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1. Fair Machine Learning for Healthcare Requires Recognizing the Intersectionality of Sociodemographic Factors, a Case Study

Author

Valentine, Alissa A., Charney, Alexander W., and Landi, Isotta

Subjects
Computer Science - Computers and Society, Computer Science - Machine Learning
Abstract

As interest in implementing artificial intelligence (AI) in medical systems grows, discussion continues on how to evaluate the fairness of these systems, or the disparities they may perpetuate. Socioeconomic status (SES) is commonly included in machine learning models to control for health inequities, with the underlying assumption that increased SES is associated with better health. In this work, we considered a large cohort of patients from the Mount Sinai Health System in New York City to investigate the effect of patient SES, race, and sex on schizophrenia (SCZ) diagnosis rates via a logistic regression model. Within an intersectional framework, patient SES, race, and sex were found to have significant interactions. Our findings showed that increased SES is associated with a higher probability of obtaining a SCZ diagnosis in Black Americans ($\beta=4.1\times10^{-8}$, $SE=4.5\times10^{-9}$, $p < 0.001$). Whereas high SES acts as a protective factor for SCZ diagnosis in White Americans ($\beta=-4.1\times10^{-8}$, $SE=6.7\times10^{-9}$, $p < 0.001$). Further investigation is needed to reliably explain and quantify health disparities. Nevertheless, we advocate that building fair AI tools for the health care space requires recognizing the intersectionality of sociodemographic factors., Comment: NeurIPS: Queer in AI Workshop [QueerIPS], New Orleans, December 2023

Published
2024

2. The Point of View of a Sentiment: Towards Clinician Bias Detection in Psychiatric Notes

Author

Valentine, Alissa A., Lepow, Lauren A., Charney, Alexander W., and Landi, Isotta

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

In psychiatry, negative patient descriptions and stigmatizing language can contribute to healthcare disparities in two ways: (1) read by patients they can harm their trust and engagement with the medical center; (2) read by future providers they may negatively influence the future perspective of a patient. By leveraging large language models, this work aims to identify the sentiment expressed in psychiatric clinical notes based on the reader's point of view. Extracting sentences from the Mount Sinai Health System's large and diverse clinical notes, we used prompts and in-context learning to adapt three large language models (GPT-3.5, Llama 2, Mistral) to classify the sentiment conveyed by the sentences according to the provider or non-provider point of view. Results showed that GPT-3.5 aligns best to provider point of view, whereas Mistral aligns best to non-provider point of view., Comment: Oral presentation at NAACL 2024 Queer in AI Workshop

Published
2024

3. (Non-)existence of Cannon-Thurston maps for Morse boundaries

Author

Charney, Ruth, Cordes, Matthew, Goldsborough, Antoine, Sisto, Alessandro, and Zbinden, Stefanie

Subjects
Mathematics - Geometric Topology, Mathematics - Group Theory, 20F65, 20F67, 20E07, 57M07
Abstract

We show that the Morse boundary exhibits interesting examples of both the existence and non-existence of Cannon-Thurston maps for normal subgroups, in contrast with the hyperbolic case., Comment: 7 pages, arguments streamlined and minor edits, accepted to Bulletin of the LMS

Published
2024

4. Acylindrical hyperbolicity for Artin groups with a visual splitting

Author

Charney, Ruth, Martin, Alexandre, and Morris-Wright, Rose

Subjects
Mathematics - Group Theory, 20F36 (Primary), 20F65, 20F67 (Secondary)
Abstract

We establish a criterion that implies the acylindrical hyperbolicity of many Artin groups admitting a visual splitting. This gives a variety of new examples of acylindrically hyperbolic Artin groups, including many Artin groups of FC-type. Our approach relies on understanding when parabolic subgroups are weakly malnormal in a given Artin group. We formulate a conjecture for when this happens, and prove it for several classes of Artin groups, including all spherical-type, all two-dimensional, and all even FC-type Artin groups. In addition, we establish some connections between several conjectures about Artin groups, related to questions of acylindrical hyperbolicity, weak malnormality of parabolic subgroups, and intersections of parabolic subgroups., Comment: 22 pages, 1 figure

Published
2024

5. Extracting Social Support and Social Isolation Information from Clinical Psychiatry Notes: Comparing a Rule-based NLP System and a Large Language Model

Author

Patra, Braja Gopal, Lepow, Lauren A., Kumar, Praneet Kasi Reddy Jagadeesh, Vekaria, Veer, Sharma, Mohit Manoj, Adekkanattu, Prakash, Fennessy, Brian, Hynes, Gavin, Landi, Isotta, Sanchez-Ruiz, Jorge A., Ryu, Euijung, Biernacka, Joanna M., Nadkarni, Girish N., Talati, Ardesheer, Weissman, Myrna, Olfson, Mark, Mann, J. John, Charney, Alexander W., and Pathak, Jyotishman

Subjects
Computer Science - Computation and Language
Abstract

Background: Social support (SS) and social isolation (SI) are social determinants of health (SDOH) associated with psychiatric outcomes. In electronic health records (EHRs), individual-level SS/SI is typically documented as narrative clinical notes rather than structured coded data. Natural language processing (NLP) algorithms can automate the otherwise labor-intensive process of data extraction. Data and Methods: Psychiatric encounter notes from Mount Sinai Health System (MSHS, n=300) and Weill Cornell Medicine (WCM, n=225) were annotated and established a gold standard corpus. A rule-based system (RBS) involving lexicons and a large language model (LLM) using FLAN-T5-XL were developed to identify mentions of SS and SI and their subcategories (e.g., social network, instrumental support, and loneliness). Results: For extracting SS/SI, the RBS obtained higher macro-averaged f-scores than the LLM at both MSHS (0.89 vs. 0.65) and WCM (0.85 vs. 0.82). For extracting subcategories, the RBS also outperformed the LLM at both MSHS (0.90 vs. 0.62) and WCM (0.82 vs. 0.81). Discussion and Conclusion: Unexpectedly, the RBS outperformed the LLMs across all metrics. Intensive review demonstrates that this finding is due to the divergent approach taken by the RBS and LLM. The RBS were designed and refined to follow the same specific rules as the gold standard annotations. Conversely, the LLM were more inclusive with categorization and conformed to common English-language understanding. Both approaches offer advantages and are made available open-source for future testing., Comment: 2 figures, 3 tables

Published
2024
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7. Natural Language Programming in Medicine: Administering Evidence Based Clinical Workflows with Autonomous Agents Powered by Generative Large Language Models

Author

Vaid, Akhil, Lampert, Joshua, Lee, Juhee, Sawant, Ashwin, Apakama, Donald, Sakhuja, Ankit, Soroush, Ali, Bick, Sarah, Abbott, Ethan, Gomez, Hernando, Hadley, Michael, Lee, Denise, Landi, Isotta, Duong, Son Q, Bussola, Nicole, Nabeel, Ismail, Muehlstedt, Silke, Freeman, Robert, Kovatch, Patricia, Carr, Brendan, Wang, Fei, Glicksberg, Benjamin, Argulian, Edgar, Lerakis, Stamatios, Khera, Rohan, Reich, David L., Kraft, Monica, Charney, Alexander, and Nadkarni, Girish

Subjects
Computer Science - Artificial Intelligence
Abstract

Generative Large Language Models (LLMs) hold significant promise in healthcare, demonstrating capabilities such as passing medical licensing exams and providing clinical knowledge. However, their current use as information retrieval tools is limited by challenges like data staleness, resource demands, and occasional generation of incorrect information. This study assessed the potential of LLMs to function as autonomous agents in a simulated tertiary care medical center, using real-world clinical cases across multiple specialties. Both proprietary and open-source LLMs were evaluated, with Retrieval Augmented Generation (RAG) enhancing contextual relevance. Proprietary models, particularly GPT-4, generally outperformed open-source models, showing improved guideline adherence and more accurate responses with RAG. The manual evaluation by expert clinicians was crucial in validating models' outputs, underscoring the importance of human oversight in LLM operation. Further, the study emphasizes Natural Language Programming (NLP) as the appropriate paradigm for modifying model behavior, allowing for precise adjustments through tailored prompts and real-world interactions. This approach highlights the potential of LLMs to significantly enhance and supplement clinical decision-making, while also emphasizing the value of continuous expert involvement and the flexibility of NLP to ensure their reliability and effectiveness in healthcare settings., Comment: Figures: 5, Tables: 3

Published
2024

8. Investigating Fire–Atmosphere Interaction in a Forest Canopy Using Wavelets

Author

Desai, Ajinkya, Guilloteau, Clément, Heilman, Warren E, Charney, Joseph J, Skowronski, Nicholas S, Clark, Kenneth L, Gallagher, Michael R, Foufoula-Georgiou, Efi, and Banerjee, Tirtha

Subjects
Earth Sciences, Atmospheric Sciences, Heading surface fire, Time-frequency plane, Ramp-cliff structures, Cross-wavelet coherence, Heat/momentum fluxes, Meteorology & Atmospheric Sciences, Atmospheric sciences
Abstract

Wildland fire–atmosphere interaction generates complex turbulence patterns, organized across multiple scales, which inform fire-spread behaviour, firebrand transport, and smoke dispersion. Here, we utilize wavelet-based techniques to explore the characteristic temporal scales associated with coherent patterns in the measured temperature and the turbulent fluxes during a prescribed wind-driven (heading) surface fire beneath a forest canopy. We use temperature and velocity measurements from tower-mounted sonic anemometers at multiple heights. Patterns in the wavelet-based energy density of the measured temperature plotted on a time–frequency plane indicate the presence of fire-modulated ramp–cliff structures in the low-to-mid-frequency band (0.01–0.33 Hz), with mean ramp durations approximately 20% shorter and ramp slopes that are an order of magnitude higher compared to no-fire conditions. We then investigate heat- and momentum-flux events near the canopy top through a cross-wavelet coherence analysis. Briefly before the fire-front arrives at the tower base, momentum-flux events are relatively suppressed and turbulent fluxes are chiefly thermally-driven near the canopy top, owing to the tilting of the flame in the direction of the wind. Fire-induced heat-flux events comprising warm updrafts and cool downdrafts are coherent down to periods of a second, whereas ambient heat-flux events operate mainly at higher periods (above 17 s). Later, when the strongest temperature fluctuations are recorded near the surface, fire-induced heat-flux events occur intermittently at shorter scales and cool sweeps start being seen for periods ranging from 8 to 35 s near the canopy top, suggesting a diminishing influence of the flame and increasing background atmospheric variability thereat. The improved understanding of the characteristic time scales associated with fire-induced turbulence features, as the fire-front evolves, will help develop more reliable fire behaviour and scalar transport models.

Published
2024

10. Neuropsychiatric complications of coronavirus disease 2019: Mount Sinai Health System cohort study

Author

Gururangan, Kapil, Peschansky, Veronica J., Van Hyfte, Grace, Agarwal, Parul, Blank, Leah J., Mathew, Brian, Goldstein, Jonathan, Kwon, Churl-Su, McCarthy, Louise, Cohen, Ariella, Chan, Andy Ho Wing, Deng, Pojen, Dhamoon, Mandip, Gutzwiller, Eveline, Hao, Qing, He, Celestine, Klenofsky, Britany, Lemus, Hernan Nicolas, Marcuse, Lara, Navis, Allison, Heredia Nunez, Wilson D., Luckey, Mallory N., Schorr, Emily M., Singh, Anuradha, Tantillo, Gabriela B., Ufongene, Claire, Young, James J., Balchandani, Priti, Festa, Joanne R., Naasan, Georges, Charney, Alexander W., Nadkarni, Girish N., and Jetté, Nathalie

Published
2024
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11. Exploring and Analyzing Wildland Fire Data Via Machine Learning Techniques

Author

Dulal, Dipak, Charney, Joseph J., Gallagher, Michael, Navasca, Carmeliza, and Skowronski, Nicholas

Subjects
Computer Science - Machine Learning
Abstract

This research project investigated the correlation between a 10 Hz time series of thermocouple temperatures and turbulent kinetic energy (TKE) computed from wind speeds collected from a small experimental prescribed burn at the Silas Little Experimental Forest in New Jersey, USA. The primary objective of this project was to explore the potential for using thermocouple temperatures as predictors for estimating the TKE produced by a wildland fire. Machine learning models, including Deep Neural Networks, Random Forest Regressor, Gradient Boosting, and Gaussian Process Regressor, are employed to assess the potential for thermocouple temperature perturbations to predict TKE values. Data visualization and correlation analyses reveal patterns and relationships between thermocouple temperatures and TKE, providing insight into the underlying dynamics. The project achieves high accuracy in predicting TKE by employing various machine learning models despite a weak correlation between the predictors and the target variable. The results demonstrate significant success, particularly from regression models, in accurately estimating the TKE. The research findings contribute to fire behavior and smoke modeling science, emphasizing the importance of incorporating machine learning approaches and identifying complex relationships between fine-scale fire behavior and turbulence. Accurate TKE estimation using thermocouple temperatures allows for the refinement of models that can inform decision-making in fire management strategies, facilitate effective risk mitigation, and optimize fire management efforts. This project highlights the valuable role of machine learning techniques in analyzing wildland fire data, showcasing their potential to advance fire research and management practices.

Published
2023

12. An AI-Guided Data Centric Strategy to Detect and Mitigate Biases in Healthcare Datasets

Author

Gulamali, Faris F., Sawant, Ashwin S., Liharska, Lora, Horowitz, Carol R., Chan, Lili, Kovatch, Patricia H., Hofer, Ira, Singh, Karandeep, Richardson, Lynne D., Mensah, Emmanuel, Charney, Alexander W, Reich, David L., Hu, Jianying, and Nadkarni, Girish N.

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence
Abstract

The adoption of diagnosis and prognostic algorithms in healthcare has led to concerns about the perpetuation of bias against disadvantaged groups of individuals. Deep learning methods to detect and mitigate bias have revolved around modifying models, optimization strategies, and threshold calibration with varying levels of success. Here, we generate a data-centric, model-agnostic, task-agnostic approach to evaluate dataset bias by investigating the relationship between how easily different groups are learned at small sample sizes (AEquity). We then apply a systematic analysis of AEq values across subpopulations to identify and mitigate manifestations of racial bias in two known cases in healthcare - Chest X-rays diagnosis with deep convolutional neural networks and healthcare utilization prediction with multivariate logistic regression. AEq is a novel and broadly applicable metric that can be applied to advance equity by diagnosing and remediating bias in healthcare datasets.

Published
2023

13. Clinical Text Deduplication Practices for Efficient Pretraining and Improved Clinical Tasks

Author

Landi, Isotta, Alleva, Eugenia, Valentine, Alissa A., Lepow, Lauren A., and Charney, Alexander W.

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

Despite being a unique source of information on patients' status and disease progression, clinical notes are characterized by high levels of duplication and information redundancy. In general domain text, it has been shown that deduplication does not harm language model (LM) pretraining, thus helping reduce the training cost. Although large LMs have proven to learn medical knowledge, they still require specialized domain adaptation for improved downstream clinical tasks. By leveraging large real-world clinical corpora, we first provided a fine-grained characterization of duplicates stemming from common writing practices and clinical relevancy. Second, we demonstrated that deduplicating clinical text can help clinical LMs encode less redundant information in a more efficient manner and do not harm classification tasks via prompt-based learning.

Published
2023

14. Cosmological shocks around galaxy clusters: A coherent investigation with DES, SPT & ACT

Author

Anbajagane, D., Chang, C., Baxter, E. J., Charney, S., Lokken, M., Aguena, M., Allam, S., Alves, O., Amon, A., An, R., Andrade-Oliveira, F., Bacon, D., Battaglia, N., Bechtol, K., Becker, M. R., Benson, B. A., Bernstein, G. M., Bleem, L., Bocquet, S., Bond, J. R., Brooks, D., Rosell, A. Carnero, Kind, M. Carrasco, Chen, R., Choi, A., Costanzi, M., Crawford, T. M., Crocce, M., da Costa, L. N., Pereira, M. E. S., Davis, T. M., De Vicente, J., Desai, S., Devlin, M. J., Diehl, H. T., Doel, P., Doux, C., Drlica-Wagner, A., Elvin-Poole, J., Ferrero, I., Ferte, A., Flaugher, B., Fosalba, P., Friedel, D., Frieman, J., Garcia-Bellido, J., Gatti, M., Giannini, G., Grandis, S., Gruen, D., Gruendl, R. A., Gutierrez, G., Harrison, I., Hill, J. C., Hilton, M., Hinton, S. R., Hollowood, D. L., Honscheid, K., Jain, B., James, D. J., Jarvis, M., Kuehn, K., Lin, M., MacCrann, N., Marshall, J. L., McCullough, J., McMahon, J. J., Mena-Fernandez, J., Menanteau, F., Miquel, R., Moodley, K., Mroczkowski, T., Myles, J., Naess, S., Navarro-Alsina, A., Ogando, R. L. C., Page, L. A., Palmese, A., Pandey, S., Patridge, B., Pieres, A., Malagon, A. A. Plazas, Porredon, A., Prat, J., Reichardt, C., Reil, K., Rodriguez-Monroy, M., Rollins, R. P., Romer, A. K., Rykoff, E. S., Sanchez, E., Sanchez, C., Cid, D. Sanchez, Schaan, E., Schubnell, M., Secco, L. F., Sevilla-Noarbe, I., Sheldon, E., Shin, T., Sifon, C., Smith, M., Staggs, S. T., Suchyta, E., Swanson, M. E. C., Tarle, G., To, C., Troxel, M. A., Tutusaus, I., Vavagiakis, E. M., Weaverdyck, N., Weller, J., Wiseman, P., Wollack, E. J., and Yanny, B.

Subjects
Astrophysics - Astrophysics of Galaxies, Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract

We search for signatures of cosmological shocks in gas pressure profiles of galaxy clusters using the cluster catalogs from three surveys: the Dark Energy Survey (DES) Year 3, the South Pole Telescope (SPT) SZ survey, and the Atacama Cosmology Telescope (ACT) data releases 4, 5, and 6, and using thermal Sunyaev-Zeldovich (SZ) maps from SPT and ACT. The combined cluster sample contains around $10^5$ clusters with mass and redshift ranges $10^{13.7} < M_{\rm 200m}/M_\odot < 10^{15.5}$ and $0.1 < z < 2$, and the total sky coverage of the maps is $\approx 15,000 \,\,{\rm deg}^2$. We find a clear pressure deficit at $R/R_{\rm 200m}\approx 1.1$ in SZ profiles around both ACT and SPT clusters, estimated at $6\sigma$ significance, which is qualitatively consistent with a shock-induced thermal non-equilibrium between electrons and ions. The feature is not as clearly determined in profiles around DES clusters. We verify that measurements using SPT or ACT maps are consistent across all scales, including in the deficit feature. The SZ profiles of optically selected and SZ-selected clusters are also consistent for higher mass clusters. Those of less massive, optically selected clusters are suppressed on small scales by factors of 2-5 compared to predictions, and we discuss possible interpretations of this behavior. An oriented stacking of clusters -- where the orientation is inferred from the SZ image, the brightest cluster galaxy, or the surrounding large-scale structure measured using galaxy catalogs -- shows the normalization of the one-halo and two-halo terms vary with orientation. Finally, the location of the pressure deficit feature is statistically consistent with existing estimates of the splashback radius., Comment: [v2]: Version accepted to MNRAS

Published
2023

15. A strategy for cost-effective large language model use at health system-scale

Author

Eyal Klang, Donald Apakama, Ethan E. Abbott, Akhil Vaid, Joshua Lampert, Ankit Sakhuja, Robert Freeman, Alexander W. Charney, David Reich, Monica Kraft, Girish N. Nadkarni, and Benjamin S. Glicksberg

Subjects
Computer applications to medicine. Medical informatics, R858-859.7
Abstract

Abstract Large language models (LLMs) can optimize clinical workflows; however, the economic and computational challenges of their utilization at the health system scale are underexplored. We evaluated how concatenating queries with multiple clinical notes and tasks simultaneously affects model performance under increasing computational loads. We assessed ten LLMs of different capacities and sizes utilizing real-world patient data. We conducted >300,000 experiments of various task sizes and configurations, measuring accuracy in question-answering and the ability to properly format outputs. Performance deteriorated as the number of questions and notes increased. High-capacity models, like Llama-3–70b, had low failure rates and high accuracies. GPT-4-turbo-128k was similarly resilient across task burdens, but performance deteriorated after 50 tasks at large prompt sizes. After addressing mitigable failures, these two models can concatenate up to 50 simultaneous tasks effectively, with validation on a public medical question-answering dataset. An economic analysis demonstrated up to a 17-fold cost reduction at 50 tasks using concatenation. These results identify the limits of LLMs for effective utilization and highlight avenues for cost-efficiency at the enterprise scale.

Published
2024
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16. Derivation, external and clinical validation of a deep learning approach for detecting intracranial hypertension

Author

Faris Gulamali, Pushkala Jayaraman, Ashwin S. Sawant, Jacob Desman, Benjamin Fox, Annette Chang, Brian Y. Soong, Naveen Arivazagan, Alexandra S. Reynolds, Son Q. Duong, Akhil Vaid, Patricia Kovatch, Robert Freeman, Ira S. Hofer, Ankit Sakhuja, Neha S. Dangayach, David S. Reich, Alexander W. Charney, and Girish N. Nadkarni

Subjects
Computer applications to medicine. Medical informatics, R858-859.7
Abstract

Abstract Increased intracranial pressure (ICP) ≥15 mmHg is associated with adverse neurological outcomes, but needs invasive intracranial monitoring. Using the publicly available MIMIC-III Waveform Database (2000–2013) from Boston, we developed an artificial intelligence-derived biomarker for elevated ICP (aICP) for adult patients. aICP uses routinely collected extracranial waveform data as input, reducing the need for invasive monitoring. We externally validated aICP with an independent dataset from the Mount Sinai Hospital (2020–2022) in New York City. The AUROC, accuracy, sensitivity, and specificity on the external validation dataset were 0.80 (95% CI, 0.80–0.80), 73.8% (95% CI, 72.0–75.6%), 73.5% (95% CI 72.5–74.5%), and 73.0% (95% CI, 72.0–74.0%), respectively. We also present an exploratory analysis showing aICP predictions are associated with clinical phenotypes. A ten-percentile increment was associated with brain malignancy (OR = 1.68; 95% CI, 1.09-2.60), intracerebral hemorrhage (OR = 1.18; 95% CI, 1.07–1.32), and craniotomy (OR = 1.43; 95% CI, 1.12–1.84; P

Published
2024
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17. Finite groups of untwisted outer automorphisms of RAAGs

Author

Bregman, Corey, Charney, Ruth, and Vogtmann, Karen

Subjects
Mathematics - Group Theory, Mathematics - Geometric Topology, 20F65, 20F28, 20F36
Abstract

For any right-angled Artin group $A_{\Gamma}$, Charney--Stambaugh--Vogtmann showed that the subgroup $U^0(A_{\Gamma}) \leq\text{Out}(A_{\Gamma})$ generated by Whitehead automorphisms and inversions acts properly and cocompactly on a contractible space $K_{\Gamma}$. In the present paper we show that any finite subgroup of $U^0(A_{\Gamma})$ fixes a point of $K_{\Gamma}$. This generalizes the fact that any finite subgroup of $\text{Out}(F_n)$ fixes a point of Outer Space, and implies that there are only finitely many conjugacy classes of finite subgroups in $U^0(A_{\Gamma})$., Comment: 30 pages, 6 figures. Minor edits. This is the final, accepted version, to appear in Algebraic and Geometric Topology

Published
2023

20. Derivation, external and clinical validation of a deep learning approach for detecting intracranial hypertension

Author

Gulamali, Faris, Jayaraman, Pushkala, Sawant, Ashwin S., Desman, Jacob, Fox, Benjamin, Chang, Annette, Soong, Brian Y., Arivazagan, Naveen, Reynolds, Alexandra S., Duong, Son Q., Vaid, Akhil, Kovatch, Patricia, Freeman, Robert, Hofer, Ira S., Sakhuja, Ankit, Dangayach, Neha S., Reich, David S., Charney, Alexander W., and Nadkarni, Girish N.

Published
2024
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22. Author Correction: Divergent landscapes of A-to-I editing in postmortem and living human brain

Author

Rodriguez de los Santos, Miguel, Kopell, Brian H., Buxbaum Grice, Ariela, Ganesh, Gauri, Yang, Andy, Amini, Pardis, Liharska, Lora E., Vornholt, Eric, Fullard, John F., Dong, Pengfei, Park, Eric, Zipkowitz, Sarah, Kaji, Deepak A., Thompson, Ryan C., Liu, Donjing, Park, You Jeong, Cheng, Esther, Ziafat, Kimia, Moya, Emily, Fennessy, Brian, Wilkins, Lillian, Silk, Hannah, Linares, Lisa M., Sullivan, Brendan, Cohen, Vanessa, Kota, Prashant, Feng, Claudia, Johnson, Jessica S., Rieder, Marysia-Kolbe, Scarpa, Joseph, Nadkarni, Girish N., Wang, Minghui, Zhang, Bin, Sklar, Pamela, Beckmann, Noam D., Schadt, Eric E., Roussos, Panos, Charney, Alexander W., and Breen, Michael S.

Published
2024
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23. Divergent landscapes of A-to-I editing in postmortem and living human brain

Author

Rodriguez de los Santos, Miguel, Kopell, Brian H., Buxbaum Grice, Ariela, Ganesh, Gauri, Yang, Andy, Amini, Pardis, Liharska, Lora E., Vornholt, Eric, Fullard, John F., Dong, Pengfei, Park, Eric, Zipkowitz, Sarah, Kaji, Deepak A., Thompson, Ryan C., Liu, Donjing, Park, You Jeong, Cheng, Esther, Ziafat, Kimia, Moya, Emily, Fennessy, Brian, Wilkins, Lillian, Silk, Hannah, Linares, Lisa M., Sullivan, Brendan, Cohen, Vanessa, Kota, Prashant, Feng, Claudia, Johnson, Jessica S., Rieder, Marysia-Kolbe, Scarpa, Joseph, Nadkarni, Girish N., Wang, Minghui, Zhang, Bin, Sklar, Pamela, Beckmann, Noam D., Schadt, Eric E., Roussos, Panos, Charney, Alexander W., and Breen, Michael S.

Published
2024
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24. Dopamine and serotonin in human substantia nigra track social context and value signals during economic exchange

Author

Batten, Seth R., Bang, Dan, Kopell, Brian H., Davis, Arianna N., Heflin, Matthew, Fu, Qixiu, Perl, Ofer, Ziafat, Kimia, Hashemi, Alice, Saez, Ignacio, Barbosa, Leonardo S., Twomey, Thomas, Lohrenz, Terry, White, Jason P., Dayan, Peter, Charney, Alexander W., Figee, Martijn, Mayberg, Helen S., Kishida, Kenneth T., Gu, Xiaosi, and Montague, P. Read

Published
2024
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25. The Artin monoid Cayley graph

Author

Boyd, Rachael, Charney, Ruth, Morris-Wright, Rose, and Rees, Sarah

Subjects
Mathematics - Group Theory, Mathematics - Geometric Topology, 20F36 (primary), 20F55, 20M32, 20F65 (secondary)
Abstract

In this paper we investigate properties of the Artin monoid Cayley graph. This is the Cayley graph of an Artin group $A_\Gamma$ with respect to the (infinite) generating set given by the associated Artin monoid $A^+_\Gamma$. In a previous paper, the first three authors introduced a monoid Deligne complex and showed that this complex is contractible for all Artin groups. In this paper, we show that the Artin monoid Cayley graph is quasi-isometric to a modification of the Deligne complex for $A_\Gamma$ obtained by coning off translates of the monoid Deligne complex. We then address the question of when the monoid Cayley graph has infinite diameter. We conjecture that this holds for all Artin groups of infinite type. We give a set of criteria that imply infinite diameter, and using existing solutions to the word problem for large-type Artin groups and 3-free Artin groups, we prove that the conjecture holds for any Artin group containing a 3-generator subgroup of one of these two types., Comment: 15 Pages, final version

Published
2023

26. Multimodal fusion learning for long QT syndrome pathogenic genotypes in a racially diverse population

Author

Joy Jiang, Ha My Thi Vy, Alexander Charney, Patricia Kovatch, Vivek Reddy, Pushkala Jayaraman, Ron Do, Rohan Khera, Sumeet Chugh, Deepak L. Bhatt, Akhil Vaid, Joshua Lampert, and Girish Nitin Nadkarni

Subjects
Computer applications to medicine. Medical informatics, R858-859.7
Abstract

Abstract Congenital long QT syndrome (LQTS) diagnosis is complicated by limited genetic testing at scale, low prevalence, and normal QT corrected interval in patients with high-risk genotypes. We developed a deep learning approach combining electrocardiogram (ECG) waveform and electronic health record data to assess whether patients had pathogenic variants causing LQTS. We defined patients with high-risk genotypes as having ≥1 pathogenic variant in one of the LQTS-susceptibility genes. We trained the model using data from United Kingdom Biobank (UKBB) and then fine-tuned in a racially/ethnically diverse cohort using Mount Sinai BioMe Biobank. Following group-stratified 5-fold splitting, the fine-tuned model achieved area under the precision-recall curve of 0.29 (95% confidence interval [CI] 0.28–0.29) and area under the receiver operating curve of 0.83 (0.82–0.83) on independent testing data from BioMe. Multimodal fusion learning has promise to identify individuals with pathogenic genetic mutations to enable patient prioritization for further work up.

Published
2024
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27. Divergent landscapes of A-to-I editing in postmortem and living human brain

Author

Miguel Rodriguez de los Santos, Brian H. Kopell, Ariela Buxbaum Grice, Gauri Ganesh, Andy Yang, Pardis Amini, Lora E. Liharska, Eric Vornholt, John F. Fullard, Pengfei Dong, Eric Park, Sarah Zipkowitz, Deepak A. Kaji, Ryan C. Thompson, Donjing Liu, You Jeong Park, Esther Cheng, Kimia Ziafat, Emily Moya, Brian Fennessy, Lillian Wilkins, Hannah Silk, Lisa M. Linares, Brendan Sullivan, Vanessa Cohen, Prashant Kota, Claudia Feng, Jessica S. Johnson, Marysia-Kolbe Rieder, Joseph Scarpa, Girish N. Nadkarni, Minghui Wang, Bin Zhang, Pamela Sklar, Noam D. Beckmann, Eric E. Schadt, Panos Roussos, Alexander W. Charney, and Michael S. Breen

Subjects
Science
Abstract

Abstract Adenosine-to-inosine (A-to-I) editing is a prevalent post-transcriptional RNA modification within the brain. Yet, most research has relied on postmortem samples, assuming it is an accurate representation of RNA biology in the living brain. We challenge this assumption by comparing A-to-I editing between postmortem and living prefrontal cortical tissues. Major differences were found, with over 70,000 A-to-I sites showing higher editing levels in postmortem tissues. Increased A-to-I editing in postmortem tissues is linked to higher ADAR and ADARB1 expression, is more pronounced in non-neuronal cells, and indicative of postmortem activation of inflammation and hypoxia. Higher A-to-I editing in living tissues marks sites that are evolutionarily preserved, synaptic, developmentally timed, and disrupted in neurological conditions. Common genetic variants were also found to differentially affect A-to-I editing levels in living versus postmortem tissues. Collectively, these discoveries offer more nuanced and accurate insights into the regulatory mechanisms of RNA editing in the human brain.

Published
2024
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28. HeartBEiT: Vision Transformer for Electrocardiogram Data Improves Diagnostic Performance at Low Sample Sizes

Author

Vaid, Akhil, Jiang, Joy, Sawant, Ashwin, Lerakis, Stamatios, Argulian, Edgar, Ahuja, Yuri, Lampert, Joshua, Charney, Alexander, Greenspan, Hayit, Glicksberg, Benjamin, Narula, Jagat, and Nadkarni, Girish

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

The electrocardiogram (ECG) is a ubiquitous diagnostic modality. Convolutional neural networks (CNNs) applied towards ECG analysis require large sample sizes, and transfer learning approaches result in suboptimal performance when pre-training is done on natural images. We leveraged masked image modeling to create the first vision-based transformer model, HeartBEiT, for electrocardiogram waveform analysis. We pre-trained this model on 8.5 million ECGs and then compared performance vs. standard CNN architectures for diagnosis of hypertrophic cardiomyopathy, low left ventricular ejection fraction and ST elevation myocardial infarction using differing training sample sizes and independent validation datasets. We show that HeartBEiT has significantly higher performance at lower sample sizes compared to other models. Finally, we also show that HeartBEiT improves explainability of diagnosis by highlighting biologically relevant regions of the EKG vs. standard CNNs. Thus, we present the first vision-based waveform transformer that can be used to develop specialized models for ECG analysis especially at low sample sizes.

Published
2022

30. Researching COVID to enhance recovery (RECOVER) adult study protocol: Rationale, objectives, and design

Author

Horwitz, Leora I, Thaweethai, Tanayott, Brosnahan, Shari B, Cicek, Mine S, Fitzgerald, Megan L, Goldman, Jason D, Hess, Rachel, Hodder, SL, Jacoby, Vanessa L, Jordan, Michael R, Krishnan, Jerry A, Laiyemo, Adeyinka O, Metz, Torri D, Nichols, Lauren, Patzer, Rachel E, Sekar, Anisha, Singer, Nora G, Stiles, Lauren E, Taylor, Barbara S, Ahmed, Shifa, Algren, Heather A, Anglin, Khamal, Aponte-Soto, Lisa, Ashktorab, Hassan, Bassett, Ingrid V, Bedi, Brahmchetna, Bhadelia, Nahid, Bime, Christian, Bind, Marie-Abele C, Black, Lora J, Blomkalns, Andra L, Brim, Hassan, Castro, Mario, Chan, James, Charney, Alexander W, Chen, Benjamin K, Chen, Li Qing, Chen, Peter, Chestek, David, Chibnik, Lori B, Chow, Dominic C, Chu, Helen Y, Clifton, Rebecca G, Collins, Shelby, Costantine, Maged M, Cribbs, Sushma K, Deeks, Steven G, Dickinson, John D, Donohue, Sarah E, Durstenfeld, Matthew S, Emery, Ivette F, Erlandson, Kristine M, Facelli, Julio C, Farah-Abraham, Rachael, Finn, Aloke V, Fischer, Melinda S, Flaherman, Valerie J, Fleurimont, Judes, Fonseca, Vivian, Gallagher, Emily J, Gander, Jennifer C, Gennaro, Maria Laura, Gibson, Kelly S, Go, Minjoung, Goodman, Steven N, Granger, Joey P, Greenway, Frank L, Hafner, John W, Han, Jenny E, Harkins, Michelle S, Hauser, Kristine SP, Heath, James R, Hernandez, Carla R, Ho, On, Hoffman, Matthew K, Hoover, Susan E, Horowitz, Carol R, Hsu, Harvey, Hsue, Priscilla Y, Hughes, Brenna L, Jagannathan, Prasanna, James, Judith A, John, Janice, Jolley, Sarah, Judd, SE, Juskowich, Joy J, Kanjilal, Diane G, Karlson, Elizabeth W, Katz, Stuart D, Kelly, J Daniel, Kelly, Sara W, Kim, Arthur Y, Kirwan, John P, Knox, Kenneth S, Kumar, Andre, Lamendola-Essel, Michelle F, Lanca, Margaret, Lee-lannotti, Joyce K, Lefebvre, R Craig, and Levy, Bruce D

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Prevention, Clinical Research, Emerging Infectious Diseases, Infectious Diseases, Infection, Good Health and Well Being
Abstract

Abstract: Importance: SARS-CoV-2 infection can result in ongoing, relapsing, or new symptoms or other health effects after the acute phase of infection; termed post-acute sequelae of SARS-CoV-2 infection (PASC), or long COVID. The characteristics, prevalence, trajectory and mechanisms of PASC are ill-defined. The objectives of the Researching COVID to Enhance Recovery (RECOVER) Multi-site Observational Study of PASC in Adults (RECOVER-Adult) are to: (1) characterize PASC prevalence; (2) characterize the symptoms, organ dysfunction, natural history, and distinct phenotypes of PASC; (3) identify demographic, social and clinical risk factors for PASC onset and recovery; and (4) define the biological mechanisms underlying PASC pathogenesis. Methods: RECOVER-Adult is a combined prospective/retrospective cohort currently planned to enroll 14,880 adults aged ≥18 years. Eligible participants either must meet WHO criteria for suspected, probable, or confirmed infection; or must have evidence of no prior infection. Recruitment occurs at 86 sites in 33 U.S. states, Washington, DC and Puerto Rico, via facility– and community-based outreach. Participants complete quarterly questionnaires about symptoms, social determinants, vaccination status, and interim SARS-CoV-2 infections. In addition, participants contribute biospecimens and undergo physical and laboratory examinations at approximately 0, 90 and 180 days from infection or negative test date, and yearly thereafter. Some participants undergo additional testing based on specific criteria or random sampling. Patient representatives provide input on all study processes. The primary study outcome is onset of PASC, measured by signs and symptoms. A paradigm for identifying PASC cases will be defined and updated using supervised and unsupervised learning approaches with cross– validation. Logistic regression and proportional hazards regression will be conducted to investigate associations between risk factors, onset, and resolution of PASC symptoms. Discussion: RECOVER-Adult is the first national, prospective, longitudinal cohort of PASC among US adults. Results of this study are intended to inform public health, spur clinical trials, and expand treatment options.

Published
2023

31. The resident experience with psychological safety during interprofessional critical event debriefings

Author

Hitchner, Lily, Yore, Mackensie, Burk, Charney, Mason, Jessica, and Vohra, Stacy Sawtelle

Subjects
Curriculum and Pedagogy, Education, Clinical Research, Quality Education, critical event debriefing, interprofessional feedback, psychological safety, Curriculum and pedagogy
Abstract

ObjectivesInterprofessional feedback and teamwork skills training are important in graduate medical education. Critical event debriefing is a unique interprofessional team training opportunity in the emergency department. While potentially educational, these varied, high-stakes events can threaten psychological safety for learners. This is a qualitative study of emergency medicine resident physicians' experience of interprofessional feedback during critical event debriefing to characterize factors that impact their psychological safety.MethodsThe authors conduced semistructured interviews with resident physicians who were the physician team leader during a critical event debriefing. Interviews were coded and themes were generated using a general inductive approach and concepts from social ecological theory.ResultsEight residents were interviewed. The findings suggest that cultivating a safe learning environment for residents during debriefings involves the following: (1) allowing space for validating statements, (2) supporting strong interprofessional relationships, (3) providing structured opportunities for interprofessional learning, (4) encouraging attendings to model vulnerability, (5) standardizing the process of debriefing, (6) rejecting unprofessional behavior, and (7) creating the time and space for the process in the workplace.ConclusionsGiven the numerous intrapersonal, interpersonal, and institutional factors at play, educators should be sensitive to times when a resident cannot engage due to unaddressed threats to their psychological safety. Educators can address these threats in real time and over the course of a resident's training to enhance psychological safety and the potential educational impact of critical event debriefing.

Published
2023

34. Applications of large language models in psychiatry: a systematic review

Author

Mahmud Omar, Shelly Soffer, Alexander W. Charney, Isotta Landi, Girish N. Nadkarni, and Eyal Klang

Subjects
LLMS, large language model, artificial intelligence, psychiatry, generative pre-trained transformer (GPT), Psychiatry, RC435-571
Abstract

BackgroundWith their unmatched ability to interpret and engage with human language and context, large language models (LLMs) hint at the potential to bridge AI and human cognitive processes. This review explores the current application of LLMs, such as ChatGPT, in the field of psychiatry.MethodsWe followed PRISMA guidelines and searched through PubMed, Embase, Web of Science, and Scopus, up until March 2024.ResultsFrom 771 retrieved articles, we included 16 that directly examine LLMs’ use in psychiatry. LLMs, particularly ChatGPT and GPT-4, showed diverse applications in clinical reasoning, social media, and education within psychiatry. They can assist in diagnosing mental health issues, managing depression, evaluating suicide risk, and supporting education in the field. However, our review also points out their limitations, such as difficulties with complex cases and potential underestimation of suicide risks.ConclusionEarly research in psychiatry reveals LLMs’ versatile applications, from diagnostic support to educational roles. Given the rapid pace of advancement, future investigations are poised to explore the extent to which these models might redefine traditional roles in mental health care.

Published
2024
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35. Exome-wide association study to identify rare variants influencing COVID-19 outcomes: Results from the Host Genetics Initiative.

Author

Butler-Laporte, Guillaume, Povysil, Gundula, Kosmicki, Jack, Cirulli, Elizabeth, Drivas, Theodore, Furini, Simone, Saad, Chadi, Schmidt, Axel, Olszewski, Pawel, Korotko, Urszula, Quinodoz, Mathieu, Çelik, Elifnaz, Kundu, Kousik, Walter, Klaudia, Jung, Junghyun, Stockwell, Amy, Sloofman, Laura, Jordan, Daniel, Thompson, Ryan, Del Valle, Diane, Simons, Nicole, Cheng, Esther, Sebra, Robert, Schadt, Eric, Kim-Schulze, Seunghee, Gnjatic, Sacha, Merad, Miriam, Buxbaum, Joseph, Beckmann, Noam, Charney, Alexander, Przychodzen, Bartlomiej, Chang, Timothy, Pottinger, Tess, Shang, Ning, Brand, Fabian, Fava, Francesca, Mari, Francesca, Chwialkowska, Karolina, Niemira, Magdalena, Pula, Szymon, Baillie, J, Stuckey, Alex, Salas, Antonio, Bello, Xabier, Pardo-Seco, Jacobo, Gómez-Carballa, Alberto, Rivero-Calle, Irene, Martinón-Torres, Federico, Ganna, Andrea, Karczewski, Konrad, Veerapen, Kumar, Bourgey, Mathieu, Bourque, Guillaume, Eveleigh, Robert, Forgetta, Vincenzo, Morrison, David, Langlais, David, Lathrop, Mark, Mooser, Vincent, Nakanishi, Tomoko, Frithiof, Robert, Hultström, Michael, Lipcsey, Miklos, Marincevic-Zuniga, Yanara, Nordlund, Jessica, Schiabor Barrett, Kelly, Lee, William, Bolze, Alexandre, White, Simon, Riffle, Stephen, Tanudjaja, Francisco, Sandoval, Efren, Neveux, Iva, Dabe, Shaun, Casadei, Nicolas, Motameny, Susanne, Alaamery, Manal, Massadeh, Salam, Aljawini, Nora, Almutairi, Mansour, Arabi, Yaseen, Alqahtani, Saleh, Al Harthi, Fawz, Almutairi, Amal, Alqubaishi, Fatima, Alotaibi, Sarah, Binowayn, Albandari, Alsolm, Ebtehal, El Bardisy, Hadeel, Fawzy, Mohammad, Cai, Fang, Soranzo, Nicole, Butterworth, Adam, Geschwind, Daniel, Arteaga, Stephanie, Stephens, Alexis, Butte, Manish, Boutros, Paul, Yamaguchi, Takafumi, and Tao, Shu

Subjects
Humans, Exome, Genome-Wide Association Study, COVID-19, Genetic Predisposition to Disease, Toll-Like Receptor 7, SARS-CoV-2
Abstract

Host genetics is a key determinant of COVID-19 outcomes. Previously, the COVID-19 Host Genetics Initiative genome-wide association study used common variants to identify multiple loci associated with COVID-19 outcomes. However, variants with the largest impact on COVID-19 outcomes are expected to be rare in the population. Hence, studying rare variants may provide additional insights into disease susceptibility and pathogenesis, thereby informing therapeutics development. Here, we combined whole-exome and whole-genome sequencing from 21 cohorts across 12 countries and performed rare variant exome-wide burden analyses for COVID-19 outcomes. In an analysis of 5,085 severe disease cases and 571,737 controls, we observed that carrying a rare deleterious variant in the SARS-CoV-2 sensor toll-like receptor TLR7 (on chromosome X) was associated with a 5.3-fold increase in severe disease (95% CI: 2.75-10.05, p = 5.41x10-7). This association was consistent across sexes. These results further support TLR7 as a genetic determinant of severe disease and suggest that larger studies on rare variants influencing COVID-19 outcomes could provide additional insights.

Published
2022

36. Atmospheric turbulence observed during a fuel-bed-scale low-intensity surface fire

Author

J. Seitz, S. Zhong, J. J. Charney, W. E. Heilman, K. L. Clark, X. Bian, N. S. Skowronski, M. R. Gallagher, M. Patterson, J. Cole, M. T. Kiefer, R. Hadden, and E. Mueller

Subjects
Physics, QC1-999, Chemistry, QD1-999
Abstract

The ambient atmospheric environment affects the growth and spread of wildland fires, whereas heat and moisture released from the fires and the reduction of the surface drag in the burned areas can significantly alter local atmospheric conditions. Observational studies on fire–atmosphere interactions have used instrumented towers to collect data during prescribed fires, but a few towers in an operational-scale burn plot (usually > 103 m2) have made it extremely challenging to capture the myriad of factors controlling fire–atmosphere interactions, many of which exhibit strong spatial variability. Here, we present analyses of atmospheric turbulence data collected using a 4 × 4 array of fast-response sonic anemometers during a fire experiment on a 10 m × 10 m burn plot. In addition to confirming some of the previous findings on atmospheric turbulence associated with low-intensity surface fires, our results revealed substantial heterogeneity in turbulent intensity and heat and momentum fluxes just above the combustion zone. Despite the small plot (100 m2), fire-induced atmospheric turbulence exhibited strong dependence on the downwind distance from the initial line fire and the relative position specific to the fire front as the surface fire spread through the burn plot. This result highlights the necessity for coupled atmosphere–fire behavior models to have 1–2 m grid spacing to resolve heterogeneities in fire–atmosphere interactions that operate on spatiotemporal scales relevant to atmospheric turbulence. The findings here have important implications for modeling smoke dispersion, as atmospheric dispersion characteristics in the vicinity of a wildland fire are directly affected by fire-induced turbulence.

Published
2024
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37. Smoke Emissions and Buoyant Plumes above Prescribed Burns in the Pinelands National Reserve, New Jersey

Author

Kenneth L. Clark, Michael R. Gallagher, Nicholas Skowronski, Warren E. Heilman, Joseph Charney, Matthew Patterson, Jason Cole, Eric Mueller, and Rory Hadden

Subjects
prescribed burning, PM2.5, wildland fire emissions, air quality, available fuels, fuel consumption, Physics, QC1-999
Abstract

Prescribed burning is a cost-effective method for reducing hazardous fuels in pine- and oak-dominated forests, but smoke emissions contribute to atmospheric pollutant loads, and the potential exists for exceeding federal air quality standards designed to protect human health. Fire behavior during prescribed burns influences above-canopy sensible heat flux and turbulent kinetic energy (TKE) in buoyant plumes, affecting the lofting and dispersion of smoke. A more comprehensive understanding of how enhanced energy fluxes and turbulence are related during the passage of flame fronts could improve efforts to mitigate the impacts of smoke emissions. Pre- and post-fire fuel loading measurements taken during 48 operational prescribed burns were used to estimate the combustion completeness factors (CC) and emissions of fine particulates (PM2.5), carbon dioxide (CO2), and carbon monoxide (CO) in pine- and oak-dominated stands in the Pinelands National Reserve of southern New Jersey. During 11 of the prescribed burns, sensible heat flux and turbulence statistics were measured by tower networks above the forest canopy. Fire behavior when fire fronts passed the towers ranged from low-intensity backing fires to high-intensity head fires with some crown torching. Consumption of forest-floor and understory vegetation was a near-linear function of pre-burn loading, and combustion of fine litter on the forest floor was the predominant source of emissions, even during head fires with some crowning activity. Tower measurements indicated that above-canopy sensible heat flux and TKE calculated at 1 min intervals during the passage of fire fronts were strongly influenced by fire behavior. Low-intensity backing fires, regardless of forest type, had weaker enhancement of above-canopy air temperature, vertical and horizontal wind velocities, sensible heat fluxes, and TKE compared to higher-intensity head and flanking fires. Sensible heat flux and TKE in buoyant plumes were unrelated during low-intensity burns but more tightly coupled during higher-intensity burns. The weak coupling during low-intensity backing fires resulted in reduced rates of smoke transport and dispersion, and likely in more prolonged periods of elevated surface concentrations. This research facilitates more accurate estimates of PM2.5, CO, and CO2 emissions from prescribed burns in the Pinelands, and it provides a better understanding of the relationships among fire behavior, sensible heat fluxes and turbulence, and smoke dispersion in pine- and oak-dominated forests.

Published
2024
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38. Measurement of circulating viral antigens post-SARS-CoV-2 infection in a multicohort study

Author

Abraham, Rachael, Ager, Arijan, Aguilar, Franchesca A., Ahmadi-Izad, Ghazal, Ahmed, Dilshad R., Alvarez, Alicarmen, Anderson, Blake, Asencios, Walter D., Atha, Mary, Beaty, Casey L., Bedi, Brahmchetna, Berry, Jasmine A., Boone, Donchel, Bower, Mary, Bremner, James D., Brent, Corbin, Brown-Smith, Ke'Ara, Bull, Rachel, Bush, Patricia A., Capo, Gustavo, Carl-Igwe, Kelechi, Chitadze, Calista, Chukwumerije, Nachi, Clyburn, Erna, Collins, Shelby, Costello, Julie, Couture, Grace, Craft, Angel, Cribbs, Sushma K., Cui, Xiangqin, Dandy, Annette, Rio, Carlos del, Jasarevic, Rijalda, Detelich, Joshua F., Dixon, Cartia, Dow, Jeanne, Doyle, D'Andrea, Elchommali, Jannah, Ibeawuchi, Carmel, Elsey, Imani, Fineman, Rebecca, Francis, Anyssa G., Franks, Nicole, Gallini, Julia, Gander, Jennifer C., Gray, Natalie, Grimes, Ash, Gutter, Evan, Han, Jenny E., Hang, Tina P., Harding, Jess, Hernandez, Liliana, Hewitt, Lauren N., Holloway, Carla, Hudgins, Alex F., Huerta, Christopher, Ifejika, Cynthia, Ingram, Ketteria D., Javia, Vidhi N., Jeter, Mykayla, Johnson, Brandi, Joseph, Yasha, Juarez, Monica, Kajan, Dana, Khalil, Lana, Kirkpatrick, Caitlin M., Kleinhenz, Dean, Kolailat, Imanii, Koumanelis, Athena, Koumanelis, Alexandra, Kozoman, Rebecca, Krishnan, Shilpa, Lainez, Jordi, Lawrence, Brianna, Lee, Matthew A., Leon, Jose D., Lew, Valerie, Lewis, Kennedy C., Litvack, Matthew, Maroney, Mara, Maier, Cheryl L., Makkaoui, Nour, Marconi, Vincent C., Martin, Christopher F., Martinez, Monica, Mbogo, Loice, McCaslin, Atuarra, McIntyre, Jerrod, Moanna, Abeer, Montoya, Miranda, Morales, Elena, Moran, Caitlin A., Morgan-Billingslea, Jan, Murray, Calista, Nelson, Roslin, Neuman, Robert B., Nguyen, Tran, Ofotokun, Ighovwerha, Ojemakinde, Elizabeth I., Ojoawo, Bukkie, Osinski, Eileen, Oviedo, Sofia, Panganiban, Bernadine, Paredes-Gaitan, Yolanda, Patzer, Rachel E., Pemu, Priscilla, Prude, Michael, Rahman, Kazi, Ramakrishnan, Grace, Rebolledo, Paulina A., Roberts, Marjorie, Robinson, Keysha, Rogers, Chantrice, Rouphael, Nadine G., Searles, Charles, Shah, Anand, Segall, Marni, Shaw, Renata M., Silva, Ruvina, Simpson, Cheryl, Simpson-Derrell, Krystal, Sirajud-Deen, Talib, Smith, Veronica E., Stringer, Andre, Stroud, Jacob, Suthar, Mehul S., Sylber, Cory, Sylvera, Ashley, Tanner, Tehquin, Teunis, Larissa J., Tolbert, Maliya, Thomas, Kodasha M., Thompson, Sierra G., Titanji, Kehmia, Toy, Christopher, Traenkner, Jessica, Truong, Alex, Unterberger, Kristen, Vaccarino, Viola, Varney, Kris, Vyas, Kartavya, Vyas, Kurt, Walker, Tiffany A., Walkow, Max, Wang, Dongli, Wesley, Tamara, Wiley, Zanthia, Wimberly, Erika, Winston, Juton R., Winter, Terra J., Wongtrakool, Cherry, Aikawa, Masanori, Alba, George A., Aung, Taing N., Baden, Lindsey, Baslet, Gaston, Bassett, Ingrid V., Bennett, Lindsey, Bhattacharyya, Shamik, Blazey-Martin, Deborah, Buring, Julie, Cagnina, Rebecca E., Chen, Li Qing, Clark, Cheryl R., Cohen, Pieter, Collier, Ai-Ris, Czeisler, Charles, Duffy, Elizabeth, Estill, Peter, Fong, Tamara, Gay, Elizabeth, Ghamloush, Maher, Ginns, Leo C., Haack, Monika, Haas, Jennifer, Hamburg, Naomi, Hauser, Kristine S., John, Janice, Jordan, Michael, Juelg, Boris D., Kanjilal, Diane G., Kim, Arthur Y., Klerman, Elizabeth B., Kobayashi, Misaki ., Kogelman, Laura, Lamas, Daniela, Levy, Bruce D., Levy-Carrick, Nomi, Lewis, Gregory, Maley, Jason H., Manson, JoAnn, Marathe, Jai G., Mullington, Janet M., O'Connor, George T., Ojikutu, Bisola, Perlis, Roy, Quintana, Yuri, Redline, Susan, Remis, Elijah J., Rosand, Jonathan, Sesso, Howard D., Shaughnessy, Lynn, Shepherd, Fitzgerald M., Solomon, Scott, Sparks, Jeffrey A., Spencer, Lia L., Stephenson, Kathryn, Systrom, David, Thomas, Robert J., Min Thu, Phyo Phyo, Ticotsky, Amberly, Torres, Robert, Wallace, Zachary S., Walt, David, Ward, Honorine D., Washko, George, Whittelsey, Maureen, Wiener, Rebecca, Williams, Charles T., Xerras, Dean, Zhang, Haihua, Zionts, Danielle, Armstrong, Donna, Binkley, Susan E., Blackwell, Kenneth, Brown, Todd, Carton, Thomas W., Causey, Annalia, Cook, Felice, Daniel, Casey L., Datri, Paula, Domingo, Julio, Donahue, Conner, Eady, Maitlyn, Edberg, Jeffrey, Erdmann, Nathaniel, Fuloria, Jyotsna, Garcia-McClaney, Noah, Garner, Melissa, Gillespie, Mark, Gray, Brandon, Hagensee, Michael, Hall, Wanda, Hansel, Jamie, Hart, Cady, Hebson, Camden L., Hidalgo, Bertha, Holtzapfel, Kaylen, Jinright, Alexis, Judd, Suzanne E., Kennedy, Teri, Kirkwood, Leigh, Leggio, Cathryn, Levitan, Emily B., Maier, Megan, McCormack, Patricia, Miele, Lucio, Mitchell, Kevin, Montgomery, Aoyjai, Peralta-Carcelen, Myriam, Perkins, Allen, Pilco, Juan P., Powell, Leigh, Shevin, Rachael, Skipworth, Sidney, Spurgeon, Leah, Sutherland, Erica, Tita, Alan T., Trauth, Amber, Trotter, Siobhan, Van Deerlin, Alexander, Ware, Gregory, Weiser, Sharon, Wilson, Rosanne, Woodruff, Dana, Wu, Jing, Young, Madeline, Alemu, Mhret, Anderson, Jordan, Ashktorab, Hassan, Brim, Hassan, Chang, Linda, Chauhan, Mahak, Cho, Sung, Durrani, Saima, Gentil, Monique P., Goodman, Karli, Laiyemo, Adeyinka O., Lanke, Gandi, Lebron, Ralph, Maheshwari, Anurag, Mehari, Alem, Nezamloo, Ali, Ngwa, Julius, Njoku, Noelle, Ok, Jina, Sherif, Zaki A., Solemani, Akbar, Thuluvath, Paul, To, Chau, Spikes, Leslie A., James, Judith A., Luciano Roman, Carlos A., Chow, Dominic C., Marshall, Gailen D., Dickinson, John D., Hoover, Susan E., Warren, David E., Emery, Ivette F., Sukhera, Fatima I., Rosen, Clifford J., Greenway, Frank L., Hodder, Sally L., Shikuma, Cecilia M., VanWagoner, Timothy M., Bardes, James M., Kirwan, John P., Wood, Jeremy P., Whiteheart, Sidney W., Shellito, Judd, Roelke, Theresa, Black, Lora, Tjarks, Brian, Fonseca, Vivian, Gupta, Shaveeta, Longo, Michele, Yang, Mei, MarGangcuangco, Louis, Bengtson, Charles, Castro, Mario, Howard, Theresa, Garvy, Beth, Simmons, Christopher, Garla, Vishnu, Kuebler, Joy, Nandi, Utsav, Vasey, Andrew, Bogie, Amanda, Scott, James, Frontera, Sigrid Perez, Bagur, Jorge Santana, Dominique-Villanueva, Daphne, Juskowich, Joy, Reece, Rebecca, Sarwari, Arif, Aponte-Soto, Lisa, Adams, Dara, Baker, Aileen, Barbera, Sunni, Basu, Sanjib, Bleasdale, Susan, Bolliger, Dawn, Boyd, Andrew D., Boyineni, Jerusha, Breiter, Taylor, Brown, Daniel, Buhimschi, Irina A., Carrithers, Michael D., Certa, Marta, Chalamalla, Rashmika, Chebrolu, Praneeth, Chestek, David, Chessier, Erica, Cook, Judith A., Cranford, Savannah, Curry, Hannah L., Darbar, Dawood, Dasgupta, Raktima, Blakley, Felicia Davis, DeLisa, Julie A., Del Rios, Marina, Diaz, Maya Z., Diviak, Kathleen R., Dixon, Jennifer, Donlon, Meghan F., Donohue, Sarah E., Dworkin, Mark S., Edmonds, Sherrie, Ellison, Angela, Everett, Emily, Flanigan, Clarie, Freedman, Michael B., Gale, Lisa, Gerald, Lynn B., Giles, Wayne H., Gordon, Howard S., Hafner, John, Hammad, Bayan, Hanson, Keith A., Harris, Pastor C., Hartwig, Kimberly, Hasek, Sharon, Hasse, Wendy, Hendrickson, Monica, Hobbs, Brianna, Hryniewicka, Martyna, Hammerl, Savannah, Hutton, Robert, Ibanez, Alejandra L., Illendula, Sai D., Ismail, Nahed, Jain, Akash, Jennette, Kyle J., Kadubek, Grace, Kent, Denise, Kotini-Shah, Pavitra, Kelly, Sara W., Kent, Denise A., Kim, Keri S., Kindred, Elijah, Klein, Jonathan D., Krishnan, Jerry A., Large, Lucia, Lash, James ., Lin, Janet Y., Lu, Jun, Mahamed, Abeer M., Maholovich, Phoebe, Malchenko, Sergey, Martinez, Miriam, Mauntel-Medici, Cammeo, Madineni, Abhigna, McCauley, Mark, Menchaca, Martha, Mermelstein, Robin, Moreno, David J., Morrissy, Liam, Muramatsu, Naoko, Musick, Hugh, Noland, Seth, Norwick, Lourdes, Novak, Richard M., Olds, Lela, Ortiz, Marilyn, Patel, Khushboo, Perez, Nicolas L., Pliskin, Neil H., Pope, Sam, Prabhakar, Bellur S., Prasad, Bharati, Predki, Barbara, Prendergast, Heather M., Quigley, John G., Ramchandran, Ramaswamy, Ramirez, Ana, Rappe, Sarah, Rehman, Jalees, Rolon, Cesar, Rowley, Matthew, Rudraraju, Gowrisree, Rutherfoord, Melissa, Sader, Samer B., Sculley, Jennifer A., Smith-Mack, Jerisha, Swearingen, Peyton, Stewart de Ramirez, Sarah A., Sudhindra, Praveen, Sun, Jun, Tartt, Nancy, Terlinde, Tracy, Thompson, Tiffany, Vanden Hoek, Terry L., Kelly, Sara Warfield, Villanueva, Laura, Welter, Hannah, Woolley, Brittany, Yazici, Cemal, Charney, Alexander W., Kovatch, Patricia, Merad, Miriam, Nadkarni, Girish N., Wisnivesky, Juan P., Aberg, Judith A., Ascolillo, Steven, Assenso, Esther, Bagiella, Emilia, Bartram, Logan, Becker, Jacqueline, Beckmann, Noam D., Bendl, Ashley, Chen, Benjamin K., Civil, Alyssa, Dhar, Kaberi, Evo-Ortega, Lorraine, Fierer, Daniel, Gallagher, Emily J., Garcia-Sastre, Adolfo, Gnjatic, Sacha, Guliyeva, Sabina, Harvey-Ingram, Lori, Herrera-Moreno, Julia, Hill, Matthew, Horowitz, Carol R., Jackson, Rachel, Kastrat, Din, Lala-Trindade, Anu, Lin, Jenny, Macaluso, Nick, Marcon, Kathryn, Meyer, Dara, Morinigo, Janice, Natelson, Benjamin H., Nussenzweig, Maya, Padua, Tiffani, Putrino, David, Quazi, Nawreen, Ramos, Michelle, Richardson, Lynne, Russo, Scott, Seifert, Alan C., Serri, Abdullah, Walker, Jordan, Yee, Michell, Adolphi, Natalie L., Alekhina, Natalya, Archuleta, David A., Barlocker, Jackson, Bateman, Lucinda, Bradfute, Steven B., Brito, Rebecca, Bryan, Tanner W., Buck, Kaitlin E., Davis, Debra, Deakyne Davies, Sara J., Decker, Lauren A., Elifritz, Jamie, Erlandson, Kristine M., Facelli, Julio C., Fudge, Harrison Z., Tran, Huong, Pitch, Chloe, Feuerriegel, Elen M., Ford, Isaac, Friedman, Naomi P., Garcia-Soberanez, Noella D., Gardner, Edward M., Stringham, Caitlyn, Ling, Leah, Gebremariam, Tewodros H., Gentry, Frederick D., Gouripeddi, Ramkiran, Graham, Paige, Gronert, Eve G., Harkins, Michelle S., Hawkins, Kellie L., Hess, Rachel, Johnny, Jace D., Johnson, Brandon M., Jolley, Sarah E., Lloyd, Jennifer, Ludwig, Katelyn R., Martinez, Noah I., McCandless, Sean A., Montoya, Lorenzo A., Oakes, Judy L., Parada, Alisha N., Quinn, Davin K., Raissy, Hengameh, Ramos, Alfredo, Reid, Kayleigh M., Reusch, Jane E., Sheehan, Elyce B., Sokol, Ron J., Treacher, Irena S., Trinity, Joel D., Truong, Dongngan T., West, Shelby C., Molden, Joie, Sharareh, Nasser, Weaver, Lisa J., Spivak, Adam M., Brown, Jeanette P., Shah, Kevin S., Pace, Laura A., Scholand, Mary Beth, Velinder, Matt, Cortez, Melissa, Morimoto, Sarah Shizuko, Vernon, Suzanne D., Lu, Yue, Owen, Megan, Hermansen, Jessica A., Lindsay, Ashton M., Donohue, Dagny K., Garg, Lohit, Wodushek, Thomas, Higgins, Janine, Lockie, Tim, Brightman, Marisa, Thurman, Brook, Powell, Jenny M., Freston, Dylan C., Medina, Juliemar C., Aguirre, Bailee, Anderson, Jeff, Bair, Tami, Bosh, Lindsay, Evans, Lorlie, Garrett, Chase, Harris, Dixie, Herrera, Katherine, Horne, Benjamin D., Juan, James, Knight, Stacey, Knowlton, Kirk, Leither, Lindsay, Maestas, Heather, May, Heidi T., Najarian, Gabriel, Woller, Scott C., Zubal, Shyanne, Jensen, McKenna M., Webb, Tiaura, Iverson, Leslie, Ayache, Mirna, Baloi, Alexis, Barnboym, Emma, Boldt, Nicholas, Bukulmez, Hulya, Chesnick, Hailey, Conrad, Ann, Consolo, Mary, Curtis, Lynette, D’anza, Brian, DiFrancesco, Kathryn, Edminston, Marissa, Eteshola, Ebenezer, Gallagher, Michelle, Gibson, Kelly S., Gordesky, Larraine, Greenwood, Carla, Haghiac, Maricela, Harris, Paul, Hernandez, Carla, Iqbal, Shirin, Kaelber, David C., Kaufman, Elizabeth S., Kennedy, Olivia, Labbato, Danielle, Lengu, Ketrin, Levert, Antonio, Levin, Jennifer, Lowenthal, Rebecca, Mackin, Bridget, Malakooti, Shahdi K., McComsey, Grace A., Minium, Judy, Mouchati, Christian, Oleson, Christine, Pearman, Ann, Hershey, Morgan, Rivera, Amanda, Rodgers, Michael, Rodgers, Theresa, Roy, Arnab, Russ, Kris, Scott, Sarah, Sheth, Niyati, Singer, Nora G., Smith, Beth, Smith, Cheryl, Stancin, Terry, Temple, Daniel, Tribout, Megan, Weinberger, Elisheva, Zhang, David, Zisis, Sokratis N., Atieh, Ornina, Yendewa, George, Baissary, Jhony, Pettinato, KImberly, Lim, Joaquin, Jacob, Joshua, Adams, Cara, Tejani, Viral, Algren, Heather A., Alicic, Radica, Baxter, Joni, Brennan, Conor, Caudill, Antonina, Chen, Peter, Chopra, Tananshi, Chu, Helen Y., Del Alcazar, James, Duven, Alexandria M., Edmark, Rick, Emerson, Sarah, Goldman, Jason D., Gutierrez, Vanessa, Hadlock, Jennifer, Harteloo, Alex, Heath, James R., Hood, Susan, Jackman, Susan, Kaneko, John, Kemp, Megan, Kim, Christina, Kuykendall, Kelli, Li, Sarah, Logue, Jennifer K., Magis, Andrew T., Manner, Paula, Mason, Carly, McCaffrey, Kathryn, McDonald, Connor, McDonald, Dylan, Murray, Kim M., Nackviseth, Callista, Nguyen, Helen, Parimon, Tanyalak, Poussier, Rachel, Rowen, Lee, Satira, Richard, Torbati, Sam, Tuttle, Katherine R., Wallick, Julie A., Yuan, Dan, Watanabe, Kino, Wilcox, Lauren E., Contreras, Fatima, Dahlke, Lea, Gudipudi, Lasya, Modes, Matthew, Muttera, Nicole, Salinas, Nancy, Tadeo, Josie, White, Shane, Alvarado, Stephanie, Anderson, Reed, Arellanes, Azaneth, Barajas, Rose A., Chauhan, Suneet P., Clarke, Geoffrey D., Farner, Cheryl E., Fischer, Melinda S., Goldberg, Mark P., Hasbani, Keren, Hastings, Gabrielyd, Heard, Patricia, Herrera, Italia, Infante, Edgar, Johnson, Hillary, Jones, Johnnie, Kellogg, Dean L., Kraig, Ellen, Longoria, Lisa, Nambiar, Anoop M., Okafor, Emeka, Paredes, Claudia C., Patterson, Thomas F., Patterson, Jan E., Pinones, Alexis, Potter, Jennifer S., Reeves, W.B., Saade, George R., Salehi, Marzieh, Scholler, Irma, Seshadri, Sudha, Shah, Dimpy P., Shah, Pankil, Sharma, Kumar, Sharma, Kavita, Soileau, Bridgette, Solis, Pamela, Stoebner, Carmen, Sullivan, Michael, Taylor, Barbara S., Tragus, Robin, Tsevat, Joel, Verduzco-Gutierrez, Monica, Ahuja, Neera, Blish, Catherine A., Blomkalns, Andra L., Bonilla, Hector, Brotherton, Richard, Clinton, Kimberly, Dingankar, Vaidehi, Geng, Linda N., Go, Minjoung, Haddad, Francois, Jagannathan, Prasanna, Jamero, Christopher, Jee, Kathryn, Jia, Xiaolin K., Khurana, Naresh, Kumar, Andre, Maldonado, Yvonne, Miglis, Mitchell G., O'Conor, Ellen, Olszewski, Kelly, Pathak, Divya, Quintero, Orlando, Scott, Jake, Singh, Upinder, Urdaneta, Alfredo E., Utz, Paul J., Varkey, Mary R., Saperia, Corey, Autry, Lynn, Bime, Christian, Borwege, Sabine, Copeland, Jacquelynn, DiLise-Russo, Marjorie, Ernst, Kacey C., Esquivel, Denise R., Fadden, Susan, Gomez, Isaias, Grischo, Garrett, Hansen, Lillian, Harris, David T., Harris, Stefanie, Hartley, William, Hernandez, Michael, Hillier, Leah, Hsu, Harvey, Hughes, Trina, Ismail, Hira, Iusim, Stephanie, James, Michelle, Kala, Mrinalini, Karnafel, Maria, Kim, Daniel, Knox, Kenneth S., Koleski, Alison, LaFleur, Bonnie, Lambert, Brenda, LaRue, Sicily, Lee-Iannotti, Joyce K., Lieberman, David, Lutrick, Karen, Merchant, Nirav, Morton, Christopher, Mosier, Jarrod M., Murthy, Ganesh, Nikolich, Janko Z., Olorunnisola, Toluwanimi, Parthasarathy, Sairam, Peralta, Jeanette, Pilling, William, Pogreba-Brown, Kristen, Reiman, Eric M., Rischard, Franz P., Ryan, Lee T., Smith, Terry, Snyder, Manuel, Soto, Francisco, Subbian, Vignesh, Suhr, Kyle, Unzek, Samuel, Vadovicky, Sheila, Velarde, Deanna, Veres, Sharry, Wilson, Cathleen, Anderson, Grace, Anglin, Khamal, Argueta, Urania, Asare, Kofi, Buitrago, Melissa, Chang Song, Celina, Clark, Alexus, Conway, Emily, Deeks, Steven G., Del Castillo, Nicole, Deswal, Monika, Durstenfeld, Matthew S., Eilkhani, Elnaz, Eun, Avery, Fehrman, Emily, Figueroa, Tony, Flores, Diana, Grebe, Halle, Henrich, Timothy J., Hoh, Rebecca, Hsue, Priscilla, Huang, Beatrice, Ibrahim, Rania, Kelly, John D., Kerbleski, Marian, Kirtikar, Raushun, Lew, Megan T., Lombardo, James, Lopez, Monica, Luna, Michael, Marquez, Carina, Martin, Jeffrey N., Munter, Sadie, Ngo, Lynn, Peluso, Michael J., Pineda-Ramirez, Jesus, Rhoads, Kim, Rodriguez, Antonio, Romero, Justin, Ryder, Dylan, So, Matthew, Somsouk, Ma, Tai, Viva, Tran, Brandon, Uy, Julian, Valdivieso, Daisy, Verma, Deepshika, Williams, Meghann, Zamora, Andhy, Newman, Lisa T., Abella, Julie, Barnette, Quinn, Bevc, Christine, Beverly, Jennifer, Ceger, Patricia, Croxford, Julie, Enger, Mike, Fain, Katie, Farris, Tonya, Hanlon, Sean, Hines, David, Johnson-Lawrence, Vicki, Jordan, Kevin, Lefebvre, Craig, Linas, Beth, Luukinen, Bryan, Mandal, Meisha, McKoy, Nikki J., Nance, Susan, Pasquarelli, Demian, Quiner, Claire, Sembajwe, Rita, Shaw, Gwendolyn, Thornburg, Vanessa, Tosco, Kendall, Wright, Hannah, Gross, Rachel S., Hochman, Judith S., Horwitz, Leora I., Katz, Stuart D., Troxel, Andrea B., Adler, Lenard, Akinbo, Precious, Almenana, Ramona, Aschalew, Malate, Balick, Lara, Bello, Ola, Bhuiyan, Sultana, Blachman, Nina, Branski, Ryan, Briscoe, Jasmine, Brosnahan, Shari, Bueler, Elliott, Burgos, Yvette, Caplin, Nina, Chaplin, Domonique, Chen, Yu, Cheng, Shen, Choe, Peter, Choi, Jess, Chung, Alicia, Church, Richard, Cobos, Stanley, Croft, Nakia, Irving, Angelique Cruz, Del Boccio, Phoebe, Díaz, Iván, Divers, Jasmin, Doshi, Vishal, Dreyer, Benard, Ebel, Samantha, Esquenazi-Karonika, Shari, Faustin, Arline, Febres, Elias, Fine, Jeffrey, Fink, Sandra, Freeland, Catherine, Frontera, Jennifer, Gallagher, Richard, Gonzalez-Duarte, Alejandra, Gross, Rachel, Hasson, Denise, Hill, Sophia, Hochman, Judith, Horwitz, Leora, Hossain, Jennifer, Islam, Shahidul, Jean, Christina Saint, Johnson, Stephen, Kansal, Neha, Katz, Stuart, Kenney, Rachel, Kershner, Tammy, Kewlani, Deepshikha, Kwak, Judy, Lamendola-Essel, Michelle F., Laury, Sarah, Laynor, Gregory, Lei, Lei, Leon, Terry, Linton, Janelle, Logan, Max, Malik, Nadia, Mamistvalova, Lia, Mandel, Hannah, Maranga, Gabrielle, Mattoo, Aprajita, Mei, Tony, Mendelsohn, Alan, Mercier, Emmanuelle, Vernetti, Patricio Millar, Miller, Marc, Mitchell, Maika, Moreira, Andre, Mudumbi, Praveen C., Nahin, Erica, Nair, Nandini, Nekulak, Joseph, Owens, Kellie, Parent, Brendan, Patibandla, Nandan, Petrov, Peter, Postelnicu, Radu, Pratt, Francesca, Randall, Isabelle, Rao, Priyatha, Rapkiewicz, Amy, Rizzo, JohnRoss, Rosas, Johana, Rose, Chelsea, Saint-Jean, Christina, Santacatterina, Michelle, Shah, Binita, Shaukat, Aasma, Simon, Naomi, Simsir, Aylin, Stinson, Miranda, Tang, Wenfei, Tatapudi, Vasishta, Thawani, Sujata, Thomas, Mary, Thorpe, Lorna, Tom, MeeLee, Treiha, Ethan, Troxel, Andrea, Truong, Jennifer, Udosen, Mmekom, Valencia, Carlos, Velazquez-Perez, Jessica, Vernetti, Patricio M., Vidal, Crystal, Viswanathan, Anand, Willerford, Amy, Williams, Natasha, Wong, Crystal, Wood, Marion J., Wuller, Shannon, Yin, Shonna H., Young, Chloe, Zaretsky, Jonah, Zavlunova, Susanna, Ahirwar, Shreya, Ahmed, Shifa, Ainsworth, Layne L., Atchley-Challenner, Rachel, Avilach, Paul, Balan, Trisha T., Benik, Nicholas, Benoit, Barbara, Bind, Marie-Abèle C., Bonaventura, William J., Boutin, Natalie, Brion, Beverly, Cagan, Andrew, Cai, Tianrun, Cao, Tingyi, Castro, Victor M., Cerretani, Xander R., Chan, James G., Cheng, David, Chibnik, Lori B., Ciriello, Mark, Costenbader, Karen, Dimitrov, Dimitar S., Estiri, Hossein, Fayad, Maria, Feldman, Candace H., Foulkes, Andrea, Gainer, Vivian, Ghosh, Bhaswati, Gollub, Randy, Guan, Zoe, Harris, Alan, Helmer, Karl, Hendrix, Andrew, Holzbach, Ana, Huang, Weixing, Karlson, Elizabeth W., Kaufman, Daniel, Keogh, Diane, Kerr, James D., Klann, Jeffrey G., Krishnamoorthy, Aparna, Lasky-Su, Jessica A., Liao, Katherine P., MacFadden, Doug, Maram, Anupama, Martel, Megan W., Mendis, Michael, Metta, Reeta, Monteiro, Jonathan, Morales, Eduardo, Morse, Richard E., Murphy, Shawn, Nazaire, Marc-Danie, Neils, Gregory, Nguyen, Amber N., Norman, James, Paik, Henry H., Pant, Deepti, Park, HeeKyong, Rabideau, Dustin J., Reeder, Harrison T., Rossi-Roh, Kathleen, Santacroce, Leah M., Schlepphorst, Katherine, Schulte, Carolin, Selvaggi, Caitlin A., Shinnick, Daniel J., Simons, William, Simpson, Lynn A., St. Jean Flanders, Mary L., Strasser, Zachary, Thakrar, Mansi R., Thaweethai, Tanayott, Thorn, Madeleine, Trewett, Philip, Van Fleet, Dustin, Wagholikar, Kavishwar B., Wang, Taowei D., Wattanasin, Nich, Weber, Griffin, Williams, Michael A., Zhang, Ren Zhe, Cicek, Mine, Chang, Nancy, Wirkus, Samantha, Zahnle, Nicole, Flotte, Thomas J., Frisch, Erika, Boysen, Erik M., Welch, Gary, Akintonwa, Teresa, Blancero, Frank, Brown, Heather-Elizabeth, Carmilani, Megan, Cerda, Marta, Clash, Victor H., Copeland, Debra, Hall, Yvonka, kevin kondo, Lerma, Lydia, Lindsay, Jacqui, Marti, Heather, Maughan, Christine, Minor, Tony, Taylor, Brittany, Vincent, Hyatt, Zissis, Mike, Anderson, Brett, Bardhan, Sujata, Castro-Baucom, Leah, Chisolm, Deena, Corchado, Claudia, Damian, April Joy, Daniel, Casey, DasGupta, Soham, Dehority, Walter, Feldman, Candace, Fessel, Josh, Rosas, Lisa Goldman, Horowitz, Carol, Khullar, Dhruv, Lopez, Keila, McDonald Pinkett, Shelly, Myaskovsky, Larissa, Regino, Lidia, St John Thomas, Gelise, Stewart de Ramirez, Sarah, Vangeepuram, Nita, Walden, Anita, Williams, Neely, Yin, Shonna, Burton, Phoebe, Catallozzi, Marina, Clark, Cheryl, Dworetzky, Beth, Edwards, Belinda, Ferrer, Robert L., Judd, Suzanne, Rothman, Russell, Wagner, Laura, Wallace, Ann, Adams, Sonseeahray (Ray), Aragon, Leyna, Bander, Bryan, Bishof, Karyn, Brooks, Gail, Carignan, Etienne, Coombs, Krista, Davis, Hannah, Blakley, Felicia D., Diggs, Marissa, Brown, Heather E., Favors, Umar, Fields, Whitney, Fisher, Liza, Fitzgerald, Megan, Gaffney, Alicia, Witvliet, Margot Gage, Garcia, Roberto, Gustafson, Tyler, Guthe, Nick, Holmes, Verna, Hornig, Mady, Hornig, Maxwell, Jefferson, Wendy, Kochis, Nancy, Kondo, Kevin, Lam, Julie, Lawrence, Fadwa, Letts, Rebecca, Lewis, Juan, Lopez, Silcia, Martinez, Thomas, McCorkell, Lisa, McGrath, Rebecca, Minor, Thomas T., Moore, Charita, Nguyen, Kian, Nichols, Lauren, O'Brien, Lisa, Olson, Holly, Peddie, Aimee, Perlowski, Alice, Lorenzo, Elizabeth P., Prentiss, Lisa, Raytselis, Nadia, Rochez, Nitza, Rockwell, Megan, Rutter, Jacqueline, Seibert, Elle, Sekar, Anisha, Smith, Chimere, Stiles, Lauren, Taylor, Emily, Thompson, Julie, Trapp, Stephen, Valdiva, Stephen, Wilensky, Rochelle, Williams, Melissa, Dawson, Kay W., Wylam, Andrew, Swank, Zoe, Borberg, Ella, Chen, Yulu, Senussi, Yasmeen, Chalise, Sujata, Manickas-Hill, Zachary, Yu, Xu G., Li, Jonathan Z., Alter, Galit, Kelly, J. Daniel, Goldberg, Sarah A., Talla, Aarthi, Li, Xiaojun, Skene, Peter, Bumol, Thomas F., Torgerson, Troy R., Czartoski, Julie L., McElrath, M. Juliana, and Walt, David R.

Published
2024
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40. Temporal Activation of Extravehicular Activity Science and Operations Data: How Rise2's Enablement of a Novel Data Management Prototype Influenced ISS and Artemis Advancements

Author

B Feist, Matthew J Miller, David Charney, Cameron Pittman, Jackie Vu, K Young, Patrick L Whelley, J E Bleacher, and P B Niles

Subjects
Computer Programming and Software
Abstract

SSERVI's RIS4E and Rise2 nodes provided early and repeated opportunities to prototype the organization and visualization of science and field operations data together via temporal vs. type-indexing. This data management approach was first explored in our Apollo in Real Time initiative through its meticulous reconciliation and integration of Apollo 11, 13, and 17 mission data, providing an unparalleled temporal and contextual understanding of these historic missions. The positive results from these prototypes served as pre-cursor examples of what eventually became the Collaborative Operations Data Activation (CODA) application at NASA Johnson Space Center (JSC).

Published
2024

43. Proteomic characterization of acute kidney injury in patients hospitalized with SARS-CoV2 infection

Author

Paranjpe, Ishan, Jayaraman, Pushkala, Su, Chen-Yang, Zhou, Sirui, Chen, Steven, Thompson, Ryan, Del Valle, Diane Marie, Kenigsberg, Ephraim, Zhao, Shan, Jaladanki, Suraj, Chaudhary, Kumardeep, Ascolillo, Steven, Vaid, Akhil, Gonzalez-Kozlova, Edgar, Kauffman, Justin, Kumar, Arvind, Paranjpe, Manish, Hagan, Ross O., Kamat, Samir, Gulamali, Faris F., Xie, Hui, Harris, Joceyln, Patel, Manishkumar, Argueta, Kimberly, Batchelor, Craig, Nie, Kai, Dellepiane, Sergio, Scott, Leisha, Levin, Matthew A., He, John Cijiang, Suarez-Farinas, Mayte, Coca, Steven G., Chan, Lili, Azeloglu, Evren U., Schadt, Eric, Beckmann, Noam, Gnjatic, Sacha, Merad, Miram, Kim-Schulze, Seunghee, Richards, Brent, Glicksberg, Benjamin S., Charney, Alexander W., and Nadkarni, Girish N.

Published
2023
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44. Circulating proteins to predict COVID-19 severity

Author

Su, Chen-Yang, Zhou, Sirui, Gonzalez-Kozlova, Edgar, Butler-Laporte, Guillaume, Brunet-Ratnasingham, Elsa, Nakanishi, Tomoko, Jeon, Wonseok, Morrison, David R., Laurent, Laetitia, Afilalo, Jonathan, Afilalo, Marc, Henry, Danielle, Chen, Yiheng, Carrasco-Zanini, Julia, Farjoun, Yossi, Pietzner, Maik, Kimchi, Nofar, Afrasiabi, Zaman, Rezk, Nardin, Bouab, Meriem, Petitjean, Louis, Guzman, Charlotte, Xue, Xiaoqing, Tselios, Chris, Vulesevic, Branka, Adeleye, Olumide, Abdullah, Tala, Almamlouk, Noor, Moussa, Yara, DeLuca, Chantal, Duggan, Naomi, Schurr, Erwin, Brassard, Nathalie, Durand, Madeleine, Del Valle, Diane Marie, Thompson, Ryan, Cedillo, Mario A., Schadt, Eric, Nie, Kai, Simons, Nicole W., Mouskas, Konstantinos, Zaki, Nicolas, Patel, Manishkumar, Xie, Hui, Harris, Jocelyn, Marvin, Robert, Cheng, Esther, Tuballes, Kevin, Argueta, Kimberly, Scott, Ieisha, Greenwood, Celia M. T., Paterson, Clare, Hinterberg, Michael A., Langenberg, Claudia, Forgetta, Vincenzo, Pineau, Joelle, Mooser, Vincent, Marron, Thomas, Beckmann, Noam D., Kim-schulze, Seunghee, Charney, Alexander W., Gnjatic, Sacha, Kaufmann, Daniel E., Merad, Miriam, and Richards, J. Brent

Published
2023
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45. Fundamentos normativos del Estado plurinacional: una reconfiguración de las categorías centrales del constitucionalismo

Author

John Charney and Manuel Núñez

Subjects
Estado plurinacional, plurinacionalismo, derecho de los pueblos indígenas, derecho a la autodeterminación, constitucionalismo, proceso constituyente chileno, Law in general. Comparative and uniform law. Jurisprudence, K1-7720
Abstract

Este artículo explora los fundamentos normativos sobre los que se construye el Estado plurinacional en el Norte global y en Latinoamérica. Mientras que la justificación normativa del primero descansa en los principios fundantes de la tradición liberal, la del segundo descansa en los postulados del derecho internacional de los derechos humanos, en particular del derecho internacional de los pueblos indígenas. A pesar de todas las diferencias que separan a ambos proyectos, algunas de las cuales son muy significativas, el artículo propone que ambos forman parte de una aproximación crítica que busca reformular algunas de las categorías centrales del constitucionalismo. El trabajo cierra proyectando estos conceptos en el actual proceso constituyente chileno.

Published
2024
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46. Remote Short Sessions of Heart Rate Variability Biofeedback Monitored With Wearable Technology: Open-Label Prospective Feasibility Study

Author

Robert P Hirten, Matteo Danieletto, Kyle Landell, Micol Zweig, Eddye Golden, Renata Pyzik, Sparshdeep Kaur, Helena Chang, Drew Helmus, Bruce E Sands, Dennis Charney, Girish Nadkarni, Emilia Bagiella, Laurie Keefer, and Zahi A Fayad

Subjects
Psychology, BF1-990
Abstract

BackgroundHeart rate variability (HRV) biofeedback is often performed with structured education, laboratory-based assessments, and practice sessions. It has been shown to improve psychological and physiological function across populations. However, a means to remotely use and monitor this approach would allow for wider use of this technique. Advancements in wearable and digital technology present an opportunity for the widespread application of this approach. ObjectiveThe primary aim of the study was to determine the feasibility of fully remote, self-administered short sessions of HRV-directed biofeedback in a diverse population of health care workers (HCWs). The secondary aim was to determine whether a fully remote, HRV-directed biofeedback intervention significantly alters longitudinal HRV over the intervention period, as monitored by wearable devices. The tertiary aim was to estimate the impact of this intervention on metrics of psychological well-being. MethodsTo determine whether remotely implemented short sessions of HRV biofeedback can improve autonomic metrics and psychological well-being, we enrolled HCWs across 7 hospitals in New York City in the United States. They downloaded our study app, watched brief educational videos about HRV biofeedback, and used a well-studied HRV biofeedback program remotely through their smartphone. HRV biofeedback sessions were used for 5 minutes per day for 5 weeks. HCWs were then followed for 12 weeks after the intervention period. Psychological measures were obtained over the study period, and they wore an Apple Watch for at least 7 weeks to monitor the circadian features of HRV. ResultsIn total, 127 HCWs were enrolled in the study. Overall, only 21 (16.5%) were at least 50% compliant with the HRV biofeedback intervention, representing a small portion of the total sample. This demonstrates that this study design does not feasibly result in adequate rates of compliance with the intervention. Numerical improvement in psychological metrics was observed over the 17-week study period, although it did not reach statistical significance (all P>.05). Using a mixed effect cosinor model, the mean midline-estimating statistic of rhythm (MESOR) of the circadian pattern of the SD of the interbeat interval of normal sinus beats (SDNN), an HRV metric, was observed to increase over the first 4 weeks of the biofeedback intervention in HCWs who were at least 50% compliant. ConclusionsIn conclusion, we found that using brief remote HRV biofeedback sessions and monitoring its physiological effect using wearable devices, in the manner that the study was conducted, was not feasible. This is considering the low compliance rates with the study intervention. We found that remote short sessions of HRV biofeedback demonstrate potential promise in improving autonomic nervous function and warrant further study. Wearable devices can monitor the physiological effects of psychological interventions.

Published
2024
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49. Comparing ChatGPT and GPT-4 performance in USMLE soft skill assessments

Author

Dana Brin, Vera Sorin, Akhil Vaid, Ali Soroush, Benjamin S. Glicksberg, Alexander W. Charney, Girish Nadkarni, and Eyal Klang

Subjects
Medicine, Science
Abstract

Abstract The United States Medical Licensing Examination (USMLE) has been a subject of performance study for artificial intelligence (AI) models. However, their performance on questions involving USMLE soft skills remains unexplored. This study aimed to evaluate ChatGPT and GPT-4 on USMLE questions involving communication skills, ethics, empathy, and professionalism. We used 80 USMLE-style questions involving soft skills, taken from the USMLE website and the AMBOSS question bank. A follow-up query was used to assess the models’ consistency. The performance of the AI models was compared to that of previous AMBOSS users. GPT-4 outperformed ChatGPT, correctly answering 90% compared to ChatGPT’s 62.5%. GPT-4 showed more confidence, not revising any responses, while ChatGPT modified its original answers 82.5% of the time. The performance of GPT-4 was higher than that of AMBOSS's past users. Both AI models, notably GPT-4, showed capacity for empathy, indicating AI's potential to meet the complex interpersonal, ethical, and professional demands intrinsic to the practice of medicine.

Published
2023
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