Chemical Profiling,Toxicity and Anti-Inflammatory Activities of Essential Oils from Three Grapefruit Cultivars from KwaZulu-Natal in South Africa

The medicinal potential and volatile composition of different parts of three cultivars of grapefruit (Citrus paradisi) were evaluated for their toxicity and anti-inflammatory activities. Fresh leaf and fruit peel were separately isolated by hydrodistillation for 4 h. The essential oils were subjected to GC/GC-MS analysis for chemical profile. Toxicity of the essential oils in mice were evaluated using Lorke’s method, while an anti-inflammatory assay was performed in a rat model using egg albumin-induced oedema. The oils obtained were light yellow in colour, and odour varied from strong citrus smell to mild. Percentage yield of fresh peel oil (0.34–0.57%) was greater than the fresh leaf oil yield (0.21–0.34%). D-limonene (86.70–89.90%) was the major compound identified in the leaf oil, while β-phellandrene (90.00–91.01%) dominated the peel oil. At a dosage level of 5000 mg/kg, none of the oils showed mortality in mice. An anti-inflammatory bioassay revealed that all the oils caused a significant (p < 0.05–0.01) reduction in oedema size when compared to the negative control group throughout the 5 h post induction assessment period. The study reveals that the oils are non-toxic and demonstrate significant anti-inflammatory activity. Our findings suggest that the leaf and peel oils obtained from waste parts of grapefruit plants can be useful as flavouring agents, as well as anti-inflammatory agents.     

Volatile constituents of Senecio pterophorus (African daisy) DC. from South Africa

The hydrodistilled essential oils of the leaves of Senecio pterophorus collected from three different localities in KwaZulu-Natal Province, South Africa have been analyzed by GC and GC/MS. Thirteen - eighteen compounds were identified, accounting for over 98% of the total oil composition. The major components of the oils were limonene (10.3 - 32.3%), myrcene (14.4-19.7%), sabinene (13.0-18.0%), alpha-phellandrene (3.4-16.9%) and p-cymene (15.6-16.7%).     

Toward computationally efficient combustion DNS with complex fuels via principal component transport

We investigate the potential of accelerating chemistry integration during the direct numerical simulation (DNS) of complex fuels based on the transport equations of representative scalars that span the desired composition space using principal component analysis (PCA). The transport of principal components (PCs) can reduce the number of transported scalars and improve the spatial and temporal resolution requirements. The strategy is demonstrated using DNS of a premixed methane–air flame in a 2D vortical flow and is extended to the 3D geometry to demonstrate the resulting enhancement in the computational efficiency of PC transport. The PCs are derived from a priori PCA of the same composition space using DNS. This analysis is used to construct and tabulate the PCs’ chemical source terms in terms of the PCs using artificial neural networks (ANN). Comparison of DNS based on a full thermo-chemical state and DNS based on PC transport with six PCs shows excellent agreement even for terms that are not included in the PCA reduction. The transported PCs reproduce some of the salient features of strongly curved and strongly strained flames. The results also show a significant reduction of two orders of magnitude in the computational cost of the simulations, which enables an extension of the solution approach to 3D DNS under similar computational requirements.     

Atomistic simulation reveals structural mechanisms underlying D614G spike glycoprotein-enhanced fitness in SARS-COV-2

D614G spike glycoprotein (sgp) mutation in rapidly spreading severe acute respiratory syndrome coronavirus-2 (SARS-COV-2) is associated with enhanced fitness and higher transmissibility in new cases of COVID-19 but the underlying mechanism is unknown. Here, using atomistic simulation, a plausible mechanism has been delineated. In G614 sgp but not wild type, increased D(G)614-T859 Cα-distance within 65 ns is interpreted as S1/S2 protomer dissociation. Overall, ACE2-binding, post-fusion core, open-state and sub-optimal antibody-binding conformations were preferentially sampled by the G614 mutant, but not wild type. Furthermore, in the wild type, only one of the three sgp chains has optimal communication route between residue 614 and the receptor-binding domain (RBD); whereas, two of the three chains communicated directly in G614 mutant. These data provide evidence that D614G sgp mutant is more available for receptor binding, cellular invasion and reduced antibody interaction; thus, providing framework for enhanced fitness and higher transmissibility in D614G SARS-COV-2 mutant.     

Composition and biological potential of essential oil from Thelechitonia trilobata growing in South Africa

Thelechitonia trilobata is regarded as a troublesome weed that grows to form a dense blanket over the soil preventing the growth of other crops in farmland. Although the plant is regarded as a notorious, invasive plant, its chemical composition and biological potential have not been reported. The essential oil was isolated from the fresh leaves of T. trilobata using hydrodistillation. alpha-Pinene (21.6%), alpha-phellendrene (21.0%), limonene (12.8%) and germacrene D (7.5%) were the major constituents of the oil. The essential oil was screened against agricultural pests. The anti-tick properties were tested on Ripicephalus e. ervertsi found on sheep, while repellency, fumigation, and contact toxicity tests were carried out with maize weevils. Except for the contact toxicity test, all other bioassays gave positive results.     

Efficient bifurcation and tabulation of multi-dimensional combustion manifolds using deep mixture of experts: An a priori study

This work describes and validates an approach for autonomously bifurcating turbulent combustion manifolds to divide regression tasks amongst specialized artificial neural networks (ANNs). This approach relies on the mixture of experts (MoE) framework, where each neural network is trained to be specialized in a given portion of the input space. The assignment of different input regions to the experts is determined by a gating network, which is a neural network classifier. In some previous studies [1], [2], [3], [4], it has been demonstrated that bifurcation of a complex combustion manifold and fitting different ANNs for each part leads to better fits or faster inference speeds. However, the manner of bifurcation in these studies was based on heuristic approaches or clustering techniques. In contrast, the proposed technique enables automatic bifurcation using non-linear planes in high-dimensional turbulent combustion manifolds that are often associated with complex behavior due to different dominating physics in various zones. The proposed concept is validated using 4-dimensional (4D) and 5D flamelet tables, showing that the errors obtained with a given network size, or conversely the network size required to achieve a given accuracy, is considerably reduced. The effect of the number of experts on inference speed is also investigated, showing that by increasing the number of experts from 1 to 8, the inference time can be approximately reduced by a factor of two. Moreover, it is shown that the MoE approach divides the input manifold in a physically intuitive manner, suggesting that the MoE framework can elucidate high-dimensional datasets in a physically meaningful way.     

Pentacyclic Triterpenoids with Nitrogen-Containing Heterocyclic Moiety,Privileged Hybrids in Anticancer Drug Discovery

Pentacyclic triterpenoids are well-known phytochemicals with various biological activities commonly found in plants as secondary metabolites. The wide range of biological activities exhibited by triterpenoids has made them the most valuable sources of pharmacological agents. A number of novel triterpenoid derivatives with many skeletal modifications have been developed. The most important modifications are the formation of analogues or derivatives with nitrogen-containing heterocyclic scaffolds. The derivatives with nitrogen-containing heterocyclic compounds are among the most promising candidate for the development of novel therapeutic drugs. About 75% of FDA-approved drugs are nitrogen-containing heterocyclic moieties. The unique properties of heterocyclic compounds have encouraged many researchers to develop new triterpenoid analogous with pharmacological activities. In this review, we discuss recent advances of nitrogen-containing heterocyclic triterpenoids as potential therapeutic agents. This comprehensive review will assist medicinal chemists to understand new strategies that can result in the development of compounds with potential therapeutic efficacy.     

Modelling the viscosity of carbon-based nanomaterials dispersed in diesel oil: a machine learning approach

Journal of Thermal Analysis and Calorimetry - The viscosity of a nanofluid is one of its fundamental thermophysical properties, and it is an important consideration in heat transfer applications....