Molecular genotyping analysis suggested high clonality and simple marker loci in Puccinia striiformis f. sp. tritici population in Uzbekistan, Central Asian region.

Puccinia striiformis f. sp. tritici (Pst) is a destructive pathogen that causes wheat stripe rust worldwide. Understanding the population structure and dynamic of pathogen spread is critical to fight against this disease. Limited information is available for the population genetic structure of Pst in Uzbekistan, Central Asia. In this study, we carried out surveillance from 9 different regions (Andijan, Fergana, Jizzakh, Kashkadarya, Namangan, Samarkand, Sirdaryo, Surkhandarya and Tashkent) of Uzbekistan to fill this gap. A total of 255 isolates were collected, which were genotyped using 17 polymorphic simple sequence repeats (SSR) markers. The DAPC analysis results showed no population subdivision in these sample-collected regions except Surkhandarya. Multilocus genotype (MLG) analysis, FST, and Nei's genetic distance results indicated a clonal population (rBarD ≤ 0.12) and merely three MLGs accounting for 70% of the overall population. MLG-34 was predominant in all Uzbekistan regions, followed by MLG-36 and MLG-42. Low genotypic diversity was observed in Andijan, Fergana, Jizzakh, Kashkadarya, Namangan, Sirdaryo, and Tashkent (0.56 to 0.76), compared with Samarkand (0.82) and Surkhandarya (0.97). No virulence against Yr5, Yr15, YrSp, and Yr26 was found, while resistant was overcome against Yr1, Yr2, Yr6, Yr9, Yr17, and Yr44 genes (Virulence frequency =≥75%). Comparative study results of Uzbekistan with previous Himalayan population were showed divergence from China and Pakistan populations. Further studies need to be conducted in a worldwide context to understand migration patterns; for that purpose, collaborative work is essential due to the Pst long-distance migration capability.

AiCarePWP: Deep learning-based novel research for Freezing of Gait forecasting in Parkinson.

BACKGROUND AND OBJECTIVES: Episodes of Freezing of Gait (FoG) are among the most debilitating motor symptoms of Parkinson's Disease (PD), leading to falls and significantly impacting patients' quality of life. Accurate assessment of FoG by neurologists provides crucial insights into patients' conditions and disease symptoms. This proposed strategy involves utilizing a Weighted Fuzzy Logic Controller, Kalman Filter, and Kaiser-Meyer-Olkin test to detect the gait parameters while walking, resting, and standing phases. Parameters such as neuromodulation format, intensity, duration, frequency, and velocity are computed to pre-empt freezing episodes, thus aiding their prevention. METHOD: The AiCarePWP is a wearable electronics device designed to identify instances when a patient is on the brink of experiencing a freezing episode and subsequently deliver a brief electrical impulse to the patient's shank muscles to stimulate movement. The AiCarePWP wearable device aims to identify impending freezing episodes in PD patients and deliver brief electrical impulses to stimulate movement. The study validates this innovative approach using plantar insoles with a 3D accelerometer and electrical stimulator, analysing data from the inertial measuring unit and plantar-pressure foot data to detect and predict FoG. RESULTS: Using a Convolutional Neural Network-based model, the study evaluated 47 gait features for their ability to differentiate resting, standing, and walking conditions. Variable selection was based on sensitivity, specificity, and overall accuracy, followed by Principal Component Analysis and Varimax rotation to extract and interpret factors. Factors with eigenvalues exceeding 1.0 were retained, and 37 features were retained. CONCLUSION: This study validates CNN's effectiveness in detecting FoG during various activities. It introduces a novel cueing method using electrical stimulation, which improves gait function and reduces FoG incidence in PD patients. Trustworthy wearable devices, based on Artificial Intelligence of Things (AIoT) and Artificial Intelligence of Medical Things (AIoMT), have been developed to support such interventions.

Distribution of Aspergillus species and risk factors for aspergillosis in mainland China: a systematic review.

Background: Aspergillus, a widespread fungus in the natural environment, poses a significant threat to human health by entering the human body via the airways and causing a disease called aspergillosis. This study comprehensively analyzed data on aspergillosis in published articles from mainland China to investigate the prevalence of Aspergillus, and risk factors, mortality rate, and underlying condition associated with aspergillosis. Methods: Published articles were retrieved from Google Scholar, PubMed, and Science Direct online search engines. In the 101 analyzed studies, 3558 Aspergillus isolates were meticulously collected and classified. GraphPad Prism 8 was used to statistically examine the epidemiology and clinical characteristics of aspergillosis. Results: Aspergillus fumigatus was prominently reported (n = 2679, 75.14%), followed by A. flavus (n = 437, 12.25%), A. niger (n = 219, 6.14%), and A. terreus (n = 119, 3.33%). Of a total of 9810 patients, 7513 probable cases accounted for the highest number, followed by confirmed cases (n = 1956) and possible cases (n = 341). In patients, cough emerged as the most common complaint (n = 1819, 18.54%), followed by asthma (n = 1029, 10.48%) and fever (1024, 10.44%). Of total studies, invasive pulmonary aspergillosis (IPA) was reported in 47 (45.53%) studies, exhibiting an increased prevalence in Beijing (n = 12, 25.53%), Guangdong (n = 7, 14.89%), and Shanghai (n = 6, 12.76%). Chronic pulmonary aspergillosis (CPA) was reported in 14 (13.86%) studies. Among the total of 14 studies, the occurrence of CPA was 5 (35.71%) in Beijing and 3 (21.42%) in Shanghai. Allergic bronchopulmonary aspergillosis (ABPA), was reported at a lower frequency (n = 8, 7.92%), Guangdong recorded a relatively high number (n = 3, 37.5%), followed by Beijing (n = 2, 25.0%), and Shanghai (n = 1, 12.5%). Percentage of death reported: IPA had the highest rate (n = 447, 68.87%), followed by CPA (n = 181, 27.88%) and ABPA (n = 14, 2.15%). Among the aspergillosis patients, 6220 had underlying conditions, including chronic lung disease (n = 3765, 60.53%), previous tuberculosis (n = 416, 6.68%), and organ transplant or organ failure (n = 648, 10.41%). Aspergillosis was also found in patients using corticosteroid therapy (n = 622, 10.0%). Conclusion: This review sheds light on the prevalence patterns of Aspergillus species, risk factors of aspergillosis, and gaps in surveillance that could be helpful for the control and treatment of aspergillosis and guide the researchers in future studies. Registration: This systematic review was prospectively registered on PROSPERO: Registration ID CRD42023476870.

Evaluation of agricultural non-point source pollution using an in-situ and automated photochemical flow analysis system.

Off-line leachate collection from agricultural landscapes cannot guarantee precise evaluation of agricultural non-point source (ANPS) due to geospatial variations, time, and transportation from the field to the laboratory. Implementing an in-situ nitrogen and phosphorous monitoring system with a robust photochemical flow analysis is imperative for precision agriculture, enabling real-time intervention to minimize non-point source pollution and overcome the limitations posed by conventional analysis in laboratory. A reliable, robust and in-situ approach was proposed to monitor nitrogen and phosphorous for determining ANPS pollution. In this study, a home-made porous ceramic probe and the frequency domain reflectometer (FDR) based water content sensors were strategically placed at different soil depths to facilitate the collection of leachates. These solutions were subsequently analyzed by in-situ photochemical flow analysis monitoring system built across the field to estimate the concentrations of phosphorus and nitrogen. After applying both natural and artificial irrigation to the agricultural landscape, at least 10 mL of soil leachates was consistently collected using the porous ceramic probe within 20 min, regardless of the depth of the soil layers when the volumetric soil water contents are greater than 19%. The experimental results showed that under different weather conditions and irrigation conditions, the soil water content of 50 cm and 90 cm below the soil surface was 19.58% and 26.08%, respectively. The average concentrations of NH4+-N, NO3--N, PO43- are 0.584 mg/L, 15.7 mg/L, 0.844 mg/L, and 0.562 mg/L, 16.828 mg/L and 0.878 mg/L at depths of 50 cm and 90 cm below the soil surface, respectively. Moreover, the comparison with conventional laboratory spectroscopic analysis confirmed R2 values of 0.9951, 0.9943, 0.9947 average concentration ranges of NH4+-N, NO3--N, and PO43-, showcasing the accuracy and reliability of robust photochemical flow analysis in-situ monitoring system. The suggested monitoring system can be helpful in the assessment of soil nutrition for precision agriculture.

Graphical Insight: Revolutionizing Seizure Detection with EEG Representation.

Epilepsy is characterized by recurring seizures that result from abnormal electrical activity in the brain. These seizures manifest as various symptoms including muscle contractions and loss of consciousness. The challenging task of detecting epileptic seizures involves classifying electroencephalography (EEG) signals into ictal (seizure) and interictal (non-seizure) classes. This classification is crucial because it distinguishes between the states of seizure and seizure-free periods in patients with epilepsy. Our study presents an innovative approach for detecting seizures and neurological diseases using EEG signals by leveraging graph neural networks. This method effectively addresses EEG data processing challenges. We construct a graph representation of EEG signals by extracting features such as frequency-based, statistical-based, and Daubechies wavelet transform features. This graph representation allows for potential differentiation between seizure and non-seizure signals through visual inspection of the extracted features. To enhance seizure detection accuracy, we employ two models: one combining a graph convolutional network (GCN) with long short-term memory (LSTM) and the other combining a GCN with balanced random forest (BRF). Our experimental results reveal that both models significantly improve seizure detection accuracy, surpassing previous methods. Despite simplifying our approach by reducing channels, our research reveals a consistent performance, showing a significant advancement in neurodegenerative disease detection. Our models accurately identify seizures in EEG signals, underscoring the potential of graph neural networks. The streamlined method not only maintains effectiveness with fewer channels but also offers a visually distinguishable approach for discerning seizure classes. This research opens avenues for EEG analysis, emphasizing the impact of graph representations in advancing our understanding of neurodegenerative diseases.

From lab to life: assessing the impact of real-world interactions on the operation of rapid serial visual presentation-based brain-computer interfaces.

Objective.Brain-computer interfaces (BCI) have been extensively researched in controlled lab settings where the P300 event-related potential (ERP), elicited in the rapid serial visual presentation (RSVP) paradigm, has shown promising potential. However, deploying BCIs outside of laboratory settings is challenging due to the presence of contaminating artifacts that often occur as a result of activities such as talking, head movements, and body movements. These artifacts can severely contaminate the measured EEG signals and consequently impede detection of the P300 ERP. Our goal is to assess the impact of these real-world noise factors on the performance of a RSVP-BCI, specifically focusing on single-trial P300 detection.Approach.In this study, we examine the impact of movement activity on the performance of a P300-based RSVP-BCI application designed to allow users to search images at high speed. Using machine learning, we assessed P300 detection performance using both EEG data captured in optimal recording conditions (e.g. where participants were instructed to refrain from moving) and a variety of conditions where the participant intentionally produced movements to contaminate the EEG recording.Main results.The results, presented as area under the receiver operating characteristic curve (ROC-AUC) scores, provide insight into the significant impact of noise on single-trial P300 detection. Notably, there is a reduction in classifier detection accuracy when intentionally contaminated RSVP trials are used for training and testing, when compared to using non-intentionally contaminated RSVP trials.Significance.Our findings underscore the necessity of addressing and mitigating noise in EEG recordings to facilitate the use of BCIs in real-world settings, thus extending the reach of EEG technology beyond the confines of the laboratory.

Short-term photovoltaic energy generation for solar powered high efficiency irrigation systems using LSTM with Spatio-temporal attention mechanism.

Solar irrigation systems should become more practical and efficient as technology advances. Automation and AI-based technologies can optimize solar energy use for irrigation while reducing environmental impacts and costs. These innovations have the potential to make agriculture more environmentally friendly and sustainable. Solar irrigation system implementation can be hampered by a lack of technical expertise in installation, operation, and maintenance. It must be technically and economically feasible to be practical and continuous. Due to weather and solar irradiation, photovoltaic power generation is difficult for high-efficiency irrigation systems. As a result, more precise photovoltaic output calculations could improve solar power systems. Customers should benefit from increased power plant versatility and high-quality electricity. As a result, an artificial intelligence-powered automated irrigation power-generation system may improve the existing efficiency. To predict high-efficiency irrigation system power outputs, this study proposed a spatial and temporal attention block-based long-short-term memory (LSTM) model. Using MSE, RMSE, and MAE, the results have been compared to pre-existing ML and a simple LSTM network. Moreover, it has been found that our model outperformed cutting-edge methods. MAPE was improved by 6-7% by increasing Look Back (LB) and Look Forward (LF). Future goals include adapting the technology for wind power production and improving the proposed model to harness customer behavior to improve forecasting accuracy.

Research on Assimilation of Unmanned Aerial Vehicle Remote Sensing Data and AquaCrop Model.

Taking the AquaCrop crop model as the research object, considering the complexity and uncertainty of the crop growth process, the crop model can only achieve more accurate simulation on a single point scale. In order to improve the application scale of the crop model, this study inverted the canopy coverage of a tea garden based on UAV multispectral technology, adopted the particle swarm optimization algorithm to assimilate the canopy coverage and crop model, constructed the AquaCrop-PSO assimilation model, and compared the canopy coverage and yield simulation results with the localized model simulation results. It is found that there is a significant regression relationship between all vegetation indices and canopy coverage. Among the single vegetation index regression models, the logarithmic model constructed by OSAVI has the highest inversion accuracy, with an R2 of 0.855 and RMSE of 5.75. The tea yield was simulated by the AquaCrop-PSO model and the measured values of R2 and RMSE were 0.927 and 0.12, respectively. The canopy coverage R2 of each simulated growth period basically exceeded 0.9, and the accuracy of the simulation results was improved by about 19.8% compared with that of the localized model. The results show that the accuracy of crop model simulation can be improved effectively by retrieving crop parameters and assimilating crop models through UAV remote sensing.

Ebselen analogues delay disease onset and its course in fALS by on-target SOD-1 engagement.

Amyotrophic lateral sclerosis (ALS) selectively affects motor neurons. SOD1 is the first causative gene to be identified for ALS and accounts for at least 20% of the familial (fALS) and up to 4% of sporadic (sALS) cases globally with some geographical variability. The destabilisation of the SOD1 dimer is a key driving force in fALS and sALS. Protein aggregation resulting from the destabilised SOD1 is arrested by the clinical drug ebselen and its analogues (MR6-8-2 and MR6-26-2) by redeeming the stability of the SOD1 dimer. The in vitro target engagement of these compounds is demonstrated using the bimolecular fluorescence complementation assay with protein-ligand binding directly visualised by co-crystallography in G93A SOD1. MR6-26-2 offers neuroprotection slowing disease onset of SOD1G93A mice by approximately 15 days. It also protected neuromuscular junction from muscle denervation in SOD1G93A mice clearly indicating functional improvement.

Demand-side load forecasting in smart grids using machine learning techniques.

Electrical load forecasting remains an ongoing challenge due to various factors, such as temperature and weather, which change day by day. In this age of Big Data, efficient handling of data and obtaining valuable information from raw data is crucial. Through the use of IoT devices and smart meters, we can capture data efficiently, whereas traditional methods may struggle with data management. The proposed solution consists of two levels for forecasting. The selected subsets of data are first fed into the "Daily Consumption Electrical Networks" (DCEN) network, which provides valid input to the "Intra Load Forecasting Networks" (ILFN) network. To address overfitting issues, we use classic or conventional neural networks. This research employs a three-tier architecture, which includes the cloud layer, fog layer, and edge servers. The classical state-of-the-art prediction schemes usually employ a two-tier architecture with classical models, which can result in low learning precision and overfitting issues. The proposed approach uses more weather features that were not previously utilized to predict the load. In this study, numerous experiments were conducted and found that support vector regression outperformed other methods. The results obtained were 5.055 for mean absolute percentage error (MAPE), 0.69 for root mean square error (RMSE), 0.37 for normalized mean square error (NRMSE), 0.0072 for mean squared logarithmic error (MSLE), and 0.86 for R2 score values. The experimental findings demonstrate the effectiveness of the proposed method.

Functionalized Single Crystal Perovskite Materials for SERS and Their Potential Detection Applications.

Recent advances in detection and diagnostic tools have improved understanding and identification of plant physiological and biochemical processes. Effective and safe Surface Enhanced Raman Spectroscopy (SERS) can find objects quickly and accurately. Raman enhancement amplifies the signal by 1014-1015 to accurately quantify plant metabolites at the molecular level. This paper shows how to use functionalized perovskite substrates for SERS. These perovskite substrates have lots of surface area, intense Raman scattering, and high sensitivity and specificity. These properties eliminate sample matrix component interference. This study identified research gaps on perovskite substrates' effectiveness, precision, and efficiency in biological metabolite detection compared to conventional substrates. This article details the synthesis and use of functionalized perovskites for plant metabolites measurement. It analyzes their pros and cons in this context. The manuscript analyzes perovskite-based SERS substrates, including single-crystalline perovskites with enhanced optoelectronic properties. This manuscript aims to identify this study gap by comprehensively reviewing the literature and using it to investigate plant metabolite detection in future studies.

Estimation of population median using bivariate auxiliary information in simple random sampling.

To estimate the unknown population median, several researchers have developed efficient estimators but these estimators are unable to provide efficient results in the existence of outliers. Keeping this point in view, the present work suggests enhanced class of robust estimators to estimate population median under simple random sampling in case of outliers/extreme observations. The suggested estimators are a mixture of bivariate auxiliary information and robust measures with the linear combination of deciles mean, tri-mean and Hodges Lehmann estimator. Mathematical properties associated with the improved class of robust estimators are evaluated in terms of bias and mean squared error. Moreover, the potentiality of our suggested estimators as compared to already available estimators is checked by considering two real-life data sets with outlier(s). In addition, a simulation study is also added in this regard. From theoretical and numerical findings, it is observed that our newly suggested estimators outperforms as compared to its competitors.

Thermal and solutal transport by Cattaneo-Christov model for the magnetohydrodynamic Williamson fluid with joule heating and heat source/sink.

This article scrutinizes the 2-dimensional and boundary layer flow of magnetohydrodynamic Williamson fluid flowing on a stretchable surface with variable viscosity. The thermal and solutal rates are examined through the Cattaneo-Christov model with Joule heating, heat source/sink, and chemical reaction. The authors are motivated to conduct this study because of its practical and scientific significance in various processes, including polymer processing, textile industries, food industries, solar energy, biomedical science, wind turbine blades, oil spill clean-up, metal rolling, and forging. With the mentioned assumptions, the partial differential equations are achieved by using the basic governing laws, including momentum law, energy law, and concentration law. This non-linear system of equations is transmuted into ordinary differential equations by taking similarity transformations. The main novelty behind the conduction of this work is the numerical technique, namely the 'Adams-Milne (Predictor-Corrector)' method along with the Runge-Kutta technique on Matlab software, which has not previously been studied by any researcher in the literature. The analytical solution of the determined equations is not possible due to their highly non-linear nature; therefore the multistep numerical method namely the 'Adams-Milne (Predictor-Corrector)' method, along with the Runge-Kutta technique is used to determine the numerical results. The outcomes are noted due to numerous parameters for velocity, temperature, and concentration profiles. The explanation of graphical and numerical results is discussed here. The graphical impression of the Williamson parameter reveals that the velocity and temperature curves diminish for higher inputs of this parameter. The movement of fluid shows the declining behavior for the Hartmann number and viscosity parameter. The solutal and thermal findings due to Cattaneo-Christov heat and mass relaxation coefficients mark the reducing behaviour in respective field. The rise in reaction coefficient decreases the mass distribution. The analyses of comparison of results are also presented here.

ALL classification using neural ensemble and memetic deep feature optimization.

Acute lymphoblastic leukemia (ALL) is a fatal blood disorder characterized by the excessive proliferation of immature white blood cells, originating in the bone marrow. An effective prognosis and treatment of ALL calls for its accurate and timely detection. Deep convolutional neural networks (CNNs) have shown promising results in digital pathology. However, they face challenges in classifying different subtypes of leukemia due to their subtle morphological differences. This study proposes an improved pipeline for binary detection and sub-type classification of ALL from blood smear images. At first, a customized, 88 layers deep CNN is proposed and trained using transfer learning along with GoogleNet CNN to create an ensemble of features. Furthermore, this study models the feature selection problem as a combinatorial optimization problem and proposes a memetic version of binary whale optimization algorithm, incorporating Differential Evolution-based local search method to enhance the exploration and exploitation of feature search space. The proposed approach is validated using publicly available standard datasets containing peripheral blood smear images of various classes of ALL. An overall best average accuracy of 99.15% is achieved for binary classification of ALL with an 85% decrease in the feature vector, together with 99% precision and 98.8% sensitivity. For B-ALL sub-type classification, the best accuracy of 98.69% is attained with 98.7% precision and 99.57% specificity. The proposed methodology shows better performance metrics as compared with several existing studies.

Repurposing of US-FDA-approved drugs as negative modulators of ubiquitin specific protease-7 (USP7).

Ubiquitin-specific protease7 (USP7) regulates the stability of the p53 tumor suppressor protein and several other proteins critical for tumor cell survival. Aberrant expression of USP7 facilitates human malignancies by altering the activity of proto-oncogenes/proteins, and tumor suppressor genes. Therefore, USP7 is a validated anti-cancer drug target. In this study, a drug repurposing approach was used to identify new hits against the USP7 enzyme. It is one of the most strategic approaches to find new uses for drugs in a cost- and time-effective way. Nuclear Magnetic Resonance-based screening of 172 drugs identified 11 compounds that bind to the catalytic domain of USP7 with dissociation constant (Kd) values in the range of 0.6-1.49 mM. These 11 compounds could thermally destabilize the USP7 enzyme by decreasing its melting temperature up to 9 °C. Molecular docking and simulation studies provided structural insights into the ligand-protein complexes, suggesting that these compounds bind to the putative substrate binding pocket of USP7, and interact with its catalytically important residues. Among the identified 11 hits, compound 6 (oxybutynin), 7 (ketotifen), 10 (pantoprazole sodium), and 11 (escitalopram) also showed anti-cancer activity with an effect on the expression of proto-oncogenes and tumor-suppressor gene at mRNA level in HCT116 cells. The compounds identified in this study can serve as potential leads for further studies.

Inversion of the Warburg Effect: Unraveling the Metabolic Nexus between Obesity and Cancer.

Obesity is a well-established risk factor for cancer, significantly impacting both cancer incidence and mortality. However, the intricate molecular mechanisms connecting adipose tissue to cancer cell metabolism are not fully understood. This Review explores the historical context of tumor energy metabolism research, tracing its origins to Otto Warburg's pioneering work in 1920. Warburg's discovery of the "Warburg effect", wherein cancer cells prefer anaerobic glycolysis even in the presence of oxygen, laid the foundation for understanding cancer metabolism. Building upon this foundation, the "reverse Warburg effect" emerged in 2009, elucidating the role of aerobic glycolysis in cancer-associated fibroblasts (CAFs) and its contribution to lactate accumulation in the tumor microenvironment, subsequently serving as a metabolic substrate for cancer cells. In contrast, within high-adiposity contexts, cancer cells exhibit a unique metabolic shift termed the "inversion of the Warburg effect". This phenomenon, distinct from the stromal-dependent reverse Warburg effect, relies on increased nutrient abundance in obesity environments, leading to the generation of glucose from lactate as a metabolic substrate. This Review underscores the heightened tumor proliferation and aggressiveness associated with obesity, introducing the "inversion of the Warburg effect" as a novel mechanism rooted in the altered metabolic landscape within an obese milieu. The insights presented here open promising avenues for therapeutic exploration, offering fresh perspectives and opportunities for the development of innovative cancer treatment strategies.

A novel immunoinformatics approach for developing a poly-epitope vaccine targeting foot and mouth disease virus, exploiting structural VP proteins.

Foot and mouth Disease virus (FMDV) belongs to Picornaviridae family and Aphthovirus genus causing Foot and mouth disease (FMD) in cloven-hoofed animals. FMDV, a prevalent virus induces both acute and chronic infections with high mutation rates resulting in seven primary serotypes, making vaccine development indispensable. Due to time and cost effectiveness of the immunoinformatic approach, we designed in-silico polyepitope vaccine (PEV) for the curtailment of FMDV. Structural and immunogenic parts of FMDV (Viral Protein 1 (VP1), Viral Protein 2 (VP2), Viral Protein 3 (VP3), and Viral Protein 4 (VP4)) were used to design the cytotoxic T Lymphocyte (CTL), Helper T Lymphocyte (HTL), and B-cell epitopes, followed by screening for antigenic, non-allergenic, Interferon (IFN) simulator, and non-toxicity, which narrowed down to 7 CTL, 3 HTL, and 12 B-cell epitopes. These selected epitopes were linked using appropriate linkers and Cholera Toxin B (CTB) adjuvant for immunological modulation. The physiochemical analyses followed by the structure prediction demonstrated the stability, hydrophilicity and solubility of the PEV. The interactions and stability between the vaccine, Toll like Receptor 3 (TLR3) and Toll like receptor 7 (TLR7) were revealed by molecular docking and Molecular Mechanics/Poisson Boltzmann Surface Area (MMPBSA) with high stability and compactness verified by MD simulation. In-silico immune simulation demonstrated a strong immunological response. FMDV-PEV (Poly epitope vaccine) will be effectively produced in an E. coli system, as codon optimization and cloning in an expression vector was performed. The effectiveness, safety, and immunogenicity profile of FMDV-PEV may be confirmed by further experimental validations.Communicated by Ramaswamy H. Sarma.

The structural and immunogenic parts of FMDV were targeted for developing VaccineCTB-adjuvant and appropriate linkers, enhancing the immunogenicity of the PEVMinimal deformability and high stability of Vaccine using immunoinformaticsStrong antigen-specific humoral and cellular immune response of potential vaccineResults indicating the effectiveness, safety, and immunogenicity of the PEV.

An efficient decision support system for leukemia identification utilizing nature-inspired deep feature optimization.

In the field of medicine, decision support systems play a crucial role by harnessing cutting-edge technology and data analysis to assist doctors in disease diagnosis and treatment. Leukemia is a malignancy that emerges from the uncontrolled growth of immature white blood cells within the human body. An accurate and prompt diagnosis of leukemia is desired due to its swift progression to distant parts of the body. Acute lymphoblastic leukemia (ALL) is an aggressive type of leukemia that affects both children and adults. Computer vision-based identification of leukemia is challenging due to structural irregularities and morphological similarities of blood entities. Deep neural networks have shown promise in extracting valuable information from image datasets, but they have high computational costs due to their extensive feature sets. This work presents an efficient pipeline for binary and subtype classification of acute lymphoblastic leukemia. The proposed method first unveils a novel neighborhood pixel transformation method using differential evolution to improve the clarity and discriminability of blood cell images for better analysis. Next, a hybrid feature extraction approach is presented leveraging transfer learning from selected deep neural network models, InceptionV3 and DenseNet201, to extract comprehensive feature sets. To optimize feature selection, a customized binary Grey Wolf Algorithm is utilized, achieving an impressive 80% reduction in feature size while preserving key discriminative information. These optimized features subsequently empower multiple classifiers, potentially capturing diverse perspectives and amplifying classification accuracy. The proposed pipeline is validated on publicly available standard datasets of ALL images. For binary classification, the best average accuracy of 98.1% is achieved with 98.1% sensitivity and 98% precision. For ALL subtype classifications, the best accuracy of 98.14% was attained with 78.5% sensitivity and 98% precision. The proposed feature selection method shows a better convergence behavior as compared to classical population-based meta-heuristics. The suggested solution also demonstrates comparable or better performance in comparison to several existing techniques.

Biosafety and toxicity assessment of transgenic cotton-harboring insecticide and herbicide tolerant genes on albino mice.

Introduction: Genetic engineering has revolutionized agriculture by transforming biotic and abiotic stress-resistance genes in plants. The biosafety of GM crops is a major concern for consumers and regulatory authorities. Methodology: A 14-week biosafety and toxicity analysis of transgenic cotton, containing 5 transgenes ((Cry1Ac, Cry2A, CP4 EPSPS, VIP3Aa, and ASAL)), was conducted on albino mice. Thirty mice were divided into three groups (Conventional, Non-transgenic, without Bt, and transgenic, containing targeted crop) according to the feed given, with 10 mice in each group, with 5 male and 5 female mice in each group. Results: During the study, no biologically significant changes were observed in the non-transgenic and transgenic groups compared to the control group in any of the study's parameters i.e. increase in weight of mice, physiological, pathological, and molecular analysis, irrespective of the gender of the mice. However, a statistically significant change was observed in the hematological parameters of the male mice, while no such change was observed in the female study group mice. The expression analysis, however, of the TNF gene increases many folds in the transgenic group as compared to the non-transgenic and conventional groups. Conclusion: Overall, no physiological, pathological, or molecular toxicity was observed in the mice fed with transgenic feed. Therefore, it can be speculated that the targeted transgenic crop is biologically safe. However, more study is required to confirm the biosafety of the product on the animal by expression profiling.