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17,660 results on '"Prasad, N."'

2. Functional Classification of Spiking Signal Data Using Artificial Intelligence Techniques: A Review

Author

Sharifrazi, Danial, Javed, Nouman, Joloudari, Javad Hassannataj, Alizadehsani, Roohallah, Paradkar, Prasad N., Tan, Ru-San, Acharya, U. Rajendra, and Bhatti, Asim

Subjects
Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Quantitative Biology - Neurons and Cognition
Abstract

Human brain neuron activities are incredibly significant nowadays. Neuronal behavior is assessed by analyzing signal data such as electroencephalography (EEG), which can offer scientists valuable information about diseases and human-computer interaction. One of the difficulties researchers confront while evaluating these signals is the existence of large volumes of spike data. Spikes are some considerable parts of signal data that can happen as a consequence of vital biomarkers or physical issues such as electrode movements. Hence, distinguishing types of spikes is important. From this spot, the spike classification concept commences. Previously, researchers classified spikes manually. The manual classification was not precise enough as it involves extensive analysis. Consequently, Artificial Intelligence (AI) was introduced into neuroscience to assist clinicians in classifying spikes correctly. This review discusses the importance and use of AI in spike classification, focusing on the recognition of neural activity noises. The task is divided into three main components: preprocessing, classification, and evaluation. Existing methods are introduced and their importance is determined. The review also highlights the need for more efficient algorithms. The primary goal is to provide a perspective on spike classification for future research and provide a comprehensive understanding of the methodologies and issues involved. The review organizes materials in the spike classification field for future studies. In this work, numerous studies were extracted from different databases. The PRISMA-related research guidelines were then used to choose papers. Then, research studies based on spike classification using machine learning and deep learning approaches with effective preprocessing were selected., Comment: 8 figures, 32 pages

Published
2024

4. Automated detection of Zika and dengue in Aedes aegypti using neural spiking analysis

Author

Sharifrazi, Danial, Javed, Nouman, Alizadehsani, Roohallah, Paradkar, Prasad N., Acharya, U. Rajendra, and Bhatti, Asim

Subjects
Computer Science - Machine Learning, Quantitative Biology - Neurons and Cognition
Abstract

Mosquito-borne diseases present considerable risks to the health of both animals and humans. Aedes aegypti mosquitoes are the primary vectors for numerous medically important viruses such as dengue, Zika, yellow fever, and chikungunya. To characterize this mosquito neural activity, it is essential to classify the generated electrical spikes. However, no open-source neural spike classification method is currently available for mosquitoes. Our work presented in this paper provides an innovative artificial intelligence-based method to classify the neural spikes in uninfected, dengue-infected, and Zika-infected mosquitoes. Aiming for outstanding performance, the method employs a fusion of normalization, feature importance, and dimension reduction for the preprocessing and combines convolutional neural network and extra gradient boosting (XGBoost) for classification. The method uses the electrical spiking activity data of mosquito neurons recorded by microelectrode array technology. We used data from 0, 1, 2, 3, and 7 days post-infection, containing over 15 million samples, to analyze the method's performance. The performance of the proposed method was evaluated using accuracy, precision, recall, and the F1 scores. The results obtained from the method highlight its remarkable performance in differentiating infected vs uninfected mosquito samples, achieving an average of 98.1%. The performance was also compared with 6 other machine learning algorithms to further assess the method's capability. The method outperformed all other machine learning algorithms' performance. Overall, this research serves as an efficient method to classify the neural spikes of Aedes aegypti mosquitoes and can assist in unraveling the complex interactions between pathogens and mosquitoes.

Published
2023

8. Engineered Antiviral Sensor Targets Infected Mosquitoes

Author

Benetta, Elena Dalla, López-Denman, Adam J, Li, Hsing-Han, Masri, Reem A, Brogan, Daniel J, Bui, Michelle, Yang, Ting, Li, Ming, Dunn, Michael, Klein, Melissa J, Jackson, Sarah, Catalan, Kyle, Blasdell, Kim R, Tng, Priscilla, Antoshechkin, Igor, Alphey, Luke S, Paradkar, Prasad N, and Akbari, Omar S

Subjects
Medical Microbiology, Biomedical and Clinical Sciences, Vector-Borne Diseases, Infectious Diseases, Prevention, Genetics, Emerging Infectious Diseases, Biotechnology, 2.2 Factors relating to the physical environment, Aetiology, Infection, Good Health and Well Being, Animals, Culicidae, CRISPR-Cas Systems, Gene Editing, Mosquito Vectors, RNA, Viral, Antiviral Agents
Abstract

Escalating vector disease burdens pose significant global health risks, as such innovative tools for targeting mosquitoes are critical. CRISPR-Cas technologies have played a crucial role in developing powerful tools for genome manipulation in various eukaryotic organisms. Although considerable efforts have focused on utilizing class II type II CRISPR-Cas9 systems for DNA targeting, these modalities are unable to target RNA molecules, limiting their utility against RNA viruses. Recently, the Cas13 family has emerged as an efficient tool for RNA targeting; however, the application of this technique in mosquitoes, particularly Aedes aegypti, has yet to be fully realized. In this study, we engineered an antiviral strategy termed REAPER (vRNA Expression Activates Poisonous Effector Ribonuclease) that leverages the programmable RNA-targeting capabilities of CRISPR-Cas13 and its potent collateral activity. REAPER remains concealed within the mosquito until an infectious blood meal is uptaken. Upon target viral RNA infection, REAPER activates, triggering programmed destruction of its target arbovirus such as chikungunya. Consequently, Cas13-mediated RNA targeting significantly reduces viral replication and viral prevalence of infection, and its promiscuous collateral activity can even kill infected mosquitoes within a few days. This innovative REAPER technology adds to an arsenal of effective molecular genetic tools to combat mosquito virus transmission.

Published
2023

9. Lung Cancer Classification and Prediction Based on Statistical Feature Selection Method Using Data Mining Techniques

Author

Kavitha, S., Prasad, N. H., Sowmya, K., Naik, Ramavathu Durga Prasad, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Tan, Kay Chen, Series Editor, Shetty, N. R., editor, Prasad, N. H., editor, and Nagaraj, H. C., editor

Published
2024
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10. Detection of Content-Based Image Retrieval Using OCR Tools

Author

MariyanRichard, A., Prasad, N. H., Kavitha, S., Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Tan, Kay Chen, Series Editor, Shetty, N. R., editor, Prasad, N. H., editor, and Nalini, N., editor

Published
2024
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14. N-Terminomic Changes in Neurons During Excitotoxicity Reveal Proteolytic Events Associated With Synaptic Dysfunctions and Potential Targets for Neuroprotection.

Author

Ameen, S Sadia, Griem-Krey, Nane, Dufour, Antoine, Hossain, M Iqbal, Hoque, Ashfaqul, Sturgeon, Sharelle, Nandurkar, Harshal, Draxler, Dominik F, Medcalf, Robert L, Kamaruddin, Mohd Aizuddin, Lucet, Isabelle S, Leeming, Michael G, Liu, Dazhi, Dhillon, Amardeep, Lim, Jet Phey, Basheer, Faiza, Zhu, Hong-Jian, Bokhari, Laita, Roulston, Carli L, Paradkar, Prasad N, Kleifeld, Oded, Clarkson, Andrew N, Wellendorph, Petrine, Ciccotosto, Giuseppe D, Williamson, Nicholas A, Ang, Ching-Seng, and Cheng, Heung-Chin

Subjects
Neurons, Cells, Cultured, Animals, Mice, Rats, Nervous System Diseases, Calpain, Glutamic Acid, Proteolysis, Neuroprotection, CRMP2, CaM kinase IIa, CaM kinase IIb, Src, calpains, excitotoxicity, neuronal death, neuroprotection, proteolytic processing, synaptic damage, Neurosciences, Stroke, Genetics, Brain Disorders, Underpinning research, Aetiology, 1.1 Normal biological development and functioning, 2.1 Biological and endogenous factors, Neurological, Biochemistry & Molecular Biology
Abstract

Excitotoxicity, a neuronal death process in neurological disorders such as stroke, is initiated by the overstimulation of ionotropic glutamate receptors. Although dysregulation of proteolytic signaling networks is critical for excitotoxicity, the identity of affected proteins and mechanisms by which they induce neuronal cell death remain unclear. To address this, we used quantitative N-terminomics to identify proteins modified by proteolysis in neurons undergoing excitotoxic cell death. We found that most proteolytically processed proteins in excitotoxic neurons are likely substrates of calpains, including key synaptic regulatory proteins such as CRMP2, doublecortin-like kinase I, Src tyrosine kinase and calmodulin-dependent protein kinase IIβ (CaMKIIβ). Critically, calpain-catalyzed proteolytic processing of these proteins generates stable truncated fragments with altered activities that potentially contribute to neuronal death by perturbing synaptic organization and function. Blocking calpain-mediated proteolysis of one of these proteins, Src, protected against neuronal loss in a rat model of neurotoxicity. Extrapolation of our N-terminomic results led to the discovery that CaMKIIα, an isoform of CaMKIIβ, undergoes differential processing in mouse brains under physiological conditions and during ischemic stroke. In summary, by identifying the neuronal proteins undergoing proteolysis during excitotoxicity, our findings offer new insights into excitotoxic neuronal death mechanisms and reveal potential neuroprotective targets for neurological disorders.

Published
2023

15. FlightTrackAI: a robust convolutional neural network-based tool for tracking the flight behaviour of Aedes aegypti mosquitoes

Author

Nouman Javed, Adam J. López-Denman, Prasad N. Paradkar, and Asim Bhatti

Subjects
disease transmission, convolutional neural network, multi-object tracking, flight behaviour, FlightTrackAI, vector control, Science
Abstract

Monitoring the flight behaviour of mosquitoes is crucial for assessing their fitness levels and understanding their potential role in disease transmission. Existing methods for tracking mosquito flight behaviour are challenging to implement in laboratory environments, and they also struggle with identity tracking, particularly during occlusions. Here, we introduce FlightTrackAI, a robust convolutional neural network (CNN)-based tool for automatic mosquito flight tracking. FlightTrackAI employs CNN, a multi-object tracking algorithm, and interpolation to track flight behaviour. It automatically processes each video in the input folder without supervision and generates tracked videos with mosquito positions across the frames and trajectory graphs before and after interpolation. FlightTrackAI does not require a sophisticated setup to capture videos; it can perform excellently with videos recorded using standard laboratory cages. FlightTrackAI also offers filtering capabilities to eliminate short-lived objects such as reflections. Validation of FlightTrackAI demonstrated its excellent performance with an average accuracy of 99.9%. The percentage of correctly assigned identities after occlusions exceeded 91%. The data produced by FlightTrackAI can facilitate analysis of various flight-related behaviours, including flight distance and volume coverage during flights. This advancement can help to enhance our understanding of mosquito ecology and behaviour, thereby informing targeted strategies for vector control.

Published
2024
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16. A Segmentation Scheme For Robust Iris Based On Improved U-Net

Author

Kumar, B. P. N. Madhu, Bhavani, V. G., Prasad, N. Ch. S., Kumar, G. P., Kumar, G. Roop, Shanmukh, K., Fournier-Viger, Philippe, Series Editor, Madhavi, K. Reddy, editor, Subba Rao, P., editor, Avanija, J., editor, Manikyamba, I. Lakshmi, editor, and Unhelkar, Bhuvan, editor

Published
2024
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17. Synthesis and Structural Properties of Flexible Ferroelectric Polymer–Ceramics Nanocomposite Films for Multifunctional Applications

Author

Puli, Venkata Sreenivas, Narendra Babu, S., Prasad, N. V., Ghosh, Arindam, Series Editor, Chua, Daniel, Series Editor, de Souza, Flavio Leandro, Series Editor, Aktas, Oral Cenk, Series Editor, Han, Yafang, Series Editor, Gong, Jianghong, Series Editor, Jawaid, Mohammad, Series Editor, Manohara Babu, Mulakaluri Rama, editor, Buragohain, Manoj Kumar, editor, and Kuchipudi, Srinivas, editor

Published
2024
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18. Potential Zones Identification to Effectively Exploit Solar and Wind Energy in the State of Assam—A MCDA Approach Using GIS and Remote Sensing

Author

Raj, P Taniya, Prasad, N. S. R., di Prisco, Marco, Series Editor, Chen, Sheng-Hong, Series Editor, Vayas, Ioannis, Series Editor, Kumar Shukla, Sanjay, Series Editor, Sharma, Anuj, Series Editor, Kumar, Nagesh, Series Editor, Wang, Chien Ming, Series Editor, Cui, Zhen-Dong, Series Editor, Mesapam, Shashi, editor, Ohri, Anurag, editor, Sridhar, Venkataramana, editor, and Tripathi, Nitin Kumar, editor

Published
2024
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19. A Study on Flexural Fatigue Performance of Cement-Treated Base in Flexible Pavements Due to Repetitive Loading

Author

Kumar, B. V. Kiran, Prasad, N. Shiva, di Prisco, Marco, Series Editor, Chen, Sheng-Hong, Series Editor, Vayas, Ioannis, Series Editor, Kumar Shukla, Sanjay, Series Editor, Sharma, Anuj, Series Editor, Kumar, Nagesh, Series Editor, Wang, Chien Ming, Series Editor, Cui, Zhen-Dong, Series Editor, Kolathayar, Sreevalsa, editor, Vinod Chandra Menon, N., editor, and Sreekeshava, K. S., editor

Published
2024
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23. Increasing Eligibility to Transplant Through the Selective Cytopheretic Device: A Review of Case Reports Across Multiple Clinical Conditions

Author

Sai Prasad N. Iyer, PhD, Christopher J. Pino, PhD, Lenar T. Yessayan, MD, Stuart L. Goldstein, MD, Matthew R. Weir, MD, Angela J. Westover, BS, David A. Catanzaro, PharmD, Kevin K. Chung, MD, and H. David Humes, MD

Subjects
Surgery, RD1-811
Abstract

A stable, minimum physiological health status is required for patients to qualify for transplant or artificial organ support eligibility to ensure the recipient has enough reserve to survive the perioperative transplant period. Herein, we present a novel strategy to stabilize and improve patient clinical status through extracorporeal immunomodulation of systemic hyperinflammation with impact on multiple organ systems to increase eligibility and feasibility for transplant/device implantation. This involves treatment with the selective cytopheretic device (SCD), a cell-directed extracorporeal therapy shown to adhere and immunomodulate activated neutrophils and monocytes toward resolution of systemic inflammation. In this overview, we describe a case series of successful transition of pediatric and adult patients with multiorgan failure to successful transplant/device implantation procedures by treatment with the SCD in the following clinical situations: pediatric hemophagocytic lymphohistiocytosis, and adult hepatorenal and cardiorenal syndromes. Application of the SCD in these cases may represent a novel paradigm in increasing clinical eligibility of patients to successful transplant outcomes.

Published
2024
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29. Highly Classified with Two Factor Authentication Encrypted Secured Mail

Author

Prasad, N. H., Narayan, B. N. Lakshmi, GovindaKishora, S., Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Shetty, N. R., editor, Patnaik, L. M., editor, and Prasad, N. H., editor

Published
2023
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30. Using Big Data and Gamification to Incentivize Sustainable Urban Transportation

Author

Mariyan Richard, A., Hamsavath, Prasad N., Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Shetty, N. R., editor, Patnaik, L. M., editor, and Prasad, N. H., editor

Published
2023
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32. EggCountAI: a convolutional neural network-based software for counting of Aedes aegypti mosquito eggs

Author

Nouman Javed, Adam J. López-Denman, Prasad N. Paradkar, and Asim Bhatti

Subjects
Fecundity, Artificial intelligence, Mask RCNN, Egg counting, EggCountAI, ICount, Infectious and parasitic diseases, RC109-216
Abstract

Abstract Background Mosquito-borne diseases exert a huge impact on both animal and human populations, posing substantial health risks. The behavioural and fitness traits of mosquitoes, such as locomotion and fecundity, are crucial factors that influence the spread of diseases. In existing egg-counting tools, each image requires separate processing with adjustments to various parameters such as intensity threshold and egg area size. Furthermore, accuracy decreases significantly when dealing with clustered or overlapping eggs. To overcome these issues, we have developed EggCountAI, a Mask Region-based Convolutional Neural Network (RCNN)-based free automatic egg-counting tool for Aedes aegypti mosquitoes. Methods The study design involves developing EggCountAI for counting mosquito eggs and comparing its performance with two commonly employed tools—ICount and MECVision—using 10 microscopic and 10 macroscopic images of eggs laid by females on a paper strip. The results were validated through manual egg counting on the strips using ImageJ software. Two different models were trained on macroscopic and microscopic images to enhance egg detection accuracy, achieving mean average precision, mean average recall, and F1-scores of 0.92, 0.90, and 0.91 for the microscopic model, and 0.91, 0.90, and 0.90 for the macroscopic model, respectively. EggCountAI automatically counts eggs in a folder containing egg strip images, offering adaptable filtration for handling impurities of varying sizes. Results The results obtained from EggCountAI highlight its remarkable performance, achieving overall accuracy of 98.88% for micro images and 96.06% for macro images. EggCountAI significantly outperformed ICount and MECVision, with ICount achieving 81.71% accuracy for micro images and 82.22% for macro images, while MECVision achieved 68.01% accuracy for micro images and 51.71% for macro images. EggCountAI also excelled in other statistical parameters, with mean absolute error of 1.90 eggs for micro, 74.30 eggs for macro, and a strong correlation and R-squared value (0.99) for both micro and macro. The superior performance of EggCountAI was most evident when handling overlapping or clustered eggs. Conclusion Accurate detection and counting of mosquito eggs enables the identification of preferred egg-laying sites and facilitates optimal placement of oviposition traps, enhancing targeted vector control efforts and disease transmission prevention. In future research, the tool holds the potential to extend its application to monitor mosquito feeding preferences. Graphical Abstract

Published
2023
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34. Attempted Blind Constrained Descent Experiments

Author

R, Prasad N

Subjects
Computer Science - Machine Learning, Electrical Engineering and Systems Science - Signal Processing
Abstract

Blind Descent uses constrained but, guided approach to learn the weights. The probability density function is non-zero in the infinite space of the dimension (case in point: Gaussians and normal probability distribution functions). In Blind Descent paper, some of the implicit ideas involving layer by layer training and filter by filter training (with different batch sizes) were proposed as probable greedy solutions. The results of similar experiments are discussed. Octave (and proposed PyTorch variants) source code of the experiments of this paper can be found at https://github.com/PrasadNR/Attempted-Blind-Constrained-Descent-Experiments-ABCDE- . This is compared against the ABCDE derivatives of the original PyTorch source code of https://github.com/akshat57/Blind-Descent .

Published
2021

35. Culex-Transmitted Diseases: Mechanisms, Impact, and Future Control Strategies using Wolbachia

Author

Mukund Madhav, Kim R. Blasdell, Brendan Trewin, Prasad N. Paradkar, and Adam J. López-Denman

Subjects
Culex, Wolbachia, arboviruses, mosquito control, Microbiology, QR1-502
Abstract

Mosquitoes of the Culex genus are responsible for a large burden of zoonotic virus transmission globally. Collectively, they play a significant role in the transmission of medically significant diseases such as Japanese encephalitis virus and West Nile virus. Climate change, global trade, habitat transformation and increased urbanisation are leading to the establishment of Culex mosquitoes in new geographical regions. These novel mosquito incursions are intensifying concerns about the emergence of Culex-transmitted diseases and outbreaks in previously unaffected areas. New mosquito control methods are currently being developed and deployed globally. Understanding the complex interaction between pathogens and mosquitoes is essential for developing new control strategies for Culex species mosquitoes. This article reviews the role of Culex mosquitos as vectors of zoonotic disease, discussing the transmission of viruses across different species, and the potential use of Wolbachia technologies to control disease spread. By leveraging the insights gained from recent successful field trials of Wolbachia against Aedes-borne diseases, we comprehensively discuss the feasibility of using this technique to control Culex mosquitoes and the potential for the development of next generational Wolbachia-based control methods.

Published
2024
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37. The Darwinian Core of Evolutionary Theory and the Extended Evolutionary Synthesis: Similarities and Differences

Author

Vidya, T. N. C., Dey, Sutirth, Prasad, N. G., Joshi, Amitabh, Delisle, Richard G., Series Editor, Bellon, Richard, Editorial Board Member, Brooks, Daniel R., Editorial Board Member, Cain, Joe, Editorial Board Member, Ceccarelli, David, Editorial Board Member, Dickins, Thomas E., Editorial Board Member, Diogo, Rui, Editorial Board Member, Esposito, Maurizio, Editorial Board Member, Kutschera, Ulrich, Editorial Board Member, Levit, Georgy S., Editorial Board Member, Loison, Laurent, Editorial Board Member, Schwartz, Jeffrey H., Editorial Board Member, Tattersall, Ian, Editorial Board Member, Turner, Derek D., Editorial Board Member, van den Meer, Jitse M., Editorial Board Member, and Dickins, Benjamin J.A., editor

Published
2023
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38. Why Evolution Is Bigger than all of Us: A Reply to Smocovitis

Author

Vidya, T. N. C., Dey, Sutirth, Prasad, N. G., Joshi, Amitabh, Delisle, Richard G., Series Editor, Bellon, Richard, Editorial Board Member, Brooks, Daniel R., Editorial Board Member, Cain, Joe, Editorial Board Member, Ceccarelli, David, Editorial Board Member, Dickins, Thomas E., Editorial Board Member, Diogo, Rui, Editorial Board Member, Esposito, Maurizio, Editorial Board Member, Kutschera, Ulrich, Editorial Board Member, Levit, Georgy S., Editorial Board Member, Loison, Laurent, Editorial Board Member, Schwartz, Jeffrey H., Editorial Board Member, Tattersall, Ian, Editorial Board Member, Turner, Derek D., Editorial Board Member, van den Meer, Jitse M., Editorial Board Member, and Dickins, Benjamin J.A., editor

Published
2023
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39. Causes and Consequences of Selection: A Commentary on Baedke and Fábregas-Tejeda

Author

Vidya, T. N. C., Dey, Sutirth, Prasad, N. G., Joshi, Amitabh, Delisle, Richard G., Series Editor, Bellon, Richard, Editorial Board Member, Brooks, Daniel R., Editorial Board Member, Cain, Joe, Editorial Board Member, Ceccarelli, David, Editorial Board Member, Dickins, Thomas E., Editorial Board Member, Diogo, Rui, Editorial Board Member, Esposito, Maurizio, Editorial Board Member, Kutschera, Ulrich, Editorial Board Member, Levit, Georgy S., Editorial Board Member, Loison, Laurent, Editorial Board Member, Schwartz, Jeffrey H., Editorial Board Member, Tattersall, Ian, Editorial Board Member, Turner, Derek D., Editorial Board Member, van den Meer, Jitse M., Editorial Board Member, and Dickins, Benjamin J.A., editor

Published
2023
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40. Effect of Fiber Drawing on Tensile Strength of UHMWPE Single Fibers: Simulation via Von Mises Stress Criterion

Author

Singh, Shubhanker, Das, Vishal, Tripathi, D. N., Eswara Prasad, N., Chaari, Fakher, Series Editor, Gherardini, Francesco, Series Editor, Ivanov, Vitalii, Series Editor, Cavas-Martínez, Francisco, Editorial Board Member, di Mare, Francesca, Editorial Board Member, Haddar, Mohamed, Editorial Board Member, Kwon, Young W., Editorial Board Member, Trojanowska, Justyna, Editorial Board Member, Vučinić, Dean, editor, Chandran, Vidya, editor, Mahbub, Alam Md., editor, and Sobhan, C. B., editor

Published
2023
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41. Cascaded H-Bridge Multilevel Inverter for PV Applications

Author

Srujay, P., Abeebunnisa, Sd., Prasad, N., Hanu Vamshi, K., Srinivas, M., Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Satapathy, Suresh Chandra, editor, Lin, Jerry Chun-Wei, editor, Wee, Lai Khin, editor, Bhateja, Vikrant, editor, and Rajesh, T. M., editor

Published
2023
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44. Blind Descent: A Prequel to Gradient Descent

Author

Gupta, Akshat and R, Prasad N

Subjects
Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

We describe an alternative learning method for neural networks, which we call Blind Descent. By design, Blind Descent does not face problems like exploding or vanishing gradients. In Blind Descent, gradients are not used to guide the learning process. In this paper, we present Blind Descent as a more fundamental learning process compared to gradient descent. We also show that gradient descent can be seen as a specific case of the Blind Descent algorithm. We also train two neural network architectures, a multilayer perceptron and a convolutional neural network, using the most general Blind Descent algorithm to demonstrate a proof of concept., Comment: We have discovered a new learning method which does random optimisation and solves the problem of initialisation for backpropagation in global optimisation. However, we also pose open questions that we discovered while performing experiments

Published
2020

47. Corrosion in the Development and Airworthiness Certification of Select Al and Mg Aerograde Alloys

Author

Prabhu, T. Ram, Kale, Shirish S., Prasad, N. Eswara, Basu, Bikramjit, Editorial Board Member, Amarendra, G., Editorial Board Member, Bhattacharjee, P. P., Editorial Board Member, Gokhale, Amol A., Editorial Board Member, Kamaraj, M., Editorial Board Member, Manna, Indranil, Editorial Board Member, Mishra, Suman K., Editorial Board Member, Muraleedharan, K., Editorial Board Member, Murty, B. S., Editorial Board Member, Murty, S. V. S. Narayana, Editorial Board Member, Padmanabham, G., Editorial Board Member, Philip, John, Editorial Board Member, Prasad, N. Eswara, Editorial Board Member, Prasad, Rajesh, Editorial Board Member, Rajulapati, Koteswara Rao, Editorial Board Member, Reddy, G. Madhusudan, Editorial Board Member, Srinivasan, A., Editorial Board Member, Sudarshan, T. S., Editorial Board Member, Tarafder, S., Editorial Board Member, Tewari, Raghavendra, Editorial Board Member, Upadhya, Anish, Editorial Board Member, Venkatraman, B., Editorial Board Member, Kamachi Mudali, U., editor, Subba Rao, Toleti, editor, Ningshen, S., editor, G. Pillai, Radhakrishna, editor, P. George, Rani, editor, and Sridhar, T. M., editor

Published
2022
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