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In silico molecular docking study of Andrographis paniculata phytochemicals against TNF-? as a potent anti-rheumatoid drug
Tumor necrosis factor-? (TNF-?) is a proinflammatory cytokine which plays a crucial role in controlling inflammatory responses. The pathway of Rheumatoid arthritis (RA) leading to TNF-alpha is activated by macrophages and quite often by natural killer cells and lymphocytes. In the inflammatory phase, it is believed to be the main mediator and to be anchored with the progression of different diseases such as ankylosing spondylitis, Crohn's disease, and Rheumatoid arthritis (RA). The major goal of this study is to use in silico docking studies to investigate the anti-inflammatory potential of a bioactive molecule from the medicinal plant Andrographis paniculata. The three-dimensional structures of different phytochemicals of A. paniculata were obtained from PubChem database, and the receptor protein was derived from PDB database. Docking analysis was executed using AutoDock vina, and the binding energies were compared. Bisandrographolide A and Andrographidine C revealed the highest score of ?8.6 Kcal/mol, followed by, Neoandrographolide (?8.5 Kcal/mol). ADME and toxicity parameters were evaluated for these high scoring ligands and results showed that Andrographidine C could be a potent drug, whereas Neoandrographolide and Bisandrographolide A can be modified in invitro and can lead to a promising drug. Further, the top scorer (Andrographidine C) and control drug (Leflunomide) were subjected to 100 ns MD Simulation. The protein complex with Andrographidine C had more stable confirmation with lower RMSD (0.28 nm) and higher binding energy (?133.927 +/? 13.866 kJ/mol). In conclusion, Andrographidine C may be a potent surrogate to the disease-modifying anti-rheumatic drugs (DMARDs) & Non-steroidal anti-inflammatory drugs (NSAIDs) that has fewer or minor adverse effects and can aid in RA management. 2022 Informa UK Limited, trading as Taylor & Francis Group. -
In Silico Identification of 1-DTP Inhibitors of Corynebacterium diphtheriae Using Phytochemicals from Andrographis paniculata
A number of phytochemicals have been identified as promising drug molecules against a variety of diseases using an in-silico approach. The current research uses this approach to identify the phyto-derived drugs from Andrographis paniculata (Burm. f.) Wall. ex Nees (AP) for the treatment of diphtheria. In the present study, 18 bioactive molecules from Andrographis paniculata (obtained from the PubChem database) were docked against the diphtheria toxin using the AutoDock vina tool. Visualization of the top four molecules with the best dockscore, namely bisandrographolide (?10.4), andrographiside (?9.5), isoandrographolide (?9.4), and neoandrographolide (?9.1), helps gain a better understanding of the molecular interactions. Further screening using molecular dynamics simulation studies led to the identification of bisandrographolide and andrographiside as hit compounds. Investigation of pharmacokinetic properties, mainly ADMET, along with Lipinskis rule and binding affinity considerations, narrowed down the search for a potent drug to bisandrographolide, which was the only molecule to be negative for AMES toxicity. Thus, further modification of this compound followed by in vitro and in vivo studies can be used to examine itseffectiveness against diphtheria. 2023 by the authors. -
In Silico Analysis of the Apoptotic and HPV Inhibitory Roles of Some Selected Phytochemicals Detected from the Rhizomes of Greater Cardamom
Occurrence of cervical cancer, caused due to persistent human papilloma virus (HPV) infection, is common in women of developing countries. As the conventional treatments are expensive and associated with severe side effects, there is a need to find safer alternatives, which is affordable and less toxic to the healthy human cells. Present study aimed to evaluate the anti-HPV and apoptotic potential of four compounds from the greater cardamom (Amomum subulatum Roxb. var. Golsey), namely rhein, phytosphingosine, n-hexadecenoic acid and coronarin E. Their anti-HPV and apoptotic potential were studied against viral E6, E7 and few anti-apoptotic proteins of host cell (BCL2, XIAP, LIVIN) by in silico docking technique. Phytochemicals from the plant extract were analysed and identified by LC/MS and GC/MS. Involvement of the target proteins in various biological pathways was determined through KEGG. Structural optimization of the three-dimensional structures of the ligands (four phytochemicals and control drug) was done by Avogadro1.1. Receptor protein models were built using ProMod3 and other advanced tools. Pharmacophore modelling of the selected phytochemicals was performed in ZINCPharmer. Swiss ADME studies were undertaken to determine drug likeness. The ligands and proteins were digitally docked in DockThor docking program. Protein flexibility-molecular dynamic simulation helped to study proteinligand stability in real time. Finally, the correlation of evaluated molecules was studied by the use of principal component analysis (PCA) based on the docking scores. All the ligands were found to possess apoptotic and anti-cancer activities and did not violate Lipinsky criteria. n-Hexadecanoic acid and its analogues showed maximum efficacy against the target proteins. All the proteinligand interactions were found to be stable. The uncommon phytochemicals identified from rhizomes of greater cardamom have anti-cancer, apoptotic and HPV inhibitory potentials as analysed by docking and other in silico studies, which can be utilized in drug development after proper experimental validation. 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. -
In silico analysis of NHP2 membrane protein, a novel vaccine candidate present in the RD7 region of Mycobacterium tuberculosis
Mycobacterium tuberculosis, the etiological agent of tuberculosis, is one of the trickiest pathogens. We have only a few protective shields, like the BCG vaccine against the pathogen, which itself has poor efficacy in preventing adult tuberculosis. Even though different vaccine trials for an alternative vaccine have been conducted, those studies have not shown much promising results. In the current study, advanced computational technology was used to study the potential of a novel hypothetical mycobacterial protein, identified by subtractive hybridization, to be a vaccine candidate. NHP2 (Novel Hypothetical Protein 2), housed in the RD7 region of the clinical strains of M. tuberculosis, was studied for its physical, chemical, immunological and structural properties using different computational tools. PFAM studies and Gene ontology studies depicted NHP2 protein to be functionally active with a possible antibiotic binding domain too. Different computational tools used to assess the toxicity, allergenicity and antigenicity of the protein indicated its antigenic nature. Immune Epitope Database (IEDB) tools were used to study the T and B cell determinants of the protein. The 3D structure of the protein was designed, refined and authenticated using bioinformatics tools. The validated tertiary structure of theprotein was docked against the TLR3 immune receptor to study the binding affinity and docking scores. Molecular dynamic simulation of the protein-protein complex formed were studied. NHP2 was found to activate host immune response against tubercle bacillus and could be explored as a potential vaccine in the fight against tuberculosis. 2023, The Author(s), under exclusive licence to Plant Science and Biodiversity Centre, Slovak Academy of Sciences (SAS), Institute of Zoology, Slovak Academy of Sciences (SAS), Institute of Molecular Biology, Slovak Academy of Sciences (SAS). -
In Service Teachers' Diffrentiated Instructional Strategy and Students' Reflective Thinking and Empowered Learning
Every educational program aims at the comprehensive growth and development of learners. Education policymakers and teachers who are part of any education system have a pivotal role in providing an environment that empowers learners. Thinking pervades all spheres of human action and the ability to think reflectively differentiates man from other animals. Psychological theories have proved that, in a classroom, each learner is unique and has different learning profiles, i.e., learning style, intelligence preference, culture and gender. Therefore, one- sized curriculum doesn't fit all. This research was conducted to measure the influence of differentiated instructional strategy of in-service- teachers as a pedagogy on students' reflective thinking and empowered learning. The researcher developed and standardized a module of 16 lesson plans on English grammar and poetry integrating essential components of reflective thinking and empowered learning into differentiated instruction. Randomly selected samples of this research consisted of 100 students of standard 9, boys and girls, from an English medium ICSE school in the urban district of Bangalore. After a try-out of a few lessons on 25 samples, the researcher taught the lessons through differentiated instruction within 3 months. Through control and experimental groups, pre-test and post-test design, data were collected through 2 measuring tools (1) a questionnaire to measure the level of reflective thinking and (2) Learner empowerment measure. Data analysis of the pre and post-test scores of the experiment group shows a significant impact of differentiated instruction on all four components of reflective thinking of students, i.e., Habitual Action, Understanding, Reflection and Critical Reflection; and on the components of empowered learning of students, i.e., Meaningfulness, Competence, Impact and Choice irrespective of the difference in the gender. The results indicate that differentiated instruction could be implemented in schools as an instructional method to include all types of students and respect their diversity. -
In search of radio emission from exoplanets: GMRT observations of the binary system HD 41004
This paper reports Giant Metrewave Radio Telescope (GMRT) observations of the binary system HD 41004 that are among the deepest images ever obtained at 150 and 400 MHz in the search for radio emission from exoplanets. The HD 41004 binary system consists of a K1 V primary star and an M2 V secondary; both stars are host to a massive planet or brown dwarf. Analogous to planets in our Solar system that emit at radio wavelengths due to their strong magnetic fields, one or both of the planet or brown dwarf in the HD 41004 binary system are also thought to be sources of radio emission. Various models predict HD 41004Bb to have one of the largest expected flux densities at 150 MHz. The observations at 150 MHz cover almost the entire orbital period of HD 41004Bb, and about 20percent of the orbit is covered at 400 MHz. We do not detect radio emission, setting 3? limits of 1.8 mJy at 150 MHz and 0.12 mJy at 400 MHz. We also discuss some of the possible reasons why no radio emission was detected from the HD 41004 binary system. 2020 The Author(s) Published by Oxford University Press on behalf of Royal Astronomical Society. -
In search for FPI trail in blue-chip Indian bourse during a phase of rehabilitation- An investigative study /
Asian Journal of Management, Vol.8, Issue 1, pp.107-111, ISSN: 0976-495X (Print), 2321-5763 (Online). -
Impulse noise recuperation from grayscale and medical images using supervised curve fitting linear regression and mean filter
Acquisition of images from electronic devices or Transmission of the image through any medium will cause an additional commotion. This study aims to investigate a framework for eliminating impulse noise from grayscale and medical images by utilizing linear regression and a mean filter. Linear regression is a supervised machine learning algorithm that computes the value of a dependent variable based on an independent variable. The value of the recuperating pixel is measured using a curve-fitting, direction-based linear regression approach or applying a mean filter to the noise-free pixels. The efficiency of the proposed technique experiments with benchmark test images and the images of the USC-SIPI and TESTIMAGES data sets. Peak signal-to-noise ratio (PSNR) and structural similarity index metrics (SSIM) are determined to prove the performance of the proposed method. The results, when compared with the seven recent state-of-the-art techniques, show the superiority of the proposed method in terms of visual quality and accuracy. The proposed model achieves an average PSNR value of 65.21dB and an SSIM value of 0.999 for the reconstruction of medical images, proving its accuracy and efficiency. The impulse noise restoration process helps the radiologist get a clear visual clarity of the medical image for diagnosis purposes. 2022 Institute of Advanced Engineering and Science. All rights reserved. -
Improvising data security measures using rajan transform
Data security has always been a concern with the use of a large amount of data in our day-to-day life. There are many methods suggested and presented to secure data during the stages of its preprocessing and post-processing. However, many of them are not following the process of Homomorphism. During the study of Fast Fourier transform (FFT), Hadamard transform (HT) and Rajan transform (RT), this research work encountered a method that uses the cyclic, dyadic and graphical inverse properties of data and encrypts them which makes them homomorphic. This paper is targeting to improvise the data security measures using Homomorphism-based Rajan Transform, a method, which can help in securing data while data processing. The proposed methodology works in such a way that the encrypted data is available for processing without decrypting data into the original form. The performance of the proposed method is described by the efficiency of the algorithm, key size, Block size, and no of rounds required to complete the encryption. It has been found, if we take 512 bits of input data to get 512-bit ciphertext, it takes 9 rounds and generates a 4608-bit key. 2021 Taylor's University. All rights reserved. -
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Improvised process model for prediction of software development effort by integration of risk
Software development involves usage of a finite quantum of resources in accordance with the estimated effort and schedule. The newlineSoftware Development Lifecycle comprises activities pertaining to software engineering. The software engineering activities could be carried out using any of the various models available in practice. The newlineprocess of estimating size and effort accurately is vital in a software project since it could influence the success of the project. However, the realistic estimation of time and resources required for a project newlinecontinues to be a challenge. Risks exist in any software project, and hence Risk management is required to be considered across various processes throughout the project. The risks could be quantified by newlinearriving at the risk score based on the probability of occurrence of the risk and its impact. This research focused on the aspect that risk factors need to be considered in software effort estimation. A total of 503 newlinesoftware projects were considered, and from this dataset, projects which had risk score information were extracted and utilized for further analysis. This research work proposed an improvised effort estimation process by including risk scores in the standard estimation process. It also analysed the relationship existing between risk score in the project and other parameters considered in the effort estimation process. Regression analysis that was done on the dataset revealed an improvement in the model fitment by inclusion of risk score. An ensemble machine learning approach was utilized through deployment of Extreme Gradient Boosting algorithm. This algorithm was chosen newlineafter a model selection process by comparing various algorithmic models. The results indicated a better model fit by including risk as one of the parameters in the effort estimation process. A validation for the newlineproposed risk-integrated effort estimation model was done through responses from industry practitioners to a research instrument. -
Improvised Model for Estimation of Cable Bending Stiffness Under Various Slip Regimes
It is well known that the bending response of a stranded cable varies between two extremes, known as a monolithic stickslip state and a completely frictionless loose wire state. While the monolithic state offers the maximum stiffness for the cable, the latter loose wire assembly results in minimum stiffness. The estimation of the actual behavior of the cable under any loading scenario demands a proper modeling that accounts for the interaction of the constituent wires in the intermittent slip stages. During loading, the wires are not only subjected to forces along their axes but are considerably acted upon with radial forces that cause clenching effect. Major research works have focused on the frictional resistance of these radial forces from the Coulomb hypothesis, which contributes to the macro slip phenomenon. As the effect of these radial clenching forces are also significant in causing high contact stresses between wires at the adjacent layers, the need for considering the micro slip at these locations is also vital in the evaluation of the net cable stiffness. In this paper, a novel model is proposed that considers the slip caused by the Coulomb friction hypothesis and the micro slip caused by the Hertzian contact friction for the evaluation of bending stiffness. The variation of the bending stiffness has been evaluated for a single-layered cable as a function of bending curvature at various locations by studying their slip regimes. The predicted results are compared with the published results to establish the refined combined slip hypothesis suggested in this paper. The suggested slip model in this paper has also been accounted with the improvised kinematic relations that consider the wire stretch effect, a parameter that has been neglected in this cable research till date. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Improving the Security of Video Embedding Using the CFP-SPE Method
With the amount of data being transferred on a daily basis, it is becoming increasingly dangerous to save data on the Internet in the face of intruders or hackers. This study paper is one of the most effective ways to transmit information in a secure and confidential manner. The authors previously disclosed a way for embedding a secret video inside a cover video in their prior work. The writers have implemented a number of techniques to incorporate the secret video. The current work improves on the existing approach by including encryption and decryption concepts into the video embedding process. The secret data for either a large or little amount of information is put on the cover video utilising the embedding technique. Our proposed method combines compression, encryption, decryption, and secret information embedding to provide a more secure data transfer. 2022 Karthick Panneerselvam et al. -
Improving supply chain progress using blockchain technology /
Patent Number: 202141034319, Applicant: Dr. E. Bhuvaneswari.
Security, distributed networks, transparency, and immutability are all attractive aspects of blockchain that may help construct safe and dependable cyber physical systems. Because each use case has its own set of criteria, worries about the scalability, cost-effectiveness, and efficiency of blockchain-based systems have arisen. Because the total cost of ownership of blockchain systems is so costly, a feasibility study for small and medium-sized companies (SMEs) such as PFL is necessary to determine the blockchain's appropriateness for SMEs. -
Improving Speaker Gender Detection by Combining Pitch and SDC
Gender detection is helpful in various applications, such as speaker and emotion recognition, which helps with online learning, telecom caller identification, etc. This process is also used in speech analysis and initiating human-machine interaction. Gender detection is a complex process but an essential part of the digital world dealing with voice. The proposed approach is to detect gender from a speech by combining acoustic features like shifted delta cepstral (SDC) and pitch. The first step is preprocessing the speech sample to retrieve valid speech data. The second step is to calculate the pitch and SDC for each frame. The multifeature fusion method combines the speech features, and the XGBoost model is applied to detect gender. This approach results in accuracy rates of 99.44 and 99.37% with the help of RAVDESS and TIMIT datasets compared to the pre-defined methods. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Improving service quality and customer engagement with marketing intelligence
To succeed, businesses must keep up with the ever-changing technological landscape and constantly introduce new advancements. The rise of digitalization has wholly transformed how companies interact with their customers, presenting both opportunities and challenges. Marketing professionals are inundated with data and need guidance on leveraging it effectively to craft successful marketing strategies. Additionally, the ethical and privacy concerns surrounding the collection and use of customer data make the marketing landscape even more complex. Improving Service Quality and Customer Engagement With Marketing Intelligence is a groundbreaking book that offers a comprehensive solution to these challenges. This book is a must-read for marketing professionals, business owners, and students, providing a practical guide to navigating the digital age. It explores the impact of digitalization on marketing practices. It offers insights into customer behavior, equipping readers with the knowledge and skills needed to thrive in today's competitive market. The book's interdisciplinary approach integrates insights from marketing, technology, data science, and ethics, giving readers a holistic understanding of marketing intelligence. With its timely and practical approach, Improving Service Quality and Customer Engagement With Marketing Intelligence is a valuable resource for anyone seeking to enhance their marketing efforts in the digital age. It features best practices, case studies, and step-by-step guides, empowering readers to make informed decisions prioritizing customer satisfaction and engagement. Reading this book will help you stay ahead of the curve and drive success in today's dynamic marketing landscape by bridging the gap between academia and industry. 2024 by IGI Global. All rights reserved. -
Improving Renewable Energy Operations in Smart Grids through Machine Learning
This paper reviews the work in the areas of machine learning's role in bolstering renewable energy within smart grids. As the global shift towards eco-friendly energy sources such as wind and solar gains momentum, the challenge lies in managing these unpredictable energy sources efficiently. Innovative learning techniques are emerging as potential solutions to these challenges, optimising the use and benefits of renewable energies. Furthermore, the landscape of energy distribution is evolving, with a growing emphasis on automated decision-making software. Central to this evolution is machine learning, with its applications spanning a range of sectors. These include enhancing energy efficiency, seamlessly integrating green energy sources, making sense of vast data sets within smart grids, forecasting energy consumption patterns, and fortifying the security of power systems. Through a comprehensive review of these areas, this paper highlights the potential of machine learning in paving the way for a greener, more efficient energy future. The Authors, published by EDP Sciences, 2024. -
Improving Organizational Sustainable Performance of Organizations Through Green Training
It is necessary to equip employees with green abilities as well as to develop their dedication towards green behaviour, in order to improve an organization's environmental performance. The purpose of this research is to evaluate the direct impact of green training on organizational environmental performance (OEP) and the mediating effect of organizational citizenship behaviour on the environment (OCBE). The study is based on responses from 107 employees of the IT sector in India. The findings suggest that green training has a significant positive impact on the organizational environmental performance, and that the impact is strengthened by organizational citizenship behaviour towards the environment. The findings are of particular importance given the growing importance of sustainability in the organizational context. 2023 IGI Global. All rights reserved. -
Improving organizational environmental performance through green training
It is necessary to equip employees with green abilities as well as to develop their dedication towards green behaviour in order to improve an organization's environmental performance. The purpose of this research is to evaluate the direct impact of green training on organizational environmental performance (OEP) and the mediating effect of organizational citizenship behaviour on the environment (OCBE). The study is based on responses from 107 employees of the IT sector in India. The findings suggest that green training has a significant positive impact on the organizational environmental performance and that the impact is strengthened by organizational citizenship behaviour towards the environment. The findings are of particular importance given the growing importance of sustainability in the organizational context. 2023, IGI Global. All rights reserved. -
Improving maternal health by predicting various pregnancy-related abnormalities using machine learning algorithms
Over the past few decades, artificial intelligence has been showing its high relevance and potential in a vast number of applications, particularly in the healthcare domain. Having a healthy pregnancy is one of the best ways to promote a healthy birth. Getting early and regular prenatal care improves the chances of a healthy pregnancy. Complications involved in the individual's pregnancy need to be predicted on time accurately. AI can help clinicians to make decisions by assisting them in decision-making. In this regard, the objective of this chapter is to provide a detailed survey of various pregnancy-related abnormalities; and to explore various machine learning algorithms to classify/predict pregnancy-related abnormalities with higher accuracy. A generic framework that focuses more on classifying various features into normal and abnormal, and to be monitored patients to provide support and care during an emergency. 2023 by IGI Global. All rights reserved.