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1,117 results on '"Vivek, D."'

3. A Critical Review of Fresh, Hardened and Durability Properties of 3D Printing Concrete

Author

Elango, K. S., Saravanakumar, R., Vivek, D., Yuvaraj, S., Prasanthni, P., Pisello, Anna Laura, Editorial Board Member, Bibri, Simon Elias, Editorial Board Member, Ahmed Salih, Gasim Hayder, Editorial Board Member, Battisti, Alessandra, Editorial Board Member, Piselli, Cristina, Editorial Board Member, Strauss, Eric J., Editorial Board Member, Matamanda, Abraham, Editorial Board Member, Gallo, Paola, Editorial Board Member, Marçal Dias Castanho, Rui Alexandre, Editorial Board Member, Chica Olmo, Jorge, Editorial Board Member, Bruno, Silvana, Editorial Board Member, He, Baojie, Editorial Board Member, Niglio, Olimpia, Editorial Board Member, Pivac, Tatjana, Editorial Board Member, Olanrewaju, AbdulLateef, Editorial Board Member, Pigliautile, Ilaria, Editorial Board Member, Karunathilake, Hirushie, Editorial Board Member, Fabiani, Claudia, Editorial Board Member, Vujičić, Miroslav, Editorial Board Member, Stankov, Uglješa, Editorial Board Member, Sánchez, Angeles, Editorial Board Member, Jupesta, Joni, Editorial Board Member, Pignatta, Gloria, Editorial Board Member, Shtylla, Saimir, Editorial Board Member, Alberti, Francesco, Editorial Board Member, Buckley, Ayşe Özcan, Editorial Board Member, Mandic, Ante, Editorial Board Member, Ahmed Ibrahim, Sherif, Editorial Board Member, Teba, Tarek, Editorial Board Member, Al-Kassimi, Khaled, Editorial Board Member, Rosso, Federica, Editorial Board Member, Abdalla, Hassan, Editorial Board Member, Trapani, Ferdinando, Editorial Board Member, Magnaye, Dina Cartagena, Editorial Board Member, Chehimi, Mohamed Mehdi, Editorial Board Member, van Hullebusch, Eric, Editorial Board Member, Chaminé, Helder, Editorial Board Member, Della Spina, Lucia, Editorial Board Member, Aelenei, Laura, Editorial Board Member, Parra-López, Eduardo, Editorial Board Member, Ašonja, Aleksandar N., Editorial Board Member, Amer, Mourad, Series Editor, K N, Subramanya, editor, Wee, Hui-Ming, editor, and Oliveira, Mario Orlando, editor

Published
2025
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4. Androgen signaling restricts glutaminolysis to drive sex-specific Th17 metabolism in allergic airway inflammation

Author

Chowdhury, Nowrin U., Cephus, Jacqueline-Yvonne, Pilier, Emely Henriquez, Wolf, Melissa M., Madden, Matthew Z., Kuehnle, Shelby N., McKernan, Kaitlin E., Jennings, Erin Q., Arner, Emily N., Heintzman, Darren R., Chi, Channing, Sugiura, Ayaka, Stier, Matthew T., Voss, Kelsey, Ye, Xiang, Scales, Kennedi, Krystofiak, Evan S., Gandhi, Vivek D., Guzy, Robert D., Cahill, Katherine N., Sperling, Anne I., Peebles, R. Stokes, Jr., Rathmell, Jeffrey C., and Newcomb, Dawn C.

Subjects
Respiratory tract diseases -- Physiological aspects -- Care and treatment, Glutamine -- Physiological aspects -- Health aspects, Androgens -- Physiological aspects -- Health aspects, Health care industry
Abstract

Female individuals have an increased prevalence of many Th17 cell-mediated diseases, including asthma. Androgen signaling decreases Th17 cell-mediated airway inflammation, and Th17 cells rely on glutaminolysis. However, it remains unclear whether androgen receptor (AR) signaling modifies glutamine metabolism to suppress Th17 cell-mediated airway inflammation. We show that Th17 cells from male humans and mice had decreased glutaminolysis compared with female individuals, and that AR signaling attenuated Th17 cell mitochondrial respiration and glutaminolysis in mice. Using allergen-induced airway inflammation mouse models, we determined that females had a selective reliance upon glutaminolysis for Th17-mediated airway inflammation, and that AR signaling attenuated glutamine uptake in [CD4.sup.+] T cells by reducing expression of glutamine transporters. In patients with asthma, circulating Th17 cells from men had minimal reliance upon glutamine uptake compared to Th17 cells from women. AR signaling thus attenuates glutaminolysis, demonstrating sexspecific metabolic regulation of Th17 cells with implications for Th17 or glutaminolysis targeted therapeutics., Introduction Cellular metabolism drives [CD4.sup.+] T cell differentiation and stability, and [CD4.sup.+] T cell metabolic pathways are promising therapeutic targets for cancers, autoimmune diseases, and asthma (1-3). Once activated, effector [...]

Published
2024
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29. Intelligent Building Automation System: A Layered Hardware and Software Architecture Approach

Author

Chavan, Pramod U., Chavan, Pratibha P., Ghanekar, Vivek D., Jadhav, Sharad T., Kale, Shilpa J., 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, Senjyu, Tomonobu, editor, So–In, Chakchai, editor, and Joshi, Amit, editor

Published
2024
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33. Data-driven Modeling of the Mechanical Behavior of Anisotropic Soft Biological Tissue

Author

Tac, Vahidullah, Sree, Vivek D., Rausch, Manuel K., and Tepole, Adrian B.

Subjects
Quantitative Biology - Quantitative Methods, Condensed Matter - Soft Condensed Matter, Computer Science - Computational Engineering, Finance, and Science
Abstract

Constitutive models that describe the mechanical behavior of soft tissues have advanced greatly over the past few decades. These expert models are generalizable and require the calibration of a number of parameters to fit experimental data. However, inherent pitfalls stemming from the restriction to a specific functional form include poor fits to the data, non-uniqueness of fit, and high sensitivity to parameters. In this study we design and train fully connected neural networks as material models to replace or augment expert models. To guarantee objectivity, the neural network takes isochoric strain invariants as inputs, and outputs the value of strain energy and its derivatives with respect to the invariants. Convexity of the material model is enforced through the loss function. Direct prediction of the derivative functions -- rather than just predicting the energy -- serves two purposes: it provides flexibility during training, and it enables the calculation of the elasticity tensor through back-propagation. We showcase the ability of the neural network to learn the mechanical behavior of porcine and murine skin from biaxial test data. Crucially, we show that a multi-fidelity scheme which combines high fidelity experimental data with low fidelity analytical data yields the best performance. The neural network material model can then be interpreted as the best extension of an expert model: it learns the features that an expert has encoded in the analytical model while fitting the experimental data better. Finally, we implemented a general user material subroutine (UMAT) for the finite element software Abaqus and thereby make our advances available to the broader computational community. We expect that the methods and software generated in this work will broaden the use of data-driven constitutive models in biomedical applications., Comment: 19 pages, 10 figures

Published
2021

43. Finite Element Modelling of Reinforced Concrete Element Under Corrosion Effects

Author

Prasath, C. Rajendra, Vivek, D., Elango, K. S., 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, Das, B. B., editor, Gomez, Christy P., editor, and Mohapatra, Benu. G., editor

Published
2022
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