Browsing by Author "Singh, A"

Now showing 1 - 12 of 12
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    A Clinical Insight on New Discovered Molecules and Repurposed Drugs for the Treatment of COVID-19
    (2023-02) Banerjee, S; Banerjee, D; Singh, A; Kumar, S
    Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) began churning out incred ulous terror in December 2019. Within several months from its first detection in Wuhan, SARS-CoV-2 spread to the rest of the world through droplet infection, making it a pandemic situation and a healthcare emergency across the globe. The available treatment of COVID-19 was only symptomatic as the disease was new and no approved drug or vaccine was available. Another challenge with COVID-19 was the continuous mutation of the SARS-CoV-2 virus. Some repurposed drugs, such as hydroxychloroquine, chloroquine, and remdesivir, received emergency use authorization in various countries, but their clinical use is compromised with either severe and fatal adverse effects or non availability of sufficient clinical data. Molnupiravir was the first molecule approved for the treatment of COVID-19, followed by Paxlovid™, monoclonal antibodies (MAbs), and others. New molecules have variable therapeutic efficacy against different variants or strains of SARS-CoV-2, which require further investigations. The aim of this review is to provide in-depth information on new molecules and repurposed drugs with emphasis on their general description, mechanism of action (MOA), correlates of protection, dose and dosage form, route of administration, clinical trials, regulatory approval, and marketing authorizations.
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    A Clinical Insight on New Discovered Molecules and Repurposed Drugs for the Treatment of COVID-19
    (2023-02) Banerjee, S; Banerjee, D; Singh, A; Kumar, S; Pooja, D
    Abstract: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) began churning out incred ulous terror in December 2019. Within several months from its first detection in Wuhan, SARS-CoV-2 spread to the rest of the world through droplet infection, making it a pandemic situation and a healthcare emergency across the globe. The available treatment of COVID-19 was only symptomatic as the disease was new and no approved drug or vaccine was available. Another challenge with COVID-19 was the continuous mutation of the SARS-CoV-2 virus. Some repurposed drugs, such as hydroxychloroquine, chloroquine, and remdesivir, received emergency use authorization in various countries, but their clinical use is compromised with either severe and fatal adverse effects or non availability of sufficient clinical data. Molnupiravir was the first molecule approved for the treatment of COVID-19, followed by Paxlovid™, monoclonal antibodies (MAbs), and others. New molecules have variable therapeutic efficacy against different variants or strains of SARS-CoV-2, which require further investigations. The aim of this review is to provide in-depth information on new molecules and repurposed drugs with emphasis on their general description, mechanism of action (MOA), correlates of protection, dose and dosage form, route of administration, clinical trials, regulatory approval, and marketing authorizations.
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    Combining Forth and Rust: A Robust and Efficient Approach for Low-Level System Programming
    (2023-12) Gupta, P; Rahar, R; Yadav, R; Singh, A; Ramandeep; Kumar, S
    Rust is a modern programming language that addresses the drawbacks of earlier languages by providing features such as memory safety at compilation and high performance. Rust’s memory safety features include ownership and borrowing, which makes it an ideal choice for systems programming, where memory safety is critical. Forth is a stack-based programming language that is widely used for low-level system programming due to its simplicity and ease of use. This research paper aims to explore the combination of Forth and Rust programming languages to create a more robust and efficient solution for low-level system programming. The primary objective is to demonstrate the implementation of essential Forth operations, including addition, subtraction, assignment, comparison, and if-else statements, while demonstrating loops, push operations, and dump operations in Rust. The implementation of these operations in Rust is demonstrated using code from actual implementation. This research paper also discusses the advantages of using Rust for low-level system programming. Rust’s memory safety features, coupled with its high performance, make it an ideal choice for systems programming, where memory safety and performance are critical. The combination of Forth and Rust provides a more efficient and safer solution for low-level system programming, making the implementation more robust. Our implementation tries to leverage these properties of both languages to make a memory-safe and low-level system programming language. This research paper also includes code snippets to provide a practical demonstration of how the Forth operations can be implemented in Rust.
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    Combining Forth and Rust: A Robust and Efficient Approach for Low-Level System Programming †
    (2023-12) Gupta, P; Rahar, R; Yadav, R; Singh, A
    Abstract: Rust is a modern programming language that addresses the drawbacks of earlier languages by providing features such as memory safety at compilation and high performance. Rust’s memory safety features include ownership and borrowing, which makes it an ideal choice for systems programming, where memory safety is critical. Forth is a stack-based programming language that is widely used for low-level system programming due to its simplicity and ease of use. This research paper aims to explore the combination of Forth and Rust programming languages to create a more robust and efficient solution for low-level system programming. The primary objective is to demonstrate the implementation of essential Forth operations, including addition, subtraction, assignment, comparison, and if-else statements, while demonstrating loops, push operations, and dump operations in Rust. The implementation of these operations in Rust is demonstrated using code from actual implementation. This research paper also discusses the advantages of using Rust for low-level system programming. Rust’s memory safety features, coupled with its high performance, make it an ideal choice for systems programming, where memory safety and performance are critical. The combination of Forth and Rust provides a more efficient and safer solution for low-level system programming, making the implementation more robust. Our implementation tries to leverage these properties of both languages to make a memory-safe and low-level system programming language. This research paper also includes code snippets to provide a practical demonstration of how the Forth operations can be implemented in Rust.
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    Federated Transfer Learning for Rice-Leaf Disease Classification across Multiclient Cross-Silo Datasets
    (2023-09) Aggarwal, M; Khullar, V; Goyal, N; Gautam, R; Singh, A
    Paddy leaf diseases encompass a range of ailments affecting rice plants’ leaves, arising from factors like bacteria, fungi, viruses, and environmental stress. Precision agriculture leverages technologies for enhanced crop production, with disease detection being a vital element. Prompt identification of diseases in paddy leaves is critical for curtailing their propagation and reducing crop damage. However, manually diagnosing paddy diseases in regions with vast agricultural areas and limited experts proves immensely difficult. The utilization of machine learning (ML) and deep learning (DL) for diagnosing diseases in agricultural crops appears to be effective and well-suited for widespread application. These ML/DL methods cannot ensure data privacy, as they involve sharing training data with a central server, overlooking competitive and regulatory considerations. As a solution, federated learning (FL) aims to facilitate decentralized training to tackle the identified limitations of centralized training. This paper utilizes the FL approach for the classification of rice-leaf diseases. The manuscript presents an effective approach for rice-leaf disease classification with a federated architecture, ensuring data privacy. We have compiled an unbalanced dataset of rice-leaf disease images, categorized into four diseases with their respective image counts: bacterial blight (1584), brown spot (1440), blast (1600), and tungro (1308). The proposed method, called federated transfer learning (F-TL), maintains privacy for all connected devices using a decentralized client-server setup. Both IID (independent and identically distributed) and non-IID datasets were utilized for testing the F-TL framework after preprocessing. Initially, we conducted an effectiveness analysis of CNN and eight transfer learning models for rice-leaf disease classification. Among them, MobileNetV2 and EfficientNetB3 outperformed the other transfer-learned models. Subsequently, we trained these models using both IID and non-IID datasets in a federated learning environment. The framework’s performance was assessed through diverse scenarios, comparing it with traditional and federated learning models. The evaluation considered metrics like validation accuracy, loss as well as resource utilization such as CPU and RAM. EfficientNetB3 excelled in training, achieving 99% accuracy with 0.1 loss for both IID and non-IID datasets. MobilenetV2 showed slightly lower training accuracy at 98% (IID) and 90% (non-IID) with losses of 0.4 and 0.6, respectively. In evaluation, EfficientNetB3 maintained 99% accuracy with 0.1 loss for both datasets, while MobilenetV2 achieved 90% (IID) and 97% (non-IID) accuracy with losses of 0.6 and 0.2, respectively. Results indicated the F-TL framework’s superiority over traditional distributed deep-learning classifiers, demonstrating its effectiveness in both single and multiclient instances. Notably, the framework’s strengths lie in its cost-effectiveness and data-privacy assurance for resource-constrained edge devices, positioning it as a valuable alternative for rice-leaf disease classification compared to existing tools.
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    Fungi based valorization of wheat straw and rice straw for cellulase and xylanase production
    (2024-01) Devi, A; Singh, A; Kothari, R
    A total of 29 fungal species were isolated from different ecological habitats of Jammu region and screened for cellulase and xylanase production. The potent fungal species were identified as Aspergillus flavus, Aspergillus terreus, Ramichloridium apiculatum, Fusarium pallidoroseum and were compared with the commercially available strains. Out of these, A. flavus and A. terreus exhibit significant cellulolytic and xylanolytic activities on alkali pretreated wheat straw. R. apiculatum was first time reported for cellulolytic and xylanolytic activities in the present study. The maximum xylanolytic activity was shown by A. flavus ITCC 11854.23 as 4.68 U/ml when utilizing alkali treated rice straw. In this investigation, the enzymatic activities of the isolated strains out performed the commercial strains. This study provides a baseline to extract enzymes from these potential fungal species on utilizing low-cost substrates to make the industrial processes more economic and sustainable.
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    Genome-Wide Analysis and Evolutionary Perspective of the Cytokinin Dehydrogenase Gene Family in Wheat
    (2022-08) Jain, P; Singh, A; Iquebal, M; Jaiswal, S; Kumar, S; Kumar, D
    Cytokinin dehydrogenase (CKX; EC.1.5.99.12) regulates the level of cytokinin (CK) in plants and is involved in CK regulatory activities. In different plants, a small gene family encodes CKX proteins with varied numbers of members. The segenes are expanded in the genome mainly due to segmental duplication events. Despite their biological importance, CKX genes in Triticum aestivum have yet to be studied in depth. A total of 11 CKX subfamilies were identified with similar gene structures, motifs, domains, cis-acting elements, and an average signal peptide of 25 amino acid length was found. Introns, ranging from one to four, were present in the coding regions at a similar interval in major CKX genes. Putative cis-elements such as abscisic acid, auxin, salicylic acid, and low temperature-, drought-, and light-responsive cis-regulatory elements were found in the promoter region of majority CKX genes. Variation in the expression pattern of CKX genes were identified across different tissues in Triticum. Phylogenetic analysis shows that the same subfamily of CKX clustered into a similar clade that reflects their evolutionary relationship. We performed a genome-wide identification of CKX family members in the Triticum aestivum genome to get their chromosomal location, gene structure, cis-element, phylogeny, synteny, and tissue- and stage-specific expression along with gene ontology. This study has also elaborately described the tissue- and stage-specific expression and is the resource for further analysis of CKX in the regulation of biotic and abiotic stress resistance, growth, and development in Triticum and other cereals to endeavor for higher production and proper management.
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    Milk‑Derived Antimicrobial Peptides: Overview, Applications, and Future Perspectives
    (2022-11) Singh, A; Duche, R; Sian, J; Panwar, H
    The growing consumer awareness towards healthy and safe food has reformed food processing strategies. Nowadays, food processors are aiming at natural, efective, safe, and low-cost substitutes for enhancing the shelf life of food products. Milk, besides being a rich source of nutrition for infants and adults, serves as a readily available source of precious functional peptides. Due to the existence of high genetic variability in milk proteins, there is a great possibility to get bioactive peptides with varied properties. Among other bioactive agents, milk-originated antimicrobial peptides (AMPs) are gaining interest as attractive and safe additive conferring extended shelf life to minimally processed foods. These peptides display broad spectrum antagonistic activity against bacteria, fungi, viruses, and protozoans. Microbial proteolytic activity, extracellular peptidases, food-grade enzymes, and recombinant DNA technology application are among few strategies to tailor specifc peptides from milk and enhance their production. These bioprotective agents have a promising future in addressing the global concern of food safety along with the possibility to be incorporated into the food matrix without compromising overall consumer acceptance. Additionally, in conformity to the current consumer demands, these AMPs also possess functional properties needed for value addition. This review attempts to present the basic properties, synthesis approaches, action mechanism, current status, and prospects of antimicrobial peptide application in food, dairy, and pharma industry along with their role in ensuring the safety and health of consumers.
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    A panoramic view of technological landscape for bioethanol production from various generations of feedstocks
    (2022-06) Devi, A; Bajar, S; Sihag, P; Sheikh, Z; Singh, A
    Bioethanol is an appropriate alternate energy option due to its renewable, nontoxic, environmentally friendly, and carbon-neutral nature. Depending upon various feedstocks, bioethanol is classified in different various generations. First-generation ethanol created a food vs fuel problem, which was overcome by second-generation, third-generation and fourth-generation ethanol. The considerable availability of lignocellulosic biomass makes it a suitable feedstock, however, its recalcitrant nature is the main hurdle in converting it to bioethanol. The present study gives a comprehensive assessment of global biofuel policies and the current status of ethanol production. Feedstocks for first-generation (sugar and starch-based), second-generation (lignocellulosic biomass and energy crops), third-genera tion (algal-based) and fourth-generation (genetically modified algal biomass or crops) are discussed in detail. The study also assessed the process for ethanol production from various feedstocks, besides giving a holistic background knowledge on the bioconversion process, factors affecting bioethanol production, and various microorganisms involved in the fermentation process. Biotechnological tools also play a pivotal role in enhancing process efficiency and product yield. In addition, most significant development in the field of genetic engineering and adaptive evolution are also highlighted.
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    A panoramic view of technological landscape for bioethanol production from various generations of feedstocks
    (2023-07) Devi, A; Bajar, S; Sihag, P; Singh, A
    Bioethanol is an appropriate alternate energy option due to its renewable, nontoxic, environmentally friendly, and carbon-neutral nature. Depending upon various feedstocks, bioethanol is classified in different various generations. First-generation ethanol created a food vs fuel problem, which was overcome by second-generation, third-generation and fourth-generation ethanol. The considerable availability of lignocellulosic biomass makes it a suitable feedstock, however, its recalcitrant nature is the main hurdle in converting it to bioethanol. The present study gives a comprehensive assessment of global biofuel policies and the current status of ethanol production. Feedstocks for first-generation (sugar and starch-based), second-generation (lignocellulosic biomass and energy crops), third-genera tion (algal-based) and fourth-generation (genetically modified algal biomass or crops) are discussed in detail. The study also assessed the process for ethanol production from various feedstocks, besides giving a holistic background knowledge on the bioconversion process, factors affecting bioethanol production, and various microorganisms involved in the fermentation process. Biotechnological tools also play a pivotal role in enhancing process efficiency and product yield. In addition, most significant development in the field of genetic engineering and adaptive evolution are also highlighted.
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    Pre-Trained Deep Neural Network-Based Features Selection Supported Machine Learning for Rice Leaf Disease Classification
    (2023-04) Aggarwal, M; Khullar, V; Goyal, N; Singh, A; Tolba, A; Thompson, EB; Kumar, S
    Rice is a staple food for roughly half of the world’s population. Some farmers prefer rice cultivation to other crops because rice can thrive in a wide range of environments. Several studies have found that about 70% of India’s population relies on agriculture in some way and that agribusiness accounts for about 17% of India’s GDP. In India, rice is one of the most important crops, but it is vulnerable to a number of diseases throughout the growing process. Farmers’ manual identification of these diseases is highly inaccurate due to their lack of medical expertise. Recent advances in deep learning models show that automatic image recognition systems can be extremely useful in such situations. In this paper, we propose a suitable and effective system for predicting diseases in rice leaves using a number of different deep learning techniques. Images of rice leaf diseases were gathered and processed to fulfil the algorithmic requirements. Initially, features were extracted by using 32 pre-trained models, and then we classified the images of rice leaf diseases such as bacterial blight, blast, and brown spot with numerous machine learning and ensemble learning classifiers and compared the results. The proposed procedure works better than other methods that are currently used. It achieves 90–91% identification accuracy and other performance parameters such as precision, Recall Rate, F1-score, Matthews Coefficient, and Kappa Statistics on a normal data set. Even after the segmentation process, the value reaches 93–94% for model EfficientNetV2B3 with ET and HGB classifiers. The proposed model efficiently recognises rice leaf diseases with an accuracy of 94%. The experimental results show that the proposed procedure is valid and effective for identifying rice diseases.