A functional SiO2-TiO2 mesoporous assembly designed for the controlled release of carvacrol

Mesoporous composites can be useful for the gentle release of biocides and fertilizers. Here, we study the structural, morphological, and drug-delivery advantages of SiO2-TiO2 (PST) particles synthesized by a hydrothermal method. The PST particles had a spherical-like assembly architecture, which was obtained by cobalt ions incorporation. Each assembly contains nanobars with acute or obtuse tip shape, notably in the sample with 0.5 wt% of Co. Drug loading/delivery evaluations were carried out using carvacrol (CVC) as biocidal molecule. PST composites functionalized with the triethanolamine presented higher load efficiencies of 30–37% and load capacities of 20–26%. A similar trend occurred for the release percentage of CVC. The PST samples without functionalization had a release of only 19–29% (at pH=7.4). After the TEOA functionalization, the release percentage was enhanced to 64–78%. Moreover, the PST had a maximum release percentage of 64.8% reached after only 3 min (at pH=7.4). In contrast, the PST doped with 1 wt% of Co reached the maximum release percentage of 64.8% after 24 h at pH=7.4. Hence, the results of this investigation demonstrate that the PST synthetized with Co and functionalized with TEOA could be used for the control of undesired weed and phytopathogens in cultivated plants by prolonging their delivery time of biocidal molecules

Probability of major depression classification based on the SCID, CIDI, and MINI diagnostic interviews: A synthesis of three individual participant data meta-analyses

Introduction: Three previous individual participant data meta-analyses (IPDMAs) reported that, compared to the Structured Clinical Interview for the DSM (SCID), alternative reference standards, primarily the Composite International Diagnostic Interview (CIDI) and the Mini International Neuropsychiatric Interview (MINI), tended to misclassify major depression status, when controlling for depression symptom severity. However, there was an important lack of precision in the results. Objective: To compare the odds of the major depression classification based on the SCID, CIDI, and MINI. Methods: We included and standardized data from 3 IPDMA databases. For each IPDMA, separately, we fitted binomial generalized linear mixed models to compare the adjusted odds ratios (aORs) of major depression classification, controlling for symptom severity and characteristics of participants, and the interaction between interview and symptom severity. Next, we synthesized results using a DerSimonian-Laird random-effects meta-analysis. Results: In total, 69,405 participants (7,574 [11%] with major depression) from 212 studies were included. Controlling for symptom severity and participant characteristics, the MINI (74 studies; 25,749 participants) classified major depression more often than the SCID (108 studies; 21,953 participants; aOR 1.46; 95% confidence interval [CI] 1.11-1.92]). Classification odds for the CIDI (30 studies; 21,703 participants) and the SCID did not differ overall (aOR 1.19; 95% CI 0.79-1.75); however, as screening scores increased, the aOR increased less for the CIDI than the SCID (interaction aOR 0.64; 95% CI 0.52-0.80). Conclusions: Compared to the SCID, the MINI classified major depression more often. The odds of the depression classification with the CIDI increased less as symptom levels increased. Interpretation of research that uses diagnostic interviews to classify depression should consider the interview characteristics. © 202

Data from Sullivan et al. (2020) Long-term thermal sensitivity of Earth’s tropical forests. Science. DOI: 10.1126/science.aaw7578.

ABSTRACT: The sensitivity of tropical forest carbon to climate is a key uncertainty in predicting global climate change. Although short-term drying and warming are known to affect forests, it is unknown if such effects translate into long-term responses. Here, we analyze 590 permanent plots measured across the tropics to derive the equilibrium climate controls on forest carbon. Maximum temperature is the most important predictor of aboveground biomass (−9.1 megagrams of carbon per hectare per degree Celsius), primarily by reducing woody productivity, and has a greater rate of decline in the hottest forests (>32.2°C). Our results nevertheless reveal greater thermal resilience than observations of short-term variation imply. To realize the long-term climate adaptation potential of tropical forests requires both protecting them and stabilizing Earth’s climate