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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.
Whether monozygotic (MZ) and dizygotic (DZ) twins differ from each other in a variety of phenotypes is important for genetic twin modeling and for inferences made from twin studies in general. We analyzed whether there were differences in individual, maternal and paternal education between MZ and DZ twins in a large pooled dataset. Information was gathered on individual education for 218,362 adult twins from 27 twin cohorts (53% females; 39% MZ twins), and on maternal and paternal education for 147,315 and 143,056 twins respectively, from 28 twin cohorts (52% females; 38% MZ twins). Together, we had information on individual or parental education from 42 twin cohorts representing 19 countries. The original education classifications were transformed to education years and analyzed using linear regression models. Overall, MZ males had 0.26 (95% CI [0.21, 0.31]) years and MZ females 0.17 (95% CI [0.12, 0.21]) years longer education than DZ twins. The zygosity difference became smaller in more recent birth cohorts for both males and females. Parental education was somewhat longer for fathers of DZ twins in cohorts born in 1990–1999 (0.16 years, 95% CI [0.08, 0.25]) and 2000 or later (0.11 years, 95% CI [0.00, 0.22]), compared with fathers of MZ twins. The results show that the years of both individual and parental education are largely similar in MZ and DZ twins. We suggest that the socio-economic differences between MZ and DZ twins are so small that inferences based upon genetic modeling of twin data are not affected.
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.
Transcranial direct current stimulation (tDCS) is a non-pharmacological intervention for depression. It has mixed results, possibly caused by study heterogeneity.
To assess tDCS efficacy and to explore individual response predictors.
Systematic review and individual patient data meta-analysis.
Data were gathered from six randomised sham-controlled trials, enrolling 289 patients. Active tDCS was significantly superior to sham for response (34% v. 19% respectively, odds ratio (OR) = 2.44, 95% CI 1.38–4.32, number needed to treat (NNT) = 7), remission (23.1% v. 12.7% respectively, OR = 2.38, 95% CI 1.22–4.64, NNT = 9) and depression improvement (B coefficient 0.35, 95% CI 0.12–0.57). Mixed-effects models showed that, after adjustment for other predictors and confounders, treatment-resistant depression and higher tDCS ‘doses' were, respectively, negatively and positively associated with tDCS efficacy.
The effect size of tDCS treatment was comparable with those reported for repetitive transcranial magnetic stimulation and antidepressant drug treatment in primary care. The most important parameters for optimisation in future trials are depression refractoriness and tDCS dose.
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.
Our knowledge about the impact of coping behavior styles in people exposed to stressful disaster events is limited. Effective coping behavior has been shown to be a psychosocial stress modifier in both occupational and nonoccupational settings.
Data were collected by using a web-based survey that administered the Post-Traumatic Stress Disorder (PTSD) Checklist–Civilian, General Coping Questionnaire-30, and a supplementary questionnaire assessing various risk factors. Logistic regression models were used to test for the association of the 3 coping styles with probable PTSD following disaster exposure among federal disaster responders.
In this sample of 549 study subjects, avoidant coping behavior was most associated with probable PTSD. In tested regression models, the odds ratios ranged from 1.19 to 1.26 and 95% confidence intervals ranged from 1.08 to 1.35. With control for various predictors, emotion-based coping behavior was also found to be associated with probable PTSD (odds ratio=1.11; 95% confidence interval: 1.01-1.22).
This study found that in disaster responders exposed to traumatic disaster events, the likelihood of probable PTSD can be influenced by individual coping behavior style and other covariates. The continued probability of disasters underscores the critical importance of these findings both in terms of guiding mental health practitioners in treating exposed disaster responders and in stimulating future research. (Disaster Med Public Health Preparedness. 2016;10:108–117)
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.
The thermal conductivities of epoxy composites of mixtures of graphite and graphene in varying ratios were measured. Thermal characterization results showed unexpectedly high conductivities at a certain ratio filler ratio. This phenomenon was exhibited by samples with three different overall filler concentrations (graphene + graphite) of 7, 14, and 35 wt%. The highest thermal conductivity of 42.4 ± 4.8 W/m K (nearly 250 times the thermal conductivity of pristine epoxy) was seen for a sample with 30 wt% graphite and 5 wt% graphene when characterized using the dual-mode heat flow meter technique. This significant improvement in thermal conductivity can be attributed to the lowering of overall thermal interface resistance due to small amounts of nanofillers (graphene) improving the thermal contact between the primary microfillers (graphite). The synergistic effect of this hybrid filler system is lost at higher loadings of the graphene relative to graphite. Graphite and graphene mixed in the ratio of 6:1 yielded the highest thermal conductivities at three different filler loadings.
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.
Previously published guidelines are available that provide comprehensive recommendations for detecting and preventing healthcare-associated infections (HAIs). The intent of this document is to highlight practical recommendations in a concise format designed to assist acute care hospitals in implementing and prioritizing their Clostridium difficile infection (CDI) prevention efforts. This document updates “Strategies to Prevent Clostridium difficile Infections in Acute Care Hospitals,” published in 2008. This expert guidance document is sponsored by the Society for Healthcare Epidemiology of America (SHEA) and is the product of a collaborative effort led by SHEA, the Infectious Diseases Society of America (IDSA), the American Hospital Association (AHA), the Association for Professionals in Infection Control and Epidemiology (APIC), and The Joint Commission, with major contributions from representatives of a number of organizations and societies with content expertise. The list of endorsing and supporting organizations is presented in the introduction to the 2014 updates.
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.
Solid phase epitaxial regrowth (SPER) has been proven to be highly advantageous for ultra shallow junction formation in advanced technologies. Application of SPER to strained Si/SiGe structures raises the concern that the Ge may out diffuse during the implantation and/or anneal steps and thus reduce the strain in the top silicon layer.
In the present studies we expose 8-30 nm strained silicon layers grown on thin relaxed SiGe-buffers, to implant conditions and anneal cycles, characteristic for formation of the junctions by solid phase epitaxial regrowth and conventional spike activation. The resulting Geredistribution is measured using SIMS. Based on the outdiffused Ge-profiles the Ge-diffusion coefficient has been determined in the temperature range of 800-1100C from which an activation energy of ∼ 3.6 eV can be deduced. Up to 1050 C, 10 min, even a 30 nm strained film remains highly stable and shows only very moderate outdiffusion.
We also have observed a far more efficient, athermal Ge-redistribution process linked to the implantation step itself. This was studied by analysing the Ge-redistribution following an Asimplant (2-15 keV, 5 1014 – 3 1015 at/cm2). It is shown that the energy of the implant species (or more specifically the position of the damage distribution function relative to the Ge-edge) plays a determining factor with respect to the Ge-migration. For implants whereby the damage distribution overlaps with the Ge-edge, a very efficient transport of the Ge is observed, even prior to any anneal cycle. The migration is entirely correlated with the collision cascade and the resulting (forward!) Ge-recoil distribution. The scaling with dose for a given energy links the observed Ge-profile with a broadening mechanism related to the number of atom displacements induced in the sample within the vicinity of the Si-SiGe-transition.
Tensile strained Si on SiGe Strain Relaxed Buffers (SRB) is an interesting candidate to increase both electron and hole mobility which results in improved device performance. Most of this work was/is based on thick (several μm), step-graded SRBs with or without Chemical Mechanical Polishing (CMP) planarisation. This approach bears several disadvantages such as issues with STI formation in the thick SiGe structure, and considerable self-heating effects due to the lower thermal conductivity of the SiGe material. Further, pMOS improvement requires SRBs with high Ge contents (> 30 %), which complicates device fabrication even more. To overcome these issues, we developed a new and cost efficient type of thin SRB (∼200 nm). The concept is based on the introduction of a thin carbon-containing layer during growth of a constant composition SiGe layer. The process relies on standard Chemical Vapor Deposition epitaxial technology without need for CMP. It is designed to allow both non-selective growth on blanket wafers and selective growth in the active area of structured wafers with Shallow Trench Isolation (STI). The selective epitaxial process for strained Si on thin SRBs proposed here, allows relatively simple and cost-effective fabrication of strained Si layers on existing STI structures without any process modification. Further, it offers a very flexible fabrication scheme to independently improve nMOS and pMOS devices. The SRB quality is comparable to the best reported in literature so far, with 70 % and 53 % mobility enhancements for long channel nMOSFETs on 22 % Ge SRBs grown on blanket and STI patterned wafers, respectively.
Junctions were formed in thin SiGe/strained Si substrates with a thickness of 250-350 nm to assess the effect of different buffer layer parameters (bandgap, dislocations, thickness) on the junction leakage density that can be expected in MOSFET devices. The implantations used are standard well, channel and Highly Doped Drain (HDD) implants. Both p+/n and n+/p junctions were evaluated. The total thickness of the buffer layers was varied to compare the effect of different structural layers on the diode leakage. This investigation shows that the effect of an increased defect density is dominant at room temperature for the strained Si samples, resulting in 4-5 orders of magnitude increase in leakage. However, there is a different gradation in leakage dependence for thick and thin buffer layers, especially at higher temperatures.
In advanced CMOS technology nodes one may achieve further enhancement of device performance by carrier mobility modification in the transistor channel. The carrier mobility enhancement can be realized by formation of strained silicon layers on a Si1−xGex strain relaxed buffer. Formation of source and drain extensions on such structures need to satisfy one additional requirement, the formation process, including the activation related thermal budget should not relax the strain in the channel. In this paper we separately investigate the role of amorphization during implantation, different doping impurities and thermal budget on the junction and the transistor channel regions properties. Two approaches of dopant activation are discussed: low temperature solid phase epitaxial regrowth and high temperature conventional spike.
Strain relaxed Si1−xGex layers are attractive virtual substrates for the epitaxial growth of strained Si. Tensile strained Si has attracted a lot of attention due its superior electronic properties. In this study, the strain relaxation of pseudomorphic Si1−xGex layers grown by chemical vapor deposition (CVD) on Si(100) substrates was investigated after He+ ion implantation and thermal annealing. The implantation induced defects underneath the SiGe/Si interface promote strain relaxation during annealing via preferred nucleation of dislocation loops which form misfit dislocations at the interface to the substrate. The amount of strain relaxation as well as the final threading dislocation density depend on the implantation dose and energy. Si1−xGex layers with thicknesses between 75 and 420 nm and Ge concentrations between 19 and 29 at% were investigated. The strain relaxation strongly depends on the layer thickness. Typically the structures show ≈70 % strain relaxation and threading dislocation densities in the low 106 cm−2 range. AFM investigations proved excellent surface morphology with an rms roughness of 0.6 nm. The samples were investigated by Rutherford backscattering spectrometry, ion channeling, transmission electron microscopy and atomic force microscopy.