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Ordered mesoscale structures in 2D materials induced by small misorientations have allowed for a wide variety of electronic, ferroelectric, and quantum phenomena to be explored. Until now, the only mechanism to induce this periodic ordering was

Two-dimensional transition metal dichalcogenides (TMDs) can adopt one of several possible structures, with the most common being the trigonal prismatic and octahedral symmetry phases. Since the structure determines the electronic properties, being able to predict phase-preferences of TMDs from just the knowledge of the constituent atoms is highly desired, but has remained a long-standing problem. In this study, we applied high-throughput quantum mechanical computations with machine learning algorithms to solve this old problem. Our analysis provides insights into determining physiochemical factors that dictate the phase-preference of a TMD, identifying and going beyond the attributes considered by earlier researchers in predicting crystal structures. A knowledge of these underlying physiochemical factors not only helps us to rationalize, but also to accurately predict structural preferences. We show that machine learning algorithms are powerful tools that can be used not only to find new materials with targeted properties, but also to find connections between elemental attributes and the target property/properties that were not previously obvious.

Due to an extremely diverse phase space, La1-xSrxMnO3, as with other manganites, offers a wide range of tunability and applications including colossal magnetoresistance and use as spin-polarized electrodes. Here, we study an unprecedented, exotic surface reconstruction (6 × 6) in La1-xSrxMnO3 (x = 0.3) observed via low-energy electron diffraction (LEED). Scanning tunneling microscopy (STM) shows the surface is relatively flat, with unit-cell step heights, and X-ray photoelectron spectroscopy (XPS) reveals a strong degree of Sr segregation at the surface. By combining electron diffraction and first-principles computations, we propose that the long-range surface reconstruction consists of a Sr-segregated surface with La (6 × 6) ordering. This study expands our understanding of manganite systems and underscores their ability to form interesting surface reconstructions, driven largely by cation segregation that can potentially be controlled for tuning surface ordering.

Glycerol is a key by-product in biodiesel production and can be utilized in the synthesis of value-added chemicals. The low cost and fairly abundant availability of glycerol can be advantageous in producing a variety of pharmaceuticals and cosmetic products. Among the various catalytic transformations, selective oxidation is a promising pathway for the valorization of glycerol. In this present report, we deliver a first proof of concept for the involvement of quinone groups adjacent to N sites on the GO surface, for the selective oxidation of glycerol to dihydroxyacetone (DHA). Graphene oxide is covalently functionalized with 2,4-dihydroxypyridine (DHP), which resembles the identified active sites in the carbon clusters. As anticipated, the DHP-functionalized graphene oxide catalyst (DHP@GO) improved the conversion of glycerol to DHA, the main product, along with minor amounts of glyceric acid (GA) and fumaric acid (FA).

The COVID-19 pandemic has substantially strained health systems across the globe. In particular, documented disruptions to voluntary family planning and reproductive health care due to competing health priorities, service disruptions, stockouts, and lockdowns are significantly impacting reproductive, maternal, newborn, and child health. As governments and family planning programmes grapple with how best to respond to the direct and indirect effects of the pandemic on family planning and reproductive health (FP/RH), the implementation and adaptation of evidence-based practices is crucial. In this commentary, we outline applications of the High Impact Practices in Family Planning (HIPs) towards COVID-19 response efforts. The HIPs are a set of evidence-based family planning practices which reflect global expert consensus on what works in family planning programming. Drawing upon preliminary COVID-19 data, documented experiences from prior health emergencies, and recommended programme adaptations from a variety of global health partners, we outline situations where specific HIPs may assist family planning programme managers in developing context-specific and evidence-based responses to COVID-19-related impacts on FP/RH, with the ultimate goal of ensuring the accessibility, availability, and continuity of voluntary family planning services across the world.

The Joint Automated Repository for Various Integrated Simulations (JARVIS) is an integrated infrastructure to accelerate materials discovery and design using density functional theory (DFT), classical force-fields (FF), and machine learning (ML) techniques. JARVIS is motivated by the Materials Genome Initiative (MGI) principles of developing open-access databases and tools to reduce the cost and development time of materials discovery, optimization, and deployment. The major features of JARVIS are: JARVIS-DFT, JARVIS-FF, JARVIS-ML, and JARVIS-tools. To date, JARVIS consists of ≈40,000 materials and ≈1 million calculated properties in JARVIS-DFT, ≈500 materials and ≈110 force-fields in JARVIS-FF, and ≈25 ML models for material-property predictions in JARVIS-ML, all of which are continuously expanding. JARVIS-tools provides scripts and workflows for running and analyzing various simulations. We compare our computational data to experiments or high-fidelity computational methods wherever applicable to evaluate error/uncertainty in predictions. In addition to the existing workflows, the infrastructure can support a wide variety of other technologically important applications as part of the data-driven materials design paradigm. The JARVIS datasets and tools are publicly available at the website: https://jarvis.nist.gov.

The chemical composition of essential oils (EOs) isolated by hydrodistillation (HD) and simultaneous distillation extraction (SDE) from Aloe debrana roots were determined for the first time by GC/M...

Ordered mesoscale structures in 2D materials induced by small misorientations have opened pathways for a wide variety of novel electronic, ferroelectric, and quantum phenomena. Until now, the only mechanism to induce this periodic ordering was via mechanical rotations between the layers, with the periodicity of the resulting moir\'e pattern being directly related to twist angle. Here we report a fundamentally new mechanism for emergence of mesoscopic periodic patterns in multilayer sulfur-containing metal phosphorous trichalcogenide, MnPS$_{3}$, induced by the electron beam. The formation under the beam of periodic hexagonal patterns with several characteristic length scales, nucleation and transitions between the phases, and local dynamics are demonstrated. The associated mechanisms are attributed to the relative contraction of the layers caused by beam-induced sulphur vacancy formation with subsequent ordering and lattice parameter change. As a result, the plasmonic response of the system is locally altered, suggesting an element of control over plasmon resonances by electron beam patterning. We pose that harnessing this phenomenon provides both insight into fundamental physics of quantum materials and opens a pathway towards device applications by enabling controlled periodic potentials on the atomic scale., Comment: Electron microscopy data and analysis codes are freely available here: https://github.com/kevinroccapriore/MnPS3

We develop a probabilistic machine learning model and use it to screen for new hybrid organic-inorganic perovskites (HOIPs) with targeted electronic band gap. The data set used for this work is highly diverse, containing multiple atomic structures for each of 192 chemically distinct HOIP formulas. Therefore, any property prediction on a given formula must be associated with an irreducible "uncertainty" that comes from its unknown atomic details. As a result, dozens of new HOIP formulas with band gap falling between 1.25 and 1.50 eV were identified and validated against suitable first-principles computations. Through this demonstration we show that the probabilistic deep learning approach is robust, versatile, and can be used to properly quantify this uncertainty. In conclusion, the probabilistic standpoint and approach described herein could be widely useful for the very common and inevitable data uncertainty which is rooted at the incompleteness of information during experiments and/or computations.

Among all the cancer-related deaths globally, pancreatic ductal adenocarcinoma (PDAC) accounts for the seventh leading cause of mortality. A dysregulated immune system disrupts anti-tumor immunity by abnormal accumulation of myeloid-derived suppressor cells (MDSCs), but the underlying mechanisms are still inconclusive. To gain new insights into the role of MDSCs in tumor settings, we aimed to determine the mechanism of expansion of various subsets of MDSCs in PDAC patients and their role in promoting invasiveness. We assessed the load of MDSCs, chemokines responsible for the recruitment of MDSCs in PDAC patients by flow cytometry. We investigated the chemokine profile of tumor tissue using qRT-PCR and the status of epithelial-mesenchymal transition (EMT) related markers E-Cadherin, N-Cadherin, Snail, and ZEB1 by qRT-PCR and immunohistochemistry. We found a higher frequency of tumor infiltrated MDSCs in PDAC patients. Chemokine ligands CCL2 and the receptor CCR4 were markedly elevated in the PDAC tumor, while CCR4

Since their discovery in late 1940s, perovskite ferroelectric materials have become one of the central objects of condensed matter physics and materials science due to the broad spectrum of functional behaviors they exhibit, including electro-optical phenomena and strong electromechanical coupling. In such disordered materials, the static properties of defects such as oxygen vacancies are well explored but the dynamic effects are less understood. In this work, the first observation of enhanced electromechanical response in BaTiO3 thin films is reported driven via dynamic local oxygen vacancy control in piezoresponse force microscopy (PFM). A persistence in peizoelectricity past the bulk Curie temperature and an enhanced electromechanical response due to a created internal electric field that further enhances the intrinsic electrostriction are explicitly demonstrated. The findings are supported by a series of temperature dependent band excitation PFM in ultrahigh vacuum and a combination of modeling techniques including finite element modeling, reactive force field, and density functional theory. This study shows the pivotal role that dynamics of vacancies in complex oxides can play in determining functional properties and thus provides a new route toward- achieving enhanced ferroic response with higher functional temperature windows in ferroelectrics and other ferroic materials.

Novel Coronavirus (nCoV-2019) is a highly infectious viral outbreak that has so far infected more than 110 million people worldwide. Fast viral transmission and high infection rates have severely affected the entire population, especially the old aged and comorbid individuals leaving significantly less time to find some effective treatment strategy. In these challenging times, convalescent plasma (CP) therapy came as a ray of hope to save humankind. It is a form of passive immunization that has been used to treat various infectious diseases since 1890, including the 1918 Spanish flu, 2002/03 SARS-CoV, 2009 H1N1, 2012 MERS-CoV, and 2014 Ebola outbreak. The transfusion includes administration of CP containing a high value of neutralizing antibodies against the virus in hospitalized patients. This chapter summarizes the potential outcome of CP therapy in the treatment of nCoV-2019 patients.

Due to an extremely diverse phase space, La

Pancreatic cancer has been identified as one of the deadliest malignancies because it remains asymptomatic and usually presents in the advanced stage. Tumor immune evasion is a well-known mechanism of tumorigenesis in various forms of human malignancies. Chronic inflammation via complex networking of various inflammatory cytokines in the local tissue microenvironment dysregulate the immune system and support tumor development. Pro-inflammatory mediators present in the tumor microenvironment increase the tumor burden by causing immune suppression through the generation of myeloid-derived suppressor cells and T regulatory cells. These cells, along-with myofibroblasts, create a highly immunosuppressive and resistant tumor microenvironment and are thus considered as one of the culprits for the failure of anti-cancer chemotherapies in pancreatic adenocarcinoma patients. Targeting these myeloid-derived suppressor cells using various combinatorial approaches might have the potential for abrogating the resistance and suppressive nature of the pancreatic tumor microenvironment. Therefore, there is more curiosity in studying the crosstalk of myeloid-derived suppressor cells with other immune cells during pathological conditions and the underlying mechanisms of immunosuppression in the current scenario. In this article, the possible role of myeloid-derived suppressor cells in inflammation-mediated tumor progression of pancreatic adenocarcinoma has been discussed.

Despite the presence of chondrogenic progenitor cells (CPCs) in knee osteoarthritis patients they are unable to repair the damaged cartilage. This study aimed to evaluate the oxidative stress, cellular senescence, and senescence-associated secretory phenotype (SASP) in the CPCs derived from osteoarthritic cartilage and compare with the CPCs of healthy articular cartilage.Isolated CPCs were characterized based on phenotypic expression of stem cell markers, clonogenicity, and tri-lineage differentiation assay. Production of ROS was measured using DCFDA assay. Cellular senescence in CPCs was assessed by senescence-associated beta-galactosidase assay and expression of senescence markers at the gene level using real-time PCR. Morphological features associated with senescent OA-CPCs were studied using scanning electron microscopy. To study SASP, the production of inflammatory cytokines was assessed in the culture supernatant using a flow-cytometer based cytometric bead array.OA-CPCs exhibited elevated ROS levels along with a relatively high percentage of senescent cells compared to non-OA CPCs, and a positive correlation exists between ROS production and senescence. The morphological assessment of senescent CPCs revealed increased cell size and multiple nuclei in senescent OA-CPCs. These results were further validated by elevated expression of senescence genes p16, p21, and p53. Additionally, culture supernatant of senescent OA-CPCs expressed IL-6 and IL-8 cytokines indicative of SASP.Despite exhibiting similar expression of stem cell markers and clonogenicity, CPCs undergo oxidative stress in diseased knee joint leading to increased production of intracellular ROS in chondrogenic progenitor cells that support cellular senescence. Further, senescence in OA-CPCs is mediated via the release of pro-inflammatory cytokines, IL-6 and IL-8.

Many technological applications depend on the response of materials to electric fields, but available databases of such responses are limited. Here, we explore the infrared, piezoelectric, and dielectric properties of inorganic materials by combining high-throughput density functional perturbation theory and machine learning approaches. We compute Γ-point phonons, infrared intensities, Born-effective charges, piezoelectric, and dielectric tensors for 5015 non-metallic materials in the JARVIS-DFT database. We find 3230 and 1943 materials with at least one far and mid-infrared mode, respectively. We identify 577 high-piezoelectric materials, using a threshold of 0.5 C/m2. Using a threshold of 20, we find 593 potential high-dielectric materials. Importantly, we analyze the chemistry, symmetry, dimensionality, and geometry of the materials to find features that help explain variations in our datasets. Finally, we develop high-accuracy regression models for the highest infrared frequency and maximum Born-effective charges, and classification models for maximum piezoelectric and average dielectric tensors to accelerate discovery.

Laggera tomentosa Sch. Bip. ex Oliv. et Hiern (Asteraceae), an endemic Ethiopian medicinal plant, is traditionally used to treat various ailments. Previously, the chemical constituents of the essential oil (EO) of its leaves and inflorescence were documented. However, no data about the chemical compositions of other parts of the EOs of the plant have been reported to date. Moreover, there are no previous biological activity reports on any parts of the EOs of this plant. Thus, in this study, the EOs were isolated from the stem bark and roots of this plant by hydrodistillation and analyzed using gas chromatography-mass spectrometry to identify their components. In addition, antibacterial potentials of the oils were evaluated using the disc diffusion and minimal inhibitory concentration (MIC) methods. 2,2-diphenyl-1-picrylhydrazyl (DPPH) and hydrogen peroxide methods were also employed to assess their antioxidant properties. Oxygenated monoterpenes (71.82% and 77.51%), of which 2,5-dimethoxy- p -cymene (57.28% and 64.76%) and thymol methyl ether (9.51% and 8.93%) were identified as major components in the EOs of stem bark and roots of L. tomentosa and the oils, were the most potent in the DPPH (IC50, 0.33 ± 1.10 and 0.39 ± 0.97 mg/mL) assay, respectively. Moreover, the EOs demonstrated appreciable activity towards the gram+ ( S. aureus and B. cereus ) bacteria. Among these oils, the oil of the stem bark showed the greatest activity to the gram+ (MIC = 0.625 mg/mL) bacteria. Therefore, the overall results suggested that the EOs of L. tomentosa may be a promising prospect for pharmaceutical, food, and other industrial applications.

Reduced graphene oxide (rGO) has attracted significant attention for electrochemical sensing applications. In this work, rGO was obtained by thermal annealing of electrospun polymeric nanofiber membrane at 500 and 600 oC. The XRD patterns reveals the phase and crystalline formation of rGO. The interlayer spacing decreases at higher temperature that indicates the removal of oxygen containing moieties. FTIR spectrum shows the absence of epoxy, carboxyland hydroxylgroups for rGO-600 that resembles the surface feature of rGO. XPS further corroborates the XRD and FTIR results and quantifies the predominant functional groups in rGO-500 and rGO-600 oC. The synthesized materials were applied for the electrochemical sensing of dopamine (DA) by cyclic voltammetry. In the case of rGO-500/SPCE, a linear relationship for the DA concentration wasobserved in the range of 0.5µM to 20µM with a detection limit of 1.11 µM.Whereas,rGO-600/SPCEalso gave a linear relationship for the DA concentration in the range of 0.5µM to 20µM with 1.23 µM detection limit. These electrodes showed good electrocatalytic activity for the oxidation of DA with a minute variation in their detection limit. Therefore, the annealed material rGO can be efficiently used for the quantitative analysis of dopamine after carefully controlling the surface functional groups.

Titanium alloys find extensive use in the aerospace and biomedical industries due to a unique combination of strength, density, and corrosion resistance. Decades of mostly experimental research has led to a large body of knowledge of the processing-microstructure-properties linkages. But much of the existing understanding of point defects that play a significant role in the mechanical properties of titanium is based on semi-empirical rules. In this work, we present the results of a detailed self-consistent first-principles study that was developed to determine formation energies of intrinsic point defects including vacancies, self-interstitials, and extrinsic point defects, such as, interstitial and substitutional impurities/dopants. We find that most elements, regardless of size, prefer substitutional positions, but highly electronegative elements, such as C, N, O, F, S, and Cl, some of which are common impurities in Ti, occupy interstitial positions. First principles, not semi-empirical rules, can systematically predict how point defects form because of impurities in titanium. A team led by Sanjeev Nayak and Rainer Herbert at the University of Connecticut, USA, used density functional theory to model the effect of more than twenty impurities in titanium, including the first twenty chemical elements as well as transition metals commonly added to titanium. Electronegative elements such as carbon and oxygen were stable at octahedral interstitial positions in the titanium lattice, in an identical manner to interstitial titanium. In contrast, metallic impurities such as vanadium preferred substitutional sites. Finally, vacancies in the lattice only persisted if they were far enough from their interstitial titanium atom. Research into point defect formation and stability may help us control materials for advanced manufacturing processes such as 3D printing.

A controlled and successful high temperature calcination of PVDF was performed to synthesize high quality CQDs from polymeric membrane via electrospinning to control its shape and size. X-Ray Diffractograms show the formation of CQDs after thermal reduction of polymeric nanofibers with an average crystallite size of ~1.9 nm. Raman spectrum reveals the formation of intense G-band which confirms the presence of carbon nanomaterials in the form of CQDs with ratio of intensities IG/ID ~3.7. The variation of FTIR spectrum before and after calcination process supports the speculation of the suppression of oxygen containing functional groups. XPS and UV–Vis absorption spectrum confirms the highly stable and rich carbon content of C C group. FE-SEM and TEM demonstrates the spherical distribution of quantum dots with an average size of ~2.6 nm.

Purpose National wealth, income inequalities, and expenditures on education can profoundly influence the health of a nation's women, children, and adolescents. We explored the association of trends in national socioeconomic status (SES) indicators with trends in adolescent birth rates (ABRs), by nation and region. Methods An ecologic research design was employed using national-level data from the World Bank on birth rates per 1,000 women aged 15–19 years, national wealth (per capita gross domestic product or GDP), income inequality (Gini index), and expenditures on education as a percentage of GDP (EduExp). Data were available for 142 countries and seven regions for 1990–2012. Multiple linear regression for repeated measures with generalized estimating equations was used to examine independent associations. Results ABRs in 2012 varied >200-fold—with the highest rates in Sub-Saharan Africa and lowest rates in the Western Europe/Central Asia region. The median national ABR fell 40% from 72.4/1,000 in 1990 to 43.6/1,000 in 2012. The largest regional declines in ABR occurred in South Asia (70%), Europe/Central Asia (63%), and the Middle East/North Africa (53%)—regions with lower income inequality. In multivariable analyses considering change over time, ABRs were negatively associated with GDP and EduExp and positively associated with greater income inequality. Conclusions ABRs have declined globally since 1990. Declines closely followed rising socioeconomic status and were greater where income inequalities were lower in 1990. Reducing poverty and income inequalities and increasing investments in education should be essential components of national policies to prevent adolescent childbearing.

We disclose a novel, heterogeneous catalytic approach for selective coupling of C1 of N-aryltetrahydroisoquinolines with C3 of indoles in the presence of mesoporous manganese oxides. Our work involves a detailed mechanistic investigation of the reaction on the catalyst surface, backed by DFT computational studies, to understand the superior catalytic activity of manganese oxides.

An intelligent model is developed to control the water level in boiler drum. There are three different types of boiler control such as i Single element boiler drum level control, ii Two element boiler drum level control and iii Three element boiler drum level control. This paper provides the knowledge about the Fuzzy PID Controller and the various PID controller design methods such as Zeigler Nichols method, Tyreus Luyben method, Internal Model Control IMC . Comparative study is made on the performance of the PID and Fuzzy PID controller for better control system design. Sumit Patel | Vinit Sharma "Analysis of Different Controllers used for Boiler Drum Level Control by using LabVIEW Simulation" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-1 , December 2019, URL: https://www.ijtsrd.com/papers/ijtsrd29704.pdf

The model reactions CH3 X + (NH-CH=O)M ➔ CH3 -NH-NH═O or NH═CH-O-CH3 + MX (M = none, Li, Na, K, Ag, Cu; X = F, Cl, Br) are investigated to demonstrate the feasibility of Marcus theory and the hard and soft acids and bases (HSAB) principle in predicting the reactivity of ambident nucleophiles. The delocalization indices (DI) are defined in the framework of the quantum theory of atoms in molecules (QT-AIM), and are used as the scale of softness in the HSAB principle. To react with the ambident nucleophile NH═CH-O- , the carbocation H3 C+ from CH3 X (F, Cl, Br) is actually a borderline acid according to the DI values of the forming C…N and C…O bonds in the transition states (between 0.25 and 0.49), while the counter ions are divided into three groups according to the DI values of weak interactions involving M (M…X, M…N, and M…O): group I (M = none, and Me4 N) basically show zero DI values; group II species (M = Li, Na, and K) have noticeable DI values but the magnitudes are usually less than 0.15; and group III species (M = Ag and Cu(I)) have significant DI values (0.30-0.61). On a relative basis, H3 C+ is a soft acid with respect to group I and group II counter ions, and a hard acid with respect to group III counter ions. Therefore, N-regioselectivity is found in the presence of group I and group II counter ions (M = Me4 N, Li, Na, K), while O-regioselectivity is observed in the presence of the group III counter ions (M = Ag, and Cu(I)). The hardness of atoms, groups, and molecules is also calculated with new functions that depend on ionization potential (I) and electron affinity (A) and use the atomic charges obtained from localization indices (LI), so that the regioselectivity is explained by the atomic hardness of reactive nitrogen atoms in the transition states according to the maximum hardness principle (MHP). The exact Marcus equation is derived from the simple harmonic potential energy parabola, so that the concepts of activation free energy, intrinsic activation barrier, and reaction energy are completely connected. The required intrinsic activation barriers can be either estimated from ab initio calculations on reactant, transition state, and product of the model reactions, or calculated from identity reactions. The counter ions stabilize the reactant through bridging N- and O-site of reactant of identity reactions, so that the intrinsic barriers for the salts are higher than those for free ambident anions, which is explained by the increased reorganization parameter Δr. The proper application of Marcus theory should quantitatively consider all three terms of Marcus equation, and reliably represent the results with potential energy parabolas for reactants and all products. For the model reactions, both Marcus theory and HSAB principle/MHP principle predict the N-regioselectivity when M = none, Me4 N, Li, Na, K, and the O-regioselectivity when M = Ag and Cu(I). © 2019 Wiley Periodicals, Inc.

Hydrocarbon production from high-pressure/high-temperature (HPHT) unconventional and tight gas reservoirs is challenging the industry with its increasing complexities, changing geological and reservoir dynamics with deeper depth and temperature, stimulation techniques and the strategic cost investment. The southern basins of India offers a complete set of such variability, uncertainties and challenges that demand a more synergic approach and effort to produce. Field X is a deep HPHT tight gas field in the Krishna Godavri Basin with a permeability of < 0.1 mD and a porosity of 10-12%. Prior attempts of hydraulic fracturing were carried out in the field with limited success. Small volume jobs were pumped, yielding low-permeability coverage, with just an initial production gain and no sustainability on the gas production increase. Frac fluid recovery was an additional concern because of the tight nature of the reservoir. This paper discusses the integration of log data, lab/fluid testing, production modelling, and fracture diagnostics that were used to design and optimize the massive hydraulic fracturing treatment. The technical methodology implemented during design, execution and evaluation phases for fracturing an HPHT tight gas well is discussed, including how the various risks were mitigated and the technical challenges were overcome. Finally, this paper elaborates the successful execution of the hydraulic fracturing treatment wherein ~332,000 lb of proppant were pumped–the largest in this field in a single stage. Initial production was enhanced by four times after the hydraulic fracturing. With the success of the hydraulic fracturing treatment execution strategy, fracturing operations were planned for the future field development wells to realize the true potential of the reservoir for increased and sustainable production.

Most species of the genus Laggera are often used in traditional and folk medicines for the treatment of jaundice, inflammation, leukemia, removing phlegm, bronchitis and bacterial diseases. The essential oils obtained from Laggera plants are rich sources of oxygenated monoterpenes and sesquiterpenes. Among oxygenated monoterpenes, aromatic ether 2,5-dimethoxy-p-cymene is the most abundant and dominant compound of many essential oils of the Laggera species. Till today, to the best of our knowledge, chemical compounds of the essential oils and/or extracts of only eight Laggera species were reported from different countries. Thus, this review presents the chemical compositions and biological activities of the essential oils of these plants studied in thirteen countries. In addition, it discusses the reported ethnobotanical and ethnopharmacological information as well as biological activities of the extracts and some of the isolated compounds of Laggera plants species.

Increasing evidence suggests a role of inflammation during the pathogenesis of osteoarthritis (OA). The local and systemic inflammation was studied in 33 patients of different KL grades, grade2 (n = 11), grade3 (n = 6) and grade4 (n = 16). The levels of cytokines, adipokines and matrix metalloproteinases (MMPs) were measured in serum and synovial fluid (SF) by flow cytometry and ELISA, respectively. The frequency of T cells and CD161 expression was measured by flow cytometry. The levels of IL-1β, IL-6 and IL-10 were significantly higher in sera and SF of patients with OA as compared to healthy control's serum. Higher levels of MMP9 and leptin and lower levels of adiponectin were observed in SF as compared to serum. The MMP9 in SF and MMP13 levels in serum and SF decreased in KL grade 4 cases. In these patients, higher levels of leptin and lower levels of adiponectin were observed in SF versus patients of lower grades. There was increased infiltration of CD8+ T cells in SF of OA cases with decreased frequency in grade 4 cases. The expression of CD161 on T cells was significantly higher in SF than peripheral blood with significant upregulation in grade 4 patients. The CD161 expression had significant positive correlation with IL-17 in the serum of patients. The ROC curves of CD161 expression significantly distinguished grade 2 and grade 4 patients. Collectively, an elevated CD161 expression on T cells in circulation and synovial compartment clearly distinguished lower and higher grade patients warranting studies to assess its role as a contributing factor towards OA progression.

Complex behavior poses challenges in extracting models from experiment. An example is spin liquid formation in frustrated magnets like Dy$_2$Ti$_2$O$_7$. Understanding has been hindered by issues including disorder, glass formation, and interpretation of scattering data. Here, we use a novel automated capability to extract model Hamiltonians from data, and to identify different magnetic regimes. This involves training an autoencoder to learn a compressed representation of three-dimensional diffuse scattering, over a wide range of spin Hamiltonians. The autoencoder finds optimal matches according to scattering and heat capacity data and provides confidence intervals. Validation tests indicate that our optimal Hamiltonian accurately predicts temperature and field dependence of both magnetic structure and magnetization, as well as glass formation and irreversibility in Dy$_2$Ti$_2$O$_7$. The autoencoder can also categorize different magnetic behaviors and eliminate background noise and artifacts in raw data. Our methodology is readily applicable to other materials and types of scattering problems., Comment: 18 pages, 6 figures

The mechanisms of phase transitions have been previously explored at various theoretical and experimental levels. For a wide variety of compounds, the majority of studies are limited by observations at fixed temperature and composition, in which case, relevant information can be determined only from the behaviors at topological and structural defects. All analyses to date utilize macroscopic descriptors derived from structural information such as polarization or octahedral tilts extracted from the atomic positions, ignoring the multiple degrees of freedom observable from atomically resolved images. In this article, we provide a solution, by exploring the mechanisms of a phase transition between the trigonal prismatic and distorted octahedral phases of layered chalcogenides in the 2D MoS2–ReS2 system from the observations of local degrees of freedom, namely atomic positions by scanning transmission electron microscopy. We employ local crystallographic analysis based on statistical learning of atomic environments to build a picture of the transition from the atomic level up and determine local and global variables controlling the local symmetry breaking. We highlight how the dependence of the average symmetry-breaking distortion amplitude on global and local concentration can be used to separate local chemical as well as global electronic effects on the transition. This approach allows for the exploring of atomic mechanisms beyond the traditional macroscopic descriptions, utilizing the imaging of compositional fluctuations in solids to explore phase transitions over a range of observed local stoichiometries and atomic configurations.

Particle sizes play a major role to mediate charge transfer, both between identical and different material surfaces. The study probes into the probable mechanism that actuates opposite polarities between two different size fractions of the same material by analyzing the charge transfer patterns of two different sizes of microcrystalline cellulose (MCC). Quantum scale calculations confirmed alteration of charge transfer capacities due to variation of moisture content predicted by multiple surface and bulk analytical techniques. Discrete Element Method (DEM) based multi-scale computational models pertinent to predict charge transfer capacities were further implemented, and the results were in accordance to the experimental charge profiles.

The dopant chemical space in LaMnO3 (LMO) is systematically explored using first-principles computations. We study a range of cationic dopants including alkali, alkaline earth metals, 3d, 4d, and 5d transition metal elements without and with an adjacent O vacancy. A linear programming approach is employed to access the energetically favorable decomposition pathway and the corresponding decomposition energy of doped LaMnO3. The decomposition energy is then used to classify the dopants for stability, site preference and tendency of O vacancy formation. We find that La site doping is more favored compared to Mn site doping. We also identify dopants previously not considered, such as K, Rb, Cs, and In, which lead to stable doped LMO and are also excellent O vacancy formers. Employing data mining techniques, we identify the dopant features that are critical to the stability of a doped oxide.

Herein we introduce an environmentally friendly approach to the synthesis of symmetrical and asymmetrical aromatic azo compounds by using air as the sole oxidant under mild reaction conditions in the presence of cost-effective and reusable mesoporous manganese oxide materials.