d) 2022 - 98 Documents

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THE PARADOX OF GENDER PERFORMATIVITY IN WINNIE-THE-POOH
(Boyhood Studies, 2021) Krishnapriya, Kamalakshan; Sumathy K, Swamy
In a heteronormative society, boys and girls are trained to dress and act in ways regarded appropriate for their respective genders. Even during play, a boy is expected to indulge only in activities that are traditionally considered masculine. A. A. Milne was inspired by his son's pretend play to write the Pooh books. From the illustrations in the book, which were modeled upon the real Christopher Robin and his toys, and various biographical material on the Pooh books, it can be discerned that the young boy was dressed in a gender-nonconforming fashion. This article probes this paradox of gender performativity in Christopher Robin's character in Winnie-the-Pooh (1926), wherein the child performs acts considered masculine in his imaginative play, while going against gender norms in his real-life appearance
PERFORMANCE ANALYSIS OF ABSTRACT-BASED CLASSIFICATION OF MEDICAL JOURNALS USING MACHINE LEARNING TECHNIQUES
(Springer Link, 2021-09-14) Deepika, A; Radha, N
Researchers face many challenges in finding the opt web-based resources by giving the queries based on keyword search. Due to advent of Internet, there are huge biological literatures that are deposited in the medical database repository in recent years. Nowadays, as many web-based medical researchers evolved in the field of medicine, there is need for an intelligent and efficient extraction technique required to filter appropriate and opt literature from the growing body of biomedical literature repository. In this research work, new combination of model is proposed in order to find the new insights in applying the combination of algorithm on biological data set. The information in the biomedical field is the basic information for healthy living. National Center for Biotechnology Information (NCBI)’s PubMed is the major source of peer-reviewed biomedical documents for researchers and health practitioners in the field of health-related management. In this paper, abstracts available in PubMed database is used for experimentation. In recent years, deep learning-based neural approach models provide an efficient way to create an end-to-end model that can accurately measure classification labels. This research work is a systematic analysis of performance of the supervised learning models such as Naïve Bayes (NB), support vector machine (SVM) and long short-term memory (LSTM) by implementing on textual medical data. The novelty in this work is the process of incorporating certain topic modelling techniques after the pre-processing phase to automatically label the documents. Topic modelling is a useful technique in increasing the efficiency and improves the ability of researchers to interpret biological information. So, the classification algorithms thus proposed are implemented in combination with popular topic modelling algorithms such as latent Dirichlet algorithm (LDA) and non-negative matrix factorization (NMF). The final performance of the combination of algorithms is also analysed and is found that SVM with NMF outperforms the other models.
SYNTHESIS, CHARACTERIZATION, PHOTOCATALYTIC AND PHOTOVOLTAIC APPLICATIONS OF A NOVEL SEMICONDUCTOR CUPBO NANOMATERIAL
(Asian Journal of Chemistry, 2021-12-16) Ponnambalam, P; Kamalakkannan, J; Jayaseelan, R; Arokia Doss, M; Selvi, G
A new semiconductor CuPbO nanomaterial has been prepared where lead nitrate and copper nitrate were co-precipitated to form this compound. The high-resolution scanning electron microscopic studies revealed that CuPbO possesses a nanobundle flower-like structure. XRD confirmed the occurrence of Cu, Pb & O in the fabricated compound. The size of the nanomaterial is determined for being ~200 nm by high-resolution transmission electron microscopic analysis. The photoluminescence analysis showed the possible electron transfer and crevasses between Cu and PbO causes recombine of electron-hole pairs. The ultraviolet-visible diffuse reflectance spectral analysis showed that the nanomaterial has a low band gap energy. The material was found to be a good recyclable photocatalyst in the decomposition of diamond green B dye. As a photoelectrode, CuPbO proved its efficiency in dye-sensitized solar cell applications.
AN INFLUENCE OF WASTEWATER DISCHARGES FROM PAPER MILLS ON FARM PRACTICES
(IOP Publishing Ltd, 2022) Karthika, S
Industrial development is a challenging issue in recent times, as its adverse impact directly influences the environment. Paper and pulp industries are generally declared as one of the highly polluting industries in the country. However, nowadays they are also identified as the industry mounting with environmental and economic pressures to reduce the volume and toxicity of generated industrial wastewaters. Paper industries generate varieties of contaminants depending upon the manufacturing process. Especially, disposal of polluted water directly affects the soil structures, not only in industrial area but also in agricultural fields. Therefore, the present work accentuates on the examination of paper effluent characteristics, its impact on soil quality, and germination of groundnut seedlings. Seedling growth in polluted soil and fertile soil were monitored for 90 days. For this process, effluent, soil samples were collected from the paper industry located in Coimbatore district, Tamil Nadu, India. Soil samples were tested for their nutrients' level, concentration of heavy metals as per the standard quality procedures. Distribution of nutrients, heavy metal concentrations were studied in the matured crops. Sample crop registered mixed concentration of nutrient levels/heavy metals against the prescribed WHO/FAO standards, whereas control crop exhibited values within standards sufficing its healthier growth. This implies that the irrigation of the farmland with industrial water alters nutrient availabilities, in turn promoting toxic leachates into the soil. Further, the soil performances due to the percolation of industrial discharges reflected in the complexities of crop growth.
CLUSTER BASED DATA-AGGREGATION USING LIGHTWEIGHT CRYPTOGRAPHIC ALGORTIHM FOR WIRELESS SENSOR NETWORKS
(Elsevier, 2022) Kowsalya, R; Rosiline Jeetha, B
Wireless Sensor Networks (WSN) comprises of large number of SNs that are distributed to capture data from other nodes. In WSN, there are diverse secure data aggregation methods are used; however, it fails to address the authentication process. It is extremely confronting to implement authentication while preserving the energy consumption in WNS. The prevailing approaches concentrates on various limitations like sharing security key, key length to enhance authentication. However, it leads data aggregation network exposed to malicious activities. This work presents a novel method to address security and energy issues in WSN which is known as a secure Lightweight cryptographic data-aggregation algorithm (SLC-DAA). The proposed SLC-DAA uses cryptographic primitives like hash functions and XOR operations. This method is used to providing promising solutions in cluster based data-aggregation using higher security and energy consumption model. When compared to existing approaches, the proposed model is simple and provides better computational efficiency and privacy protection.
DRUG EFFICACY SCORE PREDICTION USING SIGNATURE BASED APPROACHES FOR AMYOTROPHIC LATERAL SCLEROSIS DISORDER: A REVIEW
(IEEE, 2022) Devipriya, S; Vijaya, M S
Amyotrophic Lateral sclerosis is one of the inflammatory demyelinating diseases that affects the central nervous system. Demyelination occurs due to the attack of the immune system in the myelin layer of nerves. Because of the complexity of disorder in the nervous system, the pharmacological processes are unknown, which results in incorrect biomarker identification, uncertain targets, and unknown models. Gene signatures are employed as the primary method for treating complicated disorders. Signature based drug discovery strategy plays a vital role in predicting drug efficacy scores to reveal unknown pharmacological processes based on chemical perturbations and gene perturbations. Machine learning models with high computational and processing technologies are currently being adopted to predict drug efficacy scores by interconnecting OMICS data. This paper explores and reviews different computational models available for signature-based drug discovery and machine learning models for predicting drug efficacy scores. The results of the existing research are studied and reported.
ANTIMICROBIAL ACTIVITY OF COPPER NANOMATERIALS: CURRENT STATUS AND FUTURE PERSPECTIVES
(Elsevier, 2022) Bhuvaneshwari, V; Nirmal Kumar, Ramasamy; Idhaya Kumar, S; Kalaivani, S; Vaidehi, D; Karthik Kumar, D
Copper (Cu) is an essential element in governing the health of major organisms such as plants, animals, and human beings. Recently eco-friendly problem-solving methods have drawn considerable attention as they are simple and viable, alternative to numerous physical and chemical methods. A wider application and utilization of metal oxide nanoparticles (NPs) have gained momentum as an eco-friendly approach to solve numerous problems. Among the NPs, copper nanoparticles (CuNPs) or copper oxide nanoparticles (CuONPs) have a specialized role, and their use in nanobiotechnology as fertilizers to improve nutrition in the soil for sustainable crop development is also important. Cu-based agro-chemicals have traditionally been used in agriculture to maintain the nutrition and health status of the plants. Biological synthesis of CuNPs/CuONPs using plant extracts and microbial extracts, which showed better antimicrobial potential in inhibiting the growth of plant bacterial and fungal pathogens, was discussed in this chapter. The mechanistic impact of CuNPs/CuONPs against the pathogenic microorganisms of crops was also discussed.
RECURRRENT NEURAL NETWORK BASED MODEL FOR AUTISM SPECTRUM DISORDER PREDICTION USING CODON ENCODING
(Springer Link, 2022) Pream Sudha, V; Vijaya, M S
Deep learning methods are noteworthy tools that go together with traditional machine learning techniques to enable computers learn from data and create smarter applications. Deleterious gene classification is an important task in a standard computational framework for biomedical data analysis. As gene sequences are high dimensional and do not represent explicit attributes for computational modelling, extracting features from them becomes a complex task. Recently neural deep learning architectures automatically extract valuable features from input patterns. The principal idea of this work is to exploit the power of Recurrent Neural Networks (RNN) to learn sequential patterns through high-level information associated with observed signals which in turn can be used for classification. Classification of affected genes that cause disease like Autism-spectrum disorder (ASD) is a noteworthy challenge in biomedical research. Long Short Term Memory (LSTM) units go well with sequence-based tasks with long-term dependencies and hence this work examines a stacked LSTM architecture for classifying genes causing ASD. The model is trained and tested with two hand crafted datasets and a codon encoded dataset. Experiments revealed the superiority of these advanced recurrent units compared to the traditional Deep Neural Networks and Bi-directional RNNs distinctively with codon encoded dataset.
QUALITATIVE PHYTOCHEMICAL ANALYSIS OF EIGHT TURMERIC (CURCUMA LONGA L) CULTIVARS GROWN IN VARIOUS GEOGRAPHICAL LOCATIONS OF INDIA WITH SIX EXTRACTS – A COMPARATIVE STUDY
(Elsevier, 2022) Salma, S; Aariba, S; Velvizhi, M; Yasmin, Y; Sudha, U V; Anitha, M C; Naveena Reddy, S
Being integral part of Indian cuisine, spices and condiments are reservoir of medicinal and phytochemical properties. The main objective of the study was to qualitatively analyse the phytochemical constituents of eight different cultivars of Indian turmeric with six different extracts. Different turmeric cultivars showed the presence of carbohydrates, proteins and aminoacids, phenolic compounds and tannins. Alkaloids, flavonoids, gums and mucilages, phytosterols were absent in most of the turmeric cultivars. This study concludes that aqueous extracts of different turmeric cultivars have highest phytonutrients in all forms and are recommended as the most preferred option in health and disease for further analyses.
SYNTHESIS AND CRYSTAL GROWTH OF CADMIUM NAPHTHOATE CRYSTAL FOR SECOND ORDER NON-LINEAR OPTICS AND CYTOTOXIC ACTIVITY
(Taylor & Francis Online, 2022) Natarajan, Arunadevi; Ponnusamy, Kanchana; Venkatesan, Hemapriya; Shanmuga Sundari, Sankaran; Mehala, Mayilsamy
A new organometallic crystal diaquo-di(2-hydroxy-1-naphthoate)-cadmium(II) was synthesized by slow evaporation method and characterized for optical, spectral, thermal and biological applications. Single crystal X-ray diffraction revealed that the crystal structure was monoclinic with C2 space group. The crystal perfection and atomic packing of the grown crystal were also analyzed. The presence of functional groups and different vibrational modes were studied from IR spectra. Various decomposition stages and thermal stability of the grown crystal was studied from TG-DTA data. Molar conductance and dielectric of the complex were determined. Energy Band gap, refractive index, skin depth and extinction coefficient of the crystal were calculated from UV-Visible absorption studies and by using Kurtz powder method, the second harmonic generation (SHG) efficiency was examined. In addition, the crystal was investigated for its anticancer activity on lung cancer and breast cancer cells. Using AutoDock Vina software, the binding pattern of the crystal was investigated toward target proteins Leu 862, Glu 793, Trp 796, Gln 858, Tyr 857 and Lys 861.
A SCIENTIFIC PHARMACOGNOSY ON GAUCHER’S DISEASE: AN IN SILICO ANALYSIS
(Springer Link, 2022) Amritha Pozhaiparambil, Sasikumar; SathishKumar, Ramaswamy; Sreeram, Sudhir
From ancient times, studies on herbal medicine and pharmacognosy have increased gradually worldwide, due to the increased side effects, adverse drug reactions, and charge lines of modern medicines. Plants are well known for their medicinal effects and nutritional values. They contain bioactive compounds which display a wide spectrum of therapeutic effects. Gaucher’s disease (GD) is a rare autosomal recessively inherited metabolic disorder caused due to the defect in Glucosylceramidase beta gene coding for the enzyme acid-β-glucosidase in humans. We revealed the profound binding efficiency of five selected bioactive compounds from different plants against the main enzyme acid-β-glucosidase responsible for GD through molecular docking. An in silico approach along with the ADMET profiles of phytocompounds was done using the Schrodinger software. The preventive measure of GD leads to side effects, inaccessible and unaffordable which put forth the emergence of phytocompounds which have fewer toxic effects, and one such compound is β-D-Glucopyranose with the best docking score (–10.28 kcal/mol) and an excellent binding affinity than other ligands, which could be further analyzed for stability using molecular dynamics study and in vitro. Being a dietary supplement, these compounds could be prepared in any form of formulation as a drug.
PLATINUM LAYERS SANDWICHED BETWEEN BLACK PHOSPHOROUS AND GRAPHENE FOR ENHANCED SPR SENSOR PERFORMANCE
(Springer Link, 2022) Maheswari, Pandaram; Subanya, Santhanakumar; Ravi, Veeran; Rajesh Karuppaiya, Balasundaram; Rajan, Jha
Highly sensitive surface plasmon resonance (SPR) sensor consisting of Ag-Pt bimetallic films sandwiched with 2D materials black phosphorus (BP) and graphene over Pt layer in Kretschmann configuration is analyzed theoretically using the transfer matrix method. Numerical results show that upon suitable optimization of thickness of Ag-Pt layers and the number of layers of BP and graphene, sensitivity as high as 412°/RIU (degree/refractive index unit) can be achieved for p-polarized light of wavelength 633 nm. This performance can be tuned and controlled by changing the number of layers of BP and graphene. Furthermore, the addition of graphene and heterostructures of black phosphorus not only improved the sensitivity of the sensor but also kept the FWHM of the resonance curve much smaller than the conventional sensor utilizing Au as plasmonic metal and hence improved the resolution to a significant extent. We expect that this new proposed design will be useful for medical diagnosis, biomolecular detection, and chemical examination.
MIGRATION AND OIL-CENTRIC LIFE: A STUDY ON GHASSAN KANAFANI’S MEN IN THE SUN
(IAFOR Journal of Literature & Librarianship, 2022) Jeyasiba, Ponmani Sami; Narasingaram, Jayashree
The oil narratives bring in a gamut of perspectives that would redefine the outlook of life. Modern life is embedded in the discovery of oil and the usage of hydrocarbon fuels. Petrofiction offers a scope for understanding the representation of oil aesthetics in literature. The research paper aims to critically expound the transformation after the sudden boom of wealth in Kuwait due to the discovery of oil, and the migration of Palestine refugees from Iraqi camps to Kuwait in search of jobs to upgrade their living conditions with reference to Men in the Sun by Ghassan Kanafani. The study authenticates the oil-centric life in Men in the Sun by understanding that oil is the base structure that governs the “push”, “pressure” and “stay” factors of a refugee in flight with theoretical support of kinetic model of exile, displacement and resettlement as proposed by Egon F. Kunz.
TYPE DESIGNATION OF FIVE LINDLEY’S NAMES IN THE GENUS HABENARIA (ORCHIDACEAE)
(Horizon e- publishing Group, 2022) Sulaiman, M; Kiruthika, K
The present study highlights the type designation on 5 species of the genus HabenariaWilld. (Orchidaceae) namely H. cephalotesLindl., H. heyneanaLindl., H. longicornuLindl., H.macrostachyaLindl. and H. plantagineaLindl.
SYNTHESIS OF ECO-FRIENDLY NANOCOMPOSITE WITH SILVER NANOPARTICLE TO INCREASE THE ANTIMICROBIAL ACTIVITY
(Elsevier, 2022) Sheeba, Maxwalt; Kumutha, Rahupathy; Siva Nandhini, Suresh; Ramesh, Subramani; Charumathi, Pushparaj
Nanomaterials are widely used as antifungal/antibacterial agents in numerous fields including cosmetics, therapeutics, diagnostics, food and other chemical industries. These nanomaterials are synthesized using various approaches; however, the use of chemical methods is leading to toxic and non-eco-friendly products, hence, there is an urge to prepare the nanomaterials free of toxicity. In this study, we synthesized and characterized the plant mediated silver nanoparticles-based composites using Cissus quandrangularis and Ocimum tenuiflorum. These eco-friendly nanoparticles were encapsulated with natural polymers and characterized by UV–Vis spectrophotometer, FT-IR and Scanning Electron Microscopy. In addition, synthesized nanocomposites were evaluated for antibacterial activity by disc diffusion method against Gram-negative (Escherichia coli, Staphylococcus aureus and Pseudomonas sps) bacteria showed higher antibacterial activity. These eco-friendly nanocomposites may be used as antimicrobial packaging materials, wound dressings and grafting applications.
BIOSENSORS BASED ON METAL-ORGANIC FRAMEWORK (MOF): PAVING THE WAY TO POINT-OF-CARE DIAGNOSIS
(Elsevier, 2022) Sushma, Dave; Jone Kirubavathy, S
Metal-organic frameworks (MOFs) are recent versatile materials that have found application in gas storage, electrochemical sensors, drug delivery vehicles, and so on. MOFs have become an exotic class of material candidates due to their limitless combination of metal ions and organic ligands. This provides a wide range of inherent functionalities. The infinite crystalline lattice, porosity, and high surface area of MOFs have made them an important tool to sense various analytes of interest. The pore size of MOFs can also be tuned by employing organic ligands with many functional groups. The promising qualities of MOFs have extended its application in the detection of various contaminates in edible products and also as biosensors. This chapter discusses MOFs as biosensors and examines quantitative detection including the specific association of an analyte with an MOF and the utilization of both primitive and synthetic MOFs for occasional visual detection and direct in vitro or in vivo estimation.
MOLECULAR ANALYSIS OF BACTERIA ISOLATED FROM THE SOIL FOR ITS POTENTIAL AGNOSTIC ACTIVITY
(Research Journal of Pharmacy and Technology, 2022) Vinodhini, K; Kavitha, S; Saranya, T; Geethalakshmi, K
The work elucidates the agnostic action of a bacterial soil isolate, procured from different parts from Coimbatore, Tamilnadu, India. The bacteria strains were obtained via routine serial dilution plate protocols. The best isolate was tested against 3 pathogens; Pseudomonas aeruginosa, Klebsiella pneumoniae, and Staphylococcus aureus. The screening was performed via streaking, and the isolate L1An1 exhibited agnostic activity on K pneumoniae and S aureus. The maximal impact of 21mm inhibition zone was recorded. Molecular identi?cation and the resultant sequence were consigned for NCBI BLAST. The isolates L1An2 showed 99% resemblance with that of Enterobacter aerogenes. The sequence submitted to GenBank and MT192658.1 was procured the accession number.
RIVER WATER QUALITY PREDICTION AND INDEX CLASSIFICATION USING MACHINE LEARNING
(IOP Publishing Ltd, 2022) Jitha P, Nair; Vijaya, M S
Various pollutants have had a substantial impact on the quality of water in recent years. The quality of water directly impacts human health and the environment. The water quality index (WQI) is an indicator of effective water management. Water quality modelling and prediction have become essential in the fight against water pollution. The research aims to build an efficient prediction model for river water quality and to categorize the index value according to the water quality standards. The data has been collected from eleven sampling stations located in various locations across the Bhavani River, which flows through Kerala and Tamilnadu. The water quality index is determined by 27different parameters affecting water quality like dissolved oxygen, temperature, pH, alkalinity, hardness, chloride, coliform, etc. Data normalization and feature selection are done to construct the dataset to develop machine learning models. Machine learning algorithms such as linear regression, MLP regressor, support vector regressor and random forest has been employed to build a water quality prediction model. Support vector machines (SVM), naïve bayes, decision trees, MLP classifiers, have been used to develop a classification model for classifying water quality index. The experimental results revealed that the MLP regressor efficiently predicts the water Quality index with root mean squared error as 2.432, MLP classifier classifies the water quality index with 81% accuracy. The developed models show promising output concerning water quality index prediction and classification.
THE ROLE OF INTERIOR AND CLOSURE OPERATOR IN MEDICAL APPLICATIONS
(Turkish World Mathematical Society Journal of Applied and Engineering Mathematics, 2022) Sasikala, D; Divya, A; Jafari, S
In this paper, we consider the interior and closure operator as topological tools to apply in divisor cordial labeling. We investigate the properties related to the path with certain examples in a divisor cordial graphic topology. This concept is utilized in human blood circulation path and the results are analyzed.
ENCAPSULATION OF INULIN LOADED OVALBUMIN NANOFIBRILS IN TONED MILK TO ENHANCE THE NUTRITIONAL VALUE
(IOP Publishing Ltd, 2022) Praveetha, Senthilkumar; Arunadevi, Natarajan; Vladimir, Shavrov; Petr, Lega
Self-assembled nanofibrils encapsulation was performed in the application of nutritional enhancement of toned milk. Inulin loaded nanofibrils (self-assembled ovalbumin nanofibrils) were used for the encapsulation of toned milk. The physico-chemical parameters and nutritional value of inulin loaded ovalbumin nanofibrils were determined. The physic-chemical analysis of toned milk, such as pH, titrable acidity, anti-oxidant activity, encapsulation efficiency, and in-vitro release, were calculated. The results show that sensory characteristics were not affected by encapsulation of nanofibrils on toned milk. The nutritional values of inulin loaded ovalbumin nanofibrils in toned milk was performed using conventional oven drain method (moisture), ignition method (ash), gerber method (protein), kjeldahl method (fat), pearson's composition analysis (carbohydrates), titration method (lactose), and HPLC method (Vitamin D). The result shows that the protein content is raised and also increased with other nutritional values.