@article{AbeysekaraBenbowBirdetal.2018, author = {Abeysekara, A. U. and Benbow, Wystan and Bird, Ralph and Brantseg, T. and Brose, Robert and Buchovecky, M. and Buckley, J. H. and Bugaev, V. and Connolly, M. P. and Cui, Wei and Daniel, M. K. and Falcone, A. and Feng, Qi and Finley, John P. and Fortson, L. and Furniss, Amy and Gillanders, Gerard H. and Gunawardhana, Isuru and Huetten, M. and Hanna, David and Hervet, O. and Holder, J. and Hughes, G. and Humensky, T. B. and Johnson, Caitlin A. and Kaaret, Philip and Kar, P. and Kertzman, M. and Krennrich, F. and Lang, M. J. and Lin, T. T. Y. and McArthur, S. and Moriarty, P. and Mukherjee, Reshmi and Ong, R. A. and Otte, Adam Nepomuk and Park, N. and Petrashyk, A. and Pohl, Martin and Pueschel, Elisa and Quinn, J. and Ragan, K. and Reynolds, P. T. and Richards, Gregory T. and Roache, E. and Rulten, C. and Sadeh, I. and Santander, M. and Sembroski, G. H. and Shahinyan, Karlen and Wakely, S. P. and Weinstein, A. and Wells, R. M. and Wilcox, P. and Williams, D. A. and Zitzer, B. and Jorstad, Svetlana G. and Marscher, Alan P. and Lister, Matthew L. and Kovalev, Yuri Y. and Pushkarev, A. B. and Savolainen, Tuomas and Agudo, I. and Molina, S. N. and Gomez, J. L. and Larionov, Valeri M. and Borman, G. A. and Mokrushina, A. A. and Tornikoski, Merja and Lahteenmaki, A. and Chamani, W. and Enestam, S. and Kiehlmann, S. and Hovatta, Talvikki and Smith, P. S. and Pontrelli, P.}, title = {Multiwavelength Observations of the Blazar BL Lacertae}, series = {The astrophysical journal : an international review of spectroscopy and astronomical physics}, volume = {856}, journal = {The astrophysical journal : an international review of spectroscopy and astronomical physics}, number = {2}, publisher = {IOP Publ. Ltd.}, address = {Bristol}, organization = {VERITAS Collaboration}, issn = {0004-637X}, doi = {10.3847/1538-4357/aab35c}, pages = {14}, year = {2018}, abstract = {Combined with measurements made by very-long-baseline interferometry, the observations of fast TeV gamma-ray flares probe the structure and emission mechanism of blazar jets. However, only a handful of such flares have been detected to date, and only within the last few years have these flares been observed from lower-frequency-peaked BL. Lac objects and flat-spectrum radio quasars. We report on a fast TeV gamma-ray flare from the blazar BL. Lacertae observed by the Very Energetic Radiation Imaging Telescope Array System (VERITAS). with a rise time of similar to 2.3 hr and a decay time of similar to 36 min. The peak flux above 200 GeV is (4.2 +/- 0.6) x 10(-6) photon m(-2) s(-1) measured with a 4-minute-binned light curve, corresponding to similar to 180\% of the flux that is observed from the Crab Nebula above the same energy threshold. Variability contemporaneous with the TeV gamma-ray flare was observed in GeV gamma-ray, X-ray, and optical flux, as well as in optical and radio polarization. Additionally, a possible moving emission feature with superluminal apparent velocity was identified in Very Long Baseline Array observations at 43 GHz, potentially passing the radio core of the jet around the time of the gamma-ray flare. We discuss the constraints on the size, Lorentz factor, and location of the emitting region of the flare, and the interpretations with several theoretical models that invoke relativistic plasma passing stationary shocks.}, language = {en} } @article{AbeysekaraArcherBenbowetal.2019, author = {Abeysekara, A. U. and Archer, A. and Benbow, Wystan and Bird, Ralph and Brill, A. and Brose, Robert and Buchovecky, M. and Calderon-Madera, D. and Christiansen, J. L. and Cui, W. and Daniel, M. K. and Falcone, A. and Feng, Q. and Fernandez-Alonso, M. and Finley, J. P. and Fortson, Lucy and Furniss, Amy and Gent, A. and Giuri, C. and Gueta, O. and Hanna, David and Hassan, T. and Hervet, Oliver and Holder, J. and Hughes, G. and Humensky, T. B. and Johnson, Caitlin A. and Kaaret, P. and Kertzman, M. and Kieda, David and Krause, Maria and Krennrich, F. and Kumar, S. and Lang, M. J. and Maier, Gernot and Moriarty, P. and Mukherjee, Reshmi and Nievas-Rosillo, M. and Ong, R. A. and Pfrang, Konstantin Johannes and Pohl, Martin and Prado, R. R. and Pueschel, Elisa and Quinn, J. and Ragan, K. and Reynolds, P. T. and Ribeiro, D. and Richards, G. T. and Roache, E. and Rovero, A. C. and Sadeh, Iftach and Santander, M. and Sembroski, G. H. and Shahinyan, Karlen and Sushch, Iurii and Svraka, T. and Weinstein, A. and Wells, R. M. and Wilcox, Patrick and Wilhelm, Alina and Williams, David Arnold and Williamson, T. J. and Zitzer, B.}, title = {Measurement of the Extragalactic Background Light Spectral Energy Distribution with VERITAS}, series = {The astrophysical journal : an international review of spectroscopy and astronomical physics}, volume = {885}, journal = {The astrophysical journal : an international review of spectroscopy and astronomical physics}, number = {2}, publisher = {IOP Publ. Ltd.}, address = {Bristol}, issn = {0004-637X}, doi = {10.3847/1538-4357/ab4817}, pages = {8}, year = {2019}, abstract = {The extragalactic background light (EBL), a diffuse photon field in the optical and infrared range, is a record of radiative processes over the universe?s history. Spectral measurements of blazars at very high energies (>100 GeV) enable the reconstruction of the spectral energy distribution (SED) of the EBL, as the blazar spectra are modified by redshift- and energy-dependent interactions of the gamma-ray photons with the EBL. The spectra of 14 VERITAS-detected blazars are included in a new measurement of the EBL SED that is independent of EBL SED models. The resulting SED covers an EBL wavelength range of 0.56?56 ?m, and is in good agreement with lower limits obtained by assuming that the EBL is entirely due to radiation from cataloged galaxies.}, language = {en} } @article{VaidSomaniRussaketal.2020, author = {Vaid, Akhil and Somani, Sulaiman and Russak, Adam J. and De Freitas, Jessica K. and Chaudhry, Fayzan F. and Paranjpe, Ishan and Johnson, Kipp W. and Lee, Samuel J. and Miotto, Riccardo and Richter, Felix and Zhao, Shan and Beckmann, Noam D. and Naik, Nidhi and Kia, Arash and Timsina, Prem and Lala, Anuradha and Paranjpe, Manish and Golden, Eddye and Danieletto, Matteo and Singh, Manbir and Meyer, Dara and O'Reilly, Paul F. and Huckins, Laura and Kovatch, Patricia and Finkelstein, Joseph and Freeman, Robert M. and Argulian, Edgar and Kasarskis, Andrew and Percha, Bethany and Aberg, Judith A. and Bagiella, Emilia and Horowitz, Carol R. and Murphy, Barbara and Nestler, Eric J. and Schadt, Eric E. and Cho, Judy H. and Cordon-Cardo, Carlos and Fuster, Valentin and Charney, Dennis S. and Reich, David L. and B{\"o}ttinger, Erwin and Levin, Matthew A. and Narula, Jagat and Fayad, Zahi A. and Just, Allan C. and Charney, Alexander W. and Nadkarni, Girish N. and Glicksberg, Benjamin S.}, title = {Machine learning to predict mortality and critical events in a cohort of patients with COVID-19 in New York City: model development and validation}, series = {Journal of medical internet research : international scientific journal for medical research, information and communication on the internet ; JMIR}, volume = {22}, journal = {Journal of medical internet research : international scientific journal for medical research, information and communication on the internet ; JMIR}, number = {11}, publisher = {Healthcare World}, address = {Richmond, Va.}, issn = {1439-4456}, doi = {10.2196/24018}, pages = {19}, year = {2020}, abstract = {Background: COVID-19 has infected millions of people worldwide and is responsible for several hundred thousand fatalities. The COVID-19 pandemic has necessitated thoughtful resource allocation and early identification of high-risk patients. However, effective methods to meet these needs are lacking. Objective: The aims of this study were to analyze the electronic health records (EHRs) of patients who tested positive for COVID-19 and were admitted to hospitals in the Mount Sinai Health System in New York City; to develop machine learning models for making predictions about the hospital course of the patients over clinically meaningful time horizons based on patient characteristics at admission; and to assess the performance of these models at multiple hospitals and time points. Methods: We used Extreme Gradient Boosting (XGBoost) and baseline comparator models to predict in-hospital mortality and critical events at time windows of 3, 5, 7, and 10 days from admission. Our study population included harmonized EHR data from five hospitals in New York City for 4098 COVID-19-positive patients admitted from March 15 to May 22, 2020. The models were first trained on patients from a single hospital (n=1514) before or on May 1, externally validated on patients from four other hospitals (n=2201) before or on May 1, and prospectively validated on all patients after May 1 (n=383). Finally, we established model interpretability to identify and rank variables that drive model predictions. Results: Upon cross-validation, the XGBoost classifier outperformed baseline models, with an area under the receiver operating characteristic curve (AUC-ROC) for mortality of 0.89 at 3 days, 0.85 at 5 and 7 days, and 0.84 at 10 days. XGBoost also performed well for critical event prediction, with an AUC-ROC of 0.80 at 3 days, 0.79 at 5 days, 0.80 at 7 days, and 0.81 at 10 days. In external validation, XGBoost achieved an AUC-ROC of 0.88 at 3 days, 0.86 at 5 days, 0.86 at 7 days, and 0.84 at 10 days for mortality prediction. Similarly, the unimputed XGBoost model achieved an AUC-ROC of 0.78 at 3 days, 0.79 at 5 days, 0.80 at 7 days, and 0.81 at 10 days. Trends in performance on prospective validation sets were similar. At 7 days, acute kidney injury on admission, elevated LDH, tachypnea, and hyperglycemia were the strongest drivers of critical event prediction, while higher age, anion gap, and C-reactive protein were the strongest drivers of mortality prediction. Conclusions: We externally and prospectively trained and validated machine learning models for mortality and critical events for patients with COVID-19 at different time horizons. These models identified at-risk patients and uncovered underlying relationships that predicted outcomes.}, language = {en} } @article{HoehnkeJohnson1995, author = {Hoehnke, Hans-J{\"u}rgen and Johnson, K. W.}, title = {3-characters are sufficient for the group determinant}, year = {1995}, language = {en} } @article{JonesArridgeCoatesetal.2009, author = {Jones, Geraint H. and Arridge, Christopher S. and Coates, Andrew J. and Lewis, Gethyn R. and Kanani, Sheila and Wellbrock, Anne and Young, David T. and Crary, Frank J. and Tokar, Robert L. and Wilson, R. J. and Hill, Thomas W. and Johnson, Robert E. and Mitchell, Donald G. and Schmidt, J{\"u}rgen and Kempf, Sascha and Beckmann, Uwe and Russell, Christopher T. and Jia, Y. D. and Dougherty, Michele K. and Waite, J. Hunter and Magee, Brian A.}, title = {Fine jet structure of electrically charged grains in Enceladus' plume}, issn = {0094-8276}, doi = {10.1029/2009gl038284}, year = {2009}, abstract = {By traversing the plume erupting from high southern latitudes on Saturn's moon Enceladus, Cassini orbiter instruments can directly sample the material therein. Cassini Plasma Spectrometer, CAPS, data show that a major plume component comprises previously-undetected particles of nanometer scales and larger that bridge the mass gap between previously observed gaseous species and solid icy grains. This population is electrically charged both negative and positive, indicating that subsurface triboelectric charging, i.e., contact electrification of condensed plume material may occur through mutual collisions within vents. The electric field of Saturn's magnetosphere controls the jets' morphologies, separating particles according to mass and charge. Fine-scale structuring of these particles' spatial distribution correlates with discrete plume jets' sources, and reveals locations of other possible active regions. The observed plume population likely forms a major component of high velocity nanometer particle streams detected outside Saturn's magnetosphere.}, language = {en} } @article{DeFreitasJohnsonGoldenetal.2021, author = {De Freitas, Jessica K. and Johnson, Kipp W. and Golden, Eddye and Nadkarni, Girish N. and Dudley, Joel T. and B{\"o}ttinger, Erwin and Glicksberg, Benjamin S. and Miotto, Riccardo}, title = {Phe2vec}, series = {Patterns}, volume = {2}, journal = {Patterns}, number = {9}, publisher = {Elsevier}, address = {Amsterdam}, issn = {2666-3899}, doi = {10.1016/j.patter.2021.100337}, pages = {9}, year = {2021}, abstract = {Robust phenotyping of patients from electronic health records (EHRs) at scale is a challenge in clinical informatics. Here, we introduce Phe2vec, an automated framework for disease phenotyping from EHRs based on unsupervised learning and assess its effectiveness against standard rule-based algorithms from Phenotype KnowledgeBase (PheKB). Phe2vec is based on pre-computing embeddings of medical concepts and patients' clinical history. Disease phenotypes are then derived from a seed concept and its neighbors in the embedding space. Patients are linked to a disease if their embedded representation is close to the disease phenotype. Comparing Phe2vec and PheKB cohorts head-to-head using chart review, Phe2vec performed on par or better in nine out of ten diseases. Differently from other approaches, it can scale to any condition and was validated against widely adopted expert-based standards. Phe2vec aims to optimize clinical informatics research by augmenting current frameworks to characterize patients by condition and derive reliable disease cohorts.}, language = {en} }