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14. Evaluation of First-line Radiosurgery vs Whole-Brain Radiotherapy for Small Cell Lung Cancer Brain Metastases

Author

Rusthoven, Chad G, Yamamoto, Masaaki, Bernhardt, Denise, Smith, Derek E, Gao, Dexiang, et al, Guckenberger, Matthias, Andratschke, Nicolaus, University of Zurich, and Rusthoven, Chad G

Subjects
brain radiotherapy, whole, 610 Medicine & health, 2730 Oncology, 1306 Cancer Research, small cell lung cancer brain metastases, first, line stereotactic radiosurgery, 10044 Clinic for Radiation Oncology
Published
2020

17. Reply to Letter to the Editor

Author

Saltzman, Amanda F., primary, Smith, Derek E., additional, Gao, Dexiang, additional, Ghosh, Debashis, additional, Amini, Arya, additional, Aldrink, Jennifer H., additional, Dasgupta, Roshni, additional, Gow, Kenneth W., additional, Glick, Richard D., additional, Ehrlich, Peter F., additional, and Cost, Nicholas G., additional

Published
2020
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19. Evaluation of First-line Radiosurgery vs Whole-Brain Radiotherapy for Small Cell Lung Cancer Brain Metastases

Author

Rusthoven, Chad G., primary, Yamamoto, Masaaki, additional, Bernhardt, Denise, additional, Smith, Derek E., additional, Gao, Dexiang, additional, Serizawa, Toru, additional, Yomo, Shoji, additional, Aiyama, Hitoshi, additional, Higuchi, Yoshinori, additional, Shuto, Takashi, additional, Akabane, Atsuya, additional, Sato, Yasunori, additional, Niranjan, Ajay, additional, Faramand, Andrew M., additional, Lunsford, L. Dade, additional, McInerney, James, additional, Tuanquin, Leonard C., additional, Zacharia, Brad E., additional, Chiang, Veronica, additional, Singh, Charu, additional, Yu, James B., additional, Braunstein, Steve, additional, Mathieu, David, additional, Touchette, Charles J., additional, Lee, Cheng-chia, additional, Yang, Huai-che, additional, Aizer, Ayal A., additional, Cagney, Daniel N., additional, Chan, Michael D., additional, Kondziolka, Douglas, additional, Bernstein, Kenneth, additional, Silverman, Joshua S., additional, Grills, Inga S., additional, Siddiqui, Zaid A., additional, Yuan, Justin C., additional, Sheehan, Jason P., additional, Cordeiro, Diogo, additional, Nosaki, Kename, additional, Seto, Takahashi, additional, Deibert, Christopher P., additional, Verma, Vivek, additional, Day, Samuel, additional, Halasz, Lia M., additional, Warnick, Ronald E., additional, Trifiletti, Daniel M., additional, Palmer, Joshua D., additional, Attia, Albert, additional, Li, Benjamin, additional, Cifarelli, Christopher P., additional, Brown, Paul D., additional, Vargo, John A., additional, Combs, Stephanie E., additional, Kessel, Kerstin A., additional, Rieken, Stefan, additional, Patel, Samir, additional, Guckenberger, Matthias, additional, Andratschke, Nicolaus, additional, Kavanagh, Brian D., additional, and Robin, Tyler P., additional

Published
2020
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21. Development of a Prediction Model for Incident Atrial Fibrillation using Machine Learning Applied to Harmonized Electronic Health Record Data: Supplemental Material

Author

Tiwari, Premanand, Colborn, Kathryn L., Smith, Derek E., Xing, Fuyong, Ghosh, Debashis, and Rosenberg, Michael A.

Abstract

Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia, whose early detection could lead to significant improvements in outcomes through appropriate prescription of anticoagulation. Although a variety of methods exist for screening for AF, there is general agreement that a targeted approach would be preferred. Implicit within this approach is the need for an efficient method for identification of patients at risk. In this investigation, we examined the strengths and weaknesses of an approach based on application of machine-learning algorithms to electronic health record (EHR) data that has been harmonized to the Observational Medical Outcomes Partnership (OMOP) common data model. We examined data from a total of 2.3M individuals, of whom 1.16% developed incident AF over designated 6-month time intervals. We examined and compared several approaches for data reduction, sample balancing (re-sampling) and predictive modeling using cross-validation for hyperparameter selection, and out-of-sample testing for validation. Although no approach provided outstanding classification accuracy, we found that the optimal approach for prediction of 6-month incident AF used a random forest classifier, raw features (no data reduction), and synthetic minority oversampling technique (SMOTE) resampling (F1 statistic 0.12, AUC 0.65). This model performed better than a predictive model based only on known AF risk factors, and highlighted the importance of using resampling methods to optimize ML approaches to imbalanced data as exists in EHRs. Further studies using EHR data in other medical systems are needed to validate the clinical applicability of these findings.

Published
2019
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24. IL-6 and IL-8 Are Linked With Myeloid-Derived Suppressor Cell Accumulation and Correlate With Poor Clinical Outcomes in Melanoma Patients

Author

Tobin, Richard P., primary, Jordan, Kimberly R., additional, Kapoor, Puja, additional, Spongberg, Eric, additional, Davis, Dana, additional, Vorwald, Victoria M., additional, Couts, Kasey L., additional, Gao, Dexiang, additional, Smith, Derek E., additional, Borgers, Jessica S. W., additional, Robinson, Steven, additional, Amato, Carol, additional, Gonzalez, Rene, additional, Lewis, Karl D., additional, Robinson, William A., additional, Borges, Virginia F., additional, and McCarter, Martin D., additional

Published
2019
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25. How many lymph nodes are enough? Assessing the adequacy of lymph node yield for staging in favorable histology wilms tumor

Author

Saltzman, Amanda F., primary, Smith, Derek E., additional, Gao, Dexiang, additional, Ghosh, Debashis, additional, Amini, Arya, additional, Aldrink, Jennifer H., additional, Dasgupta, Roshni, additional, Gow, Kenneth W., additional, Glick, Richard D., additional, Ehrlich, Peter F., additional, and Cost, Nicholas G., additional

Published
2019
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27. Abstract A117: Tumor-produced IL-6 and IL-8 are associated with MDSC accumulation and correlate with long-term clinical outcomes in melanoma patients

Author

Tobin, Richard P., primary, Jordan, Kimberly R., additional, Davis, Dana, additional, Vorwald, Victoria M., additional, Couts, Kasey, additional, Gao, Dexiang, additional, Smith, Derek E, additional, Robinson, William A, additional, Borges, Virginia, additional, and McCarter, Martin D, additional

Published
2019
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29. Ecology under lake ice

Author

Hampton, Stephanie E., Galloway, Aaron W. E., Powers, Stephen M., Ozersky, Ted, Woo, Kara H., Batt, Ryan D., Labou, Stephanie G., O'Reilly, Catherine M., Sharma, Sapna, Lottig, Noah R., Stanley, Emily H., North, Rebecca L., Stockwell, Jason D., Adrian, Rita, Weyhenmeyer, Gesa A., Arvola, Lauri, Baulch, Helen M., Bertani, Isabella, Bowman, Larry L., Jr., Carey, Cayelan C., Catalan, Jordi, Colom-Montero, William, Domine, Leah M., Felip, Marisol, Granados, Ignacio, Gries, Corinna, Grossart, Hans-Peter, Haberman, Juta, Haldna, Marina, Hayden, Brian, Higgins, Scott N., Jolley, Jeff C., Kahilainen, Kimmo K., Kaup, Enn, Kehoe, Michael J., MacIntyre, Sally, Mackay, Anson W., Mariash, Heather L., Mckay, Robert M., Nixdorf, Brigitte, Noges, Peeter, Noges, Tiina, Palmer, Michelle, Pierson, Don C., Post, David M., Pruett, Matthew J., Rautio, Milla, Read, Jordan S., Roberts, Sarah L., Ruecker, Jacqueline, Sadro, Steven, Silow, Eugene A., Smith, Derek E., Sterner, Robert W., Swann, George E. A., Timofeyev, Maxim A., Toro, Manuel, Twiss, Michael R., Vogt, Richard J., Watson, Susan B., Whiteford, Erika J., Xenopoulos, Marguerite A., Coastal dynamics, Fluvial systems and Global change, Palaeo-ecologie, Biological Sciences, Lammi Biological Station, Environmental Sciences, Coastal dynamics, Fluvial systems and Global change, and Palaeo-ecologie

Subjects
0106 biological sciences, 010504 meteorology & atmospheric sciences, data synthesis, Limnology, AULACOSEIRA-BAICALENSIS, Aquatic ecosystem, 01 natural sciences, Zooplankton, Phytoplankton, Temperate climate, Ecosystem, Ice Cover, freshwater, lake, Ecology, Evolution, Behavior and Systematics, 0105 earth and related environmental sciences, Abiotic component, Ekologi, long-term, CLIMATE-CHANGE, SEASONAL SUCCESSION, Ecology, 010604 marine biology & hydrobiology, limnology, plankton, NORTH-ATLANTIC OSCILLATION, UNDER-ICE, Plankton, seasonal, Snow, WINTER LIMNOLOGY, PLANKTON SUCCESSION, Lakes, 13. Climate action, FRESH-WATER LAKES, COVERED LAKES, 1181 Ecology, evolutionary biology, Environmental science, Seasons, long‐term, time series, 500 Naturwissenschaften und Mathematik::570 Biowissenschaften, Biologie::577 Ökologie, COMMUNITY STRUCTURE, winter ecology
Abstract

Winter conditions are rapidly changing in temperate ecosystems, particularly for those that experience periods of snow and ice cover. Relatively little is known of winter ecology in these systems, due to a historical research focus on summer 'growing seasons'. We executed the first global quantitative synthesis on under-ice lake ecology, including 36 abiotic and biotic variables from 42 research groups and 101 lakes, examining seasonal differences and connections as well as how seasonal differences vary with geophysical factors. Plankton were more abundant under ice than expected; mean winter values were 43.2% of summer values for chlorophyll a, 15.8% of summer phytoplankton biovolume and 25.3% of summer zooplankton density. Dissolved nitrogen concentrations were typically higher during winter, and these differences were exaggerated in smaller lakes. Lake size also influenced winter-summer patterns for dissolved organic carbon (DOC), with higher winter DOC in smaller lakes. At coarse levels of taxonomic aggregation, phytoplankton and zooplankton community composition showed few systematic differences between seasons, although literature suggests that seasonal differences are frequently lake-specific, species-specific, or occur at the level of functional group. Within the subset of lakes that had longer time series, winter influenced the subsequent summer for some nutrient variables and zooplankton biomass. National Science Foundation (NSF DEB) [1431428, 1136637]; Washington State University; Russian Science Foundation [14-14-00400]; Ministry of education and science of Russia Gos-Zasanie project [1354-2014/51]; Natural Environment Research Council [NE/J00829X/1, 1230750, NE/G019622/1, NE/J010227/1] Funding was provided by the National Science Foundation (NSF DEB #1431428; NSF DEB #1136637) and Washington State University. M. Timofeyev and E. Silow were partially supported by Russian Science Foundation project No 14-14-00400 and Ministry of education and science of Russia Gos-Zasanie project No 1354-2014/51. We are grateful to Marianne Moore, Deniz Ozkundakci, Chris Polashenski and Paula Kankaala for discussions that greatly improved this work. We also gratefully acknowledge the following individuals for contributing to this project: John Anderson, Jill Baron, Rick Bourbonniere, Sandra Brovold, Lluis Camarero, Sudeep Chandra, Jim Cotner, Laura Forsstom, Guillaume Grosbois, Chris Harrod, Klaus D. Joehnk, T.Y. Kim, Daniel Langenhaun, Reet Laugaste, Suzanne McGowan, Virginia Panizzo, Giampaolo Rossetti, R.E.H. Smith, Sarah Spaulding, Helen Tammert, Steve Thackeray, Kyle Zimmer, Priit Zingel and two anonymous reviewers. Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the US Government.

Published
2017

30. Risk and subtypes of secondary primary malignancies in diffuse large B-cell lymphoma survivors change over time based on stage at diagnosis.

Author

Major, Ajay, Smith, Derek E., Ghosh, Debashis, Rabinovitch, Rachel, and Kamdar, Manali

Subjects
*DIFFUSE large B-cell lymphomas, *PROPORTIONAL hazards models, *SECONDARY primary cancer, *LOG-rank test, *REPORTING of diseases, *CANCER relapse, *B cell lymphoma, *TUMOR classification, *RESEARCH funding
Abstract

Background: Previous studies have shown an increased risk of secondary primary malignancies (SPMs) after diffuse large B-cell lymphoma (DLBCL) treatment. Whether stage of DLBCL at diagnosis affects the subtypes of SPMs that occur has not been previously described.Methods: The Surveillance, Epidemiology, and End Results database was queried for patients aged >18 years diagnosed with primary DLBCL from 1973 to 2010 and categorized by early stage (ES) (stage I-II) or advanced stage (AS) (stage III-IV) disease. Differences in overall and location-specific SPM incidence by stage and time since diagnosis were assessed in 5-year intervals using a Fine-Gray hazards model. Overall survival was compared using the log-rank test. A Cox proportional hazards model was used to assess differences in survival.Results: In total, 26,038 patients with DLBCL were identified, including 14,724 with ES and 11,314 with AS disease. The median follow-up was 13.3 years. Overall, 13.0% of patients developed SPM, with a higher but nonsignificantly increased risk of SPM development in those who had ES disease compared with those who had AS disease (14% vs 11.6%; P = .14). During the first 5 years after diagnosis, patients who had ES disease had a higher risk of SPM than those who had AS disease, specifically colorectal, pancreas, breast, and prostate SPMs. During the period from 10 to 15 years after diagnosis, patients who had AS disease had a higher risk of SPM than those who had ES disease, specifically hematologic SPMs. Development of SPM was found to significantly increase the risk of death regardless of stage at diagnosis.Conclusions: In this large, population-based study, distinctly different subtypes and temporal patterns of SPM development were identified based on stage of DLBCL at diagnosis. The current study merits consideration of tailored site-specific and time-specific surveillance for patients with DLBCL according to stage and time interval since diagnosis. [ABSTRACT FROM AUTHOR]

Published
2020
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35. A Robust Calibration Method for Continental‐Scale Soil Water Content Measurements

Author

Roberti, Joshua A., primary, Ayres, Edward, additional, Loescher, Henry W., additional, Tang, Jianwu, additional, Starr, Gregory, additional, Durden, David J., additional, Smith, Derek E., additional, de la Reguera, Elizabeth, additional, Morkeski, Kate, additional, McKlveen, Margot, additional, Benstead, Heidi, additional, SanClements, Michael D., additional, Lee, Robert H., additional, Gebremedhin, Maheteme, additional, and Zulueta, Rommel C., additional

Published
2018
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36. Ecology under lake ice

Author

Coastal dynamics, Fluvial systems and Global change, Palaeo-ecologie, Hampton, Stephanie E., Galloway, Aaron W. E., Powers, Stephen M., Ozersky, Ted, Woo, Kara H., Batt, Ryan D., Labou, Stephanie G., O'Reilly, Catherine M., Sharma, Sapna, Lottig, Noah R., Stanley, Emily H., North, Rebecca L., Stockwell, Jason D., Adrian, Rita, Weyhenmeyer, Gesa A., Arvola, Lauri, Baulch, Helen M., Bertani, Isabella, Bowman, Larry L., Jr., Carey, Cayelan C., Catalan, Jordi, Colom-Montero, William, Domine, Leah M., Felip, Marisol, Granados, Ignacio, Gries, Corinna, Grossart, Hans-Peter, Haberman, Juta, Haldna, Marina, Hayden, Brian, Higgins, Scott N., Jolley, Jeff C., Kahilainen, Kimmo K., Kaup, Enn, Kehoe, Michael J., MacIntyre, Sally, Mackay, Anson W., Mariash, Heather L., Mckay, Robert M., Nixdorf, Brigitte, Noges, Peeter, Noges, Tiina, Palmer, Michelle, Pierson, Don C., Post, David M., Pruett, Matthew J., Rautio, Milla, Read, Jordan S., Roberts, Sarah L., Ruecker, Jacqueline, Sadro, Steven, Silow, Eugene A., Smith, Derek E., Sterner, Robert W., Swann, George E. A., Timofeyev, Maxim A., Toro, Manuel, Twiss, Michael R., Vogt, Richard J., Watson, Susan B., Whiteford, Erika J., Xenopoulos, Marguerite A., Coastal dynamics, Fluvial systems and Global change, Palaeo-ecologie, Hampton, Stephanie E., Galloway, Aaron W. E., Powers, Stephen M., Ozersky, Ted, Woo, Kara H., Batt, Ryan D., Labou, Stephanie G., O'Reilly, Catherine M., Sharma, Sapna, Lottig, Noah R., Stanley, Emily H., North, Rebecca L., Stockwell, Jason D., Adrian, Rita, Weyhenmeyer, Gesa A., Arvola, Lauri, Baulch, Helen M., Bertani, Isabella, Bowman, Larry L., Jr., Carey, Cayelan C., Catalan, Jordi, Colom-Montero, William, Domine, Leah M., Felip, Marisol, Granados, Ignacio, Gries, Corinna, Grossart, Hans-Peter, Haberman, Juta, Haldna, Marina, Hayden, Brian, Higgins, Scott N., Jolley, Jeff C., Kahilainen, Kimmo K., Kaup, Enn, Kehoe, Michael J., MacIntyre, Sally, Mackay, Anson W., Mariash, Heather L., Mckay, Robert M., Nixdorf, Brigitte, Noges, Peeter, Noges, Tiina, Palmer, Michelle, Pierson, Don C., Post, David M., Pruett, Matthew J., Rautio, Milla, Read, Jordan S., Roberts, Sarah L., Ruecker, Jacqueline, Sadro, Steven, Silow, Eugene A., Smith, Derek E., Sterner, Robert W., Swann, George E. A., Timofeyev, Maxim A., Toro, Manuel, Twiss, Michael R., Vogt, Richard J., Watson, Susan B., Whiteford, Erika J., and Xenopoulos, Marguerite A.

Published
2017

37. Ecology under lake ice

Author

Biological Sciences, Hampton, Stephanie E., Galloway, Aaron W. E., Powers, Stephen M., Ozersky, Ted, Woo, Kara H., Batt, Ryan D., Labou, Stephanie G., O'Reilly, Catherine M., Sharma, Sapna, Lottig, Noah R., Stanley, Emily H., North, Rebecca L., Stockwell, Jason D., Adrian, Rita, Weyhenmeyer, Gesa A., Arvola, Lauri, Baulch, Helen M., Bertani, Isabella, Bowman, Larry L., Jr., Carey, Cayelan C., Catalan, Jordi, Colom-Montero, William, Domine, Leah M., Felip, Marisol, Granados, Ignacio, Gries, Corinna, Grossart, Hans-Peter, Haberman, Juta, Haldna, Marina, Hayden, Brian, Higgins, Scott N., Jolley, Jeff C., Kahilainen, Kimmo K., Kaup, Enn, Kehoe, Michael J., MacIntyre, Sally, Mackay, Anson W., Mariash, Heather L., McKay, Robert M., Nixdorf, Brigitte, Noges, Peeter, Noges, Tiina, Palmer, Michelle, Pierson, Don C., Post, David M., Pruett, Matthew J., Rautio, Milla, Read, Jordan S., Roberts, Sarah L., Ruecker, Jacqueline, Sadro, Steven, Silow, Eugene A., Smith, Derek E., Sterner, Robert W., Swann, George E. A., Timofeyev, Maxim A., Toro, Manuel, Twiss, Michael R., Vogt, Richard J., Watson, Susan B., Whiteford, Erika J., Xenopoulos, Marguerite A., Biological Sciences, Hampton, Stephanie E., Galloway, Aaron W. E., Powers, Stephen M., Ozersky, Ted, Woo, Kara H., Batt, Ryan D., Labou, Stephanie G., O'Reilly, Catherine M., Sharma, Sapna, Lottig, Noah R., Stanley, Emily H., North, Rebecca L., Stockwell, Jason D., Adrian, Rita, Weyhenmeyer, Gesa A., Arvola, Lauri, Baulch, Helen M., Bertani, Isabella, Bowman, Larry L., Jr., Carey, Cayelan C., Catalan, Jordi, Colom-Montero, William, Domine, Leah M., Felip, Marisol, Granados, Ignacio, Gries, Corinna, Grossart, Hans-Peter, Haberman, Juta, Haldna, Marina, Hayden, Brian, Higgins, Scott N., Jolley, Jeff C., Kahilainen, Kimmo K., Kaup, Enn, Kehoe, Michael J., MacIntyre, Sally, Mackay, Anson W., Mariash, Heather L., McKay, Robert M., Nixdorf, Brigitte, Noges, Peeter, Noges, Tiina, Palmer, Michelle, Pierson, Don C., Post, David M., Pruett, Matthew J., Rautio, Milla, Read, Jordan S., Roberts, Sarah L., Ruecker, Jacqueline, Sadro, Steven, Silow, Eugene A., Smith, Derek E., Sterner, Robert W., Swann, George E. A., Timofeyev, Maxim A., Toro, Manuel, Twiss, Michael R., Vogt, Richard J., Watson, Susan B., Whiteford, Erika J., and Xenopoulos, Marguerite A.

Abstract

Winter conditions are rapidly changing in temperate ecosystems, particularly for those that experience periods of snow and ice cover. Relatively little is known of winter ecology in these systems, due to a historical research focus on summer 'growing seasons'. We executed the first global quantitative synthesis on under-ice lake ecology, including 36 abiotic and biotic variables from 42 research groups and 101 lakes, examining seasonal differences and connections as well as how seasonal differences vary with geophysical factors. Plankton were more abundant under ice than expected; mean winter values were 43.2% of summer values for chlorophyll a, 15.8% of summer phytoplankton biovolume and 25.3% of summer zooplankton density. Dissolved nitrogen concentrations were typically higher during winter, and these differences were exaggerated in smaller lakes. Lake size also influenced winter-summer patterns for dissolved organic carbon (DOC), with higher winter DOC in smaller lakes. At coarse levels of taxonomic aggregation, phytoplankton and zooplankton community composition showed few systematic differences between seasons, although literature suggests that seasonal differences are frequently lake-specific, species-specific, or occur at the level of functional group. Within the subset of lakes that had longer time series, winter influenced the subsequent summer for some nutrient variables and zooplankton biomass.

Published
2017

38. Inferior outcomes and treatment disparities in elderly patients with classical Hodgkin lymphoma: a national cancer data base analysis.

Author

Major, Ajay, Jackson, Matthew W., Smith, Derek E., Kamdar, Manali, and Rabinovitch, Rachel

Subjects
HODGKIN'S disease, OLDER patients, DATABASES, TREATMENT effectiveness, DATA analysis
Abstract

Elderly patients with classical Hodgkin lymphoma (cHL) are understudied and poorly defined. The National Cancer Data Base was queried for adults with cHL diagnosed 2004–2013: 22,547 age 18–39, 12,841 age 40–59, and 10,873 age ≥60 were identified. Two-year overall survival (OS) was 97%, 91%, and 65% for the three age cohorts, respectively (p <.0001). Elderly patients age ≥60 had greater advanced comorbidity scores, stage III–IV disease, and lymphocyte-depleted histology. Elderly patients were treated less with chemotherapy, radiotherapy for stage I–II disease, and at academic/research centers (p <.001). There was improved OS in elderly patients who received chemotherapy and/or radiotherapy and were treated at academic/research centers. This largest analysis of elderly cHL demonstrates that patients ≥60 are distinct from those 40–59. Age ≥60 should be a stratification in future trials and merit distinct studies. Improving the poor rate of treatment delivery and directing care to academic centers may improve outcomes. [ABSTRACT FROM AUTHOR]

Published
2019
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40. Ecology under lake ice

Author

Hampton, Stephanie E., primary, Galloway, Aaron W. E., additional, Powers, Stephen M., additional, Ozersky, Ted, additional, Woo, Kara H., additional, Batt, Ryan D., additional, Labou, Stephanie G., additional, O'Reilly, Catherine M., additional, Sharma, Sapna, additional, Lottig, Noah R., additional, Stanley, Emily H., additional, North, Rebecca L., additional, Stockwell, Jason D., additional, Adrian, Rita, additional, Weyhenmeyer, Gesa A., additional, Arvola, Lauri, additional, Baulch, Helen M., additional, Bertani, Isabella, additional, Bowman, Larry L., additional, Carey, Cayelan C., additional, Catalan, Jordi, additional, Colom‐Montero, William, additional, Domine, Leah M., additional, Felip, Marisol, additional, Granados, Ignacio, additional, Gries, Corinna, additional, Grossart, Hans‐Peter, additional, Haberman, Juta, additional, Haldna, Marina, additional, Hayden, Brian, additional, Higgins, Scott N., additional, Jolley, Jeff C., additional, Kahilainen, Kimmo K., additional, Kaup, Enn, additional, Kehoe, Michael J., additional, MacIntyre, Sally, additional, Mackay, Anson W., additional, Mariash, Heather L., additional, McKay, Robert M., additional, Nixdorf, Brigitte, additional, Nõges, Peeter, additional, Nõges, Tiina, additional, Palmer, Michelle, additional, Pierson, Don C., additional, Post, David M., additional, Pruett, Matthew J., additional, Rautio, Milla, additional, Read, Jordan S., additional, Roberts, Sarah L., additional, Rücker, Jacqueline, additional, Sadro, Steven, additional, Silow, Eugene A., additional, Smith, Derek E., additional, Sterner, Robert W., additional, Swann, George E. A., additional, Timofeyev, Maxim A., additional, Toro, Manuel, additional, Twiss, Michael R., additional, Vogt, Richard J., additional, Watson, Susan B., additional, Whiteford, Erika J., additional, and Xenopoulos, Marguerite A., additional

Published
2016
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41. ROS1Gene Rearrangements Are Associated With an Elevated Risk of Peridiagnosis Thromboembolic Events

Author

Ng, Terry L., Smith, Derek E., Mushtaq, Rao, Patil, Tejas, Dimou, Anastasios, Yang, Shuo, Liu, Qian, Li, Xuefei, Zhou, Caicun, Jones, Robert T., Tu, Megan M., Yan, Flora, Bowman, I. Alex, Liu, Stephen V., Newkirk, Siera, Bauml, Joshua, Doebele, Robert C., Aisner, Dara L., Gao, Dexiang, Ren, Shengxiang, and Camidge, D. Ross

Abstract

This study aims to determine whether advanced ROS1gene-rearranged NSCLC (ROS1+ NSCLC) has a higher than expected thromboembolic event (TEE) rate.

Published
2019
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44. SIZE-FRACTIONATED PHYTOPLANKTON GROWTH AND MICROZOOPLANKTON GRAZING RATES IN THE UPPER ST. LAWRENCE RIVER.

Author

Twiss, Michael R. and Smith, Derek E.

Abstract

ABSTRACT Chlorophyll- a-specific growth and grazing rates of phytoplankton in three size fractions (0.2 to 2 µm, 2 to 20 µm, and 20 to 153 µm) were determined using dilution assays ( n = 38) that were conducted from May to December 2009 in the St. Lawrence River (discharge approx. 7135 m3 s−1) from its headwaters at Lake Ontario to 180 km downstream. There was no discernible difference in phytoplankton growth or grazing rates as a function of river reach sampled. At water temperatures below 20°C specific rates of growth and grazing were closely matched (0.43 day−1), suggesting tight coupling of phytoplankton grazing in the water column. However, specific phytoplankton growth rates exceeded specific grazing rates by 0.29 day−1 when river water temperature exceeded 20°C. Nevertheless, the greater specific growth rate did not manifest in greater phytoplankton biomass with transit downstream suggesting the importance of grazer organisms on benthic surfaces that graze phytoplankton in this reach of a large river. Copyright © 2011 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Published
2012
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45. Abstract 13948: Machine Learning Using Electronic Health Records to Predict 6-Month Incident Atrial Fibrillation

Author

Tiwari, Premanand, Colborn, Kathryn L, Smith, Derek E, Xing, Fuyong, Ghosh, Debashis, and Rosenberg, Michael A

Abstract

Introduction:Early detection of atrial fibrillation (AF) has potential to significantly reduce stroke risk via initiation of anticoagulation. Although the best screening method for AF remains uncertain, there is general agreement that a targeted approach based on predictive modeling might be preferred.Hypothesis:We hypothesize that machine-learning (ML) algorithms applied to the electronic health record (EHR) can predict incidence of AF better than standard approaches.Methods:Using data on over 2.3M individuals in the UCHealth EHR, we examined several ML techniques to predict 6-month incident AF. We included an initial feature set of ~26K diagnoses and medications, harmonized to the Observational Medical Outcomes Partnership common data model. In a subsequent model, we included procedure codes (~55K features), from which we selected the 200 most common features, as well as age and sex.Results:Over 8 years of follow-up, ~26K subjects (1.16%) developed incident AF within 6-month time intervals. We examined and compared several ML approaches using dimensionality reduction, sample balancing, and predictive modeling using out-of-sample testing (20%) for validation. Using only diagnoses and medications, we found that the optimal approach for prediction used a random forest classifier, with raw features and synthetic minority oversampling technique (AUC 0.65), and performed better than a predictive model based only on known AF risk factors (excluding age and sex). However, with inclusion of procedure codes, age, and sex, the optimal model was a 5-layer deep neural network (Early stopping, 20% dropout), and random oversampling, which had an AUC of 0.79 (Figure).Conclusions:ML approaches provide the opportunity to employ large amounts of data from the EHR to predict incident AF. Inclusion of the age and sex improved model performance over larger numbers of weaker predictors. More work is needed to identify the optimal EHR-based model to predict incident AF.

Published
2019
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46. Ecology under lake ice.

Author

Hampton SE, Galloway AW, Powers SM, Ozersky T, Woo KH, Batt RD, Labou SG, O'Reilly CM, Sharma S, Lottig NR, Stanley EH, North RL, Stockwell JD, Adrian R, Weyhenmeyer GA, Arvola L, Baulch HM, Bertani I, Bowman LL Jr, Carey CC, Catalan J, Colom-Montero W, Domine LM, Felip M, Granados I, Gries C, Grossart HP, Haberman J, Haldna M, Hayden B, Higgins SN, Jolley JC, Kahilainen KK, Kaup E, Kehoe MJ, MacIntyre S, Mackay AW, Mariash HL, McKay RM, Nixdorf B, Nõges P, Nõges T, Palmer M, Pierson DC, Post DM, Pruett MJ, Rautio M, Read JS, Roberts SL, Rücker J, Sadro S, Silow EA, Smith DE, Sterner RW, Swann GE, Timofeyev MA, Toro M, Twiss MR, Vogt RJ, Watson SB, Whiteford EJ, and Xenopoulos MA

Subjects
Seasons, Ecosystem, Ice Cover, Lakes, Plankton physiology
Abstract

Winter conditions are rapidly changing in temperate ecosystems, particularly for those that experience periods of snow and ice cover. Relatively little is known of winter ecology in these systems, due to a historical research focus on summer 'growing seasons'. We executed the first global quantitative synthesis on under-ice lake ecology, including 36 abiotic and biotic variables from 42 research groups and 101 lakes, examining seasonal differences and connections as well as how seasonal differences vary with geophysical factors. Plankton were more abundant under ice than expected; mean winter values were 43.2% of summer values for chlorophyll a, 15.8% of summer phytoplankton biovolume and 25.3% of summer zooplankton density. Dissolved nitrogen concentrations were typically higher during winter, and these differences were exaggerated in smaller lakes. Lake size also influenced winter-summer patterns for dissolved organic carbon (DOC), with higher winter DOC in smaller lakes. At coarse levels of taxonomic aggregation, phytoplankton and zooplankton community composition showed few systematic differences between seasons, although literature suggests that seasonal differences are frequently lake-specific, species-specific, or occur at the level of functional group. Within the subset of lakes that had longer time series, winter influenced the subsequent summer for some nutrient variables and zooplankton biomass., (© 2016 The Authors. Ecology Letters published by CNRS and John Wiley & Sons Ltd.)

Published
2017
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