Innovative Self-Regulation Strategies to Reduce Weight Gain in Young Adults: The Study of Novel Approaches to Weight Gain Prevention (SNAP) Randomized Clinical Trial

Weight gain occurs commonly in young adults and has adverse effects on health. Reducing weight gain in young adults would have significant public health impact

Linking in vitro lipolysis and microsomal metabolism for the quantitative prediction of oral bioavailability of BCS II drugs administered in lipidic formulations

Lipidic formulations (LFs) are increasingly utilized for the delivery of drugs that belong to class II of the Biopharmaceutics Classification System (BCS). The current work proposes, for the first time, the combination of in vitro lipolysis and microsomal metabolism studies for the quantitative prediction of human oral bioavailability of BCS II drugs administered in LFs. Marinol® and Neoral® were selected as model LFs and their observed oral bioavailabilities (Fobserved) obtained from published clinical studies in humans. Two separate lipolysis buffers, differing in the level of surfactant concentrations, were used for digestion of the LFs. The predicted fraction absorbed (Fabs) was calculated by measuring the drug concentration in the micellar phase after completion of the lipolysis process. To determine first-pass metabolism (Fg∙Fh), drug depletion studies with human microsomes were performed. Clearance values were determined by applying the “in vitro half-life approach”. The estimated Fabs and Fg∙Fh values were combined for the calculation of the predicted oral bioavailability (Fpredicted). Results showed that there was a strong correlation between Fobserved and Fpredicted values only when Fabs was calculated using a buffer with surfactant concentrations closer to physiological conditions. The general accuracy of the predicted values suggests that the novel in vitro lipolysis/metabolism approach could quantitatively predict the oral bioavailability of lipophilic drugs administered in LFs

Predicting residue contacts using pragmatic correlated mutations method: reducing the false positives

BACKGROUND: Predicting residues' contacts using primary amino acid sequence alone is an important task that can guide 3D structure modeling and can verify the quality of the predicted 3D structures. The correlated mutations (CM) method serves as the most promising approach and it has been used to predict amino acids pairs that are distant in the primary sequence but form contacts in the native 3D structure of homologous proteins. RESULTS: Here we report a new implementation of the CM method with an added set of selection rules (filters). The parameters of the algorithm were optimized against fifteen high resolution crystal structures with optimization criterion that maximized the confidentiality of the predictions. The optimization resulted in a true positive ratio (TPR) of 0.08 for the CM without filters and a TPR of 0.14 for the CM with filters. The protocol was further benchmarked against 65 high resolution structures that were not included in the optimization test. The benchmarking resulted in a TPR of 0.07 for the CM without filters and to a TPR of 0.09 for the CM with filters. CONCLUSION: Thus, the inclusion of selection rules resulted to an overall improvement of 30%. In addition, the pair-wise comparison of TPR for each protein without and with filters resulted in an average improvement of 1.7. The methodology was implemented into a web server that is freely available to the public. The purpose of this implementation is to provide the 3D structure predictors with a tool that can help with ranking alternative models by satisfying the largest number of predicted contacts, as well as it can provide a confidence score for contacts in cases where structure is known

Automating nitrogen fertiliser management for cereals (Auto-N). AHDB Project Report No.561

Uncertainty in estimating fertiliser N requirements is large, with differences between recommended and measured N optima frequently exceeding 50 kg/ha. Precision farming technologies including yield mapping, canopy sensing, satellite imaging and soil mapping are now common-place on farm. The Auto-N project sought to apply the information readily available from these technologies within an ‘Auto-N logic’ to improve the precision of N fertiliser decision making. The ‘Auto-N logic’ was derived from that used to estimate fertiliser N requirements as set out in the AHDB Cereals & Oilseeds guide Nitrogen for winter wheat – management guidelines; this guide suggests that N requirements should be calculated by subtracting Soil N Supply (SNS) from Crop N Demand (CND: grain yield x crop N content) and dividing by Fertiliser N Recovery (FNR); thus the ‘Auto-N logic’ uses yield and protein maps to inform estimates of CND, canopy sensing to inform estimates of SNS and soil sensing to inform estimates of FNR. Novel chessboard N response experiments were set up on six commercial fields between harvest years 2010 and 2012 to quantify spatial variation in N requirement, to explain it in terms of CND, SNS and FNR, hence to develop the ‘Auto-N logic’. At each site, each farmer applied N as liquid urea plus ammonium nitrate (UAN) using the farm sprayer twice, in perpendicular directions, to create a systematic grid of ~400 plots (~12m × 12m) fertilised with N rates of 0, 120, 240 or 360 kg/ha; the area of each experiment exceeded 4 ha. Grain yields were measured by small-plot combine, grain samples were analysed for protein, and N harvest index and total N uptake were determined from pre-harvest grab samples. Values were then estimated for all variates and all N levels for all plots by kriging. Response curves were fitted, and N optima and their components (SNS, CND, FNR) were derived assuming 5 kg grain would pay for 1 kg fertiliser N. Within field variation in optimum N exceeded 100 kg/ha at all sites; spatial variation in optimal yield was greater than 2 t/ha at all sites and variation in SNS was generally greater than 50 kg/ha. Some of the spatial variation in optimum N was explained in terms of SNS and CND. However, the tendency for positive correlations between SNS and optimum yield was striking, and hindered complete explanation of spatial variation in optimum N: i.e. high yielding areas tended to have greater SNS, so the increased requirement from higher crop N demand was counteracted by the reduced requirement from higher SNS. Spatial variation in CND and SNS was reasonably well estimated from the use of past yield maps and crop sensing, respectively; often, similar within-field patterns showed through for both. However, variation in FNR was also large and was unpredictable. Using clustering techniques, zoning, performance mapping or simple averaging of data from five farms, it was shown that past yield maps could be used usefully to estimate variation in CND. In addition, variation in SNS could be predicted from canopy sensing in early spring (an algorithm was developed based on sensed NDVI and thermal time since sowing). Calibrations for crop N uptake, biomass and crop N status (Nitrogen Nutrition Index) from canopy sensing were explored, but no rational basis could be found to justify their inclusion in the ‘Auto-N logic’. Validation trials were set up with farmers on 11 fields in 2013 & 2014; these used adjacent tramlines to compare the Auto-N logic with the farm’s own practice, 50 kg/ha more N and 50 kg/ha less N. Evaluation of these trials along with economic analysis of the chessboard trials showed the benefits of precision in judging N requirements to be modest, whereas benefits of accuracy (proximity to the measured mean) were much greater. Whilst this work demonstrated the feasibility of automating judgements of N requirements within fields using precision information, the variability in CND, SNS and FNR, and crucially the interactions between them, meant that the use of such systems would not guarantee increased accuracy or precision of N use. The evidence suggests that variable rate N management can give only modest returns, even with a system making perfect predictions, if the field is already receiving the right average N rate. The results showed that the most important decisions concern N use for whole farms, then for whole fields, then for areas within fields. Precision technologies can help with all of these, especially through comparisons of crops between and within farms. However, the most effective aspect of precision farming technologies is probably the empowerment of farmers to test retrospectively the effects of their N decisions (or indeed any decisions) on-farm. Given the variation in and unpredictability of N requirements between fields and between farms the only way farmers can know for sure whether their chosen N rates were right is to test yield effects of different N rates – this is relatively easy now, by simply applying (say) 60 kg/ha more and 60 kg/ha less to adjacent tramlines. The chessboard trials initiated here have transformed our understanding of N responses and shown new possibilities for spatial experimentation, not only to empower on-farm testing, but to understand how soil variation affects husbandry outcomes. These trials show that N use is not the major cause of the very large spatial variation seen in yield. Thus, understanding the soil-related causes of yield variation should, and can, now become a priority for soil and agronomic research

Maternal and Cord Blood Manganese Concentrations and Early Childhood Neurodevelopment among Residents near a Mining-Impacted Superfund Site

Background: Environmental manganese exposure has been associated with adverse neurodevelopmental outcomes among school-aged children; yet, few studies have evaluated prenatal exposure. Objectives: Our study examines associations between prenatal manganese concentrations and placental transfer of manganese with neurodevelopment in 224 2-y-old children residing near the Tar Creek Superfund Site. Methods: We collected maternal and cord blood at delivery, measured manganese using inductively coupled plasma mass spectrometry, and assessed neurodevelopment using the Bayley Scales of Infant Development-II. Associations between manganese and mental (MDI) and psychomotor (PDI) development indices were estimated in multivariable models. Placental transfer, approximated by cord/maternal manganese ratio, cord/total manganese ratio (total=maternal+cord), and by joint classification according to high or low (above or below median) maternal and cord manganese, was evaluated as a predictor of neurodevelopment. Results: Median levels [interquartile ranges (IQR)] of manganese in maternal and cord blood, respectively, were 24.0 (19.5–29.7) and 43.1 (33.5–52.1) μg/L. Adjusting for lead, arsenic, and other potential confounders, an IQR increase in maternal manganese was associated with −3.0 (95% CI: −5.3, −0.7) points on MDI and −2.3 (95% CI: −4.1, −0.4) points on PDI. Cord manganese concentrations were not associated with neurodevelopment scores. Cord/maternal and cord/total manganese ratios were positively associated with MDI [cord/maternal: β=2.6 (95% Cl: −0.04, 5.3); cord/total: β=22.0 (95% Cl: 3.2, 40.7)] and PDI (cord/maternal: β=1.7 (95% Cl: −0.5, 3.9); cord/total: β=15.6 (95% Cl: 0.3, 20.9)). Compared to mother–child pairs with low maternal and cord manganese, associations with neurodevelopment scores were negative for pairs with either high maternal, high cord, or high maternal and cord manganese. Conclusions: Maternal blood manganese concentrations were negatively associated with early childhood neurodevelopment scores in our study. Findings highlight the importance of understanding maternal exposures during pregnancy and factors influencing placental transfer. https://doi.org/10.1289/EHP92

The importance of canopy complexity in shaping seasonal spider and beetle assemblages in saltmarsh habitats

1. Habitat structure, including vegetation structural complexity, largely determines invertebrate assemblages in semi-natural grasslands. The importance of structural complexity to the saltmarsh invertebrate community, where the interplay between vegetation characteristics and tidal inundation is key, is less well known. 2. It was hypothesised that canopy complexity would be a more important predictor of spider and beetle assemblages than simple vegetation attributes (e.g. height, community type) and environmental variables (e.g. elevation) alone, measured in two saltmarsh regions, south-east (Essex) and north-west (Morecambe Bay) U.K. Canopy complexity (number of non-vegetated ‘gaps’ in canopy ≥ 1 mm wide) was assessed using side-on photography. Over 1500 spiders and beetles were sampled via suction sampling, winter and summer combined. 3. In summer, saltmarshes with abundant spider and beetle populations were characterised by high scores for canopy complexity often associated with tussocky grass or shrub cover. Simple vegetation attributes (plant cover, height) accounted for 26% of variation in spider abundance and 14% in spider diversity, rising to 46% and 41%, respectively, with the addition of canopy complexity score. Overwintering spider assemblages were associated with elevation and vegetation biomass. Summer beetle abundance, in particular the predatory and zoophagous group, and diversity were best explained by elevation and plant species richness. 4. Summer canopy complexity was identified as a positive habitat feature for saltmarsh spider communities (ground-running hunters and sheet weavers) with significant ‘added value’ over more commonly measured attributes of vegetation structure