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258 results on '"Gerkin, Richard C"'

1. Multiple models for outbreak decision support in the face of uncertainty.

Author

Shea, Katriona, Borchering, Rebecca K, Probert, William JM, Howerton, Emily, Bogich, Tiffany L, Li, Shou-Li, van Panhuis, Willem G, Viboud, Cecile, Aguás, Ricardo, Belov, Artur A, Bhargava, Sanjana H, Cavany, Sean M, Chang, Joshua C, Chen, Cynthia, Chen, Jinghui, Chen, Shi, Chen, YangQuan, Childs, Lauren M, Chow, Carson C, Crooker, Isabel, Del Valle, Sara Y, España, Guido, Fairchild, Geoffrey, Gerkin, Richard C, Germann, Timothy C, Gu, Quanquan, Guan, Xiangyang, Guo, Lihong, Hart, Gregory R, Hladish, Thomas J, Hupert, Nathaniel, Janies, Daniel, Kerr, Cliff C, Klein, Daniel J, Klein, Eili Y, Lin, Gary, Manore, Carrie, Meyers, Lauren Ancel, Mittler, John E, Mu, Kunpeng, Núñez, Rafael C, Oidtman, Rachel J, Pasco, Remy, Pastore Y Piontti, Ana, Paul, Rajib, Pearson, Carl AB, Perdomo, Dianela R, Perkins, T Alex, Pierce, Kelly, Pillai, Alexander N, Rael, Rosalyn Cherie, Rosenfeld, Katherine, Ross, Chrysm Watson, Spencer, Julie A, Stoltzfus, Arlin B, Toh, Kok Ben, Vattikuti, Shashaank, Vespignani, Alessandro, Wang, Lingxiao, White, Lisa J, Xu, Pan, Yang, Yupeng, Yogurtcu, Osman N, Zhang, Weitong, Zhao, Yanting, Zou, Difan, Ferrari, Matthew J, Pannell, David, Tildesley, Michael J, Seifarth, Jack, Johnson, Elyse, Biggerstaff, Matthew, Johansson, Michael A, Slayton, Rachel B, Levander, John D, Stazer, Jeff, Kerr, Jessica, and Runge, Michael C

Subjects
Humans, Uncertainty, Public Health, Disease Outbreaks, Pandemics, COVID-19, cognitive biases, decision theory, multi-model aggregation, Prevention, Brain Disorders, Good Health and Well Being
Abstract

Policymakers must make management decisions despite incomplete knowledge and conflicting model projections. Little guidance exists for the rapid, representative, and unbiased collection of policy-relevant scientific input from independent modeling teams. Integrating approaches from decision analysis, expert judgment, and model aggregation, we convened multiple modeling teams to evaluate COVID-19 reopening strategies for a mid-sized United States county early in the pandemic. Projections from seventeen distinct models were inconsistent in magnitude but highly consistent in ranking interventions. The 6-mo-ahead aggregate projections were well in line with observed outbreaks in mid-sized US counties. The aggregate results showed that up to half the population could be infected with full workplace reopening, while workplace restrictions reduced median cumulative infections by 82%. Rankings of interventions were consistent across public health objectives, but there was a strong trade-off between public health outcomes and duration of workplace closures, and no win-win intermediate reopening strategies were identified. Between-model variation was high; the aggregate results thus provide valuable risk quantification for decision making. This approach can be applied to the evaluation of management interventions in any setting where models are used to inform decision making. This case study demonstrated the utility of our approach and was one of several multimodel efforts that laid the groundwork for the COVID-19 Scenario Modeling Hub, which has provided multiple rounds of real-time scenario projections for situational awareness and decision making to the Centers for Disease Control and Prevention since December 2020.

Published
2023

3. On the role of theory and modeling in neuroscience

Author

Levenstein, Daniel, Alvarez, Veronica A., Amarasingham, Asohan, Azab, Habiba, Chen, Zhe Sage, Gerkin, Richard C., Hasenstaub, Andrea, Iyer, Ramakrishnan, Jolivet, Renaud B., Marzen, Sarah, Monaco, Joseph D., Prinz, Astrid A., Quraishi, Salma, Santamaria, Fidel, Shivkumar, Sabyasachi, Singh, Matthew F., Traub, Roger, Rotstein, Horacio G., Nadim, Farzan, and Redish, A. David

Subjects
Quantitative Biology - Neurons and Cognition, Quantitative Biology - Quantitative Methods
Abstract

In recent years, the field of neuroscience has gone through rapid experimental advances and a significant increase in the use of quantitative and computational methods. This growth has created a need for clearer analyses of the theory and modeling approaches used in the field. This issue is particularly complex in neuroscience because the field studies phenomena across a wide range of scales and often requires consideration of these phenomena at varying degrees of abstraction, from precise biophysical interactions to the computations they implement. We argue that a pragmatic perspective of science, in which descriptive, mechanistic, and normative approaches each play a distinct role in defining and bridging levels of abstraction will facilitate neuroscientific practice. This analysis leads to methodological suggestions, including selecting a level of abstraction that is appropriate for a given problem, identifying transfer functions to connect models and data, and the use of models themselves as a form of experiment.

Published
2020
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4. Machine Learning for Scent: Learning Generalizable Perceptual Representations of Small Molecules

Author

Sanchez-Lengeling, Benjamin, Wei, Jennifer N., Lee, Brian K., Gerkin, Richard C., Aspuru-Guzik, Alán, and Wiltschko, Alexander B.

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

Predicting the relationship between a molecule's structure and its odor remains a difficult, decades-old task. This problem, termed quantitative structure-odor relationship (QSOR) modeling, is an important challenge in chemistry, impacting human nutrition, manufacture of synthetic fragrance, the environment, and sensory neuroscience. We propose the use of graph neural networks for QSOR, and show they significantly out-perform prior methods on a novel data set labeled by olfactory experts. Additional analysis shows that the learned embeddings from graph neural networks capture a meaningful odor space representation of the underlying relationship between structure and odor, as demonstrated by strong performance on two challenging transfer learning tasks. Machine learning has already had a large impact on the senses of sight and sound. Based on these early results with graph neural networks for molecular properties, we hope machine learning can eventually do for olfaction what it has already done for vision and hearing., Comment: 18 pages, 13 figures

Published
2019

5. More than smell – COVID-19 is associated with severe impairment of smell, taste, and chemesthesis

Author

Parma, Valentina, Ohla, Kathrin, Veldhuizen, Maria G, Niv, Masha Y, Kelly, Christine E, Bakke, Alyssa J, Cooper, Keiland W, Bouysset, Cédric, Pirastu, Nicola, Dibattista, Michele, Kaur, Rishemjit, Liuzza, Marco Tullio, Pepino, Marta Y, Schöpf, Veronika, Pereda-Loth, Veronica, Olsson, Shannon B, Gerkin, Richard C, Domínguez, Paloma Rohlfs, Albayay, Javier, Farruggia, Michael C, Bhutani, Surabhi, Fjaeldstad, Alexander W, Kumar, Ritesh, Menini, Anna, Bensafi, Moustafa, Sandell, Mari, Konstantinidis, Iordanis, Di Pizio, Antonella, Genovese, Federica, Öztürk, Lina, Thomas-Danguin, Thierry, Frasnelli, Johannes, Boesveldt, Sanne, Saatci, Özlem, Saraiva, Luis R, Lin, Cailu, Golebiowski, Jérôme, Hwang, Liang-Dar, Ozdener, Mehmet Hakan, Guàrdia, Maria Dolors, Laudamiel, Christophe, Ritchie, Marina, Havlícek, Jan, Pierron, Denis, Roura, Eugeni, Navarro, Marta, Nolden, Alissa A, Lim, Juyun, Whitcroft, KL, Colquitt, Lauren R, Ferdenzi, Camille, Brindha, Evelyn V, Altundag, Aytug, Macchi, Alberto, Nunez-Parra, Alexia, Patel, Zara M, Fiorucci, Sébastien, Philpott, Carl M, Smith, Barry C, Lundström, Johan N, Mucignat, Carla, Parker, Jane K, van den Brink, Mirjam, Schmuker, Michael, Fischmeister, Florian Ph S, Heinbockel, Thomas, Shields, Vonnie DC, Faraji, Farhoud, Santamaría, Enrique, Fredborg, William EA, Morini, Gabriella, Olofsson, Jonas K, Jalessi, Maryam, Karni, Noam, D’Errico, Anna, Alizadeh, Rafieh, Pellegrino, Robert, Meyer, Pablo, Huart, Caroline, Chen, Ben, Soler, Graciela M, Alwashahi, Mohammed K, Welge-Lüssen, Antje, Freiherr, Jessica, de Groot, Jasper HB, Klein, Hadar, Okamoto, Masako, Singh, Preet Bano, Hsieh, Julien W, Reed, Danielle R, Hummel, Thomas, Munger, Steven D, Hayes, John E, Abdulrahman, Olagunju, Dalton, Pamela, Yan, Carol H, Voznessenskaya, Vera V, Chen, Jingguo, Sell, Elizabeth A, and Walsh-Messinger, Julie

Subjects
Neurosciences, Dental/Oral and Craniofacial Disease, Clinical Research, Adult, Aged, Betacoronavirus, COVID-19, Coronavirus Infections, Female, Humans, Male, Middle Aged, Olfaction Disorders, Pandemics, Pneumonia, Viral, SARS-CoV-2, Self Report, Smell, Somatosensory Disorders, Surveys and Questionnaires, Taste, Taste Disorders, Young Adult, head and neck surgery, olfaction, somatosensation, GCCR Group Author, Biological Sciences, Neurology & Neurosurgery
Abstract

Recent anecdotal and scientific reports have provided evidence of a link between COVID-19 and chemosensory impairments, such as anosmia. However, these reports have downplayed or failed to distinguish potential effects on taste, ignored chemesthesis, and generally lacked quantitative measurements. Here, we report the development, implementation, and initial results of a multilingual, international questionnaire to assess self-reported quantity and quality of perception in 3 distinct chemosensory modalities (smell, taste, and chemesthesis) before and during COVID-19. In the first 11 days after questionnaire launch, 4039 participants (2913 women, 1118 men, and 8 others, aged 19-79) reported a COVID-19 diagnosis either via laboratory tests or clinical assessment. Importantly, smell, taste, and chemesthetic function were each significantly reduced compared to their status before the disease. Difference scores (maximum possible change ±100) revealed a mean reduction of smell (-79.7 ± 28.7, mean ± standard deviation), taste (-69.0 ± 32.6), and chemesthetic (-37.3 ± 36.2) function during COVID-19. Qualitative changes in olfactory ability (parosmia and phantosmia) were relatively rare and correlated with smell loss. Importantly, perceived nasal obstruction did not account for smell loss. Furthermore, chemosensory impairments were similar between participants in the laboratory test and clinical assessment groups. These results show that COVID-19-associated chemosensory impairment is not limited to smell but also affects taste and chemesthesis. The multimodal impact of COVID-19 and the lack of perceived nasal obstruction suggest that severe acute respiratory syndrome coronavirus strain 2 (SARS-CoV-2) infection may disrupt sensory-neural mechanisms.

Published
2020

8. Requirements for storing electrophysiology data

Author

Teeters, Jeff, Benda, Jan, Davison, Andrew, Eglen, Stephen, Gerkin, Richard C., Grethe, Jeffrey, Grewe, Jan, Harris, Kenneth, Kellner, Christian, Franc, Yann Le, Mouček, Roman, Prodanov, Dimiter, Pröpper, Robert, Sessions, Hyrum L., Smith, Leslie, Sobolev, Andrey, Sommer, Friedrich, Stoewer, Adrian, Wachtler, Thomas, and Wark, Barry

Subjects
Computer Science - Other Computer Science, E.1, E.2, E.5
Abstract

The purpose of this document is to specify the basic data types required for storing electrophysiology and optical imaging data to facilitate computer-based neuroscience studies and data sharing. These requirements are being developed within a working group of the Electrophysiology Task Force in the International Neuroinformatics Coordinating Facility (INCF) Program on Standards for Data Sharing. While this document describes the requirements of the standard independent of the actual storage technology, the Task Force has recommended basing a standard on HDF5. This is in line with a number of groups who are already using HDF5 to store electrophysiology data, although currently without being based on a standard., Comment: 5 pages, 1 table

Published
2016

10. Improved diagnosis of Parkinson's disease from a detailed olfactory phenotype

Author

Gerkin, Richard C, Adler, Charles H, Hentz, Joseph G, Shill, Holly A, Driver‐Dunckley, Erika, Mehta, Shyamal H, Sabbagh, Marwan N, Caviness, John N, Dugger, Brittany N, Serrano, Geidy, Belden, Christine, Smith, Brian H, Sue, Lucia, Davis, Kathryn J, Zamrini, Edward, and Beach, Thomas G

Subjects
Biological Psychology, Psychology, Parkinson's Disease, Dementia, Prevention, Neurodegenerative, Acquired Cognitive Impairment, Clinical Research, Brain Disorders, Aging, Neurosciences, 4.2 Evaluation of markers and technologies, Detection, screening and diagnosis, 4.1 Discovery and preclinical testing of markers and technologies, Neurological, Clinical Sciences, Clinical and health psychology
Abstract

ObjectiveTo assess the predictive potential of the complete response pattern from the University of Pennsylvania Smell Identification Test for the diagnosis of Parkinson's disease.MethodsWe analyzed a large dataset from the Arizona Study of Aging and Neurodegenerative Disorders, a longitudinal clinicopathological study of health and disease in elderly volunteers. Using the complete pattern of responses to all 40 items in each subject's test, we built predictive models of neurodegenerative disease, and we validated these models out of sample by comparing model predictions against postmortem pathological diagnosis.ResultsConsistent with anatomical considerations, we found that the specific test response pattern had additional predictive power compared with a conventional measure - total test score - in Parkinson's disease, but not Alzheimer's disease. We also identified specific test questions that carry the greatest predictive power for disease diagnosis.InterpretationOlfactory ability has typically been assessed with either self-report or total score on a multiple choice test. We showed that a more accurate clinical diagnosis can be made using the pattern of responses to all the test questions, and validated this against the "gold standard" of pathological diagnosis. Information in the response pattern also suggests specific modifications to the standard test that may optimize predictive power under the typical clinical constraint of limited time. We recommend that future studies retain the individual item responses for each subject, and not just the total score, both to enable more accurate diagnosis and to enable additional future insights.

Published
2017

11. Predicting human olfactory perception from chemical features of odor molecules

Author

Keller, Andreas, Gerkin, Richard C, Guan, Yuanfang, Dhurandhar, Amit, Turu, Gabor, Szalai, Bence, Mainland, Joel D, Ihara, Yusuke, Yu, Chung Wen, Wolfinger, Russ, Vens, Celine, schietgat, leander, De Grave, Kurt, Norel, Raquel, Consortium, DREAM Olfaction Prediction, Stolovitzky, Gustavo, Cecchi, Guillermo A, Vosshall, Leslie B, and meyer, pablo

Subjects
Neurosciences, Adult, Datasets as Topic, Humans, Male, Models, Biological, Odorants, Olfactory Perception, Smell, DREAM Olfaction Prediction Consortium, General Science & Technology
Abstract

It is still not possible to predict whether a given molecule will have a perceived odor or what olfactory percept it will produce. We therefore organized the crowd-sourced DREAM Olfaction Prediction Challenge. Using a large olfactory psychophysical data set, teams developed machine-learning algorithms to predict sensory attributes of molecules based on their chemoinformatic features. The resulting models accurately predicted odor intensity and pleasantness and also successfully predicted 8 among 19 rated semantic descriptors ("garlic," "fish," "sweet," "fruit," "burnt," "spices," "flower," and "sour"). Regularized linear models performed nearly as well as random forest-based ones, with a predictive accuracy that closely approaches a key theoretical limit. These models help to predict the perceptual qualities of virtually any molecule with high accuracy and also reverse-engineer the smell of a molecule.

Published
2017

12. Unit Testing, Model Validation, and Biological Simulation

Author

Sarma, Gopal P., Jacobs, Travis W., Watts, Mark D., Ghayoomi, Vahid, Gerkin, Richard C., and Larson, Stephen D.

Subjects
Quantitative Biology - Quantitative Methods, Computer Science - Software Engineering
Abstract

The growth of the software industry has gone hand in hand with the development of tools and cultural practices for ensuring the reliability of complex pieces of software. These tools and practices are now acknowledged to be essential to the management of modern software. As computational models and methods have become increasingly common in the biological sciences, it is important to examine how these practices can accelerate biological software development and improve research quality. In this article, we give a focused case study of our experience with the practices of unit testing and test-driven development in OpenWorm, an open-science project aimed at modeling Caenorhabditis elegans. We identify and discuss the challenges of incorporating test-driven development into a heterogeneous, data-driven project, as well as the role of model validation tests, a category of tests unique to software which expresses scientific models., Comment: 13 pages, 8 figures

Published
2015
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13. Humans can discriminate trillions of olfactory stimuli, or more, or fewer

Author

Gerkin, Richard C. and Castro, Jason B.

Subjects
Quantitative Biology - Neurons and Cognition
Abstract

A recent Science paper (Bushdid et al, 2014)[1] proposed that humans can discriminate between at least a trillion olfactory stimuli. Here we show that this claim is the result of a fragile estimation framework capable of producing nearly any result from the reported data, including values tens of orders of magnitude larger or smaller than the one originally reported in [1]. We conclude that there is no evidence for the original claim., Comment: 11 pages, 10 figures

Published
2015

18. Predicting human olfactory perception from chemical features of odor molecules

Author

DREAM Olfaction Prediction Consortium, Keller, Andreas, Gerkin, Richard C., Guan, Yuanfang, Dhurandhar, Amit, Turu, Gabor, Szalai, Bence, Mainland, Joel D., Ihara, Yusuke, Yu, Chung Wen, Wolfinger, Russ, Vens, Celine, Schietgat, Leander, De Grave, Kurt, Norel, Raquel, Stolovitzky, Gustavo, Cecchi, Guillermo A., Vosshall, Leslie B., and Meyer, Pablo

Published
2017

21. Metabolic activity organizes olfactory representations

Author

Qian, Wesley W, primary, Wei, Jennifer N, additional, Sanchez-Lengeling, Benjamin, additional, Lee, Brian K, additional, Luo, Yunan, additional, Vlot, Marnix, additional, Dechering, Koen, additional, Peng, Jian, additional, Gerkin, Richard C, additional, and Wiltschko, Alexander B, additional

Published
2023
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26. On the Role of Theory and Modeling in Neuroscience

Author

Levenstein, Daniel, primary, Alvarez, Veronica A., additional, Amarasingham, Asohan, additional, Azab, Habiba, additional, Chen, Zhe S., additional, Gerkin, Richard C., additional, Hasenstaub, Andrea, additional, Iyer, Ramakrishnan, additional, Jolivet, Renaud B., additional, Marzen, Sarah, additional, Monaco, Joseph D., additional, Prinz, Astrid A., additional, Quraishi, Salma, additional, Santamaria, Fidel, additional, Shivkumar, Sabyasachi, additional, Singh, Matthew F., additional, Traub, Roger, additional, Nadim, Farzan, additional, Rotstein, Horacio G., additional, and Redish, A. David, additional

Published
2023
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27. Pyrfume: A Window to the World’s Olfactory Data

Author

Castro, Jason B., primary, Gould, Travis J., additional, Pellegrino, Robert, additional, Liang, Zhiwei, additional, Coleman, Liyah A., additional, Patel, Famesh, additional, Wallace, Derek S., additional, Bhatnagar, Tanushri, additional, Mainland, Joel D., additional, and Gerkin, Richard C., additional

Published
2022
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28. A deep learning and digital archaeology approach for mosquito repellent discovery

Author

Wei, Jennifer N., primary, Vlot, Marnix, additional, Sanchez-Lengeling, Benjamin, additional, Lee, Brian K., additional, Berning, Luuk, additional, Vos, Martijn W., additional, Henderson, Rob W.M., additional, Qian, Wesley W., additional, Ando, D. Michael, additional, Groetsch, Kurt M., additional, Gerkin, Richard C., additional, Wiltschko, Alexander B., additional, and Dechering, Koen J., additional

Published
2022
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29. A Principal Odor Map Unifies Diverse Tasks in Human Olfactory Perception

Author

Lee, Brian K., primary, Mayhew, Emily J., additional, Sanchez-Lengeling, Benjamin, additional, Wei, Jennifer N., additional, Qian, Wesley W., additional, Little, Kelsie, additional, Andres, Matthew, additional, Nguyen, Britney B., additional, Moloy, Theresa, additional, Parker, Jane K., additional, Gerkin, Richard C., additional, Mainland, Joel D., additional, and Wiltschko, Alexander B., additional

Published
2022
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32. Metabolic activity organizes olfactory representations

Author

Qian, Wesley W., primary, Wei, Jennifer N., additional, Sanchez-Lengeling, Benjamin, additional, Lee, Brian K., additional, Luo, Yunan, additional, Vlot, Marnix, additional, Dechering, Koen, additional, Peng, Jian, additional, Gerkin, Richard C., additional, and Wiltschko, Alexander B., additional

Published
2022
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36. The perception of odor pleasantness is shared across cultures

Author

Arshamian, Artin, Gerkin, Richard C., Kruspe, Nicole, Wnuk, Ewelina, Floyd, Simeon, O'Meara, Carolyn, Garrido Rodriguez, Carolyn, Lundström, Johan N., Mainland, Joel D., Majid, Asifa, Arshamian, Artin, Gerkin, Richard C., Kruspe, Nicole, Wnuk, Ewelina, Floyd, Simeon, O'Meara, Carolyn, Garrido Rodriguez, Carolyn, Lundström, Johan N., Mainland, Joel D., and Majid, Asifa

Abstract

Humans share sensory systems with a common anatomical blueprint, but individual sensory experience nevertheless varies. In olfaction, it is not known to what degree sensory perception, particularly the perception of odor pleasantness, is founded on universal principles dictated by culture or merely a matter of personal taste. To address this, we asked 225 individuals from 9 diverse nonwestern cultures—hunter-gatherer to urban dwelling—to rank the monomolecular odorants from most to least pleasant. Contrary to expectations, culture explained only 6% of the variance in pleasantness rankings, whereas individual variability or personal taste explained 54%. Importantly, there was substantial global consistency, with molecular identity explaining 41% of the variance in odor pleasantness rankings. Critically, these universal rankings were predicted by the physicochemical properties of out-of-sample molecules and out-of-sample pleasantness ratings given by a tenth group of western urban participants. Taken together, this shows human olfactory perception is strongly constrained by universal principles.

Published
2022
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38. The perception of odor pleasantness is shared across cultures

Author

Arshamian, Artin, primary, Gerkin, Richard C., additional, Kruspe, Nicole, additional, Wnuk, Ewelina, additional, Floyd, Simeon, additional, O’Meara, Carolyn, additional, Garrido Rodriguez, Gabriela, additional, Lundström, Johan N., additional, Mainland, Joel D., additional, and Majid, Asifa, additional

Published
2022
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39. Transport features predict if a molecule is odorous

Author

Mayhew, Emily J., primary, Arayata, Charles J., additional, Gerkin, Richard C., additional, Lee, Brian K., additional, Magill, Jonathan M., additional, Snyder, Lindsey L., additional, Little, Kelsie A., additional, Yu, Chung Wen, additional, and Mainland, Joel D., additional

Published
2022
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42. Mere end lugtesans - COVID-19 er associeret med svær påvirkning af lugtesansen, smagssansen og mundfølelsen

Author

Parma, Valentina, Ohla, Kathrin, Veldhuizen, Maria G, Niv, Masha Y, Kelly, Christine E, Bakke, Alyssa J, Cooper, Keiland W, Bouysset, Cédric, Pirastu, Nicola, Dibattista, Michele, Kaur, Rishemjit, Liuzza, Marco Tullio, Pepino, Marta Y, Schöpf, Veronika, Pereda-Loth, Veronica, Olsson, Shannon B, Gerkin, Richard C, Rohlfs Domínguez, Paloma, Albayay, Javier, Farruggia, Michael C, Bhutani, Surabhi, Fjaeldstad, Alexander W, Kumar, Ritesh, Menini, Anna, Bensafi, Moustafa, Sandell, Mari, Konstantinidis, Iordanis, Di Pizio, Antonella, Genovese, Federica, Öztürk, Lina, Thomas-Danguin, Thierry, Frasnelli, Johannes, Boesveldt, Sanne, Saatci, Özlem, Saraiva, Luis R, Lin, Cailu, Golebiowski, Jérôme, Hwang, Liang-Dar, Ozdener, Mehmet Hakan, Guàrdia, Maria Dolors, Laudamiel, Christophe, Ritchie, Marina, Havlícek, Jan, Pierron, Denis, Roura, Eugeni, Navarro, Marta, Nolden, Alissa A, Lim, Juyun, Whitcroft, Katherine L, Colquitt, Lauren R, Ferdenzi, Camille, Brindha, Evelyn V, Altundag, Aytug, Macchi, Alberto, Nunez-Parra, Alexia, Patel, Zara M, Fiorucci, Sébastien, Philpott, Carl M, Smith, Barry C, Lundström, Johan N, Mucignat, Carla, Parker, Jane K, van den Brink, Mirjam, Schmuker, Michael, Fischmeister, Florian Ph S, Heinbockel, Thomas, Shields, Vonnie D C, Faraji, Farhoud, Santamaría, Enrique, Fredborg, William E A, Morini, Gabriella, Olofsson, Jonas K, Jalessi, Maryam, Karni, Noam, D’Errico, Anna, Alizadeh, Rafieh, Pellegrino, Robert, Meyer, Pablo, Huart, Caroline, Chen, Ben, Soler, Graciela M, Alwashahi, Mohammed K, Welge-Lüssen, Antje, Freiherr, Jessica, de Groot, Jasper H B, Klein, Hadar, Okamoto, Masako, Singh, Preet Bano, Hsieh, Julien W, Abdulrahman, Olagunju, Dalton, Pamela, Yan, Carol H, Voznessenskaya, Vera V, Chen, Jingguo, Sell, Elizabeth A, Walsh-Messinger, Julie, Archer, Nicholas S, Koyama, Sachiko, Deary, Vincent, Roberts, S Craig, Yanık, Hüseyin, Albayrak, Samet, Nováková, Lenka Martinec, Croijmans, Ilja, Mazal, Patricia Portillo, Moein, Shima T, Margulis, Eitan, Mignot, Coralie, Mariño, Sajidxa, Georgiev, Dejan, Kaushik, Pavan K, Malnic, Bettina, Wang, Hong, Seyed-Allaei, Shima, Yoluk, Nur, Razzaghi-Asl, Sara, Justice, Jeb M, Restrepo, Diego, Reed, Danielle R, Hummel, Thomas, Munger, Steven D, Hayes, John E, Indústries Alimentàries, Qualitat i Tecnologia Alimentària, Tecnologia Alimentària, Temple University [Philadelphia], Pennsylvania Commonwealth System of Higher Education (PCSHE), Forschungszentrum Jülich GmbH | Centre de recherche de Juliers, Helmholtz-Gemeinschaft = Helmholtz Association, Mersin University, The Hebrew University of Jerusalem (HUJ), AbScent, Pennsylvania State University (Penn State), Penn State System, University of California [Irvine] (UC Irvine), University of California (UC), Université Côte d'Azur (UCA), University of Edinburgh, Università degli studi di Bari Aldo Moro = University of Bari Aldo Moro (UNIBA), Central Scientific Instruments Organisation (CSIR), Università degli Studi 'Magna Graecia' di Catanzaro = University of Catanzaro (UMG), University of Illinois at Urbana-Champaign [Urbana], University of Illinois System, Medizinische Universität Wien = Medical University of Vienna, Groupement scientifique de Biologie et de Medecine Spatiale (GSBMS), Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Centre National d'Études Spatiales [Toulouse] (CNES), Tata Institute for Fundamental Research (TIFR), Arizona State University [Tempe] (ASU), Universidad de Extremadura - University of Extremadura (UEX), Università degli Studi di Padova = University of Padua (Unipd), Yale School of Medicine [New Haven, Connecticut] (YSM), San Diego State University (SDSU), Aarhus University [Aarhus], University of Hertfordshire [Hatfield] (UH), Scuola Internazionale Superiore di Studi Avanzati / International School for Advanced Studies (SISSA / ISAS), Neurosciences Sensorielles Comportement Cognition, Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS), Helsingin yliopisto = Helsingfors universitet = University of Helsinki, University of Turku, Aristotle University of Thessaloniki, Leibniz-Institute for Food Systems Biology at the Technical University of Munich, Monell Chemical Senses Center, Centre des Sciences du Goût et de l'Alimentation [Dijon] (CSGA), Université de Bourgogne (UB)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Université Bourgogne Franche-Comté [COMUE] (UBFC), Université de Montréal (UdeM), Wageningen University and Research Centre (WUR), Medical Science University, Sidra Medicine [Doha, Qatar], Institut de Chimie de Nice (ICN), Université Nice Sophia Antipolis (1965 - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA), University of Southern Queensland (USQ), Institut de Recerca i Tecnologia Agroalimentàries = Institute of Agrifood Research and Technology (IRTA), DreamAir Llc, Charles University [Prague] (CU), Anthropologie Moléculaire et Imagerie de Synthèse (AMIS), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS), University of Massachusetts System (UMASS), Oregon State University (OSU), Ear Institute, UCL, Lyon Neuroscience Research center, Karunya University, Biruni University, Assi Sette Llaghi Varese, Stanford School of Medicine [Stanford], Stanford Medicine, Stanford University-Stanford University, University of East Anglia [Norwich] (UEA), California Department of Food and Agriculture (CDFA), Unité mixte de recherche interactions plantes-microorganismes, Institut National de la Recherche Agronomique (INRA)-Université Toulouse III - Paul Sabatier (UT3), Maastricht University [Maastricht], Institute for Biology - Neurobiology, Freie Universität Berlin, Karl-Franzens-Universität Graz, Howard University College of Medicine, Towson University, University of California [San Diego] (UC San Diego), Proteomics, Center for Applied Medical Research (CIMA), Stockholm University, University of Gastronomic Sciences, Iran University of Medical Sciences, Goethe Universität Frankfurt, University of Tennessee, IBM T.J. Watson Research Center, Université libre de Bruxelles (ULB), Guangzhou Medical University, Buenos Aires University and GEOG (Grupo de Estudio de Olfato y Gusto), Sultan Qaboos University (SQU), Federal University of Technology of Akure (FUTA), A.N. Severtsov Institute of Ecology and Evolution, Russian Academy of Sciences [Moscow] (RAS), Hospital of Xi'an Jiaotong University, University of Pennsylvania, University of Dayton, CSIRO Agriculture and Food (CSIRO), Indiana University [Bloomington], Indiana University System, University of Northumbria at Newcastle [United Kingdom], University of Stirling, Middle East Technical University [Ankara] (METU), Utrecht University [Utrecht], Instituto Universitario del Hospital Italiano [Buenos Aires, Argentina], Institute for Research in Fundamental Sciences [Tehran] (IPM), Hebrew University of Jerusalem, Technische Universität Dresden = Dresden University of Technology (TU Dresden), Terrazas del Club Hipico, University Medical Centre Ljubljana [Ljubljana, Slovenia] (UMCL), Tata Institute of Fundamental Research [Bangalore], Universidade de São Paulo = University of São Paulo (USP), University of Florida [Gainesville] (UF), University of Colorado Anschutz [Aurora], Center for Smell and Taste, Department of Food Science, Pennsylvania State University., Julien, Sabine, Tıp Fakültesi, UCL - SSS/IONS/NEUR - Clinical Neuroscience, UCL - (SLuc) Service d'oto-rhino-laryngologie, Department of Food and Nutrition, Senses and Food, Research Center Jülich, University of California [Irvine] (UCI), University of California, Università degli studi di Bari Aldo Moro (UNIBA), Università degli Studi 'Magna Graecia' di Catanzaro [Catanzaro, Italie] (UMG), University of Extremadura, University of Padova, Yale University School of Medicine, Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon, University of Helsinki, Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA), Institute of Agrifood Research and Technology (IRTA), Universita degli Studi di Padova, Centre National de la Recherche Scientifique (CNRS)-Institut National de la Recherche Agronomique (INRA)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, Karl-Franzens-Universität [Graz, Autriche], University of California San Diego Health, University of Brussels, University of Pennsylvania [Philadelphia], Tata Institute of Fundamental Research, University of São Paulo (USP), UCL - SSS/IONS - Institute of NeuroScience, FSE Campus Venlo, and RS: FSE UCV

Subjects
Male, Taste, Physiology, Smagstab, Audiology, AcademicSubjects/SCI01180, Settore BIO/09 - Fisiologia, Behavioral Neuroscience, chemistry.chemical_compound, Olfaction Disorders, Taste Disorders, 0302 clinical medicine, RATINGS, Hyposmia, Surveys and Questionnaires, CHEMOSENSITIVITY, [SDV.IDA]Life Sciences [q-bio]/Food engineering, Viral, PALADAR, 030223 otorhinolaryngology, Sensory Science and Eating Behaviour, media_common, TASTE, US NATIONAL-HEALTH, [SDV.IDA] Life Sciences [q-bio]/Food engineering, Middle Aged, Biological Sciences, 16. Peace & justice, Sensory Systems, 3. Good health, Smell, GCCR Group Author, ddc:540, Smell loss, Female, Original Article, medicine.symptom, Corrigendum, Coronavirus Infections, olfaction, Adult, somatosensation, medicine.medical_specialty, 663/664, Coronavirus disease 2019 (COVID-19), OLFACTORY DISORDERS, Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), media_common.quotation_subject, Pneumonia, Viral, head and neck surgery, Aged, Betacoronavirus, COVID-19, Humans, Pandemics, SARS-CoV-2, Self Report, Somatosensory Disorders, Young Adult, Anosmia, Sensory system, Olfaction, 03 medical and health sciences, Chemesthesis, Physiology (medical), Perception, medicine, Neurology & Neurosurgery, Behaviour Change and Well-being, business.industry, R-PACKAGE, 3112 Neurosciences, Pneumonia, Parosmia, COMPONENT, Smagssans, [SDV.AEN] Life Sciences [q-bio]/Food and Nutrition, Sensoriek en eetgedrag, chemistry, Lugtetab, business, [SDV.AEN]Life Sciences [q-bio]/Food and Nutrition, 030217 neurology & neurosurgery, Lugtesans
Abstract

Correction: Chemical Senses, Volume 46, 2021, bjab050, https://doi.org/10.1093/chemse/bjab050 Published: 08 December 2021 Recent anecdotal and scientific reports have provided evidence of a link between COVID-19 and chemosensory impairments, such as anosmia. However, these reports have downplayed or failed to distinguish potential effects on taste, ignored chemesthesis, and generally lacked quantitative measurements. Here, we report the development, implementation, and initial results of a multilingual, international questionnaire to assess self-reported quantity and quality of perception in 3 distinct chemosensory modalities (smell, taste, and chemesthesis) before and during COVID-19. In the first 11 days after questionnaire launch, 4039 participants (2913 women, 1118 men, and 8 others, aged 19-79) reported a COVID-19 diagnosis either via laboratory tests or clinical assessment. Importantly, smell, taste, and chemesthetic function were each significantly reduced compared to their status before the disease. Difference scores (maximum possible change +/- 100) revealed a mean reduction of smell (-79.7 +/- 28.7, mean +/- standard deviation), taste (-69.0 +/- 32.6), and chemesthetic (-37.3 +/- 36.2) function during COVID-19. Qualitative changes in olfactory ability (parosmia and phantosmia) were relatively rare and correlated with smell loss. Importantly, perceived nasal obstruction did not account for smell loss. Furthermore, chemosensory impairments were similar between participants in the laboratory test and clinical assessment groups. These results show that COVID-19-associated chemosensory impairment is not limited to smell but also affects taste and chemesthesis.The multimodal impact of COVID-19 and the lack of perceived nasal obstruction suggest that severe acute respiratory syndrome coronavirus strain 2 (SARS-CoV-2) infection may disrupt sensory-neural mechanisms.

Published
2020

44. The United States COVID-19 Forecast Hub dataset

Author

US COVID-19 Forecast Hub Consortium, Cramer, Estee Y., Huang, Yuxin, Wang, Yijin, Ray, Evan L., Cornell, Matthew, Bracher, Johannes, Brennen, Andrea, Rivadeneira, Alvaro J. Castro, Gerding, Aaron, House, Katie, Jayawardena, Dasuni, Kanji, Abdul Hannan, Khandelwal, Ayush, Le, Khoa, Mody, Vidhi, Mody, Vrushti, Niemi, Jarad, Stark, Ariane, Shah, Apurv, Wattanchit, Nutcha, Zorn, Martha W., Reich, Nicholas G., Gneiting, Tilmann, Mühlemann, Anja, Gu, Youyang, Chen, Yixian, Chintanippu, Krishna, Jivane, Viresh, Khurana, Ankita, Kumar, Ajay, Lakhani, Anshul, Mehrotra, Prakhar, Pasumarty, Sujitha, Shrivastav, Monika, You, Jialu, Bannur, Nayana, Deva, Ayush, Jain, Sansiddh, Kulkarni, Mihir, Merugu, Srujana, Raval, Alpan, Shingi, Siddhant, Tiwari, Avtansh, White, Jerome, Adiga, Aniruddha, Hurt, Benjamin, Lewis, Bryan, Marathe, Madhav, Peddireddy, Akhil Sai, Porebski, Przemyslaw, Venkatramanan, Srinivasan, Wang, Lijing, Dahan, Maytal, Fox, Spencer, Gaither, Kelly, Lachmann, Michael, Meyers, Lauren Ancel, Scott, James G., Tec, Mauricio, Woody, Spencer, Srivastava, Ajitesh, Xu, Tianjian, Cegan, Jeffrey C., Dettwiller, Ian D., England, William P., Farthing, Matthew W., George, Glover E., Hunter, Robert H., Lafferty, Brandon, Linkov, Igor, Mayo, Michael L., Parno, Matthew D., Rowland, Michael A., Trump, Benjamin D., Chen, Samuel, Faraone, Stephen V., Hess, Jonathan, Morley, Christopher P., Salekin, Asif, Wang, Dongliang, Zhang-James, Yanli, Baer, Thomas M., Corsetti, Sabrina M., Eisenberg, Marisa C., Falb, Karl, Huang, Yitao, Martin, Emily T., McCauley, Ella, Myers, Robert L., Schwarz, Tom, Gibson, Graham Casey, Sheldon, Daniel, Gao, Liyao, Ma, Yian, Wu, Dongxia, Yu, Rose, Jin, Xiaoyong, Wang, Yu-Xiang, Yan, Xifeng, Chen, YangQuan, Guo, Lihong, Zhao, Yanting, Chen, Jinghui, Gu, Quanquan, Wang, Lingxiao, Xu, Pan, Zhang, Weitong, Zou, Difan, Chattopadhyay, Ishanu, Huang, Yi, Lu, Guoqing, Pfeiffer, Ruth, Sumner, Timothy, Wang, Dongdong, Wang, Liqiang, Zhang, Shunpu, Zou, Zihang, Biegel, Hannah, Lega, Joceline, Hussain, Fazle, Khan, Zeina, Van Bussel, Frank, McConnell, Steve, Guertin, Stephanie L., Hulme-Lowe, Christopher, Nagraj, V. P., Turner, Stephen D., Bejar, Benjamín, Choirat, Christine, Flahault, Antoine, Krymova, Ekaterina, Lee, Gavin, Manetti, Elisa, Namigai, Kristen, Obozinski, Guillaume, Sun, Tao, Thanou, Dorina, Ban, Xuegang, Shi, Yunfeng, Walraven, Robert, Hong, Qi-Jun, Van De Walle, Axel, Ben-Nun, Michal, Riley, Steven, Riley, Pete, Turtle, James, Cao, Duy, Galasso, Joseph, Cho, Jae H., Jo, Areum, DesRoches, David, Forli, Pedro, Hamory, Bruce, Koyluoglu, Ugur, Kyriakides, Christina, Leis, Helen, Milliken, John, Moloney, Michael, Morgan, James, Nirgudkar, Ninad, Ozcan, Gokce, Piwonka, Noah, Ravi, Matt, Schrader, Chris, Shakhnovich, Elizabeth, Siegel, Daniel, Spatz, Ryan, Stiefeling, Chris, Wilkinson, Barrie, Wong, Alexander, Cavany, Sean, España, Guido, Moore, Sean, Oidtman, Rachel, Perkins, Alex, Ivy, Julie S., Mayorga, Maria E., Mele, Jessica, Rosenstrom, Erik T., Swann, Julie L., Kraus, Andrea, Kraus, David, Bian, Jiang, Cao, Wei, Gao, Zhifeng, Ferres, Juan Lavista, Li, Chaozhuo, Liu, Tie-Yan, Xie, Xing, Zhang, Shun, Zheng, Shun, Chinazzi, Matteo, Vespignani, Alessandro, Xiong, Xinyue, Davis, Jessica T., Mu, Kunpeng, Piontti, Ana Pastore Y, Baek, Jackie, Farias, Vivek, Georgescu, Andreea, Levi, Retsef, Sinha, Deeksha, Wilde, Joshua, Zheng, Andrew, Lami, Omar Skali, Bennouna, Amine, Ndong, David Nze, Perakis, Georgia, Singhvi, Divya, Spantidakis, Ioannis, Thayaparan, Leann, Tsiourvas, Asterios, Weisberg, Shane, Jadbabaie, Ali, Sarker, Arnab, Shah, Devavrat, Celi, Leo A., Penna, Nicolas D., Sundar, Saketh, Berlin, Abraham, Gandhi, Parth D., McAndrew, Thomas, Piriya, Matthew, Chen, Ye, Hlavacek, William, Lin, Yen Ting, Mallela, Abhishek, Miller, Ely, Neumann, Jacob, Posner, Richard, Wolfinger, Russ, Castro, Lauren, Fairchild, Geoffrey, Michaud, Isaac, Osthus, Dave, Wolffram, Daniel, Karlen, Dean, Panaggio, Mark J., Kinsey, Matt, Mullany, Luke C., Rainwater-Lovett, Kaitlin, Shin, Lauren, Tallaksen, Katharine, Wilson, Shelby, Brenner, Michael, Coram, Marc, Edwards, Jessie K., Joshi, Keya, Klein, Ellen, Hulse, Juan Dent, Grantz, Kyra H., Hill, Alison L., Kaminsky, Kathryn, Kaminsky, Joshua, Keegan, Lindsay T., Lauer, Stephen A., Lee, Elizabeth C., Lemaitre, Joseph C., Lessler, Justin, Meredith, Hannah R., Perez-Saez, Javier, Shah, Sam, Smith, Claire P., Truelove, Shaun A., Wills, Josh, Gardner, Lauren, Marshall, Maximilian, Nixon, Kristen, Burant, John C., Budzinski, Jozef, Chiang, Wen-Hao, Mohler, George, Gao, Junyi, Glass, Lucas, Qian, Cheng, Romberg, Justin, Sharma, Rakshith, Spaeder, Jeffrey, Sun, Jimeng, Xiao, Cao, Gao, Lei, Gu, Zhiling, Kim, Myungjin, Li, Xinyi, Wang, Yueying, Wang, Guannan, Wang, Lily, Yu, Shan, Jain, Chaman, Bhatia, Sangeeta, Nouvellet, Pierre, Barber, Ryan, Gaikedu, Emmanuela, Hay, Simon, Lim, Steve, Murray, Chris, Pigott, David, Reiner, Robert C., Baccam, Prasith, Gurung, Heidi L., Stage, Steven A., Suchoski, Bradley T., Fong, Chung-Yan, Yeung, Dit-Yan, Adhikari, Bijaya, Cui, Jiaming, Prakash, B. Aditya, Rodríguez, Alexander, Tabassum, Anika, Xie, Jiajia, Asplund, John, Baxter, Arden, Keskinocak, Pinar, Oruc, Buse Eylul, Serban, Nicoleta, Arik, Sercan O., Dusenberry, Mike, Epshteyn, Arkady, Kanal, Elli, Le, Long T., Li, Chun-Liang, Pfister, Tomas, Sinha, Rajarishi, Tsai, Thomas, Yoder, Nate, Yoon, Jinsung, Zhang, Leyou, Wilson, Daniel, Belov, Artur A., Chow, Carson C., Gerkin, Richard C., Yogurtcu, Osman N., Ibrahim, Mark, Lacroix, Timothee, Le, Matthew, Liao, Jason, Nickel, Maximilian, Sagun, Levent, Abbott, Sam, Bosse, Nikos I., Funk, Sebastian, Hellewell, Joel, Meakin, Sophie R., Sherratt, Katharine, Kalantari, Rahi, Zhou, Mingyuan, Karimzadeh, Morteza, Lucas, Benjamin, Ngo, Thoai, Zoraghein, Hamidreza, Vahedi, Behzad, Wang, Zhongying, Pei, Sen, Shaman, Jeffrey, Yamana, Teresa K., Bertsimas, Dimitris, Li, Michael L., Soni, Saksham, Bouardi, Hamza Tazi, Adee, Madeline, Ayer, Turgay, Chhatwal, Jagpreet, Dalgic, Ozden O., Ladd, Mary A., Linas, Benjamin P., Mueller, Peter, Xiao, Jade, Bosch, Jurgen, Wilson, Austin, Zimmerman, Peter, Wang, Qinxia, Wang, Yuanjia, Xie, Shanghong, Zeng, Donglin, Bien, Jacob, Brooks, Logan, Green, Alden, Hu, Addison J., Jahja, Maria, McDonald, Daniel, Narasimhan, Balasubramanian, Politsch, Collin, Rajanala, Samyak, Rumack, Aaron, Simon, Noah, Tibshirani, Ryan J., Tibshirani, Rob, Ventura, Valerie, Wasserman, Larry, Drake, John M., O’Dea, Eamon B., Abu-Mostafa, Yaser, Bathwal, Rahil, Chang, Nicholas A., Chitta, Pavan, Erickson, Anne, Goel, Sumit, Gowda, Jethin, Jin, Qixuan, Jo, HyeongChan, Kim, Juhyun, Kulkarni, Pranav, Lushtak, Samuel M., Mann, Ethan, Popken, Max, Soohoo, Connor, Tirumala, Kushal, Tseng, Albert, Varadarajan, Vignesh, Vytheeswaran, Jagath, Wang, Christopher, Yeluri, Akshay, Yurk, Dominic, Zhang, Michael, Zlokapa, Alexander, Pagano, Robert, Jain, Chandini, Tomar, Vishal, Ho, Lam, Huynh, Huong, Tran, Quoc, Lopez, Velma K., Walker, Jo W., Slayton, Rachel B., Johansson, Michael A., and Biggerstaff, Matthew

Subjects
ddc:510, Mathematics
Abstract

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages.

Published
2022

46. Adaptive Olfactory Measure of Threshold

Author

Weir, Elisabeth M., primary, Hannum, Mackenzie E., additional, Reed, Danielle R., additional, Joseph, Paule V., additional, Munger, Steven D., additional, Hayes, John E., additional, and Gerkin, Richard C., additional

Published
2022
Full Text
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49. Deconstructing the mouse olfactory percept through an ethological atlas

Author

Manoel, Diogo, primary, Makhlouf, Melanie, additional, Arayata, Charles J., additional, Sathappan, Abbirami, additional, Da’as, Sahar, additional, Abdelrahman, Doua, additional, Selvaraj, Senthil, additional, Hasnah, Reem, additional, Mainland, Joel D., additional, Gerkin, Richard C., additional, and Saraiva, Luis R., additional

Published
2021
Full Text
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50. Facilitating the sharing of data analysis results through in-depth provenance capture

Author

Köhler, Cristiano, Ulianych, Danylo, Gerkin, Richard C., Davison, Andrew P., Grün, Sonja, and Denker, Michael

Abstract

INTRODUCTION/MOTIVATION Workflows for the analysis of electrophysiology activity data are typically composed of multiple steps. In the simplest case, these comprise several scripts executed in sequence, with several dependencies on data and parameters sets. However, workflows can become increasingly complex during the course of an analysis project: researchers can investigate alternative analysis paths or adjust the workflow components according to new hypotheses or additional experimental data. Considering this complexity and iterative nature, robust tools forming the basis of the workflow are necessary [1] to fully document the workflow and improve the reproducibility of the results. Provenance is the capture and characterization of data manipulations and parameters throughout the workflow [2]. This requires complete and self-explanatory descriptions of the generated data and a method to minimize the need for manually tracking the workflow execution, while maximizing the information content of the provenance trail. While frameworks to structure the input data and associated metadata exist, a similar representation for the outputs of the analysis part of the workflow is missing. Moreover, workflow management systems capture limited provenance information, as they do not provide details about the functions used inside each analysis script. Finally, the workflow output lacks information pertaining to its generation. Therefore, to satisfy the requirements of a practically useful provenance trail, existing tools must be improved to implement a data model that captures analysis outputs and their detailed provenance and, ultimately, represents the analysis and its results in accordance with the FAIR principles [3]. METHODS We focus on two open-source tools for the analysis of electrophysiology data developed in EBRAINS. The Neo (RRID:SCR_000634) framework provides an object model to standardize neural activity data acquired from different sources [4]. Elephant (RRID:SCR_003833) is a Python toolbox for analyses of electrophysiology data [5]. We implemented two synergistic prototype solutions that extend the functionality of these tools with respect to (i) the systematic standardization of analysis results and (ii) the automatical capture of provenance information during the execution of a Python analysis script. Both solutions are under development and being incorporated as new functionality into the Elephant package. The first solution represents the output of Elephant functions in a data model inspired by Neo. Objects for specific analysis results (e.g., a time histogram) are inherited from a base Python class that supports storage of provenance information such as timestamps and unique identifiers. The second solution is a provenance tracker implemented as a function decorator. It identifies the objects that are input to and output from the function, creating unique hashes. It also captures timestamps, statement code lines, and additional function parameters. Extended dependencies between objects (such as indexing and attributes) are mapped using the analysis of the abstract syntax tree (AST) obtained from the code. RESULTS AND DISCUSSION The solutions presented here capture provenance during the analysis of electrophysiology data with minimal user intervention. The data objects support a hierarchical standardization of the output of Elephant functions (e.g., a time histogram is a specific type of histogram) while encapsulating all the information about the generation of an analysis output. Therefore, these objects can be easily re-used or shared. This will eliminate the need to manually annotate the output of the analysis with corresponding parameters. The new objects also seamlessly extend the functionality of the Neo classes currently used as output of Elephant functions, and can be integrated into the existing code bases with minimal disruption. Additionally, we describe how to capture provenance information throughout the Python analysis script using decorators. These track the Elephant and user-defined functions used in the script while mapping the inputs to the outputs. We demonstrate how the captured information can be used to build a graph showing the steps followed in the script, and that can be stored as metadata. The analysis results obtained with or without the use of the two solutions are compared, highlighting the potential benefits for reproducibility and data re-use. The provenance tracker and the standard data objects capture and manage distinct aspects of the provenance information. In the end, both solutions are complementary. On one hand, the decorator is focused on building the provenance trail and the relationships between the different steps of the analysis within the script. On the other hand, the standard objects focus on the representation of the data, standardizing information that is similar among the outputs of different functions together with the storage of the relevant provenance information as metadata. Ultimately, those two developments aim to increase data interoperability and reusability in accordance with the FAIR principles. REFERENCES: [1] Denker, M. and Grün, S. (2016). Designing Workflows for the Reproducible Analysis of Electrophysiological Data. In Brain-Inspired Computing, Amunts, K. et al., eds. (Cham: Springer International Publishing), pp. 58-72. [2] Ragan, E.D. et al. (2016). Characterizing Provenance in Visualization and Data Analysis: An Organizational Framework of Provenance Types and Purposes. IEEE Transactions on Visualization and Computer Graphics. 22(1):31–40. [3] Wilkinson, M.D. et al. (2016). The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data 3, 160018. [4] Garcia, S. et al. (2014) Neo: an object model for handling electrophysiology data in multiple formats. Frontiers in Neuroinformatics 8:10. [5] http://python-elephant.org

Published
2021

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