Heavy Drinking in University Students With and Without Attention-Deficit/Hyperactivity Disorder: Contributions of Drinking Motives and Protective Behavioral Strategies

This study examined rates of heavy drinking and alcohol problems in relation to drinking motives and protective behavioral strategies in university students with a documented current diagnosis of attention-deficit/hyperactivity disorder (ADHD; n = 31) compared with students with no history of ADHD (n = 146). Participants completed a Web-based questionnaire, and logistic regression models tested interactions between ADHD/comparison group membership and motives and protective strategies. Group differences in rates of heavy drinking and alcohol problems were not statistically significant, but medium-sized risk ratios showed that students without ADHD reported heavy drinking at a rate 1.44 times higher than students with ADHD and met screening criteria for problematic alcohol use at a rate of 1.54 times higher than students with ADHD. Other key findings were, first, that drinking to enhance positive affect (e.g., drinking because it is exciting), but not to cope with negative affect (e.g., drinking to forget your worries), predicted both heavy drinking and alcohol problems. Second, only protective behavioral strategies that emphasize alcohol avoidance predicted both heavy drinking and alcohol problems. Contrary to expectations, we found no ADHD-related moderation of effects of motives or protective strategies on our alcohol outcomes. Results of this study are limited by the small sample of students with ADHD but highlight tentative similarities and differences in effects of motives and strategies on drinking behaviors and alcohol problems reported by students with and without ADHD

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2,3,4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease

Predicting the yield of Douglas-Fir from site factors on better quality sites in Scotland

In Scotland, as a result of recent changes in agricultural policy and grant schemes, there is now greater potential for planting a wider range of more productive forestry species on better quality land. In order to permit accurate production forecasting and financial appraisals for any such afforestation, it is necessary to develop predictive yield models. This article describes the development of a multiple linear regression model for the prediction of General Yield Class (GYC) of Douglas fir using readily assessed, or derived, site factors. Climate surfaces developed by spatial analysis of weather data were used to predict temperature and rainfall for 87 sample sites to a resolution of 1 km(2). Estimates of wind climate were derived from a regression model using geographic location, elevation and topographic exposure. Multivariate analysis of these and other soil and topographic variables indicate that temperature and exposure are most important in determining the productivity of Douglas fir on better quality sites in Scotland. As crop age increases, GYC declines and the possible reasons for this effect are discussed. Other factors are also discussed, such as the genetic variability of Douglas fir, and problems associated with establishment and form

Procedure for the estimation of carbon oxidation rates in porous spent oil shale. [Based on free stream oxygen concentration and temperature]

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Models to predict the General Yield Class of Douglas fir, Japanese larch and Scots pine on better quality land in Scotland

Recent changes in forestry incentives mean that there is potential for an increase in afforestation on better quality agricultural land in Scotland. As a result improved information is required about timber yields from a range of species on better quality sites for production forecasting, financial appraisal, and planning at the local, regional and strategic levels. This paper describes the development of models that enable General Yield Class (GYC) to be predicted from site factors for Douglas fir, Japanese larch and Scots pine. Temporary sample plots were established in stands below 350 m on Land Capability for Forestry class I to V sites. At each location GYC was assessed, as well as the soil, climate and topographic factors which had been demonstrated to influence forest productivity in earlier studies in Scotland. The models, based on a step-wise multiple regression procedure, indicate that mean spring temperature, geomorphic shelter (topex), and crop age are most important in determining the productivity of Douglas fir and Japanese larch. For Scots pine, mean spring temperature, mean winter temperature, and crop age are the most important factors. The models accounted for between 34 and 45 per cent of variation in General Yield Class and are sufficiently precise for estimating mean productivity at regional and national levels

The mycotoxins 4-deoxynivalenol, zearalenone and aflatoxin in weather-damaged wheat harvested 1983-1985 in south-east Queensland

A survey for various mycotoxins was carried out on samples of all wheat delivered to nine storage and marketing depots in south-eastern Queensland, selected as most likely to receive mycotoxin-contaminated grain. All wheat was surveyed during 1983, when the degree of weather damage was high. Samples of the poorest grade of wheat from these depots were also surveyed in 1984 and 1985. The surveys included all regions where head scab of wheat caused by Fusariurn graminearurn Schwabe Group 2 had been reported to occur at significant levels. 4-Deoxynivalenol was detected in nearly all pooled samples representing bulk wheat at concentrations ranging from traces of <0.01 up to 1.7 mg kg-1. The highest concentration of zearlenone detected in a pooled wheat sample was 0.04 mg kg-1. In a few samples representing individual wheat deliveries and with up to 2.8% by weight of pink grains, 4-deoxynivalenol concentrations ranged up to 11.7 mg kg-' and zearalenone up to 0.43 mg kg-l. Aflatoxins B,, B2, G1 and G2 were detected in only one pooled sample of wheat, at a total aflatoxin concentration of 0.003 mg kg-'. Ochratoxin A, sterigmatocystin and T-2 toxin were not detected. Higher concentrations of mycotoxins were found in the poorer grades of wheat