Plant Trait Diversity Buffers Variability in Denitrification Potential over Changes in Season and Soil Conditions

BACKGROUND: Denitrification is an important ecosystem service that removes nitrogen (N) from N-polluted watersheds, buffering soil, stream, and river water quality from excess N by returning N to the atmosphere before it reaches lakes or oceans and leads to eutrophication. The denitrification enzyme activity (DEA) assay is widely used for measuring denitrification potential. Because DEA is a function of enzyme levels in soils, most ecologists studying denitrification have assumed that DEA is less sensitive to ambient levels of nitrate (NO(3)(-)) and soil carbon and thus, less variable over time than field measurements. In addition, plant diversity has been shown to have strong effects on microbial communities and belowground processes and could potentially alter the functional capacity of denitrifiers. Here, we examined three questions: (1) Does DEA vary through the growing season? (2) If so, can we predict DEA variability with environmental variables? (3) Does plant functional diversity affect DEA variability? METHODOLOGY/PRINCIPAL FINDINGS: The study site is a restored wetland in North Carolina, US with native wetland herbs planted in monocultures or mixes of four or eight species. We found that denitrification potentials for soils collected in July 2006 were significantly greater than for soils collected in May and late August 2006 (p<0.0001). Similarly, microbial biomass standardized DEA rates were significantly greater in July than May and August (p<0.0001). Of the soil variables measured--soil moisture, organic matter, total inorganic nitrogen, and microbial biomass--none consistently explained the pattern observed in DEA through time. There was no significant relationship between DEA and plant species richness or functional diversity. However, the seasonal variance in microbial biomass standardized DEA rates was significantly inversely related to plant species functional diversity (p<0.01). CONCLUSIONS/SIGNIFICANCE: These findings suggest that higher plant functional diversity may support a more constant level of DEA through time, buffering the ecosystem from changes in season and soil conditions

A cluster randomised controlled trial of an intervention to promote healthy lifestyle habits to school leavers:Study rationale, design, and methods

BACKGROUND: Physical inactivity and a poor diet predict lifestyle diseases such as diabetes, cardiovascular disease, and certain types of cancer. Marked declines in physical activity occur during late adolescence, coinciding with the point at which many young people leave school and enter the workforce and begin to take greater control over their lifestyle behaviours. The work outlined within this paper sought to test a theoretically-informed intervention aimed at supporting increased engagement in physical activity and healthy eating habits in young people at the point of transition from school to work or work-based learning. As actively engaging young people in initiatives based on health messages is challenging, we also tested the efficacy of financial incentives in promoting initial engagement with the programme. METHODS/DESIGN: A three-arm cluster-randomised design was used. Participants were school pupils from Year 11 and 13 (i.e., in their final year of study), aged 16–18 years. To reduce contamination effects, the unit of randomisation was school. Participants were randomly allocated to receive (i) a 12-week behavioural support intervention consisting of six appointments, (ii) a behavioural support intervention plus incentives (totalling £40), or (iii) an information-only control group. Behavioural support was provided by fitness advisors at local leisure centres following an initial consultation with a dietician. Sessions focused on promoting habit formation through setting implementation intentions as part of an incremental goal setting process. Consistent with self-determination theory, all advisors were trained to provide guidance in an autonomy-supportive manner so that they were equipped to create a social context supportive of autonomous forms of participant motivation. The primary outcome was objectively assessed physical activity (via GT1M accelerometers). Secondary outcome measures were diet, motivation and habit strength. Data were collected at baseline, post-intervention (12 weeks) and 12 months. DISCUSSION: Findings of this trial will provide valuable insight into the feasibility of promoting autonomous engagement in healthy physical activity and dietary habits among school leavers. The research also provides much needed data and detailed information related to the use of incentives for the initial promotion of young peoples’ behaviour change during this important transition. TRIAL REGISTRATION: The trial is registered as Current Controlled Trials ISRCTN55839517

Minimal-uncertainty prediction of general drug-likeness based on Bayesian neural networks

Triaging unpromising lead molecules early in the drug discovery process is essential for accelerating its pace while avoiding the costs of unwarranted biological and clinical testing. Accordingly, medicinal chemists have been trying for decades to develop metrics-ranging from heuristic measures to machine-learning models-that could rapidly distinguish potential drugs from small molecules that lack drug-like features. However, none of these metrics has gained universal acceptance and the very idea of &apos;drug-likeness&apos; has recently been put into question. Here, we evaluate drug-likeness using different sets of descriptors and different state-of-the-art classifiers, reaching an out-of-sample accuracy of 87-88%. Remarkably, because these individual classifiers yield different Bayesian error distributions, their combination and selection of minimal-variance predictions can increase the accuracy of distinguishing drug-like from non-drug-like molecules to 93%. Because total variance is comparable with its aleatoric contribution reflecting irreducible error inherent to the dataset (as opposed to the epistemic contribution due to the model itself), this level of accuracy is probably the upper limit achievable with the currently known collection of drugs. When designing new drugs, there are countless ways to create molecules, yet only a few interact with biological targets. Beker and colleagues provide here a graph neural network based metric for drug-likeness that can guide the search