Extended Berry Curvature Tail in Ferromagnetic Weyl Semimetals NiMnSb and PtMnSb.

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.

Deep Kernel learning for reaction outcome prediction and optimization.

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.

An insight into MDR Acinetobacter baumannii infection and its pathogenesis: Potential therapeutic targets and challenges.

Acinetobacter baumannii is observed as a common species of Gram-negative bacteria that exist in soil and water. Despite being accepted as a typical component of human skin flora, it has become an important opportunistic pathogen, especially in healthcare settings. The pathogenicity of A. baumannii is attributed to its virulence factors, which include adhesins, pili, lipopolysaccharides, outer membrane proteins, iron uptake systems, autotransporter, secretion systems, phospholipases etc. These elements provide the bacterium the ability to cling to and penetrate host cells, get past the host immune system, and destroy tissue. Its infection is a major contributor to human pathophysiological conditions including pneumonia, bloodstream infections, urinary tract infections, and surgical site infections. It is challenging to treat infections brought on by this pathogen since this bacterium has evolved to withstand numerous drugs and further emergence of drug-resistant A. baumannii results in higher rates of morbidity and mortality. The long-term survival of this bacterium on surfaces of medical supplies and hospital furniture facilitates its frequent spread in humans from one habitat to another. There is a need for urgent investigations to find effective drug targets for A. baumannii as well as designing novel drugs to reduce the survival and spread of infection. In the current review, we represent the specific features, pathogenesis, and molecular intricacies of crucial drug targets of A. baumannii. This would also assist in proposing strategies and alternative therapies for the prevention and treatment of A. baumannii infections and their spread.

Relationship between serum lipids and depression: A cross sectional survey among adults in Haryana, India.

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.

Recent Advancements in Polyurethane-based Tissue Engineering.

In tissue engineering, polyurethane-based implants have gained significant traction because of their high compatibility and inertness. The implants therefore show fewer side effects and lasts longer. Also, the mechanical properties can be tuned and morphed into a particular shape, owing to which polyurethanes show immense versatility. In the last 3 years, scientists have devised methods to enhance the strength of and induce dynamic properties in polyurethanes, and these developments offer an immense opportunity to use them in tissue engineering. The focus of this review is on applications of polyurethane implants for biomedical application with detailed analysis of hard tissue implants like bone tissues and soft tissues like cartilage, muscles, skeletal tissues, and blood vessels. The synthetic routes for the preparation of scaffolds have been discussed to gain a better understanding of the issues that arise regarding toxicity. The focus here is also on concerns regarding the biocompatibility of the implants, given that the precursors and byproducts are poisonous.

Molecular Machine Learning for Chemical Catalysis: Prospects and Challenges.

ConspectusIn the domain of reaction development, one aims to obtain higher efficacies as measured in terms of yield and/or selectivities. During the empirical cycles, an admixture of outcomes from low to high yields/selectivities is expected. While it is not easy to identify all of the factors that might impact the reaction efficiency, complex and nonlinear dependence on the nature of reactants, catalysts, solvents, etc. is quite likely. Developmental stages of newer reactions would typically offer a few hundreds of samples with variations in participating molecules and/or reaction conditions. These "observations" and their "output" can be harnessed as valuable labeled data for developing molecular machine learning (ML) models. Once a robust ML model is built for a specific reaction under development, it can predict the reaction outcome for any new choice of substrates/catalyst in a few seconds/minutes and thus can expedite the identification of promising candidates for experimental validation. Recent years have witnessed impressive applications of ML in the molecular world, most of them aimed at predicting important chemical or biological properties. We believe that an integration of effective ML workflows can be made richly beneficial to reaction discovery.As with any new technology, direct adaptation of ML as used in well-developed domains, such as natural language processing (NLP) and image recognition, is unlikely to succeed in reaction discovery. Some of the challenges stem from ineffective featurization of the molecular space, unavailability of quality data and its distribution, in making the right choice of ML model and its technically robust deployment. It shall be noted that there is no universal ML model suitable for an inherently high-dimensional problem such as chemical reactions. Given these backgrounds, rendering ML tools conducive for reactions is an exciting as well as challenging endeavor at the same time. With the increased availability of efficient ML algorithms, we focused on tapping their potential for small-data reaction discovery (a few hundreds to thousands of samples).In this Account, we describe both feature engineering and feature learning approaches for molecular ML as applied to diverse reactions of high contemporary interest. Among these, catalytic asymmetric hydrogenation of imines/alkenes, ß-C(sp3)-H bond functionalization, and relay Heck reaction employed a feature engineering approach using the quantum-chemically derived physical organic descriptors as the molecular features─all designed to predict the enantioselectivity. The selection of molecular features to customize it for a reaction of interest is described, along with emphasizing the chemical insights that could be gathered through the use of such features. Feature learning methods for predicting the yield of Buchwald-Hartwig cross-coupling, deoxyfluorination of alcohols, and enantioselectivity of N,S-acetal formation are found to offer excellent predictions. We propose a transfer learning protocol, wherein an ML model such as a language model is trained on a large number of molecules (105-106) and fine-tuned on a focused library of target task reactions, as an effective alternative for small-data reaction discovery (102-103 reactions). The exploitation of deep neural network latent space as a method for generative tasks to identify useful substrates for a reaction is demonstrated as a promising strategy.

Effectiveness of health promoting schools: A comparative health profile assessment of higher as compared to low accredited schools in Chandigarh, Union Territory of North India.

OBJECTIVES: 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. DESIGN: Quasi experimental study with pre and post assessment with comparison of higher with lower accredited schools. SETTINGS: 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. INTERVENTIONS: 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. OUTCOMES: Accreditation levels (bronze, silver, gold and platinum levels) as health promoting schools after pre and post intervention. RESULTS: 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). CONCLUSIONS: 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.

A transfer learning approach for reaction discovery in small data situations using generative model.

Sustainable practices in chemical sciences can be better realized by adopting interdisciplinary approaches that combine the advantages of machine learning (ML) on the initially acquired small data in reaction discovery. Developing new reactions generally remains heuristic and even time and resource intensive. For instance, synthesis of fluorine-containing compounds, which constitute ∼20% of the marketed drugs, relies on deoxyfluorination of abundantly available alcohols. Herein, we demonstrate the use of a recurrent neural network-based deep generative model built on a library of just 37 alcohols for effective learning and exploration of the chemical space. The proof-of-concept ML model is able to generate good quality, synthetically accessible, higher-yielding novel alcohol molecules. This protocol would have superior utility for deployment into a practical reaction discovery pipeline.

Ir-Catalyzed Ligand-Free Directed C-H Borylation of Arenes and Pharmaceuticals: Detailed Mechanistic Understanding.

An efficient method for Ir-catalyzed ligand free ortho borylation of arenes (such as, 2-phenoxypyridines, 2-anilinopyridines, benzylamines, benzylpiperazines, benzylmorpholines, benzylpyrrolidine, benzylpiperidines, benzylazepanes, α-amino acid derivatives, aminophenylethane derivatives, and other important scaffolds) and pharmaceuticals has been developed. The reaction underwent via an interesting mechanistic pathway, as revealed by the detailed mechanistic investigations by using kinetic isotope studies and DFT calculations. The catalytic cycle is found to involve the intermediacy of an Ir-boryl complex where the substrate C-H activation is the turnover determining step, intriguingly without any appreciable primary KIE. The method displays a broad range of substrate scope and functional group tolerance. Numerous late-stage borylation of various important molecules and drugs were achieved using this developed strategy. The borylated compounds were further converted into more valuable functionalities. Moreover, utilizing the benefit of the B-N intramolecular interaction of the mono borylated compounds, an operationally simple method has been developed for the selective diborylation of 2-phenoxypyridines and numerous functionalized arenes. Furthermore, the synthetic utility has been showcased with the removal of the pyridyl directing group from the borylated product to achieve ortho borylated phenol along with the ipso-borylation for the preparation of 1,2-diborylated benzene.

Anisotropic large diamagnetism in Dirac semimetals ZrTe5and HfTe5.

Dirac semimetals, e.g., ZrTe5and HfTe5, have been widely investigated and have exhibited various exotic physical properties. Nevertheless, several properties of these compounds, including diamagnetism, are still unclear. In this study, we measured the temperature- and field-dependent diamagnetism of ZrTe5and HfTe5along all three crystallographic axes (a-,b-, andc-axis). The temperature-dependent magnetization shows an anomaly, which is a characteristic of Dirac crossing. Diamagnetic signal reaches the highest value of 17.3 × 10-4emu mol-1Oe-1along the van der Waals layers, i.e., theb-axis. However, the diamagnetism remains temperature-independent along the other two axes. The field-dependent diamagnetic signal grows linearly without any sign of saturation and maintains a large value along theb-axis. Interestingly, the observed diamagnetism is anisotropic like other physical properties of these compounds and is strongly related to the effective mass, indicating the dominating contribution of orbital diamagnetism in Dirac semimetals induced by interband effects. ZrTe5and HfTe5show one of the largest diamagnetic value among previously reported state-of-the-art topological semimetals. Our present study adds another important experimental aspect to characterize nodal crossing and search for other topological materials with large magnetic susceptibility.

Giant Topological Hall Effect in the Noncollinear Phase of Two-Dimensional Antiferromagnetic Topological Insulator MnBi4Te7.

Magnetic topological insulators provide an important platform for realizing several exotic quantum phenomena, such as the axion insulating state and the quantum anomalous Hall effect, owing to the interplay between topology and magnetism. MnBi4Te7 is a two-dimensional Z2 antiferromagnetic (AFM) topological insulator with a Néel temperature of ∼13 K. In AFM materials, the topological Hall effect (THE) is observed owing to the existence of nontrivial spin structures. A material with noncollinearity that develops in the AFM phase rather than at the onset of the AFM order is particularly important. In this study, we observed that such an unanticipated THE starts to develop in a MnBi4Te7 single crystal when the magnetic field is rotated away from the easy axis (c-axis) of the system. Furthermore, the THE resistivity reaches a giant value of ∼7 µΩ-cm at 2 K when the angle between the magnetic field and the c-axis is 75°. This value is significantly higher than the values for previously reported systems with noncoplanar structures. The THE can be ascribed to the noncoplanar spin structure resulting from the canted state during the spin-flip transition in the ground AFM state of MnBi4Te7. The large THE at a relatively low applied field makes the MnBi4Te7 system a potential candidate for spintronic applications.

Anisotropic Nodal-Line-Derived Large Anomalous Hall Conductivity in ZrMnP and HfMnP.

The nontrivial band structure of semimetals has attracted substantial research attention in condensed matter physics and materials science in recent years owing to its intriguing physical properties. Within this class, a group of nontrivial materials known as nodal-line semimetals is particularly important. Nodal-line semimetals exhibit the potential effects of electronic correlation in nonmagnetic materials, whereas they enhance the contribution of the Berry curvature in magnetic materials, resulting in high anomalous Hall conductivity (AHC). In this study, two ferromagnetic compounds, namely ZrMnP and HfMnP, are selected, wherein the abundance of mirror planes in the crystal structure ensures gapped nodal lines at the Fermi energy. These nodal lines result in one of the largest AHC values of 2840 Ω-1 cm-1 , with a high anomalous Hall angle of 13.6% in these compounds. First-principles calculations provide a clear and detailed understanding of nodal line-enhanced AHC. The finding suggests a guideline for searching large AHC compounds.

Progress and challenges in achieving noncommunicable diseases targets for the sustainable development goals.

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.

Burden of noncommunicable diseases and implementation challenges of National NCD Programmes in India.

BACKGROUND: Out of the total deaths globally, noncommunicable diseases (NCDs) account for 72% of the deaths. In India, as per the global burden of disease 2016 estimates, NCDs contributed to 62% of the deaths and 55% of the disability-adjusted life years, thereby posing a huge burden. Before 2010, there was no integrated programme, which addresses these NCDs, but there were many programmes parallelly running and catering to different aspects of these NCDs. Now almost 13 programmes are directly or indirectly contributing to the NCD prevention and control with many implementation challenges. METHODS: A review on the status of NCD burden estimates globally and nationally was undertaken. The National NCD Programme and other strategies associated with addressing the NCDs were searched using the search engines PubMed and Google Scholar along with the websites of national ministries, government portals and meeting proceedings. RESULTS: Health is a state subject, with National Health Mission (NRHM/NUHM) as a flagship programme of Ministry of Health and Family Welfare. There are 13 programmes contributing to NCD prevention and control directly or indirectly and the major one is the National Programme for prevention and control of cancer, diabetes, cardiovascular diseases and stroke (NPCDCS). The other initiatives taken for NCD prevention and control include the National Action Plan to achieve NCD targets by 2025 and development of Multisectoral Action Plan (2017-2022). The infrastructure for NPCDCS includes 524 district NCD cells, 565 district NCD clinics, 167 district cardiac care units, 164 district day care centres and 2759 Community Health Centre NCD clinics. The key challenges are with trained human resources for the screening of the NCDs, low budget allocation and utilisation, lack of access to diagnostics and regular supply of essential medicines. There is also poor focus on health promotion, multisectoral participation, surveillance, monitoring and evaluation of the programme at different levels of health care delivery. CONCLUSION: The government has taken different initiatives for the prevention and control but effective implementation is the major challenge in India. A health system strengthening with focus on health promotion in different settings, robust surveillance and access to individual clinical services is required. Collaborations with ministries, multisectoral approach, strengthening of referral system along with involvement/training of grassroot level workers who efficiently implement are needed. Bolstering of screening, diagnostic and treatment service will be fruitful.

Pd-Catalyzed γ-C(sp3)-H Fluorination of Free Amines.

The first example of free amine γ-C(sp3)-H fluorination is realized using 2-hydroxynicotinaldehyde as the transient directing group. A wide range of cyclohexyl and linear aliphatic amines could be fluorinated selectively at the γ-methyl and methylene positions. Electron withdrawing 3,5-disubstituted pyridone ligands were identified to facilitate this reaction. Computational studies suggest that the turnover determining step is likely the oxidative addition step for methylene fluorination, while it is likely the C-H activation step for methyl fluorination. The explicit participation of Ag results in a lower energetic span for methylene fluorination and a higher energetic span for methyl fluorination, which is consistent with the experimental observation that the addition of silver salt is desirable for methylene but not for methyl fluorination. Kinetic studies on methyl fluorination suggest that the substrate and PdL are involved in the rate-determining step, indicating that the C-H activation step may be partially rate-determining. Importantly, an energetically preferred pathway has identified an interesting pyridone-assisted bimetallic transition state for the oxidative addition step in methylene fluorination, thus uncovering a potential new role of the pyridone ligand.

Is silver a mere terminal oxidant in palladium catalyzed C-H bond activation reactions?

In the contemporary practice of palladium catalysis, a molecular understanding of the role of vital additives used in such reactions continues to remain rather vague. Herein, we disclose an intriguing and a potentially general role for one of the most commonly used silver salt additives, discovered through rigorous computational investigations on four diverse Pd-catalyzed C-H bond activation reactions involving sp2 aryl C-H bonds. The catalytic pathways of different reactions such as phosphorylation, arylation, alkynylation, and oxidative cycloaddition are analyzed, with and without the explicit inclusion of the silver additive in the respective transition states and intermediates. Our results indicate that the pivotal role of silver salts is likely to manifest in the form of a Pd-Ag heterobimetallic species that facilitates intermetallic electronic communication. The Pd-Ag interaction is found to provide a consistently lower energetic span as compared to an analogous pathway devoid of such interaction. Identification of a lower energy pathway as well as enhanced catalytic efficiency due to Pd-Ag interaction could have broad practical implications in the mechanism of transition metal catalysis and the current perceptions on the same.

A unified machine-learning protocol for asymmetric catalysis as a proof of concept demonstration using asymmetric hydrogenation.

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.

Mechanism and Origin of Enantioselectivity in Nickel-Catalyzed Alkyl-Alkyl Suzuki Coupling Reaction.

Enantioselective Suzuki coupling reactions are a widely used method in asymmetric synthesis of chiral compounds. In an important extension of this protocol, 1-bromo-1-fluoroalkanes were coupled with alkyl-9-BBN using chiral NiCl2L* as the catalyst (where L* = bis(pyrrolidine) ligand) under Suzuki conditions to obtain a product with a stereogenic center bearing a fluorine. In view of the current interest in chiral fluorine-containing compounds as well as lack of clarity on the mechanism of Ni-catalyzed asymmetric Suzuki coupling reactions, we decided to examine various mechanistic pathways of the title reaction. The (U)M06 density functional theory computations have been employed to identify the energetically preferred pathway first and then to probe the origin of high enantioselectivity. In particular, we have compared the likely involvement of different redox couples such as Ni(0)/Ni(II) and Ni(I)/Ni(III) in the catalytic cycle. For the Ni(0)/Ni(II) pathway, both singlet and triplet spin states have been considered whereas a doublet spin multiplicity has been examined in the case of the Ni(I)/Ni(III) system. The most preferred catalytic pathway is found to proceed through a Ni(I)/Ni(III) redox cycle with key mechanistic steps such as (a) a transmetalation involving the transfer of the alkyl group of 9-BBN to the Ni-catalyst, (b) an oxidative addition of bromo(fluoro) alkane to give a penta-coordinate Ni(III) intermediate, and (c) an enantio-controlling reductive elimination (RE) that facilitates the C-C bond formation between the Ni-bound fluoroalkyl and alkyl moieties to yield the final product. The transmetalation is found to be the turnover determining transition state (TS) according to the activation span model. The RE is found to be the enantio-controlling step, wherein the TS for the addition of the si prochiral face of the Ni-bound fluoro alkyl moiety to the alkyl group is 4.3 kcal/mol lower than the corresponding re face addition. Distortion-Interaction analysis suggested that the extent of distortion in the catalyst Ni(Br)L* fragment in the si face reductive elimination TS is much lower than in the re face addition, thus making a vital contribution to the energy difference between diastereomeric TS.

Influence of heat-moisture treatment (HMT) on physicochemical and functional properties of starches from different Indian oat (Avena sativa L.) cultivars.

The present work was aimed to study the effect of heat-moisture treatment (HMT) at 30% moisture content and temperature of 100 °C/12 h on physicochemical and functional properties of starches from eight Indian oat cultivars. As compared to native starches, HMT starches had significantly (p < 0.05) lower amylose content (6.18-16.87%), swelling power (11.4-14.3 g/g), solubility (4.7-8.5%) and proportion of B and C type starch granules. A reduction in starch paste clarity after HMT for all the cultivar starches was observed. The results revealed lower pasting viscosities for HMT starches in comparison to native starches, thereby indicating their thermostability, and lower retrogradation. The shape of oat starch granules did not change after HMT. Both native and HMT starches showed similar A-type X-ray diffraction pattern. Among all cultivars, HMT starch from OS-7 cv. had the lowest amylose content, swelling power, solubility, B- and C-type starch granules and lower peak and breakdown viscosities. HMT starches could be used as a substitute of chemically modified starches with the advantage of being safe and a greener modification. Such modifications will enhance versatility of oat starches in development of various fabricated food products.

Aliphatic C(sp3)-H Bond Activation Using Nickel Catalysis: Mechanistic Insights on Regioselective Arylation.

Transition-metal-catalyzed C(sp3)-H bond activation in aliphatic compounds are of current interest. Lack of mechanistic insights on Ni-catalyzed C(sp3)-H activation using 8-aminoquinoline as a directing group motivated us to examine an interesting direct arylation of an aliphatic tertiary amide by using density functional theory. The catalysis employed Ni(II) precatalyst, 4-iodoanisole as an arylating agent, sodium carbonate, and mesitylenic acid as additives in DMF solvent. Examination of a comprehensive set of mechanistic pathways helped us learn that the most preferred route begins with a bidentate chelate binding of deprotonated substrate to the Ni. The C-H activation in the catalyst-substrate complex via a cyclometalation deprotonation provides a five-membered nickelacycle intermediate, which upon the rate-limiting oxidative insertion to aryl iodide forms a Ni(IV)-aryl intermediate. The ensuing reductive elimination furnishes the desired arylated product. We note that the explicit inclusion of sodium carbonate, mesitylenic acid, and solvent molecules on sodium ion all are critical in identifying the most favorable pathway. Of the two types of C(sp3)-H bonds in the substrate [2-methyl-2-phenyl-N-(quinolin-8-yl)heptanamide], the energies for the regiocontrolling reductive elimination is predicted to be more in favor of the methyl group than the methylene of the pentyl chain, in excellent agreement with the previous experimental observation.