Objective: This paper aims to build an Effort Estimation Model for design, coding and testing Web Applications Based Fuzzy and Practical Models, which will help in optimizing the efforts in software development. Methods/Analysis: Soft computing approach is adopted and applied in the effort estimation and then compared with practical efforts in the development process with interpreting the historical data available for the existing functionalities. Findings: The effort estimation model presented in this paper focuses on the first level estimates published by Project Managers and the second level estimates presented by Project Leaders or Developers for any new requirement or enhancement for a web application built on 3-tier architecture using Microsoft technologies. The model considers the classification of each task as either Low or Medium or High complexity. These tasks pertain to the lowest level parts in bottom-up estimation. Efforts are estimated for designing, coding and unit testing of these tasks and the efforts are summed up to get the effort estimation for the higher level which is a feature to be implemented. Novelty/Improvement: The paper also discusses about the application of the effort estimation model by taking a new requirement as a case study. The first level estimates calculated using the effort estimation model has a variance of about 25% when compared with the actual effort. This variance is very much acceptable considering the fact that the first level estimates can be tolerable up to 35%. The proposed effort estimation tool would help the project managers to efficiently control the project, manage the resources effectively, and improve the software development process and also trade off analyses among schedule, performance, quality and functionality. Fuzzy logic is used to verify the claims made in efforts estimation. It is proposed a new relation between the number of data and efforts value membership for actual data. And converts it into crisp value in the range [0…1] which helps to classify the complexity of the task and subtask in the design, coding and testing phases.

Oxidative stress-mediated damage is often a downstream result of Parkinson’s disease (PD), which is marked by sharp decline in dopaminergic neurons within the nigrostriatal regions of the brain, accounting for the symptomatic motor deficits in patients. Regulating the level of oxidative stress may present a beneficial approach in preventing PD pathology. Here, we assessed the efficacy of a nicotinamide adenine phosphate (NADPH) oxidase (NOX) inhibitor, an exogenous reactive oxygen species (ROS) regulator synthesized by Aptabio therapeutics with the specificity to NOX-1, 2, and 4. Utilizing N27 rat dopaminergic cells and C57Bl/6 mice, we confirmed that the exposures of alpha-synuclein preformed fibrils (PFF) induced protein aggregation, a hallmark in PD pathology. In vitro assessment of the novel compound revealed an increase in cell viability and decreases in cytotoxicity, ROS, and protein aggregation (Thioflavin-T stain) against PFF exposure at the optimal concentration of 10nM. Concomitantly, the oral treatment alleviated motor deficits in behavioral tests, such as hindlimb clasping, rotarod, pole, nesting, and grooming test, via reducing protein aggregation, based on rescued dopaminergic neuronal loss. The suppression of NOX-1, 2, and 4 within the striatum and ventral midbrain regions including SNc contributed to neuroprotective/recovery effects, making it a potential therapeutic option for PD.

COVID-19 (coronavirus disease 2019) is a public health emergency of international concern caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). As of this time, there is no known effective pharmaceutical, phytopharmaceutical or traditional medicine for cure or prevention of COVID-19, although it is urgently needed. In this review, based on the current understanding of the disease molecular mechanisms of novel Coronavirus SARS-CoV-2 and its closest relative SARS-CoV and other human Coronaviruses, I have identified some naturally occurring plant based substances and Ayurvedic medicinal herbs that could feasibly be tested as a matter of urgency for prevention as well as therapeutic option for COVID-19 in India and other parts of the world. I conclude that dried rhizome of Curcuma longa L. i.e. turmeric, and its active ingredient curcumin may be effective in preventing as well as cure the COVID-19 pandemic due to its proven antiviral activities, this however need to be tested by appropriate clinical trials as research priority.

"The wisdom of crowd'' is so often observed in social discourses and activities around us. The manifestations of it are, however, so intrinsically embedded and behaviorally accepted that an elaboration of a social phenomenon evidencing such wisdom is often cheered as a discovery; or at least an astonishing fact. One such scenario is explored here, namely conceptualization and modeling of a food safety system, a system directly related to social cognition. Food safety is an area of concern these days. Models representing the food safety systems are recently published to study the effect of interactions between important entities of the system. For example, Knowles’s model finds conditions leading to a more efficient and dependable system of entities like consumers, regulators and stores with specific focus on regulators behavior and their impact on the food safety. The first contribution of this paper is reevaluation of Knowles’s model towards a more conscious understanding of ``the wisdom of crowd'' effects on inspection and consuming behaviors. The second contribution is augmenting of the model with social networking capabilities, which acts as a medium to spread information about stores and help consumers find stores which are not contaminated. Simulation results reveal that stores’ respecting social cognition improve effectiveness of the food safety system for consumers and stores both. Simulation findings also reveals that an active society has a capability to self-organize effectively even in the absence of any regulatory compulsion.

Long COVID is a phenomenon in which individuals experience persistent symptoms after recovering from COVID-19. The symptoms are discovered to be unique for every individual and can affect multiple organs systems in the body. This study aims to identify effective natural remedies for long COVID by analyzing hub genes associated with the symptoms of the condition and evaluating the repurposed drugs catered and used for treating the symptoms of long COVID. The most common and prevalent symptoms of long COVID were identified; Fatigue, Shortness of Breath, Loss of Smell, Headache, Brain Fog, Chest Pain, Insomnia, Heart Palpitations, Dizziness, Joint Pain, Depression, Anxiety, Tinnitus, Loss of Appetite. Hub genes for each of the symptoms provided an insight on the key biological pathways of the symptom. Repurposed drugs identified, provided the template to identify the natural compounds with similar structure as a potential therapeutic drug. The natural compounds were retrieved using fingerprint search of the repurposed drugs from the NPASS Database. The findings of this study suggest several natural remedies for each symptom based on the molecular docking of the hub gene and natural compound using iGEMDOCK. The identified natural remedies may hold promise in treating long COVID, but further research is required to explore the efficacy and effectiveness of the proposed natural compounds. The results of the study pose important implications for the development of effective treatments for long COVID.

It is one of the central goals of cognitive neuroscience to understand how structure and function relate in the brain. We review how cognitive function characterization has been approached in the past. In addition, we examine the ongoing efforts, as well as the implications for the future. Clinical studies on patients with lesions have provided key insights into the relationship between brain areas and behavior over the past century. We describe cognitive function according to localization considering these early efforts for characterization. We chose a perceptual-cognitive function, namely body perception, to describe our current efforts. Using body perception as an example, we summarize contemporary techniques. Finally, we outline the trajectory of current progress into the future and discuss the implications for clinical and basic neuroscience.

DC-connected parallel inverter systems are gaining popularity in industrial applications. However, such parallel systems generate excess current ripple (harmonics) at the DC-link due to harmonic interactions between the inverters in addition to the harmonics from the PWM switching. These DC-link harmonics cause the failure of fragile components such as DC-link capacitors. This paper proposes an interleaving scheme to minimize the current harmonics induced in the DC-link of such a system. The results show that when the carrier waves of the two inverters are phase-shifted by 90° angle, the maximum high-frequency harmonic ripple cancellation occurs, which reduces the overall RMS value of the DC-capacitor current.The outcome of this proposed solution is a cost-effective DC-harmonics mitigating strategy for the industrial designers to practically configuring multi-inverter systems, even when most of the drives are not operating at rated power levels. Experimental and simulation results presented in this paper verify the effectiveness of the proposed carrier-based phase-shifting scheme for two different configurations of common DC connected multi-converter systems.

Rice (Oryza sativa L.) supplies nourishment to about half of the population of the world's inhabitants. Of them, more than 2 billion people suffer from "hidden hunger" in which they are unable to meet the recommended nutrients or micronutrients from their daily dietary intake. Bio-fortification refers to developing micronutrient-rich diet foods using traditional breeding methods and modern biotechnology, a promising approach to nutrition enrichment as part of an integrated strategy for food systems. To improve the profile of rice grain for the biofortification related traits, understanding the genetics of important biofortification traits is required. Moreover, these attributes are quantitative in nature and are influenced by several genes and environmental variables. In the course of past decades, several endeavours such, as finding the important quantitative trait loci (QTLs) for improving the nutrient profile of rice seeds were successfully undertaken. In this review, we have presented the information regarding the QTLs identified for the biofortification traits in the rice.

Municipal solid waste management is a growing worldwide challenge. Landfill mining is a promising technique to reduce MSW landfills. In the present study, arbitrary embankments made of different MSW fractions are analyzed to find the safety factor of their slopes. Properties of Municipal solid waste fines (MSWF), Shredded MSW, MSW Incineration Ash, and MSW having particles less than 35 mm are used in the analysis. For doing the same, finite element-based application 'Plaxis-2D' is used. The safety of slopes is assessed while the water table is inserted, and a uniform load is applied to the embankment to establish how well they will perform in critical situations. It was found that the slope made of shredded MSW is most safe due to its low density and high friction angle, while the least safe slope is made of MSW having less than 35 mm fractions.

Wheat is the essential constituent of cereal-based diets and one of the most significant sources of calories. However, there is an inherently low bioavailability of proteins, mineral, and vitamins in modern wheat grains. Biofortification has earned recognition as an outstanding approach, at the same time as a cure for world hunger. The developments in the identifications of quantitative trait loci (QTL) analysis and understanding of the physiological and molecular basis of QTLs controlling the biofortification traits in wheat has revealed new horizons for the improvement of modern wheat varieties. Within this review, we have compiled the information from the studies carried out in wheat using QTL mapping methodologies that is among the best methods for biofortification traits. We hope this review will serve as an essential reference for the QTLs identified for the several important biofortification traits in wheat.

Lesion studies are crucial in establishing brain-behavior relationships, and accurately segmenting the lesion represents the first step in achieving this. Manual lesion segmentation is the gold standard for chronic strokes. However, it is labor-intensive, subject to bias, and limits sample size. Therefore, our objective is to develop an automatic segmentation algorithm for chronic stroke lesions on T1-weighted MR images. Methods: To train our model, we utilized an open-source dataset: ATLAS v2.0 (Anatomical Tracings of Lesions After Stroke). We partitioned the dataset of 655 T1 images with manual segmentation labels into five subsets and performed a 5-fold cross-validation to avoid overfitting of the model. We used a deep neural network (DNN) architecture for model training. Results: To evaluate the model performance, we used three metrics that pertain to diverse aspects of volumetric segmentation including shape, location, and size. The Dice similarity coefficient (DSC) compares the spatial overlap between manual and machine segmentation. The average DSC was 0.65 (0.61- 0.67; 95% bootstrapped CI). Average symmetric surface distance (ASSD) measures contour distances between the two segmentations. ASSD between manual and automatic segmentation was 12mm. Finally, we compared the total lesion volumes and the Pearson correlation coefficient (ρ) between the manual and automatically segmented lesion volumes, which was 0.97(p-value < 0.001). Conclusions: We present the first automated segmentation model trained on a large multicentric dataset. This model will enable automated on-demand processing of MRI scans and quantitative chronic stroke lesion assessment.

Wound healing is a process through which skin maintains itself. Once a wound occurs, the inflammatory and proliferative stages are instigated in reaction to injury. It is established that wound restorative comprises four stages including haemostasis, inflammation, proliferation, and remodeling. The amelioration of wound healing is very challenging as tumors can develop at the site of chronic injury. There are numerous plants, plant extracts and plant based natural products were widely used by tribal communities from ancient times for the treatment of cuts, burns, scars, burns and wounds. The therapeutic potential of these plants is recognized due to the presence of phytomolecules such as phenolic compounds, flavonoids, triterpenoids, saponins, tannins, alkaloids and glycosides. The plant used for the treatments of wound healing includes Achillea millefolium, Andrographis paniculata, Boswellia sacra, Calendula officinalis, Crocus sativus, Curcuma longa, Ehretia laevis, Ehretia microphylla, Glycyrrhiza glabra, Malva sylvestris, Rosmarinus officinalis and Salvia officinalis. This assemblage comprises the structures of phytomolecules isolated from the different extracts of these plants, mechanistic insights and important key findings responsible for wound healing. The mechanistic insights involved in wound healing are similar to cytotoxic, anti-inflammatory and antioxidant agents such as ROS generation, DNA fragmentation and western blotting. This review article is an effort to bridge the gaps in the prevailing literature and thus offers gigantic scope for researchers and academicians betrothed in validation of the customary claims and development of safer and efficient and worldwide recognized natural potential candidates as drugs for healing of wounds, burns and cuts.

The diagnosis of Cardio-Vascular diseases (CVD) is highly dependent on analysis of ECG signals. ECG analysis can be helpful in estimating the underlying cause and condition of heart in cardiac abnormality. The effectiveness of ECG signal analysis in detection of CVDs is widely accepted by professional healthcare service provider. Many algorithms have been proposed but almost all of them have some kind of limitations, and these limitations largely influence the effectiveness of ECG analysis. The performed research work is dedicated for design of unique self-organizing maps (SOMs) based neural network for classification of arrhythmia according to a particular ECG signal, the generation of SOMs is based on the certain unique signatures of ECG signals and have potential to classify different cardiac conditions. For extraction of unique features from ECG signals, we have proposed to use Fast Discrete Stockwell Transform (FDST). Since the proposed technique is a result of combining two different techniques hence called as hybrid technology. The purpose of using FDST is to identify unique signatures of ECG signals in a more improved manner than existing one, the term improved is used because it has several advantages over existing techniques such as wavelet and Fourier Transform based methods. Results obtained from the implementation of the technique are capable in visualizing the ECG sinus rhythm and arrhythmia conditions in form of unique SOM for each associated arrhythmia condition. This unique SOM based classification makes them ideal for being used as a diagnostic tool. This ability of arrhythmia classification using FDST and SOMs makes the technique unique and useful providing valuable information about patient condition. Using proposed technology a portable diagnosis tool for monitoring of patient at their site may be facilitated later, that will improve the quality of life of the patient by diagnosing cardiac condition.

Around the world, every year, about 17 million people death cause happen due to CardioVascular Diseases (CVD). As per clinical records, primarily sufferers exhibit myocardial infarctions and Heart Failures (HF). Creatinine is a Musculo - skeletal waste product. The kidneys filter creatinine from the blood and excrete it through the urine in a healthy body. High creatinine levels can suggest renal problems. Elevated Serum Creatinine (SC) has been well established in the HF. Patients’ electronic medical records can be used to quantify symptoms and other related clinical laboratory test values, which would then be utilized to direct biostatistics exploration to uncover patterns and associations that doctors would otherwise miss. The latest American Heart Association guidelines for 1500 mg/d sodium tend to be sufficiently relevant for patients with stage A and B with HF. In this article, we used a dataset of the year 2015 of heart patients records of 299 patients. The present paper used the data analytic and statistical tools to verify the significant differences between alive and dead patients’ SC and Serum Sodium (SS). It also demonstrates the impact of significant features on abnormal SC and SS on the Survival-Status levels. The Age-Group feature, which is derived from age attribute and, Ejection Fraction (EF), anemia, platelets, Creatinine Phosphokinase (CPK), Blood-Pressure (BP), gender, diabetes, and smoking-status were utilized to determine the potential contributing features to mortality with Cox regression model. The Kaplan Meier plot was used to investigate the overall pattern of survival concerning age-group. During pre-processing of the dataset, Age and SS were removed due to multicollinear features during performing machine learning algorithms experiments. This paper also predicted patients’ survival, age group, and gender using supervised machine learning classifiers. Detection of significant features would help in making informed decisions to balance the lifestyle of heart patients. The author revealed that the patient’s follow-up months, as well as SC, EF, CPK, and platelets, are sufficient key features to predict heart patient survival using Random Forest (RF) stratified 10-fold CV method with accuracy (96%) with 5% Standard Deviation (SD) from medical records dataset. We identified the age-group and gender of the patient, and the RF model outperformed others with the best accuracy 96% and 94% in both cases having 11% SD. Also, prominent features such as CPK, SC, follow-up month, platelets, and ejection were found to be significant factors in predicting the patient’s age-group. Smoking habits, CPK, platelets, follow-up month, and SC of each patient were discovered to be significant predictors of patient gender. The hypothetical study proved that SC and SS making substantial differences in the survival of patients (p < 0.05) and failed to reject that anemia, diabetes, and BP making a significant impact on the creatinine and sodium of each patient (p > 0.05). With χ2(1) = 8.565, the Kaplan Meier plot revealed that mortality was high in the extremely elder age-group. The finding has possible effects on clinical practice and becomes a new medical support system when predicting whether a patient can survive a heart attack or not. The doctor should primarily concentrate on follow-up month, SC and EF, CPK, and platelet count since the aim is to understand whether a patient survives after HF.

Glioblastoma multiforme (GBM) is classified as WHO grade IV Astrocytoma & is the most common highly aggressive form of primary brain tumor. Garde IV tumor are highly recurrent even after treatment, with patient survival rate is less than two years from the time of diagnosis. This might be due to overexpression of one of the factors such as ATP-binding cassette transporters (ABC transporters) responsible for drug resistance. ABCC transporter family a member of ABC transporter was found to mostly responsible for multi drug resistance (MDR) in cancer cells. On the other hand, heterogeneous nuclear ribonucleoprotein (hnRNP's) an alternative splicing factor play different role in various cellular process such as nucleic acid metabolism, transcription and translation regulation, among them hnRNPA1 is best studied and its aberrant deregulation favor development of cancer. This study was focused on to study the function of hnRNPA1 in the expression analysis of ABCC transporter (responsible for MDR) in glioma cell lines. The expression of ABCC transporter (ABCC4 and ABCC6) gene was examined in two glioma cell line i.e. U87MG and T98G in normal and knockdown two alternative variants of hnRNPA1 by Quantitative Realtime PCR and Reverse Transcription PCR. We found that ABCC4 was significantly overexpressed in hnRNPA1 Variant 2 knockdown cells (si hnRNPA1 V2) in U87 (3-fold) and in T98G (18.34-fold), While hnRNPA1 Variant 1 knockdown cells (hnRNPA1 V1i) does not shows any significant effect. Further, the expression of ABCC6 was decreased in both hnRNPA1 V1i (0.40-fold) and hnRNPA1 V2i (0.48-fold) in U87. Reverse transcription-based result was complemented with normal PCR based detection strategy after running in agarose gel for U87MG and T98G glioma cell line. Finally, this result indicates that hnRNPA1 an alternative splicing factor regulate the expression of ABCC4 and ABCC6 transporter which are responsible for multiple drug resistance in cancer. This information will help in future for the development of an alternative method for the treatment of drug resistance cases in brain tumor and other tumors by targeting hnRNPA1 splicing factor.

Trash mulches are very effective in preventing soil erosion; reduce sediment transport rate, runoff rate and increasing infiltration. The study was carried out with the objectives to observe the sediment outflow from sugar cane leaf (trash) mulch treatments at selected land slopes under simulated rainfall conditions by using rainfall simulator of size 10 m × 1.2 m × 0.5 m with the locally available soil material collected from Pantnagar. In the present study, trash mulches with different quantities were selected to observe the effect of mulching in soil loss reduction. The quantity of mulch was taken as, 6 t/ha, 8 t/ha and 10 t/ha, three rainfall intensities viz. 11cm/h, 13cm/h and 14.65cm/h at 0%, 2% and 4% land slopes were selected. The duration of rainfall was fixed (10 minutes) for every mulch treatment. The total runoff volume was found to be varying with different mulch rates for particular rainfall input and land slope. The runoff distribution pattern was observed to be increasing with the increase in land slope. The average sediment concentration (SC) and outflow was found to be increasing with the increasing land slope, but SC and outflow decreased with increasing mulch rate for particular land slope and rainfall intensity. The SOR (SOR) for no mulch treated land was higher as compared to trash mulch treated lands. Mathematical relationships were developed for relating SOR, SC, land slope and rainfall intensity for a particular mulch treatment. It was observed that values of SOR and average SC had a good correlation with rainfall intensity and land slope for each mulch treatment. The correlation coefficients of developed models were found to be more than 90%.

Maize (Zea mays subsp. mays) is the staple food crop in the world. In this study, multi-layer perceptron (MLP), support vector machine (SVM), genetic algorithm-based multi-layer perceptron (MLP-GA), and genetic algorithm-based support vector machine (SVM-GA) hybrid artificial intelligence algorithms were used for the prediction of drought tolerance and stress tolerance indices in teosinte maize lines. Correspondingly, the gamma test technique was applied to determine efficient input and output vectors. The potential of developed models was evaluated based on statistical indices and graphical representation. Results of gamma test based on the least value of gamma and standard error indices show that day of anthesis (DOA), day of silking (DOS), yield index (YI), and gross yield per plant (GYP) information vector arrangements were determined as efficient information vector combination for drought-tolerant index (DTI) as well as the stress-tolerant index (STI). The results of MLP, SVM, MLP-GA, and SVM-GA algorithms were compared based on statistical indices and visual interpretation that have satisfactory for prediction of the drought-tolerant index and stress-tolerant index in maize crop. It has also seemed that genetic algorithm-based hybrid models (MLP-GA and SVM-GA) were found a better prediction of the drought-tolerant index and stress-tolerant index in maize crop. Similarly, the SVM-GA model has the highest potential to forecast the DTI and STI in maize crops as compared to MLP, SVM, MLP-GA models.

Alcohol use disorder (AUD) is a chronic and relapsing biopsychosocial condition that leads to a wide range of medical, psychological, social, economic, and personal issues. The study investigated the predictors of quality of life (QoL) among male patients seeking treatment for AUD based on attention, cognitive instability, motor impulsiveness, perseverance, and self-control dimensions of trait impulsivity. The study also investigated the potential mediating effect of anxiety and alcohol use severity in this relationship. After an initial assessment, participants underwent comprehensive evaluations, including sociodemographic data, clinical history, and QoL domains. Evaluations also covered AUD severity, impulsivity dimensions, cognitive functions, craving intensity, depression, anxiety and self-efficacy. Multiple linear regressions were employed to identify associations between independent variables and QoL. Attention dimension of impulsivity and anxiety symptoms significantly predicted physical health QoL. Self-control dimension and severity of alcohol use disorder predicted psychological QoL. Indirect effect of attention dimension on physical health was significant [effect =-1.082,95% CI (-2.008, -0.3598)].The significance of impulsivity in influencing QoL for individuals with AUD has profound clinical implications. These findings underscore the importance of addressing anxiety and impulsivity in managing AUD, given their considerable impact, particularly on QoL, ultimately shaping treatment outcomes.

SARS-CoV-2 is a newly emerging, highly transmissible, and pathogenic coronavirus in humans, which has caused global public health emergency and economic crisis. To date, millions of infections and thousands of deaths have been reported worldwide, and the numbers continue to rise. Currently, there is no specific drug or vaccine against this deadly virus; therefore, there is a pressing need to understand the mechanism through which this virus enters the host cell. Viral entry into the host cell is a multistep process in which SARS-CoV-2 utilizes the receptor binding domain of the spike glycoprotein (S) to recognize ACE2 receptors on the human cells; this initiates host cell entry by promoting viral-host cell membrane fusion through large scale conformational changes in the S protein. Receptor recognition and fusion are critical and essential steps of viral infections and are key determinants of the viral host range and cross-species transmission. In this review, we summarize the current knowledge on the origin and evolution of SARS-CoV-2 and the roles of key viral factors. We discuss the RNA dependent RNA polymerase structure of SARS-CoV-2, its significance in drug discovery, and explain the receptor recognition mechanisms of coronaviruses. We provide a comparative analysis of the SARS-CoV and SARS-CoV-2 S proteins, receptor-binding specificity, and discuss the differences in their antigenicity based on biophysical and structural characteristics.

Multiple lines of research have led to the hypothesis that antimicrobial peptides (AMPs) are an important component of the innate immune response, playing a vital role in the defense against a wide range of infectious diseases. In this review we explore the occurrence and availability of antimicrobial proteins and peptides across various species, highlighting their natural abundance and evolutionary significance. The design of AMPs has been driven by the identification of key structural and functional features, which are essential for optimizing their antimicrobial activity and reducing toxicity to host cells. We discuss various approaches, including rational design, high-throughput screening, and computational modeling, that have been employed to develop novel AMPs with enhanced efficacy. A particular focus is given to the identification and characterization of peptide fragments derived from naturally occurring host defense proteins, which offer a promising avenue for the discovery of new AMPs. The incorporation of artificial intelligence (AI) and machine learning (ML) tools into AMP research has further accelerated the identification, optimization, and application of these peptides. This review also discusses the current status and therapeutic potential of AMPs, emphasizing their role in addressing the growing issue of antibiotic resistance. The conclusion highlights the importance of continued research and innovation in AMP development to fully harness their potential as next-generation antimicrobial agents.

Acquired Immunodeficiency Syndrome (AIDS) which is chiefly originated by a retrovirus named Human Immunodeficiency Virus (HIV), has influenced about 70 million populations worldwide. Even though several advancements have been invented in the field of antiretroviral combination therapy, still HIV has become the dominant reason for death in South Africa, for example. The current antiretroviral therapies have achieved success in providing instant HIV suppression but with countless undesirable adverse effects. In the present day, the biodiversity of the plant kingdom is being explored by several researchers for the discovery of potent anti-HIV drugs with different mechanisms of action. The primary challenge is to afford a treatment that is free from any sort of risk of drug resistance and serious side effects. Hence, there is a strong demand to evaluate the drugs obtained from natural plants as well as the synthetic derivatives that have been derived from the natural compounds by various chemical reactions. Several plants such as Andrographis paniculata, Dioscorea bulbifera, Aegle marmelos, Wistaria floribunda, Lindera chunii, Xanthoceras sorbifolia and others have displayed significant anti-HIV activity showing more potent anti-HIV activity along with their structures, SARs & important key findings.

ABSTRACT Underwater mines may destroy friendly submarines and marine habitats, compromising nonmilitary security. Finding credible information is vital to avoiding scams and spotting hazards. This article employs Supervised Machine literacy models to appropriately categorize subsea items like mines or gems based on Sonar sounds. Python constructs three double classifier models using Gorman and Sejnowski (1988) training data. Through the extraction of key aspects from the Sonar signals, such as spectral components and intensity, the models are able to match newly obtained data with the gemstone order or mine. Delicacy decreases the chance of misidentifications in nonmilitary activities. Machine literacy offers full validation that accommodates real-world Sonar data noise and unpredictability. Based on test results, the highest performing algorithm will deliver the required ratio of accuracy to perceptivity for the bracket task. Central keywords: vaticination model, bracket algorithms, supervised machine literacy, SONAR, and aquatic mining emphasize particular foundations. Improved sophistication in spotting undersea Mines let nonmilitary defence systems respond to proven threats only, preventing friendly submarines or sea life from unnecessary damage owing to false duplicity. This work is a promising start to real-world mine detection devices. Further developments may incorporate greater training data sets, real-time testing on subsea platforms equipped with integrated sonar, and optimization for the discovery of new mine-types. Overall, machine learning and Sonar vision decrease signal fluctuation and noise, making it easier to determine accurate boundaries for non-military applications and environmental protection. The recommended automated discovery technique must balance computational efficiency, generalizability, and delicacy. Prior a real crime, new quality control and verification procedures could boost trust even further.

The current study was planned to simulate runoff due to the snowmelt in the Lidder River catchment of Himalayan region under climate change scenarios. A basic degree-day model, Snowmelt-Runoff Model (SRM) was utilized to assess the hydrological consequences of change in climate. The SRM model performance during the calibration and validation was assessed using volume difference (Dv) and coefficient of determination (R2). The Dv was found as 11.7, -10.1, -11.8, 1.96, and 8.6 during 2009-2014, respectively, while the R2 is 0.96, 0.92, 0.95, 0.90, and 0.94, respectively. The Dv and R2 values indicating that the simulated snowmelt runoff has a close agreement with the observed value. The simulated findings were also assessed under the different scenarios of climate change: a) increases in precipitation by +20 %, b) temperature rise of +2 °C, and c) temperature rise of +2 °C with a 20 % increase in snow cover. In scenario "b", the simulated results showed that runoff increased by 53 % in summer (April–September). In contrast, the projected increased discharge for scenarios "a" and "c" was 37 % and 67 %, respectively. In high elevation data-scarce mountain environments, the SRM is efficient in forecasting future water supplies due to the snowmelt runoff.

Background: India leads in road traffic accident (RTA) deaths, according to the 2018 World Road Statistics. Most unidentified patients exhibit poor Glasgow Coma Scale (GCS) scores ,often less than eight , heightened trauma severity, and suboptimal outcomes despite standard treatment. Our study concentrates on individuals involved in hit-and-run RTAs or found unconscious and brought to the emergency department and their final outcome.Case Presentation: We are presenting a case series featuring five patients admitted to our hospital for treatment. At the time of admission, all these patients' identities were unknown.We did not face any legal challenges in managing the unidentified patients as we followed preestablished protocols. We report a case series of five male patients who had been admitted after road traffic accidents. All of them regained consciousness after conservative management. These complex cases frequently led to extended hospital stays, underscoring the intricacies of providing care to individuals with uncertain medical histories. All of the patients were treated and successfully discharged. Conclusion: Addressing challenges with unidentified adult patients requires proactive measures like staff training and improved resources. In a developing nation, prioritizing public awareness on road safety, prehospital trauma care, and enhancing healthcare delivery, especially in hospitals, is essential for optimal outcomes.

The Internet of Healthcare Things (IoHT) is an essential IoT domain that encompasses the necessary capabilities to transform the current healthcare system. IoHT generates, transfers, manages, stores, and processes highly confidential medical data and sensitive personal information. Thus, IoHT is a critical application from a security perspective and is most favorably targeted by attackers. Several security-breaching attempts can be carried out due to the vulnerabilities of the IoHT that put classified data at constant risk. Henceforth, this work aims to inspect the performance of 6LoWPAN-RPL-based `Patient-Centric IoHT’ in the presence of malicious intruders conducting blackhole, rank, and DoDAG attacks in both normal and emergency operational modes of the mentioned resource-constraint network. PC-IoHT is simulated on the Cooja simulator and Contiki-OS for each attack with two and four intruders (out of 10 motes) for both operation modes. Several performance evaluation parameters are used to assess the overall performance of the considered PC-IoHT. The attacks’ impact on PC-IoHT is analyzed by comparing blackhole, rank, and DoDAG attacks with the no-attack scenario.

BMAP-18, derived from the N-terminal region of bovine myeloid antimicrobial peptide BMAP-27, demonstrated potent antimicrobial activity without cytotoxicity. This study aimed to compare the antibacterial, antibiofilm, and anti-inflammatory properties of BMAP-18, rich in aromatic phenylalanine residues, with its aliphatic analogue, BMAP-18-FL. Both aromatic BMAP-18 and aliphatic BMAP-18-FL exhibited equally potent antimicrobial activities against Gram-positive and Gram-negative bacteria, particularly methicillin-resistant Staphylococcus aureus (MRSA) and multidrug-resistant Pseudomonas aeruginosa (MDRPA). Mechanistic investigations employing SYTOX green uptake, DNA binding, and FACScan analysis revealed that both peptides acted by inducing membrane permeabilization and subsequent intracellular targeting. Moreover, both BMAP-18 and BMAP-18-FL effectively prevented biofilm formation and eradicated existing biofilms of MRSA and MDRPA. Notably, BMAP-18-FL displayed superior anti-inflammatory activity compared to BMAP-18, significantly reducing the expression levels of pro-inflammatory cytokines in lipopolysaccharide-stimulated macrophages. This study emphasizes the similarities and differences in the antimicrobial, antibiofilm, and anti-inflammatory properties between aromatic BMAP-18 and aliphatic BMAP-18-FL, providing valuable insights for the development of multifunctional antimicrobial peptides against drug-resistant bacteria.

Self-compacting concrete is essential for vertical structures due to high reinforcement and usage of pumps for transporting the concrete at the working site. SCC is mainly influenced by self-curing for arid areas. To explore the SCC (Self-Compacting Concrete) mixture and condition influencing the self-curing condition were determined using the experimental procedure. Determination of various parameters was done using T50 slump flow, V-funnel, L-box-box, J ring test, compressive strength, splitting tensile strength, and flexural strength test. After the experimental investigation, it shows that if the dose if the dosage of superplasticizer (SNF%) increases, it will improve the concrete mixture's different properties, such as flowability and ability to pass. Higher water-to-cement ratios (w/c) generally result in enhanced flowability and stability. Mixture 9 (SNF 0.35, w/c 0.5) demonstrates favourable results across all parameters, indicating excellent flowability and stability. Mixture 9 (SNF 0.35, w/c 0.5) also shows favourable flowability and stability characteristics. Lower concentrations, such as 0.5%, may have a positive effect up to a certain point but might not provide sustained improvement over the curing period. On the other hand, concentrations of 1% and 2% demonstrated consistent and significant enhancement in strength throughout the 28 days. It is found that the higher concentrations of PEG 4000 may be more effective in improving the material strength.

Abstract: Lung cancer, characterized by its heterogeneity, presents a significant challenge in ther-apeutic management, primarily due to the development of resistance to conventional drugs. This resistance is often compounded by the tumour’s ability to reprogram its metabolic pathways, a survival strategy that enables cancer cells to thrive in adverse conditions. This review article ex-plores the complex link between drug resistance and metabolic reprogramming in lung cancer, offering a detailed analysis of the molecular mechanisms and treatment strategies. It emphasizes the interplay between drug resistance and changes in metabolic pathways, crucial for developing effective lung cancer therapies. The review examines the impact of current treatments on metabolic pathways and the significance of considering metabolic factors to combat drug resistance. It highlights the different challenges and metabolic alterations in non-small cell lung cancer and small cell lung cancer, underlining the need for subtype-specific treatments. Key signalling pathways, including PI3K/AKT/mTOR, MAPK, and AMPK, have been discussed for their roles in promoting drug resistance and metabolic changes, alongside the complex regulatory networks involved. The article evaluates emerging treatments targeting metabolism, such as metabolic inhibitors, dietary management, and combination therapies, assessing their potential and challenges. It concludes with insights into the role of precision medicine and metabolic biomarkers in crafting personalized lung cancer treatments, advocating for metabolic targeting as a promising approach to enhance treatment efficacy and overcome drug resistance. The review underscores ongoing advancements and hurdles in integrating metabolic considerations into lung cancer therapy strategies.

Immune surveillance and adaptive immune responses, involving continuously circulating and tissue-resident T-lymphocytes, provide host defense against infectious agents and possible malignant transformation while avoiding autoimmune tissue damage. Activation, migration, and deployment of T-cells to affected tissue sites are crucial for mounting an adaptive immune response. An effective adaptive immune defense depends on the ability of T-cells to dynamically reprogram their metabolic requirements in response to environmental cues. Inability of the T-cells to adapt to specific metabolic demands may skew cells to become either hyporesponsive creating immunocompromised conditions or hyperactive causing autoimmune tissue destruction. Here, we review maladaptive T-cell metabolic fitness that can cause autoimmune diseases and discuss how T-cell metabolic programs can potentially be modulated to achieve therapeutic benefits.

Cytochrome P450 (CYPs) is a functionally diversified third-largest gene family that exploded in the plant kingdom. Their role in different organ development has been illustrated by the intervention of phytohormone. Cotton is a model organism for cell differentiation and cell elongation. To decipher the participation of CYPs in different cotton fiber developmental stages, we identified and characterized 2460 CYPs in three diploids and two allotetraploid cottons. Furthermore, In-silico expression and cluster analysis of cotton CYPs was conducted to distinguish the fiber stage-specific clusters that have the determining role in different stages of fiber development. The subgenome expression of two conserved Gossypium hirsutum CYPs, namely, GhCYP78A197 and GhCYP78A198 contributed to fiber initiation at an early stage of fiber development, governed by the co-occurrence of TATA and MYB TFs binding sites. Coexpression network partners of these two GhCYP78A annotated as auxin, kinases, chromatin remodeler, epigenetic regulator and cyclin-related genes that possibly induce the endoreduplication and cell proliferation for fiber cell initiation to define the high yield and biomass.

The major arboviruses belong mainly to the families Bunyaviridae, Togaviridae, and Flaviviridae amongst which chikungunya virus and dengue virus have emerged as a global public health problem. The main objective of the study was to develop a specific, sensitive, and cost-effective molecular multiplex RT-PCR and RT-qPCR assay for the rapid and simultaneous detection of CHIKV and the four serotypes of DENV for arbovirus surveillance. Specific primers of all virus were designed, one step multiplex RT-PCR (mRT-PCR) and RT-qPCR (mRT-qPCR) was developed using reference strains of CHIKV and DENV serotypes. The specificity of the test for all the viruses was confirmed by sequencing. The standard curves showed a high correlation coefficient, R2= 0.99, for DENV-2 and DENV-3; R2= 0.98, for DENV-4 and CHIKV; R2= 0.93, for DENV-1. The limits of detection were calculated to be 10-2 copies/µL for all viruses. The specificity and sensitivity of the newly developed mRT-PCR and mRT-qPCR were validated using positive serum samples collected in India and Burkina Faso. The sensitivity of mRT-PCR and mRT-qPCR are 91%, and 100% respectively. The specificity of both assays was 100% specific. mRT-PCR and mRT-qPCR assays are low cost and a combination of both will be a useful tool for arbovirus surveillance.

Transparent, flexible, biaxially oriented polyethylene terephthalate (PET) sheets were modified by bioactive polymer-fibronectin top layers for the attachment of cells and growth of muscle fibers. Towards this end, PET sheets were grafted with 4-(dimethylamino)phenyl (DMA) groups from the in situ generated corresponding diazonium compound. The arylated sheets served as macro-hydrogen donors for benzophenone and the growth of poly(2-hydroxy ethyl methacrylate) (PHEMA) top layer by surface-confined free radical photopolymerization. The PET-PHEMA sheets were further grafted with fibronectin (FBN) through the 1,1-carbonyldiimidazole coupling procedures. The bioactive PET-PHEMA-I-FBN was then employed as a platform for the attachment, proliferation and differentiation of eukaryotic cells which after a few days gave remarkable muscle fibers, of ~120 µm length and ~45 µm thickness. We demonstrate that PET-PHEMA yields a fast growth of cells followed by muscle fibers of excellent levels of differentiation compared to pristine PET or standard microscope glass slides. The positive effect is exacerbated by crosslinking PHEMA chains with ethylene glycol dimethacrylate at initial HEMA/EGDMA concentration ratio = 9/1. This works conclusively shows that in situ generated diazonium salts provide aryl layers for the efficient UV-induced grafting of biocompatible coating that beneficially serve as platform for cell attachment and growth of muscle fibers. Beyond this work, diazonium coupling agents constitute the corner stone of next generation processes for building flexible platforms for cell adhesion and uses thereof.

Ruxolitinib has been approved by FDA for the treatment of myeloproliferative neoplasms such as polycythemia vera and idiopathic myelofibrosis. Although ruxolitinib has been in the clinic for more than ten years, surprisingly, no JAK2 drug resistant mutations have been identified in patients. At the moment, it is not clear whether the absence of JAK2 mutations in MPN patients is due to a lack of JAK2 addiction or residues that mediate the drug resistance compromising the JAK2 function. To find out this, we established a Ba/F3 cell-based screening strategy to predict mutations in JAK2-V617F that cause resistance towards ruxolitinib. In this study, we have recovered seven residues in the kinase domain of JAK2 that affect ruxolitinib sensitivity. All these mutations confer cross-resistance across the panel of JAK2 kinase inhibitors except JAK2-L983F. JAK2-L983F reduces the sensitivity towards ruxolitinib. However, it is sensitive towards fedratinib indicating that our screen identifies the drug-specific resistance profiles. All the ruxolitinib resistant JAK2 variants displayed sensitivity towards Type II JAK2 inhibitor CHZ-868. Finally, our study also shows that HSP90 inhibitors are potent against ruxolitinib resistant variants through the JAK2 degradation and provides the rationale for clinical evaluation of potent HSP90 inhibitors in genetic resistance driven by JAK2 inhibitors.

This article investigates the effectiveness of feature extraction and selection techniques for enhancing the performance of classifiers accuracy in Type II Diabetes Mellitus (DM) detection using microarray gene data. To address the inherent high dimensionality of the data by employing three Feature Extraction (FE) methods are used namely, Short-Time Fourier Transform (STFT), Ridge Regression (RR), and Pearson Correlation Coefficient (PCC). To further refine the data, meta-heuristic algorithms like Bald Eagle Search Optimization (BESO) and Red Deer Optimization (RDO) are utilized for feature selection. The performance of seven classification techniques such as Non-Linear Regression - NLR, Linear Regression - LR, Gaussian Mixture Model - GMM, Expectation Maximization - EM, Logistic Regression - LoR, Softmax Discriminant Classifier - SDC, and Support Vector Machine with Radial Basis Function kernel - SVM-RBF are evaluated with and without feature selection. The analysis reveals that the combination of PCC with SVM-RBF achieved a promising accuracy of 92.85% even without feature selection. Notably, employing BESO with PCC and SVM-RBF maintained this high accuracy. However, the highest overall accuracy of 97.14% was achieved when RDO was used for feature selection alongside PCC and SVM-RBF. These findings highlight the potential of feature extraction and selection techniques, particularly RDO with PCC, in improving the accuracy of DM detection using microarray gene data.

The complexity and interconnection of smart cities provide huge political, technical, and socioeconomic challenges for the designers, integrators, and organisations who are responsible for the administration of these new entities. A growing number of studies are concentrating their attention on the safety, privacy, and potential dangers that exist within smart cities. These studies are drawing attention to the dangers that are associated with information security as well as the challenges that smart city infrastructure faces in the management and processing of personal data. This state of the art review of the literature analyses a number of issues pertaining to smart homes, offers a helpful synthesis of the important information found in the primary research, and creates a model for the interaction between smart cities. An overview of smart home research incorporating machine learning, including everything from definition to current research state. The state of art begins with a smart home definition, followed by descriptions of smart home elements, typical research projects, smart home network research status, smart home appliances, and difficulties. The term "smart home" refers to a subcategory of "everyday computing" that includes "smart technology" in order to improve a person's level of convenience, health, safety, and security while also reducing their When controlled by artificial intelligence, the applications for the smart home provide users with context-aware settings, services, and remote control, which significantly increases the level of user satisfaction.

Background: Green synthesis of silver (Ag) nanoparticle was performed with the fruit extract of Scindapsus officinalis and test against HepG-2, MCF-7 cancer cell lines. These biosynthesized metal nanoparticles have a potential of therapeutic use as an alternative medicine for the treatment of hepatic & breast cancer cases. Results : AgNPs were identified by change of color & their absobtion at 340 nm measured by UV-visible spectroscopy, FTIR spectral analysis confirmed phenolic compounds presence, morphology & size visualized in SEM, TEM used for determination of size, shape & light scattering analysis. Synthesized silver nanoparticles were spherical in shape and size less than 50 nm. XRD analysis was affirmed the crystalline nature of metal particles. In-vitro cytotoxic result showed an excellent IC50 value of 155.81μg/ml and 114.74 μg/ml against HepG-2 & MCF-7 cell lines. Conclusion: The current study reveals green synthesized AgNPs possess high cytotoxic action against HepG-2 & MCF-7 cell lines which suggested the use of silver nanoparticles as a medicine to treat hepatic & breast cancer.

Environmental interventions and ecological adaptations harbor millions of valued substances and metabolites in plants which can be employed and commercialized for human benefits. Present study encompasses the untapped potential of pine needles of Indo-Himalayan region for the production of different metabolites and their pharmacological significance in terms of antioxidant and antimicrobial activity. Total phenolic and flavonoid content from the needles of ten pine species was quantified using three different solvent systems. Results revealed that out of 10 different selected Pinus species, Pinus taeda showed highest concentration of total phenolics, soluble-F phenolics and flavonoids content (approx.147.02 mg/g, 141.08 mg/g and 21.91mg/g respectively) as compared to other species. On the other hand P. greggii showed highest Bound-W phenolic content (approx.3.62mg/g). Among all the selected plant species, the needles of P.echinata exhibited the highest and P.thunbergii had the lowest ratio of total flavonoids to total phenolics. Most of these compounds were found to have effective antioxidant activities as well as antimicrobial activity, as estimated by oxygen radical absorbance capacity (ORAC) and disk diffusion test respectively.

Nowadays, great attention has been attributed to the study of runoff and its fluctuation over space and time. There is a crucial need for a good soil and water management system to overcome the challenges of water scarcity and other natural adverse events like floods and landslides, among others. Rainfall-runoff modeling is an appropriate approach for runoff prediction, making it possible to take preventive measures to avoid damage caused by natural hazards such as floods. In the present study, several data driven models, namely: Multiple linear regression (MLR), Multiple adaptive regression splines (MARS), Support vector machine (SVM), and Random Forest (RF), were used for rainfall-runoff prediction of the Gola watershed, located in the south-eastern part of the Uttarakhand. The performance of the models was evaluated based on the coefficient of determination (R2), root mean square error (RMSE), Nash-Sutcliffe efficiency (NSE), and percent bias (PBAIS) indices. In addition to the numerical comparison, the models were evaluated and their performances were evaluated base on graphical plotting, i.e., line diagram, scatter plot, Violin plot, relative error plot and Taylor diagram (TD). The comparison results revealed that the four heuristic methods gave higher accuracy than the MLR model. Among the machine learning models, the RF (RMSE (m3/s), R2, NSE, and PBIAS (%) = 6.31, 0.96, 0.94, and -0.20 during the training period, respectively, and 5.53, 0.95, 0.92, and -0.20 during the testing period, respectively) surpassed the MARS, SVM, and the MLR models in forecasting daily runoff for all cases studies. Among all four models, the RF model outperformed in the training and testing periods. It can be summarized that the RF model is best-in-class and delivers a strong potential for runoff prediction of the Gola watershed.

Oxygen based heterocyclic moieties hold an ample range of therapeutic activities. Heterocyclic molecules are nominated as vital components of an extensive array of structural motifs with both biological and pharmaceutical significance. The oxygen-based scaffolds act as anticancer candidates and are also present in numerous phytomolecules viz; irinotecan, camptothecin, topotecan, taxol, taxotere, podophylotoxin, etoposide, daunorubicin and teniposide. The architectural design of numerous structural motifs for amelioration of cancer has become progressively amplified in recent past years. Until now presently there is no strategic treatment which is so capable that can cure cancer from its roots. Henceforth, it is very indispensible to design novel anticancer structural motifs with least side effects. The oxygen containing heterocyclic scaffolds includes flavonoids, pyrans, xanthones and coumarins are of utmost importance in medical chemistry for the mitigation of cancer. This assemblage offers several recent developments as anticancer oxygen containing heterocyclic molecules all-round the globe and attracted the structural motifs of auspicious molecules, along with their mechanistic insights, IC50 values, structure–activity relationships, and molecular docking studies. The encouraging properties discovered by these oxygen-based scaffolds unconditionally engaging them at frontline in invention of potential drug candidates. Consequently, these probably will be of amazing attention to scientists working on the design and synthesis of antitumor candidates.

Background: The use of animal models for biomedical research provides us with a convenient and feasible route to establish causal relationships by recapitulating the temporal sequence of events in a controlled environment with a potential to manipulate the variables at multiple levels including genetic, protein, physiological or environmental. Objectives: The current review was conducted to gain insights into various animal models for the SARS-CoV-2 virus. Material and Methods: A literature review (PUBMED, PUBMED Central, PMC, Google Scholar, Google search engine) following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines performed in early April 2020 revealed 9 articles of interest. Search terms included covid 19, covid-19, novel corona virus, SARS-CoV-2, animal models, experimental models, laboratory models & covid 19 animal models. Two independent reviewers extracted the data; the third reviewer was involved in case of discrepancy. Results: SARS-CoV-2 shares an identical receptor binding domain with the SARS-CoV virus and has a superior binding affinity to the host ACE2. Based on this, the role of rhesus macaques, golden Syrian hamsters, transgenic hACE2 mice and ferrets as animal models have been studied. All four animals are susceptible to infection with SARS-CoV-2 with variable clinical presentation but universal recovery. The respiratory tract is primarily involved in all four models. Involvement of intestines was also seen in at least one study in each animal. Transfer to naïve animals in close contact has been documented in case of hamsters and ferrets. Seroconversion was documented in all although the role of convalescent sera was tested in hamsters only, with positive results though. Air-borne transmission was documented in ferrets and the possibility of feco-oral transmission was suggested for hamsters. The possibilities of recurrence and re-infection were ruled out by experiments upon the rhesus macaques. The fulfilment of Koch’s postulates has been highlighted. Discussion: The various studies available on animal models have been able to establish models of infection and transmission that recapitulate different aspects of disease in humans. However, the response between different animals and the same animal in different experiments is not completely coherent. Some of them do not manifest the disease clinically while others behave differently at molecular and immunological levels. Moreover, the physiology of these animals is not identical to human beings and the findings may not be extrapolated to human beings in an ‘as-is’ manner. Conclusions: The review acknowledges the achievements made by these experiments in a short span of time and highlighted the urgent need for a deeper dive in search of a quintessential animal model which can be studied for efficacy and safety of newer drugs and vaccines before a make-shift from the petri-dish to the human body can be contemplated.

Breast cancer has become the most diagnosed cancer in women globally with 2.3 million new diagnosis each year. Accurate early staging is essential for improving survival rates with metastatic spread from loco regional to distant metastasis decreasing rates by 50%. Current guidelines do not advice the routine use of Positron Emission Tomography (PET)-Computed Tomography (CT) in staging of early breast cancer in the absence of symptoms. However, there is a growing body of evidence to suggest that PET-CT used in this early stage can benefit the patient by improving staging and as a result treatment and outcome as well as psychological burden without increasing costs to the health service. Ongoing research in PET radiomics and artificial intelligence is showing promising future prospects in its use in diagnosis, staging, prognosticatication and assessing response to treatment in breast cancer. Furthermore ongoing research to address current limitations of PET-CT by improving techniques and tracers is encouraging.

This research aims to find out whether the synthetic 1, 2, 4-triazine and its derivatives have antifungal effects and can protect humans from infection with Candida albicans. Molecular docking and molecular dynamic simulation are widely used in modern drug design to target a We are interested in using molecular docking and molecular dynamics modelling to investigate the interaction between the derivatives of 1, 2, 4-triazine and the resulting lanosterol 14 - demethylase (CYP51) of Candida albicans The inhibition of Candida albicans CYP51 is the main goal of our research. The 1, 2, 4-triazine and its derivatives have been docked to the CYP51 enzyme, which is involved in Candida albicans Multidrug Drug Resistance (MDR). Autodock tools were used to identifying the binding affinities of molecules against the target proteins. Compared to conventional fluconazole, the molecular docking results indicated that each drug has a high binding affinity for CYP51 proteins and forms unbound interactions and hydrogen bonds with their active residues and surrounding allosteric residues. The docking contacts were made using a 10 ns MD simulation with nine molecules. RMSD, RMSF, hydrogen bonds, and the Rg all confirm these conclusions. In addition, these compounds were expected to have a favorable pharmacological profile and low toxicity. The compounds are being offered as scaffolds for the development of new antifungal drugs and as candidates for future in vitro testing.

Standard Model is well-performing under the three-generation, but in the wake of more physics, fourth-generation leptons and quarks are introduced. In this paper, We will be reviewing various constraints surrounding quarks and leptons of 4th Generation and tried to show the importance of quarks to understand the baryogenesis. We talked about, reviewed existing parameters and set the updated lower limits of t’ and b’ by analysis. And predicted an updated version of lower mass bounds quarks and leptons. It is also reviewed that fourth generation leptons are primarily not possible because of disintegration of Higgs Boson and an interpretation has been setup. And in final, we talked about the Unified Standard Model.

This note will be a brief account of an unknown vectorial mathematical current in SM4 Field based on disintegrating the Lagrangian of quarks in SM4 into four parts. It is shown that the current breaks while interacting with the quarks and makes the quarks to not interact with fourth-generation quarks. This note also shows that gauge bosons are produced while these currents symmetry break and provides the service as mediators.

The rapid growth of urban areas has led to a decline in the amount of green space available to residents. This has a number of negative consequences for human health and well-- being. Green spaces provide a number of benefits for human health, including improved physical, mental, and social health. They also have a number of economic benefits, such as increased property values and reduced crime rates. Given the many benefits of green spaces, it is clear that they are essential for the health and well-being of urban residents. Policymakers and planners should prioritize the creation and preservation of green spaces in urban areas.

In this note, the toy-model of Fourth Generation quarks coupling to Standard Model particles through unknown Lagrangian has been figured. Eventually producing a 103 TeV vector gauge boson, which is a special gauge boson that appears in unification models and here as a mediator for WeakSM4 interaction. Another anomaly of different gauge boson has been reported.

Neuron degeneration is the most common old age complications worldwide. Alzheimer's disease (AD) is one of the most common neurodegenerative condition with limited drug options and significant adverse effects. Hence, there is a growing interest in exploring natural products for their potential pharmacological activities against AD. This study investigates the efficacy of Verbesina encelioides flower extract (VFE) in alleviating neurobehavioral damage induced by Scopolamine in rat mimicking AD model. GC-MS analysis showed that V. encelioides flower extract is rich in carotenoids like spirilloxanthin. Various behavioural models were used to assess the rats' learning and memory capabilities. Additionally, biochemical tests were conducted on the AD rat models to evaluate oxidative stress in the brain, including superoxide dismutase (SOD), nitric oxide (NO), reduced glutathione (GSH), and anti-acetylcholinesterase (AChE) enzyme assays. Findings from this study suggest that the VFE significantly improved the rats' cognitive aptitude and alleviated them from oxidative stress by reducing free radical generation. Based on the results obtained from GC-MS analysis, in silico interaction analysis of gingkolide A and spirilloxanthin with acetylcholinesterase was carried out. In summary, this study suggests that V. enceloides flower extract improves learning abilities and mitigates cognitive deficits caused by scopolamine. Thus V. enceloides could be potentially seen as natural therapeutic intervention against neurodegenerative disorders likes Alzheimer’s disease and dementia.

Fermatean fuzzy linguistic (FFL) set theory provides an efficient tool for modeling a higher level of uncertain and imprecise information, which cannot be represented using intuitionistic fuzzy linguistic (IFL)/Pythagorean fuzzy linguistic (PFL) sets. On the other hand, the linguistic scale function is the better way to consider the semantics of the linguistic terms during the evaluation process. In the present paper, we first define some new modified operational laws for Fermatean fuzzy linguistic numbers (FFLNs) based on linguistic scale function (LSF) to overcome the shortcomings of the existing operational laws and prove some important mathematical properties of them. Based on it, the work defines several new aggregation operators (AOs), namely, the FFL-weighted averaging (FFLWA) operator, the FFL-weighted geometric (FFLWG) operator, the FFL-ordered weighted averaging (FFLOWA) operator, the FFL-ordered weighted geometric (FFLOWG) operator, the FFL-hybrid averaging (FFLHA) operator and the FFL-hybrid geometric (FFLHG) operator under FFL environment. Several properties of these AOs are investigated in detail. Further, based on these operators, a multiple attribute group decision-making (MAGDM) approach with FFL information is developed. Finally, to illustrate the effectiveness of the present approach, a real-life supplier selection problem is presented where the evaluation information of the alternatives is given in terms of FFLNs.

Diabetes mellitus is a chronic disease that affects millions of people worldwide. Article focuses on detecting the presence of diabetes in patients using microarray gene data obtained from the pancreas. Handle the high-dimensional nature of the data, four different dimensionality reduction techniques, namely Bessel Function, Discrete Cosine Transform (DCT), Least Square Linear Regression (LSLR), and Artificial Algae Algorithm (AAA) are used. After reduced the data, Meta-heuristic algorithms are applied like Dragonfly Optimization Algorithm (DOA) and Elephant Herding Optimization Algorithm (EHO) for feature selection. Ten classification techniques are using to classify the data in both the format like without feature selection method and with feature selection method. The classification techniques are Non-Linear Regression (NLR), Linear Regression (LR), Gaussian Mixture Model (GMM), Expectation Maximum (EM), Bayesian Linear Discriminant Classifier (BLDC), Logistic Regression (LoR), Softmax Discriminant Classifier (SDC), Support Vector Machine (SVM) with linear kernel, Support Vector Machine (SVM) with polynomial kernel, and Support Vector Machine (SVM) with Radial Basis Function (RBF) kernel. Results showed that the AAA with SVM(RBF) achieved an accuracy of 90% without feature selection. However, when feature selection was applied, with EHO of AAA with SVM(RBF) exhibited the highest accuracy of 95.714%, followed closely by with DOA of AAA with SVM (RBF) at 94.28%.

Diabetes becomes a life threatening non-communicable disease in the world, according to International Diabetic Federation (IDF) in 2023, an estimated 537 million adults (20-79 years) are living with diabetes, which is equivalent to 9.3% of the global adult population. This number is predicted to rise to 643 million by 2030 and 783 million by 2045. Over 3 in 4 adults with diabetes live in low- and middle-income countries. Diabetes is a persistent metabolic condition marked by increased levels of glucose in the bloodstream. It is a significant global health concern, affecting millions of individuals worldwide. It having the symptoms of drowsiness for the whole day if not properly examined or treated well. It is targeted to spread over the younger age community and the number is growing in the exponential fashion. Even day to day many advancements have come from the researcher to diagnose, to find solutions to prevent from newer entry. Authors have taken a dataset with 57 non diabetic and 20 diabetic patients with the total 28735 micro array gene to undergone pre-processing process and reduced up to 22960 gene data using Dimensionality Reduction (DR) such as Detrend Fluctuation Analysis (DFA), Chi square probability density function (Chi2PDF), Firefly algorithm Cuckoo search were used in this research. Meta heuristic algorithms like Particle swarm Optimization (PSO) and Harmonic Search (HS) are used for feature selection. Further seven classification techniques such as Non-Linear Regression (NLR), Linear Regression (LR), Logistics Regression (LoR), Gaussian Mixture Model (GMM), Bayesian Linear Discriminant Classifier (BLDC), Softmax Discriminant Classifier (SDC), Support Vector Machine – Radial Basis Function (SVM-RBF) are using to make a decision, predictive analysis and segregate the data according to the level of blood glucose as Diabetic Patient (DP) and Non-Diabetic Patient (NDP).

In the current scenario of changing climatic conditions and rising global population, there is always a need to explore novel, efficient, and economical natural products for the benefit of human kind. Biosurfactant is one of the latest explored microbial biomolecules that have been used in numerous fields, including agriculture, pharmaceuticals, cosmetics, food processing, and environment-cleaning industries as raw material, lubrication, wetting, foaming softening, making emulsions, and stabilizing dispersions. The amphiphilic nature of biosurfactant biomolecules showed great advantage, distributing themselves into two immiscible surfaces by reducing interfacial surface tension and increasing the solubility of hydrophobic compounds. Furthermore, their eco-friendly nature, low or even no toxic nature, durability at higher temperatures, and wide range of pH fluctuations making the microbial surfactants preferable compared to their chemical counterparts. Additionally, the biosurfactants can obviate the oxidation flow by eliciting the antioxidant property, antimicrobial, anticancer activity, and drug delivery system, further broadening their applicability in the food, pharmaceutical, and pharma industries. Nowadays, biosurfactant has been broadly utilized to improve the soil quality by improving the concentration of trace elements and mixed with pesticides or applied singly on the plant surfaces for plant disease management. In the present review, we summarise the latest aspect of microbial synthesized biosurfactant compounds, limiting factors of biosurfactant production, and their application in improving soil quality, plant disease management.

This study investigates the synthesis and characterization of zirconium oxide (ZrO₂)-based metal-composites doped with cerium (Ce) and yttrium (Y), using chromium (Cr) and nickel (Ni) as base metals. These constituents were selected for their superior mechanical properties and compatibility with the ceramic phase. High-purity powders were homogenized via high-energy ball milling, followed by cold isostatic pressing (CIP) and sintering in a controlled atmosphere. The sintering process was conducted at various temperatures (850°C to 1350°C) to examine densification, grain growth, and microstructure evolution. Scanning Electron Microscopy (SEM) revealed a homogeneous distribution of phases, with distinct microstructural features attributed to each element at different sintering temperatures. Density measurements using the water immersion method and theoretical calculations demonstrated increased density and reduced porosity with higher sintering temperatures. Vickers hardness testing indicated that hardness improved with increased sintering temperature, peaking around 1150°C due to cerium and yttrium reinforcement within the matrix. Flexural strength tests showed the highest value at 1350°C, with a significant drop in porosity, suggesting enhanced mechanical properties due to efficient doping and phase transformations. Overall, the incorporation of cerium and yttrium significantly improved the mechanical behavior and phase stability of the ZrO₂-Cr-Ni composites, highlighting their potential for advanced engineering applications.

The study of Graviton as Goldstone bosons appeared in the 1960s, after Bjorken interacting idea of Electrodynamics. However, no recent advancement has been done in the field, because of very constraints as well as low-attractiveness of the theory. We do the non-metric tensor (covariant derivative of the metric tensor) case of Gravitation and eventually get SO(1,3) broken in the vacuum state of quantized field theory, then find the Graviton as Goldstone Boson. We, in final, see that Gravitons can have appearances in many modified (and extended) theories of Gravitation.

In an effort to discover an effective and selective antitumour agent, synthesis and anti-cancer potential of 4-(pyridin-4-yl)-6-(thiophen-2-yl)pyrimidin-2(1H)-one (SK-25), which has been reported earlier by us with significant cytotoxicity towards MiaPaCa-2 malignant cells, with an IC₅₀ value of 1.95 μM and was found to instigate apoptosis. In the present study, the antitumour efficacy of SK-25 was investigated on Ehrlich ascites tumour (solid), Sarcoma 180 (solid) tumour and Ehrlich ascites carcinoma. The compound was found to inhibit tumour development by 94.71% in Ehrlich ascites carcinoma (EAC), 59.06% in Ehrlich tumour (ET, solid) and 45.68% in Sarcoma-180 (solid) at 30 mg/kg dose. Additionally, SK-25 was established to be non-toxic at a maximum tolerated dose of 1000 mg/kg in acute oral toxicity in Swiss-albino mice. Computer-based predictions also show that the compounds could have an interesting DMPK profile. The current study provides insight for further investigation of the antitumour potential of the molecule.

The elevated environment temperature in summer plays adverse effects on bovine fertility. Antioxidant treatment may have positive impact to enhance oocyte competence could be possible strategy for alleviating the summer effects on bovine fertility. The present study was aimed to investigate the effects of exogenous melatonin treatment on oocyte development competence, blastocyst development rate, activity of antioxidant enzymes, expression profiles of development related genes and pregnancy rates in cattle during summer. The experiment was conducted in 44 non-lactating indigenous Sahiwal cattle divided into control and treatment group. Treatment group received (day 0) inj. Melatonin @ 18mg/50kg body weight while control group received inj. Corn oil, 4ml i.m. On day 4, all the cows were subjected to ovum pickup (OPU) to aspirate oocytes for in-vitro embryo production. Blood samples were used to determine lipid peroxidation (MDA) and antioxidant enzymes concentration (SOD, GSH), and also for plasma hormonal concentration analysis. The results revealed that blastocyst development rate (%) was higher in treatment group (p<0.05). Treatment group had increased (p<0.05) antioxidant enzymes (SOD and GSH), plasma melatonin and estradiol concentrations, and decreased (p<0.05) MDA concentrations than controls. The mRNA expression level of SOD2, HSPB1, BCL2, MT2 and STAR in oocytes, cumulus cells, COCs (immature and mature) and blastocyst were found to be higher, however, BAX was downregulated in melatonin-treated than control group. In conclusion, the results suggesting probable beneficial role of exogenous melatonin administration in reducing heat stress and enhancing blastocyst developmental competence in indigenous cattle.

This paper focuses on the relationship between Mental Health and the Landscape. It aims to find out how people respond to Biophilic inspired landscape design in a healthcare setting. The focal point is not to cure diseases but to help them deal with the physiological, psychological, and psycho-social imbalance and provide a healing environment for the overall well-being of an individual. For this study, a Multispecialty Hospital was selected where an Indoor Healing Garden is used as a retrofitting tool to reduce stress and thus reconnect humans with nature. A multi-method approach is being used for this study. Initially, a questionnaire was conducted for the targeted users categorized into three types – patients, staff, and visitors to know their longing for the landscape. Based on this data and available literature, an evidence-based design was proposed. This conceptual design model is then shown to the targeted user and the response is recorded. The data has then collaborated with similar studies done earlier and design elements are highlighted which helps in creating a restorative environment by reducing stress and increasing recovery rate and thus approaching sustainable development.

The field of solar technology has experienced remarkable advancements, transforming from early photovoltaic discoveries to a diverse array of high-efficiency, cost-effective solutions. This review explores the evolution of solar technology, detailing its development from the initial discovery of the photovoltaic effect to contemporary innovations. The current generation of solar technologies, including silicon photovoltaic cells, thin-film solar cells, perovskite solar cells, bifacial panels, concentrated solar power, and building-integrated photovoltaics, is examined for their efficiencies, applications, and contributions to the renewable energy landscape. Future prospects highlight promising trends such as next-generation photovoltaics, advanced energy storage solutions, agrivoltaics, floating solar farms, and artificial photosynthesis. Despite the progress, challenges related to efficiency, stability, scalability, cost, grid integration, environmental impact, policy support, and public acceptance remain. Addressing these challenges through continued research, supportive policies, and collaborative efforts is crucial for realizing the full potential of solar technology. This review underscores the importance of solar energy in achieving a sustainable future and emphasizes the need for ongoing innovation and commitment to overcome existing barriers.

The environmental and economic concern is the biggest challenge that concrete industry is facing today. Advancement in utilization of wastes in concrete as a mixture reduces usage of natural resources. Phyllite is a kind of foliated metamorphic rock generates during underground mining .Phyllite was calcined at 850 to 900oC in furnace and ground in ball mill. In this study, cement was partially replaced by weight with calcined phyllite to make M30 grade of concrete with 0% (Control mix), 2%, 4%, 6%, 8%, & 10%, (which are designated as M1, M2, M3, M4, M5 and M6). The laboratory tests such as slump value, compressive strength, flexural strength, water absorption, chloride ion penetration and durability in acidic and basic medium were conducted on the phyllite concrete and results are compared with the control mix. Through results it is concluded that cement can be replaced in concrete at the tune of 8% with Calcine Phyllite (CP) without affecting the strength and durability. The aim of the experiment is to find the maximum content of mines calcined Phyllite that can be used as a partial replacement of cement without compromising the quality on any of the characteristics of concrete.

Introduced in 2009, Bitcoin has demonstrated a huge potential as the world’s first digital currency and has been widely used as a financial investment. Our research aims to uncover the relationship between Bitcoin prices and people’s sentiments about Bitcoin on social media. Among various social media platforms, micro-blogging is one of the most popular. Millions of people use micro-blogging platforms to exchange ideas, broadcast views, and to provide opinions on different topics related to politics, culture, science, and technology. This makes them a potentially rich source of data for sentiment analysis. Therefore we chose one of the busiest micro-blogging platforms, Twitter, to perform sentiment analysis on Bitcoin. We used ELMo embedding model to convert Bitcoin-related tweets into a vector form and SVM classifier to divide the tweets into three sentiment categories - positive, negative, and neutral. We then used the sentiment data to find its relation with Bitcoin price fluctuation using the linear mixed model.

Esophageal cancer is a formidable challenge in the realm of cancer treatment. Conventional methods such as surgery, chemotherapy, and immunotherapy have demonstrated limited success rates in managing this disease. In response, targeted drug therapies have emerged as a promising strategy to improve outcomes for patients. These therapies aim to disrupt specific pathways involved in the growth and development of esophageal cancer cells. This review explores various drugs used to target specific pathways, including cetuximab and monoclonal antibodies (gefitinib) that target the epidermal growth factor receptor (EGFR), trastuzumab that targets human epidermal growth factor receptor 2 (HER-2), drugs targeting the vascular endothelial growth factor receptor (VEGFR), mTOR inhibitors, and cMET inhibitors. Additionally, the article discusses the impact of drug resistance on the effectiveness of these therapies, highlighting factors such as cancer stem cells, cancer-associated fibroblasts, immune-inflammatory cells, cytokines, hypoxia, and growth factors. While drug targeting approaches do not provide a complete cure for esophageal cancer due to drug resistance and associated side effects, they offer potential for improving patient survival rates.

The integration of diverse distributed generation (DG) units, encompassing both Renewable Energy Sources (RES) and non-RES technologies delves into voltage levels, current distributions, and power flows, aiming to optimize and effectively manage the distribution network. An RES-based DGs such as photovoltaic systems and wind turbines, alongside non-RES units like fuel cells and micro turbines. This study proposes a hybrid approach the Multi-Objective Genetic Algorithm - Grey Wolf Optimizer (MOGA-GWO). By merging the strengths of multi-objective genetic algorithms with the efficiency of the Grey Wolf Optimizer, this advanced algorithm seeks semi-optimal solutions for multi-objective optimization problems. The algorithm aims to improve convergence speed without compromising solution quality, offering a promising solution for optimizing the characteristics of DG and RES integration in distribution networks compared to Cuckoo Search and Simulation Annealing Techniques. Key algorithmic operations include individual representation, initialization, fitness function computation, selection, crossover, mutation, and ending conditions. The DG optimal power flow problem is explored with a focus on minimizing generation costs, improving voltage profiles, minimizing losses, enhancing reliability, integrating renewable energy, reducing environmental impact, managing congestion, and ensuring compliance with operational constraints. Beyond traditional considerations, the study emphasizes neglected objectives and constraints, such as cyber-security concerns, real-time operation, regulatory frameworks, social acceptance, lifecycle environmental impact, grid resilience, data privacy, aging infrastructure compatibility, and economic viability.

In this work the existence and nonexistence of stationary radial solutions to the elliptic partial differential equation arising in the molecular beam epitaxy are studied. Since we are interested in radial solutions we arrive at the following fourth-order differential equation

Ranked Set Sampling (RSS) is a useful technique for improving the estimator of population mean when the sampling units in a study can be easily ranked than the actual measurement. RSS performs better than simple random sampling (SRS) when the mean of units corresponding to each rank is used. The performance of RSS can be increased further by assigning weights to the ranked observations. In this paper, we propose weighted RSS procedures to estimate the population mean of positively skew distributions. It is shown that the gain in the relative precisions of the population mean for chosen distributions are uniformly higher than those based on RSS. The gains in relative precisions are substantially high. Further, the relative precisions of our estimator are slightly higher than the ones based on Neyman’s optimal allocation model for small sample sizes. Moreover, it is shown that, the performance of the proposed estimator increases as the skewness increases by using the example of lognormal family of distributions.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections have triggered a recent pandemic of respiratory disease and affected almost every country all over the world. A large amount of natural bioactive compounds is under clinical investigation for various diseas-es. Especially, marine natural compounds are gaining more attention in the new drug develop-ment process. The present study has aimed to identify potential marine-derived inhibitors against the target proteins of COVID-19 using a computational approach. Currently, 16 marine clinical-level compounds were selected for computational screening against the four SARS-CoV-2 main proteases. Computational screening resulted from the best drug candidates for each target based on the binding affinity scores and amino acid interactions. Among these, five marine-derived compounds namely Chrysophaentin A (-6.6 kcal/mol), Geodisterol sulfates (-6.6 kcal/mol), Hymenidin (-6.4 kcal/mol), Plinabulin (-6.4 kcal/mol) and Tetrodotoxin (-6.3 kcal/mol) expressed the minimized binding energy and molecular interactions such as covalent and hydrophobic interactions to the SARS CoV-2 Main Protease. Using Molecular dynamic stud-ies, the Root-Mean-Square Deviation (RMSD), Root-Mean-Square Fluctuation (RMSF), Radius of Gyration (ROG), and Hydrogen bonds (H-Bonds) values were calculated for SARS-CoV-2 Main Protease with Hymenidin docked complex. Additionally, in silico Druglikeness and pharmaco-kinetic property assessments of the compounds demonstrated favorable druggability. These re-sults suggested that marine natural compounds are capable of fighting SARS-CoV-2. Further, in vitro and in vivo studies need to be carried out to confirm their inhibitory potential.

In this scientific research, thermally stable nickel nanoparticles were synthesized and characterized. Nickel nanoparticles were synthesized using phenol –formaldehyde by chemical method followed by calcination. The polymer metal complex was confirmed by FTIR and NMR. The spherical morphology of nickel nanoparticles confirmed by SEM. The crystallographic structure is confirmed by XRD and size of cobalt nanoparticles is 24.0 nm. The TGA analysis was performed over a range of 29-600OC. The TGA thermograph predicts mass decomposition of 11%, for nickel phenol-formaldehyde nanocomposite. The decomposition rate of composites is very low 2% weight loss per 100OC increment in temperature.

The present study aimed to undertake a detailed investigation of pathological epidemiology and molecular confirmation of Ehrlichia canis among pet dogs in smart city Bhubaneswar, Odisha, a state in eastern India. A total of 178 dogs were screened for Ehrlichiosis on the basis of history, clinical signs, blood, and buffy coat smear examination, resulting in only 56 dogs (31.46%) screening positive. The epidemiological study recorded an increase in incidences among dogs less than one year of age (41%), males (68%), German Shepherds (25%), dogs more than 20 kg body weight (75%), summer months (55%) and dogs housed on pukka houses with exposure to the outside (59%). The majority of the infected dogs had a history of tick infestation (79%) at some point in their life. Clinical signs showed non-typical manifestations like fever, lethargy, diarrhoea, epistaxis, hind limb edema, and corneal opacity. Haematological studies revealed anaemia and thrombocytopenia along with neutrophilia with relative lymphopenia and monocytosis. A decreasing trend was observed in the levels of total protein and albumin with an increase in the levels of globulin, AST, ALT, BUN, and creatinine. The ultrasonography studies revealed hepatosplenomegaly along with hyper-echogenicity in various organs. Pro-teinuria and haematuria were consistent, along with the presence of bile salts in the urine of affected dogs. Molecular confirmation from n-type PCR data using Ehrlichia-specific primers targeting p28 gene (843 bp) was done, and the identified gene sequences submitted to NCBI databases have accession numbers OQ383671-OQ383674 and OP886674-OP886677. Ticks collected from dogs were identified morphologically through microscopy and scanning electron microscopy as Rhipicephalus sanguineus.

In this article, bio-composites derived from starch-glycerol biodegradable matrix reinforced with jute fibers have been fabricated using the wet hand lay-up and compression moulding techniques. Samples having different weight percentages of jute fiber in the starch matrix have been analysed. The fibers surface was chemically treated by alkaline sodium hydroxide for improving the interphase bonding between fiber and matrix. Tensile test for the composites were done and the sample with highest tensile strength was selected for further tests that included water absorption, scanning electron microscopy and thermal analysis. It has been concluded that the ultimate tensile strength was found to be maximum for the composition of 15% fiber by weight composite as 7.547 MPa without epoxy coating and 10.43 MPa with epoxy coating. The major disadvantage of bio-composite is its high water absorption property, which in this study has been inhibited by the epoxy resin layer. Herein, the results of various tests done disclose a noteworthy improvement in the overall properties of bio-composite, in comparison to the neat biodegradable starch matrix.

Artificial Intelligence (AI) and Machine Learning (ML) are rapidly advancing. These technologies have significantly impacted the consumer goods market, including the smart footwear industry. AI and ML are extensively utilized in the design and production of smart and agile footwear. There are various methods for manufacturing smart footwear, which can be categorized based on their applications. These include medical and healthcare smart footwear, assistive footwear for individuals with impairments, footwear designed for navigation in unfamiliar areas, foot-wear for studying foot movements and gait analysis, safety footwear, anti-skid footwear, foot-wear for the elderly, army shoes, and energy-harvesting footwear, among others. These smart shoes enable real-time patient monitoring and provide appropriate suggestions for their care. Additionally, smart footwear can be classified based on the tools and techniques used in their production, as well as the sensors and Internet of Things (IoT) devices integrated into them. This paper reviews the various tools and techniques used in the creation of smart footwear for dif-ferent applications.

The space shuttle, a revolutionary spacecraft that has played a significant role in human space exploration, was composed of various advanced materials that were carefully selected to meet the extreme demands of spaceflight. This review paper provides a comprehensive examination of the materials historically employed in the construction of space shuttles and explores the latest trends shaping the field. The evolution of space shuttle materials is traced from the inception of the space program to contemporary missions, highlighting key milestones, challenges, and breakthroughs. Emphasis is placed on the critical role that materials play in the overall performance, safety, and sustainability of space shuttles. The paper begins by elucidating the diverse requirements that materials must fulfill in the harsh and complex environment of space, encompassing extreme temperatures, radiation exposure, and mechanical stresses. A detailed analysis of the materials utilized in the fabrication of various shuttle components, such as thermal protection systems, structural elements, and propulsion systems, is presented. Special attention is given to the challenges posed by re-entry and the strategies employed to mitigate heat-related issues. Furthermore, the review explores recent innovations and emerging materials that are reshaping the landscape of space shuttle design. Advancements in nanotechnology, composite materials, and additive manufacturing are discussed in the context of their potential applications for enhancing shuttle performance and reducing mission costs. The paper also addresses the importance of sustainability in space exploration, exploring materials with lower environmental impact and improved recyclability. The review concludes with a forward-looking perspective on the future of materials, considering ongoing research, development, and the potential incorporation of cutting-edge technologies. Insights are provided into how the evolving landscape of materials science may influence the design and manufacturing processes of the next generation of space vehicles. This paper provides an overview of the materials used in the construction of the space shuttle, including their properties, applications, and challenges. The materials used in the space shuttle are critical in ensuring the safety, performance, and longevity of the spacecraft, and understanding their characteristics and performance in space is crucial for the advancement of aerospace engineering.

This paper presents an on-device semi-supervised human activity detection system that can learn and predict human activity patterns in real time. The proposed semi-supervised learning (SSL) framework uses sparsely labelled user activity events acquired from Inertial Measurement Unit (IMU) sensors installed as wearable device. The objective is to learn classification of multiple classes of human activities in real-time. The proposed cluster-based learning model in this approach is trained with data from the same target user, thus preserving data privacy while providing personalized activity detection services. Two different cluster labelling strategies, namely, population-based, and distance-based, are employed to achieve the desired classification performance. A comparative study of these strategies has been presented in terms of classifiability and classification accuracies. The proposed system is shown to be computationally efficient, which is relevant in the context of limited computing resources on typical wearable devices. The trade-off between classification accuracy and computation complexity is analyzed for different algorithmic hyper-parameters and other system parameters. Extensive experimentation and simulation study have been conducted on a multi-user human activity data from the public do-main in order to validate the proposed learning paradigm.

The pandemic history of pathogenic SARS-CoV-2 associated COVID-19 infection began in December 2019, with its emergence in Wuhan, China. Pertaining to its high transmissibility and wide host adaptability, this new and unique human coronavirus spread across the planet affecting almost every country, inflicting 91 million people and causing 1.9 million deaths (as of January 17th, 2021). Limited or negligible pre-existing immunity to multiple SARS-CoV-2 variants has resulted in severe morbidity and mortality worldwide, as well as a record-breaking surge in the use of medical-surgical supplies and personal protective equipments. In response to the global need for effective sterilization techniques, this study evaluated the virucidal efficacy of FATHHOME’s self-contained, ozone-based dry-sanitizing device, by dose and time response assessment. We tested inactivation of human coronavirus, HCoV-OC43, a close genetic model of SARS-CoV-2, on porous (N95 filtering facepiece respirator/FFR) and nonporous (glass) surfaces. We started our assays with 20 ppm of ozone for 10 min exposure, which was able to effectively reduce, 99.8% and 99.9% of virus from glass and N95 FFR surfaces, respectively. Importantly, the virus was completely inactivated, below the detection limit (over 6-log10 reduction) with 25 ppm ozone for 15 mins on both tested surfaces. As expected, a higher ozone concentration (50 ppm) resulted in faster inactivation of HCoV-OC43 with 100% inactivation in 10 mins from both the surfaces, with no residual ozone present after completion of the 5-minute post exposure recapture cycle and no measurable increase in ambient ozone levels. These results confirmed that FATHHOME’s device may provide a safe and viable solution for rapid decontamination of SARS-CoV-2- from worn items, frequently touched items, and PPE including N95 FFRs, face shields and other personal items.

In the present era, infertility is one of the major issues which restricts many couples to have their own kids. Infertility is the inability to achieve a clinical pregnancy after regular unprotected sexual intercourse for the period of one year or more. Various factors including defective male or female germ cell development, unhealthy and improper lifestyles, diseases like cancer and associated chemo-or-radiation therapies, congenital disorders etc. may be responsible for infertility. Therefore, it is highly important to understand the basic concepts of germ cell development including primordial germ cell (PGC) formation, specification, migration, entry to genital ridges and their molecular mechanisms, activated pathways, paracrine and autocrine signaling, along with possible alteration which can hamper germ cell development and can cause adversities like cancer progression and infertility. Knowing all these aspects in a proper way can be very much helpful in improving our understanding about gametogenesis and finding possible ways to cure related disorders. Here in this review, various aspects of gametogenesis especially female gametes and relevant factors causing functional impairment have been thoroughly discussed.

Myofascial Pain Syndrome (MPS) is a prevalent chronic pain disorder characterized by myofascial trigger points (MTrPs). Current diagnosis relies on manual detection of MTrPs, which has low inter-rater reliability. This study aims to enhance diagnosis through machine learning (ML) and a combination of texture-features extracted from B-mode ultrasound (B-mode-US) images. Four texture-features were investigated: statistical-features, and their combination with Gabor, local binary (LBP), and SEGL, LBP + gray-level-co-occurrence-matrices + Edge for the classification of MTrPs on the B-mode-US images. B-mode-US images of trapezius muscles (n=90) were examined for MTrPs and healthy muscle. Three methods of LBP, SEGL, and Gabor were separately calculated for each B-mode-US image. Then, the statistical-features (e.g., entropy) were calculated over the B-mode-US images and those three methods. Seven Machine learning (ML) classifiers (e.g., neural network) with the calculated statistical-features were applied to discriminate MTrPs from healthy muscle. Additionally, traditional statistical analysis (i.e., ANOVA) was calculated between statistical-features within each method. The results indicated that the combination of statistical-features effectively discriminated between MTrPs and healthy muscles, based on traditional statistical analysis. However, ML classifiers struggled due to high variation and similar mean values among them. This discrepancy between them prompts further exploration of US texture-based automated ML for MTrPs diagnosis.

Balancing the levels of ammonia, in poultry facilities plays a role in enhancing the welfare and productivity of animals. Various environmental factors such as temperature, humidity, ammonia concentrations, carbon dioxide concentration and exposure to light have an impact on the wellbeing and efficiency of poultry. It is essential to monitor these elements to achieve outcomes necessitating practical, reliable and cost-effective solutions. Leveraging the Internet of Things (IoT) in conjunction with LoRa-WAN (Long Range Wide Area Network) technology presents an opportunity in the industry by enhancing connectivity among devices and facilitating real time data transmission. This research introduces a hardware and software setup for monitoring conditions at poultry farms through LoRa WAN and IoT technologies. More-over researchers employed Random Forest Machine Learning prediction models to tune the concentration of Ammonia (NH3) in poultry facilities across a wide area or multiple Poultry houses. Comparative assessments were conducted to evaluate the proposed system against devices revealing a correlation exceeding 0.9830 between the sensors and standard equipment while costing 28% less than traditional tools. The prototype displayed outcomes underscoring its potential, for managing poultry facilities across multiple farms with centralized oversight.

Seasonal changes and varying degree of corneal hydration has been linked to excimer laser corneal ablation rates. Use of PMMA as calibration material in refractive lasers is well established. However, PMMA ablation may be equally affected by seasonal variations in temperature and humidity, intern affecting the calibration process. Aim of this work is to analyze the effect of seasonal changes in PMMA Performance using SCHWIND AMARIS laser system. PET and PMMA ablations conducted in climate-controlled environment with 826 consecutive AMARIS systems manufactured over 6 years were retrospectively analyzed. Lasers were stratified depending on seasons and months of the year. Metrics like Single laser pulse fluence, nominal number of laser pulses, mean performance, standard deviation, and technical performance of system were compared to global average values. Cyclic winter-summer variation was confirmed with seasons winter and summer showing statistically significant variations with respect to global values. Metric technical performance showed deeper PMMA ablation performance in summertime, with maximum seasonal deviation of 6%. Results were consistently confirmed in seasonal as well as monthly analyses. These findings could help minimize variance among laser systems by implementing compensation factors depending on seasons such that laser systems installed worldwide follow the same trend line of variation.

Genetic variability in rice breeding program plays the very crucial role. It provides outstanding pool of superior alleles governing better agronomic and quality characters through association mapping. For the understanding of population structure and genetic relationship among the different rice lines is indispensable prior to setting of correlation among dynamic alleles and traits. In the present investigation, genetic diversity and population structure of 116 rice accessions by using 64 polymorphic SSR markers was targeted for the evaluation of the genetic relatedness and diversity. Genotyping assessment based on SSR markers revealed a total of 225 alleles, with an average PIC value of 0.755. The germplasm lines were classified into three distinct subgroups through population structure analysis, utilizing both model and distance-based approaches. AMOVA analysis showed that 11% of the total variation could be attributed to differences between groups, while the remaining 89% of the variation was likely due to differences within groups. The study suggests that the population structure and genetic relatedness should be considered when working with the core collection of 116 rice germplasm lines for association mapping, aiming to establish marker-trait associations.

Type 2 diabetes mellitus (T2DM) is a polygenic metabolic disease described by hyperglycemia, which is caused by insulin resistance or reduced insulin secretion. Interaction between various genetic variants and environmental factors triggers T2DM. The main aim of this study was to find the risk associated with genetic variant (rs5210) of KCNJ11gene in the development of T2D in Indian Population. A total number of 300 cases of T2D and 100 control samples were studied to find the polymorphism in KCNJ11 through PCR-RFLP. The genotype and allele frequencies in T2DM cases were significantly different from the control population. We found a significant association of KCNJ11 (rs5210) gene polymorphism with T2DM in North Indian patients indicating the role of this variant in developing risk for T2DM.

Long COVID, also referred to as post-COVID-19 condition, describes the lingering after-effects of COVID-19 infection. This complex syndrome encompasses a range of symptoms affecting physical well-being, cognitive function, and mental health. The widespread impact of long COVID extends beyond individual sufferers, placing significant strain on healthcare systems and causing far-reaching socioeconomic consequences. The scale of this multifaceted health crisis is unparalleled in recent history, affecting millions globally and challenging our understanding of post-viral conditions. This study aimed to explore the possibility of targeting G protein-coupled receptors (GPCRs) signaling pathway-related genes as therapeutics for long COVID using computational analysis. Targeting GPCRs and their signaling pathways holds significant promise for long COVID therapeutics, as these receptors are involved in crucial physiological processes including immune regulation, inflammation control, and neurotransmission. All of these are implicated in the diverse and persistent symptoms of long COVID, potentially offering a versatile approach to addressing multiple aspects of the condition simultaneously. This study identified 21 genes associated with GPCRs for further exploration as potential targets. Notably, GPCR genes such as HRAS, KRAS, GNAQ, and GNA11 were determined to be possible therapeutic targets for the pathophysiological mechanisms of long COVID. FDA-approved drugs targeting these genes were identified, including Binimetinib, Cabozantinib, Selumetinib, Panitumumab, Cetuximab, Adagrasib, Tipifarnib, and Sotorasib. Additionally, Naltrexone and Tipifarnib have emerged as potential new treatment options for long COVID. SARS-CoV-2 may hijack GPCR signaling pathways to dysregulate lung ion and fluid transport, contributing to the pathophysiology of pulmonary edema in patients with COVID-19. Further research is required to translate the findings into the development of therapeutics and to investigate the relationship between viral infection and GPCR signalling.

As the outbreak of coronavirus disease 2019 (COVID-19) is continuously increasing in India, so epidemiological modeling of COVID-19 data is urgently required for administrative strategies. Time series and is capable to predict future observations by modeling the data based on past and present data. Here, we have modeled the epidemiological COVID-19 Indian data using various models. Based on the collected COVID-19 outbreak data, we try to find the propagation rule of this outbreak disease and predict the outbreak situations in India. For India data, the time series model gives the best results in the form of predication as compared to other models for all variables of COVID-19. For new cases, new deaths, total cases and total deaths, the best fitted ARIMA models are as follows: ARIMA(0,2,3), ARIMA(0,1,1), ARIMA(0,2,0) and ARIMA(0,2,1). Based on time series analysis, we predict all variables for next month and conclude that the predictive value of Indian COVID-19 data of total cases is more than 20 lakhs with more than 43 thousand total deaths. The present chapter recommended that a comparison between various predictive models provide the accurate and better forecast value of the COVID-19 outbreak for all study variables.

To utilize heterosis and combining ability for grain yield and its attributing traits along with three physiological traits viz., leaf area index, chlorophyll fluorescence and chlorophyll content in bread wheat (Tritium aestivum L.) the crosses were attempted during rabi, 2019-20 in a half diallel mating scheme among 8 wheat varieties and the study was carried out during rabi, 2020-21 at Block No. D-13 field of Mahua block, Simradha research farm, Rani Lakshmi Bai Central Agricultural University, Jhansi. Analysis of variance showed significant differences for treatments. Based on per se performance the hybrids NW 5054 x PBW 723 (23.3 g), and HI 1544 x NIAW 34 (23.14 g), were identified for the higher grain yield. The hybrid HI 544 x NIAW 34 (18.41 %) was identified as the best heterotic cross for grain yield per plant and its component traits. The hybrids HI 1544 x NIAW 34 (3.40) and HI 1544 x GW 322 (2.76), as well as the parents HD 3086 (21.85) and PBW 723 (20.68), were identified as superior economic hybrids and parents based on SCA/GCA effects, per se performance for grain yield per plant, and its component trait in bread wheat. Days to heading, flag leaf length, biological yield per plant, harvest index, number of grains per spike, 1000 grain weight, spike length, leaf area index, chlorophyll fluorescence, and chlorophyll content were all positively correlated with grain yield per plant.

Tuning tensor program generation involves navigating a vast search space to find optimal program transformations and measurements for a program on target hardware. The complexity of this process is further amplified by the exponential combinations of transformations, especially in heterogeneous environments. This research addresses these challenges by introducing a novel approach that learns the joint neural network and hardware features space, facilitating knowledge transfer to new, unseen target hardware. A comprehensive analysis is conducted on the existing state-of-the-art dataset, TenSet, including a thorough examination of test split strategies and the proposal of methodologies for dataset pruning. Leveraging an attention-inspired technique, we tailor the tuning of tensor programs to embed both neural network and hardware-specific features. Notably, our approach substantially reduces the dataset size by up to 53% compared to the baseline without compromising Pairwise Comparison Accuracy (PCA). Furthermore, our proposed methodology demonstrates competitive or improved mean inference times with only 25%40% of the baseline tuning time across various networks and target hardware. The attention-based tuner can effectively utilize schedules learned from previous hardware program measurements to optimize tensor program tuning on previously unseen hardware, achieving a top-5 accuracy exceeding 90%. This research introduces a significant advancement in auto-tuning tensor program generation, addressing the complexities associated with heterogeneous environments and showcasing promising results regarding efficiency and accuracy.

The mental and emotional health of an athlete is crucial for their performance and well-being. Sports-related stress can significantly impair that mental health. Yoga of Immortals (YOI) is a unique combination of specific yogic postures, breathing exercises, sound therapy &amp; meditation, which has demonstrated benefit in improving measures of mental health. This study used the Sports Mind Inventory (SMI) to examine whether YOI can improve the resilience of athletes to sports-related stress. Participants were a diverse set of athletes based in Mauritius who routinely engage in a wide range of athletic activities. Participants were randomly assigned to receive four weeks of YOI or no intervention. Both groups completed the SMI questionarie at baseline and again after four weeks. The YOI intervention significantly increased the total mean SMI scores by 14%, indicating improved sports resilience and psychological health. No improvement was observed in the control group. This study demonstrates that YOI is a promising intervention in improving sports related stress.

Corona Virus Infectious Disease-2019 (COVID-19) outbreak originated recently at Wuhan, China in December 2019. It has already spread rapidly to more than 200 countries and has been declared a pandemic by WHO. It is caused by a beta-coronavirus named as SARS-CoV-2. There is no definitive cure, either drug or vaccine, to treat or prevent this viral disease. Recently, the crystal structure of the main protease M^pro has been determined. M^pro is responsible for the proteolytic maturation of the polyprotein essential for the viral replication and transcription, which makes it an important drug target. The discovery of new drug molecules may take years before getting to the clinics. So, considering urgency we performed molecular docking studies using FDA approved drugs to identify molecules that could potentially bind to the substrate-binding site and inhibit SARS-CoV-2 main protease (M^pro). We used the Glide module in Schrodinger software suite to perform molecular docking studies followed by MM-GBSA based energy calculations to score the hit molecules. Molecular docking and manual analysis suggest that several drugs may bind and potentially inhibit M^pro. We also performed molecular simulations studies for selected compounds to evaluate protein-drug interactions. Interestingly, we observed only one antiviral compound, Adefovir, in the top50 list of compounds. Considering bioavailability, lesser toxicity, route of administration some of the top-ranked drugs including lumefantrine (antimalarial), dipyridamole (coronary vasodilator), dihydroergotamine (used for treating migraine), hexoprenaline (anti- asthmatic), riboflavin (vitamin B2) and pantethine (vitamin B5) may be taken forward for further in vitro and in vivo experiments to investigate their therapeutic potential.

As the demand of heating and air conditioning is increasing day by day due to getting a comfortable life, for that reason, the market for heat pump and air conditioning systems or both, is seeing a steady increase in low global warming potential. All researchers on heat pump and air conditioning systems are searching for environmentally friendly refrigerants. However, a significant number of contemporary refrigerants (HCFCs) have the potential to cause global warming and ozone depletion, such as those found in heat pump air conditioning systems. Using more environmentally friendly refrigerants, including hydrocarbon and blend refrigerant mixtures, is one way to lower GWP and Ozone depletion potential. This study examines the performance of several alternative refrigerants, including R32, R134A, R410A, R22, and R290. In order to examine the performance of R290, theoretical and simulation results are compared on various refrigerants while taking into account a number of performance parameters, including the refrigerant energy efficiency ratio, mass flow rate, coefficient of performance, cooling capacity, and compressor work for one ton of refrigeration (1TR) air conditioners. One player with a very low GWP is R290. However, the only drawback is that it can catch fire and is hazardous to use in split-type commercial heat pump air conditioning applications. The environmental and thermo physical properties of HC-290 are significantly better than those of other materials, according to theoretical and simulation studies utilizing Cool Pack software, hence it is a viable replacement.

The human interferon-induced transmembrane (IFITM) proteins inhibit the fusion of a broad spectrum of enveloped viruses, both when expressed in target cells and when present in infected cells. Upon expression in infected cells, IFITMs incorporate into progeny virions and reduce their infectivity by a poorly understood mechanism. Since only a few Env glycoproteins are present on HIV-1 particles, and Env clustering has been proposed to be essential for optimal infectivity, we asked if IFITM protein incorporation modulates HIV-1 Env clustering. The incorporation of two members of the IFITM family, IFITM1 and IFITM3, into HIV-1 pseudoviruses verified by Western blotting correlated with a marked reduction of infectivity. Super-resolution imaging of Env distribution on single HIV-1 pseudoviruses did not reveal significant effects of IFITMs on Env clustering. However, IFITM3 markedly reduced the Env processing and incorporation into virions relative to control and IFITM1-containing viruses. These results suggest that IFITM1 and IFITM3 restrict progeny HIV-1 infectivity via distinct mechanisms. We propose that IFITM1 interferes with viral fusion by altering the properties of the viral membrane or Env functionality by means other than disrupting Env clusters, while IFITM3 also targets Env processing and incorporation into progeny virions.

Urinary peptides are the products of systemic protein turn over which are excreted out of the body and hence can serve as an important biomarker for various pathophysiology. These peptides in other species of bovine have been reported to possess several bioactive properties. To investigate the potential of urinary peptides we generated a peptidome profile from the urine of Murrah Buffaloes (n=10). Urine samples were processed using &lt;10 kDa MWCO filter, filtrate obtained was used for peptide extraction using Solid Phase Extraction (SPE). Eluate, obtained after SPE was extracted using ethyl acetate and the peptide-containing aqueous phase was collected. We extensively assessed the antimicrobial properties of the urinary aqueous phase. The nLC-MS/MS of the aqueous phase from 10 animals resulted in the identification of 8165 peptides originating from 6041 parent proteins. 76 Proteases responsible for the release of these sequences from precursor proteins were also identified. We further analyzed these peptide sequences to identify bioactive peptides and classify them into anti-cancerous, anti-hypertensive, anti-microbial, and anti-inflammatory groups with a special emphasis on antimicrobial properties. With this in mind, we simultaneously conducted experiments to evaluate antimicrobial properties of urinary aqueous extract on three pathogenic bacterial strains S. aureus, E. coli, &amp; S. agalactiae.

Study in black rice has gain prominence in recent times due to its high nutritive value, curative effect, and anti-oxidant properties. However, its poor agronomic traits, including low yield necessitates the incorporation of the colour-grain trait into elite varieties through plant breeding techniques. SSR markers play an important role in plant identification and breeding. Here, the generation of reference-based transcriptome, annotation of transcriptome datasets, and a large set of simple sequence repeat (SSR) markers derived from Black rice have been described. In all 28664 SSRs were predicted in 34978 (48.59%) expressed transcripts. However, 7068 (20.20%) transcripts were found to have more than one SSR. The identified SSRs were dominated by tri-nucleotide and tetra-nucleotide repeats representing about 54.11% and 33.31% respectively, of total SSRs. Validation of selected markers associated with anthocyanin trait performed across different black rice accessions established the reliability of the process used for mining SSR markers. The SSR markers identified in this study could be used to select varieties with desired traits, and to investigate the genetic mechanism underlying anthocyanin accumulation in the pericarps of black rice. Furthermore, the findings from this study may prove beneficial in future genetic diversity studies, primer development, and selective breeding programs.

Valorization of food and fruit wastes has the potential for the production of sustainable energy and biochemicals. Approximately 70% of the weight of the original jackfruit (Artocarpus heterophyllus L.) fruit is lost during processing as waste in the form of peeled skin and core, both of which have not been utilized and, thus contribute to disposal as well as pollution issues. The major components, cellulose, and hemicellulose, can be biologically transformed easily into bioenergy sources like ethanol, methanol, and butanol; valuable phenolics and biotechnological products like pectin, citric acid, bromelain, ferulic acid, and vanillin; and many other products. These residues can also be utilized as essential sources for the biological transformation process leading to the production of numerous products with added value, such as phenolic antioxidants, phenolic flavor compounds, and organic acids. Thus, the value addition of jackfruit waste can support the sustainable solution towards food and nutritional security. In this way, zero waste can be achieved through novel biorefineries which are critically highlighted in this paper. Furthermore, novel technologies for the conversion of jackfruit wastes are summarized with recent findings.

In this study, global response analysis was performed to explore the mechanism of action of Usnic acid and its synergy with Norfloxacin, a well-known quinolone antibiotic to which MRSA clinical isolates showed resistance (MIC, 500 µg/mL). Microdilution assay, growth kinetics, microscopic analysis, and cell-based assays consistently showed that Usnic acid possess strong anti-staphylococcal activity (MIC, 7.8 µg/ml), causes cell leakage, modulates efflux pump activity, and synergizes with Norfloxacin against multi-drug resistant clinical isolate MRSA 2071. Whole cell proteome profiling using gel-free based nano-LC-ESI-QTOF-MS/MS revealed several proteins whose expression was significantly modulated by Usnic acid and Norfloxacin alone or in combination. Usnic acid downregulated the abundance of RNA polymerase subunits (RpoB, RpoC), carbamoyl phosphate synthase large subunit (PyrAB), chaperone (GroEL), and adenylosuccinate synthetase (PurA). Interestingly, proteins found to be upregulated in the presence of Usnic acid and Norfloxacin included oxidative stress-related proteins such as peroxidase (Tpx), alkyl hydroperoxide reductase (AphC), and general stress protein (UspA). This study clearly showed that Usnic acid affects numerous cellular targets and can potentiate the action of Norfloxacin. Furthermore, an in-vivo study emphasized the use of Usnic acid to develop as a safe phytopharmaceutical to repurpose old antibiotics through combination therapy against MRSA infections.

Game-based learning assessments lean on educational data mining approaches such as stealth assessments and quasi mixed-methods that help gather student learning proficiency. Rarely do we see approaches where student proficiency in learning is woven into the game's design. Educational Burst Games (EBGs) is a new approach to improving learning proficiency by designing fast-paced, short, repetitive, and skill-based games. They have the potential to be effective learning interventions both during instruction in the classroom and during after-school activities such as assignments and homework. Over five years we have developed two EBGs aimed at improving linear algebra concepts of undergraduate students. In this study, we provide results of an in-depth evaluation of the two EBGs developed with 45 participants that represent our target population. We discuss the role of EBGs and their design constructs such as pace and repetition, the effect of the format (2D vs. 3D), the complexity of the levels and the influence of prior knowledge on the learning outcomes.

Circulating miRNA-451a has recently emerged as an important biomolecule with potential clinical values as a diagnostic marker for several diseases. However, to be used as such, it is critical to accurately assay miRNA-451a in the clinic. Yet, pre-analytical factors that can affect such an error-free clinical assay of miRNA-451a have not been explored. This study aimed at investigating several of these pre-analytical factors that may affect the accurate quantification of miRNA-451a in human blood samples. We initially evaluated miRNA-451a levels in red blood cells (RBCs), white blood cells (WBCs), platelets, and plasma by droplet digital PCR (ddPCR). Next, we quantified miRNA-451a levels in whole blood or plasma stored at different temperatures for different time periods by ddPCR and used hemolyzed plasma samples to study the effects of hemolysis on plasma miRNA-451a concentration. We found that WBCs and platelets contain trace amounts of miRNA-451a and that about 99.9% of miRNA-451a in the blood are localized in RBCs. Hemolyzed plasma showed 58.5-fold increase in miRNA-451 concentration as compared to non-hemolyzed plasma. Importantly, plasma stored at room temperature (RT) or 4ºC showed a steady statistically significant decrease in miRNA-451a levels over a 7-day period whereas plasma samples stored at -20 or -80ºC for up to 7 days showed stable miRNA-451a levels. Together, our data indicate that hemolysis and blood storage at RT or 4ºC may have significant negative effects on the levels of circulating miRNA-451a.

The reinforcing ability of the fillers results in significant improvements in properties of polymer matrix at extremely low filler loadings compared to conventional fillers. In view of this, present review article describes the different methods used in preparation of different rubber nanocomposites reinforced with nanodimensional individual carbonaceous fillers, such as graphene, expanded graphite, single walled carbon nanotubes, multiwalled carbon nanotubes and graphite oxide, graphene oxide and hybrid fillers consisting combination of individual fillers. This is followed by review of mechanical properties (tensile strength, elongation at break, Young modulus, and fracture toughness) and dynamic mechanical properties (glass transition temperature, crystallization temperature, melting point) of these rubber nanocomposites. Finally, Payne and Mullin Effects have also been reviewed in rubber filled with different carbon based nanofillers.

Proprotein convertase subtilisin/Kexin type 9 (PCSK9) is a proteolytic enzyme that indirectly regulates serum LDL cholesterol by destroying LDL receptors. In clinical studies, the main role of the proprotein convertase subtilisin/Kexin type9 (PCSK9) inhibitor in cholesterol regulation was elucidated. It is produced in the liver but is also present in the kidney and intestine. It prevents HMGCo from synthesizing cholesterol. SREBP-2 is a reductase that is induced by statins. In a dose-dependent manner, increasing SREBP-2 levels enhanced LDL-R and PCSK9 gene expression. At the minimum, two procedures have been developed to overcome the plasma level of PCSK9. This is the LDLR test, polyclonal antibodies, and sentience oligonucleotide. Lower dosage statin treatment with a proprotein convertase subtilisin/Kexin type9 inhibitor will be most efficient in lowering LDL and avoiding statin adverse effects. In multiple long-term trials, statins have been found to reduce cardiovascular mortality by 30% and stroke incidence by 20%. In this way, we conclude the role of PCSK9 in hypercholesterolemia.

Endocytosis in mammalian cells is a fundamental cellular machinery that regulates vital physiological processes, such as the absorption of metabolites, release of neurotransmitters, hormonal secretion, cellular defense, and delivery of biomolecules across the plasma membrane. A remarkable characteristic of the endocytic machinery is the sequential assembly of the complex proteins at the plasma membrane followed by transportation of various cargo molecules to different cellular compartments. In all eukaryotic cells, functional characterization of endocytic pathways is based on dynamics of the protein complex modules. To coordinate the assembly and functions of the numerous parts of the endocytic machinery, the endocytic proteins interact significantly within and between the modules. Clathrin dependent and independent endocytosis, caveolar pathway and receptor mediated endocytosis have been attributed in a greater variety of physiological and pathophysiological roles such as, autophagy, metabolism, cell division, apoptosis, cellular defense, and intestinal permeabilization. Notably, any defect or alteration in the endocytic machinery results in the development of pathological consequences associated with human diseases such as cancer, cardiovascular diseases, neurological diseases, and inflammatory diseases. In this review, an in-depth endeavor has made to illustrate the process of endocytosis, and associated mechanisms describing pathological manifestation associated with dysregulated endocytosis machinery.

Laser-induced breakdown spectroscopy (LIBS) of pharmaceutical drugs that contain paracetamol is investigated in air and argon atmospheres. Characteristic neutral and ionic spectral lines of various elements and molecular signatures of CN violet and C₂ Swan band systems are observed. The relative hardness of all drug samples is measured as well. Principal component analysis, a multivariate method, is applied in data analysis for demarcation purposes of the drug samples. The CN violet and C₂ Swan spectral radiances are investigated for evaluation of possible correlation of the chemical and molecular structures of the pharmaceuticals. Complementary Raman and Fourier-transform-infra-red spectroscopies are used record molecular spectra of the drug samples. The applicationof the above techniques for the drug screening are important for identification and mitigation of drugs that reveal additives that may cause adverse side-effects.

Little is understood about the impact of nebulisation on the viability of SARS-CoV-2. In this study, a range of nebulisers with differing methods of aerosol generation were evaluated to determine SARS-CoV-2 viability following aerosolisation. The aerosol particle size distribution was assessed using an aerosol particle sizer (APS) and SARS-CoV-2 viability was determined after collection into liquid media using All-Glass Impingers (AGI). Viable particles of SARS-CoV-2 were further characterised using the Collison 6-jet nebuliser in conjunction with novel sample techniques in an Andersen size-fractioning sampler to predict lung deposition profiles. Results demonstrate that all the tested nebulisers can generate stable, polydisperse aerosols (Geometric standard deviation (GSD) circa 1.8) in the respirable range (1.2 to 2.2µm). Viable fractions (PFU/particle, the virus viability as a function of total particles produced) were circa 5x10^-3 and were not significantly affected by relative humidity. The novel Andersen sample collection methods successfully captured viable virus particles across all sizes; with most particle sizes below 3.3µm. MMADs (Mass Median Aerodynamic Diameters) were calculated from linear regression of log10-log10 transformed cumulative PFU data, and calculated MMADs accorded well with APS measurements and did not differ across collection method types. This data will be vital in informing animal aerosol challenge models, and infection prevention and control policies.

Background: Thyroid nodules are common, affecting nearly 65% of the general population. While they are widespread, it's crucial to recognize that the majority of these nodules are not cancerous and can be effectively managed through careful monitoring. Thyroid ultrasound is commonly used to evaluate thyroid nodules owing to its low cost, wide availability and technical ease of application. In sonography, it's crucial to assess characteristics and vascular flow. Yet, conventional color and power Doppler ultrasound may not always effectively capture small vessels and low velocities due to their limitations. Superior Microvascular Imaging can display the blood flow of small vessels and low-velocity blood flow, which is difficult to visualize with conventional Doppler ultrasound. Meanwhile other thyroid scans, that utilize either [99mTc] pertechnetate or (18)F-fluorodeoxyglucose, can categorize nodules as hyperactive ('hot'), exhibiting normal activity ('warm'), or having reduced/no activity ('cold'). Thus, the main goal of screening and tracking thyroid nodules is to improve the management of potentially worrisome thyroid malignancies. Methodology: The study was conducted within the structural framework of a network meta-analysis, with the index tests being - SMI, Radionuclide Scanning and Color Doppler, and the general link between them being the reference gold standard. The data collection involved searching for all pertinent literature using PubMed, Google Scholar, and the Cochrane Library databases. The essential data was extracted following the application of specific inclusion and exclusion criteria.This meta-analysis included 7 RCTs with 1,152 patients for Radionuclide Scanning, 10 RCTs with 1,847 patients for SMI and 8 RCTs with 4,480 patients for Color Doppler. They were analyzed using the QualSyst tool and the risk of bias was evaluated by using QUADAS-2 analysis.The statistical software packages MetaDiSc 1.4, RevMan (Review Manager, version 5.3), SPSS (Statistical Package for the Social Sciences, version 20) and Excel in Stata 14 were used to perform the statistical analyses. Result: According to the findings, the sensitivity for Radionuclide Scanning is calculated as 0.79 (range: 0.76 to 0.81) and specificity as 0.55 (range: 0.50 to 0.60) with a 95% confidence interval. The overall diagnostic odds ratio (DOR) for Radionuclide Scanning Is 10.54.Additionally, with a 95% confidence interval, SMI exhibits a sensitivity of 0.82 (with a range of 0.79 to 0.84) and a specificity of 0.86 (with a range of 0.84 to 0.88), while the overall DOR is approximately 25.73. Color Doppler demonstrates a calculated sensitivity of 0.71 (ranging from 0.69 to 0.74) and specificity of 0.81 (ranging from 0.79 to 0.83) with a 95% confidence interval, while its DOR is 13.48.Conclusion: As evidenced by this meta-analysis, blood flow dependent imaging techniques are superior in their abilities to be used as diagnostic tests, especially in conjunction with their use in detecting thyroid nodules. This study offers a starting point for delving deeper into the utilization of Radionuclide Imaging, Superior Microvascular Imaging and Color Doppler in diagnosing thyroid nodules in a clinical setting. Key Words: - Thyroid Nodule, Radionuclide Scanning, Superior Microvascular Imaging, Color Doppler Diagnosis, Sensitivity, Specificity, Diagnostic imaging

Long COVID, characterized by persistent symptoms lasting from weeks to several months following initial infection with Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), significantly impacts individuals' quality of life and poses a potential long-term burden on global healthcare systems. Clinical manifestations of Long COVID are diverse, affecting multiple systems including respiratory, cardiovascular, neurological, and gastrointestinal. While the precise mechanisms underlying Long COVID remain unclear, emerging evidence suggests involvement of various bodily pathways. This study employs bioinformatics tools to investigate the mechanisms of Long COVID and identify potential drug targets associated with key pathways, including inflammation, cytokines, immune response, coagulation, mitochondrial function, oxidative stress, and autonomic dysfunction. Tools such as the National Center for Biotechnology Information (NCBI) database, PubChem, UniProt, and iGEMDOCK were utilized for pathway analysis and molecular docking. The analysis aimed to identify drug molecules that may interact with target proteins to regulate or inhibit pathway activity, yielding several potential drug candidates for further investigation.

Purpose: We hypothesized that a recently introduced epibulbar micro-shunt (PRESERFLO, P) produces nycthemeral (24h) intraocular pressure (IOP) profiles different from ab-interno trabeculectomy (Trabectome, T). P is a flow restrictor that drains fluid into the sub-tenon space. In contrast, T increases conventional outflow, which is limited by episcleral venous pressure. Methods: In this prospective cohort, we analyzed 68 patients (34 P and 34 T) who presented for 24-h IOP monitoring 6 to 12 months after surgery. IOP and tonographic outflow facility were measured in the habitual position using a pneumatonometer. The IOP variation was considered the primary outcome measure. Glaucoma medications were also compared. Results: P had a higher baseline IOP than T (24.8±10.0 vs. 17.3±7.9 mmHg, p=0.001). Postoperatively, P and T had similar nycthemeral IOP profiles, but IOP in P was significantly lower than in T, except at 4 pm. P had a lower absolute IOP variation than T (5.8±2.6 vs. 7.1±2.7 mmHg, p=0.049). The relative IOP variation was similar in both (34.8±13.2 vs. 37.2±13.1, p=0.45) as was the tonographic outflow facility (0.35±0.23 vs. 0.26±0.18 µl/min/mmHg, p=0.097). Conclusion: Nycthemeral IOP profiles of P and T were similar, but P had lower IOPs and less variation than T. This could reflect how T, unlike P, is more impacted by habitual, positional factors, especially at night.

1) Landsat operational land imager (OLI) data and consequent laboratory measurements were used to predict Chlorophyll-a (Chl-a) concentration and the trophic states for an inland lake within the East Kolkata Wetland, India; 2) The most suitable band ratio was identified by performing Pearson correlation analysis between Chl-a concentrations and possible OLI band and band ratios from the study points; 3) The results showed highest correlation coefficient from the band ratio OLI5/OLI4 with an R value of 0.85. The prediction model was then developed by applying regression analysis between the band ratio OLI5/OLI4 and Chl-a concentration of the study points. The reflectance ratios of the validation points were given as input on the prediction model and the model output was considered as predicted Chl-a values of the validation points to check the efficiency of the prediction model. The regression model between laboratory-derived Chl-a value and model-fitted Chl-a value of the validation points revealed a high correlation with an R² value of 0.78. Trophic State Index (TSI) of the lake was also calculated from laboratory-derived Chl-a value and model-fitted Chl-a value of the validation points. The study presented a high correlation of TSI determined from predicted data with TSI from laboratory reference data (R = 0.88). The TSI values of the lake ranged from 65 to 75 which indicate that the lake is appeared to be eutrophic to hypereutrophic conditions. 4) This empirical study showed that Landsat 8 OLI imagery can be effectively applied to estimate Chl-a levels and trophic states for inland lakes.