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Agriculture plays a vital role in Bangladesh's economy. It is essential to ensure the proper growth and health of crops for the development of the agricultural sector. In the context of Bangladesh, crop diseases pose a significant threat to agricultural output and, consequently, food security. This necessitates the timely and precise identification of such diseases to ensure the sustainability of food production. This study focuses on building a hybrid deep learning model for the identification of three specific diseases affecting three major crops: late blight in potatoes, brown spot in rice, and common rust in corn. The proposed model leverages EfficientNetB0's feature extraction capabilities, known for achieving rapid high learning rates, coupled with the classification proficiency of SVMs, a well-established machine learning algorithm. This unified approach streamlines data processing and feature extraction, potentially improving model generalizability across diverse crops and diseases. It also aims to address the challenges of computational efficiency and accuracy that are often encountered in precision agriculture applications. The proposed hybrid model achieved 97.29% accuracy. A comparative analysis with other models, CNN, VGG16, ResNet50, Xception, Mobilenet V2, Autoencoders, Inception v3, and EfficientNetB0 each achieving an accuracy of 86.57%, 83.29%, 68.79%, 94.07%, 90.71%, 87.90%, 94.14%, and 96.14% respectively, demonstrated the superior performance of our proposed model.

The classification of a pre-processed fMRI dataset using functional connectivity (FC)-based features is considered a challenging task because of the set of high-dimensional FC features and the small dataset size. To tackle this specific set of FC high-dimensional features and a small-sized dataset, we propose here a conditional Generative Adversarial Network (cGAN)-based dataset augmenter to first train the cGAN on computed connectivity features of NYU dataset and use the trained cGAN to generate synthetic connectivity features per category. After obtaining a sufficient number of connectivity features per category, a Multi-Head attention mechanism is used as a head for the classification. We name our proposed approach "ASD-GANNet", which is end-to-end and does not require hand-crafted features, as the Multi-Head attention mechanism focuses on the features that are more relevant. Moreover, we compare our results with the six available state-of-the-art techniques from the literature. Our proposed approach results using the "NYU" site as a training set for generating a cGAN-based synthetic dataset are promising. We achieve an overall 10-fold cross-validation-based accuracy of 82%, sensitivity of 82%, and specificity of 81%, outperforming available state-of-the art approaches. A sitewise comparison of our proposed approach also outperforms the available state-of-the-art, as out of the 17 sites, our proposed approach has better results in the 10 sites.

Ethylene is an essential plant hormone, critical in various physiological processes. These processes include seed germination, leaf senescence, fruit ripening, and the plant's response to environmental stressors. Ethylene biosynthesis is tightly regulated by two key enzymes, namely 1-aminocyclopropane-1-carboxylate synthase (ACS) and 1-aminocyclopropane-1-carboxylate oxidase (ACO). Initially, the prevailing hypothesis suggested that ACS is the limiting factor in the ethylene biosynthesis pathway. Nevertheless, accumulating evidence from various studies has demonstrated that ACO, under specific circumstances, acts as the rate-limiting enzyme in ethylene production. Under normal developmental processes, ACS and ACO collaborate to maintain balanced ethylene production, ensuring proper plant growth and physiology. However, under abiotic stress conditions, such as drought, salinity, extreme temperatures, or pathogen attack, the regulation of ethylene biosynthesis becomes critical for plants' survival. This review highlights the structural characteristics and examines the transcriptional, post-transcriptional, and post-translational regulation of ACS and ACO and their role under abiotic stress conditions. Reviews on the role of ethylene signaling in abiotic stress adaptation are available. However, a review delineating the role of ACS and ACO in abiotic stress acclimation is unavailable. Exploring how particular ACS and ACO isoforms contribute to a specific plant's response to various abiotic stresses and understanding how they are regulated can guide the development of focused strategies. These strategies aim to enhance a plant's ability to cope with environmental challenges more effectively.

The major cause of hyperglycemia can generally be attributed to β-glucosidase as per its involvement in non-alcoholic fatty liver disease. This clinical condition leads to liver carcinoma (HepG2 cancer). The phthalimides and phthalamic acid classes possess inhibitory potential against glucosidase, forming the basis for designing new phthalimide and phthalamic acid analogs to test their ability as potent inhibitors of β-glucosidase. The study also covers in silico (molecular docking and MD simulations) and in vitro (β-glucosidase and HepG2 cancer cell line assays) analyses. The phthalimide and phthalamic acid derivatives were synthesized, followed by spectroscopic characterization. The mechanistic complexities associated with β-glucosidase inhibition were identified via the docking of the synthesized compounds inside the active site of the protein, and the results were analyzed in terms of the best binding energy and appropriate docking pose. The top-ranked compounds were subjected to extensive MD simulation studies to understand the mode of interaction of the synthesized compounds and binding energies, as well as the contribution of individual residues towards binding affinities. Lower RMSD/RMSF values were observed for 2c and 3c , respectively, in the active site, confirming more stabilized, ligand-bound complexes when compared to the free state. An anisotropic network model was used to unravel the role of loop fluctuation in the context of ligand binding and the dynamics that are distinct to the bound and free states, supported by a 3D surface plot. An in vitro study revealed that 1c (IC 50 = 1.26 µM) is far better than standard acarbose (2.15 µM), confirming the potential of this compound against the target protein. Given the appreciable potential of the candidate compounds against β-glucosidase, the synthesized compounds were further tested for their cytotoxic activity against hepatic carcinoma on HepG2 cancer cell lines. The cytotoxicity profile of the synthesized compounds was performed against HepG2 cancer cell lines. The resultant IC 50 value (0.048 µM) for 3c is better than the standard (thalidomide: IC 50 0.053 µM). The results promise the hypothesis that the synthesized compounds might become potential drug candidates, given the fact that the β-glucosidase inhibition of 1c is 40% better than the standard, whereas compound 3c holds more anti-tumor activity (greater than 9%) against the HepG2 cell line than the known drug.

The ameliorative effect of ethanolic extract of M. oleifera (MOEE) leaves in combination with curcumin against seizures, cognitive impairment, and oxidative stress in the molecular docking of PTZ-induced kindled rats was performed to predict the potential phytochemical effects of MOEE and curcumin against epilepsy. The effect of pretreatment with leaves of M. oleifera ethanolic extracts (MOEE) (250 mg/kg and 500 mg/kg, orally), curcumin (200 mg/kg and 300 mg/kg, orally), valproic acid used as a standard (100 mg/kg), and the combined effect of MOEE (250 mg/kg) and curcumin (200 mg/kg) at a low dose on Pentylenetetrazole was used for (PTZ)-induced kindling For the development of kindling, individual Wistar rats (male) were injected with pentyletetrazole (40 mg/kg, i.p.) on every alternate day. Molecular docking was performed by the Auto Dock 4.2 tool to merge the ligand orientations in the binding cavity. From the RCSB website, the crystal structure of human glutathione reductase (PDB ID: 3DK9) was obtained. Curcumin and M. oleifera ethanolic extracts (MOEE) showed dose-dependent effects. The combined effects of MOEE and curcumin leaves significantly improved the seizure score and decreased the number of myoclonic jerks compared with a standard dose of valproic acid. PTZ kindling induced significant oxidative stress and cognitive impairment, which was reversed by pretreatment with MOEE and curcumin. Glutathione reductase (GR) is an enzyme that plays a key role in the cellular control of reactive oxygen species (ROS). Therefore, activating GR can uplift antioxidant properties, which leads to the inhibition of ROS-induced cell death in the brain. The combination of the ethanolic extract of M. oleifera (MOEE) leaves and curcumin has shown better results than any other combination for antiepileptic effects by virtue of antioxidant effects. As per the docking study, chlorogenic acid and quercetin treated with acombination of curcumin have much more potential.

The sense of taste determines the choice of nutrients and food intake and, consequently, influences feeding behaviors. The taste papillae are primarily composed of three types of taste bud cells (TBC), i.e., type I, type II, and type III. The type I TBC, expressing GLAST (glutamate--aspartate transporter), have been termed as glial-like cells. We hypothesized that these cells could play a role in taste bud immunity as glial cells do in the brain. We purified type I TBC, expressing F4/80, a specific marker of macrophages, from mouse fungiform taste papillae. The purified cells also express CD11b, CD11c, and CD64, generally expressed by glial cells and macrophages. We further assessed whether mouse type I TBC can be polarized toward M1 or M2 macrophages in inflammatory states like lipopolysaccharide (LPS)-triggered inflammation or obesity, known to be associated with low-grade inflammation. Indeed, LPS-treatment and obesity state increased TNFα, IL-1β, and IL-6 expression, both at mRNA and protein levels, in type I TBC. Conversely, purified type I TBC treated with IL-4 showed a significant increase in arginase 1 and IL-4. These findings provide evidence that type I gustatory cells share many features with macrophages and may be involved in oral inflammation.

This paper presents a trainable hybrid approach involving a shallow autoencoder (AE) and a conventional classifier for epileptic seizure detection. The signal segments of a channel of electroencephalogram (EEG) (EEG epochs) are classified as epileptic and non-epileptic by employing its encoded AE representation as a feature vector. Analysis on a single channel-basis and the low computational complexity of the algorithm allow its use in body sensor networks and wearable devices using one or few EEG channels for wearing comfort. This enables the extended diagnosis and monitoring of epileptic patients at home. The encoded representation of EEG signal segments is obtained based on training the shallow AE to minimize the signal reconstruction error. Extensive experimentation with classifiers has led us to propose two versions of our hybrid method: (a) one yielding the best classification performance compared to the reported methods using the k-nearest neighbor (kNN) classifier and (b) the second with a hardware-friendly architecture and yet with the best classification performance compared to other reported methods in this category using a support-vector machine (SVM) classifier. The algorithm is evaluated on the Children's Hospital Boston, Massachusetts Institute of Technology (CHB-MIT), and University of Bonn EEG datasets. The proposed method achieves 98.85% accuracy, 99.29% sensitivity, and 98.86% specificity on the CHB-MIT dataset using the kNN classifier. The best figures using the SVM classifier for accuracy, sensitivity, and specificity are 99.19%, 96.10%, and 99.19%, respectively. Our experiments establish the superiority of using an AE approach with a shallow architecture to generate a low-dimensionality yet effective EEG signal representation capable of high-performance abnormal seizure activity detection at a single-channel EEG level and with a fine granularity of 1 s EEG epochs.

Climate changes abruptly affect optimum growth temperatures, leading to a negative influence on plant physiology and productivity. The present study aimed to investigate the extent of low-temperature stress effects on date palm growth and physiological indicators under the exogenous application of silicon (Si). Date palm seedlings were treated with Si (1.0 mM) and exposed to different temperature regimes (5, 15, and 30 °C). It was observed that the application of Si markedly improved fresh and dry biomass, photosynthetic pigments (chlorophyll and carotenoids), plant morphology, and relative water content by ameliorating low-temperature-induced oxidative stress. Low-temperature stress (5 and 15 °C), led to a substantial upregulation of ABA-signaling-related genes ( NCED-1 and PyL-4 ) in non Si treated plants, while Si treated plants revealed an antagonistic trend. However, jasmonic acid and salicylic acid accumulation were markedly elevated in Si treated plants under stress conditions (5 and 15 °C) in comparison with non Si treated plants. Interestingly, the upregulation of low temperature stress related plant plasma membrane ATPase ( PPMA3 and PPMA4 ) and short-chain dehydrogenases/reductases ( SDR ), responsible for cellular physiology, stomatal conductance and nutrient translocation under silicon applications, was observed in Si plants under stress conditions in comparison with non Si treated plants. Furthermore, a significant expression of LSi-2 was detected in Si plants under stress, leading to the significant accumulation of Si in roots and shoots. In contrast, non Si plants demonstrated a low expression of LSi-2 under stress conditions, and thereby, reduced level of Si accumulation were observed. Less accumulation of oxidative stress was evident from the expression of superoxide dismutase (SOD) and catalase (CAT) . Additionally, Si plants revealed a significant exudation of organic acids (succinic acid and citric acid) and nutrient accumulation (K and Mg) in roots and shoots. Furthermore, the application of Si led to substantial upregulation of the low temperature stress related soybean cold regulated gene ( SRC-2 ) and ICE-1 ( inducer of CBF expression 1 ), involved in the expression of CBF/DREB (C-repeat binding factor/dehydration responsive element binding factor) gene family under stress conditions in comparison with non Si plants. The current research findings are crucial for exploring the impact on morpho-physio-biochemical attributes of date palms under low temperature and Si supplementation, which may provide an efficient strategy for growing plants in low-temperature fields.

The possession of two X chromosomes may come with the risk of various illnesses, females are more likely to be affected by osteoarthritis, heart disease, and anxiety. Given the reported correlations between gut microbiome dysbiosis and various illnesses, the female gut microbiome is worthy of exploration. Herein, we discuss the composition of the female gut microbiota and its dysbiosis in pathologies affecting the female population. Using PubMed, we performed a literature search, using key terms, namely: "gut microbiome", "estrogen", "menopause", "polycystic ovarian syndrome", "pregnancy", and "menstruation". In polycystic ovarian syndrome (PCOS), the abundance of Bacteroides vulgatus, Firmicutes, Streptococcus, and the ratio of Escherichia/Shigella was found to be increased while that of Tenericutes ML615J-28 , Tenericutes 124-7 , Akkermansia, Ruminococcaceae, and Bacteroidetes S24-7 was reduced. In breast cancer, the abundance of Clostridiales was enhanced, while in cervical cancer, Prevotella, Porphyromonas, and Dialister were enhanced but Bacteroides, Alistipes, and members of Lachnospiracea, were decreased. In ovarian cancer, Prevotella abundance was increased. Interestingly, the administration of Lactobacillus acidophilus , Bifidobacterium bifidum, Lactobacillus reuteri, and Lactobacillus fermentum ameliorated PCOS symptoms while that of a mix of Bifidobacterium lactis W51, Bifidobacterium bifidum W23, Lactobacillus brevis W63, Bifidobacterium lactis W52, Lactobacillus salivarius W24, Lactobacillus acidophilus W37, Lactococcus lactis W19, Lactobacillus casei W56, and Lactococcus lactis W58 alleviated vascular malfunction and arterial stiffness in obese postmenopausal women, and finally, while further research is needed, Prevotella maybe protective against postmenopausal bone mass loss. As several studies report the therapeutic potential of probiotics and since the gut microbiota of certain female pathological states has been relatively characterized, we speculate that the administration of certain bacterial species as probiotics is warranted, as novel independent or adjunct therapies for various female pathologies.

Colorectal cancer is the third most diagnosed cancer worldwide and the second most prevalent cause of cancer-related mortality. It is believed that alterations within the gut microbiome may impact the development and progression of cancer. Additionally, the diet an individual maintains and the amount of alcohol consumed can alter the microbiome, thus impacting the development of colorectal cancer. A diet focused on fiber intake is considered beneficial, as it contains short-chain fatty acids such as butyrate, which have antitumor properties. Furthermore, current treatment strategies, such as chemotherapy, have various side effects. In this review, we discuss the role of the gut microbiome and oral bacteria in relation to colorectal cancer. We also deliberate on the role of diet and alcohol consumption in the development of colorectal cancer. Moreover, the influence of the various metabolites within the gut and the importance of gut inflammation in the development of colorectal cancer are explained. Finally, potential therapies such as fecal microbiota transfer and post/prebiotics are elaborated on. To further comprehend risk factors in the development of colorectal cancer, future studies are warranted to determine the precise mechanisms of action between the gut microbiome and carcinogenesis in order to develop therapies that may target gut microbial dysbiosis.

Small molecules with nitrogen-containing scaffolds have gained much attention due to their biological importance in the development of new anticancer agents. The present paper reports the synthesis of a library of new dihydropyridine and pyridine analogs with diverse pharmacophores. All compounds were tested against the human tissue nonspecific alkaline phosphatase ( h -TNAP) enzyme. Most of the compounds showed excellent enzyme inhibition against h -TNAP, having IC 50 values ranging from 0.49 ± 0.025 to 8.8 ± 0.53 µM, which is multi-fold higher than that of the standard inhibitor (levamisole = 22.65 ± 1.60 µM) of the h -TNAP enzyme. Furthermore, an MTT assay was carried out to evaluate cytotoxicity against the HeLa and MCF-7 cancer cell lines. Among the analogs, the most potent dihydropyridine-based compound 4d was selected to investigate pro-apoptotic behavior. The further analysis demonstrated that compound 4d played a significant role in inducing apoptosis through multiple mechanisms, including overproduction of reactive oxygen species, mitochondrial dysfunction, DNA damaging, and arrest of the cell cycle at the G1 phase by inhibiting CDK4/6. The apoptosis-inducing effect of compound 4d was studied through staining agents, microscopic, and flow cytometry techniques. Detailed structure-activity relationship (SAR) and molecular docking studies were carried out to identify the core structural features responsible for inhibiting the enzymatic activity of the h -TNAP enzyme. Moreover, fluorescence emission studies corroborated the binding interaction of compound 4d with DNA through a fluorescence titration experiment.

The present study concerns the modeling of the thermal behavior of a porous longitudinal fin under fully wetted conditions with linear, quadratic, and exponential thermal conductivities surrounded by environments that are convective, conductive, and radiative. Porous fins are widely used in various engineering and everyday life applications. The Darcy model was used to formulate the governing non-linear singular differential equation for the heat transfer phenomenon in the fin. The universal approximation power of multilayer perceptron artificial neural networks (ANN) was applied to establish a model of approximate solutions for the singular non-linear boundary value problem. The optimization strategy of a sports-inspired meta-heuristic paradigm, the Tiki-Taka algorithm (TTA) with sequential quadratic programming (SQP), was utilized to determine the thermal performance and the effective use of fins for diverse values of physical parameters, such as parameter for the moist porous medium, dimensionless ambient temperature, radiation coefficient, power index, in-homogeneity index, convection coefficient, and dimensionless temperature. The results of the designed ANN-TTA-SQP algorithm were validated by comparison with state-of-the-art techniques, including the whale optimization algorithm (WOA), cuckoo search algorithm (CSA), grey wolf optimization (GWO) algorithm, particle swarm optimization (PSO) algorithm, and machine learning algorithms. The percentage of absolute errors and the mean square error in the solutions of the proposed technique were found to lie between 10-4 to 10-5 and 10-8 to 10-10, respectively. A comprehensive study of graphs, statistics of the solutions, and errors demonstrated that the proposed scheme's results were accurate, stable, and reliable. It was concluded that the pace at which heat is transferred from the surface of the fin to the surrounding environment increases in proportion to the degree to which the wet porosity parameter is increased. At the same time, inverse behavior was observed for increase in the power index. The results obtained may support the structural design of thermally effective cooling methods for various electronic consumer devices.

The proliferation of the internet of things (IoT) technology has led to numerous challenges in various life domains, such as healthcare, smart systems, and mission-critical applications. The most critical issue is the security of IoT nodes, networks, and infrastructures. IoT uses the routing protocol for low-power and lossy networks (RPL) for data communication among the devices. RPL comprises a lightweight core and thus does not support high computation and resource-consuming methods for security implementation. Therefore, both IoT and RPL are vulnerable to security attacks, which are broadly categorized into RPL-specific and sensor-network-inherited attacks. Among the most concerning protocol-specific attacks are rank attacks and wormhole attacks in sensor-network-inherited attack types. They target the RPL resources and components including control messages, repair mechanisms, routing topologies, and sensor network resources by consuming. This leads to the collapse of IoT infrastructure. In this paper, a lightweight multiclass classification-based RPL-specific and sensor-network-inherited attack detection model called MC-MLGBM is proposed. A novel dataset was generated through the construction of various network models to address the unavailability of the required dataset, optimal feature selection to improve model performance, and a light gradient boosting machine-based algorithm optimized for a multiclass classification-based attack detection. The results of extensive experiments are demonstrated through several metrics including confusion matrix, accuracy, precision, and recall. For further performance evaluation and to remove any bias, the multiclass-specific metrics were also used to evaluate the model, including cross-entropy, Cohn's kappa, and Matthews correlation coefficient, and then compared with benchmark research.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causing coronavirus disease 2019 (COVID-19) has been a rising concern since its declaration as a pandemic by the World Health Organization on 11 March 2020. Recently, its association with multiple underlying organs has been identified that includes cardiac, renal, gastrointestinal, nervous systems, and cutaneous manifestations. Cutaneous COVID-19 findings have been supposedly classified into the following categories: vesicular (varicella-like), papulo-vesiculsar, chilblains-like ("COVID toes") maculopapular, and urticarial morphologies. In this review, we aim to focus on the proposed pathophysiology behind the various dermatological manifestations associated with COVID-19 and their associated management. We also included prevalence and clinical features of the different COVID-19-related skin lesions in our review. A comprehensive narrative review of the literature was performed in PubMed databases. Data from case reports, observational studies, case series, and reviews till June 2022 were all screened and included in the review.

The present analysis explores the chemical constituents and determines the in vitro antimicrobial, antidiabetic, and antioxidant significance of the essential oils (EOs) of the stem, leaves, and flowers of Ochradenus arabicus for the first time. The EOs of the flowers presented seventy-four constituents contributing to 81.46% of the total EOs, with the major compounds being 24-norursa-3,12-diene (13.06%), 24-norursa-3,12-dien-11-one (6.61%), and 24-noroleana-3,12-diene (6.25%). The stem EOs with sixty-one compounds contributed 95.95% of the total oil, whose main bioactive compounds were (+)-camphene (21.50%), eremophilene (5.87%), and δ-selinene (5.03%), while a minimum of fifty-one compounds in the leaves’ EOs (98.75%) were found, with the main constituents being n-hexadecanoic acid (12.32%), octacosane (8.62%), tetradecanoic acid (8.54%), and prehydro fersenyl acetone (7.27%). The antimicrobial activity of the EOs of O. arabicus stem, leaves, and flowers was assessed against two bacterial strains (Escherichia coli and Streptococcus aureus) and two fungal strains (Penicillium simplicissimum and Rhizoctonia solani) via the disc diffusion assay. However, the EOs extracted from the stem were found effective against one bacterial strain, E. coli, and one fungal strain, R. Solani, among the examined microbes in comparison to the standard and negative control. The tested EOs samples of the O. arabicus stem displayed a maximum potential to cure diabetes with an IC50 = 0.40 ± 0.10 µg/mL, followed by leaves and flowers with an IC50 = 0.71 ± 0.11 µg/mL and IC50 = 10.57 ± 0.18 µg/mL, respectively, as compared to the standard acarbose (IC50 = 377.26 ± 1.20 µg/mL). In addition, the EOs of O. arabicus flowers had the highest antioxidant activity (IC50 = 106.40 ± 0.19 µg/mL) as compared to the standard ascorbic acid (IC50 = 73.20 ± 0.17 µg/mL) using the 2,2-diphenyl-1-picrylhydrazyl (DPPH) assay. In the ABTS assay, the EOs of the same sample (flower) depicted the utmost potential to scavenge the free radicals with an IC50 = 178.0 ± 0.14 µg/mL as compared with the ascorbic acid, having an IC50 of 87.34 ± 0.10 µg/mL the using 2,2-Azino-Bis-3-Ethylbenzothiazoline-6-Sulfonic acid (ABTS) assay. The EOs of all parts of O. arabicus have useful bioactive components due to which they present antidiabetic and antioxidant significance. Furthermore, additional investigations are considered necessary to expose the responsible components of the examined biological capabilities, which would be effective in the production of innovative drugs.

The development of novel bioactive molecules is urgently needed, especially with increasing fatalities occurring due to infections by bacteria and escalating numbers of multiple-drug-resistant bacteria. Several lines of evidence show that the gut microbiome of cockroaches, snakes, crocodiles, water monitor lizards, and other species may possess molecules that are bioactive. As these animals are routinely exposed to a variety of microorganisms in their natural environments, it is likely that they have developed methods to counter these microbes, which may be a contributing factor in their persistence on the planet for millions of years. In addition to the immune system, the gut microbiota of a host may thwart colonization of the gastro-intestine by pathogenic and/or foreign microorganisms through two mechanisms: (i) production of molecules with antibacterial potential targeting foreign microorganisms, or (ii) production of molecules that trigger host immunity targeting foreign microorganisms that penetrate the host. Herein, we discuss and deliberate on the current literature examining antibacterial activities that stem from the gut bacteria of animals such as crocodiles, cockroaches, and water monitor lizards, amongst other interesting species, which likely encounter a plethora of microorganisms in their natural environments. The overall aim is to unveil a potential library of novel bioactive molecules for the benefit of human health and for utilization against infectious diseases.

The objective of this study was the preparation and characterization of a sustained-release matrix tablet containing a high-dose hydrophobic drug and its comparison with marketed products. In the present study, HPMC was applied as the matrix-forming polymer for the sustained release of clarithromycin (500 mg). The compatibility of clarithromycin and excipients was studied using a binary mixture approach and compatible excipients were selected. Matrix tablets were prepared using the high-shear wet granulation technique. Tablets were compressed using oblong (19 mm), shallow concave punches, under a compression weight of 900 mg/tablet. The flow of granules was evaluated by determining their bulk density, tapped density, angle of repose, Hausner ratio, and Car's index. Compressed tablets were tested for their physical parameters, mechanical characteristics, drug content, and in vitro drug release, as per United States Pharmacopeia (USP). Clarithromycin is a drug having poor water solubility and showed compatibility with all the excipients used in the formulation of polymeric matrix tablets. FTIR spectra of clarithromycin, before and after being subjected to the stress conditions, confirmed the compatibility of clarithromycin and other ingredients of the matrix tablets. All the formulations exhibited good rheological characteristics and all the parameters related to flow showed results in the acceptable range. Physically, matrix tablets were smooth and shiny, without any surface defects. Weight variation (±5%) and drug content of the tablets (95-102%) were within the pharmacopeial limits. HPMC successfully sustained the drug release for 24 h. It is concluded from the study that dissolution rate of clarithromycin can be sustained using hydrophilic polymer (HPMC) as a release-controlling agent.

Plants encounter several abiotic stresses, among which heat stress is gaining paramount attention because of the changing climatic conditions. Severe heat stress conspicuously reduces crop productivity through changes in metabolic processes and in growth and development. Ethylene and hydrogen sulfide (H 2 S) are signaling molecules involved in defense against heat stress through modulation of biomolecule synthesis, the antioxidant system, and post-translational modifications. Other compounds containing the essential mineral nutrient sulfur (S) also play pivotal roles in these defense mechanisms. As biosynthesis of ethylene and H 2 S is connected to the S-assimilation pathway, it is logical to consider the existence of a functional interplay between ethylene, H 2 S, and S in relation to heat stress tolerance. The present review focuses on the crosstalk between ethylene, H 2 S, and S to highlight their joint involvement in heat stress tolerance.

Isopropyl Isothiocyanate (IPI) is a poorly water-soluble drug used in different biological activities. So, the present work was designed to prepare and evaluate IPI loaded vesicles and evaluated for vesicle size, polydispersity index (PDI) and zeta potential, encapsulation efficiency, drug release, and drug permeation. The selected formulation was coated with chitosan and further assessed for the anti-platelet and anti-thrombotic activity. The prepared IPI vesicles (F3) exhibited a vesicle size of 298 nm ± 5.1, the zeta potential of −18.7 mV, encapsulation efficiency of 86.2 ± 5.3% and PDI of 0.33. The chitosan-coated IPI vesicles (F3C) exhibited an increased size of 379 ± 4.5 nm, a positive zeta potential of 23.5 ± 2.8 mV and encapsulation efficiency of 77.3 ± 4.1%. IPI chitosan vesicle (F3C) showed enhanced mucoadhesive property (2.7 folds) and intestinal permeation (~1.8-fold) higher than IPI vesicles (F3). There was a significant (p < 0.05) enhancement in size, muco-adhesion, and permeation flux achieved after coating with chitosan. The IPI chitosan vesicle (F3C) demonstrated an enhanced bleeding time of 525.33 ± 12.43 s, anti-thrombin activity of 59.72 ± 4.21, and inhibition of platelet aggregation 68.64 ± 3.99%, and anti-platelet activity of 99.47%. The results of the study suggest that IPI chitosan vesicles showed promising in vitro results, as well as improved anti-platelet and anti-thrombotic activity compared to pure IPI and IPI vesicles.

We have previously found that sera from Crocodylus porosus contain anticancer agents and the treatment of MCF7 cells with this serum resulted in the differential expression of 51 genes. The purpose of this study was to use in silico analysis to identify genes that might be epigenetically modulated in cells treated with crocodile serum and to understand the role of potential genes as novel candidates with epigenetic therapeutic potential. The findings report five proto-oncogenes ( TUBA1B , SLC2A1 , PGK1 , CCND1 , and NCAPD2 ) and two tumor suppressor genes ( RPLP2 , RPL37 ) as novel therapeutic targets. Furthermore, we present a comprehensive overview of relevant studies on epigenetic regulation of these genes along with an insight into their clinical implications. Therefore, elucidating the molecules present in the serum and gut bacteria of reptiles such as crocodiles may offer insights into the role of these genes on longevity, health, disease, and life expectancy.

The timely detection of seizure activity in the case of newborns can help save lives. Clinical signs of seizures in newborns are difficult to observe, so, in this study, we propose an automated method of detecting seizures in newborns using multi-channel electroencephalogram (EEG) recording acquired from 36 newborns admitted to Royal Women's Hospital, Brisbane, Australia. A novel set of time-frequency marginal features are defined to detect seizure activity in newborns. The proposed set is based on the observation that EEG seizure signals appear either as a train of spikes or as a summation of frequency-modulated chirps with slow variation in the instantaneous frequency curve. The proposed set of features is obtained by extracting the time-frequency (TF) signature of seizure spikes and frequency-modulated chirps by exploiting the direction of ridges in the TF plane. Based on extracted TF signature of spikes, the modified time-marginal is computed whereas based on the extracted TF signature of frequency-modulated chirps, the modified frequency-marginal is computed. It is demonstrated that features extracted from the modified time-domain marginal and frequency-domain marginal in combination with TF statistical and frequency-related features lead to better accuracy than the existing TF signal classification method, i.e., the proposed method achieves an F1 score of 70.93% which is 5% greater than the existing method.

Instantaneous frequency in multi-sensor recordings is an important parameter for estimation of direction of arrival estimation, source separation, and sparse reconstruction. The instantaneous frequency estimation problem becomes challenging when signal components have close or overlapping signatures and the number of sensors is less than the number of sources. In this study, we develop a computationally efficient method that exploits the direction of the IF curve in addition to the angle of arrival as additional features for the accurate tracking of IF curves. Experimental results show that the proposed scheme achieves better accuracy compared to the-state-of-art method in terms of mean square error (MSE) with a slight increase in the computational cost, i.e., the proposed method achieves MSE of -50 dB at the signal to noise ratio of 0 dB whereas the existing method achieves the MSE of -38 dB.

Antimicrobial resistance (AMR) is a global health challenge with methicillin resistant Staphylococcus aureus (MRSA), a leading cause of nosocomial infection. In the search for novel antibiotics, marine sponges have become model organisms as they produce diverse bioactive compounds. We investigated and compared the antibacterial potential of 3 bis-indole alkaloids-bromodeoxytopsentin, bromotopsentin and spongotine A-isolated from the Northeastern Atlantic sponge Spongosorites calcicola . Antimicrobial activity was determined by MIC and time-kill assays. The mechanism of action of bis-indoles was assessed using bacterial cytological profiling via fluorescence microscopy. Finally, we investigated the ability of bis-indole alkaloids to decrease the cytotoxicity of pathogens upon co-incubation with HeLa cells through the measurement of mammalian cell lysis. The bis-indoles were bactericidal to clinically relevant Gram-positive pathogens including MRSA and to the Gram-negative gastroenteric pathogen Vibrio parahaemolyticus . Furthermore, the alkaloids were synergistic in combination with conventional antibiotics. Antimicrobial activity of the bis-indole alkaloids was due to rapid disruption and permeabilization of the bacterial cell membrane. Significantly, the bis-indoles reduced pathogen cytotoxicity toward mammalian cells, indicating their ability to prevent bacterial virulence. In conclusion, sponge bis-indole alkaloids are membrane-permeabilizing agents that represent good antibiotic candidates because of their potency against Gram-positive and Gram-negative bacterial pathogens.

The effect of exogenously-applied ethylene sourced from ethephon (2-chloroethyl phosphonic acid)was studied on photosynthesis, carbohydrate metabolism, and high-temperature stress tolerance in Taipei-309 and Rasi cultivars of rice ( Oryza sativa L.). Heat stress increased the content of H 2 O 2 and thiobarbituric acid reactive substances (TBARS)more in Rasi than Taipei-309. Further, a significant decline in sucrose, starch, and carbohydrate metabolism enzyme activity and photosynthesis was also observed in response to heat stress. The application of ethephon reduced H 2 O 2 and TBARS content by enhancing the enzymatic antioxidant defense system and improved carbohydrate metabolism, photosynthesis, and growth more conspicuously in Taipei-309 under heat stress. The ethephon application enhanced photosynthesis by up-regulating the psbA and psbB genes of photosystem II in heat-stressed plants. Interestingly, foliar application of ethephoneffectively down-regulated high-temperature-stress-induced elevated ethylene biosynthesis gene expression. Overall, ethephon application optimized ethylene levels under high-temperature stress to regulate the antioxidant enzymatic system and carbohydrate metabolism, reducing the adverse effects on photosynthesis. These findings suggest that ethylene regulates photosynthesis via carbohydrate metabolism and the antioxidant system, thereby influencing high-temperature stress tolerance in rice.

In this paper, a mathematical model for the rolling motion of ships in random beam seas has been investigated. The ships' steady-state rolling motion with a nonlinear restoring moment and damping effect is modeled by the nonlinear second-order differential equation. Furthermore, an artificial neural network (NN)-based, backpropagated Levenberg-Marquardt (LM) algorithm is utilized to interpret a numerical solution for the roll angle (x(t)), velocity (x'(t)), and acceleration (x''(t)) of the ship in random beam seas. A reference data set based on numerical examples of the mathematical model for a rolling ship for the LM-NN algorithm is generated by the numerical solver Runge-Kutta method of order 4 (RK-4). The LM-NN algorithm further uses the created data set for the validation, testing, and training of approximate solutions. The outcomes of the design paradigm are compared with those of the homotopy perturbation method (HPM), optimal homotopy analysis method (OHAM), and RK-4. Statistical analyses of the mean square error (MSE), regression, error histograms, proportional performance, and computational complexity further validate the worth of the LM-NN algorithm.

In this paper, a novel soft computing technique is designed to analyze the mathematical model of the steady thin film flow of Johnson-Segalman fluid on the surface of an infinitely long vertical cylinder used in the drainage system by using artificial neural networks (ANNs). The approximate series solutions are constructed by Legendre polynomials and a Legendre polynomial-based artificial neural networks architecture (LNN) to approximate solutions for drainage problems. The training of designed neurons in an LNN structure is carried out by a hybridizing generalized normal distribution optimization (GNDO) algorithm and sequential quadratic programming (SQP). To investigate the capabilities of the proposed LNN-GNDO-SQP algorithm, the effect of variations in various non-Newtonian parameters like Stokes number (St), Weissenberg number (We), slip parameters ( a ), and the ratio of viscosities (ϕ) on velocity profiles of the of steady thin film flow of non-Newtonian Johnson-Segalman fluid are investigated. The results establish that the velocity profile is directly affected by increasing Stokes and Weissenberg numbers while the ratio of viscosities and slip parameter inversely affects the fluid's velocity profile. To validate the proposed technique's efficiency, solutions and absolute errors are compared with reference solutions calculated by RK-4 (ode45) and the Genetic algorithm-Active set algorithm (GA-ASA). To study the stability, efficiency and accuracy of the LNN-GNDO-SQP algorithm, extensive graphical and statistical analyses are conducted based on absolute errors, mean, median, standard deviation, mean absolute deviation, Theil's inequality coefficient (TIC), and error in Nash Sutcliffe efficiency (ENSE). Statistics of the performance indicators are approaching zero, which dictates the proposed algorithm's worth and reliability.

In this paper, we have analyzed the mathematical model of various nonlinear oscillators arising in different fields of engineering. Further, approximate solutions for different variations in oscillators are studied by using feedforward neural networks (NNs) based on the backpropagated Levenberg-Marquardt algorithm (BLMA). A data set for different problem scenarios for the supervised learning of BLMA has been generated by the Runge-Kutta method of order 4 (RK-4) with the "NDSolve" package in Mathematica. The worth of the approximate solution by NN-BLMA is attained by employing the processing of testing, training, and validation of the reference data set. For each model, convergence analysis, error histograms, regression analysis, and curve fitting are considered to study the robustness and accuracy of the design scheme.

In this study, we have investigated the mathematical model of an immobilized enzyme system that follows the Michaelis-Menten (MM) kinetics for a micro-disk biosensor. The film reaction model under steady state conditions is transformed into a couple differential equations which are based on dimensionless concentration of hydrogen peroxide with enzyme reaction (H) and substrate (S) within the biosensor. The model is based on a reaction-diffusion equation which contains highly non-linear terms related to MM kinetics of the enzymatic reaction. Further, to calculate the effect of variations in parameters on the dimensionless concentration of substrate and hydrogen peroxide, we have strengthened the computational ability of neural network (NN) architecture by using a backpropagated Levenberg-Marquardt training (LMT) algorithm. NNs-LMT algorithm is a supervised machine learning for which the initial data set is generated by using MATLAB built in function known as "pdex4". Furthermore, the data set is validated by the processing of the NNs-LMT algorithm to find the approximate solutions for different scenarios and cases of mathematical model of micro-disk biosensors. Absolute errors, curve fitting, error histograms, regression and complexity analysis further validate the accuracy and robustness of the technique.

Internet of Things (IoT) and 5G are enabling intelligent transportation systems (ITSs). ITSs promise to improve road safety in smart cities. Therefore, ITSs are gaining earnest devotion in the industry as well as in academics. Due to the rapid increase in population, vehicle numbers are increasing, resulting in a large number of road accidents. The majority of the time, casualties are not appropriately discovered and reported to hospitals and relatives. This lack of rapid care and first aid might result in life loss in a matter of minutes. To address all of these challenges, an intelligent system is necessary. Although several information communication technologies (ICT)-based solutions for accident detection and rescue operations have been proposed, these solutions are not compatible with all vehicles and are also costly. Therefore, we proposed a reporting and accident detection system (RAD) for a smart city that is compatible with any vehicle and less expensive. Our strategy aims to improve the transportation system at a low cost. In this context, we developed an android application that collects data related to sound, gravitational force, pressure, speed, and location of the accident from the smartphone. The value of speed helps to improve the accident detection accuracy. The collected information is further processed for accident identification. Additionally, a navigation system is designed to inform the relatives, police station, and the nearest hospital. The hospital dispatches UAV (i.e., drone with first aid box) and ambulance to the accident spot. The actual dataset from the Road Safety Open Repository is used for results generation through simulation. The proposed scheme shows promising results in terms of accuracy and response time as compared to existing techniques.

In this paper, we analyzed the mass transfer model with chemical reactions during the absorption of carbon dioxide (CO2) into phenyl glycidyl ether (PGE) solution. The mathematical model of the phenomenon is governed by a coupled nonlinear differential equation that corresponds to the reaction kinetics and diffusion. The system of differential equations is subjected to Dirichlet boundary conditions and a mixed set of Neumann and Dirichlet boundary conditions. Further, to calculate the concentration of CO2, PGE, and the flux in terms of reaction rate constants, we adopt the supervised learning strategy of a nonlinear autoregressive exogenous (NARX) neural network model with two activation functions (Log-sigmoid and Hyperbolic tangent). The reference data set for the possible outcomes of different scenarios based on variations in normalized parameters (α1,&nbsp;α2,&nbsp;β1,&nbsp;β2,&nbsp;k) are obtained using the MATLAB solver "pdex4". The dataset is further interpreted by the Levenberg-Marquardt (LM) backpropagation algorithm for validation, testing, and training. The results obtained by the NARX-LM algorithm are compared with the Adomian decomposition method and residual method. The rapid convergence of solutions, smooth implementation, computational complexity, absolute errors, and statistics of the mean square error further validate the design scheme's worth and efficiency.

In this research article, an organic polymer based polypyrrole (Ppy) composite material has been synthesized and analyzed for the design and fabrication purposes of a fast-responsive, highly sensitive, and an economical resistive-type novel humidity detection sensor. This humidity sensor most suitably serves the purpose for industrial humidity (i.e., values ranging from low to high) detection applications. First, a polypyrrole composite material (a mixture of polypyrrole, polypyrrole-NiO, polypyrrole-CeO 2 , and polypyrrole-Nb 2 O 5 ) has been synthesized by chemical oxidative polymerization method, and then is treated at various temperatures, i.e., 100, 150 and 200 °C, respectively. After this treatment, the synthesized samples were then characterized by using FTIR, SEM, and DTA/TGA techniques for analyzing humidity sensing properties. The polypyrrole samples with the best morphological structure and properties were then incorporated on interdigitated electrodes. For the fabrication purposes of this thin film structure, at first a few drops of polyvinyl alcohol (PVA) were placed over interdigitated electrodes (IDE) and then the synthesized polypyrrole composite was uniformly deposited in the form of a thin film over it. The plots show that this is a good resistive-type humidity detection device for the relative humidity range of 30% to 90%. The response and recovery times of this newly fabricated humidity sensor were reported to be the same as 128 s at room temperature. Additionally, the stability and the repeatability response behavior of this Ppy sensor were verified up to five cycles of multiple repetitions. This presents an excellent stability and repeatability performance of the sensor. Furthermore, the capacitances versus humidity response and recovery properties of the designed sensor were studied too. This illustrates an excellent capacitive verses humidity response and shows a linear and an active behavior. Lastly, the experimental result proves that polypyrrole composite thin film shows a reasonable best performance up to a temperature of 100 °C.

Crocodiles are remarkable animals that have the ability to endure extremely harsh conditions and can survive up to a 100 years while being exposed to noxious agents that are detrimental to Homo sapiens . Besides their immunity, we postulate that the microbial gut flora of crocodiles may produce substances with protective effects. In this study, we isolated and characterized selected bacteria colonizing the gastrointestinal tract of Crocodylus porosus and demonstrated their inhibitory effects against three different cancerous cell lineages. Using liquid chromatography-mass spectrometry, several molecules were identified. For the first time, we report partial analyses of crocodile's gut bacterial molecules.

In this study, a novel application of neurocomputing technique is presented for solving nonlinear heat transfer and natural convection porous fin problems arising in almost all areas of engineering and technology, especially in mechanical engineering. The mathematical models of the problems are exploited by the intelligent strength of Euler polynomials based Euler neural networks (ENN's), optimized with a generalized normal distribution optimization (GNDO) algorithm and Interior point algorithm (IPA). In this scheme, ENN's based differential equation models are constructed in an unsupervised manner, in which the neurons are trained by GNDO as an effective global search technique and IPA, which enhances the local search convergence. Moreover, a temperature distribution of heat transfer and natural convection porous fin are investigated by using an ENN-GNDO-IPA algorithm under the influence of variations in specific heat, thermal conductivity, internal heat generation, and heat transfer rate, respectively. A large number of executions are performed on the proposed technique for different cases to determine the reliability and effectiveness through various performance indicators including Nash-Sutcliffe efficiency (NSE), error in Nash-Sutcliffe efficiency (ENSE), mean absolute error (MAE), and Thiel's inequality coefficient (TIC). Extensive graphical and statistical analysis shows the dominance of the proposed algorithm with state-of-the-art algorithms and numerical solver RK-4.

Background: The trauma incidence follows specific patterns in different societies and is expected to increase over the weekend and nighttime. We aimed to explore and analyze the incidence, pattern, and severity of trauma at different times (working hours vs. out off-working hours, weekdays vs. weekends and season)., Methods: A retrospective analysis was conducted at a level 1 trauma facility in Qatar. All injured patients admitted between June 2017 and May 2018 were included. The data were analyzed to determine whether outcomes and care parameters of these patients differed between regular working hours and off-working hours, weekdays vs. weekends, and between season intervals., Results: During the study period, 2477 patients were admitted. A total of 816 (32.9%) patients presented during working hours and 1500 (60.6%) during off-working hours. Off-working hours presentations differed significantly with the injury severity score (ISS) ( p   <  0.001), ICU length of stay ( p   =  0.001), blood transfusions ( p = 0.001), intubations ( p = 0.001), mortality rate (9.7% vs. 0.7%; p < 0.001), and disposition to rehabilitation centers. Weekend presentations were significantly associated with a higher ISS ( p = 0.01), Priority 1 trauma activation (19.1% vs. 14.7%; p = 0005), and need for intubation (21% vs. 16%; p = 0.002). The length of stay (ICU and hospital), mortality, and disposition to rehabilitation centers and other clinical parameters did not show any significant differences. No significant seasonal variation was observed in terms of admissions at the trauma center., Conclusions: The off-working hours admission showed an apparent demographic effect in involved mechanisms, injury severity, and trauma activations, while outcomes, especially the mortality rate, were significantly different during nights but not during the weekends. The only observed seasonal effect was a decrease in the number of admissions during the summer break.

Attention Deficit Hyperactivity Disorder (ADHD) is a brain disorder with characteristics such as lack of concentration, excessive fidgeting, outbursts of emotions, lack of patience, difficulty in organizing tasks, increased forgetfulness, and interrupting conversation, and it is affecting millions of people worldwide. There is, until now, not a gold standard test using which an ADHD expert can differentiate between an individual with ADHD and a healthy subject, making accurate diagnosis of ADHD a challenging task. We are proposing a Knowledge Distillation-based approach to search for discriminating features between the ADHD and healthy subjects. Learned embeddings from a large neural network, trained on the functional connectivity features, were fed to one hidden layer Autoencoder for reproduction of the embeddings using the same connectivity features. Finally, a forward feature selection algorithm was used to select a combination of most discriminating features between the ADHD and the Healthy Controls. We achieved promising classification results for each of the five individual sites. A combined accuracy of 81% in KKI, 60% Peking, 56% in NYU, 64% NI, and 56% OHSU and individual site wise accuracy of 72% in KKI, 60% Peking, 73% in NYU, 70% NI, and 71% OHSU were obtained using our extracted features. Our results also outperformed state-of-the-art methods in literature which validates the efficacy of our proposed approach.

Bio-nanocomposites-based packaging materials have gained significance due to their prospective application in rising areas of packaged food. This research aims to fabricate biodegradable packaging films based upon polyvinyl alcohol (PVA) and starch integrated with metal-organic frameworks (MOFs) or organic additives. MOFs offer unique features in terms of surface area, mechanical strength, and chemical stability, which make them favourable for supporting materials used in fabricating polymer-based packaging materials. zeolitic imidazolate frameworks (ZIFs) are one of the potential candidates for this application due to their highly conductive network with a large surface area and high porosity. Present research illustrates a model system based on ZIF-67 (C 8 H 10 N 4 Co) bearing 2-10 wt.% loading in a matrix of PVA/starch blend with or without pyrolysis to probe the function of intermolecular interaction in molecular packing, tensile properties, and glass transition process. ZIF-67 nanoparticles were doped in a PVA/starch mixture, and films were fabricated using the solution casting method. It was discovered through scanning electron microscopy (SEM), X-ray diffraction (XRD), thermogravimetric analysis (TGA), and Fourier transform infrared spectroscopy (FTIR) that addition of ZIF-67 and pyrolyzed ZIF-67 changed and enhanced the thermal stability of the membrane. Moreover, 2-10 wt.% loading of ZIF-67 effected the thermal stability, owing to an interlayer aggregation of ZIF-67. The membranes containing pyrolyzed ZIF-67 showed mechanical strength in the order of 25 MPa in a moderate loading of pyrolyzed ZIF-67 (i.e., at 4 wt.%). The crystallinity enhanced by an increment in ZIF-67 loading. On the other hand, pyrolyzed ZIF-67 carbon became amorphous because of the inert environment and elevated temperature. The surface area also increased after the pyrolysis, which helped to increase the strength of the composite films.

The promising chemical, mechanical, and electrical properties of silver from nano scale to bulk level make it useful to be used in a variety of applications in the biomedical and electronic fields. Recently, several methods have been proposed and applied for the small-scale and mass production of silver in the form of nanoparticles, nanowires, and nanofibers. In this research, we have proposed a novel method for the fabrication of silver nano fibers (AgNFs) that is environmentally friendly and can be easily deployed for large-scale production. Moreover, the proposed technique is easy for device fabrication in different applications. To validate the properties, the synthesized silver nanofibers have been examined through Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), and X-ray diffraction (XRD). Further, the synthesized silver nanofibers have been deposited over sensors for Relative humidity (RH), Ammonia (NH 3 ), and temperature sensing applications. The sensor was of a resistive type, and found 4.3 kΩ for relative humidity (RH %) 30-90%, 400 kΩ for NH 3 (40,000 ppm), and 5 MΩ for temperature sensing (69 °C). The durability and speed of the sensor verified through repetitive, response, and recovery tests of the sensor in a humidity and gas chamber. It was observed that the sensor took 13 s to respond, 27 s to measure the maximum value, and took 33 s to regain its minimum value. Furthermore, it was observed that at lower frequencies and higher concentration of NH 3 , the response of the device was excellent. Furthermore, the device has linear and repetitive responses, is cost-effective, and is easy to fabricate.

The effects of nitric oxide (NO) as 100 µM sodium nitroprusside (SNP, NO donor) on photosynthetic-nitrogen use efficiency (NUE), photosynthetic-sulfur use efficiency (SUE), photosynthesis, growth and agronomic traits of rice ( Oryza sativa L.) cultivars, Taipie-309 (high photosynthetic-N and SUE) and Rasi (low photosynthetic-N and SUE) were investigated under high temperature stress (40 °C for 6 h). Plants exposed to high temperature stress caused significant reduction in photosynthetic activity, use efficiency of N and S, and increment in H 2 O 2 and thiobarbituric acid reactive substance (TBARS) content. The drastic effects of high temperature stress were more pronounced in cultivar Rasi than Taipie-309. However, foliar spray of SNP decreased the high temperature induced H 2 O 2 and TBARS content and increased accumulation of proline and activity of ascorbate-glutathione cycle that collectively improved tolerance to high temperature stress more effectively in Taipie-309. Exogenously applied SNP alleviated the high temperature induced decrease in photosynthesis through maintaining higher photosynthetic-NUE and photosynthetic-SUE, activity of ribulose 1,5 bisphosphate carboxylase/oxygenase (Rubisco), and synthesis of reduced glutathione (GSH). The use of 2-4-carboxyphenyl-4,4,5,5-tetramethylimidazoline-1-oxy-3-oxide (cPTIO, NO scavenger) substantiated the study that in the absence of NO oxidative stress increased, while NO increased photosynthetic-NUE and photosynthetic-SUE, net photosynthesis and plant dry mass. Taken together, the present investigation reveals that NO increased heat stress tolerance and minimized high temperature stress adversaries more effectively in cultivar Taipie-309 than Rasi by enhancing photosynthetic-NUE and SUE and strengthening the antioxidant defense system.

Nutrition transition can be defined as shifts in food habits, and it is characterized by high-fat (chiefly saturated animal fat), hypercaloric and salty food consumption at the expense of dietary fibers, minerals and vitamins. Western dietary patterns serve as a model for studying the impact of nutrition transition on civilization diseases, such as obesity, which is commonly associated with oxidative stress and inflammation. In fact, reactive oxygen species (ROS) overproduction can be associated with nuclear factor-κB (NF-κB)-mediated inflammation in obesity. NF-κB regulates gene expression of several oxidant-responsive adipokines including tumor necrosis factor-α (TNF-α). Moreover, AMP-activated protein kinase (AMPK), which plays a pivotal role in energy homeostasis and in modulation of metabolic inflammation, can be downregulated by IκB kinase (IKK)-dependent TNF-α activation. On the other hand, adherence to a Mediterranean-style diet is highly encouraged because of its healthy dietary pattern, which includes antioxidant nutraceuticals such as polyphenols. Indeed, hydroxycinnamic derivatives, quercetin, resveratrol, oleuropein and hydroxytyrosol, which are well known for their antioxidant and anti-inflammatory activities, exert anti-obesity proprieties. In this review, we highlight the impact of the most common polyphenols from Mediterranean foods on molecular mechanisms that mediate obesity-related oxidative stress and inflammation. Hence, we discuss the effects of these polyphenols on a number of signaling pathways. We note that Mediterranean diet (MedDiet) dietary polyphenols can de-regulate nicotinamide adenine dinucleotide phosphate (NADPH) oxidase (NOX) and NF-κB-mediated oxidative stress, and metabolic inflammation. MedDiet polyphenols are also effective in upregulating downstream effectors of several proteins, chiefly AMPK.

Autism disorder, generally known as Autism Spectrum Disorder (ASD) is a brain disorder characterized by lack of communication skills, social aloofness and repetitions in the actions in the patients, which is affecting millions of the people across the globe. Accurate identification of autistic patients is considered a challenging task in the domain of brain disorder science. To address this problem, we have proposed a three-stage feature selection approach for the classification of ASD on the preprocessed Autism Brain Imaging Data Exchange (ABIDE) rs-fMRI Dataset. In the first stage, a large neural network which we call a "Teacher " was trained on the correlation-based connectivity matrix to learn the latent representation of the input. In the second stage an autoencoder which we call a "Student" autoencoder was given the task to learn those trained "Teacher" embeddings using the connectivity matrix input. Lastly, an SFFS-based algorithm was employed to select the subset of most discriminating features between the autistic and healthy controls. On the combined site data across 17 sites, we achieved the maximum 10-fold accuracy of 82% and for the individual site-wise data, based on 5-fold accuracy, our results outperformed other state of the art methods in 13 out of the total 17 site-wise comparisons.

Despite intensive research, cancer incidence and mortality continue to rise. Consequently, the necessity to develop effective anti-cancer therapy is apparent. We have recently shown that the gut bacteria of animals living in polluted environments, such as crocodiles, are a potential source of novel anti-tumor molecules. To extend this work to other resilient species, we investigated the anti-tumor effects of gut bacteria of Heterometrus spinifer (a scorpion). Bacteria from the feces and gut were isolated, identified and evaluated for their anti-tumor effects. Bacterial-conditioned media was prepared in Roswell Park Memorial Institute (RPMI) 1640 media, and cytotoxicity and growth inhibitory properties were examined against cervical (HeLa) cancer cells. Liquid chromatography-mass spectrometry (LC-MS) was conducted to establish the identity of the molecules. Eighteen bacteria species from the gut (HSG01-18) and ten bacteria species from feces (HSF01-10) were tested for anti-tumor effects. Bacterial-conditioned media from scorpion gut and feces exhibited significant growth inhibitory effects against HeLa cells of 66.9% and 83.8%, respectively. Microscopic analysis of cancer cells treated with conditioned media HSG12 and HSG16 revealed apoptosis-like effects. HSG12 was identified as Pseudomonas aeruginosa and HSG16 was identified as Bacillus subtilis . Both conditioned media exhibited 100% growth inhibitory effects versus a selection of cancer cells, comprising cervical, breast and prostate cancer cells. LC-MS indicated the presence of 72 and 38 compounds, detected from HSG12 and HSG16, respectively. Out of these compounds, 47 were successfully identified while the remainder were unidentified and are possibly novel. This study suggests that the fecal and gut microbiota of scorpions might possess molecules with anti-cancer properties, however, further intensive research is needed to assess these expectations.

Cancer recurrence has remained a significant challenge, despite advances in therapeutic approaches. In part, this is due to our incomplete understanding of the biology of cancer stem cells and the underlying molecular mechanisms. The phenomenon of differentiation and dedifferentiation (phenotypic switching) is not only unique to stem cells but it is also observed in several other organisms, as well as evolutionary-related microbes. Here, we propose the use of a primitive eukaryotic unicellular organism, Acanthamoeba castellanii , as a model to study the molecular mechanisms of cellular differentiation and dedifferentiation., Competing Interests: No conflict of interest exists.

Active commuting to school (ACS) is an important source of physical activity among children. Recent research has focused on ACS and its benefits on cognition and academic achievement (AA), factors important for success in school. This review aims to synthesize literature on the relationship between ACS and cognition or AA among children and adolescents. Peer-reviewed articles in PubMed, Web of Science, PsycINFO and Cochrane Library assessing ACS with cognition and/or AA among children, until February 2019, were selected. Twelve studies across nine countries (age range 4-18.5 years) were included. One study used accelerometers, whereas all others used self-report measures of ACS. A wide range of objective assessments of cognitive functioning and AA domains were used. Five among eight studies, and four among six found a positive relationship between ACS and cognitive or AA measure, respectively. Four studies found dose-response relationships, and some studies found sex differences. The quantitative analysis found that ACS was not significantly associated with mathematics score (odds ratio = 1.18; CI = 0.40, 3.48). Findings are discussed in terms of methodological issues, potential confounders, and the strength of the evidence. Future studies should conduct longitudinal studies and use objective measures of ACS to understand this relationship further.

Bis-aldehyde monomers 4-(4'-formyl-phenoxy)benzaldehyde ( 3a ), 3-methoxy-4-(4'-formyl-phenoxy)benzaldehyde ( 3b ), and 3-ethoxy-4-(4'-formyl-phenoxy)benzaldehyde ( 3c ) were synthesized by etherification of 4-fluorobenzaldehyde ( 1 ) with 4-hydroxybenzaldehyde ( 2a ), 3-methoxy-4-hydroxybenzaldehyde ( 2b ), and 3-ethoxy-4-hydroxybenzaldehyde ( 2c ), respectively. Each monomer was polymerized with p -phenylenediamine and 4,4'-diaminodiphenyl ether to yield six poly(azomethine)s. Single crystal X-ray diffraction structures of 3b and 3c were determined. The structural characterization of the monomers and poly(azomethine)s was performed by FT-IR and NMR spectroscopic techniques and elemental analysis. Physicochemical properties of polymers were investigated by powder X-ray diffraction, thermogravimetric analysis (TGA), viscometry, UV-vis, spectroscopy and photoluminescence. These polymers were subjected to electrical conductivity measurements by the four-probe method, and their conductivities were found to be in the range 4.0 × 10 -5 to 6.4 × 10 -5 Scm -1 , which was significantly higher than the values reported so far.

The objective of this study is to evaluate the acute and subacute toxicity of the ethanolic extract of Marsdenia tenacissima (MTE) leaves (family: Asclepiadaceae) in albino rats. The acute toxicity was performed where the limit dose of 5000 mg/kg body weight used. Observations were made and recorded for 24 h, and once daily further for a period of 14 days. The rats were weighed and various observations, like mortality, behavior, injury, or any signs of illness were conducted once daily during the period. For subacute study, four groups of 10 animals (female rats) received 10% Tween 20 in distilled water (control), and 250, 500, and 1000 mg/kg of freshly-prepared extracts, respectively, every 24 h orally for 28 days. At the end of each study, hematological analysis and biochemical parameters were evaluated. Histopathological examination of vital organs of the animals were taken for gross findings, compared to controls. There was no significant difference ( p > 0.05) observed in the relative organs, body weights, hematological, biochemical parameters, and gross abnormalities, compared to the control. No mortality was recorded. Therefore, analysis of results may lead to the conclusion that the medium-term oral administration of the MTE leaves for 28 days does not cause toxicity., Competing Interests: The authors declare no conflict of interest.