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Despite established clinical associations among major depression (MD), alcohol dependence (AD), and alcohol consumption (AC), the nature of the causal relationship between them is not completely understood. We leveraged genome-wide data from the Psychiatric Genomics Consortium (PGC) and UK Biobank to test for the presence of shared genetic mechanisms and causal relationships among MD, AD, and AC.
Linkage disequilibrium score regression and Mendelian randomization (MR) were performed using genome-wide data from the PGC (MD: 135 458 cases and 344 901 controls; AD: 10 206 cases and 28 480 controls) and UK Biobank (AC-frequency: 438 308 individuals; AC-quantity: 307 098 individuals).
Positive genetic correlation was observed between MD and AD (rgMD−AD = + 0.47, P = 6.6 × 10−10). AC-quantity showed positive genetic correlation with both AD (rgAD−AC quantity = + 0.75, P = 1.8 × 10−14) and MD (rgMD−AC quantity = + 0.14, P = 2.9 × 10−7), while there was negative correlation of AC-frequency with MD (rgMD−AC frequency = −0.17, P = 1.5 × 10−10) and a non-significant result with AD. MR analyses confirmed the presence of pleiotropy among these four traits. However, the MD-AD results reflect a mediated-pleiotropy mechanism (i.e. causal relationship) with an effect of MD on AD (beta = 0.28, P = 1.29 × 10−6). There was no evidence for reverse causation.
This study supports a causal role for genetic liability of MD on AD based on genetic datasets including thousands of individuals. Understanding mechanisms underlying MD-AD comorbidity addresses important public health concerns and has the potential to facilitate prevention and intervention efforts.
One of the typical roles of industrial–organizational (I-O) psychologists working as practitioners is administering employee surveys measuring job satisfaction/engagement. Traditionally, this work has involved developing (or choosing) the items for the survey, administering the items to employees, analyzing the data, and providing stakeholders with summary results (e.g., percentages of positive responses, item means). In recent years, I-O psychologists moved into uncharted territory via the use of survey key driver analysis (SKDA), which aims to identify the most critical items in a survey for action planning purposes. Typically, this analysis involves correlating (or regressing) a self-report criterion item (e.g., “considering everything, how satisfied are you with your job”) with (or on) each of the remaining survey items in an attempt to identify which items are “driving” job satisfaction/engagement. It is also possible to use an index score (i.e., a scale score formed from several items) as the criterion instead of a single item. That the criterion measure (regardless of being a single item or an index) is internal to the survey from which predictors are drawn distinguishes this practice from linkage research. This methodology is not widely covered in survey methodology coursework, and there are few peer-reviewed articles on it. Yet, a number of practitioners are marketing this service to their clients. In this focal article, a group of practitioners with extensive applied survey research experience uncovers several methodological issues with SKDA. Data from a large multiorganizational survey are used to back up claims about these issues. One issue is that SKDA ignores the psychometric reality that item standard deviations impact which items are chosen as drivers. Another issue is that the analysis ignores the factor structure of survey item responses. Furthermore, conducting this analysis each time a survey is administered conflicts with the lack of situational and temporal specificity. Additionally, it is problematic to imply causal relationships from the correlational data seen in most surveys. Most surprisingly, randomly choosing items out of a hat yields validities similar to those from conducting the analysis. Thus, we recommend that survey providers stop conducting SKDA until they can produce science that backs up this practice. These issues, in concert with the lack of literature examining the practice, make rigorous evaluations of SKDA a timely inquiry.
Euclid is a Europe-led cosmology space mission dedicated to a visible and near infrared survey of the entire extra-galactic sky. Its purpose is to deepen our knowledge of the dark content of our Universe. After an overview of the Euclid mission and science, this contribution describes how the community is getting organized to face the data analysis challenges, both in software development and in operational data processing matters. It ends with a more specific account of some of the main contributions of the Swiss Science Data Center (SDC-CH).
The public health burden of alcohol is unevenly distributed across the life course, with levels of use, abuse, and dependence increasing across adolescence and peaking in early adulthood. Here, we leverage this temporal patterning to search for common genetic variants predicting developmental trajectories of alcohol consumption. Comparable psychiatric evaluations measuring alcohol consumption were collected in three longitudinal community samples (N = 2,126, obs = 12,166). Consumption-repeated measurements spanning adolescence and early adulthood were analyzed using linear mixed models, estimating individual consumption trajectories, which were then tested for association with Illumina 660W-Quad genotype data (866,099 SNPs after imputation and QC). Association results were combined across samples using standard meta-analysis methods. Four meta-analysis associations satisfied our pre-determined genome-wide significance criterion (FDR < 0.1) and six others met our ‘suggestive’ criterion (FDR <0.2). Genome-wide significant associations were highly biological plausible, including associations within GABA transporter 1, SLC6A1 (solute carrier family 6, member 1), and exonic hits in LOC100129340 (mitofusin-1-like). Pathway analyses elaborated single marker results, indicating significant enriched associations to intuitive biological mechanisms, including neurotransmission, xenobiotic pharmacodynamics, and nuclear hormone receptors (NHR). These findings underscore the value of combining longitudinal behavioral data and genome-wide genotype information in order to study developmental patterns and improve statistical power in genomic studies.
In cats, the incidence of obesity and diabetes is increasing, and little is known about specific aspects of the endocrine control of food intake in this species. Recent data suggest that ghrelin has an important role in the control of insulin secretion and vice versa, but this role has never been demonstrated in cats. Here we aimed to improve our understanding about the relationship between insulin, amylin and ghrelin secretion in response to a nutrient load in overweight cats. After a 16 h fast, weekly, six overweight male cats underwent randomly one of the four testing sessions: saline, glucose, arginine and TAG. All solutions were isoenergetic and isovolumic, and were injected intravenously as a bolus. Glucose, insulin, acylated ghrelin (AG), amylin and prolactin were assayed in plasma before and 10, 20, 40, 60, 80 and 100 min after the nutrient load. A linear mixed-effects model was used to assess the effect of bolus and time on the parameters. A parenteral bolus of glucose or arginine increased insulin and ghrelin concentrations in cats. Except for with the TAG bolus, no suppression of ghrelin was observed. The absence of AG suppression after the intravenous load of arginine and glucose may suggest: (1) that some nutrients do not promote satiation in overweight cats; or that (2) AG may be involved in non-homeostatic consumption mechanisms. However, the role of ghrelin in food reward remains to be assessed in cats.
Motivated by repeated price spikes and crashes over the last decade, we investigate whether the growing market shares of futures speculators destabilize commodity spot prices. We approximate conditional volatility and analyze how it is affected by speculative open interest. In this context, we split our sample into two equally long subperiods and document whether the speculative impact on conditional volatility increases. With respect to six heavily traded agricultural and energy commodities, we do not find robust evidence that this is the case. We thus conclude that the financialization of raw material markets does not make them more volatile.
A robust processing route at low cost is an essential requirement for high-temperature materials used in automotive engines. Because of their excellent high-temperature properties, their low density, high elastic modulus as well as high specific strength, intermetallic γ-TiAl based alloys are potential candidates for application in advanced automotive turbochargers. So-called 3rd generation alloys, such as TNM™ alloys with a nominal composition of Ti-43.5Al-4Nb-1Mo-0.1B (in at%), are multi-phase alloys consisting of γ-TiAl, α2-Ti3Al and a low volume fraction of βo-TiAl phase. In this paper a novel hot-processing route, which is a combination of a one-shot hot-forging step and a controlled cooling treatment, leads to mechanical properties required for turbocharger turbine wheels. The observed strength can be attributed to the small lamellar spacing within the α2/γ colonies of the nearly lamellar microstructure. In order to analyze the microstructure and the prevailing phase fractions microscopic examinations and X-ray diffraction measurements were conducted. The mechanical properties were determined by hardness measurements as well as tensile and creep tests. The evolution of the microstructure during the hot-forming process is described and its relation to the obtained mechanical properties.
Electromigration failures occur at locations where Al mass transport flux divergences exhibit a maximum. In VLSI circuits, these locations correspond to tungsten plug contact / via areas. Characterizing and understanding electromigration failures at tungsten plug contact / via areas are essential in assuring the reliability of 0.5 μm and smaller devices. In this study, a novel test structure with four vias connecting metal 1 and metal 2 levels was used to separate two stages in the Al-Cu interconnect electromigration process. These stages are the incubation time during which Cu in the Al-Cu alloy is swept away from the via area and an Al drift time which leads to interconnect failure. By the use of this structure, a non-destructive method has been established to measure incubation times and Al drift times separately, monitoring discrete increases in line resistance corresponding to a sequential depletion of all four vias. Activation energies for both processes have been determined. The temperature dependence of the incubation time is characterized by a thermal activation energy of Q=1.08 ± 0.15 eV, indicative of Cu grain boundary diffusion through Al grains. Al depletion was found to occur with an activation energy of Q=0.72 ± 0.12 eV. We therefore conclude that Al self-grain boundary diffusion constitutes the Al depletion mechanism. The application of this novel test structure enables distinct determination of dominating failure mechanisms under operating conditions and therefore provides a realistic reliability assessment of actual on-chip interconnect structures.
Even after the successful introduction of Cu-based metallization, the electromigration (EM) failure risk has remained one of the important reliability concerns for most advanced process technologies. Ever increasing operating current densities and the introduction of low-k materials in the backend process scheme are some of the issues that threaten reliable, long-term operation at elevated temperatures. The main factors requiring attention and careful control are the activation energy related to the dominating diffusion mechanism, the resulting median lifetimes, and the lognormal standard deviation of experimentally acquired failure time distributions. Whereas the origin of the EM activation energy and the behavior of median lifetimes with continuing device scaling are relatively well understood, detailed models explaining the origin and scaling behavior of the lognormal standard deviation are scarce. The statistical behavior of EM-induced void sizes and resulting lifetime distributions appear to be explainable by geometrical variations of the void shapes and the consideration of kinetic aspects of the EM process. Using these models, expected lifetime distributions for future technology nodes can be simulated from current, experimentally obtained void size and lifetime distributions. These simulations have to include geometrical factors of the EM test structures and actual, on-chip interconnects, as well as kinetic aspects of the mass transport process, such as differences in interface diffusivity between the lines. By extrapolating the expected lifetime distributions for future technology nodes from current EM data, it is possible to predict when insertion of new process schemes, such as Cu-alloys and/or metallic coating of the Cu/passivation interface is required.
We have prepared five perovskite compounds which have two different A-site cations and examined the ordering of the A-site lattice through powder X-ray diffraction, TEM, HRTEM, HAADF-STEM, STEM-EELS. NaLaTi2O6 and NaLaZr2O6 show no indication of cation ordering by XRD but TEM analysis reveals that the A-site cations are ordered into layers in nanometer domains which have perpendicular orientations of the layers. NaLaMgTeO6 has larger domains which can be detected by XRD. XRD has revealed that both NaLaMgWO6 and KLaMnWO6 have nearly complete long range layered ordering of the A-site cations. TEM microscopy has further shown that a compositional modulation exists in both compounds. NaLaMgWO6 organizes into alternating stripes ˜24Å wide that are compositionally distinct from each other. KLaMnWO6 shows similar behaviour except with a chessboard pattern.