Enhanced cardiovascular disease prediction through self-improved Aquila optimized feature selection in quantum neural network & LSTM model.

Introduction: Cardiovascular disease (CVD) stands as a pervasive catalyst for illness and mortality on a global scale, underscoring the imperative for sophisticated prediction methodologies within the ambit of healthcare data analysis. The vast volume of medical data available necessitates effective data mining techniques to extract valuable insights for decision-making and prediction. While machine learning algorithms are commonly employed for CVD diagnosis and prediction, the high dimensionality of datasets poses a performance challenge. Methods: This research paper presents a novel hybrid model for predicting CVD, focusing on an optimal feature set. The proposed model encompasses four main stages namely: preprocessing, feature extraction, feature selection (FS), and classification. Initially, data preprocessing eliminates missing and duplicate values. Subsequently, feature extraction is performed to address dimensionality issues, utilizing measures such as central tendency, qualitative variation, degree of dispersion, and symmetrical uncertainty. FS is optimized using the self-improved Aquila optimization approach. Finally, a hybridized model combining long short-term memory and a quantum neural network is trained using the selected features. An algorithm is devised to optimize the LSTM model's weights. Performance evaluation of the proposed approach is conducted against existing models using specific performance measures. Results: Far dataset-1, accuracy-96.69%, sensitivity-96.62%, specifity-96.77%, precision-96.03%, recall-97.86%, F1-score-96.84%, MCC-96.37%, NPV-96.25%, FPR-3.2%, FNR-3.37% and for dataset-2, accuracy-95.54%, sensitivity-95.86%, specifity-94.51%, precision-96.03%, F1-score-96.94%, MCC-93.03%, NPV-94.66%, FPR-5.4%, FNR-4.1%. The findings of this study contribute to improved CVD prediction by utilizing an efficient hybrid model with an optimized feature set. Discussion: We have proven that our method accurately predicts cardiovascular disease (CVD) with unmatched precision by conducting extensive experiments and validating our methodology on a large dataset of patient demographics and clinical factors. QNN and LSTM frameworks with Aquila feature tuning increase forecast accuracy and reveal cardiovascular risk-related physiological pathways. Our research shows how advanced computational tools may alter sickness prediction and management, contributing to the emerging field of machine learning in healthcare. Our research used a revolutionary methodology and produced significant advances in cardiovascular disease prediction.

Genome Editing Approaches Using Zinc Finger Nucleases (ZFNs) for the Treatment of Motor Neuron Diseases.

Motor neuron disorders are diseases that can be passed through generations by heredity or they occur due to spontaneous mutations in the gene. These are the disorders that weaken the connection between motor neurons and the muscles, due to this the coordination between the neurons and muscles gets disturbed and thereby the actions become abnormal, every year millions of people around the world suffer from these different types of motor neuron disorders. Till now there is no proper known treatment for this type of disorder, there is active research work going on to treat these diseases permanently. Some gene therapy treatments are giving promising results in the treatment of these diseases, specifically, genetic modification techniques are the front liners, and many types of nucleases are doing their work to replace the mutated gene with a functional one. Zinc finger nucleases (ZFNs) are one of them with good disease treatment potential with accurate and desirable effects. In this review, we note the complete information about ZFNs and their drawbacks along with their future prospective in gene therapy and also shortly with other types of nucleases-mediated gene therapies. There also some factors that influence the gene therapy treatment are also noted along with some detailed information.

Chimeric states induced by higher-order interactions in coupled prey-predator systems.

Higher-order interactions have been instrumental in characterizing the intricate complex dynamics in a diverse range of large-scale complex systems. Our study investigates the effect of attractive and repulsive higher-order interactions in globally and non-locally coupled prey-predator Rosenzweig-MacArthur systems. Such interactions lead to the emergence of complex spatiotemporal chimeric states, which are otherwise unobserved in the model system with only pairwise interactions. Our model system exhibits a second-order transition from a chimera-like state (mixture of oscillating and steady state nodes) to a chimera-death state through a supercritical Hopf bifurcation. The origin of these states is discussed in detail along with the effect of the higher-order non-local topology which leads to the rise of a distinct and dynamical state termed as "amplitude-mediated chimera-like states." Our study observes that the introduction of higher-order attractive and repulsive interactions exhibit incoherence and promote persistence in consumer-resource population dynamics as opposed to susceptibility shown by synchronized dynamics with only pairwise interactions, and these results may be of interest to conservationists and theoretical ecologists studying the effect of competing interactions in ecological networks.

Identification of structural fingerprints among natural inhibitors of HDAC1 to accelerate nature-inspired drug discovery in cancer epigenetics.

Histone deacetylase 1 (HDAC1), a class I HDAC enzyme, is crucial for histone modification. Currently, it is emerged as one of the important biological targets for designing small molecule drugs through cancer epigenetics. Along with synthetic inhibitors different natural inhibitors are showing potential HDAC1 inhibitions. In order to gain insights into the relationship between the molecular structures of the natural inhibitors and HDAC1, different molecular modelling techniques (Bayesian classification, recursive partitioning, molecular docking and molecular dynamics simulations) have been applied on a dataset of 155 HDAC1 nature-inspired inhibitors with diverse scaffolds. The Bayesian study showed acceptable ROC values for both the training set and test sets. The Recursive partitioning study produced decision tree 1 with 6 leaves. Further, molecular docking study was processed for generating the protein ligand complex which identified some potential amino acid residues such as F205, H28, L271, P29, F150, Y204 for the binding interactions in case of natural inhibitors. Stability of these HDAC1-natutal inhibitors complexes has been also evaluated by molecular dynamics simulation study. The current modelling study is an attempt to get a deep insight into the different important structural fingerprints among different natural compounds modulating HDAC1 inhibition.Communicated by Ramaswamy H. Sarma.

Maize leaf disease recognition using PRF-SVM integration: a breakthrough technique.

The difficulty of collecting maize leaf lesion characteristics in an environment that undergoes frequent changes, suffers varying illumination from lighting sources, and is influenced by a variety of other factors makes detecting diseases in maize leaves difficult. It is critical to monitor and identify plant leaf diseases during the initial growing period to take suitable preventative measures. In this work, we propose an automated maize leaf disease recognition system constructed using the PRF-SVM model. The PRFSVM model was constructed by combining three powerful components: PSPNet, ResNet50, and Fuzzy Support Vector Machine (Fuzzy SVM). The combination of PSPNet and ResNet50 not only assures that the model can capture delicate visual features but also allows for end-to-end training for smooth integration. Fuzzy SVM is included as a final classification layer to accommodate the inherent fuzziness and uncertainty in real-world image data. Five different maize crop diseases (common rust, southern rust, grey leaf spot, maydis leaf blight, and turcicum leaf blight along with healthy leaves) are selected from the Plant Village dataset for the algorithm's evaluation. The average accuracy achieved using the proposed method is approximately 96.67%. The PRFSVM model achieves an average accuracy rating of 96.67% and a mAP value of 0.81, demonstrating the efficacy of our approach for detecting and classifying various forms of maize leaf diseases.

ß1-Integrin-Mediated Uptake of Chondrocyte Extracellular Vesicles Regulates Chondrocyte Homeostasis.

Osteoarthritis (OA) is the most prevalent age-related degenerative disorder, which severely reduces the quality of life of those affected. Whilst management strategies exist, no cures are currently available. Virtually all joint resident cells generate extracellular vesicles (EVs), and alterations in chondrocyte EVs during OA have previously been reported. Herein, we investigated factors influencing chondrocyte EV release and the functional role that these EVs exhibit. Both 2D and 3D models of culturing C28I/2 chondrocytes were used for generating chondrocyte EVs. We assessed the effect of these EVs on chondrogenic gene expression as well as their uptake by chondrocytes. Collectively, the data demonstrated that chondrocyte EVs are sequestered within the cartilage ECM and that a bi-directional relationship exists between chondrocyte EV release and changes in chondrogenic differentiation. Finally, we demonstrated that the uptake of chondrocyte EVs is at least partially dependent on ß1-integrin. These results indicate that chondrocyte EVs have an autocrine homeostatic role that maintains chondrocyte phenotype. How this role is perturbed under OA conditions remains the subject of future work.

The Potential of Natural Products in the Management of Cardiovascular Disease.

Cardiovascular Disease (CVD) is one of the most prevalent diseases in the world, comprising a variety of disorders such as hypertension, heart attacks, Peripheral Vascular Disease (PVD), dyslipidemias, strokes, coronary heart disease, and cardiomyopathies. The World Health Organization (WHO) predicts that 22.2 million people will die from CVD in 2030. Conventional treatments for CVDs are often quite expensive and also have several side effects. This potentiates the use of medicinal plants, which are still a viable alternative therapy for a number of diseases, including CVD. Natural products' cardio-protective effects result from their anti-oxidative, anti-hypercholesterolemia, anti-ischemic, and platelet aggregation-inhibiting properties. The conventional therapies used to treat CVD have the potential to be explored in light of the recent increase in the popularity of natural goods and alternative medicine. Some natural products with potential in the management of cardiovascular diseases such as Allium sativum L., Ginkgo biloba, Cinchona ledgeriana, Ginseng, Commiphora mukul, Digitalis lanata, Digitalis purpurea L., Murrayakoenigii, Glycyrrhiza glabra, Polygonum cuspidatum, Fenugreek, Capsicum annuum, etc. are discussed in this article.

Exploring the Potential of Essential Oil from Plectranthus amboinicus Leaves against Breast Cancer: In vitro and In silico Analysis.

Plectranthus amboinicus leaves were subjected to hydrodistillation to obtain essential oil (EO). Phytochemical analysis using gas chromatography-mass spectrometry revealed a diverse range of compounds in the EO, with p-cymen-4-ol (18.57%) emerging as the most predominant, followed by isocaryophyllene (12.18%). The in vitro antiproliferative activity of EO against breast cancer was assessed in MCF-7 and MDA-MB-231 cell lines. The MTT assay results revealed that EO showed IC50 values of 42.25 µg/mL and 13.44 µg/mL in MCF-7 cells and 63.67 µg/mL and 26.58 µg/mL in MDA-MB-231 cells after 24 and 48 h, respectively. The in silico physicochemical and pharmacokinetic profiles of the EO constituents were within acceptable limits. Molecular docking was conducted to investigate the interactions between the constituents of the EO and protein Aromatase (PDB ID:3S79). Among the EO constituents, 4-tert-butyl-2-(5-tert-butyl-2-hydroxyphenyl)phenol (4BHP) exhibited the highest dock score of -6.580 kcal/mol when compared to the reference drug, Letrozole (-5.694 kcal/mol), but was slightly lesser than Anastrozole (-7.08 kcal/mol). Molecular dynamics simulation studies (100 ns) of the 4BHP complex were performed to study its stability patterns. The RMSD and RMSF values of the 4BHP protein complex were found to be 2.03 Å and 4.46 Å, respectively. The binding free energy calculations revealed that 4BHP displayed the highest negative binding energy of -43 kcal/mol with aromatase protein, compared to Anastrozole (-40.59 kcal/mol) and Letrozole (-44.54 kcal/mol). However, further research is required to determine the safety, efficacy, and mechanism of action of the volatile oil. Taking into consideration the key findings of the present work, the development of a formulation of essential oil remains a challenging task and novel drug delivery systems may lead to site-specific and targeted delivery for the effective treatment of breast cancer.

Prevalence and risk factors analysis of postpartum depression at early stage using hybrid deep learning model.

Postpartum Depression Disorder (PPDD) is a prevalent mental health condition and results in severe depression and suicide attempts in the social community. Prompt actions are crucial in tackling PPDD, which requires a quick recognition and accurate analysis of the probability factors associated with this condition. This concern requires attention. The primary aim of our research is to investigate the feasibility of anticipating an individual's mental state by categorizing individuals with depression from those without depression using a dataset consisting of text along with audio recordings from patients diagnosed with PPDD. This research proposes a hybrid PPDD framework that combines Improved Bi-directional Long Short-Term Memory (IBi-LSTM) with Transfer Learning (TL) based on two Convolutional Neural Network (CNN) architectures, respectively CNN-text and CNN audio. In the proposed model, the CNN section efficiently utilizes TL to obtain crucial knowledge from text and audio characteristics, whereas the improved Bi-LSTM module combines written material and sound data to obtain intricate chronological interpersonal relationships. The proposed model incorporates an attention technique to augment the effectiveness of the Bi-LSTM scheme. An experimental analysis is conducted on the PPDD online textual and speech audio dataset collected from UCI. It includes textual features such as age, women's health tracks, medical histories, demographic information, daily life metrics, psychological evaluations, and 'speech records' of PPDD patients. Data pre-processing is applied to maintain the data integrity and achieve reliable model performance. The proposed model demonstrates a great performance in better precision, recall, accuracy, and F1-score over existing deep learning models, including VGG-16, Base-CNN, and CNN-LSTM. These metrics indicate the model's ability to differentiate among women at risk of PPDD vs. non-PPDD. In addition, the feature importance analysis demonstrates that specific risk factors substantially impact the prediction of PPDD. The findings of this research establish a basis for improved precision and promptness in assessing the risk of PPDD, which may ultimately result in earlier implementation of interventions and the establishment of support networks for women who are susceptible to PPDD.

An Update on Pharmacological and Phytochemical Aspects of Costus pictus D. Don - A Promising Anti-diabetic Plant.

BACKGROUND: The genus Costus is the largest genus in the family Costaceae and encompasses about 150 known species. Among these, Costus pictus D. Don (Synonym: Costus mexicanus) is a traditional medicinal herb used to treat diabetes and other ailments. Currently, available treatment options in modern medicine have several adverse effects. Herbal medicines are gaining importance as they are cost-effective and display improved therapeutic effects with fewer side effects. Scientists have been seeking therapeutic compounds in plants, and various in vitro and in vivo studies report Costus pictus D. Don as a potential source in treating various diseases. Phytochemicals with various pharmacological properties of Costus pictus D. Don, viz. anti-cancer, anti-oxidant, diuretic, analgesic, and anti-microbial have been worked out and reported in the literature. OBJECTIVE: The aim of the review is to categorize and summarize the available information on phytochemicals and pharmacological properties of Costus pictus D. Don and suggest outlooks for future research. METHODS: This review combined scientific data regarding the use of Costus pictus D. Don plant for the management of diabetes and other ailments. A systematic search was performed on Costus pictus plant with anti-diabetic, anti-cancer, anti-microbial, anti-oxidant, and other pharmacological properties using several search engines such as Google Scholar, PubMed, Science Direct, Sci-Finder, other online journals and books for detailed analysis. RESULTS: Research data compilation and critical review of the information would be beneficial for further exploration of its pharmacological and phytochemical aspects and, consequently, new drug development. Bioactivity-guided fractionation, isolation, and purification of new chemical entities from the plant as well as pharmacological evaluation of the same will lead to the search for safe and effective novel drugs for better healthcare. CONCLUSION: This review critically summarizes the reports on natural compounds, and different extract of Costus pictus D. Don with their potent anti-diabetic activity along with other pharmacological activity. Since this review has been presented in a very interactive manner showing the geographical region of availability, parts of plant used, mechanism of action and phytoconstituents in different extracts of Costus pictus responsible for particular action, it will be of great importance to the interested readers to focus on the development of the new drug leads for the treatment of diseases.

A hybrid LBP-CNN with YOLO-v5-based fire and smoke detection model in various environmental conditions for environmental sustainability in smart city.

Smart, secure, and environmentally friendly smart cities are all the rage in urban planning. Several technologies, including the Internet of Things (IoT) and edge computing, are used to develop smart cities. Early and accurate fire detection in a Smart city is always desirable and motivates the research community to create a more efficient model. Deep learning models are widely used for fire detection in existing research, but they encounter several issues in typical climate environments, such as foggy and normal. The proposed model lends itself to IoT applications for authentic fire surveillance because of its minimal configuration load. A hybrid Local Binary Pattern Convolutional Neural Network (LBP-CNN) and YOLO-V5 model-based fire detection model for smart cities in the foggy scenario is presented in this research. Additionally, we recommend a two-part technique for extracting features to be applied to YOLO throughout this article. Using a transfer learning technique, the first portion of the proposed approach for extracting features retrieves standard features. The section part is for retrieval of additional valuable information related to the current activity using the LBP (Local Binary Pattern) protective layer and classifications layers. This research utilizes an online Kaggle fire and smoke dataset with 13950 normal and foggy images. The proposed hybrid model is premised on a two-cascaded YOLO model. In the initial cascade, smoke and fire are detected in the normal surrounding region, and the second cascade fire is detected with density in a foggy environment. In experimental analysis, the proposed model achieved a fire and smoke detection precision rate of 96.25% for a normal setting, 93.2% for a foggy environment, and a combined detection average precision rate of 94.59%. The proposed hybrid system outperformed existing models in terms of better precision and density detection for fire and smoke.

Exploring the Promising Role of Guggulipid in Rheumatoid Arthritis Management: An In-depth Analysis.

BACKGROUND: Guggulipid, an oleo-gum resin extracted from the bark of Commiphora wightii of the Burseraceae family, holds a significant place in Ayurvedic medicine due to its historical use in treating various disorders, including inflammation, gout, rheumatism, obesity, and lipid metabolism imbalances. OBJECTIVE: This comprehensive review aims to elucidate the molecular targets of guggulipids and explore their cellular responses. Furthermore, it summarizes the findings from in-vitro, in-vivo, and clinical investigations related to arthritis and various inflammatory conditions. METHODS: A comprehensive survey encompassing in-vitro, in-vivo, and clinical studies has been conducted to explore the therapeutic capacity of guggulipid in the management of rheumatoid arthritis. Various molecular pathways, such as cyclooxygenase-2 (COX-2), vascular endothelial growth factor (VEGF), PI3-kinase/AKT, JAK/STAT, nitric oxide synthase (iNOS), and NFκB signaling pathways, have been targeted to assess the antiarthritic and anti-inflammatory effects of this compound. RESULTS: The research findings reveal that guggulipid demonstrates notable antiarthritic and anti-inflammatory effects by targeting key molecular pathways involved in inflammatory responses. These pathways include COX-2, VEGF, PI3-kinase/AKT, JAK/STAT, iNOS, and NFκB signaling pathways. in-vitro, in-vivo, and clinical studies collectively support the therapeutic potential of guggulipid in managing rheumatoid arthritis and related inflammatory conditions. CONCLUSION: This review provides a deeper understanding of the therapeutic mechanisms and potential of guggulipid in the management of rheumatoid arthritis. The collective evidence strongly supports the promising role of guggulipid as a therapeutic agent, encouraging further research and development in guggulipid-based treatments for these conditions.

A hybrid cloud load balancing and host utilization prediction method using deep learning and optimization techniques.

Virtual machine (VM) integration methods have effectively proven an optimized load balancing in cloud data centers. The main challenge with VM integration methods is the trade-off among cost effectiveness, quality of service, performance, optimal resource utilization and compliance with service level agreement violations. Deep Learning methods are widely used in existing research on cloud load balancing. However, there is still a problem with acquiring noisy multilayered fluctuations in workload due to the limited resource-level provisioning. The long short-term memory (LSTM) model plays a vital role in the prediction of server load and workload provisioning. This research presents a hybrid model using deep learning with Particle Swarm Intelligence and Genetic Algorithm ("DPSO-GA") for dynamic workload provisioning in cloud computing. The proposed model works in two phases. The first phase utilizes a hybrid PSO-GA approach to address the prediction challenge by combining the benefits of these two methods in fine-tuning the Hyperparameters. In the second phase, CNN-LSTM is utilized. Before using the CNN-LSTM approach to forecast the consumption of resources, a hybrid approach, PSO-GA, is used for training it. In the proposed framework, a one-dimensional CNN and LSTM are used to forecast the cloud resource utilization at various subsequent time steps. The LSTM module simulates temporal information that predicts the upcoming VM workload, while a CNN module extracts complicated distinguishing features gathered from VM workload statistics. The proposed model simultaneously integrates the resource utilization in a multi-resource utilization, which helps overcome the load balancing and over-provisioning issues. Comprehensive simulations are carried out utilizing the Google cluster traces benchmarks dataset to verify the efficiency of the proposed DPSO-GA technique in enhancing the distribution of resources and load balancing for the cloud. The proposed model achieves outstanding results in terms of better precision, accuracy and load allocation.

Supraorbital neuralgia in leprosy.

Two patients presented with side-locked frontal head pain, involving the supraorbital nerve territory, with an associated hypopigmented macule. The clinical progress and nerve biopsy in one indicated leprosy. In endemic regions, supraorbital neuralgia may be caused by leprosy sometimes without other neurocutaneous markers.

Abdominal fat depots and their association with insulin resistance in patients with type 2 diabetes.

BACKGROUND: Asian-Indians show thin fat phenotype, characterized by predominantly central deposition of excess fat. The roles of abdominal subcutaneous fat (SAT), intra-peritoneal adipose tissue, and fat depots surrounding the vital organs (IPAT-SV) and liver fat in insulin resistance (IR), type-2 diabetes (T2D) and metabolic syndrome (MetS) in this population are sparsely investigated. AIMS AND OBJECTIVES: Assessment of liver fat, SAT and IPAT-SV by MRI in subjects with T2D and MetS; and to investigate its correlation with IR, specifically according to different quartiles of HOMA-IR. METHODS: Eighty T2D and the equal number of age sex-matched normal glucose tolerant controls participated in this study. Abdominal SAT, IPAT-SV and liver fat were measured using MRI. IR was estimated by the Homeostatic Model Assessment for Insulin Resistance (HOMA-IR). RESULTS: T2D and MetS subjects have higher quantity liver fat and IPAT-SV fat than controls (P = 9 x 10-4 and 4 x 10-4 for T2D and 10-4 and 9 x 10-3 for MetS subjects respectively). MetS subjects also have higher SAT fat mass (P = 0.012), but not the BMI adjusted SAT fat mass (P = 0.48). Higher quartiles of HOMA-IR were associated with higher BMI, W:H ratio, waist circumference, and higher liver fat mass (ANOVA Test P = 0.020, 0.030, 2 x 10-6 and 3 x 10-3 respectively with F-values 3.35, 3.04, 8.82, 4.47 respectively). In T2D and MetS subjects, HOMA-IR showed a moderately strong correlation with liver fat (r = 0.467, P < 3 x 10-5 and r = 0.493, P < 10-7), but not with SAT fat and IPAT-SV. However, in MetS subjects IPAT-SV fat mass showed borderline correlation with IR (r = 0.241, P < 0.05), but not with the BMI adjusted IPAT-SV fat mass (r = 0.13, P = 0.26). In non-T2D and non-MetS subjects, no such correlation was seen. On analyzing the correlation between the three abdominal adipose compartment fat masses and IR according to its severity, the correlation with liver fat mass becomes stronger with increasing quartiles of HOMA-IR, and the strongest correlation is seen in the highest quartile (r = 0.59, P < 10-3). On the other hand, SAT fat mass tended to show an inverse relation with IR with borderline negative correlation in the highest quartile (r = -0.284, P < 0.05). IPAT-SV fat mass did not show any statistically significant correlation with HOMA-IR, but in the highest quartile it showed borderline, but statistically insignificant positive correlation (P = 0.07). CONCLUSION: In individuals suffering from T2D and MetS, IR shows a trend towards positive and borderline negative correlation with liver fat and SAT fat masses respectively. The positive trend with liver fat tends to become stronger with increasing quartile of IR. Therefore, these findings support the theory that possibly exhaustion of protective compartment's capacity to store excess fat results in its pathological deposition in liver as ectopic fat.

Bibliometric analysis, physical properties, and experimental evaluation of additives-diesel ternary blends.

The availability of petroleum fuels is being challenged due to high demand and heavy dependence on imports, raising awareness of the need for cleaner alternatives. Urbanization, air quality, economic factors, and emissions limits motivate the search for alternative fuels compatible with compression ignition engines. A comprehensive bibliometric analysis further underscores the escalating worldwide research efforts in this critical domain. According to the existing literature, nitromethane and 2-ethoxy ethyl acetate have demonstrated superior physical and combustion properties compared to other additives. To explore their potential, a meticulous performance and emission analysis was conducted using a single-cylinder, 4-stroke VCR CI engine, employing varying proportions of 2-ethoxy ethyl acetate and a constant 2% blend of nitromethane, with EEA concentrations ranging from 5, 10, to 15% (v/v). This research delved into the influence of these diverse fuel blends on the performance of CI engines and exhaust characteristics within a compression ratio spectrum spanning from 17 to 20. The experimental findings revealed that ternary blends, although having a marginal impact on engine performance, exhibited lower emissions compared to pure diesel. The pinnacle of this investigation emerged with the EEA5NM2D93 blend, which yielded optimal results in terms of both performance and emission characteristics.

First-order transition to oscillation death in coupled oscillators with higher-order interactions.

We investigate the dynamical evolution of Stuart-Landau oscillators globally coupled through conjugate or dissimilar variables on simplicial complexes. We report a first-order explosive phase transition from an oscillatory state to oscillation death, with higher-order (2-simplex triadic) interactions, as opposed to the second-order transition with only pairwise (1-simplex) interactions. Moreover, the system displays four distinct homogeneous steady states in the presence of triadic interactions, in contrast to the two homogeneous steady states observed with dyadic interactions. We calculate the backward transition point analytically, confirming the numerical results and providing the origin of the dynamical states in the transition region. The results are robust against the application of noise. The study will be useful in understanding complex systems, such as ecological and epidemiological, having higher-order interactions and coupling through conjugate variables.

Nature to Nurture: Chitosan nanopowder a natural carbohydrate polymer choice of egg parasitoid, Trichogramma Japonicum Ashmead.

Chitosan is a naturally occurring linear biopolymer made of partially deacetylated acetyl and N-acetyl glucosamine. Its biocompatible physiochemical and biochemical properties are unmatched. Chitosan is transformed to nanopowder for use in agriculture and associated industries as nanocarriers for existing agrochemicals, ensuring the delayed release of chemicals with better solubility. Chitosan nanopowder applied to leaves or soil can activate a plant's natural defences against insects and pathogens. These studies were carried out because there is a potential for toxicological risk linked with products created utilizing nanotechnology, such as chitosan nanopowder, and therefore researchers felt the need to investigate this. The egg parasitoides Trichogramma Japonicum Ashmead was used as a low-cost biomarker to determine the potential toxicity of chitosan nanopowder. This study looked into the possibility that the adult stage of the egg parasitoids, Trichogramma Japonicum Ashmead might be negatively impacted by chitosan nanopowder (80-100 nm). Unpaired t-test statistical analysis has been carried out. According to the statistical analysis, host eggs exposed to chitosan nanopowder showed noticeably greater parasitization than the control group. As a natural supply of carbohydrate polymers chitosan nanopowder promotes the parasitization of T. Japonicum. The findings showed that T. Japonicum favoured chitosan nanopowder. Through Y dual choice, eight-arm multiple choice, and no-choice olfactometer experiments, as well as images from a stereozoom microscope and a scanning electron microscope (SEM), the data was thoroughly supported. Future agricultural applications of chitosan nanopowder will benefit from a deeper understanding of our findings.