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3. INVESTIGATING INHIBITING FACTORS AFFECTING MOOCS ADOPTION AMONG TEACHERS IN HIGHER EDUCATION INSTITUTIONS OF INDIA

Author

Bhaskar, Priyanka, Joshi, Amit, and Dayalan, Padmalosani

Subjects
Management. Industrial management, HD28-70, Business, HF5001-6182
Abstract

Massive Open Online Courses (MOOCs) has the potential to eradicate the digital divide and endorse education globally among the learners in the education industry. Through MOOCs platforms, teachers can upgrade their knowledge skills and abilities in their respective fields. The present study aims to identify the inhibiting factors underlying the adoption of MOOCs by teachers employed in higher education institutions. The research has employed an Interpretative Phenomenological Analysis of qualitative research methodology. In this research, teachers from the identified Universities of Uttarakhand, India are selected as the respondent to investigate the factors affecting the adoption of MOOCs. Findings reveal that technological barriers, financial barriers, and lack of awareness act as inhibiting factors in adopting MOOCs by teachers of HEI. This study also contributes to the literature by assessing the factors influencing teachers’ intention to adopt Massive Open Online Courses for learning. Through this paper, the three stakeholders of MOOCs, that is teachers, HEI and MOOC provider are suggested measures that will enhance the support and acceptability of the online courses.

Published
2021
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4. A Comparative Analysis of Distributed Training Strategies for GPT-2

Author

Patwardhan, Ishan, Gandhi, Shubham, Khare, Om, Joshi, Amit, and Sawant, Suraj

Subjects
Computer Science - Distributed, Parallel, and Cluster Computing
Abstract

The rapid advancement in Large Language Models has been met with significant challenges in their training processes, primarily due to their considerable computational and memory demands. This research examines parallelization techniques developed to address these challenges, enabling the efficient and scalable training of Large Language Models. A comprehensive analysis of both data and model parallelism strategies, including Fully Sharded Data Parallelism and Distributed Data-Parallel frameworks, is provided to assess methods that facilitate efficient model training. Furthermore, the architectural complexities and training methodologies of the Generative Pre-Trained Transformer-2 model are explored. The application of these strategies is further investigated, which is crucial in managing the substantial computational and memory demands of training sophisticated models. This analysis not only highlights the effectiveness of these parallel training strategies in enhancing training efficiency but also their role in enabling the scalable training of large language models. Drawing on recent research findings, through a comprehensive literature review, this research underscores the critical role of parallelization techniques in addressing the computational challenges of training state-of-the-art Large Language Models, thereby contributing to the advancement of training more sophisticated and capable artificial intelligence systems., Comment: Submitted to the International Journal of Parallel Programming and is currently under review

Published
2024

6. Genetic risk impacts the association of menopausal hormone therapy with colorectal cancer risk

Author

Tian, Yu, Lin, Yi, Qu, Conghui, Arndt, Volker, Baurley, James W., Berndt, Sonja I., Bien, Stephanie A., Bishop, D. Timothy, Brenner, Hermann, Buchanan, Daniel D., Budiarto, Arif, Campbell, Peter T., Carreras-Torres, Robert, Casey, Graham, Chan, Andrew T., Chen, Rui, Chen, Xuechen, Conti, David V., Díez-Obrero, Virginia, Dimou, Niki, Drew, David A., Figueiredo, Jane C., Gallinger, Steven, Giles, Graham G., Gruber, Stephen B., Gunter, Marc J., Harlid, Sophia, Harrison, Tabitha A., Hidaka, Akihisa, Hoffmeister, Michael, Huyghe, Jeroen R., Jenkins, Mark A., Jordahl, Kristina M., Joshi, Amit D., Keku, Temitope O., Kawaguchi, Eric, Kim, Andre E., Kundaje, Anshul, Larsson, Susanna C., Marchand, Loic Le, Lewinger, Juan Pablo, Li, Li, Moreno, Victor, Morrison, John, Murphy, Neil, Nan, Hongmei, Nassir, Rami, Newcomb, Polly A., Obón-Santacana, Mireia, Ogino, Shuji, Ose, Jennifer, Pardamean, Bens, Pellatt, Andrew J., Peoples, Anita R., Platz, Elizabeth A., Potter, John D., Prentice, Ross L., Rennert, Gad, Ruiz-Narvaez, Edward A., Sakoda, Lori C., Schoen, Robert E., Shcherbina, Anna, Stern, Mariana C., Su, Yu-Ru, Thibodeau, Stephen N., Thomas, Duncan C., Tsilidis, Konstantinos K., van Duijnhoven, Franzel J. B., Van Guelpen, Bethany, Visvanathan, Kala, White, Emily, Wolk, Alicja, Woods, Michael O., Wu, Anna H., Peters, Ulrike, Gauderman, W. James, Hsu, Li, and Chang-Claude, Jenny

Published
2024
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10. Deepfake Detection: Leveraging the Power of 2D and 3D CNN Ensembles

Author

Bakliwal, Aagam and Joshi, Amit D.

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

In the dynamic realm of deepfake detection, this work presents an innovative approach to validate video content. The methodology blends advanced 2-dimensional and 3-dimensional Convolutional Neural Networks. The 3D model is uniquely tailored to capture spatiotemporal features via sliding filters, extending through both spatial and temporal dimensions. This configuration enables nuanced pattern recognition in pixel arrangement and temporal evolution across frames. Simultaneously, the 2D model leverages EfficientNet architecture, harnessing auto-scaling in Convolutional Neural Networks. Notably, this ensemble integrates Voting Ensembles and Adaptive Weighted Ensembling. Strategic prioritization of the 3-dimensional model's output capitalizes on its exceptional spatio-temporal feature extraction. Experimental validation underscores the effectiveness of this strategy, showcasing its potential in countering deepfake generation's deceptive practices., Comment: 6 pages, 2 figures

Published
2023

11. iGLU 4.0: A continuous glucose monitoring and balancing paradigm with physiological parameters

Author

Jain, Prateek, Joshi, Amit, and Mohanty, Saraju

Subjects
Physics - Medical Physics, Physics - Data Analysis, Statistics and Probability, Physics - Instrumentation and Detectors
Abstract

The conventional method of glucose measurement such as pricking blood from the body is prevalent which brings pain and trauma. Invasive methods of measurement sometimes raise the risk of blood infection to the patient. Sometimes, some of the physiological parameters such as body temperature and systolic blood pressure (SBP) are responsible for blood glucose level fluctuations. Moreover, diabetes for a long duration usually becomes a critical issue. In such situation, patients need to consult diabetologist frequently, which is not possible in normal life. Therefore, it is required to develop non-invasive glucose balancing paradigm, which measures blood glucose without pricking blood along with physiological parameters measurement and decision model. The proposed paradigm helps to doctor, who is even available at remote location. There will not be any need to consult frequently. In the way of optimized non-invasive system design, an NIRS technique with specific wavelengths along with physiological parameters is taken to predict the precise glucose value. The all parameters (glucose, Blood pressure and body temperature), food intake and insulin levels are parts of decision model, which would help to the doctor to take decision related to the further medicine doses and diet plan. The patients would have suggestions according to maintain their blood glucose level. The proposed system demonstrated an accurate model with MARD and AvgE 12.50% and 12.10% respectively using DNN model. Coefficient of determination R2 has been found 0.97.

Published
2023

12. Genome-wide interaction analysis of folate for colorectal cancer risk.

Author

Bouras, Emmanouil, Kim, Andre, Lin, Yi, Morrison, John, Du, Mengmeng, Albanes, Demetrius, Barry, Elizabeth, Baurley, James, Berndt, Sonja, Bien, Stephanie, Bishop, Timothy, Brenner, Hermann, Budiarto, Arif, Burnett-Hartman, Andrea, Campbell, Peter, Carreras-Torres, Robert, Casey, Graham, Cenggoro, Tjeng, Chan, Andrew, Chang-Claude, Jenny, Conti, David, Cotterchio, Michelle, Devall, Matthew, Diez-Obrero, Virginia, Dimou, Niki, Drew, David, Figueiredo, Jane, Giles, Graham, Gruber, Stephen, Gunter, Marc, Harrison, Tabitha, Hidaka, Akihisa, Hoffmeister, Michael, Huyghe, Jeroen, Joshi, Amit, Kawaguchi, Eric, Keku, Temitope, Kundaje, Anshul, Le Marchand, Loic, Lewinger, Juan, Li, Li, Lynch, Brigid, Mahesworo, Bharuno, Männistö, Satu, Moreno, Victor, Murphy, Neil, Newcomb, Polly, Obón-Santacana, Mireia, Ose, Jennifer, Palmer, Julie, Papadimitriou, Nikos, Pardamean, Bens, Pellatt, Andrew, Peoples, Anita, Platz, Elizabeth, Potter, John, Qi, Lihong, Qu, Conghui, Rennert, Gad, Ruiz-Narvaez, Edward, Sakoda, Lori, Schmit, Stephanie, Shcherbina, Anna, Stern, Mariana, Su, Yu-Ru, Tangen, Catherine, Thomas, Duncan, Tian, Yu, Um, Caroline, van Duijnhoven, Franzel, Van Guelpen, Bethany, Visvanathan, Kala, Wang, Jun, White, Emily, Wolk, Alicja, Woods, Michael, Ulrich, Cornelia, Hsu, Li, Gauderman, W, Peters, Ulrike, and Tsilidis, Konstantinos

Subjects
CRC, European, GWIS, SYN2, TIMP4, colorectal cancer, folate, folic acid, genome-wide, interaction, synapsin, tissue inhibitor of metalloproteinase 4, Humans, Folic Acid, Risk Factors, Colorectal Neoplasms, Case-Control Studies, Dietary Supplements
Abstract

BACKGROUND: Epidemiological and experimental evidence suggests that higher folate intake is associated with decreased colorectal cancer (CRC) risk; however, the mechanisms underlying this relationship are not fully understood. Genetic variation that may have a direct or indirect impact on folate metabolism can provide insights into folates role in CRC. OBJECTIVES: Our aim was to perform a genome-wide interaction analysis to identify genetic variants that may modify the association of folate on CRC risk. METHODS: We applied traditional case-control logistic regression, joint 3-degree of freedom, and a 2-step weighted hypothesis approach to test the interactions of common variants (allele frequency >1%) across the genome and dietary folate, folic acid supplement use, and total folate in relation to risk of CRC in 30,550 cases and 42,336 controls from 51 studies from 3 genetic consortia (CCFR, CORECT, GECCO). RESULTS: Inverse associations of dietary, total folate, and folic acid supplement with CRC were found (odds ratio [OR]: 0.93; 95% confidence interval [CI]: 0.90, 0.96; and 0.91; 95% CI: 0.89, 0.94 per quartile higher intake, and 0.82 (95% CI: 0.78, 0.88) for users compared with nonusers, respectively). Interactions (P-interaction < 5×10-8) of folic acid supplement and variants in the 3p25.2 locus (in the region of Synapsin II [SYN2]/tissue inhibitor of metalloproteinase 4 [TIMP4]) were found using traditional interaction analysis, with variant rs150924902 (located upstream to SYN2) showing the strongest interaction. In stratified analyses by rs150924902 genotypes, folate supplementation was associated with decreased CRC risk among those carrying the TT genotype (OR: 0.82; 95% CI: 0.79, 0.86) but increased CRC risk among those carrying the TA genotype (OR: 1.63; 95% CI: 1.29, 2.05), suggesting a qualitative interaction (P-interaction = 1.4×10-8). No interactions were observed for dietary and total folate. CONCLUSIONS: Variation in 3p25.2 locus may modify the association of folate supplement with CRC risk. Experimental studies and studies incorporating other relevant omics data are warranted to validate this finding.

Published
2023

18. Programmable Transimpedance Amplifier with Integrated Bandgap Reference for Glucose Concentration Measurement

Author

Ahmad, Riyaz, Joshi, Amit M., and Boolchandani, Dharmendra

Subjects
Electrical Engineering and Systems Science - Systems and Control
Abstract

For glucose electrochemical sensors, a comprehensive electronics interface is designed and constructed in 0.18 um, CMOS process technology, and 1.5 V supply voltage. This interface includes a programmable readout amplifier and bandgap reference voltage potentiostat circuit. The programmable transimpedance amplifier (PTIA), the proposed readout circuit, provides a large dynamic range and low noise. The overall transimpedance increase for the PTIA is 17.3-50.5 kohm. For an input current range of 4.2-180 uA, the PTIA response has a linear output voltage range of 0.55-1.44 V. The output rms noise value is calculated to be 5.101 Vrms, and the overall power consumption of the design is 2.33 mW. The THD percentage spans from 7.6 to 10.2 in the current range mentioned above. All bandgap reference voltage potentiostat measurements are made using the reference potential of 0.6 V. The working electrode was a glassy carbon electrode (GCE) loaded with a CuO/Cu0:76CO2:25O4 (copper cobaltite) coating. An electrochemical glucose sensing setup has been used to measure glucose concentrations between 1 and 10 mM, and an emulated circuit has been used to verify the viability of the proposed glucose sensing design. The suggested glucose sensor architecture has a total size of 0.0684 mm2., Comment: 17 Pages, 14 Figures

Published
2023

21. iCardo 3.0: A Machine Learning Framework for Prediction of Conduction Disturbance in Heart

Author

Sinha, Nidhi, Joshi, Amit, Mohanty, Saraju P., 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, Nanda, Satyasai Jagannath, editor, Yadav, Rajendra Prasad, editor, Gandomi, Amir H., editor, and Saraswat, Mukesh, editor

Published
2024
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24. A Comparative Study of Cryorolling and Cryo-Cross Rolling Treatment on Tensile and Fracture Properties of Al 5052 Alloy

Author

Pant, Ruby, Joshi, Amit, Singh, Shushant, Pathak, Manoj Kumar, Gairola, Saurabh, 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, Velmurugan, R., editor, Balaganesan, G., editor, Kakur, Naresh, editor, and Kanny, Krishnan, editor

Published
2024
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25. A Signal Conditioning Circuit with Integrated Bandgap Reference for Glucose Concentration Measurement

Author

Ahmad, Riyaz, Joshi, Amit Mahesh, Boolchandani, Dharmendar, Rannenberg, Kai, Editor-in-Chief, Soares Barbosa, Luís, Editorial Board Member, Goedicke, Michael, Editorial Board Member, Tatnall, Arthur, Editorial Board Member, Neuhold, Erich J., Editorial Board Member, Stiller, Burkhard, Editorial Board Member, Stettner, Lukasz, Editorial Board Member, Pries-Heje, Jan, Editorial Board Member, Kreps, David, Editorial Board Member, Rettberg, Achim, Editorial Board Member, Furnell, Steven, Editorial Board Member, Mercier-Laurent, Eunika, Editorial Board Member, Winckler, Marco, Editorial Board Member, Malaka, Rainer, Editorial Board Member, Puthal, Deepak, editor, Mohanty, Saraju, editor, and Choi, Baek-Young, editor

Published
2024
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27. iCardo: A Machine Learning Based Smart Healthcare Framework for Cardiovascular Disease Prediction

Author

Sinha, Nidhi, Jangid, Teena, Joshi, Amit M., and Mohanty, Saraju P.

Subjects
Computer Science - Computers and Society
Abstract

The point of care services and medication have become simpler with efficient consumer electronics devices in a smart healthcare system. Cardiovascular disease is a critical illness which causes heart failure, and early and prompt identification can lessen damage and prevent premature mortality. Machine learning has been used to predict cardiovascular disease (CVD) in the literature. The article explains choosing the best classifier model for the selected feature sets and the distinct feature sets selected using four feature selection models. The paper compares seven classifiers using each of the sixteen feature sets. Originally, the data had 56 attributes and 303 occurrences, of which 87 were in good health, and the remainder had cardiovascular disease (CVD). Demographic data with several features make up the four groups of overall features. Lasso, Tree-based algorithms, Chi-Square and RFE have all been used to choose the four distinct feature sets, each containing five, ten, fifteen, and twenty features, respectively. Seven distinct classifiers have been trained and evaluated for each of the sixteen feature sets. To determine the most effective blend of feature set and model, a total of 112 models have been trained, tested, and their performance has been compared. SVM classifier with fifteen chosen features is shown to be the best in terms of overall accuracy. The healthcare data has been maintained in the cloud and would be accessible to patients, caretakers, and healthcare providers through integration with the Internet of Medical Things (IoMT) enabled smart healthcare. Subsequently, the feature selection model chooses the most appropriate feature for CVD prediction to calibrate the system, and the proposed framework can be utilised to anticipate CVD., Comment: 19 Pages, 9 Figures, 5 Tables

Published
2022

32. The Blue Ribbon Committee II Report and Recommendations on Surgical Education and Training in the United States: 2024

Author

Stain, Steven C., Ellison, E. Christopher, Farmer, Diana L., Flynn, Timothy C., Freischlag, Julie A., Matthews, Jeffrey B., Newman, Rachel Williams, Chen, Xiaodong, Stefanidis, Dimitrios, Britt, L.D., Buyske, Jo, Fisher, Karen, Sachdeva, Ajit K., Turner, Patricia L., Flynn, Timothy C., Bass, Barbara L., Brennan, Murray F., Debas, Haile T., Freischlag, Julie A., Jones, R. Scott, Malangoni, Mark A., Pellegrini, Carlos A., Sachdeva, Ajit K., Turner, Patricia L., Warshaw, Andrew L., Welling, Richard E., Zinner, Michael J., Alseidi, Adnan A., Ford, Henri R., Craver, H. William, Dennis, Andrew J., Fingeret, Abbey, Fried, Gerald M., Mason, Bonnie Simpson, Sudan, Ranjan, Yang, Stephen C., Conrad-Schnetz, Kristen, Terhune, Kyla P., Johnson, Wali R., Jenkins Turner, Megan E., Prabhu, Ajita S., Mason, Bonnie Simpson, Brasel, Karen J., Mellinger, John D., Elster, Eric A., Fried, Gerald M., Hashimoto, Daniel A., Jarman, Benjamin T., Joshi, Amit R. T., Kelz, Rachel R., Lindeman, Brenessa M., Pugh, Carla M., Reznick, Richard K., Freischlag, Julie A., Klingensmith, Mary E., Berry, Cherisse D., Cooke, David T., Fisher, Karen, Minter, Rebecca M., Phillips, Linda G., Sidawy, Anton N., Farmer, Diana L., Damewood, Richard B., Freischlag, Julie A., Mansour, M. Ashraf, Michelassi, Fabrizio, Shabahang, Mohsen M., Sharp, Kenneth, Hawn, Mary T., Matthews, Jeffrey B., Bumgardner, Ginny L., Economou, James S., Itani, Kamal M. F., Kelz, Rachel R., Tracy, Thomas F., Jr, Zeiger, Martha A., Fried, Gerald M., Pugh, Carla M., Greenberg, Caprice C., Hashimoto, Daniel A., Paige, John T., Telem, Dana A., Varas, Julian E., Chen, Xiaodong, Stefanidis, Dimitrios, Ellison, E. Christopher, Stain, Steven C., Britt, L.D., Buyske, Jo, Karen Fisher, JD, Sachdeva, Ajit K., Turner, Patricia L., Blair, Patrice Gabler, Bura, Connie, Lupi, Linda K., McGory, Deirdre M., Newman, Rachel Williams, and Schmitt, Peter J.

Published
2024
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35. List of contributors

Author

Ababio, Grace K., primary, AbdelHamid, Sherihan G., additional, Aguilar, Cristóbal N., additional, Aher, Abhijeet A., additional, Ahire, Eknath D., additional, Ansari, Atifa Haseeb, additional, Arunachalam, Kantha Deivi, additional, Asati, Vivek, additional, Bajaj, Sakshi, additional, Balekundri, Amruta, additional, Bharti, Sanjay Kumar, additional, Bisht, Dheeraj, additional, Chauhan, Nidhi, additional, Chávez-Gonzalez, Monica L., additional, Dagar, Komal, additional, Gabr, Mariam M., additional, Gaur, Anchal, additional, Goyal, Yogeeta S., additional, Gujarathi, Nayan A., additional, Gulia, Monika, additional, Gupta, Charu, additional, Gupta, Madan Mohan, additional, Gupta, Manish, additional, Hamdy, Nadia M., additional, Jain, Meenu, additional, Jaiswal, Arvind Kumar, additional, Jarouliya, Urmila, additional, Jhawat, Vikas, additional, Joshi, Amit, additional, Kachave, Ramanlal N., additional, Kant Arya, Rajeshwar Kamal, additional, Kapoor, Devesh, additional, Karuppannan, Sathish Kumar, additional, Kaur, Ramandeep, additional, Kausar, Meena, additional, Keservani, Raj K., additional, Kesharwani, Rajesh K., additional, Khairnar, Shubham J., additional, Khan, Shahbaz, additional, Khulbe, Preeti, additional, Kshirsagar, Sanjay J., additional, Kulkarni, Shrushti, additional, Kumar, Amit, additional, Kumar, Prashant, additional, Kumar, Vipul, additional, Kumar, Virendra, additional, Kumar, Yatindra, additional, Laddha, Umesh D., additional, Lohani, Alka, additional, Mahapatra, Debarshi Kar, additional, Maurya, Mohit, additional, Nikam, Vikrant Kisanrao, additional, Nishal, Suchitra, additional, Pandit, Nisha, additional, Patil, Tulshidas S., additional, Rai, Sweta, additional, Rajora, Amit Kumar, additional, Ramirez-Guzmán, Nathiely, additional, Rane, Bhushan Rajendra, additional, Rani, Sweta, additional, Sahu, D., additional, Sandoval-Cortes, José, additional, Saranya, S, additional, Sen, Priya, additional, Sepúlveda, Leonardo, additional, Sharma, Anil K., additional, Sharma, Deepak, additional, Sharma, J.B., additional, Sharma, Prateek, additional, Shekhar, Nikhila, additional, Shukla, Deepali, additional, Singh, Durgesh, additional, Singh, Sippy, additional, Singh, Supriya, additional, Singhvi, Gautam, additional, Sukhia, Amey, additional, Surana, Khecmhand R., additional, Suryavanshi, Anjali, additional, Swellam, Menha M., additional, Thakur, Ajit Kumar, additional, Thakur, Shyamli, additional, Tyagi, Sakshi, additional, Verma, Navneet, additional, Wiwanitkit, Viroj, additional, and Yasri, Sora, additional

Published
2024
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37. Dielectric-Modulated Double Gate Bilayer Electrode Organic Thin Film Transistor-based Biosensor for Label-Free Detection: Simulation Study and Sensitivity Analysis

Author

Jain, Sushil Kumar and Joshi, Amit M.

Subjects
Physics - Instrumentation and Detectors
Abstract

A dielectric-modulated double gate bilayer electrodes organic thin-film transistor (DMDGBE-OTFT) based sensor is proposed for label-free biomolecule detection. The double gate of DMDGBE-OTFT is used for creating two symmetrical gates underlap regions on both sides of the organic semiconductor. The parallel immobilization of biomolecule in two gates underlaps region changes the on-current (ION) of the DMDGBEOTFT. Bilayer electrode is also used for significant reduction of barrier height to enhance the performance of the proposed device. The change in the drain current has been utilized to evaluate the sensitivity of the DMDGBE-OTFT for biomolecule recognition having different dielectric constants and corresponding charge densities using 2-D physics-based numerical simulation when biomolecules are immobilized in the gate underlap area. The ATLAS TCAD tool is used to investigate the sensitivity performance of the DMDGBE-OTFT. The proposed DMDGBE-OTFT has 24.2% higher sensitivity in comparison to the recently reported OTFT-based biosensors for label-free detection of biomolecules. The DMDGBE-OTFT biosensor has a lot of potential for flexible biosensing applications in the future because of its flexibility, high sensitivity, biocompatibility and low cost., Comment: 16 pages, 11 Figures, 4 Tables

Published
2022

42. Genome-Wide Interaction Analysis of Genetic Variants With Menopausal Hormone Therapy for Colorectal Cancer Risk.

Author

Tian, Yu, Kim, Andre E, Bien, Stephanie A, Lin, Yi, Qu, Conghui, Harrison, Tabitha A, Carreras-Torres, Robert, Díez-Obrero, Virginia, Dimou, Niki, Drew, David A, Hidaka, Akihisa, Huyghe, Jeroen R, Jordahl, Kristina M, Morrison, John, Murphy, Neil, Obón-Santacana, Mireia, Ulrich, Cornelia M, Ose, Jennifer, Peoples, Anita R, Ruiz-Narvaez, Edward A, Shcherbina, Anna, Stern, Mariana C, Su, Yu-Ru, van Duijnhoven, Franzel JB, Arndt, Volker, Baurley, James W, Berndt, Sonja I, Bishop, D Timothy, Brenner, Hermann, Buchanan, Daniel D, Chan, Andrew T, Figueiredo, Jane C, Gallinger, Steven, Gruber, Stephen B, Harlid, Sophia, Hoffmeister, Michael, Jenkins, Mark A, Joshi, Amit D, Keku, Temitope O, Larsson, Susanna C, Le Marchand, Loic, Li, Li, Giles, Graham G, Milne, Roger L, Nan, Hongmei, Nassir, Rami, Ogino, Shuji, Budiarto, Arif, Platz, Elizabeth A, Potter, John D, Prentice, Ross L, Rennert, Gad, Sakoda, Lori C, Schoen, Robert E, Slattery, Martha L, Thibodeau, Stephen N, Van Guelpen, Bethany, Visvanathan, Kala, White, Emily, Wolk, Alicja, Woods, Michael O, Wu, Anna H, Campbell, Peter T, Casey, Graham, Conti, David V, Gunter, Marc J, Kundaje, Anshul, Lewinger, Juan Pablo, Moreno, Victor, Newcomb, Polly A, Pardamean, Bens, Thomas, Duncan C, Tsilidis, Konstantinos K, Peters, Ulrike, Gauderman, W James, Hsu, Li, and Chang-Claude, Jenny

Subjects
Humans, Colorectal Neoplasms, Estrogens, Progestins, Risk Factors, Case-Control Studies, Menopause, Polymorphism, Single Nucleotide, Female, Aging, Digestive Diseases, Estrogen, Colo-Rectal Cancer, Genetics, Prevention, Human Genome, Clinical Research, Cancer, 2.1 Biological and endogenous factors, Aetiology, Oncology and Carcinogenesis, Oncology & Carcinogenesis
Abstract

BackgroundThe use of menopausal hormone therapy (MHT) may interact with genetic variants to influence colorectal cancer (CRC) risk.MethodsWe conducted a genome-wide, gene-environment interaction between single nucleotide polymorphisms and the use of any MHT, estrogen only, and combined estrogen-progestogen therapy with CRC risk, among 28 486 postmenopausal women (11 519 CRC patients and 16 967 participants without CRC) from 38 studies, using logistic regression, 2-step method, and 2- or 3-degree-of-freedom joint test. A set-based score test was applied for rare genetic variants.ResultsThe use of any MHT, estrogen only and estrogen-progestogen were associated with a reduced CRC risk (odds ratio [OR] = 0.71, 95% confidence interval [CI] = 0.64 to 0.78; OR = 0.65, 95% CI = 0.53 to 0.79; and OR = 0.73, 95% CI = 0.59 to 0.90, respectively). The 2-step method identified a statistically significant interaction between a GRIN2B variant rs117868593 and MHT use, whereby MHT-associated CRC risk was statistically significantly reduced in women with the GG genotype (OR = 0.68, 95% CI = 0.64 to 0.72) but not within strata of GC or CC genotypes. A statistically significant interaction between a DCBLD1 intronic variant at 6q22.1 (rs10782186) and MHT use was identified by the 2-degree-of-freedom joint test. The MHT-associated CRC risk was reduced with increasing number of rs10782186-C alleles, showing odds ratios of 0.78 (95% CI = 0.70 to 0.87) for TT, 0.68 (95% CI = 0.63 to 0.73) for TC, and 0.66 (95% CI = 0.60 to 0.74) for CC genotypes. In addition, 5 genes in rare variant analysis showed suggestive interactions with MHT (2-sided P

Published
2022

43. Author Correction: Application of Mendelian randomization to explore the causal role of the human gut microbiome in colorectal cancer

Author

Hatcher, Charlie, Richenberg, George, Waterson, Samuel, Nguyen, Long H., Joshi, Amit D., Carreras-Torres, Robert, Moreno, Victor, Chan, Andrew T., Gunter, Marc, Lin, Yi, Qu, Conghui, Song, Mingyang, Casey, Graham, Figueiredo, Jane C., Gruber, Stephen B., Hampe, Jochen, Hampel, Heather, Jenkins, Mark A., Keku, Temitope O., Peters, Ulrike, Tangen, Catherine M., Wu, Anna H., Hughes, David A., Rühlemann, Malte C., Raes, Jeroen, Timpson, Nicholas J., and Wade, Kaitlin H.

Published
2023
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44. Application of Mendelian randomization to explore the causal role of the human gut microbiome in colorectal cancer

Author

Hatcher, Charlie, Richenberg, George, Waterson, Samuel, Nguyen, Long H., Joshi, Amit D., Carreras-Torres, Robert, Moreno, Victor, Chan, Andrew T., Gunter, Marc, Lin, Yi, Qu, Conghui, Song, Mingyang, Casey, Graham, Figueiredo, Jane C., Gruber, Stephen B., Hampe, Jochen, Hampel, Heather, Jenkins, Mark A., Keku, Temitope O., Peters, Ulrike, Tangen, Catherine M., Wu, Anna H., Hughes, David A., Rühlemann, Malte C., Raes, Jeroen, Timpson, Nicholas J., and Wade, Kaitlin H.

Published
2023
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46. Inhibiting Factors Influencing Adoption of Simulation-Based Teaching from Management Teacher's Perspective: Prioritisation Using Analytic Hierarchy Process

Author

Bhaskar, Preeti, Bhaskar, Priyanka, Anthonisamy, Ananth, Dayalan, Padmalosani, and Joshi, Amit

Abstract

Simulation-based teaching provides opportunities to simulate the scenarios/situations by integrating different technology into educational practice to make it look like a real situation. The use of a simulation approach has been widely used in health, engineering, nursing, architecture, medicine, aviation, and marine courses, ironically, management education has not been at the forefront of simulation-based learning. This research aims to prioritise the inhibiting barriers faced by teachers to adopting simulation-based teaching in higher education institutions of India. Previous studies have identified inhibiting factor but none of the studies has prioritise those factors therefore this research has used AHP approach to prioritise the inhibiting barriers. This study has identified and prioritises four main inhibiting factors. Institutional-level factors have secured the highest rank, followed by technological-level factors, operational-level factors and personal-level factors.

Published
2023
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48. Novel EEG based Schizophrenia Detection with IoMT Framework for Smart Healthcare

Author

Sharma, Geetanjali and Joshi, Amit M.

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

In the field of neuroscience, Brain activity analysis is always considered as an important area. Schizophrenia(Sz) is a brain disorder that severely affects the thinking, behaviour, and feelings of people all around the world. Electroencephalography (EEG) is proved to be an efficient biomarker in Sz detection. EEG is a non-linear time-seriesi signal and utilizing it for investigation is rather crucial due to its non-linear structure. This paper aims to improve the performance of EEG based Sz detection using a deep learning approach. A novel hybrid deep learning model known as SzHNN (Schizophrenia Hybrid Neural Network), a combination of Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) has been proposed. CNN network is used for local feature extraction and LSTM has been utilized for classification. The proposed model has been compared with CNN only, LSTM only, and machine learning-based models. All the models have been evaluated on two different datasets wherein Dataset 1 consists of 19 subjects and Dataset 2 consists of 16 subjects. Several experiments have been conducted for the same using various parametric settings on different frequency bands and using different sets of electrodes on the scalp. Based on all the experiments, it is evident that the proposed hybrid model (SzHNN) provides the highest classification accuracy of 99.9% in comparison to other existing models. The proposed model overcomes the influence of different frequency bands and even showed a much better accuracy of 91% with only 5 electrodes. The proposed model is also evaluated on the Internet of Medical Things (IoMT) framework for smart healthcare and remote monitoring applications., Comment: 18 pages, 9 Figures

Published
2021

49. Genome-wide interaction study of dietary intake of fibre, fruits, and vegetables with risk of colorectal cancer

Author

Papadimitriou, Nikos, Kim, Andre, Kawaguchi, Eric S., Morrison, John, Diez-Obrero, Virginia, Albanes, Demetrius, Berndt, Sonja I., Bézieau, Stéphane, Bien, Stephanie A., Bishop, D Timothy, Bouras, Emmanouil, Brenner, Hermann, Buchanan, Daniel D., Campbell, Peter T., Carreras-Torres, Robert, Chan, Andrew T., Chang-Claude, Jenny, Conti, David V., Devall, Matthew A., Dimou, Niki, Drew, David A., Gruber, Stephen B., Harrison, Tabitha A., Hoffmeister, Michael, Huyghe, Jeroen R., Joshi, Amit D., Keku, Temitope O., Kundaje, Anshul, Küry, Sébastien, Le Marchand, Loic, Lewinger, Juan Pablo, Li, Li, Lynch, Brigid M., Moreno, Victor, Newton, Christina C., Obón-Santacana, Mireia, Ose, Jennifer, Pellatt, Andrew J., Peoples, Anita R., Platz, Elizabeth A., Qu, Conghui, Rennert, Gad, Ruiz-Narvaez, Edward, Shcherbina, Anna, Stern, Mariana C., Su, Yu-Ru, Thomas, Duncan C., Thomas, Claire E., Tian, Yu, Tsilidis, Konstantinos K., Ulrich, Cornelia M., Um, Caroline Y., Visvanathan, Kala, Wang, Jun, White, Emily, Woods, Michael O., Schmit, Stephanie L., Macrae, Finlay, Potter, John D., Hopper, John L., Peters, Ulrike, Murphy, Neil, Hsu, Li, Gunter, Marc J., and Gauderman, W. James

Published
2024
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