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2,873 results on '"Husain, Syed"'

1. Fixing the Double Penalty in Data-Driven Weather Forecasting Through a Modified Spherical Harmonic Loss Function

Author

Subich, Christopher, Husain, Syed Zahid, Separovic, Leo, and Yang, Jing

Subjects
Computer Science - Machine Learning, Physics - Atmospheric and Oceanic Physics, I.2.6, I.2.1, J.2
Abstract

Recent advancements in data-driven weather forecasting models have delivered deterministic models that outperform the leading operational forecast systems based on traditional, physics-based models. However, these data-driven models are typically trained with a mean squared error loss function, which causes smoothing of fine scales through a "double penalty" effect. We develop a simple, parameter-free modification to this loss function that avoids this problem by separating the loss attributable to decorrelation from the loss attributable to spectral amplitude errors. Fine-tuning the GraphCast model with this new loss function results in sharp deterministic weather forecasts, an increase of the model's effective resolution from 1,250km to 160km, improvements to ensemble spread, and improvements to predictions of tropical cyclone strength and surface wind extremes.

Published
2025

2. Attend-Fusion: Efficient Audio-Visual Fusion for Video Classification

Author

Awan, Mahrukh, Nadeem, Asmar, Awan, Muhammad Junaid, Mustafa, Armin, and Husain, Syed Sameed

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence
Abstract

Exploiting both audio and visual modalities for video classification is a challenging task, as the existing methods require large model architectures, leading to high computational complexity and resource requirements. Smaller architectures, on the other hand, struggle to achieve optimal performance. In this paper, we propose Attend-Fusion, an audio-visual (AV) fusion approach that introduces a compact model architecture specifically designed to capture intricate audio-visual relationships in video data. Through extensive experiments on the challenging YouTube-8M dataset, we demonstrate that Attend-Fusion achieves an F1 score of 75.64\% with only 72M parameters, which is comparable to the performance of larger baseline models such as Fully-Connected Late Fusion (75.96\% F1 score, 341M parameters). Attend-Fusion achieves similar performance to the larger baseline model while reducing the model size by nearly 80\%, highlighting its efficiency in terms of model complexity. Our work demonstrates that the Attend-Fusion model effectively combines audio and visual information for video classification, achieving competitive performance with significantly reduced model size. This approach opens new possibilities for deploying high-performance video understanding systems in resource-constrained environments across various applications.

Published
2024

3. Leveraging data-driven weather models for improving numerical weather prediction skill through large-scale spectral nudging

Author

Husain, Syed Zahid, Separovic, Leo, Caron, Jean-François, Aider, Rabah, Buehner, Mark, Chamberland, Stéphane, Lapalme, Ervig, McTaggart-Cowan, Ron, Subich, Christopher, Vaillancourt, Paul A., Yang, Jing, and Zadra, Ayrton

Subjects
Physics - Atmospheric and Oceanic Physics, Computer Science - Machine Learning
Abstract

Operational meteorological forecasting has long relied on physics-based numerical weather prediction (NWP) models. Recently, this landscape is facing disruption by the advent of data-driven artificial intelligence (AI)-based weather models, which offer tremendous computational performance and competitive forecasting skill. However, data-driven models for medium-range forecasting generally suffer from major limitations, including low effective resolution and a narrow range of predicted variables. This study illustrates the relative strengths and weaknesses of these competing paradigms using the GEM (Global Environmental Multiscale) and GraphCast models to represent physics-based and AI-based approaches, respectively. By analyzing global predictions from these two models against observations and analyses in both physical and spectral spaces, this study demonstrates that GraphCast-predicted large scales outperform GEM, particularly for longer lead times. Building on this insight, a hybrid NWP-AI system is proposed, wherein GEM-predicted large-scale state variables are spectrally nudged toward GraphCast predictions, while allowing GEM to freely generate fine-scale details critical for weather extremes. Results indicate that this hybrid approach is capable of leveraging the strengths of GraphCast to enhance the prediction skill of the GEM model. Importantly, trajectories of tropical cyclones are predicted with enhanced accuracy without significant changes in intensity. Furthermore, this new hybrid system ensures that meteorologists have access to a complete set of forecast variables, including those relevant for high-impact weather events.

Published
2024

4. NarrativeBridge: Enhancing Video Captioning with Causal-Temporal Narrative

Author

Nadeem, Asmar, Sardari, Faegheh, Dawes, Robert, Husain, Syed Sameed, Hilton, Adrian, and Mustafa, Armin

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Human-Computer Interaction
Abstract

Existing video captioning benchmarks and models lack coherent representations of causal-temporal narrative, which is sequences of events linked through cause and effect, unfolding over time and driven by characters or agents. This lack of narrative restricts models' ability to generate text descriptions that capture the causal and temporal dynamics inherent in video content. To address this gap, we propose NarrativeBridge, an approach comprising of: (1) a novel Causal-Temporal Narrative (CTN) captions benchmark generated using a large language model and few-shot prompting, explicitly encoding cause-effect temporal relationships in video descriptions, evaluated automatically to ensure caption quality and relevance and validated through human evaluation; and (2) a dedicated Cause-Effect Network (CEN) architecture with separate encoders for capturing cause and effect dynamics independently, enabling effective learning and generation of captions with causal-temporal narrative. Extensive experiments demonstrate that CEN significantly outperforms state-of-the-art models, including fine-tuned vision-language models, and is more accurate in articulating the causal and temporal aspects of video content than the second best model (GIT): 17.88 and 17.44 CIDEr on the MSVD and MSR-VTT datasets, respectively. Cross-dataset evaluations further showcase CEN's strong generalization capabilities. The proposed framework understands and generates nuanced text descriptions with intricate causal-temporal narrative structures present in videos, addressing a critical limitation in video captioning.

Published
2024

6. Smart Pebbles to Monitor Aggregate Response Under Repeated Loading

Author

Husain, Syed Faizan, Abbas, Mohammad Shoaib, Wang, Han, Qamhia, Issam I. A., Tutumluer, Erol, Wallace, John, Hammond, Matthew, 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, Lu, Xinzheng, Series Editor, Rujikiatkamjorn, Cholachat, editor, Xue, Jianfeng, editor, and Indraratna, Buddhima, editor

Published
2025
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12. Associations between eGFR and albuminuria with right ventricular measures: the MESA-Right Ventricle study.

Author

Husain-Syed, Faeq, DiFrancesco, Matthew, Deo, Rajat, Barr, R, Scialla, Julia, Bluemke, David, Kronmal, Richard, Lima, Joao, Praestgaard, Amy, Tracy, Russell, Shlipak, Michael, Kawut, Steven, and Kim, John

Subjects
cardiac magnetic resonance, cardiorenal syndromes, chronic kidney disease, kidney function, pulmonary hypertension
Abstract

BACKGROUND: Chronic kidney disease (CKD) is associated with an increased risk of pulmonary hypertension, which may lead to right ventricular (RV) pressure overload and RV dysfunction. However, the presence of subclinical changes in RV structure or function in early CKD and the influence of these changes on mortality are not well studied. We hypothesized that early CKD, as indicated by elevated albuminuria or mild reductions in estimated glomerular filtration rate (eGFR), is associated with greater RV dilation and RV mass. METHODS: We included 4063 participants (age 45-84 years) without baseline clinical cardiovascular disease from the Multi-Ethnic Study of Atherosclerosis. The associations of baseline creatinine-cystatin C-based eGFR and albuminuria with cardiac magnetic resonance-derived RV measures (2000-02) were examined cross-sectionally with linear regression models. Cox regression models were used to examine whether RV parameters modified the associations of eGFR and albuminuria with all-cause mortality. RESULTS: Participants with reductions in eGFR primarily within the 60-89 mL/min/1.73 m2 category had smaller RV end-diastolic and end-systolic volumes and stroke volume (all adjusted P-trends

Published
2023

16. Histopathology of congestive nephropathy: a case description and literature review

Author

Faeq Husain‐Syed, Janani Rangaswami, Julio Núñez, Susanne Skrzypek, Christian Jux, Hermann‐Josef Gröne, and Horst‐Walter Birk

Subjects
Acute heart failure, Acute kidney injury, Cardiorenal syndrome, Worsening renal function, Diseases of the circulatory (Cardiovascular) system, RC666-701
Abstract

Abstract Congestive nephropathy is an underappreciated manifestation of cardiorenal syndrome and is characterized by a potentially reversible kidney dysfunction caused by a reduced renal venous outflow secondary to right‐sided heart failure or intra‐abdominal hypertension. To date, the histological diagnostic criteria for congestive nephropathy have not been defined. We herein report a case of acute renal dysfunction following cardiac allograft failure and present a review of the relevant literature to elucidate the current understanding of the disease. Our case demonstrated that congestion‐driven nephropathy may be histopathologically characterized by markedly dilated veins and peritubular capillaries, focally accentuated low‐grade acute tubular damage, small areas of interstitial fibrosis, and tubular atrophy on a background of normal glomeruli and predominantly normal tubular cell differentiation.

Published
2024
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17. Concentration inequalities for Paley-Wiener spaces

Author

Husain, Syed and Littmann, Friedrich

Subjects
Mathematics - Classical Analysis and ODEs, 42A05
Abstract

This article considers the question of how much of the mass of an element in a Paley-Wiener space can be concentracted on a given set. We seek bounds in terms of relative densities of the given set. We extend a result of Donoho and Logan from 1992 in one dimension and consider similar results in higher dimensions.

Published
2022

20. An Innovative Review on the Oscillating Drum Ball Milling Process

Author

Mishra, Devesh, Manohar, Abhishek, Pal, Mohit, Chaudhary, Kritagya, Nath, Nikita, Kumar, Nitin, Bhati, Girendra, Khan, Iqbal Ahmed, Husain, Syed Qaisar, Chaari, Fakher, Series Editor, Gherardini, Francesco, Series Editor, Ivanov, Vitalii, Series Editor, Haddar, Mohamed, Series Editor, Cavas-Martínez, Francisco, Editorial Board Member, di Mare, Francesca, Editorial Board Member, Kwon, Young W., Editorial Board Member, Trojanowska, Justyna, Editorial Board Member, Xu, Jinyang, Editorial Board Member, Sachdeva, Anish, editor, Goyal, Kapil Kumar, editor, Garg, Rajiv Kumar, editor, and Davim, J. Paulo, editor

Published
2024
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25. Single-cell Subcellular Protein Localisation Using Novel Ensembles of Diverse Deep Architectures

Author

Husain, Syed Sameed, Ong, Eng-Jon, Minskiy, Dmitry, Bober-Irizar, Mikel, Irizar, Amaia, and Bober, Miroslaw

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Unravelling protein distributions within individual cells is key to understanding their function and state and indispensable to developing new treatments. Here we present the Hybrid subCellular Protein Localiser (HCPL), which learns from weakly labelled data to robustly localise single-cell subcellular protein patterns. It comprises innovative DNN architectures exploiting wavelet filters and learnt parametric activations that successfully tackle drastic cell variability. HCPL features correlation-based ensembling of novel architectures that boosts performance and aids generalisation. Large-scale data annotation is made feasible by our "AI-trains-AI" approach, which determines the visual integrity of cells and emphasises reliable labels for efficient training. In the Human Protein Atlas context, we demonstrate that HCPL defines state-of-the-art in the single-cell classification of protein localisation patterns. To better understand the inner workings of HCPL and assess its biological relevance, we analyse the contributions of each system component and dissect the emergent features from which the localisation predictions are derived.

Published
2022

26. C3 Glomerulopathy Recurs Early after Kidney Transplantation in Serial Biopsies Performed within the First Two Years Post-Transplantation

Author

Tarragón, Blanca, Peleg, Yonatan, Jagannathan, Geetha, Sekulic, Miroslav, Chang, Jae-Hyung, Cohen, David J, Crew, Russell J, Dube, Geoffrey K, Fernandez, Hilda E, Husain, Syed Ali, Mohan, Sumit, Morris, Heather K, Appel, Gerald, Jadav, Paresh, Santoriello, Dominick, Kudose, Satoru, Stokes, M Barry, Batal, Ibrahim, and Bomback, Andrew

Published
2024
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37. Hemoadsorption

Author

Bellomo, Rinaldo, Mehta, Ravindra L., Forni, Lui G., Zarbock, Alexander, Ostermann, Marlies, Ronco, Claudio, Ankawi, Ghada, Bagshaw, Sean M., Baldwin, Ian, Basu, Rajit, Bottari, Gabriella, Cantaluppi, Vincenzo, Clark, William, De Rosa, Silvia, Forni, Lui G., Fuhrman, Dana, Gomez, Hernando, Husain-Syed, Faeq, Joannidis, Michael, Kashani, Kianoush, Lorenzin, Anna, Mehta, Ravindra, Murray, Patrick T., Murugan, Ragi, Ostermann, Marlies, Pannu, Neesh, Premuzic, Vedran, Prowle, John, Reis, Thiago, Rimmelé, Thomas, Rosner, Mitch, Schneider, Antoine, See, Emily, Soranno, Danielle, Villa, Gianluca, Whaley-Connell, Adam, and Zarbock, Alexander

Published
2024
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40. The Banff 2022 Kidney Meeting Work Plan: Data-driven refinement of the Banff Classification for renal allografts

Author

Roufosse, Candice, Naesens, Maarten, Haas, Mark, Lefaucheur, Carmen, Mannon, Roslyn B., Afrouzian, Marjan, Alachkar, Nada, Aubert, Olivier, Bagnasco, Serena M., Batal, Ibrahim, Bellamy, Chris O.C., Broecker, Verena, Budde, Klemens, Clahsen-Van Groningen, Marian, Coley, Shana M., Cornell, Lynn D., Dadhania, Darshana, Demetris, Anthony J., Einecke, Gunilla, Farris, Alton B., Fogo, Agnes B., Friedewald, John, Gibson, Ian W., Horsfield, Catherine, Huang, Edmund, Husain, Syed A., Jackson, Annette M., Kers, Jesper, Kikić, Željko, Klein, Amanda, Kozakowski, Nicolas, Liapis, Helen, Mangiola, Massima, Montgomery, Robert A., Nankinvell, Brian, Neil, Desley A.H., Nickerson, Peter, Rabant, Marion, Randhawa, Parmjeet, Riella, Leonardo V., Rosales, Ivy, Royal, Virginie, Sapir-Pichhadze, Ruth, Sarder, Pinaki, Sarwal, Minnie, Schinstock, Carrie, Stegall, Mark, Solez, Kim, van der Laak, Jeroen, Wiebe, Chris, Colvin, Robert B., Loupy, Alexandre, and Mengel, Michael

Published
2024
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42. Sepsis-associated acute kidney injury: consensus report of the 28th Acute Disease Quality Initiative workgroup

Author

Zarbock, Alexander, Nadim, Mitra K., Pickkers, Peter, Gomez, Hernando, Bell, Samira, Joannidis, Michael, Kashani, Kianoush, Koyner, Jay L., Pannu, Neesh, Meersch, Melanie, Reis, Thiago, Rimmelé, Thomas, Bagshaw, Sean M., Bellomo, Rinaldo, Cantaluppi, Vicenzo, Deep, Akash, De Rosa, Silvia, Perez-Fernandez, Xose, Husain-Syed, Faeq, Kane-Gill, Sandra L., Kelly, Yvelynne, Mehta, Ravindra L., Murray, Patrick T., Ostermann, Marlies, Prowle, John, Ricci, Zaccaria, See, Emily J., Schneider, Antoine, Soranno, Danielle E., Tolwani, Ashita, Villa, Gianluca, Ronco, Claudio, and Forni, Lui G.

Published
2023
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43. Acute kidney injury in neurocritical care

Author

Faeq Husain-Syed, Tomonori Takeuchi, Javier A. Neyra, Gonzalo Ramírez-Guerrero, Mitchell H. Rosner, Claudio Ronco, and Ashita J. Tolwani

Subjects
Dialysis disequilibrium syndrome, Intracerebral hemorrhage, Renal replacement therapy, Stroke, Subarachnoid hemorrhage, Traumatic brain injury, Medical emergencies. Critical care. Intensive care. First aid, RC86-88.9
Abstract

Abstract Approximately 20% of patients with acute brain injury (ABI) also experience acute kidney injury (AKI), which worsens their outcomes. The metabolic and inflammatory changes associated with AKI likely contribute to prolonged brain injury and edema. As a result, recognizing its presence is important for effectively managing ABI and its sequelae. This review discusses the occurrence and effects of AKI in critically ill adults with neurological conditions, outlines potential mechanisms connecting AKI and ABI progression, and highlights AKI management principles. Tailored approaches include optimizing blood pressure, managing intracranial pressure, adjusting medication dosages, and assessing the type of administered fluids. Preventive measures include avoiding nephrotoxic drugs, improving hemodynamic and fluid balance, and addressing coexisting AKI syndromes. ABI patients undergoing renal replacement therapy (RRT) are more susceptible to neurological complications. RRT can negatively impact cerebral blood flow, intracranial pressure, and brain tissue oxygenation, with effects tied to specific RRT methods. Continuous RRT is favored for better hemodynamic stability and lower risk of dialysis disequilibrium syndrome. Potential RRT modifications for ABI patients include adjusted dialysate and blood flow rates, osmotherapy, and alternate anticoagulation methods. Future research should explore whether these strategies enhance outcomes and if using novel AKI biomarkers can mitigate AKI-related complications in ABI patients. Graphical abstract

Published
2023
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44. Evaluation of Compounds from Balanites aegyptiaca against Squalene Epoxidase of Micropsorum gypseum—In Vitro and In Silico Studies

Author

Mohamed Husain Syed Abuthakir, V. Hemamalini, Reham M. Alahmadi, Anis Ahamed, Ashraf Atef Hatamleh, Razack Abdullah, and Jeyam Muthusamy

Subjects
dermatophytes, Microsporum gypseum, squalene epoxidase, docking, MD simulation, Microbiology, QR1-502
Abstract

Microsporum gypseum is a dermatophyte with a geophilic nature that is found all over the globe. It mainly causes tinea in the scalp, arms, and legs in humans. Squalene epoxidase (SE) is a crucial enzyme in M. gypseum for the biosynthesis of ergosterol. The medicinal plant Balanites aegyptiaca is an abundant supply of secondary constituents with great therapeutic values. In this research, the fruit epicarp portion was used to inhibit M. gypseum using experimental and computational techniques. The anti-dermatophytic activity of epicarp extracts on M. gypseum was evaluated using the poison plate method at five different concentrations. At 3 mg/mL, the M. gypseum was completely controlled by the fractioned chloroform extract of epicarp. The compounds from previous research were utilized for docking studies (Abuthakir et al., 2022). The ideal compounds and the drug terbinafine were then docked using Schrödinger’s Glide module. It demonstrates that (3E)-7-Hydroxy-3,7-dimethyl-3-octen-1-yl-6-O-(6-deoxy-alpha-L-mannopyranosyl)-beta-D-glucopyranoside outperforms other substances and the drug terbinafine in docking analysis. Desmond, Schrödinger Molecular Dynamics simulations were also performed for (3E)-7-Hydroxy-3,7-dimethyl-3-octen-1-yl-6-O-(6-deoxy-alpha-L-mannopyranosyl)-beta-D-glucopyranoside-squalene epoxidase complexes. The complex appears to be more stable, according to the MD simulation research. This study indicates that (3E)-7-Hydroxy-3,7-dimethyl-3-octen-1-yl-6-O-(6-deoxy-alpha-L-mannopyranosyl)-beta-D-glucopyranoside could be used as a potential inhibitor of M. gypseum growth, and it could be studied further.

Published
2023
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47. Innovation Diffusion with Intergroup Suppression: A Complexity Perspective

Author

Husain, Syed Shariq, Whitmeyer, Joseph, Chakraborti, Anirban, Faggini, Marisa, Series Editor, Gallegati, Mauro, Series Editor, Kirman, Alan P., Series Editor, Lux, Thomas, Series Editor, Arecchi, Fortunato Tito, Editorial Board Member, Barile, Sergio, Editorial Board Member, Chakrabarti, Bikas K., Editorial Board Member, Chatterjee, Arnab, Editorial Board Member, Colander, David, Editorial Board Member, Day, Richard H., Editorial Board Member, Keen, Steve, Editorial Board Member, Lines, Marji, Editorial Board Member, Medio, Alfredo, Editorial Board Member, Ormerod, Paul, Editorial Board Member, Rosser, J. Barkley, Editorial Board Member, Solomon, Sorin, Editorial Board Member, Velupillai, Kumaraswamy, Editorial Board Member, Vriend, Nicolas, Editorial Board Member, Chakraborti, Anirban, editor, Haven, Emmanuel, editor, Patra, Sudip, editor, and Singh, Naresh, editor

Published
2023
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48. Evolution of Cryptocurrencies and Sentiment Analysis: A Correlation Analysis

Author

Husain, Syed Saad, Amdani, Salim Y., 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, Tuba, Milan, editor, Akashe, Shyam, editor, and Joshi, Amit, editor

Published
2023
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49. Association of COVID-19 Versus COVID-19 Vaccination With Kidney Function and Disease Activity in Primary Glomerular Disease: A Report of the Cure Glomerulonephropathy Study

Author

Ahn, Wooin, Appel, Gerald, Appelbaum, Paul, Babayev, Revekka, Chan, Brenda, D’Agati, Vivette Denise, Dogra, Samitri, Fernandez, Hilda, Gharavi, Ali, Hines, William, Husain, Syed Ali, Jain, Namrata, Kiryluk, Krzysztof, Lin, Fangming, Marasa, Maddalena, Markowitz, Glen, Rasouly, Hila Milo, Mohan, Sumit, Mongera, Nicola, Nestor, Jordan, Nickolas, Thomas, Radhakrishnan, Jai, Rao, Maya, Sanna-Cherchi, Simone, Shirazian, Shayan, Stokes, Michael Barry, Uy, Natalie, Valeri, Anthony, Vena, Natalie, Foroncewicz, Bartosz, Moszczuk, Barbara, Mucha, Krzysztof, Perkowska-Ptasińska, Agnieszka, Ghiggeri, Gian Marco, Lugani, Francesca, Ambruzs, Josephine, Liapis, Helen, Baracco, Rossana, Jain, Amrish, Isa Ashoor, Aviles, Diego, Srivastava, Tarak, Ahn, Sun-Young, Devarajan, Prasad, Erkan, Elif, Claes, Donna, Stone, Hillarey, Mason, Sherene, Gbadegesin, Rasheed, Gomez-Mendez, Liliana, Yin, Hong (Julie), Cai, Yi, Jens, Goebel, Steinke, Julia, Weaver, Donald, Lane, Jerome, Cramer, Carl, Pan, Cindy, Paloian, Neil, Sreedharan, Rajasree, Selewski, David, Twombley, Katherine, Bowers, Corinna, Dreher, Mary, Kallash, Mahmoud, Mahan, John, Sharpe, Samantha, Al-Uzri, Amira, Iragorri, Sandra, Belsha, Craig, Alge, Joseph, Braun, Michael, Gomez, A.C., Wenderfer, Scott, Vasylyeva, Tetyana, Feig, Daniel, Fuentes, Gabriel Cara, Hannah, Melisha, Nester, Carla, Chishti, Aftab, Klein, Jon, Katsoufis, Chryso, Seeherunvong, Wacharee, Wong, Craig, Mathews, Nisha, Barcia, John, Swiatecka-Urban, Agnes, Bartosh, Sharon, Hunley, Tracy, Dharnidharka, Vikas, Gaut, Joseph, Laurin, Louis-Philippe, Royal, Virginie, Achanti, Anand, Budisavljevic, Milos, Self, Sally, Ghossein, Cybele, Peleg, Yonatan, Wadhwani, Shikha, Almaani, Salem, Ayoub, Isabelle, Nadasdy, Tibor, Parikh, Samir, Rovin, Brad, Chang, Anthony, Fatima, Huma, Julian, Bruce, Novak, Jan, Renfrow, Matthew, Rizk, Dana, Chen, Dhruti, Derebail, Vimal, Falk, Ronald, Gibson, Keisha, Hogan, Susan, Jain, Koyal, Jennette, J. Charles, Mottl, Amy, Poulton, Caroline, Saha, Manish Kanti, Fogo, Agnes, Sanghani, Neil, Kidd, Jason, Muthusamy, Selvaraj, Hou, Jean, Lemley, Kevin, Mika, Warren, Russo, Pierre, Denburg, Michelle, Kogon, Amy, Meyers, Kevin, Pradhan, Madhura, O’Toole, John, Sedor, John, Sethna, Christine, Vento, Suzanne, Atta, Mohamed, Bagnasco, Serena, Neu, Alicia, Sperati, John, Adler, Sharon, Dai, Tiane, Dukkipati, Ram, Fervenza, Fernando, Sethi, Sanjeev, Kaskel, Frederick, Brathwaite, Kaye, Reidy, Kimberly, Weisstuch, Joseph, Wu, Ming, Zhdanova, Olga, Heymann, Jurgen, Kopp, Jeffrey, Waldman, Meryl, Winkler, Cheryl, Krissberg, Jill, Lafayette, Richard, Fahmeedah, Kamal, Talley, Elizabeth, Hladunewich, Michelle, Parekh, Rulan, Avila-Casado, Carmen, Cattran, Daniel, Heather, Reich, Boll, Philip, Drexler, Yelena, Fornoni, Alessia, Blazius, Brooke, Hodgin, Jeffrey, Oliverio, Andrea, Hogan, Jon, Palmer, Matthew, Abromovitz, Blaise, Mortiz, Michael, Alpers, Charles, Jefferson, J. Ashley, Brown, Elizabeth, Sambandam, Kamal, Roehm, Bethany, Graff, John, Gillespie, Brenda, Kretzler, Matthias, Nast, Cynthia, Barisoni, Laura, Guay-Woodford, Lisa M., Wang, Chia-shi, Glenn, Dorey A., Helmuth, Margaret, Smith, Abigail R., Bomback, Andrew S., Canetta, Pietro A., Coppock, Gaia M., Khalid, Myda, Tuttle, Katherine R., Bou-Matar, Raed, Greenbaum, Larry A., Robinson, Bruce M., Holzman, Lawrence B., Smoyer, William E., Rheault, Michelle N., Gipson, Debbie, and Mariani, Laura H.

Published
2024
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