or differential treatment outcomes (i.e., intervention A v. intervention B). Similar evidence has been found in observational studies of MDD persistence-severity. However, no treatment studies have yet attempted to develop treatment outcome equations using the full set of these predictors. Promising preliminary empirical results coupled with recent developments in statistical methodology suggest that models could be developed to provide useful clinical decision support in personalised treatment selection. These tools could also provide a strong foundation to increase statistical power in focused studies of biomarkers and MDD heterogeneity of treatment response in subsequent controlled trials.
Coordinated efforts are needed to develop a protocol for systematically collecting information about established predictors of heterogeneity of MDD treatment response in large observational treatment studies, applying and refining these models in subsequent pragmatic trials, carrying out pooled secondary analyses to extract the maximum amount of information from these coordinated studies, and using this information to focus future discovery efforts in the segment of the patient population in which continued uncertainty about treatment response exists.
A trend toward greater body size in dizygotic (DZ) than in monozygotic (MZ) twins has been suggested by some but not all studies, and this difference may also vary by age. We analyzed zygosity differences in mean values and variances of height and body mass index (BMI) among male and female twins from infancy to old age. Data were derived from an international database of 54 twin cohorts participating in the COllaborative project of Development of Anthropometrical measures in Twins (CODATwins), and included 842,951 height and BMI measurements from twins aged 1 to 102 years. The results showed that DZ twins were consistently taller than MZ twins, with differences of up to 2.0 cm in childhood and adolescence and up to 0.9 cm in adulthood. Similarly, a greater mean BMI of up to 0.3 kg/m2 in childhood and adolescence and up to 0.2 kg/m2 in adulthood was observed in DZ twins, although the pattern was less consistent. DZ twins presented up to 1.7% greater height and 1.9% greater BMI than MZ twins; these percentage differences were largest in middle and late childhood and decreased with age in both sexes. The variance of height was similar in MZ and DZ twins at most ages. In contrast, the variance of BMI was significantly higher in DZ than in MZ twins, particularly in childhood. In conclusion, DZ twins were generally taller and had greater BMI than MZ twins, but the differences decreased with age in both sexes.
For over 100 years, the genetics of human anthropometric traits has attracted scientific interest. In particular, height and body mass index (BMI, calculated as kg/m2) have been under intensive genetic research. However, it is still largely unknown whether and how heritability estimates vary between human populations. Opportunities to address this question have increased recently because of the establishment of many new twin cohorts and the increasing accumulation of data in established twin cohorts. We started a new research project to analyze systematically (1) the variation of heritability estimates of height, BMI and their trajectories over the life course between birth cohorts, ethnicities and countries, and (2) to study the effects of birth-related factors, education and smoking on these anthropometric traits and whether these effects vary between twin cohorts. We identified 67 twin projects, including both monozygotic (MZ) and dizygotic (DZ) twins, using various sources. We asked for individual level data on height and weight including repeated measurements, birth related traits, background variables, education and smoking. By the end of 2014, 48 projects participated. Together, we have 893,458 height and weight measures (52% females) from 434,723 twin individuals, including 201,192 complete twin pairs (40% monozygotic, 40% same-sex dizygotic and 20% opposite-sex dizygotic) representing 22 countries. This project demonstrates that large-scale international twin studies are feasible and can promote the use of existing data for novel research purposes.
Fruit and vegetable consumption produces changes in several biomarkers in blood. The present study aimed to examine the dose–response curve between fruit and vegetable consumption and carotenoid (α-carotene, β-carotene, β-cryptoxanthin, lycopene, lutein and zeaxanthin), folate and vitamin C concentrations. Furthermore, a prediction model of fruit and vegetable intake based on these biomarkers and subject characteristics (i.e. age, sex, BMI and smoking status) was established. Data from twelve diet-controlled intervention studies were obtained to develop a prediction model for fruit and vegetable intake (including and excluding fruit and vegetable juices). The study population in the present individual participant data meta-analysis consisted of 526 men and women. Carotenoid, folate and vitamin C concentrations showed a positive relationship with fruit and vegetable intake. Measures of performance for the prediction model were calculated using cross-validation. For the prediction model of fruit, vegetable and juice intake, the root mean squared error (RMSE) was 258·0 g, the correlation between observed and predicted intake was 0·78 and the mean difference between observed and predicted intake was − 1·7 g (limits of agreement: − 466·3, 462·8 g). For the prediction of fruit and vegetable intake (excluding juices), the RMSE was 201·1 g, the correlation was 0·65 and the mean bias was 2·4 g (limits of agreement: − 368·2, 373·0 g). The prediction models which include the biomarkers and subject characteristics may be used to estimate average intake at the group level and to investigate the ranking of individuals with regard to their intake of fruit and vegetables when validating questionnaires that measure intake.
Anorexia nervosa (AN) and body dysmorphic disorder (BDD) are characterized by distorted body image and are frequently co-morbid with each other, although their relationship remains little studied. While there is evidence of abnormalities in visual and visuospatial processing in both disorders, no study has directly compared the two. We used two complementary modalities – event-related potentials (ERPs) and functional magnetic resonance imaging (fMRI) – to test for abnormal activity associated with early visual signaling.
We acquired fMRI and ERP data in separate sessions from 15 unmedicated individuals in each of three groups (weight-restored AN, BDD, and healthy controls) while they viewed images of faces and houses of different spatial frequencies. We used joint independent component analyses to compare activity in visual systems.
AN and BDD groups demonstrated similar hypoactivity in early secondary visual processing regions and the dorsal visual stream when viewing low spatial frequency faces, linked to the N170 component, as well as in early secondary visual processing regions when viewing low spatial frequency houses, linked to the P100 component. Additionally, the BDD group exhibited hyperactivity in fusiform cortex when viewing high spatial frequency houses, linked to the N170 component. Greater activity in this component was associated with lower attractiveness ratings of faces.
Results provide preliminary evidence of similar abnormal spatiotemporal activation in AN and BDD for configural/holistic information for appearance- and non-appearance-related stimuli. This suggests a common phenotype of abnormal early visual system functioning, which may contribute to perceptual distortions.
Although variation in the long-term course of major depressive disorder (MDD) is not strongly predicted by existing symptom subtype distinctions, recent research suggests that prediction can be improved by using machine learning methods. However, it is not known whether these distinctions can be refined by added information about co-morbid conditions. The current report presents results on this question.
Data came from 8261 respondents with lifetime DSM-IV MDD in the World Health Organization (WHO) World Mental Health (WMH) Surveys. Outcomes included four retrospectively reported measures of persistence/severity of course (years in episode; years in chronic episodes; hospitalization for MDD; disability due to MDD). Machine learning methods (regression tree analysis; lasso, ridge and elastic net penalized regression) followed by k-means cluster analysis were used to augment previously detected subtypes with information about prior co-morbidity to predict these outcomes.
Predicted values were strongly correlated across outcomes. Cluster analysis of predicted values found three clusters with consistently high, intermediate or low values. The high-risk cluster (32.4% of cases) accounted for 56.6–72.9% of high persistence, high chronicity, hospitalization and disability. This high-risk cluster had both higher sensitivity and likelihood ratio positive (LR+; relative proportions of cases in the high-risk cluster versus other clusters having the adverse outcomes) than in a parallel analysis that excluded measures of co-morbidity as predictors.
Although the results using the retrospective data reported here suggest that useful MDD subtyping distinctions can be made with machine learning and clustering across multiple indicators of illness persistence/severity, replication with prospective data is needed to confirm this preliminary conclusion.
All stars are born in molecular clouds, and most in giant molecular clouds (GMCs), which thus set the star formation activity of galaxies. We first review their observed properties, including measures of mass surface density, Σ, and thus mass, M. We discuss cloud dynamics, concluding most GMCs are gravitationally bound. Star formation is highly clustered within GMCs, but overall is very inefficient. We compare properties of star-forming clumps with those of young stellar clusters (YSCs). The high central densities of YSCs may result via dynamical evolution of already-formed stars during and after star cluster formation. We discuss theoretical models of GMC evolution, especially addressing how turbulence is maintained, and emphasizing the importance of GMC collisions. We describe how feedback limits total star formation efficiency, ε, in clumps. A turbulent and clumpy medium allows higher ε, permitting formation of bound clusters even when escape speeds are less than the ionized gas sound speed.
So far, no comprehensive answer has emerged to the question of whether transcranial direct current stimulation (tDCS) can make a clinically useful contribution to the treatment of major depression. We aim to present a systematic review and meta-analysis of tDCS in the treatment of depression.
Medline and Embase were searched for open-label and randomized controlled trials of tDCS in depression using the expressions (‘transcranial direct current stimulation’ or ‘tDCS’) and (‘depression’ or ‘depressed’). Study data were extracted with a standardized data sheet. For randomized controlled trials, effect size (Hedges' g) was calculated and the relationships between study variables and effect size explored using meta-regression.
A total of 108 citations were screened and 10 studies included in the systematic review. Six randomized controlled trials were included in the meta-analysis, with a cumulative sample of 96 active and 80 sham tDCS courses. Active tDCS was found to be more effective than sham tDCS for the reduction of depression severity (Hedges' g=0.743, 95% confidence interval 0.21–1.27), although study results differed more than expected by chance (Q=15.52, df=6, p=0.017, I2=61.35). Meta-regression did not reveal any significant correlations.
Our study was limited by the small number of studies included, which often had small sample size. Future studies should use larger, if possible representative, health service patient samples, and optimized protocols to evaluate the efficacy of tDCS in the treatment of depression further.
The fat mass and obesity associated (FTO) gene has been implicated with obesity and dietary intake predominantly in European populations. We assessed the association between the FTO rs9939609 variant with body fat distribution and dietary intake in a multi-ethnic population. Aboriginal, Chinese, European and South Asian participants living in Canada (n=706) were assessed for body fat and inner-abdominal fat using imaging techniques, dietary intake and genotyped for the FTO rs9939609 variant. Linear regression was used to study the associations between the minor allele of the variant and measures of adiposity and dietary intake. Minor allele frequencies were: Aboriginals (17%), Chinese (17%), Europeans (39%) and South Asians (31%). The rs9939609 variant was associated with intake of dietary macronutrients in Aboriginals and Europeans only. In the total population, there were positive associations between the rs9939609 minor allele and greater fat mass (0·94±0·56 kg, P=0·045), per cent body fat (0·7±0·4%, P=0·031), relative greater subcutaneous abdominal adipose tissue (4·9±2·8%, P=0·039) and percent daily calories from fat (0·4±0·2%, P=0·064). Our findings suggest that the FTO rs9939609 minor allele may be associated with dietary intake in adults and is positively associated with regional fat deposition.
This paper discusses the characterization and optimization of organic solar cells based on a bulk heterojunction consisting of an alternating copolymer, containing a fluorene and a benzathiadiazole unit with two neighboring thiophene rings, and a fullerene derivative (PCBM). The resulting power conversion efficiency amounts 3.9±0.2 % (AM1.5, 100 mW/cm2) and these polymer solar cells are therefore considered auspicious for further research.
Organic electronic systems offer the advantage of low weight and flexibility at potentially lower cost. Although the fabrication of functioning plastic transistors using approaches such as ink jet, lithography and stamping has been described i1–3, chemically compatible materials that allow for the sequential application of liquid layers is a technical barrier. Material issues maybe the Achilles heel of ultimately printing organic electronic devices as newspapers today, at high speeds and in a reel to reel process. We introduce a novel process–thermal transfer–a non-lithographic technique that enables printing multiple, successive layers via a dry additive process. This method is capable of patterning a range of organic materials at high speed over large areas with micron size resolution and excellent electrical performance. Such a dry, potentially reel-to-reel printing method may provide a practical route to realizing the expected benefits of plastics for electronics. We illustrate the viability of thermal transfer and the ability to develop suitable printable organics conductors by fabricating a functioning 4000 cm2 transistor array.
Detailed investigations of strain generation and relaxation in Si films grown on thin Si0.78Ge0.22 virtual substrates using Raman spectroscopy are presented. Good virtual substrate relaxation (>90%) is achieved by incorporating C during the initial growth stage. The robustness of the strained layers to relaxation is studied following high temperature rapid thermal annealing typical of CMOS processing (800-1050 °C). The impact of strained layer thickness on thermal stability is also investigated. Strain in layers below the critical thickness did not relax following any thermal treatments. However for layers above the critical thickness the annealing temperature at which the onset of strain relaxation occurred appeared to decrease with increasing layer thickness. Strain in Si layers grown on thin and thick virtual substrates having identical Ge composition and epilayer thickness has been compared. Relaxation through the introduction of defects has been assessed through preferential defect etching in order to verify the trends observed. Raman signals have been analysed by calibrated deconvolution and curve-fitting of the spectra peaks. Raman spectroscopy has also been used to study epitaxial layer thickness and the impact of Ge out-diffusion during processing. Improved device performance and reduced self-heating effects are demonstrated in thin virtual substrate devices when fabricated using strained layers below the critical thickness. The results suggest that thin virtual substrates offer great promise for enhancing the performance of a wide range of strained Si devices.
High purity lanthanum oxide and praseodymium oxide thin films (C< 1 at.-%) have been deposited by liquid injection MOCVD using the volatile alkoxide precursos [La(mmp)3] and [Pr(mmp)3] in toluene-solution (mmp = OCMe2CH2OMe). 1H NMR solution studies have shown that the addition of donor species, such as tetraglyme (CH3O(CH2CH2O)4CH3) or mmpH prevent molecular aggregation and help stabilise the precursors.