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Transition metal-catalyzed asymmetric reactions are of high contemporary importance in organic synthesis. Recently, machine learning (ML) has shown promise in accelerating the development of newer catalytic protocols. However, the need for large amount of experimental data can present a bottleneck for implementing ML models. Here, we propose a meta-learning workflow that can harness the literature-derived data to extract shared reaction features and requires only a few examples to predict the outcome of new reactions. Prototypical networks are used as a meta-learning method to predict the enantioselectivity of asymmetric hydrogenation of olefins. This meta-learning model consistently provides significant performance improvement over other popular ML methods such as random forests and graph neural networks. The performance of our meta-model is analyzed with varying sizes of training examples to demonstrate its utility even with limited data. A good model performance on an out-of-sample test set further indicates the general applicability of our approach. We believe this work will provide a leap forward in identifying promising reactions in the early phases of reaction development when minimal data is available. The need for large amount of experimental data can present a bottleneck for implementing machine learning models. Here, the authors propose a meta-learning workflow that can harness the literature-derived data to extract shared reaction features and requires only a few examples to predict the outcome of new reactions.

Design of asymmetric catalysts generally involves time- and resource-intensive heuristic endeavors. In view of the steady increase in interest toward efficient catalytic asymmetric reactions and the rapid growth in the field of machine learning (ML) in recent years, we envisaged dovetailing these two important domains. We selected a set of quantum chemically derived molecular descriptors from five different asymmetric binaphthyl-derived catalyst families with the propensity to impact the enantioselectivity of asymmetric hydrogenation of alkenes and imines. The predictive power of the random forest (RF) built using the molecular parameters of a set of 368 substrate–catalyst combinations is found to be impressive, with a root-mean-square error (rmse) in the predicted enantiomeric excess (%ee) of about 8.4 ± 1.8 compared to the experimentally known values. The accuracy of RF is found to be superior to other ML methods such as convolutional neural network, decision tree, and eXtreme gradient boosting as well as stepwise linear regression. The proposed method is expected to provide a leap forward in the design of catalysts for asymmetric transformations.

The 2030 Agenda for Sustainable Development adopted by the United Nations in 2015 recognizes noncommunicable diseases (NCDs) as a major public health challenge. Sustainable Development Goal (SDG) 3 includes target 3.4 to reduce premature NCD mortality by one‐third by 2030. This review article analyzes the progress towards the attainment of targets within 3.4, the gaps in meeting the targets, and implementation challenges correlated with those gaps. A literature review was performed in September 2020 to identify the published literature and data discussing the SDGs and NCDs, its progress since 2015, and the associated challenges. The analysis reveals SDG target 3.4 is interrelated to at least nine SDGs. There have been many positive SDG initiatives, but the progress has been slow. Data from various countries show that only two out of the ten NCD progress indicators are being met by at least half of the 176 countries who signed the SDGs. The ongoing COVID‐19 pandemic is expected to further aggravate the prevalence and hinder the progress towards the achievement of goals and the targets of the SDGs. The next decade is critical to advance progress on reducing NCDs across countries. The article concludes with a commentary and recommended actions. A combination of prevention, early detection, and treatment are the key to achieve the SDG 3.4 targets. Increased funding and commitments at international and national levels are required to bring about the transformative changes.

Recent years have seen a rapid growth in the application of various machine learning methods for reaction outcome prediction. Deep learning models have gained popularity due to their ability to learn representations directly from the molecular structure. Gaussian processes (GPs), on the other hand, provide reliable uncertainty estimates but are unable to learn representations from the data. We combine the feature learning ability of neural networks (NNs) with uncertainty quantification of GPs in a deep kernel learning (DKL) framework to predict the reaction outcome. The DKL model is observed to obtain very good predictive performance across different input representations. It significantly outperforms standard GPs and provides comparable performance to graph neural networks, but with uncertainty estimation. Additionally, the uncertainty estimates on predictions provided by the DKL model facilitated its incorporation as a surrogate model for Bayesian optimization (BO). The proposed method, therefore, has a great potential towards accelerating reaction discovery by integrating accurate predictive models that provide reliable uncertainty estimates with BO. Deep learning models have gained popularity for chemical reaction outcome prediction due to their ability to learn representations directly from the molecular structure, whereas Gaussian processes provide reliable uncertainty estimates but are unable to learn representations from the data. Here, the authors combine the feature learning ability of neural networks with uncertainty quantification of Gaussian processes in a deep kernel learning framework to predict reaction outcomes.

To assess and classify all private and government schools located in a northern city of India for accreditation as health promoting schools and comparative health profile assessment of selected higher accredited schools with lower accredited and non-accredited schools Quasi experimental study with pre and post assessment with comparison of higher with lower accredited schools. The current study was conducted in 206 schools of Chandigarh City of Northern India. Comparative health profile assessment was undertaken in 8 schools with 754 children from higher accredited (platinum, gold, silver) and 8 schools with 700 children from lower accredited (bronze) and non-accredited (below bronze) schools. Multicomponent and multilevel intervention was undertaken with self-quality improvement by schools with help of a manual of accreditation of school as health promoting schools. Key intervention included capacity building, technical visits, supportive supervision, sensitization of policymakers and key stakeholders, implementation of policy initiatives, use of social media, technical support and monitoring of activities. Out of 206 schools, 203 participated in the baseline assessment and 204 in the endline assessment. The response rate was 99%. Two schools which refused participation were excluded and not assessed. Schools (N = 17) which participated in the 2011-2013 study were excluded from analysis. There was a statistically difference (p = 0.01) in the improvement of accreditation level of the baseline and endline assessment after intervention(p<0.05). Overall, the proportion of schools at the gold level increased from 1(0.5%) in 2016 to 71(38%). Silver level from 9(5%) to 57 (31%) of schools after intervention. The response rate in health profile assessment in higher(8) and lower(8) accredited schools was 95.9% and 92.7% respectively. The health profile of children higher accreditation level schools (N = 754) were found better in hygiene practices protective factors (peer support at school, parental or guardian supervision), handling stress and less prone to injury as compared to lower accreditation level schools (N = 700),(p<0.05). The health promoting school programme was found to be feasible and effective and lead to significant improvement in accreditation level as compared to baseline assessment after continuous self-quality improvement by schools(p<0.05). The health profile of children studying in higher accredited schools was better as compared to lower accredited schools.

ABSTRACT Introduction: Dyslipidemia and mental illnesses are significant contributors to the global noncommunicable disease burden and studies suggest an association between them. Aim: Using data from a noncommunicable disease risk factor survey conducted in Haryana, India, we undertook a secondary data analysis to examine the association between lipids and depressive symptoms. Methods: The survey involved 5,078 participants and followed the World Health Organisation STEPwise approach to NCD risk factor surveillance approach. Biochemical assessments were undertaken in a subset of participants. Lipid markers were measured using wet chemistry methods. Depressive symptoms were assessed using the Patient Health Questionnaire-9. Descriptive statistics were presented for all variables; logistic regression was used for association analyses. Results: The mean age of the study population was 38 years and 55% of them were females. A majority of the participants belonged to a rural background. The mean total cholesterol was 176 mg/dL and approximately 5% of the participants were found to have moderate to severe depression. The association of total cholesterol (odds ratio [OR] 0.99, P = 0.84), LDL-cholesterol (OR = 1.00, P = 0.19), HDL-cholesterol (OR = 0.99, P = .76), and triglycerides (OR 1.00, P = .12) with depressive symptoms was not significant. Conclusion: This study did not find any association between lipids and depressive symptoms. However, further investigations using prospective designs are warranted to understand this relationship and complex interactions with other mediating factors better.

Heusler compounds belong to a large family of materials and exhibit numerous physical phenomena with promising applications, particularly ferromagnetic Weyl semimetals for their use in spintronics and memory devices. Here, anomalous Hall transport is reported in the room‐temperature ferromagnets NiMnSb (half‐metal with a Curie temperature (TC) of 660 K) and PtMnSb (pseudo half‐metal with a TC of 560 K). They exhibit 4 µB/f.u. magnetic moments and non‐trivial topological states. Moreover, NiMnSb and PtMnSb are the first half‐Heusler ferromagnets to be reported as Weyl semimetals, and they exhibit anomalous Hall conductivity (AHC) due to the extended tail of the Berry curvature in these systems. The experimentally measured AHC values at 2 K are 1.8 × 102 Ω−1 cm−1 for NiMnSb and 2.2 × 103 Ω−1 cm−1 for PtMnSb. The comparatively large value between them can be explained in terms of the spin‐orbit coupling strength. The combined approach of using ab initio calculations and a simple model shows that the Weyl nodes located far from the Fermi energy act as the driving mechanism for the intrinsic AHC. This contribution of topological features at higher energies can be generalized. Anomalous Hall conductivity (AHC) is strongly influenced by topological property type and its proximity to the Fermi energy (EF). It is generally assumed that the topology near to the EF has a greater impact on the AHC than the topology further away from the EF. However, the new discovery, as reported in the manuscript, demonstrates that the AHC is determined solely by Berry curvature (BC), even when it is located far away from the EF (right panel). This effect is named as extended Berry curvature tail effect, where the source of BC, i.e., Weyl node does not necessarily have to be close to EF. The effect can spread beyond EF as long as the topological band is clean (left panel: orange and gree bands)and there is no interference from other trivial bands.

Series of catalysts comprising of parent phosphotungstate (PW 12 ) and mono lacunary phosphotungstate (PW 11 ) anchored to different mesoporous materials (MCM-41 and MCM-48) were prepared. Environmentally benign oxidation of alkenes and alcohols were carried out with H 2 O 2 and molecular oxygen as oxidants. The influence of different parameters on the conversion as well as the selectivity was investigated. Comparative study was ascertained over anchored parent, lacunary phosphotungstates as active species and the supports. The kinetic and thermodynamic studies were correlated with the effect of support as well as active species. Moreover, the catalysts could be recovered and reused four times without significant loss in their activity and selectivity. Graphical Abstract

Thermomyces lanouginosa lipase has been used to develop improved methods for carrier-free immobilization, the Cross-Linked Enzyme Aggregates (CLEAs), for its application in detergent products. An activator step has been introduced to the CLEAs preparation process with the addition of Tween 80 as activator molecule, in order to obtain a higher number of the individual lipase molecules in the ”open lid” conformation prior to the cross-linking step. A terminator step has been introduced to quench the cross-linking reaction at an optimal time by treatment with an amine buffer in order to obtain smaller and more homogenous cross-linked particles. This improved immobilization method has been compared to a commercially available enzyme and has been shown to be made up of smaller and more homogenous particles with an average diameter of 1.85 ± 0.28 µm which are 129.7% more active than the free enzyme. The CLEAs produced show improved features for commercial applications such as an improved wash performance comparable with the free enzyme, improved stability to proteolysis and a higher activity after long-term storage.

12-Tungstophosphoric acid and lacunary phosphotungstate anchored to MCM-41 and ZrO 2 were synthesized, characterized and used as bifunctional catalyst for oxidative esterification of benzaldehyde with methanol. The different aldehyde substrates study show excellent selectivity for esters, indicating the scope of the catalysts. A tentative reaction mechanism for oxidative esterification of aldehyde is also proposed. Graphical Abstract