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Neurological ailments, including stroke, Alzheimer’s disease (AD), epilepsy, Parkinson’s disease (PD), and other related diseases, have affected around 1 billion people globally to date. PD stands second among the common neurodegenerative diseases caused as a result of dopaminergic neuron loss in the midbrain’s substantia nigra regions. It affects cognitive and motor activities, resulting in tremors during rest, slow movement, and muscle stiffness. There are various traditional approaches for the management of PD, but they provide only symptomatic relief. Thus, a survey for finding new biomolecules or substances exhibiting the therapeutic potential to patients with PD is the main focus of present-day research. Medicinal plants, herbal formulations, and natural bioactive molecules have been gaining much more attention in recent years as synthetic molecules orchestrate a number of undesired effects. Several in vitro, in vivo, and in silico studies in the recent past have demonstrated the therapeutic potential of medicinal plants, herbal formulations, and plant-based bioactives. Among the plant-based bioactives, polyphenols, terpenes, and alkaloids are of particular interest due to their potent anti-inflammatory, antioxidant, and brain-health-promoting properties. Further, there are no concise, elaborated articles comprising updated mechanism-of-action-based reviews of the published literature on potent, recently investigated (2019–2023) medicinal plants, herbal formulations, and plant based-bioactive molecules, including polyphenols, terpenes, and alkaloids, as a method for the management of PD. Therefore, we designed the current review to provide an illustration of the efficacious role of various medicinal plants, herbal formulations, and bioactives (polyphenols, terpenes, and alkaloids) that can become potential therapeutics against PD with greater specificity, target approachability, bioavailability, and safety to the host. This information can be further utilized in the future to develop several value-added formulations and nutraceutical products to achieve the desired safety and efficacy for the management of PD.

The problems of atmospheric pollutants are causing significant concern across the globe and in India. The aggravated level of atmospheric pollutants in the surrounding environment poses serious threats to normal living conditions by deteriorating air quality and causing adverse health impacts. Pollutant concentration increases during harvesting seasons of Kharif/Rabi due to stubble burning and is aggravated by other points or mobile sources. The present study is intended to monitor the spatio-temporal variation of the major atmospheric pollutants using Sentinel-5P TROPOMI data through cloud computing. Land Use/Land Cover (LULC-categorization or classification of human activities and natural coverage on the landscape) was utilised to extract the agricultural area in the study site. It involves the cloud computing of MOD64A1 (MODIS Burned monthly gridded data) and Sentinel-5P TROPOMI (S5P Tropomi) data for major atmospheric pollutants, such as CH4, NO2, SOX, CO, aerosol, and HCHO. The burned area output provided information regarding the stubble burning period, which has seen post-harvesting agricultural residue burning after Kharif crop harvesting (i.e., rice from April to June) and Rabi crop harvesting (i.e., wheat from September to November). The long duration of stubble burning is due to variation in farmers’ harvesting and burning stubble/biomass remains in the field for successive crops. This period was used as criteria for considering the cloud computing of the Sentinel-5P TROPOMI data for atmospheric pollutants concentration in the study site. The results showed a significant increase in CH4, SO2, SOX, CO, and aerosol concentration during the AMJ months (stubble burning of Rabi crops) and OND months (stubble burning of Kharif crops) of each year. The results are validated with the ground control station data for PM2.5/PM10. and patterns of precipitation and temperature-gridded datasets. The trajectory frequency for air mass movement using the HYSPLIT model showed that the highest frequency and concentration were observed during OND months, followed by the AMJ months of each year (2018, 2019, 2020, and 2021). This study supports the role and robustness of Earth observation Sentinel-5P TROPOMI to monitor and evaluate air quality and pollutants distribution.

To match the current life-style, there is a huge demand and market for the processed food whose manufacturing requires multiple steps. The mounting demand increases the pressure on the producers and the regulatory bodies to provide sensitive, facile, and cost-effective methods to safeguard consumers’ health. In the multistep process of food processing, there are several chances that the food-spoiling microbes or contaminants could enter the supply chain. In this contest, there is a dire necessity to comprehend, implement, and monitor the levels of contaminants by utilizing various available methods, such as single-cell droplet microfluidic system, DNA biosensor, nanobiosensor, smartphone-based biosensor, aptasensor, and DNA microarray-based methods. The current review focuses on the advancements in these methods for the detection of food-borne contaminants and pathogens.

In recent years, erythropoietin (EPO) has emerged as a useful neuroprotective and neurotrophic molecule that produces antidepressant and cognitive-enhancing effects in psychiatric disorders. However, EPO robustly induces erythropoiesis and elevates red blood cell counts. Chronic administration is therefore likely to increase blood viscosity and produce adverse effects in non-anemic populations. Carbamoylated erythropoietin (CEPO), a chemically engineered modification of EPO, is non-erythropoietic but retains the neurotrophic and neurotrophic activity of EPO. Blood profile analysis after EPO and CEPO administration showed that CEPO has no effect on red blood cell or platelet counts. We conducted an unbiased, quantitative, mass spectrometry-based proteomics study to comparatively investigate EPO and CEPO-induced protein profiles in neuronal phenotype PC12 cells. Bioinformatics enrichment analysis of the protein expression profiles revealed the upregulation of protein functions related to memory formation such as synaptic plasticity, long term potentiation (LTP), neurotransmitter transport, synaptic vesicle priming, and dendritic spine development. The regulated proteins, with roles in LTP and synaptic plasticity, include calcium/calmodulin-dependent protein kinase type 1 (Camk1), Synaptosomal-Associated Protein, 25 kDa (SNAP-25), Sectretogranin-1 (Chgb), Cortactin (Cttn), Elongation initiation factor 3a (Eif3a) and 60S acidic ribosomal protein P2 (Rplp2). We examined the expression of a subset of regulated proteins, Cortactin, Grb2 and Pleiotrophin, by immunofluorescence analysis in the rat brain. Grb2 was increased in the dentate gyrus by EPO and CEPO. Cortactin was induced by CEPO in the molecular layer, and pleiotrophin was increased in the vasculature by EPO. The results of our study shed light on potential mechanisms whereby EPO and CEPO produce cognitive-enhancing effects in clinical and preclinical studies.

Radial neuron migration in the developing cerebral cortex is a complex journey, starting in the germinal zones and ending in the cortical plate. In mice, migratory distances can reach several hundreds of microns, or millimeters in humans. Along the migratory path, radially migrating neurons slither through cellularly dense and complex territories before they reach their final destination in the cortical plate. This task is facilitated by radial glia, the neural stem cells of the developing cortex. Indeed, radial glia have a unique bipolar morphology, enabling them to serve as guides for neuronal migration. The key guiding structure of radial glia is the basal process, which traverses the entire thickness of the developing cortex. Neurons recognize the basal process as their guide and maintain physical interactions with this structure until the end of migration. Thus, the radial glia basal process plays a key role during radial migration. In this review, we highlight the pathways enabling neuron-basal process interactions during migration, as well as the known mechanisms regulating the morphology of the radial glia basal process. Throughout, we describe how dysregulation of these interactions and of basal process morphology can have profound effects on cortical development, and therefore lead to neurodevelopmental diseases.

A wideband non-resonant absorber is proposed, and its radar cross section (RCS) reduction is investigated. A discussion on the functional materials available is followed by the design of an absorber on a Plexiglas substrate with polyaniline-graphene nanocomposite as layered square inclusions with thicknesses and conductivities scaled to golden ratio. The measured dielectric properties of polyaniline-graphene nanocomposites are used in the fullwave simulation. The design parameters have been identified and optimized using CST Microwave Studio. As designed structure is fabricated and the reflection is measured. The objective of the work is to demonstrate the use of non-metallic conducting polymer composites devoid of metals for radar absorbing material (RAM) structural designs. The structure is an all-polymer and electrically thin design with a potential to be 3D printed to suit the target object.

Herbal medicines are widely used worldwide and much appreciated because of their fewer side effects and the ability to fight diseases at the root cause. Active ‘phyto’ ingredients require a scientific approach and a mechanism to distribute components at the target site for better therapeutic results. Nanotechnology, on the other hand, has created new hope for cancer treatment but is still far from being proven in clinical settings. This article combines a unique approach to synthesis with the use of Pleurotus sajor-caju, followed by microwave irritation of silver and gold nanoparticles that ensures the capping of the active phyto ingredient and further enhances the effects of nanomedicine to fight colon cancer, thus opening a new era of what we call herbonanoceutics. The article also compares the characteristics and properties of silver (Au) and gold (Ag) nanoparticles synthesized by an in house developed novel microwave-assisted rapid green synthesis method. The as-prepared Ag NPs and Au NPs were compared using ultraviolet-visible spectroscopy (UV-Vis), Fourier transform infrared (FTIR) spectroscopy, X-ray diffraction (XRD), transmission electron microscopy (TEM), and energy dispersive spectroscopy (EDS). Our comparative study revealed that both assemblies display face-centred cubic structures (FCCs) and are nanocrystalline in nature. The advantage of the approach was that the sizes of gold and silver were identical in range with a similar distribution pattern. This has helped us to study the activity against colon cancer cell line (HCT-116) without incoherence since size plays a key role in the application. More specifically, morphological changes, cell viability, the production of reactive oxygen species (ROS) and the fragmentation of DNA have been further reported to assess better the results obtained with the two metals. Our results suggest that the newly adopted synthesis method may ensure the dual benefits from phyto ingredients which further enhances the effectiveness of advanced nanomedicine.

Optimizing the design and operational parameters for tillage tools is crucial for improved performance. Recently, artificial intelligence approaches, like ANN with learning capabilities, have gained attention for cost-effective and timely problem solving. Soil-bin experiments were conducted and data were used to develop ANN and regression models using gang angle, velocity ratio, soil CI, and depth as input parameters, while tractor equivalent PTO (PTOeq) power was used as an output. Both models were trained with a randomly selected 90% of the data, reserving 10% for testing purposes. In regression, models were iteratively fitted using nonlinear least-squares optimization. The ANN model utilized a multilayer feed-forward network with a backpropagation algorithm. The comparative performance of both models was evaluated in terms of R2 and mean square error (MSE). The ANN model outperformed the regression model in the training, testing, and validation phases. A well-trained ANN model was integrated with the particle-swarm optimization (PSO) technique for optimization of the operational parameters. The optimized configuration featured a 36.6° gang angle, 0.50 MPa CI, 100 mm depth, and 3.90 velocity ratio for a predicted tractor PTOeq power of 3.36 kW against an actual value of 3.45 kW. ANN–PSO predicted the optimal parameters with a variation between the predicted and the actual tractor PTOeq power within ±6.85%.

The use of lightweight drones has surged in recent years across both personal and commercial applications, necessitating the ability to conduct live video analytics on drones with limited computational resources. While edge computing offers a solution to the throughput bottleneck, it also opens the door to potential privacy invasions by exposing sensitive visual data to risks. In this work, we present a lightweight, privacy-preserving framework designed for real-time video analytics. By integrating a novel split-model architecture tailored for distributed deep learning through edge computing, our approach strikes a balance between operational efficiency and privacy. We provide comprehensive evaluations on privacy, object detection, latency, bandwidth usage, and object-tracking performance for our proposed privacy-preserving model.

(1) Background: Diabetic retinopathy (DR) is a major complication of diabetes, marked by abnormal angiogenesis, microaneurysms, and retinal hemorrhages. Traditional Ayurvedic medicine advocates multi-target strategies for DR management. However, the mechanisms by which Solanum xanthocarpum (SX) and Plumbago zeylanica (PZ) exert therapeutic effects are not well understood; (2) Methods: To investigate these mechanisms, we employed network pharmacology and molecular docking techniques. Phytochemicals from SX and PZ were identified using the IMPPAT database and Swiss Target Prediction tool. DR-related protein targets were sourced from the GeneCards database, and common targets were identified through Venn diagram analysis. STRING and Cytoscape were used to construct and analyze protein–protein interaction networks. Pathway enrichment was performed with Gene Ontology and KEGG databases; (3) Results: We identified 28 active phytoconstituents, targeting proteins such as EGFR, SRC, STAT3, AKT1, and HSP90AA1. Molecular docking and dynamics simulations confirmed the strong binding affinities of these compounds to their targets; (4) Conclusions: The study highlights the multi-target activity of SX and PZ, particularly in pathways related to EGFR tyrosine kinase inhibitor resistance and PI3K–AKT signaling. These findings provide valuable insights into their therapeutic potential for DR, suggesting the effective modulation of key molecular pathways involved in the disease.

Our hypothesis posited that incorporating alpha-linolenic acid (ALA) into liposomes containing Paclitaxel (PTX) could augment cellular uptake, decrease the therapeutic dosage, and alleviate PTX-related side effects. Our investigation encompassed characterization of the liposomal formulation, encompassing aspects like particle size, surface morphology, chemical structure, drug release kinetics, and stability. Compatibility studies were performed through Fourier transform infrared spectroscopy (FTIR). By utilizing the Box–Behnken design (BBD), we developed ALA-based liposomes with satisfactory particle size and entrapment efficiency. It is noteworthy that ALA incorporation led to a slight increase in particle size but did not notably affect drug entrapment. In vitro drug release assessments unveiled a sustained release pattern, with ALA-PTX liposomes demonstrating release profiles comparable to PTX liposomes. Morphological examinations confirmed the spherical structure of the liposomes, indicating that substituting ALA with phosphatidylcholine did not alter the physicochemical properties. Cellular uptake investigations showcased enhanced uptake of ALA-based liposomes in contrast to PTX liposomes, likely attributed to the heightened fluidity conferred by ALA. Efficacy against MCF-7 cells demonstrated concentration-dependent reductions in cell viability, with ALA-PTX liposomes exhibiting the lowest IC50 value. Morphological analysis confirmed apoptotic changes in cells treated with all formulations, with ALA-PTX liposomes eliciting more pronounced changes, indicative of enhanced anticancer efficacy.

The outbreak of SARS-CoV-2, also known as the COVID-19 pandemic, is still a critical risk factor for both human life and the global economy. Although, several promising therapies have been introduced in the literature to inhibit SARS-CoV-2, most of them are synthetic drugs that may have some adverse effects on the human body. Therefore, the main objective of this study was to carry out an in-silico investigation into the medicinal properties of Petiveria alliacea L. (P. alliacea L.)-mediated phytocompounds for the treatment of SARS-CoV-2 infections since phytochemicals have fewer adverse effects compared to synthetic drugs. To explore potential phytocompounds from P. alliacea L. as candidate drug molecules, we selected the infection-causing main protease (Mpro) of SARS-CoV-2 as the receptor protein. The molecular docking analysis of these receptor proteins with the different phytocompounds of P. alliacea L. was performed using AutoDock Vina. Then, we selected the three top-ranked phytocompounds (myricitrin, engeletin, and astilbin) as the candidate drug molecules based on their highest binding affinity scores of −8.9, −8.7 and −8.3 (Kcal/mol), respectively. Then, a 100 ns molecular dynamics (MD) simulation study was performed for their complexes with Mpro using YASARA software, computed RMSD, RMSF, PCA, DCCM, MM/PBSA, and free energy landscape (FEL), and found their almost stable binding performance. In addition, biological activity, ADME/T, DFT, and drug-likeness analyses exhibited the suitable pharmacokinetics properties of the selected phytocompounds. Therefore, the results of this study might be a useful resource for formulating a safe treatment plan for SARS-CoV-2 infections after experimental validation in wet-lab and clinical trials.

Vaccine development against group A Streptococcus (GAS) has gained traction in the last decade, fuelled by recognition of the significant worldwide burden of the disease. Several vaccine candidates are currently being evaluated in preclinical and early clinical studies. Here, we investigate two conjugate vaccine candidates that have shown promise in mouse models of infection. Two antigens, the J8 peptide from the conserved C-terminal end of the M protein, and the group A carbohydrate lacking N-acetylglucosamine side chain (ΔGAC) were each conjugated to arginine deiminase (ADI), an anchorless surface protein from GAS. Both conjugate vaccine candidates combined with alum adjuvant were tested in a non-human primate (NHP) model of pharyngeal infection. High antibody titres were detected against J8 and ADI antigens, while high background antibody titres in NHP sera hindered accurate quantification of ΔGAC-specific antibodies. The severity of pharyngitis and tonsillitis signs, as well as the level of GAS colonisation, showed no significant differences in NHPs immunised with either conjugate vaccine candidate compared to NHPs in the negative control group.

Winter sprouting potential and red rot resistance are two key parameters for successful sugarcane breeding in the subtropics. However, the cultivated sugarcane hybrids had a narrow genetic base; hence, the present study was planned to evaluate the Erianthus procerus genome introgressed Saccharum hybrids for their ratooning potential under subtropical climates and red rot tolerance under tropical and subtropical climates. A set of 15 Erianthus procerus derived hybrids confirmed through the 5S rDNA marker, along with five check varieties, were evaluated for agro-morphological, quality, and physiological traits for two years (2018–2019 and 2019–2020) and winter sprouting potential for three years (2018–2019, 2019–2020, and 2020–2021). The experimental material was also tested against the most prevalent isolates of the red rot pathogen in tropical (Cf671 and Cf671 + Cf9401) and subtropical regions (Cf08 and Cf09). The E. procerus hybrid GU 12—19 had the highest winter sprouting potential, with a winter sprouting index (WSI) of 10.6, followed by GU 12—22 with a WSI of 8.5. The other top-performing hybrids were as follows: GU 12—21 and GU 12—29 with a WSI of 7.2 and 6.9, respectively. A set of nine E. procerus-derived hybrids, i.e., GU04 (28) EO—2, GU12—19, GU12—21, GU12—22, GU12—23, GU12—26, GU12—27, GU12—30, and GU12—31, were resistant to the most prevalent isolates of red rot in both tropical and subtropical conditions. The association analysis revealed significant correlations between the various traits, particularly the fibre content, with a maximum number of associations, which indicates its multifaceted impact on sugarcane characteristics. Principal component analysis (PCA) summarised the data, explaining 57.6% of the total variation for the measured traits and genotypes, providing valuable insights into the performance and characteristics of the Erianthus procerus derived hybrids under subtropical climates. The anthocyanin content of Erianthus procerus hybrids was better than the check varieties, ranging from 0.123 to 0.179 (2018–2019) and 0.111 to 0.172 (2019–2020); anthocyanin plays a vital role in mitigating cold injury, acting as an antioxidant in cool weather conditions, particularly in sugarcane. Seven hybrids recorded a more than 22% fibre threshold, indicating their industrial potential. These hybrids could serve as potential donors for cold tolerance and a high ratooning ability, along with red rot resistance, under subtropical climates.

Diabetes is a long-term (chronic), challenging lifestyle (metabolic) disorder in which the body cannot regulate the amount of sugar in the blood and majorly affecting endocrine system and metabolic functions. Its complications majorly affect the neurons (as developing neuropathy), kidney (as nephropathy), and eye (as retinopathy). Diabetes also results in other associated problems, such as diabetic foot ulcers, sexual dysfunction, heart diseases etc. In the traditional medicines, the search for effective hypoglycemic agents is a continuous and challenging approach. Plant-derived bioactives, including alkaloids, phenols, glycosides, anthocyanins, flavonoids, saponins, tannins, polysaccharides, and terpenes, have been established to target cellular and molecular mechanisms involved in carbohydrate metabolism. Numerous diabetic patients select the herbal or traditional medicine system as an alternative therapeutic approach along with the mainstream anti-diabetic drugs. However, due to restrictive hurdles related to solubility and bioavailability, the bioactive compound cannot deliver the requisite effect. In this review, information is presented concerning well researched phytoconstituents established as potential hypoglycemic agents for the prevention and treatment of diabetes and its associated disorders having restricted solubility and bioavailability related issues. This information can be further utilized in future to develop several value added formulation and nutraceutical products to achieve the desired safety and efficacy for the prevention and treatment of diabetes and its related diseases.

This perspective sheds light on the transformative impact of recent computational advancements in the field of protein therapeutics, with a particular focus on the design and development of antibodies. Cutting-edge computational methods have revolutionized our understanding of protein–protein interactions (PPIs), enhancing the efficacy of protein therapeutics in preclinical and clinical settings. Central to these advancements is the application of machine learning and deep learning, which offers unprecedented insights into the intricate mechanisms of PPIs and facilitates precise control over protein functions. Despite these advancements, the complex structural nuances of antibodies pose ongoing challenges in their design and optimization. Our review provides a comprehensive exploration of the latest deep learning approaches, including language models and diffusion techniques, and their role in surmounting these challenges. We also present a critical analysis of these methods, offering insights to drive further progress in this rapidly evolving field. The paper includes practical recommendations for the application of these computational techniques, supplemented with independent benchmark studies. These studies focus on key performance metrics such as accuracy and the ease of program execution, providing a valuable resource for researchers engaged in antibody design and development. Through this detailed perspective, we aim to contribute to the advancement of antibody design, equipping researchers with the tools and knowledge to navigate the complexities of this field.

The goal of this investigation is to improve the topical delivery of medicine by preparing and maximizing the potential of a nanotransferosome gel infused with Solanum xanthocarpum methanolic extract (SXE) to provide localized and regulated distribution. Thin-film hydration was used to create SXE-infused nanotransferosomes (SXE-NTFs), and a Box–Behnken design was used to improve them. Phospholipon 90G (X1), cholesterol (X2) and sodium cholate (X3) were chosen as the independent variables, and their effects on vesicle size (Y1), polydispersity index (PDI) (Y2) and the percentage of entrapment efficiency (EE) (Y3) were observed both individually and in combination. For the SXE-NTFs, the vesicle size was 146.3 nm, the PDI was 0.2594, the EE was 82.24 ± 2.64%, the drug-loading capacity was 8.367 ± 0.07% and the drug release rate was 78.86 ± 5.24%. Comparing the antioxidant activity to conventional ascorbic acid, it was determined to be 83.51 ± 3.27%. Ex vivo permeation tests revealed that the SXE-NTF gel (82.86 ± 2.38%) considerably outperformed the SXE gel (35.28 ± 1.62%) in terms of permeation. In addition, it seemed from the confocal laser scanning microscopy (CLSM) picture of the Wistar rat’s skin that the rhodamine-B-loaded SXE-NTF gel had a higher penetration capability than the control. Dermatokinetic studies showed that the SXE-NTF gel had a better retention capability than the SXE gel. According to the experimental results, the SXE-NTF gel is a promising and successful topical delivery formulation.

Group B Streptococcus (Streptococcus agalactiae or GBS) is the leading infectious cause of neonatal mortality, causing roughly 150,000 infant deaths and stillbirths annually across the globe. Approximately 20% of pregnant women are asymptomatically colonized by GBS, which is a major risk factor for severe fetal and neonatal infections as well as preterm birth, low birth weight, and neurodevelopmental abnormalities. Current clinical interventions for GBS infection are limited to antibiotics, and no vaccine is available. We previously described VAX-A1 as a highly effective conjugate vaccine against group A Streptococcus that is formulated with three antigens, SpyAD, streptolysin O, and C5a peptidase (ScpA). ScpA is a surface-expressed, well-characterized GAS virulence factor that shares nearly identical sequences with the lesser studied GBS homolog ScpB. Here, we show that GBS C5a peptidase ScpB cleaves human complement factor C5a and contributes to disease severity in the murine models of pneumonia and sepsis. Furthermore, antibodies elicited by GAS C5a peptidase bind to GBS in an ScpB-dependent manner, and VAX-A1 immunization protects mice against lethal GBS heterologous challenge. These findings support the contribution of ScpB to GBS virulence and underscore the importance of choosing vaccine antigens; a universal GAS vaccine such as VAX-A1 whose formulation includes GAS C5a peptidase may have additional benefits through some measure of cross-protection against GBS infections.

Powdery mildew (PM), caused by the fungal pathogen Blumeria graminis f. sp. tritici (Bgt), significantly threatens global bread wheat production. Although the use of resistant cultivars is an effective strategy for managing PM, currently available wheat cultivars lack sufficient levels of resistance. To tackle this challenge, we conducted a comprehensive genome-wide association study (GWAS) using a diverse panel of 286 bread wheat genotypes. Over three consecutive years (2020–2021, 2021–2022, and 2022–2023), these genotypes were extensively evaluated for PM severity under field conditions following inoculation with virulent Bgt isolates. The panel was previously genotyped using the Illumina 90K Infinium iSelect assay to obtain genome-wide single-nucleotide polymorphism (SNP) marker coverage. By applying FarmCPU, a multilocus mixed model, we identified a total of 113 marker–trait associations (MTAs) located on chromosomes 1A, 1B, 2B, 3A, 3B, 4A, 4B, 5A, 5B, 6B, 7A, and 7B at a significance level of p ≤ 0.001. Notably, four novel MTAs on chromosome 6B were consistently detected in 2020–2021 and 2021–2022. Furthermore, within the confidence intervals of the identified SNPs, we identified 96 candidate genes belonging to different proteins including 12 disease resistance/host–pathogen interaction-related protein families. Among these, protein kinases, leucine-rich repeats, and zinc finger proteins were of particular interest due to their potential roles in PM resistance. These identified loci can serve as targets for breeding programs aimed at developing disease-resistant wheat cultivars.

Identifying a congenially targeted production environment and understanding the effects of genotype by environmental interactions on the adaption of chickpea genotypes is essential for achieving an optimal yield stability. Different models like additive main effect and multiplicative interactions (AMMI 1, AMM2), weighted average absolute scores of BLUPs (WAASB), and genotype plus genotype–environment (GGE) interactions were used to understand their suitability in the precise estimation of variance and their interaction. Our experiment used genotypes that represent the West Asia–North Africa (WANA) region. This trial involved two different sowing dates, two distinct seasons, and three different locations, resulting in a total of 12 environments. Genotype IG 5871(G1) showed a lower heat susceptibility index (HSI) across environments under study. The first four interactions principal component axis (IPCA) explain 93.2% of variations with significant genotype–environment interactions. Considering the AMMI stability value (ASV), the genotypes IG5862(G7), IG5861(G6), ILC239(G40), IG6002(G26), and ILC1932(G39), showing ASV scores of 1.66, 1.80, 2.20, 2.60, and 2.84, respectively, were ranked as the most stable and are comparable to the weighted average absolute scores of BLUPs (WAASB) ranking of genotypes. The which–won–where pattern of genotype plus genotype–environment (GGE) interactions suggested that the target environment consists of one mega environment. IG5866(G10), IG5865(G9), IG5884(G14), and IG5862(G7) displayed higher stability, as they were nearer to the origin. The genotypes that exhibited a superior performance in the tested environments can serve as ideal parental lines for heat-stress tolerance breeding programs. The weighted average absolute scores of BLUPs (WAASB) serve as an ideal tool to discern the variations and identify the stable genotype among all methods.

With the passage of time, scale gradually forms inside the oil pipeline. The produced scale, which has a high density, strongly attenuates photons, which lowers the measurement accuracy of three-phase flow meters based on gamma radiation. It is worth mentioning that the need for multiphase flow metering arises when it is necessary or desirable to meter well stream(s) upstream of inlet separation and/or commingling. In this investigation, a novel technique based on artificial intelligence is presented to overcome the issue mentioned earlier. Initially, a detection system was comprised of two NaI detectors and a dual-energy gamma source (241 Am and 133 Ba radioisotopes) using Monte Carlo N particle (MCNP) code. A stratified flow regime with varying volume percentages of oil, water, and gas was modeled inside a pipe that included a scale layer with varying thicknesses. Two detectors record the attenuated photons that could travel through the pipe. Four characteristics with the names of the amplitude of the first and second dominant signal frequencies were extracted from the received signals by both detectors. The aforementioned obtained characteristics were used to train two Radial Basis Function (RBF) neural networks to forecast the volumetric percentages of each component. The RMSE value of the gas and oil prediction neural networks are equal to 0.27 and 0.29, respectively. By measuring two phases of fluids in the pipe, the volume of the third phase can be calculated by subtracting the volume of two phases from the total volume of the pipe. Extraction and introduction of suitable characteristics to determine the volume percentages, reducing the computational burden of the detection system, considering the scale value thickness the pipe, and increasing the accuracy in determining the volume percentages of oil pipes are some of the advantages of the current research, which has increased the usability of the proposed system as a reliable measuring system in the oil and petrochemical industry.

In this paper, we investigate the outage performance of the three-hop mixed system integrating radio frequency (RF), free space optics (FSO), and under water optical communication (UWOC) system. The closed-form analytical expressions for the outage probability of the system are derived. In the considered system, the RF channel follows the Nakagami-m distribution, the FSO channel observes the Gamma-Gamma fading statistics, and the UWOC link experiences a mixture Exponential Generalized Gamma (EGG) fading distribution. To verify the derived analytical expressions, numerical simulations are also carried out, and we present the influence of the various link parameters such as path loss, atmospheric turbulence, pointing errors, angle-of-arrival fluctuations, water salinity, and scintillation on the performance of the decode and forward (DF) relayed multi-hop communication system.

Diabetes treatment requires focused administration with quality systemic circulation to determine the optimal therapeutic window. Intestinal distribution through oral administration with nanoformulation provides several benefits. Therefore, the purpose of this study is to create plumbagin enclosed within niosomes using the quality by design (QbD) strategy for efficient penetration and increased bioavailability. The formulation and optimization of plumbagin-loaded niosomes (P-Ns-Opt) involved the use of a Box–Behnken Design. The particle size (PDI) and entrapment efficiency of the optimized P-Ns-Opt were 133.6 nm, 0.150, and 75.6%, respectively. TEM, DSC, and FTIR were used to analyze the morphology and compatibility of the optimized P-Ns-Opt. Studies conducted in vitro revealed a controlled release system. P-Ns-Opt’s antioxidant activity, α-amylase, and α-glucosidase were evaluated, and the results revealed a dose-dependent efficacy with 60.68 ± 0.02%,90.69 ± 2.9%, and 88.43 ± 0.89%, respectively. In summary, the created P-Ns-Opt demonstrate remarkable potential for antidiabetic activity by inhibiting oxygen radicals, α-amylase, and α-glucosidase enzymes and are, therefore, a promising drug delivery nanocarrier in the management and treatment of diabetes.

Landmine contamination is a significant problem that has devastating consequences worldwide. Unmanned aerial vehicles (UAVs) can play an important role in solving this problem. The technology has the potential to expedite, simplify, and improve the safety and efficacy of the landmine detection process prior to physical intervention. Although the process of detecting landmines in contaminated environments is systematic, it is proven to be rather costly and overwhelming, especially if prior information about the location of the lethal objects is unknown. Therefore, automation of the process to orchestrate the search for landmines has become necessary to utilize the full potential of system components, particularly the UAV, which is the enabling technology used to airborne the sensors required in the discovery stage. UAVs have a limited amount of power at their disposal. Due to the complexity of target locations, the coverage route for UAV-based surveys must be meticulously designed to optimize resource usage and accomplish complete coverage. This study presents a framework for autonomous UAV-based landmine detection to determine the coverage route for scanning the target area. It is performed by extracting the area of interest using segmentation based on deep learning and then constructing the coverage route plan for the aerial survey. Multiple coverage path patterns are used to identify the ideal UAV route. The effectiveness of the suggested framework is evaluated using several target areas of differing sizes and complexities.

In the present work, a novel methodology was developed for the fabrication of clay-based nano pigments with enhanced thermal stability and used further as a colorant to prepare polymeric membranes. Initially, the batch extraction studies were performed to analyze the maximum adsorption of Safranin O (SO) dye onto pristine montmorillonite (Mt) and organo montmorillonite (OMt) by varying different parameters like pH, contact time, and concentration. It was confirmed from batch extraction studies that the adsorption efficacy of pristine Mt for SO was found to be more than OMt due to their negatively charged surface. Clay-based nano pigments were fabricated by considering the optimized condition where the maximum uptake of SO was observed and further characterized by XRD, FTIR, TGA, and SEM techniques. XRD studies confirmed the intercalation of SO dye while FTIR spectra revealed surface interaction of the dye with Mt/OMt. TGA studies showed that the clay-based nano pigments had more thermal stability than pure SO. Nano pigments were used as colorants to prepare thin, transparent, and homogeneously dispersed polymeric membranes through the solvent casting method. XRD studies of the polymeric membrane confirmed that the intercalation of poly methylmethacrylate (PMMA) into the interlayer of clay increases interlayer spacing, which was further confirmed by the TEM analysis. The mechanical properties of the PMMA polymeric membrane were also enhanced after the dispersion of clay-based nano pigments.

Over time, the accumulation of scale within the transmission pipeline results in a decrease in the internal diameter of the pipe, leading to a decline in efficiency and energy waste. The employment of a gamma ray attenuation system that is non-invasive has been found to be a highly precise diagnostic technique for identifying volumetric percentages across various states. The most appropriate setup for simulating a volume percentage detection system through Monte Carlo N particle (MCNP) simulations involves a system consisting of two NaI detectors and dual-energy gamma sources, namely 241Am and 133Ba radioisotopes. A three-phase flow consisting of oil, water, and gas exhibits symmetrical homogenous flow characteristics across varying volume percentages as it traverses through scaled pipes of varying thicknesses. It is worth mentioning that there is an axial symmetry of flow inside the pipe that creates a homogenous flow pattern. In this study, the experiment involved the emission of gamma rays from one end of a pipe, with photons being absorbed by two detectors located at the other end. The resulting data included three distinct features, namely the counts under the photopeaks of 241Am and 133Ba from the first detector as well as the total count from the second detector. Through the implementation of a two-output MLP neural network utilising the aforementioned inputs, it is possible to accurately forecast the volumetric percentages with an RMSE of under 1.22, regardless of the thickness of the scale. The minimal error value ensures the efficacy of the proposed technique and the practicality of its implementation in the domains of petroleum and petrochemicals.

The phenomenon of acoustic wave reflection off fluid–solid surfaces is the focus of this research. This research aims to measure the effect of material physical qualities on oblique incidence acoustic attenuation across a large frequency range. To construct the extensive comparison shown in the supporting documentation, reflection coefficient curves were generated by carefully adjusting the porousness and permeability of the poroelastic solid. The next stage in determining its acoustic response is to determine the pseudo-Brewster angle shift and the reflection coefficient minimum dip for the previously indicated attenuation permutations. This circumstance is made possible by modeling and studying the reflection and absorption of acoustic plane waves encountering half-space and two-layer surfaces. For this purpose, both viscous and thermal losses are taken into account. According to the research findings, the propagation medium has a significant impact on the form of the curve that represents the reflection coefficient, whereas the effects of permeability, porosity, and driving frequency are relatively less significant to the pseudo-Brewster angle and curve minima, respectively. This research additionally found that as permeability and porosity increase, the pseudo-Brewster angle shifts to the left (proportionally to porosity increase) until it reaches a limiting value of 73.4 degrees, and that the reflection coefficient curves for each level of porosity exhibit a greater angular dependence, with an overall decrease in magnitude at all incident angles. These findings are given within the framework of the investigation (in proportion to the increase in porosity). The study concluded that when permeability declined, the angular dependence of frequency-dependent attenuation reduced, resulting in iso-porous curves. The study also discovered that the matrix porosity largely affected the angular dependency of the viscous losses in the range of 1.4 × 10−14 m2 permeability.

A new generation of clay-based nano pigments has been introduced, providing the advantage of both inorganic pigments and organic dyes. These nano pigments have been synthesized through a stepwise procedure where, initially, an organic dye is adsorbed onto the surface of the adsorbent, and then dye adsorbed adsorbent is used as pigment for further applications. The objective of the current paper was to examine the interaction of non-biodegradable toxic dyes, Crystal Violet (CV) and Indigo Carmine (IC), with clay minerals (montmorillonite (Mt), vermiculite (Vt), and clay bentonite (Bent)) and their organically modified forms (OMt, OBent, and OVt) and to develop a novel methodology for the synthesis of the value-added products and clay-based nano pigments without creating second generation waste materials. In our observation, the uptake of CV was more intense onto pristine Mt, Bent, and Vt, and the uptake of IC was more onto OMt, OBent, and OVt. CV was found to be in the interlayer region of Mt and Bent, as supported by XRD data. Zeta potential values confirmed the presence of CV on their surface. In contrast, in the case of Vt and organically modified forms, the dye was found on the surface, confirmed by XRD and zeta potential values. In the case of indigo carmine, the dye was found only on the surface of pristine Mt, Bent, Vt, and organo Mt, Bent, Vt. During the interaction of CV and IC with clay and organoclays, intense violet and blue-colored solid residues were obtained (also known as clay-based nano pigments). The nano pigments were used as colorants in a poly (methyl-methacrylate) (PMMA) polymer matrix to form transparent polymer films.

Stem cells’ self-renewal and multi-lineage differentiation are regulated by a complex network consisting of signaling factors, chromatin regulators, transcription factors, and non-coding RNAs (ncRNAs). Diverse role of ncRNAs in stem cell development and maintenance of bone homeostasis have been discovered recently. The ncRNAs, such as long non-coding RNAs, micro RNAs, circular RNAs, small interfering RNA, Piwi-interacting RNAs, etc., are not translated into proteins but act as essential epigenetic regulators in stem cells’ self-renewal and differentiation. Different signaling pathways are monitored efficiently by the differential expression of ncRNAs, which function as regulatory elements in determining the fate of stem cells. In addition, several species of ncRNAs could serve as potential molecular biomarkers in early diagnosis of bone diseases, including osteoporosis, osteoarthritis, and bone cancers, ultimately leading to the development of new therapeutic strategies. This review aims to explore the specific roles of ncRNAs and their effective molecular mechanisms in the growth and development of stem cells, and in the regulation of osteoblast and osteoclast activities. Furthermore, we focus on and explore the association of altered ncRNA expression with stem cells and bone turnover.

Coronavirus disease (COVID-19) has killed millions of people since first reported in Wuhan, China, in December 2019. Intriguingly, Withania somnifera (WS) has shown promising antiviral effects against numerous viral infections, including SARS-CoV and SARS-CoV-2, which are contributed by its phytochemicals. This review focused on the updated testing of therapeutic efficacy and associated molecular mechanisms of WS extracts and their phytochemicals against SARS-CoV-2 infection in preclinical and clinical studies with the aim to develop a long-term solution against COVID-19. It also deciphered the current use of the in silico molecular docking approach in developing potential inhibitors from WS targeting SARS-CoV-2 and host cell receptors that may aid the development of targeted therapy against SARS-CoV-2 ranging from prior to viral entry until acute respiratory distress syndrome (ARDS). This review also discussed nanoformulations or nanocarriers in achieving effective WS delivery to enhance its bioavailability and therapeutic efficacy, consequently preventing the emergence of drug resistance, and eventually therapeutic failure.

A supercapacitor is a type of electrical component that has larger capacitance, due to asymmetric behavior with better power density, and lower ESR (effective series resistance) than conventional energy-storage components. Supercapacitors can be used with battery technology to create an effective energy storage system due to their qualities and precise characterization. Studies have shown that the use of quantum dots as electrodes in supercapacitors can significantly increase their effectiveness. In this research article, we have used a Drude model based on free electrons (asymmetric nature) to describe the supercapacitor’s discharging characteristics. Commercially available Nippon DLA and Green-cap supercapacitors were used to verify the Drude model by discharging them through a constant current source using a simple current mirror circuit. The parameters of both the fractional-order models and our suggested method were estimated using the least-squares regression fitting approach. An intriguing finding from the Drude model is the current-dependent behavior of the leakage-parallel resistance in the constant current discharge process. Instead of using the traditional exponential rule, supercapacitors discharge according to a power law. This work reflects the strong symmetry of different aspects of designing a hybrid supercapacitor with high efficiency and reliability.

Gingipains (RgpA, RgpB, and Kgp) are major virulence factors of the periodontitis-causing bacterium Porphyromonas gingivalis. Isolation of gingipains from the crude protein sample of P. gingivalis is critical for studying the underlying invasion mechanism that contributes to periodontitis, Alzheimer’s disease, and cardiovascular disease (CVD). Chromatographic processes and molecular cloning are two standard techniques often used for gingipains isolation, which are time-consuming and costly. In this study, considerably easier methods based on passive-mediated diffusion gel elution and gelatin zymogram were used to isolate and characterize gingipains. Importantly, proteins eluted from Native-PAGE showed enzymatic activity for both Rgp and Kgp. In gelatin zymography, the proteins with a molecular size of ~50 kDa and above 245 kDa were suggested as arginine-specific gingipains. The passive diffusion-mediated gel elution method is a simpler technique to isolate gingipains from crude protein samples of P. gingivalis. By using covalent and highly specific gingipain inhibitors, gelatin zymography enabled an individual characterization of gingipain activity and inhibition. Finally, this protocol can be easily extended by adding the isoelectric focusing to further improve the protein separation and characterization.

This review study examines flow boiling heat transfer in micro-fin tubes using mixed and pure refrigerants with zero ozone-depleting potential (ODP) and minimal global warming potential (GWP). This investigation focuses on the extraordinary relationship between heat transfer coefficients (HTCs) and vapor quality. Since the introduction of micro-fin heat exchanger tubes over 30 years ago, refrigerant-based cooling has improved significantly. Air conditioning and refrigeration companies are replacing widely used refrigerants, with substantial global warming impacts. When space, weight, or efficiency are limited, micro-fin heat exchangers with improved dependability are preferred. This review article discusses flow boiling concepts. The researchers used several refrigerants under different testing conditions and with varying micro-fin tube parameters. Micro-fin tubes are promising for improved heat transfer techniques. This tube increases the heat transfer area, fluid disturbance, flow speed, and direction owing to centrifugal force and HTC. As the focus shifts to improving heat transfer, pressure drop, mean vapor quality, and practical devices, this subject will grow more intriguing. A radical shift will reduce equipment size for certain traditional heat transfer systems and bring new products using micro-scale technologies. This suggested review effort helps comprehend saturation flow boiling through micro-fin tubes and find the right correlation for a given application. This domain’s challenges and future relevance are also discussed.

Periodontal disease (PD) is multifactorial oral disease that damages tooth-supporting tissue. PD treatment includes proper oral hygiene, deep cleaning, antibiotics therapy, and surgery. Despite the availability of basic treatments, some of these are rendered undesirable in PD treatment due to side effects and expense. Therefore, the aim of the present study is to develop novel molecules to combat the PD triggering pathogens. The study involved the synthesis of 4-((5-(substituted-phenyl)-1,3,4-oxadiazol-2-yl)methoxy)benzamidine (5a-e), by condensation of 2-(4-carbamimidoylphenoxy)acetohydrazide (3) with different aromatic acids; and synthesis of 4-((4-(substituted benzylideneamino)-4H-1,2,4-triazol-3-yl)methoxy)benzamidine (6a-b) by treatment of compound 3 with CS2 followed by hydrazination and a Schiff reaction with different aromatic aldehydes. Synthesized compounds were characterized based on the NMR, FTIR, and mass spectrometric data. To assess the effectiveness of the newly synthesized compound in PD, new compounds were subjected to antimicrobial evaluation against P. gingivalis and E. coli using the micro-broth dilution method. Synthesized compounds were also subjected to cytotoxicity evaluation against HEK-293 cells using an MTT assay. The present study revealed the successful synthesis of heterocyclic derivatives of benzamidine with significant inhibitory potential against P. gingivalis and E. coli. Synthesized compounds exhibited minimal to the absence of cytotoxicity. Significant antimicrobial potential and least/no cytotoxicity of new heterocyclic analogs of benzamidine against PD-triggering bacteria supports their potential application in PD treatment.