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Chloroform fraction of Chaetomorpha brachygona, a marine green alga from Indian Sundarbans inducing autophagy in cervical cancer cells in vitro
Sundarbans Mangrove Ecosystem (SME) is a rich repository of bioactive natural compounds, with immense nutraceutical and therapeutic potential. Till date, the algal population of SME was not explored fully for their anticancer activities. Our aim is to explore the potential of these algal phytochemicals against the proliferation of cervical cancer cells (in vitro) and identify the mode of cell death induced in them. In the present work, the chloroform fraction of marine green alga, Chaetomorpha brachygona was used on SiHa cell line. The algal phytochemicals were identified by GCMS, LCMS and column chromatography and some of the identified compounds, known for significant anticancer activities, have shown strong Bcl-2 binding capacity, as analyzed through molecular docking study. The extract showed cytostatic and cytotoxic activity on SiHa cells. Absence of fragmented DNA, and presence of increased number of acidic vacuoles in the treated cells indicate nonapoptotic cell death. The mode of cell death was likely to be autophagic, as indicated by the enhanced expression of Beclin 1 and LC3BII (considered as autophagic markers) observed by Western blotting. The study indicates that, C. brachygona can successfully inhibit the proliferation of cervical cancer cells in vitro. 2020, The Author(s). -
Cholesterol reduction and heavy metal assimilation characteristics of Pediococcus pentosaceus strains isolated from pickle and moor-kuzhambu
Aims: Consumption of fermented foods are known to provide various health benefits. The present study was aimed to isolate novel potent probiotic strains from the homemade fermented Indian recipe Moor Kuzhambu and Pickle and its characterization to elucidate the efficacy of isolate in cholesterol and heavy metal reduction. Methodology and results: Cultures isolated from Pickle (CK2) and Moor Kuzhambu (CK3) were identified as different strains of Pediococcus pentosaceus using the 16S rDNA sequence-based bacterial identification method. The study analysed the survival of probiotic strains under the influence of various chemical and natural stimulants. The isolated strains exhibited tolerance to gastric juice and were able to exhibit a broad range of tolerance to varying temperatures, pH, NaCl, solvent, phenol, trypsin and artificial gastric juice. Microencapsulation studies were conducted using alginate and chitosan to increase the shelf life of the isolated probiotic. Preliminary analysis regarding cell surface studies such as autoaggregation, co-aggregations and cell surface hydrophobicity determined the ability of the strains to aggregate on to intestinal cell surface and manifest competitive pathogen displacement. Conclusion, significance and impact of study: Remarkable biofilm reduction of 48% to 80% was observed in the probiotic-supplemented samples. Similarly, a reduction of 80% to 85% free cholesterol was noted in cholesterol assimilation assays and heavy metal (Cu+, Pb+, Zn+ and Fe+) assimilation ability was observed. Further studies are required to characterize the nature of the secretory products and their mode of action in the survival and immune enhancement in animal models. 2023, Malaysian Journal of Microbiology. All Rights Reserved. -
Chromatic completion number
The well known concept of proper vertex colouring of a graph is used to introduce the construction of a chromatic completion graph and determining its related parameter, the chromatic completion number of a graph. The chromatic completion numbers of certain classes of cycle derivative graphs and helm graphs are then presented. Finally, we discuss further problems for research related to this concept. 2020 the author(s). -
Chromatic Zagreb and irregularity polynomials of graphs
Graph coloring is an assignment of colors, labels or weights to elements of a graph subject to certain constraints. Coloring the vertices of a graph in such a way that adjacent vertices are having different colors is called proper vertex coloring. A proper vertex coloring using minimum parameters of colors is studied extensively in recent literature. In this paper, we define new coloring related polynomials, called chromatic Zagreb polynomials and chromatic irregularity polynomials, in terms of minimal parameter coloring and structural characteristics of graphs such as distances and degrees of vertices. 2021 World Scientific Publishing Company. -
Cipher Block Chaining Support Vector Machine for Secured Decentralized Cloud Enabled Intelligent IoT Architecture
The growth of internet era leads to a major transformation in a storage of data and accessing the applications. One such new trend that promises the endurance is the Cloud computing. Computing resources offered by the Cloud includes the servers, networks, storage, and applications, all as services. With the advent of Cloud, a single application is delivered as a metered service to numerous users, via an Application Programming Interface (API) accessible over the network. The services offered via the Cloud are such as the infrastructure, software, platform, database and web services. The main motivation of this application model is to provide computationally secure key generation to protect the data via encryption. This key generation in the cryptography process falls into three categories in this research work. In the first part, SVM based encryption service model is constructed for which the key generation is from the conventional encryption operation mode with some improvements. To make the process more complex, the optimization techniques are taken into account for the key generation in descendant two methods application model that acts computationally more secure specifically for Cloud environment. The results of security analysis confirm the effectiveness of the proposed application model withstands potentially against various attacks such as Chosen Cipher Attack, Chosen Plain text Attack indistinguishable attacks for files. In case of images, it resists well against statistical and differential attacks. Comparative Analysis shows evidence of the efficiency of the developed pioneering application model quality and strength compared with that of the existing services. 2013 IEEE. -
Circular supply chains in manufacturingQuo vadis? Accomplishments, challenges and future opportunities
Circular approach in manufacturing supply chain (SC) operations yields multiple benefits through optimal utilisation and consumption of resources. This study maps the scope and structure of circularity in the manufacturing SC discipline and explores the evolution of the domain over time. We review 946 journal articles published between 2013 and September 2023. Our study identifies key drivers and barriers to circular economy (CE) deployment in manufacturing SC operations, bibliometric parameters, emerging research themes, decision support tools, theories and applications. Using the theory extension approach, we propose a strategic framework to fortify the deployment of circularity in SCs. This comprehensive study renders a methodological contribution through combined descriptive content analysis and bibliometric and network analyses to evaluate the circular manufacturing SC operations concepts, theories and applications. We posit that manufacturing firms require to deploy innovation-led approaches to embed the CE strategies in their SC operations. We find that the studies investigating green skill development and circularity-culture adoption can facilitate manufacturers to understand the efficacy of circularity in their SC operations. The findings of this study can facilitate the practitioners to identify the links between the CE approaches and their strategic implications and examine CE implementation at the strategic level. 2024 The Authors. Business Strategy and The Environment published by ERP Environment and John Wiley & Sons Ltd. -
Citizens Perception on Livability in Indian Metropolitan Cities
Background: Livability is a complex and multifaceted concept, but is important in creating places where people can thrive. Investing in the factors contributing to livability can create more attractive, sustainable, and equitable communities. Livability is important for several reasons. It can affect peoples health, well-being, and productivity. It can also influence whether people live in a particular place and whether businesses are willing to invest there. Methods: A survey was conducted to review the top livable cities of India as per the Global Livability Index parameters such as sociocultural, environmental, healthcare, education, and infrastructure aspects. A mixed method approach, having both qualitative and quantitative analyses, was adopted for the survey. The targeted sample size was 100 people, covering stakeholders from around the campus. Findings: As an outcome of the survey analysis, the most livable cities were categorized. Conclusion: In conclusion, policy narratives and frameworks are outlined emphasizing the need and the importance of citizens participation in assessing the quality of life across Indian metropolitan cities. 2024 Springer Publishing Company. -
Citric-Acid-Catalyzed Green and Sustainable Synthesis of Novel Functionalized Pyrano[2, 3-e]pyrimidin- and Pyrano[2, 3-d]pyrazol-amines in Water via One-Pot Multicomponent Approaches
An efficient entry into the preparation of elusive, novel pyrano[2, 3-e]pyrimidin-amines and pyrano[2, 3-d]pyrazol-amines has been accomplished using citric acid as a green catalyst in aqueous medium at 25 C. The strategy successively tolerates a variety of functional groups and interestingly, it is eco-compatible, environment-friendly, propitious and the products are obtained in excellent yields without chromatographic purification. The current methodology unfolds the benefits of citric acid as an effective, expeditious, economical, green catalyst and thus adheres to the principles of green chemistry. Ecstatically, the reaction was scaled to the gram level ascertaining the wide applicability of the protocol in academia and industry. The green metrics (E-factor: 0.0497, Mass intensity: 1.1022, PMI: 1.0497 and Emw: 0.0497) for the reaction was also envisaged and the pathway was found to acquaint excellent green chemistry metrics. 2018 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim -
Citrus for wellness: Exploring the bioactive properties of Citrus medica fruit peel with emphasis on its anticancer, antioxidant, antimicrobial and anthelmintic properties
Citrus medica (Citron) is an underutilised plant consisting of various bioactive elements with numerous medicinal benefits. The present study aimed to evaluate the bioactive properties, including anthelmintic, antimicrobial, antioxidant and anticancer activities, of chloroform extract obtained from the of fruit peel of C. medica. The different types of phytochemicals present in the chloroform extract were analysed using GC-MS. The major components detected included n-hexadecanoic acid, octadecanoic acid, t-tetradecenal, 1-nonadecene etc. Anthelmintic study was conducted using Eisenia fetida as a test organism, revealing a significant anthelmintic effect in the C. medica fruit peel extract compared to the standard drug. Antimicrobial properties were assessed against five test bacterial and fungal strains. Antibacterial tests showed zones of inhibition ranging from 8 to 11 mm, while no prominent zones of inhibition were observed in antifungal tests. The DPPH assay demonstrated significant antioxidant properties of Citron fruit peel extract compared to the standard ascorbic acid. The Chloroform extract of citron fruit peel exhibited significant cytotoxic properties against FaDu (human hypopharyngeal tumour) cell line. The present study indicates the potential of the chloroform extract of C. medica fruit peel to be employed as an anthelmintic, antibacterial, antioxidant and anticancer agent. Hence, it emphasises the prominence that can be given to the dietary consumption of citrus fruit peel in various forms, such as dried peel, powder etc. The Author(s). -
Citrus Medica-derived Fluorescent Carbon Dots for the Imaging of Vigna Radiate Root Cells
Bio-imaging is a crucial tool for researchers in the fields of cell biology and developmental biomedical sector. Among the various available imaging techniques, fluorescence based imaging stands out due to its high sensitivity and specificity. However, traditional fluorescent materials used in biological imaging often suffer from issues such as photostability and biocompatibility. Moreover, plant tissues contain compounds that cause autofluorescence and light scattering, which can hinder fluorescence microscopy effectiveness. This study explores the development of fluorescent carbon dots (Cm-CDs) synthesized from Citrus medica fruit extract for the fluorescence imaging of Vigna radiata root cells. The successful synthesis of CDs with an average size of 6.7nm is confirmed by Transmission Electron Microscopy (TEM). The X-ray diffraction (XRD) analysis and raman spectroscopy indicated that the obtained CDs are amorphous in nature. The presence of various functional groups on the surface of CDs were identified by Fourier transform infrared (FTIR) spectra. The optical characteristics of Cm-CDs were studied by UV-Visible spectroscopy and photoluminescence spectroscopy. Cm-CDs demonstrated strong excitation-dependent fluorescence, good solubility, and effective penetration in to the Vigna radiata root cells with multicolor luminescence, and addressed autofluorescence issues. Additionally, a comparative analysis determined the optimal concentration for high-resolution, multi-color root cell imaging, with Cm-CD2 (2.5mg/ml) exhibiting the highest photoluminescence (PL) intensity. These findings highlight the potential of Cm-CDs in enhancing direct endocytosis and overcoming autofluorescence in plant cell imaging, offering promising advancements for cell biology research. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. -
Clan Culture in Organizational Leadership and Strategic Emphases: Expectations Among School Teachers in India
Understanding the current and preferred organizational culture among school teachers in India is a primary requirement, particularly when the National Educational Policy (NEP) is being implemented. Measuring the competing values using the Organizational Culture Assessment Instrument (OCAI) provides information about the dominant characteristics of the organizational culture and the school teachers' preferences. We surveyed school teachers and received 273 responses. Research revealed that clan culture is the overall current and preferred organizational culture type. Many of the results are not a surprise. However, we found that organizational leadership is currently in the hierarchy culture and strategic emphasis is on adhocracy, whereas teachers prefer a clan culture on these dimensions. Teachers expect school leaders to be the ones who facilitate the path to achieve, provide mentoring, and are instrumental in team building. They prefer a culture that provides for the development of human capital, promotes high trust and transparency among teachers, and offers an opportunity to participate in decision-making. This study is unique as it measures schools' organizational culture that has not been done earlier in the Indian context. The results suggest implications on the leadership practices and the strategic emphasis that need to change, in order to facilitate the implementation of the National Education Policy (NEP). 2022. All Rights Reserved. -
Classification and characterization using HCT/HFOSC spectra of carbon stars selected from the HES survey
We present results from the analysis of 88 carbon stars selected from Hamburg/ESO (HES) survey using low-resolution spectra (R ?1330 & 2190). The spectra were obtained with the Himalayan Faint Object Spectrograph Camera (HFOSC) attached to the 2-m Himalayan Chandra Telescope (HCT). Using well-defined spectral criteria based on the strength of carbon molecular bands, the stars are classified into different groups. In our sample, we have identified 53 CH stars, four C-R stars, and two C-N type stars. Twenty-nine stars could not be classified due to the absence of prominent C2 molecular bands in their spectra. We could derive the atmospheric parameters for 36 stars. The surface temperature was determined using photometric calibrations and synthesis of the H-alpha line profile. The surface gravity log g estimates are obtained using parallax estimates from the Gaia DR3 database whenever possible. Microturbulent velocity (?) was derived using calibration equation of log g & ? . We could determine metallicity for 48 objects from near-infrared Ca II triplet features using calibration equations. The derived metallicity ranges from ?0.43 ? [Fe/H] ? ?3.49. Nineteen objects were found to be metal-poor ([Fe/H] ? ?1), 14 very metal-poor ([Fe/H] ? ?2), and five extremely metal-poor ([Fe/H] ? ?3.0) stars. Eleven objects were found to have a metallicity in the range ?0.43 ? [Fe/H] ? ?0.97. We could derive the carbon abundance for 25 objects using the spectrum synthesis calculation of the C2 band around 5165 The most metal-poor objects found will make important targets for follow-up detailed chemical composition studies based on high-resolution spectroscopy, and are likely to provide insight into the Galactic chemical evolution. 2024, The Author(s), under exclusive licence to Springer Nature B.V. -
Classification and correlational analysis on lower spine parameters using data mining techniques
The application of data mining in the field of medical science is slowly gaining popularity. This is due to the fact that enormous statistical inferences from data related to the human body and medicine was a possible with high accuracy rates which was a tedious task in the past. This had led to discoveries and breakthroughs which has saved thousands of lives. Lower back pain is one of the most common issues faced by majority of the population throughout the world. The early detection and treatment of LBP can avoid life threatening issues in the body. Objective: This study aims to create a classification model which can be used to detect an unhealthy spine using the lumbar and sacral parameters. Correlational analysis was performed between different attributes to find distinguishing factors between healthy and unhealthy spine. Method: Classification methods were used such as decision tree and SVM. Correlational analysis was performed using pearson method between each attribute. Results: After creating the model using the different classification methods it was found that Ctree produced the highest accuracy with 92.80% on average. It was also found that there were 6 attribute pairs that had high correlation coefficient to distinguish unhealthy and healthy spine observations. BEIESP. -
Classification of a New-Born Infant's Jaundice Symptoms Using a Binary Spring Search Algorithm with Machine Learning
A yellowing of the skin and eyes, called jaundice, is the consequence of an abnormally high bilirubin concentration in the blood. All across the world, both newborns and adults are afflicted by this illness. Jaundice is common in new-borns because their undeveloped livers have an imbalanced metabolic rate. Kernicterus is caused by a delay in detecting jaundice in a newborn, which can lead to other complications. The degree to which a newborn is affected by jaundice depends in large part on the mitotic count. Nonetheless, a promising tool is early diagnosis using AI-based applications. It is straightforward to implement, does not require any special skills, and comes at a minimal cost. The demand for AI in healthcare has led to the realisation that it may have practical applications in the medical industry. Using a deep learning algorithm, we created a method to categorise jaundice cases. In this study, we suggest using the binary spring search procedure (BSSA) to identify features and the XGBoost classifier to grade histopathology images automatically for mitotic activity. This investigation employs real-time and benchmark datasets, in addition to targeted methods, for identifying jaundice in infants. Evidence suggests that feature quality can have a negative effect on classification accuracy. Furthermore, a bottleneck in classification performance may emerge from compressing the classification approach for unique key attributes. Therefore, it is necessary to discover relevant features to use in classifier training. This can be achieved by integrating a feature selection strategy with a classification classical. Important findings from this study included the use of image processing methods in predicting neonatal hyperbilirubinemia. Image processing involves converting photos from analogue to digital form in order to edit them. Medical image processing aims to acquire data that can be used in the detection, diagnosis, monitoring, and treatment of disease. Newburn jaundice detection accuracy can be verified using image datasets. As opposed to more traditional methods, it produces more precise, timely, and cost-effective outcomes. Common performance metrics such as accuracy, sensitivity, and specificity were also predictive. 2023 Lavoisier. All rights reserved. -
Classification of adaptive back propagation neural network along with fuzzy logic in chronic kidney disease
A steady deterioration in kidney function over months or years is known as chronic kidney disease (CKD). Through a range of techniques, such as pharmacological intervention in moderate cases and hemodialysis and renal transport in severe cases, early identification of CKD is crucial and has a substantial influence on reducing the patient's health development. The outcomes show the patient's kidneys' present state. It is suggested to develop a system for detecting chronic renal disease using machine learning. Finding the best feature sets typically involves using metaheuristic algorithms since feature selection is an NP-hard issue with amorphous polynomials. Semi-crystalline tabu search (TS) is frequently used for both local and global searches. In this study, we employ a brand-new hybrid TS with stochastic diffusion search (SDX)-based feature selection. The adaptive backpropagation neural network (ABPNN-ANFIS) is then classified using fuzzy logic. Fuzzy logic may be used to combine the ABPNN findings. Consequently, these techniques can aid experts in determining the stage of chronic renal disease. The Adaptive Neuron Clearing Inference System (ABPNN-ANFIS) was utilised to develop adaptive inverse neural networks using the MATLAB programme. The outcomes demonstrate that the suggested ABPNN-ANFIS is 98 % accurate in terms of efficiency. 2024 -
Classification of countries based on development indices by using K-means and grey relational analysis
Clustering countries based on their development profile is important, as it helps in the efficient allocation and use of resources for institutions like the World Bank, IMF and many others. However, measuring the status of development in each country is challenging, as development encompasses several facets such as economic, social, environmental and institutional aspects. These dimensions should be captured and aggregated appropriately before attempting to classify countries based on development. In this context, this paper attempts to measure various dimensions of development through four indices namely, Economic Index (EI), Social Index (SI), Sustainability Index (SUI) and Institutional Index (II) for the period between 1996 through 2015 for 102 countries. And then we categorize the countries based on these development indices using the grey relational analysis and K-means clustering method. Our study classifies countries into four clusters with twelve countries in the first cluster, fifty in second, twenty-seven and thirteen countries in third and fourth clusters respectively. Having taken each of the dimensions of development independently, our results show that no cluster has performed poorly in all four aspects. 2021, The Author(s), under exclusive licence to Springer Nature B.V. -
Classification of epileptic seizures using wavelet packet log energy and norm entropies with recurrent Elman neural network classifier
Electroencephalogram shortly termed as EEG is considered as the fundamental segment for the assessment of the neural activities in the brain. In cognitive neuroscience domain, EEG-based assessment method is found to be superior due to its non-invasive ability to detect deep brain structure while exhibiting superior spatial resolutions. Especially for studying the neurodynamic behavior of epileptic seizures, EEG recordings reflect the neuronal activity of the brain and thus provide required clinical diagnostic information for the neurologist. This specific proposed study makes use of wavelet packet based log and norm entropies with a recurrent Elman neural network (REN) for the automated detection of epileptic seizures. Three conditions, normal, pre-ictal and epileptic EEG recordings were considered for the proposed study. An adaptive Weiner filter was initially applied to remove the power line noise of 50Hz from raw EEG recordings. Raw EEGs were segmented into 1s patterns to ensure stationarity of the signal. Then wavelet packet using Haar wavelet with a five level decomposition was introduced and two entropies, log and norm were estimated and were applied to REN classifier to perform binary classification. The non-linear Wilcoxon statistical test was applied to observe the variation in the features under these conditions. The effect of log energy entropy (without wavelets) was also studied. It was found from the simulation results that the wavelet packet log entropy with REN classifier yielded a classification accuracy of 99.70% for normal-pre-ictal, 99.70% for normal-epileptic and 99.85% for pre-ictal-epileptic. 2016, Springer Science+Business Media Dordrecht. -
Classification of HHO-based Machine Learning Techniques for Clone Attack Detection in WSN
Thanks to recent technological advancements, low-cost sensors with dispensation and communication capabilities are now feasible. As an example, a Wireless Sensor Network (WSN) is a network in which the nodes are mobile computers that exchange data with one another over wireless connections rather than relying on a central server. These inexpensive sensor nodes are particularly vulnerable to a clone node or replication assault because of their limited processing power, memory, battery life, and absence of tamper-resistant hardware. Once an attacker compromises a sensor node, they can create many copies of it elsewhere in the network that share the same ID. This would give the attacker complete internal control of the network, allowing them to mimic the genuine nodes' behavior. This is why scientists are so intent on developing better clone assault detection procedures. This research proposes a machine learning based clone node detection (ML-CND) technique to identify clone nodes in wireless networks. The goal is to identify clones effectively enough to prevent cloning attacks from happening in the first place. Use a low-cost identity verification process to identify clones in specific locations as well as around the globe. Using the Optimized Extreme Learning Machine (OELM), with kernels of ELM ideally determined through the Horse Herd Metaheuristic Optimization Algorithm (HHO), this technique safeguards the network from node identity replicas. Using the node identity replicas, the most reliable transmission path may be selected. The procedure is meant to be used to retrieve data from a network node. The simulation result demonstrates the performance analysis of several factors, including sensitivity, specificity, recall, and detection. 2023, Modern Education and Computer Science Press. All rights reserved. -
Classification of Psychological Disorders by Feature Ranking and Fusion using Gradient Boosting Classification of Psychological Disorders
Negative emotional regulation is a defining element of psychological disorders. Our goal was to create a machine-learning model to classify psychological disorders based on negative emotions. EEG brainwave dataset displaying positive, negative, and neutral emotions. However, negative emotions are responsible for psychological health. In this paper, research focused solely on negative emotional state characteristics for which the divide-and-conquer approach has been applied to the feature extraction process. Features are grouped into four equal subsets and feature selection has been done for each subset by feature ranking approach based on their feature importance determined by the Random Forest-Recursive Feature Elimination with Cross-validation (RF-RFECV) method. After feature ranking, the fusion of the feature subset is employed to obtain a new potential dataset. 10-fold cross-validation is performed with a grid search created using a set of predetermined model parameters that are important to achieving the greatest possible accuracy. Experimental results demonstrated that the proposed model has achieved 97.71% accuracy in predicting psychological disorders 2023, International Journal of Advanced Computer Science and Applications.All Rights Reserved. -
Classification of Silicon (Si) Wafer Material Defects in Semiconductor Choosers using a Deep Learning ShuffleNet-v2-CNN Model
The silicon wafer is one of the raw materials used to make semiconductor chipsets. Semiconductor failure or dysfunction could be the result of defects in the layers of this material. As a result, it is essential to work toward the development of a system that is both quick and precise in identifying and classifying wafer defects. Wafer map analysis is necessary for the quality control and analysis of the semiconductor manufacturing process. There are some failure patterns that can be displayed by wafer maps. These patterns can provide essential details that can assist engineers in determining the reason of wafer failures. In this research, a deep-learning-based silicon wafer defect identification and classification model is proposed. The main objective of this research is to identify and classify the silicon wafer defects using the wafer map images. This proposed model identifies and classifies the defects based on the wafer map images from the WM-811K dataset. The proposed model is composed of a pretrained deep transfer learning model called ShuffleNet-v2 with convolutional neural network (CNN) architecture. This ShuffleNet-v2-CNN performs the defects identification and classification process following the workflow of data preprocessing, data augmentation, feature extraction, and classification. For performance evaluation, the proposed ShuffleNet-v2-CNN is evaluated with performance metrics like accuracy, recall, precision, and f1-score. The proposed model has obtained an overall accuracy of 96.93%, 95.40% precision, 96.26% recall, and 95.75% F1-score in classifying the silicon wafer defects based on the wafer map images. 2022 Rajesh Doss et al.