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Strigolactones (SLs) are carotenoid-derived terpenoid lactones. Natural SLs are grouped into two types, namely, strigol-type and orobanchol-type, and 20 SLs have been identified from different plant kingdoms or species. The more stable SLs, called GR24, derived synthetically, were utilized for the investigation of SL responses. SLs are crucial endogenous plant hormones that play a multifactorial role in plant and rhizosphere interactions by controlling mycorrhization and lateral shoot branching. SLs participate in the organization of plant architecture by reducing bud outgrowth in addition to various morphological and developmental processes, collectively with other plant growth hormones such as auxins, cytokinins, gibberellins, and abscisic acid. Strigolactones regulate root growth and structure by inhibiting lateral root production and enhancing root hair elongation. Targeted genetic engineering of SL biosynthetic genes leads to potential alterations in rooting and vegetative systems and assists in generating plants that are more appropriate to different adverse environmental conditions, such as drought, salt stress, cold, and water deficit. The present review provides a clear outline of the structure, type, biosynthesis, and signaling mechanisms of SLs. In addition, their potential functions in plant growth, development, and response to stress conditions are also discussed.

The COVID-19 pandemic has infected tens of millions of individuals around the world, and it is currently posing a worldwide health calamity. Wearing a face mask in public places is one of the most effective protection strategies, according to the World Health Organization (WHO). Moreover, their effectiveness has declined due to incorrect use of the face mask. In this scenario, effective recognition systems are anticipated to ensure that people's faces are covered with masks in public locations. Many people do not correctly wear the masks due to inadequate practices, undesirable behaviour, or individual vulnerabilities. As a result, there has been an increase in demand for automatic real-time face mask detection and mask position detection to substitute manual reminders. This proposed work classifies people into three categories such as with mask, without mask and mask with incorrect position. The dataset is tested using three different variants of object detection models, namely YOLOv4, Tiny YOLOv4, and YOLOv5. The experimental result shows that YOLOv5 model outperforms with the highest mAP value of 99.40% compared to the other two models.

AbstractMultidrug Resistance (MDR) in tumors is caused by the over-expression of ATP Binding Cassette transporter proteins such as Multidrug Resistance Protein 1 and Breast Cancer Resistance Protein 1. This in silicostudy focuses on identifying a MDR inhibitor among acetogenins (AGEs) of Annona muricataand also aims at predicting colorectal cancer (CRC) core targets of AGEs through a network pharmacological approach. Twenty-four AGEs were initially screened for their ADME properties. Molecular interaction studies were performed with the two proteins MRP1 and BCRP1. As the structure of MRP1 was not available, an inward-facing conformation of MRP1 was modeled. A Protein-protein interaction network was constructed for the correlating targets of CRC. KEGG pathway and Gene Ontology analysis were performed for the predicted CRC targets. We identified four lead AGEs: Muricatocin B, Annonacinone, Annonacin A and Annomuricin E having a higher binding affinity towards MDR proteins. MD simulation studies performed with the three lead AGEs and the MDR proteins showed that MRP1(DBD): Annomuricin E complex was stable throughout the simulation. Our analysis revealed ABCG2, ERBB2, STAT3, AR, SRC and ABCC1 as CRC targets of the lead molecules. The top 10 signaling pathways and functions of correlative CRC targets were also predicted. We conclude that the identified lead molecules might act as competitive inhibitors for reversing MDR in CRC. Additionally, network pharmacological studies established the correlative CRC targets and their mechanisms of action. Further experimental studies are needed to validate our findings. Communicated by Ramaswamy H. Sarma

A biosurfactant is an amphiphilic molecule that has both hydrophobic and hydrophilic properties. Biosurfactants from microbial sources with oil degradation potential were isolated from hydrocarbon-contaminated sites. Oil displacement and emulsification tests in this study identified the biosurfactant produced. The biosurfactants produced by the isolated bacteria possess anti-microbial properties. The cell-free supernatants of Bacillus subtilisand Pseudomonas aeruginosashowed positive results for the oil displacement method. The drop collapse test supported the biosurfactant production by these isolates. The synthesis of silver nanoparticles from Acalypha indicaplant leaf extract was carried out. The biosurfactants (BS) and silver nanoparticles (AgNP) was also carried out. Their application as individual and synergistic effects in the bioremediation of hydrocarbons (engine waste oil) was discussed. Gravimetric analysis was used to analyse the degradation of engine oil. Silver nanoparticles showed absorbance in the range of 200 and 400 nm. Seven discrete peaks were obtained in FT-IR, representing the main functional group of AgNPs. The reduction of silver nitrate (AgNO3) into silver nanoparticles (AgNP) occurred due to the presence of biomolecules. The SEM analysis showed that the size of the nanoparticle is between 69 and 87 nm and is crystalline. The average distribution of the diameter of the silver nanoparticles was 186.9 nm. Broader peaks of XRD reflect the influence of the experimental circumstances on the nucleation and development of the crystal nuclei and indicate smaller particle sizes. The biosurfactants showed less degradation than the synergistic method employed with biosurfactants and silver nanoparticles. Therefore, it is concluded that the silver nanoparticles influenced the biosurfactant and increased the rate of degradation of the engine waste oil.

AbstractBrassica juncea(BJ) is a familiar edible crop, which has been used as a dietary ingredient and to prepare anti-inflammatory/anti-arthritic formulations in Ayurveda. But, the scientific validation or confirmation of its therapeutic properties is very limited. This study was performed to determine the efficiency of BJ leaves for the treatment of Rheumatoid arthritis using in vivoand in silicosystems. Standard in vitroprocedures was followed to study the total phenolic, flavonoid contents and free radical scavenging ability of the extracts of BJ. The effective extract was screened and the presence of bioactive chemicals was studied using HPLC. Further, the possible therapeutic actions of the BJ active principles against the disease targets were studied using PPI networking and docking analysis. IL2RA, IL18 and VEGFA are found to be the potential RA target and the compounds detected from BJ extract have shown great binding efficiency towards the target from molecular docking study. The resulting complexes were then subject to 100 ns molecular dynamics simulation studies with the GROMACS package to analyze the stability of docked protein-ligand complexes and to assess the fluctuation and conformational changes during protein-ligand interactions. To confirm the anti-arthritic activity of BJ, the extract was tested in CFA-induced arthritic Wistar rats. The test groups administered with BJ extract showed retrieval of altered hematological parameters and substantial recovery from inflammation and degeneration of rat hind paw.Communicated by Ramaswamy H. Sarma

Many strategic industrial sectors prefer Directed Energy Deposition (DED) to other Additive Manufacturing (AM) technologies due to the high material deposition and build rates. However, the inadvertent formation of defects such as porosity, micro-cracks and microstructure anomalies hinders its adoption in industries that require specific mechanical and microstructural properties. These defects are caused by undesirable fluctuations in process conditions such as material flow rate, laser power, melt pool dynamics, environment gas composition, temperature gradients. This research proposes in situ quality monitoring of DED using images of process zone and contrastive learning-based Convolutional Neural Network (CNN). Experiments included deposition of titanium powder (Cp-Ti, grade 1) with the particle size ranging between 45 and 106 μm on the base plate (99.6 % Ti6Al4V grade 1), forming a cube geometry. The process parameters were tuned to achieve six quality grades. The video of the process zone was recorded co-axially to the laser beam during the entire manufacturing, which was eventually used as the input to train CNN's based on contrastive losses. An in situ monitoring strategy for classifying the different quality grades was demonstrated in a supervised and semi-supervised manner, with an accuracy ranging between 89 % and 97 %. The performance of the developed framework was compared to an alternative clustering technique, namely t-distributed stochastic neighbour embedding, justifying the efficiency of our approach. The developed methodology demonstrates the possibility to track workpiece manufacturing quality using simple CCD cameras with minimum interventions on the commercial machines.

Accurate and fast tomato plant disease identification is significant to enhance its sustainable agricultural productivity. In the conventional technique, human experts in the field of agriculture have been accommodated to find out the anomalies in tomato plants caused by pests, diseases, climatic conditions, and nutritional deficiencies. Automatic tomato leaf disease identification is initially solved through conventional image processing and machine learning approaches which result in less accuracy. In order to produce greater prediction accuracy, deep learning-based classification is introduced. This paper provides an overall review of recent work performed in the field of tomato leaf disease identification using image processing, machine learning, and deep learning approaches. And also discuss both public and private datasets available to detect tomato leaf disease, methods employed, and adopted deep learning frameworks. Consequently, suggestions are provided to figure out the appropriate techniques in order to obtain the better prediction accuracy. Finally, the challenges encountered in implementing the machine learning and deep learning models are discussed.

An expansion plan of a real-world power system will be more optimal when it combines the Electricity Demand Forecasting (EDF) and Generation Expansion Planning (GEP) problem. It should consider minimum error and cost; more reliability and environment concern. In this study, the EDF problem of Tamil Nadu, an Indian state, is handled by having the input data for instance Gross State Domestic Product (GSDP), Per Capita Income (PCI) and population. The electricity demand has been estimated and validated through Mean Absolute Percentage Error (MAPE) using the optimization techniques such as Artificial Immune System (AIS), Genetic Algorithm (GA) and Differential Evolution (DE). The outcomes reveal that DE outdid other algorithms. The future demands which are estimated from the EDF problem are provided as input to the GEP problem. From the results of the EDF problem, three different cases, for instance low, average and high growth rates, are framed to solve the GEP problem by minimizing the cost, environmental pollution and by enhancing the reliability. The uncertainties in demand, cost, Forced Outage Rate (FOR) and capacity credit of the power plants are considered when handling the GEP problem for short-term (6-year) and long-term (12-year) planning horizons using DE, Self-adaptive Differential Evolution (SaDE) and Opposition-based Differential Evolution (ODE). The simulation results reveal that SaDE outdid other techniques in estimating the ideal solution.

Green renewable technologies have become a focus of energy research due to the adverse impacts of fossil fuels, greenhouse gases, climate change, global warming, and battery short life. A new generation of biomaterials with spontaneous piezoelectric properties is highly emerging for generating electricity from ubiquitous mechanical energy. Recent years, there has been a concerted effort to engineer robust 1D functional materials for nanogenerators, leveraging cellulose as the foundational material. This research work produced nanofiber composite of zinc oxide (ZnO) nanoparticles and MXene (Ti3C2) nanosheets incorporated into cellulose acetate (CA) polymer through electrospinning process forms the basis for ecofriendly highly durable smart textile fabrication. Formation of MXene nanosheets heterostructures significantly promoted the low conversion efficiency of conventional ZnO to highest output voltage of ⁓35 V, and a short circuit current of ⁓3.34 µA. Synergistic contribution of the piezo-enhanced photocatalytic activity of MXene/ZnO hetero-structured smart nanofibers offers greater environmental remediation of water resources from the contamination of methyl orange (MO) dye with a rate constant (k) of 66.14×10−3min−1. In addition, intelligent dual mechanistic membranes support sustainable operations (20000 cycles) with strong morphological and performance retention (⁓92 %), showing good chemical and mechanical stability even under harsh operating conditions.

The disposal of magnesite mine tailings (MMT), a by-product of magnesite mining, raises significant environmental concerns due to its adverse effects on soil, water and air quality. Likewise, the improper disposal of used face masks exacerbates environmental burdens. The innovative use of polypropylene fibres (PPF) derived from disposable face masks to reinforce. This study explores the compaction and strength characteristics of PPF-MMT composites with varying fibre content to develop a sustainable composite for subgrade construction. The findings indicate that the addition of PPF increases optimal moisture content and decreases maximum dry density. Shear strength analysis reveals a linear failure envelope for both MMT and PPF-MMT, with initial angle of internal friction improvement at lower PPF content (0.25% and 0.5%) but a decline at higher contents (0.75% and 1%). Importantly, PPF-MMT consistently exhibits a unique strain-hardening behaviour across all stress levels, distinguishing it from MMT, which only transitions to strain-hardening at higher stresses. Under vertical load, MMT shows contraction, while the PPF-MMT composite initially contracts but later dilates due to increased fibre-MMT interaction during horizontal displacement. Furthermore, California bearing ratio (CBR) tests demonstrate increased dry CBR with PPF, reaching a peak of 33.85% at 0.5% fibre content. The soaked CBR tests affirm the remarkable durability of PPF-MMT, maintaining significantly higher values than MMT even after 60 days of soaking. The study concludes that 0.5% fibre content as optimum dosage.