Morphology of Three Imported Aphthona Flea Beetles Used as Biological Control Agents of Leafy Spurge

The following morphological study of the three imported Aphtona flea beetles supplies a detailed description of selected structures that will distinguish each of the three imported species and will separate them from a common native fle beetle (Glyptina atriventris) found at South Dakota release sites

CATH functional families predict functional sites in proteins

MOTIVATION: Identification of functional sites in proteins is essential for functional characterization, variant interpretation and drug design. Several methods are available for predicting either a generic functional site, or specific types of functional site. Here, we present FunSite, a machine learning predictor that identifies catalytic, ligand-binding and protein-protein interaction functional sites using features derived from protein sequence and structure, and evolutionary data from CATH functional families (FunFams). RESULTS: FunSite's prediction performance was rigorously benchmarked using cross-validation and a holdout dataset. FunSite outperformed other publicly-available functional site prediction methods. We show that conserved residues in FunFams are enriched in functional sites. We found FunSite's performance depends greatly on the quality of functional site annotations and the information content of FunFams in the training data. Finally, we analyse which structural and evolutionary features are most predictive for functional sites. AVAILABILITY: https://github.com/UCL/cath-funsite-predictor. CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online

Food environment and obesity: A systematic review and meta-analysis

BACKGROUND: Obesity is influenced by a complex, multifaceted system of determinants, including the food environment. Governments need evidence to act on improving the food environment. The aim of this study was to review the evidence from spatial environmental analyses and to conduct the first series of meta-analyses to assess the impact of the retail food environment on obesity. METHODS: We performed a systematic review and random-effects meta-analyses, focusing on geographical–statistical methods to assess the associations between food outlet availability and obesity. We searched OvidSP-Medline, Scielo, Scopus and Google Scholar databases up to January 2022. The search terms included spatial analysis, obesity and the retail food environment. Effect sizes were pooled by random-effects meta-analyses separately according to food outlet type and geographical and statistical measures. FINDINGS: Of the 4118 retrieved papers, we included 103 studies. Density (n=52, 50%) and linear and logistic regressions (n=68, 66%) were the main measures used to assess the association of the food environment with obesity. Multilevel or autocorrelation analyses were used in 35 (34%) studies. Fast-food outlet proximity was positively and significantly associated with obesity (OR: 1.15, 95% CI: 1.02 to 1.30, p=0.02). Fresh fruit and vegetable outlet density and supermarket proximity were inversely associated with obesity (OR: 0.93, 95% CI: 0.90 to 0.96, p<0.001; OR: 0.90, 95% CI: 0.82 to 0.98, p=0.02). No significant associations were found for restaurants, convenience stores or any of the body mass index measures. CONCLUSIONS: Food outlets which sell mostly unhealthy and ultra-processed foods were associated with higher levels of obesity, while fruit and vegetable availability and supermarket accessibility, which enable healthier food access, were related to lower levels of obesity. The regulation of food outlets through zoning laws may not be enough to tackle the burden of obesity. Regulations that focus on increasing the availability of healthy food within stores and ensure overall healthy food environments require further attention

Ab-Initio Calculation of Molecular Aggregation Effects: a Coumarin-343 Case Study

We present time-dependent density functional theory (TDDFT) calculations for single and dimerized Coumarin-343 molecules in order to investigate the quantum mechanical effects of chromophore aggregation in extended systems designed to function as a new generation of sensors and light-harvesting devices. Using the single-chromophore results, we describe the construction of effective Hamiltonians to predict the excitonic properties of aggregate systems. We compare the electronic coupling properties predicted by such effective Hamiltonians to those obtained from TDDFT calculations of dimers, and to the coupling predicted by the transition density cube (TDC) method. We determine the accuracy of the dipole-dipole approximation and TDC with respect to the separation distance and orientation of the dimers. In particular, we investigate the effects of including Coulomb coupling terms ignored in the typical tight-binding effective Hamiltonian. We also examine effects of orbital relaxation which cannot be captured by either of these models

Exploring the protonation properties of photosynthetic phycobiliprotein pigments from molecular modeling and spectral line shapes

In photosynthesis, specialized light harvesting pigment- protein complexes (PPCs) are used to capture incident sunlight and funnel its energy to the reaction center. In Cryptophyte algae these complexes are suspended in the lumen, where the pH ranges between ~5-7, depending on the prolongation of the incident sunlight. However, the pKa of the several kinds of bilin chromophores encountered in these complexes and the effect of its protonation state on the energy transfer process is still unknown. Here, we combine quantum chemical and continuum solvent calculations to estimate the intrinsic aqueous pKas of different bilin pigments. We then use Propka and APBS classical electrostatic calculations to estimate the change in protonation free energies when the bilins are embedded inside five different phycobiliproteins (PE545, PC577, PC612, PC630 and PC645), and critically asses our results by analysis of the changes in the absorption spectral line shapes measured within a pH range from 4.0 to 9.4. Our results suggest that each individual protein environment strongly impacts the intrinsic pKa of the different chomophores, being the final responsible of their protonation state

Drivers of inter-annual variability in Net Ecosystem Exchange in a semi-arid savanna ecosystem, South Africa

Inter-annual variability in primary production and ecosystem respiration was explored using eddy-covariance data at a semi-arid savanna site in the Kruger Park, South Africa. New methods of extrapolating night-time respiration to the entire day and filling gaps in eddy-covariance data in semi-arid systems were developed. Net ecosystem exchange (NEE) in these systems occurs as pulses associated with rainfall events, a pattern not well-represented in current standard gap-filling procedures developed primarily for temperate flux sites. They furthermore do not take into account the decrease in respiration at high soil temperatures. An artificial neural network (ANN) model incorporating these features predicted measured fluxes accurately (MAE 0.42 gC/m&lt;sup&gt;2&lt;/sup&gt;/day), and was able to represent the seasonal patterns of photosynthesis and respiration at the site. The amount of green leaf area (indexed using satellite-derived estimates of fractional interception of photosynthetically active radiation &lt;i&gt;f&lt;/i&gt;&lt;sub&gt;APAR&lt;/sub&gt;), and the timing and magnitude of rainfall events, were the two most important predictors used in the ANN model. These drivers were also identified by multiple linear regressions (MLR), with strong interactive effects. The annual integral of the filled NEE data was found to range from &amp;minus;138 to +155 g C/m&lt;sup&gt;2&lt;/sup&gt;/y over the 5 year eddy covariance measurement period. When applied to a 25 year time series of meteorological data, the ANN model predicts an annual mean NEE of 75(&amp;plusmn;105) g C/m&lt;sup&gt;2&lt;/sup&gt;/y. The main correlates of this inter-annual variability were found to be variation in the amount of absorbed photosynthetically active radiation (APAR), length of the growing season, and number of days in the year when moisture was available in the soil

Modifiable risk factors for 9-year mortality in older English and Brazilian adults: The ELSA and SIGa-Bagé ageing cohorts

To quantify and compare 9-year all-cause mortality risk attributable to modifiable risk factors among older English and Brazilian adults. We used data for participants aged 60 years and older from the English Longitudinal Study of Ageing (ELSA) and the Bagé Cohort Study of Ageing (SIGa-Bagé). The five modifiable risk factors assessed at baseline were smoking, hypertension, diabetes, obesity and physical inactivity. Deaths were identified through linkage to mortality registers. For each risk factor, estimated all-cause mortality hazard ratios (HR) and population attributable fractions (PAF) were adjusted by age, sex, all other risk factors and socioeconomic position (wealth) using Cox proportional hazards modelling. We also quantified the risk factor adjusted wealth gradients in mortality, by age and sex. Among the participants, 659 (ELSA) and 638 (SIGa-Bagé) died during the 9-year follow-up. Mortality rates were higher in SIGa-Bagé. HRs and PAFs showed more similarities than differences, with physical inactivity (PAF 16.5% ELSA; 16.7% SIGa-Bagé) and current smoking (PAF 4.9% for both cohorts) having the strongest association. A clear graded relationship existed between the number of risk factors and subsequent mortality. Wealth gradients in mortality were apparent in both cohorts after full adjustment, especially among men aged 60-74 in ELSA. A different pattern was found among older women, especially in SIGa-Bagé. These findings call attention for the challenge to health systems to prevent and modify the major risk factors related to non-communicable diseases, especially physical inactivity and smoking. Furthermore, wealth inequalities in mortality persist among older adults