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1. Sparse-grid Discontinuous Galerkin Methods for the Vlasov-Poisson-Lenard-Bernstein Model

Author

Schnake, Stefan, Kendrick, Coleman, Endeve, Eirik, Stoyanov, Miroslav, Hahn, Steven, Hauck, Cory D, Green, David L, Snyder, Phil, and Canik, John

Subjects
Mathematics - Numerical Analysis
Abstract

Sparse-grid methods have recently gained interest in reducing the computational cost of solving high-dimensional kinetic equations. In this paper, we construct adaptive and hybrid sparse-grid methods for the Vlasov-Poisson-Lenard-Bernstein (VPLB) model. This model has applications to plasma physics and is simulated in two reduced geometries: a 0x3v space homogeneous geometry and a 1x3v slab geometry. We use the discontinuous Galerkin (DG) method as a base discretization due to its high-order accuracy and ability to preserve important structural properties of partial differential equations. We utilize a multiwavelet basis expansion to determine the sparse-grid basis and the adaptive mesh criteria. We analyze the proposed sparse-grid methods on a suite of three test problems by computing the savings afforded by sparse-grids in comparison to standard solutions of the DG method. The results are obtained using the adaptive sparse-grid discretization library ASGarD.

Published
2024

2. Stable predictions for health related anticausal prediction tasks affected by selection biases: the need to deconfound the test set features

Author

Neto, Elias Chaibub, Snyder, Phil, Sieberts, Solveig K, and Omberg, Larsson

Subjects
Statistics - Machine Learning, Computer Science - Machine Learning
Abstract

In health related machine learning applications, the training data often corresponds to a non-representative sample from the target populations where the learners will be deployed. In anticausal prediction tasks, selection biases often make the associations between confounders and the outcome variable unstable across different target environments. As a consequence, the predictions from confounded learners are often unstable, and might fail to generalize in shifted test environments. Stable prediction approaches aim to solve this problem by producing predictions that are stable across unknown test environments. These approaches, however, are sometimes applied to the training data alone with the hope that training an unconfounded model will be enough to generate stable predictions in shifted test sets. Here, we show that this is insufficient, and that improved stability can be achieved by deconfounding the test set features as well. We illustrate these observations using both synthetic data and real world data from a mobile health study., Comment: Machine Learning for Health (ML4H) at NeurIPS 2020 - Extended Abstract. This workshop paper draws some material from arXiv:2001.03998 and arXiv:2004.09466

Published
2020

3. Indicators of retention in remote digital health studies: A cross-study evaluation of 100,000 participants

Author

Pratap, Abhishek, Neto, Elias Chaibub, Snyder, Phil, Stepnowsky, Carl, Elhadad, Noémie, Grant, Daniel, Mohebbi, Matthew H., Mooney, Sean, Suver, Christine, Wilbanks, John, Mangravite, Lara, Heagerty, Patrick, Arean, Pat, and Omberg, Larsson

Subjects
Statistics - Applications, Computer Science - Computers and Society
Abstract

Digital technologies such as smartphones are transforming the way scientists conduct biomedical research using real-world data. Several remotely-conducted studies have recruited thousands of participants over a span of a few months. Unfortunately, these studies are hampered by substantial participant attrition, calling into question the representativeness of the collected data including generalizability of findings from these studies. We report the challenges in retention and recruitment in eight remote digital health studies comprising over 100,000 participants who participated for more than 850,000 days, completing close to 3.5 million remote health evaluations. Survival modeling surfaced several factors significantly associated(P < 1e-16) with increase in median retention time i) Clinician referral(increase of 40 days), ii) Effect of compensation (22 days), iii) Clinical conditions of interest to the study (7 days) and iv) Older adults(4 days). Additionally, four distinct patterns of daily app usage behavior that were also associated(P < 1e-10) with participant demographics were identified. Most studies were not able to recruit a representative sample, either demographically or regionally. Combined together these findings can help inform recruitment and retention strategies to enable equitable participation of populations in future digital health research.

Published
2019

5. Accelerometer data collected with a minimum set of wearable sensors from subjects with Parkinson’s disease

Author

Daneault, Jean-Francois, Vergara-Diaz, Gloria, Parisi, Federico, Admati, Chen, Alfonso, Christina, Bertoli, Matilde, Bonizzoni, Edoardo, Carvalho, Gabriela Ferreira, Costante, Gianluca, Fabara, Eric Eduardo, Fixler, Naama, Golabchi, Fatemah Noushin, Growdon, John, Sapienza, Stefano, Snyder, Phil, Shpigelman, Shahar, Sudarsky, Lewis, Daeschler, Margaret, Bataille, Lauren, Sieberts, Solveig K., Omberg, Larsson, Moore, Steven, and Bonato, Paolo

Published
2021
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6. Limb and trunk accelerometer data collected with wearable sensors from subjects with Parkinson’s disease

Author

Vergara-Diaz, Gloria, Daneault, Jean-Francois, Parisi, Federico, Admati, Chen, Alfonso, Christina, Bertoli, Matilde, Bonizzoni, Edoardo, Carvalho, Gabriela Ferreira, Costante, Gianluca, Fabara, Eric Eduardo, Fixler, Naama, Golabchi, Fatemah Noushin, Growdon, John, Sapienza, Stefano, Snyder, Phil, Shpigelman, Shahar, Sudarsky, Lewis, Daeschler, Margaret, Bataille, Lauren, Sieberts, Solveig K., Omberg, Larsson, Moore, Steven, and Bonato, Paolo

Published
2021
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10. Meta-Analysis of the Alzheimer’s Disease Human Brain Transcriptome and Functional Dissection in Mouse Models

Author

Wan, Ying-Wooi, Al-Ouran, Rami, Mangleburg, Carl G., Perumal, Thanneer M., Lee, Tom V., Allison, Katherine, Swarup, Vivek, Funk, Cory C., Gaiteri, Chris, Allen, Mariet, Wang, Minghui, Neuner, Sarah M., Kaczorowski, Catherine C., Philip, Vivek M., Howell, Gareth R., Martini-Stoica, Heidi, Zheng, Hui, Mei, Hongkang, Zhong, Xiaoyan, Kim, Jungwoo Wren, Dawson, Valina L., Dawson, Ted M., Pao, Ping-Chieh, Tsai, Li-Huei, Haure-Mirande, Jean-Vianney, Ehrlich, Michelle E., Chakrabarty, Paramita, Levites, Yona, Wang, Xue, Dammer, Eric B., Srivastava, Gyan, Mukherjee, Sumit, Sieberts, Solveig K., Omberg, Larsson, Dang, Kristen D., Eddy, James A., Snyder, Phil, Chae, Yooree, Amberkar, Sandeep, Wei, Wenbin, Hide, Winston, Preuss, Christoph, Ergun, Ayla, Ebert, Phillip J., Airey, David C., Mostafavi, Sara, Yu, Lei, Klein, Hans-Ulrich, Carter, Gregory W., Collier, David A., Golde, Todd E., Levey, Allan I., Bennett, David A., Estrada, Karol, Townsend, T. Matthew, Zhang, Bin, Schadt, Eric, De Jager, Philip L., Price, Nathan D., Ertekin-Taner, Nilüfer, Liu, Zhandong, Shulman, Joshua M., Mangravite, Lara M., Logsdon, Benjamin A., Wan, Ying-Wooi, Al-Ouran, Rami, Mangleburg, Carl G., Perumal, Thanneer M., Lee, Tom V., Allison, Katherine, Swarup, Vivek, Funk, Cory C., Gaiteri, Chris, Allen, Mariet, Wang, Minghui, Neuner, Sarah M., Kaczorowski, Catherine C., Philip, Vivek M., Howell, Gareth R., Martini-Stoica, Heidi, Zheng, Hui, Mei, Hongkang, Zhong, Xiaoyan, Kim, Jungwoo Wren, Dawson, Valina L., Dawson, Ted M., Pao, Ping-Chieh, Tsai, Li-Huei, Haure-Mirande, Jean-Vianney, Ehrlich, Michelle E., Chakrabarty, Paramita, Levites, Yona, Wang, Xue, Dammer, Eric B., Srivastava, Gyan, Mukherjee, Sumit, Sieberts, Solveig K., Omberg, Larsson, Dang, Kristen D., Eddy, James A., Snyder, Phil, Chae, Yooree, Amberkar, Sandeep, Wei, Wenbin, Hide, Winston, Preuss, Christoph, Ergun, Ayla, Ebert, Phillip J., Airey, David C., Mostafavi, Sara, Yu, Lei, Klein, Hans-Ulrich, Carter, Gregory W., Collier, David A., Golde, Todd E., Levey, Allan I., Bennett, David A., Estrada, Karol, Townsend, T. Matthew, Zhang, Bin, Schadt, Eric, De Jager, Philip L., Price, Nathan D., Ertekin-Taner, Nilüfer, Liu, Zhandong, Shulman, Joshua M., Mangravite, Lara M., and Logsdon, Benjamin A.

Abstract

© 2020 The Authors We present a consensus atlas of the human brain transcriptome in Alzheimer's disease (AD), based on meta-analysis of differential gene expression in 2,114 postmortem samples. We discover 30 brain coexpression modules from seven regions as the major source of AD transcriptional perturbations. We next examine overlap with 251 brain differentially expressed gene sets from mouse models of AD and other neurodegenerative disorders. Human-mouse overlaps highlight responses to amyloid versus tau pathology and reveal age- and sex-dependent expression signatures for disease progression. Human coexpression modules enriched for neuronal and/or microglial genes broadly overlap with mouse models of AD, Huntington's disease, amyotrophic lateral sclerosis, and aging. Other human coexpression modules, including those implicated in proteostasis, are not activated in AD models but rather following other, unexpected genetic manipulations. Our results comprise a cross-species resource, highlighting transcriptional networks altered by human brain pathophysiology and identifying correspondences with mouse models for AD preclinical studies.

Published
2022

11. Large eQTL meta-analysis reveals differing patterns between cerebral cortical and cerebellar brain regions

Author

Sieberts, Solveig K., Perumal, Thanneer M., Carrasquillo, Minerva M., Allen, Mariet, Reddy, Joseph S., Hoffman, Gabriel E., Dang, Kristen K., Calley, John, Ebert, Philip J., Eddy, James, Wang, Xue, Greenwood, Anna K., Mostafavi, Sara, Akbarian, Schahram, Bendl, Jaroslav, Breen, Michael S., Brennand, Kristen, Brown, Leanne, Browne, Andrew, Buxbaum, Joseph D., Charney, Alexander, Chess, Andrew, Couto, Lizette, Crawford, Greg, Devillers, Olivia, Devlin, Bernie, Dobbyn, Amanda, Domenici, Enrico, Filosi, Michele, Flatow, Elie, Francoeur, Nancy, Fullard, John, Gil, Sergio Espeso, Girdhar, Kiran, Gulyás-Kovács, Attila, Gur, Raquel, Hahn, Chang-Gyu, Haroutunian, Vahram, Hauberg, Mads Engel, Huckins, Laura, Jacobov, Rivky, Jiang, Yan, Johnson, Jessica S., Kassim, Bibi, Kim, Yungil, Klei, Lambertus, Kramer, Robin, Lauria, Mario, Lehner, Thomas, Lewis, David A., Lipska, Barbara K., Montgomery, Kelsey, Park, Royce, Rosenbluh, Chaggai, Roussos, Panagiotis, Ruderfer, Douglas M., Senthil, Geetha, Shah, Hardik R., Sloofman, Laura, Song, Lingyun, Stahl, Eli, Sullivan, Patrick, Visintainer, Roberto, Wang, Jiebiao, Wang, Ying-Chih, Wiseman, Jennifer, Xia, Eva, Zhang, Wen, Zharovsky, Elizabeth, Addis, Laura, Addo, Sadiya N., Airey, David Charles, Arnold, Matthias, Bennett, David A., Bi, Yingtao, Biber, Knut, Blach, Colette, Bradhsaw, Elizabeth, Brennan, Paul, Canet-Aviles, Rosa, Cao, Sherry, Cavalla, Anna, Chae, Yooree, Chen, William W., Cheng, Jie, Collier, David Andrew, Dage, Jeffrey L., Dammer, Eric B., Davis, Justin Wade, Davis, John, Drake, Derek, Duong, Duc, Eastwood, Brian J., Ehrlich, Michelle, Ellingson, Benjamin, Engelmann, Brett W., Esmaeelinieh, Sahar, Felsky, Daniel, Funk, Cory, Gaiteri, Chris, Gandy, Samuel, Gao, Fan, Gileadi, Opher, Golde, Todd, Grosskurth, Shaun E., Gupta, Rishi R., Gutteridge, Alex X., Hooli, Basavaraj, Humphryes-Kirilov, Neil, Iijima, Koichi, James, Corey, Jung, Paul M., Kaddurah-Daouk, Rima, Kastenmuller, Gabi, Klein, Hans-Ulrich, Kummer, Markus, Lacor, Pascale N., Lah, James, Laing, Emma, Levey, Allan, Li, Yupeng, Lipsky, Samantha, Liu, Yushi, Liu, Jimmy, Liu, Zhandong, Louie, Gregory, Lu, Tao, Ma, Yiyi, Matsuoka, Yasuji Y., Menon, Vilas, Miller, Bradley, Misko, Thomas P., Mollon, Jennifer E., Mukherjee, Sumit, Noggle, Scott, Pao, Ping-Chieh, Pearce, Tracy Young, Pearson, Neil, Penny, Michelle, Petyuk, Vladislav A., Price, Nathan, Quarless, Danjuma X., Ravikumar, Brinda, Ried, Janina S., Ruble, Cara Lee Ann, Runz, Heiko, Saykin, Andrew J., Schadt, Eric, Scherschel, James E., Seyfried, Nicholas, Shulman, Joshua M., Snyder, Phil, Soares, Holly, Srivastava, Gyan P., Stockmann, Henning, Taga, Mariko, Tasaki, Shinya, Tenenbaum, Jessie, Tsai, Li-Huei, Vasanthakumar, Aparna, Wachter, Astrid, Wang, Yaming, Wang, Hong, Wang, Minghui, Whelan, Christopher D., White, Charles, Woo, Kara H., Wren, Paul, Wu, Jessica W., Xi, Hualin S., Yankner, Bruce A., Younkin, Steven G., Yu, Lei, Zavodszky, Maria, Zhang, Wenling, Zhang, Guoqiang, Zhang, Bin, Zhu, Jun, Omberg, Larsson, Peters, Mette A., Logsdon, Benjamin A., De Jager, Philip L., Ertekin-Taner, Nilüfer, Mangravite, Lara M., Sieberts, Solveig K., Perumal, Thanneer M., Carrasquillo, Minerva M., Allen, Mariet, Reddy, Joseph S., Hoffman, Gabriel E., Dang, Kristen K., Calley, John, Ebert, Philip J., Eddy, James, Wang, Xue, Greenwood, Anna K., Mostafavi, Sara, Akbarian, Schahram, Bendl, Jaroslav, Breen, Michael S., Brennand, Kristen, Brown, Leanne, Browne, Andrew, Buxbaum, Joseph D., Charney, Alexander, Chess, Andrew, Couto, Lizette, Crawford, Greg, Devillers, Olivia, Devlin, Bernie, Dobbyn, Amanda, Domenici, Enrico, Filosi, Michele, Flatow, Elie, Francoeur, Nancy, Fullard, John, Gil, Sergio Espeso, Girdhar, Kiran, Gulyás-Kovács, Attila, Gur, Raquel, Hahn, Chang-Gyu, Haroutunian, Vahram, Hauberg, Mads Engel, Huckins, Laura, Jacobov, Rivky, Jiang, Yan, Johnson, Jessica S., Kassim, Bibi, Kim, Yungil, Klei, Lambertus, Kramer, Robin, Lauria, Mario, Lehner, Thomas, Lewis, David A., Lipska, Barbara K., Montgomery, Kelsey, Park, Royce, Rosenbluh, Chaggai, Roussos, Panagiotis, Ruderfer, Douglas M., Senthil, Geetha, Shah, Hardik R., Sloofman, Laura, Song, Lingyun, Stahl, Eli, Sullivan, Patrick, Visintainer, Roberto, Wang, Jiebiao, Wang, Ying-Chih, Wiseman, Jennifer, Xia, Eva, Zhang, Wen, Zharovsky, Elizabeth, Addis, Laura, Addo, Sadiya N., Airey, David Charles, Arnold, Matthias, Bennett, David A., Bi, Yingtao, Biber, Knut, Blach, Colette, Bradhsaw, Elizabeth, Brennan, Paul, Canet-Aviles, Rosa, Cao, Sherry, Cavalla, Anna, Chae, Yooree, Chen, William W., Cheng, Jie, Collier, David Andrew, Dage, Jeffrey L., Dammer, Eric B., Davis, Justin Wade, Davis, John, Drake, Derek, Duong, Duc, Eastwood, Brian J., Ehrlich, Michelle, Ellingson, Benjamin, Engelmann, Brett W., Esmaeelinieh, Sahar, Felsky, Daniel, Funk, Cory, Gaiteri, Chris, Gandy, Samuel, Gao, Fan, Gileadi, Opher, Golde, Todd, Grosskurth, Shaun E., Gupta, Rishi R., Gutteridge, Alex X., Hooli, Basavaraj, Humphryes-Kirilov, Neil, Iijima, Koichi, James, Corey, Jung, Paul M., Kaddurah-Daouk, Rima, Kastenmuller, Gabi, Klein, Hans-Ulrich, Kummer, Markus, Lacor, Pascale N., Lah, James, Laing, Emma, Levey, Allan, Li, Yupeng, Lipsky, Samantha, Liu, Yushi, Liu, Jimmy, Liu, Zhandong, Louie, Gregory, Lu, Tao, Ma, Yiyi, Matsuoka, Yasuji Y., Menon, Vilas, Miller, Bradley, Misko, Thomas P., Mollon, Jennifer E., Mukherjee, Sumit, Noggle, Scott, Pao, Ping-Chieh, Pearce, Tracy Young, Pearson, Neil, Penny, Michelle, Petyuk, Vladislav A., Price, Nathan, Quarless, Danjuma X., Ravikumar, Brinda, Ried, Janina S., Ruble, Cara Lee Ann, Runz, Heiko, Saykin, Andrew J., Schadt, Eric, Scherschel, James E., Seyfried, Nicholas, Shulman, Joshua M., Snyder, Phil, Soares, Holly, Srivastava, Gyan P., Stockmann, Henning, Taga, Mariko, Tasaki, Shinya, Tenenbaum, Jessie, Tsai, Li-Huei, Vasanthakumar, Aparna, Wachter, Astrid, Wang, Yaming, Wang, Hong, Wang, Minghui, Whelan, Christopher D., White, Charles, Woo, Kara H., Wren, Paul, Wu, Jessica W., Xi, Hualin S., Yankner, Bruce A., Younkin, Steven G., Yu, Lei, Zavodszky, Maria, Zhang, Wenling, Zhang, Guoqiang, Zhang, Bin, Zhu, Jun, Omberg, Larsson, Peters, Mette A., Logsdon, Benjamin A., De Jager, Philip L., Ertekin-Taner, Nilüfer, and Mangravite, Lara M.

Abstract

© 2020, The Author(s). The availability of high-quality RNA-sequencing and genotyping data of post-mortem brain collections from consortia such as CommonMind Consortium (CMC) and the Accelerating Medicines Partnership for Alzheimer’s Disease (AMP-AD) Consortium enable the generation of a large-scale brain cis-eQTL meta-analysis. Here we generate cerebral cortical eQTL from 1433 samples available from four cohorts (identifying >4.1 million significant eQTL for >18,000 genes), as well as cerebellar eQTL from 261 samples (identifying 874,836 significant eQTL for >10,000 genes). We find substantially improved power in the meta-analysis over individual cohort analyses, particularly in comparison to the Genotype-Tissue Expression (GTEx) Project eQTL. Additionally, we observed differences in eQTL patterns between cerebral and cerebellar brain regions. We provide these brain eQTL as a resource for use by the research community. As a proof of principle for their utility, we apply a colocalization analysis to identify genes underlying the GWAS association peaks for schizophrenia and identify a potentially novel gene colocalization with lncRNA RP11-677M14.2 (posterior probability of colocalization 0.975).

Published
2022

16. Crowdsourcing digital health measures to predict Parkinson’s disease severity: the Parkinson’s Disease Digital Biomarker DREAM Challenge

Author

Sieberts, Solveig K., Schaff, Jennifer, Duda, Marlena, Pataki, Bálint Ármin, Sun, Ming, Snyder, Phil, Daneault, Jean-Francois, Parisi, Federico, Costante, Gianluca, Rubin, Udi, Banda, Peter, Chae, Yooree, Chaibub Neto, Elias, Dorsey, E. Ray, Aydın, Zafer, Chen, Aipeng, Elo, Laura L., Espino, Carlos, Glaab, Enrico, Goan, Ethan, Golabchi, Fatemeh Noushin, Görmez, Yasin, Jaakkola, Maria K., Jonnagaddala, Jitendra, Klén, Riku, Li, Dongmei, McDaniel, Christian, Perrin, Dimitri, Perumal, Thanneer M., Rad, Nastaran Mohammadian, Rainaldi, Erin, Sapienza, Stefano, Schwab, Patrick, Shokhirev, Nikolai, Venäläinen, Mikko S., Vergara-diaz, Gloria, Zhang, Yuqian, Wang, Yuanjia, Guan, Yuanfang, Brunner, Daniela, Bonato, Paolo, Mangravite, Lara M., Omberg, Larsson, Sieberts, Solveig K., Schaff, Jennifer, Duda, Marlena, Pataki, Bálint Ármin, Sun, Ming, Snyder, Phil, Daneault, Jean-Francois, Parisi, Federico, Costante, Gianluca, Rubin, Udi, Banda, Peter, Chae, Yooree, Chaibub Neto, Elias, Dorsey, E. Ray, Aydın, Zafer, Chen, Aipeng, Elo, Laura L., Espino, Carlos, Glaab, Enrico, Goan, Ethan, Golabchi, Fatemeh Noushin, Görmez, Yasin, Jaakkola, Maria K., Jonnagaddala, Jitendra, Klén, Riku, Li, Dongmei, McDaniel, Christian, Perrin, Dimitri, Perumal, Thanneer M., Rad, Nastaran Mohammadian, Rainaldi, Erin, Sapienza, Stefano, Schwab, Patrick, Shokhirev, Nikolai, Venäläinen, Mikko S., Vergara-diaz, Gloria, Zhang, Yuqian, Wang, Yuanjia, Guan, Yuanfang, Brunner, Daniela, Bonato, Paolo, Mangravite, Lara M., and Omberg, Larsson

Abstract

Consumer wearables and sensors are a rich source of data about patients’ daily disease and symptom burden, particularly in the case of movement disorders like Parkinson’s disease (PD). However, interpreting these complex data into so-called digital biomarkers requires complicated analytical approaches, and validating these biomarkers requires sufficient data and unbiased evaluation methods. Here we describe the use of crowdsourcing to specifically evaluate and benchmark features derived from accelerometer and gyroscope data in two different datasets to predict the presence of PD and severity of three PD symptoms: tremor, dyskinesia, and bradykinesia. Forty teams from around the world submitted features, and achieved drastically improved predictive performance for PD status (best AUROC = 0.87), as well as tremor- (best AUPR = 0.75), dyskinesia- (best AUPR = 0.48) and bradykinesia-severity (best AUPR = 0.95)

Published
2021

17. Long-Term Monitoring of Cloud Water Chemistry at Whiteface Mountain: The Emergence of a New Chemical Regime.

Author

Lawrence, Christopher E., Casson, Paul, Brandt, Richard, Schwab, James J., Dukett, James E., Snyder, Phil, Yerger, Elizabeth, Kelting, Daniel, VandenBoer, Trevor C., and Lance, Sara

Abstract

Atmospheric aqueous chemistry can have profound effects on our environment. The importance of chemistry within the atmospheric aqueous phase was first realized in the 1970s as there was growing concern over the negative impacts on ecosystem health from acid deposition. Research at mountaintop observatories including Whiteface Mountain (WFM) showed that gas phase sulfur dioxide emissions react in cloud droplets to form sulfuric acid, which also impacted aerosol mass loadings. Cloud chemistry research has experienced a major resurgence in scientific interest due to the potential for aqueous chemical processes to fill the gap between modeled and observed organic aerosol mass. The current study updates the long-term trends in cloud water composition at WFM for the past 28 years (1994–2021). Substantial decreases in sulfate (SO2-4) and nitrate (NO-3) concentrations have not been matched by an equivalent decrease in ammonium (NH+4) concentrations, leading to significantly higher cloud droplet pH. Meanwhile, Total Organic Carbon (TOC) concentrations may be increasing (both in relative and absolute terms). In the past, samples were excluded from trend analysis if they did not meet an approximate ion balance, which resulted in approximately half of samples being excluded in recent years. We show that, when including the entire available dataset, decreasing trends in SO2-4, NO-3 and NH+4 become more modest, TOC concentrations increase at a faster rate, and increasing trends in Ca2+ and Mg2+ emerge. A growing trend in cation / anion ratios is also observed, implying that a significant fraction of anions are not being measured with the current suite of measurements, and these missing anions are growing in importance. Organic acids are identified as the most likely candidates for the missing anions, since the measured ion imbalance correlates strongly with measured TOC concentrations. The TOC trend becomes statistically insignificant when evaluating cloud water loadings (or air equivalent mass loadings), possibly due to the complex role that LWC may play in TOC concentrations. We highlight the emergence of a new chemical regime characterized by low acidity and relatively high conductivity, and increasing TOC and base cation concentrations. With the increasing impact from Ca2+ and Mg2+ on the bulk cloud water pH, which are largely thought to reside within coarse mode aerosol that only represent a small fraction of cloud droplets, an "inferred cloud droplet pH" is introduced, to better represent the pH of the vast majority of cloud droplets as they reside in the atmosphere. While measured pH has increased during the history of the monitoring site, the cloud droplet pH has remained relatively flat since 2009. We also show that there is a missing source of acidity in the system that correlates with TOC. The chemical system at WFM has shifted away from a system dominated by SO2-4 to a system controlled by base cation, nitrogen containing species and TOC. Further research is required to understand the effects on air quality, climate, and ecosystem health. [ABSTRACT FROM AUTHOR]

Published
2022
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18. Meta-Analysis of the Alzheimer’s Disease Human Brain Transcriptome and Functional Dissection in Mouse Models

Author

Wan, Ying-Wooi, primary, Al-Ouran, Rami, additional, Mangleburg, Carl G., additional, Perumal, Thanneer M., additional, Lee, Tom V., additional, Allison, Katherine, additional, Swarup, Vivek, additional, Funk, Cory C., additional, Gaiteri, Chris, additional, Allen, Mariet, additional, Wang, Minghui, additional, Neuner, Sarah M., additional, Kaczorowski, Catherine C., additional, Philip, Vivek M., additional, Howell, Gareth R., additional, Martini-Stoica, Heidi, additional, Zheng, Hui, additional, Mei, Hongkang, additional, Zhong, Xiaoyan, additional, Kim, Jungwoo Wren, additional, Dawson, Valina L., additional, Dawson, Ted M., additional, Pao, Ping-Chieh, additional, Tsai, Li-Huei, additional, Haure-Mirande, Jean-Vianney, additional, Ehrlich, Michelle E., additional, Chakrabarty, Paramita, additional, Levites, Yona, additional, Wang, Xue, additional, Dammer, Eric B., additional, Srivastava, Gyan, additional, Mukherjee, Sumit, additional, Sieberts, Solveig K., additional, Omberg, Larsson, additional, Dang, Kristen D., additional, Eddy, James A., additional, Snyder, Phil, additional, Chae, Yooree, additional, Amberkar, Sandeep, additional, Wei, Wenbin, additional, Hide, Winston, additional, Preuss, Christoph, additional, Ergun, Ayla, additional, Ebert, Phillip J., additional, Airey, David C., additional, Mostafavi, Sara, additional, Yu, Lei, additional, Klein, Hans-Ulrich, additional, Carter, Gregory W., additional, Collier, David A., additional, Golde, Todd E., additional, Levey, Allan I., additional, Bennett, David A., additional, Estrada, Karol, additional, Townsend, T. Matthew, additional, Zhang, Bin, additional, Schadt, Eric, additional, De Jager, Philip L., additional, Price, Nathan D., additional, Ertekin-Taner, Nilüfer, additional, Liu, Zhandong, additional, Shulman, Joshua M., additional, Mangravite, Lara M., additional, and Logsdon, Benjamin A., additional

Published
2020
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20. HSR Aerodynamic Performance Status and Challenges

Author

Gilbert, William P, Antani, Tony, Ball, Doug, Calloway, Robert L, and Snyder, Phil

Subjects
Aerodynamics
Abstract

This paper describes HSR (High Speed Research) Aerodynamic Performance Status and Challenges. The topics include: 1) Aero impact on HSR; 2) Goals and Targets; 3) Progress and Status; and 4) Remaining Challenges. This paper is presented in viewgraph form.

Published
1999

21. Design of a virtual longitudinal observational study in Parkinson's disease (AT‐HOME PD).

Author

Schneider, Ruth B., Omberg, Larsson, Macklin, Eric A., Daeschler, Margaret, Bataille, Lauren, Anthwal, Shalini, Myers, Taylor L., Baloga, Elizabeth, Duquette, Sidney, Snyder, Phil, Amodeo, Katherine, Tarolli, Christopher G, Adams, Jamie L., Callahan, Katherine F, Gottesman, Joshua, Kopil, Catherine M., Lungu, Codrin, Ascherio, Alberto, Beck, James C., and Biglan, Kevin

Subjects
PARKINSON'S disease, VIRTUAL design, LONGITUDINAL method, COVID-19 pandemic, SCIENTIFIC observation
Abstract

Objective: The expanding power and accessibility of personal technology provide an opportunity to reduce burdens and costs of traditional clinical site‐centric therapeutic trials in Parkinson's disease and generate novel insights. The value of this approach has never been more evident than during the current COVID‐19 pandemic. We sought to (1) establish and implement the infrastructure for longitudinal, virtual follow‐up of clinical trial participants, (2) compare changes in smartphone‐based assessments, online patient‐reported outcomes, and remote expert assessments, and (3) explore novel digital markers of Parkinson's disease disability and progression. Methods: Participants from two recently completed phase III clinical trials of inosine and isradipine enrolled in Assessing Tele‐Health Outcomes in Multiyear Extensions of Parkinson's Disease trials (AT‐HOME PD), a two‐year virtual cohort study. After providing electronic informed consent, individuals complete annual video visits with a movement disorder specialist, smartphone‐based assessments of motor function and socialization, and patient‐reported outcomes online. Results: From the two clinical trials, 226 individuals from 42 states in the United States and Canada enrolled. Of these, 181 (80%) have successfully downloaded the study's smartphone application and 161 (71%) have completed patient‐reported outcomes on the online platform. Interpretation: It is feasible to conduct a large‐scale, international virtual observational study following the completion of participation in brick‐and‐mortar clinical trials in Parkinson's disease. This study, which brings research to participants, will compare established clinical endpoints with novel digital biomarkers and thereby inform the longitudinal follow‐up of clinical trial participants and design of future clinical trials. [ABSTRACT FROM AUTHOR]

Published
2021
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24. LETTERS TO THE EDITOR

Author

Foster, Lawrence, Wilks, Justin, Buchanan, Frederick S., Eyre, Richard M., Schindler, Marc A., Howard, Susan W., Riggs, Frank, Pugh, Warren S., Sonne, Conway B., Russell, William D., Wilcox, Ronald, Pedersen, Cherie, Boyack, Douglas C., Hansen, John, Collins, William P., Kenney, Scott, Pendleton, Lloyd, Hansen, Earl William, Snyder, Phil L., Tanner, George S., and Thayne, Emma Lou

Published
1982
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25. Live Sliplining

Author

Snyder, Phil

Subjects
Hobas Pipe USA Inc. -- Contracts, Water-supply -- Installation, Water-pipes -- Installation, Technology installation instructions, Contract agreement, Business, Business, regional, Construction and materials industries
Abstract

By Phil Snyder, The Snyder Agency, for HOBAS Pipe USA Summit Contracting installs HOBAS pipe under live runway in McAllen. A federal mandate, rehabilitation of a water supply line at [...]

Published
2006

26. ECIS Interceptor

Author

Snyder, Phil

Subjects
Construction industry -- Contracts, Construction industry -- Management, Company business management, Contract agreement, Business, Business, regional, Construction and materials industries
Abstract

By Phil Snyder Ongoing upgrade needed on L.A. sewer system. The city of Los Angeles operates more than 620 miles of sewers, numerous pumping plants and four wastewater treatment plants. [...]

Published
2005

28. Readers report.

Author

Gay, Wilson A., Lupardi, Vincent, Svensk, Robert E., Hense, R. A., Wildenberg, Marvin, Snyder, Phil, Berven, John A., Houser, Harold L., Oliver, Peter B., Askin, William D., Koltnow, Peter G., and Levi, Morton W.

Subjects
LETTERS to the editor, NATURAL gas, EUROBOND market, INTERNATIONAL finance, LABOR incentives, GOVERNMENT policy
Abstract

Several letters to the editor are presented in response to articles in previous issues including "Needed More Than Ever Realistic Gas Pricing" in the February 24, 1977 issue, "A Continental Swing in Eurobond Financing" in the January 24, 1977 issue, and "Airline Commissions Go Sky-High" in the February 7, 1977.

Published
1977

29. Viewpoints

Author

Hellyer, Rob, Ross, Randall J., Snyder, Phil, Gordon, Al, Baillio, Paul, Lewis, Leigh, Pezdek, Alex, and Turner, Michael, American congressional representative

Subjects
General interest, News, opinion and commentary
Published
1999

30. I am team business

Author

Snyder, Phil

Subjects
Business, Sports, sporting goods and toys industry
Abstract

HOW DID YOU GET STARTED IN THE TEAM SPORTS BUSINESS? In 1982 I applied for and was offered a part-time retail sales position with Schuylkill Valley Sports in Quakertown, PA. [...]

Published
2010

31. Balky Bryco

Author

Snyder, Phil

Subjects
Pistols -- Maintenance and repair, Sports, sporting goods and toys industry, Bryco JA-9mm (Hand gun) -- Maintenance and repair
Abstract

I have an Bryco JA-9mm semi-auto pistol that's in need of repair. If I chamber a round by hand, it works, and chambers like it should. But when I fire [...]

Published
2007

33. The Fans Speak Out.

Author

Briczinski, Stan, Jones, Bill, Carner, William J., Franson, Gary, Cummings, John, Snyder, Phil, Gorman, Stephen, Davis, Tommy, Petersen, Marty, Anderson, Larry, Ranney, Judy G., Newton, Brent E., Hodges, Tony, Picconi, David, Wulf, Doug, Grossfield, William A., Marzo, Vince, Lally, Jim, Tiemann, Maynard, and Cooke, Doug

Subjects
LETTERS to the editor, BASEBALL players, BASEBALL injuries, BASEBALL
Abstract

Several letters to the editor are presented including the susceptibility of baseball players to an inordinate number of strange injuries, explanation about the concept of double switch in baseball, and details about the fall of baseball player Stan Musial of winning the Triple Crown in the National League.

Published
2008

39. Letters to the Editor.

Author

SNYDER, PHIL and HASTED, DEREK

Abstract

Two letters to the editor are presented which includes comments from Phil Snyder about the review of his arrangement for the classical music "Etude op.10 no.3," composed by Chopin in the February 2010 issue, and a response from the reviewer Derek Hasted about the correction given by Snyder on his music arrangement.

Published
2010

42. Crowdsourcing digital health measures to predict Parkinson's disease severity: the Parkinson's Disease Digital Biomarker DREAM Challenge.

Author

Sieberts SK, Schaff J, Duda M, Pataki BÁ, Sun M, Snyder P, Daneault JF, Parisi F, Costante G, Rubin U, Banda P, Chae Y, Chaibub Neto E, Dorsey ER, Aydın Z, Chen A, Elo LL, Espino C, Glaab E, Goan E, Golabchi FN, Görmez Y, Jaakkola MK, Jonnagaddala J, Klén R, Li D, McDaniel C, Perrin D, Perumal TM, Rad NM, Rainaldi E, Sapienza S, Schwab P, Shokhirev N, Venäläinen MS, Vergara-Diaz G, Zhang Y, Wang Y, Guan Y, Brunner D, Bonato P, Mangravite LM, and Omberg L

Abstract

Consumer wearables and sensors are a rich source of data about patients' daily disease and symptom burden, particularly in the case of movement disorders like Parkinson's disease (PD). However, interpreting these complex data into so-called digital biomarkers requires complicated analytical approaches, and validating these biomarkers requires sufficient data and unbiased evaluation methods. Here we describe the use of crowdsourcing to specifically evaluate and benchmark features derived from accelerometer and gyroscope data in two different datasets to predict the presence of PD and severity of three PD symptoms: tremor, dyskinesia, and bradykinesia. Forty teams from around the world submitted features, and achieved drastically improved predictive performance for PD status (best AUROC = 0.87), as well as tremor- (best AUPR = 0.75), dyskinesia- (best AUPR = 0.48) and bradykinesia-severity (best AUPR = 0.95).

Published
2021
Full Text
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43. A Cormac McCarthy Companion : The Border Trilogy

Author

Arnold, Edwin T., Luce, Dianne C., Canfield, J. Douglas, Chollier, Christine, Guillemin, George, Scoones, Jacqueline, Snyder, Phillip A., Sullivan, Nell, Wegner, John, Arnold, Edwin T., Luce, Dianne C., Canfield, J. Douglas, Chollier, Christine, Guillemin, George, Scoones, Jacqueline, Snyder, Phillip A., Sullivan, Nell, and Wegner, John

Published
2001
Full Text
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