Platinum–rhodium–tin/carbon electrocatalysts for ethanol oxidation in acid media: effect of the precursor addition order and the amount of tin

Carbon-supported Pt x –Rh y –Sn z catalysts (x:y:z = 3:1:4, 6:2:4, 9:3:4) are prepared by Pt, Rh, and Sn precursors reduction in different addition order. The materials are characterized by X-ray diffraction, transmission electron microscopy, and X-ray photoelectron spectroscopy techniques and are evaluated for the electrooxidation of ethanol in acidic media by cyclic voltammetry, chronoamperometry, and anode potentiostatic polarization. The influence of both the order in which the precursors are added and the composition of metals in the catalysts on the electrocatalytic activity and physico-chemical characteristics of Pt x –Rh y –Sn z /C catalysts is evaluated. Oxidized Rh species prevail on the surface of catalysts synthesized by simultaneous co-precipitation, thus demonstrating the influence of synthesis method on the oxidation state of catalysts. Furthermore, high amounts of Sn in composites synthesized by co-precipitation result in very active catalysts at low potentials (bifunctional effect), while medium Sn load is needed for sequentially deposited catalysts when the electronic effect is most important (high potentials), since more exposed Pt and Rh sites are needed on the catalyst surface to alcohol oxidation. The Pt3–Rh1–Sn4/C catalyst prepared by co-precipitation is the most active at potentials lower than 0.55 V (related to bifunctional effect), while the Pt6–Rh2–Sn4/C catalyst, prepared by sequential precipitation (first Rh and, after drying, Pt + Sn), is the most active above 0.55 V.The authors thank the Brazilian National Council of Technological and Scientific Development-CNPq (Grants: 402243/2012-9, 303630/2012-4, 474261/2013-1, 407274/2013-8, and 310282/2013-6) for the scholarships and financial support for this work

Gene expression clines reveal local adaptation and associated trade-offs at a continental scale

Local adaptation, where fitness in one environment comes at a cost in another, should lead to spatial variation in trade-offs between life history traits and may be critical for population persistence. Recent studies have sought genomic signals of local adaptation, but often have been limited to laboratory populations representing two environmentally different locations of a species' distribution. We measured gene expression, as a proxy for fitness, in males of Drosophila subobscura, occupying a 20° latitudinal and 11 °C thermal range. Uniquely, we sampled six populations and studied both common garden and semi-natural responses to identify signals of local adaptation. We found contrasting patterns of investment: transcripts with expression positively correlated to latitude were enriched for metabolic processes, expressed across all tissues whereas negatively correlated transcripts were enriched for reproductive processes, expressed primarily in testes. When using only the end populations, to compare our results to previous studies, we found that locally adaptive patterns were obscured. While phenotypic trade-offs between metabolic and reproductive functions across widespread species are well-known, our results identify underlying genetic and tissue responses at a continental scale that may be responsible for this. This may contribute to understanding population persistence under environmental change

Development and external validation of the 'Global Surgical-Site Infection' (GloSSI) predictive model in adult patients undergoing gastrointestinal surgery

Background: Identification of patients at high risk of surgical-site infections may allow surgeons to minimize associated morbidity. However, there are significant concerns regarding the methodological quality and transportability of models previously developed. The aim of this study was to develop a novel score to predict 30-day surgical-site infection risk after gastrointestinal surgery across a global context and externally validate against existing models. Methods: This was a secondary analysis of two prospective international cohort studies: GlobalSurg-1 (July-November 2014) and GlobalSurg-2 (January-July 2016). Consecutive adults undergoing gastrointestinal surgery were eligible. Model development was performed using GlobalSurg-2 data, with novel and previous scores externally validated using GlobalSurg-1 data. The primary outcome was 30-day surgical-site infections, with two predictive techniques explored: penalized regression (least absolute shrinkage and selection operator ('LASSO')) and machine learning (extreme gradient boosting ('XGBoost')). Final model selection was based on prognostic accuracy and clinical utility. Results: There were 14 019 patients (surgical-site infections = 12.3%) for derivation and 8464 patients (surgical-site infections = 11.4%) for external validation. The LASSO model was selected due to similar discrimination to extreme gradient boosting (AUC 0.738 (95% c.i. 0.725 to 0.750) versus 0.737 (95% c.i. 0.709 to 0.765)), but greater explainability. The final score included six variables: country income, ASA grade, diabetes, and operative contamination, approach, and duration. Model performance remained good on external validation (AUC 0.730 (95% c.i. 0.715 to 0.744); calibration intercept -0.098 and slope 1.008) and demonstrated superior performance to the external validation of all previous models. Conclusion: The 'Global Surgical-Site Infection' score allows accurate prediction of the risk of surgical-site infections with six simple variables that are routinely available at the time of surgery across global settings. This can inform the use of intraoperative and postoperative interventions to modify the risk of surgical-site infections and minimize associated harm