Predictors of Increases in Alcohol Problems and Alcohol Use Disorders in Offspring in the San Diego Prospective Study.

BackgroundThe 35-year-long San Diego Prospective Study documented 2-fold increases in alcohol problems and alcohol use disorders (AUDs) in young-adult drinking offspring compared to rates in their fathers, the original probands. The current analyses use the same interviews and questionnaires at about the same age in members of the 2 generations to explore multiple potential contributors to the generational differences in adverse alcohol outcomes.MethodsUsing data from recent offspring interviews, multiple cross-generation differences in characteristics potentially related to alcohol problems were evaluated in 3 steps: first through direct comparisons across probands and offspring at about age 30; second by backward linear regression analyses of predictors of alcohol problems within each generation; and finally third through R-based bootstrapped linear regressions of differences in alcohol problems in randomly matched probands and offspring.ResultsThe analyses across the analytical approaches revealed 3 consistent predictors of higher alcohol problems in the second generation. These included the following: (i) a more robust relationship to alcohol problems for offspring with a low level of response to alcohol; (ii) higher offspring values for alcohol expectancies; and (iii) higher offspring impulsivity.ConclusionsThe availability of data across generations offered a unique perspective for studying characteristics that may have contributed to a general finding in the literature of substantial increases in alcohol problems and AUDs in recent generations. If replicated, these results could suggest approaches to be used by parents, healthcare workers, insurance companies, and industry in their efforts to mitigate the increasing rates of alcohol problems in younger generations

Recommendations for Future Efforts in RANS Modeling and Simulation

The roadmap laid out in the CFD Vision 2030 document suggests that a decision to move away from RANS research needs to be made in the current timeframe (around 2020). This paper outlines industry requirements for improved predictions of turbulent flows and the cost-barrier that is often associated with reliance on scale resolving methods. Capabilities of RANS model accuracy for simple and complex flow flow fields are assessed, and modeling practices that degrade predictive accuracy are identified. Suggested research topics are identified that have the potential to improve the applicability and accuracy of RANS models. We conclude that it is important that some part of a balanced turbulence modeling research portfolio should include RANS efforts

A Bivariate Time Series Approach to Anthropogenic Trend Detection in Hemispheric Mean Temperatures

A bivariate time series regression approach is used to model observed variations in hemispheric mean temperature over the period 1900-96. The regression equations include deterministic predictor variables and lagged values of the two predictands, and two different forms of this basic structure are employed. The deterministic predictors considered are simple linear trends, various climate model-generated time series based on different combinations of greenhouse gas, sulfate aerosol, and solar forcing, and the Southern Oscillation index (SOI). With linear trends as the only predictors, the best model is a fourth-order bivariate autoregressive model including lagged Southern Hemisphere (SH) to Northern Hemisphere (NH) dependence, as in previous work by Kaufmann and Stern. The estimated NH and SH trends are both + 0.67°C century-1, and both are highly statistically significant. If SOI is included as an additional predictor, however, a first-order time series model, with no SH to NH dependence, is an adequate fit to the data. This shows that SOI may be an important covariate in this kind of analysis. Further analysis uses climate model-generated forcing terms representing greenhouses gases, sulfate aerosols, and solar effects, as well as SOI. The statistical analysis makes extensive use of Bayes factors as a device for discriminating among a wide spectrum of possible models. The best fits to the data are obtained when all three forcing terms are included. Total sulfate aerosol forcing of 1.1 W m-2(with a corresponding climate sensitivity of ΔT2+ = 4.2cC) is preferred to -0.7 W m-2(with sensitivity of 2.3°C), but the Bayes factor discrimination between these cases is weak

The Birth of a Galaxy - III. Propelling reionisation with the faintest galaxies

Starlight from galaxies plays a pivotal role throughout the process of cosmic reionisation. We present the statistics of dwarf galaxy properties at z > 7 in haloes with masses up to 10^9 solar masses, using a cosmological radiation hydrodynamics simulation that follows their buildup starting with their Population III progenitors. We find that metal-enriched star formation is not restricted to atomic cooling ($T_{\rm vir} \ge 10^4$ K) haloes, but can occur in haloes down to masses ~10^6 solar masses, especially in neutral regions. Even though these smallest galaxies only host up to 10^4 solar masses of stars, they provide nearly 30 per cent of the ionising photon budget. We find that the galaxy luminosity function flattens above M_UV ~ -12 with a number density that is unchanged at z < 10. The fraction of ionising radiation escaping into the intergalactic medium is inversely dependent on halo mass, decreasing from 50 to 5 per cent in the mass range $\log M/M_\odot = 7.0-8.5$. Using our galaxy statistics in a semi-analytic reionisation model, we find a Thomson scattering optical depth consistent with the latest Planck results, while still being consistent with the UV emissivity constraints provided by Ly$\alpha$ forest observations at z = 4-6.Comment: 21 pages, 15 figures, 4 tables. Accepted in MNRA

Harnessing Whole Genome Polygenic Risk Scores to Stratify Individuals Based on Cardiometabolic Risk Factors and Biomarkers at Age 10 in the Lifecourse - Brief Report

In this study, we investigated the capability of polygenic risk scores to stratify a cohort of young individuals into risk deciles based on 10 different cardiovascular traits and circulating biomarkers. METHODS: We first conducted large-scale genome-wide association studies using data on adults (mean age 56.5 years) enrolled in the UK Biobank study (n=393 193 to n=461 460). Traits and biomarkers analyzed were body mass index, systolic blood pressure, diastolic blood pressure, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, triglycerides, apolipoprotein B, apolipoprotein A-I, C-reactive protein and vitamin D. Findings were then leveraged to build whole genome polygenic risk scores in participants from the Avon Longitudinal Study of Parents and Children (mean age, 9.9 years) which were used to stratify this cohort into deciles in turn and analyzed against their respective traits. RESULTS: For each of the 10 different traits assessed, we found strong evidence of an incremental trend across deciles (all P<0.0001). Large differences were identified when comparing top and bottom deciles; for example, using the apolipoprotein B polygenic risk scores there was a mean difference of 13.2 mg/dL for this established risk factor of coronary heart disease in later life. CONCLUSIONS: Although the use of polygenic prediction in a clinical setting may currently be premature, our findings suggest they are becoming increasingly powerful as a means of predicting complex trait variation at an early stage in the lifecourse

Predictors of subgroups based on maximum drinks per occasion over six years for 833 adolescents and young adults in COGA.

ObjectiveA person's pattern of heavier drinking often changes over time, especially during the early drinking years, and reflects complex relationships among a wide range of characteristics. Optimal understanding of the predictors of drinking during times of change might come from studies of trajectories of alcohol intake rather than cross-sectional evaluations.MethodThe patterns of maximum drinks per occasion were evaluated every 2 years between the average ages of 18 and 24 years for 833 subjects from the Collaborative Study on the Genetics of Alcoholism. Latent class growth analysis identified latent classes for the trajectories of maximum drinks, and then logistic regression analyses highlighted variables that best predicted class membership.ResultsFour latent classes were found, including Class 1 (69%), with about 5 maximum drinks per occasion across time; Class 2 (15%), with about 9 drinks at baseline that increased to 18 across time; Class 3 (10%), who began with a maximum of 18 drinks per occasion but decreased to 9 over time; and Class 4 (6%), with a maximum of about 22 drinks across time. The most consistent predictors of higher drinking classes were female sex, a low baseline level of response to alcohol, externalizing characteristics, prior alcohol and tobacco use, and heavier drinking peers.ConclusionsFour trajectory classes were observed and were best predicted by a combination of items that reflected demography, substance use, level of response and externalizing phenotypes, and baseline environment and attitudes