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Evidence of microRNAs origination from chloroplast genome and their role in regulating Photosystem II protein N (psbN) mRNA
The microRNAs are endogenous, regulating gene expression either at the DNA or RNA level. Despite the availability of extensive studies on microRNA generation in plants, reports on their abundance, biogenesis, and consequent gene regulation in plant organelles remain naVve. Building on previous studies involving pre-miRNA sequencing in Abelmoschus esculentus, we demonstrated that three putative microRNAs were raised from the chloroplast genome. In the current study, we have characterized the genesis of these three microRNAs through a combination of bioinformatics and experimental approaches. The gene sequence for a miRNA, designated as AecpmiRNA1 (A. esculentus chloroplast miRNA), is potentially located in both the genomic DNA, i.e., nuclear and chloroplast genome. In contrast, the gene sequences for the other two miRNAs (AecpmiRNA2 and AecpmiRNA3) are exclusively present in the chloroplast genome. Target prediction revealed many potential mRNAs as targets for AecpmiRNAs. Further analysis using 5N RACE-PCR determined the AecpmiRNA3 binding and cleavage site at the photosystem II protein N (psbN). These results indicate that AecpmiRNAs are generated from the chloroplast genome, possessing the potential to regulate mRNAs arising from chloroplast gene(s). On the other side, the possibility of nuclear genome-derived mRNA regulation by AecpmiRNAs cannot be ruled out. 2024, Termedia Publishing House Ltd.. All rights reserved. -
Medicinal plants, phytochemicals, and herbs to combat viral pathogens including sars-cov-2
The coronavirus disease 2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome corona virus-2 (SARS-CoV-2), is the most important health issue, internationally. With no specific and effective antiviral therapy for COVID-19, new or repurposed antiviral are urgently needed. Phytochemicals pose a ray of hope for human health during this pandemic, and a great deal of research is concentrated on it. Phytochemicals have been used as antiviral agents against several viruses since they could inhibit several viruses via different mechanisms of direct inhibition either at the viral entry point or the replication stages and via immunomodulation potentials. Recent evidence also suggests that some plants and its components have shown promising antiviral properties against SARS-CoV-2. This review summarizes certain phytochemical agents along with their mode of actions and potential antiviral activities against important viral pathogens. A special focus has been given on medicinal plants and their extracts as well as herbs which have shown promising results to combat SARS-CoV-2 infection and can be useful in treating patients with COVID-19 as alternatives for treatment under phytotherapy approaches during this devastating pandemic situation. 2021 by the authors. Licensee MDPI, Basel, Switzerland. -
Evolutionary algorithm based feature extraction for enhanced recommendations
A major challenge to Collaborative Filtering systems is high dimensional and sparse data which they have to deal with. Feature selection techniques partly address this problem by reducing the feature space and retaining only a representative subset of features. However these techniques do not address the sparsity problem which affects both quality and quantity of recommendations. A more promising direction would be to construct/extract new features which are low dimensional, dense and have more discriminative power. Content based construction of features has been explored in the past. This work proposes a evolutionary algorithm based feature extraction techniques which discover hidden features with high discriminative capacity. Such an approach offers the advantage of discovering features even in the absence of additional information such as item contents etc. The proposed approach is contrasted with content based feature extraction techniques through experiments and the ability of the new approach in discovering interesting and useful features is established. -
Exploring graph-based global similarity estimates for quality recommendations
Data sparsity or the insufficiency of past user preferences in predicting future user needs continues to be a major challenge for RS engines. We propose a solution to the sparsity problem by exploring similarity measures that capture the global patterns of commonality between users or items by leveraging on indirect ways of connecting users (items) through a user (item) graph. Entities (users or items) sharing common features are connected to each other by edges weighted by their proximity or distance. Graph-based techniques, for estimating transitive similarity between entities not directly connected, are exploited to bring the entities closer thus facilitating collaboration. Furthermore, we also propose a combined user-item graph approach for exploiting the similarity between users preferring similar items (and vice versa). In this work, we have suggested alternatives to the already existing global similarity assessment and we aim to investigate the appropriateness of the proposed techniques under differing data features. 2014 Inderscience Enterprises Ltd. -
Folksonomy-based fuzzy user profiling for improved recommendations
Genre is a major factor influencing user decisions to peruse an item in domains such as movies, books etc. Recommender systems, generally have, at their disposal, information regarding genres/categories that a movie/book belongs to. However, the degree of membership of the objects in these categories is typically unavailable. Such information, if available, would provide a better description of items and consequently lead to quality recommendations. In this paper, we propose an approach to infer the degree of genre presence in a movie by examining the various tags conferred on them by various users. Tags are user-defined metadata for items and embed abundant information about various facets of user likes, their opinion on the quality and the type of object tagged. Leveraging on tags to guide the genre degree determination exploits crowd sourcing to enrich item content description. Fuzzy logic naturally models human logic allowing for the nuanced representation of features of objects and thus is utilized to derive such gradual representation as well as for modeling user profiles. To the best of our knowledge ours is one of the first approaches to utilize such folksonomy information to infer genre degrees subsequently used for recommendations. The proposed method has the twin advantages of utilizing enriched content information for recommendation as well as squeezing the information from the user-item-tag and user-item ratings spaces and condensing them into fuzzy user profiles. The fuzzy user and object representations are leveraged both for the design of content-based as well as collaborative recommender systems. Experimental evaluations establish the effectiveness of the proposed approaches as compared to other baselines. 2013 Elsevier Ltd. All rights reserved. -
User profiling based on keyword clusters for improved recommendations
Recommender Systems (RS) have risen in popularity over the years, and their ability to ease decision-making for the user in various domains has made them ubiquitous. However, the sparsity of data continues to be one of the biggest shortcomings of the suggestions offered. Recommendation algorithms typically model user preferences in the form of a profile, which is then used to match user preferences to items of their interest. Consequently, the quality of recommendations is directly related to the level of detail contained in these profiles. Several attempts at enriching the user profiles leveraging both user preference data and item content details have been explored in the past. We propose a method of constructing a user profile, specifically for the movie domain, based on user preference for keyword clusters, which indirectly captures preferences for various narrative styles. These profiles are then utilized to perform both content-based (CB) filtering as well as collaborative filtering (CF). The proposed approach scores over the direct keyword-matching, genre-based user profiling and the traditional CF methods under sparse data scenarios as established by various experiments. It has the advantage of a compact user model representation, while at the same time capturing the essence of the styles or genres preferred by the user. The identification of implicit genres is captured effectively through clustering without requiring labeled data for training. 2014 Springer International Publishing Switzerland. -
Impact of Blockchain Technology in the Healthcare Systems
The healthcare industry is one of the most important industries in the world which is in dire need of a restructuring process because of its poor and outdated techniques of data management. Healthcare system has adopted a centralized environment and deals with a lot of intermediaries which makes it prone to issues of single point of failure, lack of traceability of transactions, and privacy issues such as data leakage. Blockchain is a relatively new technology which is able to tackle the obsolete methods and practices existing in the healthcare industry. In this chapter, we analyzed the applications of blockchains in the healthcare industry which can solve the issues prevalent in the healthcare industry. The aim of this chapter is to reveal the potential benefits that comes from using blockchain technology in the healthcare industry and identify the various challenges that this technology has. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Bibliometric analysis of the impact of blockchain technology on the tourism industry
The tourism sector is one of the world's fastest-expanding industries. Because of the benefits, it provides to individuals and organizations, the tourism sector has attracted a lot of attention throughout the years. But because of its poor and obsolete data management techniques, this industry is in desperate need of reform. Blockchain technology is one method for managing and exploring data relevant to the tourism industry. This study used bibliometric methods to analyze the impact of blockchain technology on the tourism sector from 2017 to 2022. The publications were extracted from the dimensions database, and the VOS viewer software was used to visualize research patterns. The findings provided valuable information on the publication year, authors, author's country, author's organizational affiliations, publishing journals, etc. Based on the findings of this analysis, researchers may be able to design their studies better and add more insights into their empirical studies. 2024 Srinesh Thakur, Anvita Electronics, 16-11-762, Vijetha Golden Empire, Hyderabad. -
Recurrent Neural Networks in Predicting the Popularity of Online Social Networks Content: A Review
An online social network is a web platform that individuals use to make social relationships with people who share similar interests, activities, connections, and backgrounds. All online social networks differ in the number of features they provide and their format. In recent years, drastic growth has been seen in the users of online social networks like Flickr, Instagram, Pinterest, Twitter, etc. Among all the features of online social networks, content sharing is the one being widely used by individual users and large organizations. Due to this, content popularity prediction has been extensively studied nowadays, considering various aspects related to it. The study throws light on the use of machine learning techniques in this field. Various algorithms have been used to handle popularity prediction, including classification, regression, and clustering techniques. It is feasible to extract the essential information from such content using machine learning algorithms and utilize the retrieved information in a variety of ways, the majority of which are commercial in nature. The goal of this study is to review and analyze various recurrent neural network (RNN) approaches for predicting the popularity of social media content. The Electrochemical Society -
Popularity Prediction of Online Social Media Content: A Bibliometric Analysis
An online social network is a platform that enables individuals to interact with others who have similar backgrounds, preferences, activities, and associations. The number of features available and the format of each online social network range widely. Users of online social networks, such as Twitter, Instagram, Flicker, and Pinterest, have increased dramatically in recent years. Content sharing is the most popular feature of online social networks, used by both specific users and big enterprises. This study has used bibliometric methods to analyze the growth of the social media popularity prediction on online social network content from 2013 to 2022. The publications have been extracted from the dimensions database, and the VOS viewer software was used to visualize research patterns. The finding provides valuable information on the publication year, authors, authors country, authors organizational affiliations, publishing journals, etc. Based on the findings of this analysis, researchers will be able to design their studies better and add more insights into their empirical studies. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
A study on the relationship between internal branding and affective commitment of customer contact employees in multi-brand retail stores in Bangalore /
International Journal of Business and Administration Research, Vol.1, Issue 7, pp.189-197, ISSN No: 2348-0653. -
Studies on K X Ray fluorescence parameters of low and medium Z elements
K X-ray fluorescence parameters for pure elements have been determined using different single and double reflection geometry by several researchers over the years. Horakeri et al.have shown that the K X-ray fluorescence parameters can also be determined by a simple 2and#960;-geometrical configuration method and a NaI(Tl) detector spectrometer for high Z elements. newlineHowever, in order to study the K XRF parameters for low Z elements, high resolution detector spectrometers are required.But in high resolution detectors like HPGe and Si(Li), due to the gap between window and the active area of the detector, the solid angle subtended by the detector at the target is not 2and#960;. Hence a suitable geometry correction is required for accurate newlinemeasurement of incident photons and the emitted K X-ray photons in order to determine the K XRF parameters in low Z elements. In the present study, employing a nearly 2and#960;-geometrical configuration and applying suitable geometry correction, we have determined the K X-ray fluorescence parameters of a few low and medium Z elements in the range of 27 and#8804; Z and#8804; 30 and 42 and#8804; Z and#8804; 47 respectively. The elements were procured in the form of thin foils and were irradiated by a weak radioactive source. The emitted K X-ray photons were detected using a low energy high resolution HPGe detector spectrometer. The incident photons, emitted K X-ray photons and the transmitted photons at newlinethe incident energy is measured and were corrected for window attenuation, efficiency, self-attenuation and geometry correction newlineto obtain the true intensities of incident photons, emitted K Xray photons and the transmitted photons at the incident energy. -
K-Shell X-Ray Fluorescence Parameters of a Few Low Z Elements
K-shell X-ray fluorescence parameters of low Z elements cobalt, nickel, copper, and zinc have been measured employing a simple method. These elemental targets were excited by using 32.86 keV barium K X-ray photons from a weak 137Cs ?-ray source, and the emitted K-shell X-rays from these targets were detected using a low-energy high-purity germanium X-ray detector spectrometer. The results are compared with the standard theoretical, semi-empirical, fitted values and with the others experimental values. 2018, Pleiades Publishing, Inc. -
K? to K? X-ray intensity ratios and K to L shell vacancy transfer probabilities of Co, Ni, Cu, and Zn
The K to L shell total vacancy transfer probabilities of low Z elements Co, Ni, Cu, and Zn are estimated by measuring the K? to K? intensity ratio adopting the 2?-geometry. The target elements were excited by 32.86 keV barium K-shell X-rays from a weak 137Cs ?-ray source. The emitted K-shell X-rays were detected using a low energy HPGe X-ray detector coupled to a 16 k MCA. The measured intensity ratios and the total vacancy transfer probabilities are compared with theoretical results and others work, establishing a good agreement. 2015, Pleiades Publishing, Inc. -
K-shell X-ray intensity ratios and vacancy transfer probabilities of Pt, Au, and Pb by a simple method
The K-shell X-ray intensity ratios, radiative and total vacancy transfer probablities of platinum, gold, and lead are measured by employing the 2?-geometrical configuration and a weak gamma source, a simple method proposed previously by our group. The targets of Pt, Au, and Pb were excited using ?-rays of weighted energy 123.6 keV from a weak 57Co source and the emitted K-shell X-rays were detected using an HPGe X-ray detector spectrometer coupled to a 16k multichannel analyzer. The measured values of these parameters are compared with the theoretical values and experimental data of other researchers, finding a good agreement. Thus, the 2?-geometrical configuration method with a weak gamma source can be alternative simple method to measure various atomic parameters in the field of X-ray spectroscopy. 2014, Pleiades Publishing, Inc. -
K-shell jump ratio and jump factor of 3d elements
Employing a simple 2?-geometrical configuration method, K-shell absorption jump ratio and jump factor have been estimated in a few 3d elements viz. Co, Ni, Cu and Zn. The target elements in the form of thin foils were excited using 32.86 keV K X-ray photons from a weak137Cs radioactive source. The emitted K X-rays were detected using a low energy HPGe X-ray detector spectrometerand the K X-ray production cross-section and K X-ray intensity ratios for all the target elements were measured. Then, using the measured data, the K-shell absorption jump factor and jump ratios have been evaluated. The obtained results agree within the experimental uncertainties with previous values reported in the literature. 2018 Author(s). -
A critical study on acetylene as an alternative fuel for transportation
With the traditional power sector hindered by fuel shortage and climate changes, the promotion of green energy becomes the most prioritized objective of the government. The ministry's move becomes significant because conversion to cleaner energy sources is the best way to minimize global warming and to reenergize the global economy. Among the available alternative gaseous fuels, acetylene caters to these needs because of its property similarities with hydrogen. In this research, the suitability of acetylene as an engine fuel is analyzed. Also, the production methods, combustion properties, abnormal combustion, and safety issues were discussed. This review paper describes about the various possible modes of fuel induction techniques to be adopted. The research establishes the utility of acetylene as a commercial fuel for internal combustion engines in the future years by the adoption of suitable methodologies. 2021 Author(s). -
Low cost energy management for demand side integration
Numerous batteries are outfitted with a state ofcharge (SoC) demonstrating the relaxation about the charge. Building a beneficial BMS is to check while thinking about, to that amount regardless we work now not holds a reliable method in imitation of study condition state of-charge, the nearly imperative measurement concerning a battery. Perusing the relaxation about the vigour of a battery is more unpredictable than administering thin fuel as in automobiles. An electrochemical cell diminishes its greatness and the in-and-streaming coulombs are counted for SOC. The BMS together which offers commitment while charging yet releasing; that detaches the battery if the SOC is below certain percentage. Various laws like Peukerts Law for battery capacity have been employed to determine the discharge rate of the battery. Arduino Uno is used for the input parameters required for Peukerts Law and various other calculations for significant monitoring. To address the existing complicated BMS, a new approach has been provided using IoT platform and making the understanding of BMS in much similar perspective. 2018 IEEE. -
ALLEVIATING DATA STORAGE CHALLENGE THROUGH VIRTUALIZATION OF BLOCKCHAIN EMBEDDED WITH INTERNET OF THINGS
Internet of things is evolving day by day with connected devices with continuous advancement in the devices but the security of IoT is not assured due to its trusted third party with centralized servers. Blockchain is a peer-to-peer network, where each peer is responsible for their task without centralized server, and no need to trust anyone in the network. Blockchain is integrated with IoT to improve their security, because of its feature of tamper-proof. Few issues are happening while integrating blockchain to IoT. The main issue that has to be resolved for a blockchain is the storage issue. Whenever the blockchain is evolving the storage of the blockchain is also increasing. IoT peers in the network have to store the entire blockchain to perform the verification of data and the IoT nodes are not having the capability to store the entire data. In this paper, we are discussing the storage issue of blockchain while integrating it into IoT. We proposed a navel approach to resolve the issues of storage by the virtualization technique. The result shows that virtualization reduces the storage capacity for the IoT peers as compared with the previously proposed methods. 2022, Engg Journals Publications. All rights reserved. -
Security Intensification using Blockchain coupled with Internet of Things: Proposal, Challenges and Anatomization
Internet of things is an important part of our day-to-day life where all things are connected in the network with the internet. The number of devices linked to the network grows steadily each day in recent years. The innovation in the manufacturing industry also the reason for the production of different devices that uses various technologies to make a possible connection between the devices. Even though the Internet of Things has been developing and demonstrating its potential in recent years, its security when connected to the internet is in doubt. Blockchain is a disruptive technology that provides security to their network without tampering with the data in the network. Researchers and experts have recommended using the blockchain to address security vulnerabilities in the Internet of Things. In this paper, we have analyzed some of the issues which are occurring while integrating blockchain into the Internet of things. The major issues were discussed and which will be helpful to move towards the research direction to solve those problems. 2022 IEEE.