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1. Multi-view biomedical foundation models for molecule-target and property prediction

Author

Suryanarayanan, Parthasarathy, Qiu, Yunguang, Sethi, Shreyans, Mahajan, Diwakar, Li, Hongyang, Yang, Yuxin, Eyigoz, Elif, Saenz, Aldo Guzman, Platt, Daniel E., Rumbell, Timothy H., Ng, Kenney, Dey, Sanjoy, Burch, Myson, Kwon, Bum Chul, Meyer, Pablo, Cheng, Feixiong, Hu, Jianying, and Morrone, Joseph A.

Subjects
Quantitative Biology - Biomolecules, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

Foundation models applied to bio-molecular space hold promise to accelerate drug discovery. Molecular representation is key to building such models. Previous works have typically focused on a single representation or view of the molecules. Here, we develop a multi-view foundation model approach, that integrates molecular views of graph, image and text. Single-view foundation models are each pre-trained on a dataset of up to 200M molecules and then aggregated into combined representations. Our multi-view model is validated on a diverse set of 18 tasks, encompassing ligand-protein binding, molecular solubility, metabolism and toxicity. We show that the multi-view models perform robustly and are able to balance the strengths and weaknesses of specific views. We then apply this model to screen compounds against a large (>100 targets) set of G Protein-Coupled receptors (GPCRs). From this library of targets, we identify 33 that are related to Alzheimer's disease. On this subset, we employ our model to identify strong binders, which are validated through structure-based modeling and identification of key binding motifs., Comment: 34 pages including supplement. 9 figures, 4 tables

Published
2024

2. Improving Primary Healthcare Workflow Using Extreme Summarization of Scientific Literature Based on Generative AI

Author

Stiglic, Gregor, Kopitar, Leon, Gosak, Lucija, Kocbek, Primoz, He, Zhe, Chakraborty, Prithwish, Meyer, Pablo, and Bian, Jiang

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence, 68T50, I.2.7
Abstract

Primary care professionals struggle to keep up to date with the latest scientific literature critical in guiding evidence-based practice related to their daily work. To help solve the above-mentioned problem, we employed generative artificial intelligence techniques based on large-scale language models to summarize abstracts of scientific papers. Our objective is to investigate the potential of generative artificial intelligence in diminishing the cognitive load experienced by practitioners, thus exploring its ability to alleviate mental effort and burden. The study participants were provided with two use cases related to preventive care and behavior change, simulating a search for new scientific literature. The study included 113 university students from Slovenia and the United States randomized into three distinct study groups. The first group was assigned to the full abstracts. The second group was assigned to the short abstracts generated by AI. The third group had the option to select a full abstract in addition to the AI-generated short summary. Each use case study included ten retrieved abstracts. Our research demonstrates that the use of generative AI for literature review is efficient and effective. The time needed to answer questions related to the content of abstracts was significantly lower in groups two and three compared to the first group using full abstracts. The results, however, also show significantly lower accuracy in extracted knowledge in cases where full abstract was not available. Such a disruptive technology could significantly reduce the time required for healthcare professionals to keep up with the most recent scientific literature; nevertheless, further developments are needed to help them comprehend the knowledge accurately., Comment: 5 pages, 5 figures

Published
2023

3. Informing clinical assessment by contextualizing post-hoc explanations of risk prediction models in type-2 diabetes

Author

Chari, Shruthi, Acharya, Prasant, Gruen, Daniel M., Zhang, Olivia, Eyigoz, Elif K., Ghalwash, Mohamed, Seneviratne, Oshani, Saiz, Fernando Suarez, Meyer, Pablo, Chakraborty, Prithwish, and McGuinness, Deborah L.

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

Medical experts may use Artificial Intelligence (AI) systems with greater trust if these are supported by contextual explanations that let the practitioner connect system inferences to their context of use. However, their importance in improving model usage and understanding has not been extensively studied. Hence, we consider a comorbidity risk prediction scenario and focus on contexts regarding the patients clinical state, AI predictions about their risk of complications, and algorithmic explanations supporting the predictions. We explore how relevant information for such dimensions can be extracted from Medical guidelines to answer typical questions from clinical practitioners. We identify this as a question answering (QA) task and employ several state-of-the-art LLMs to present contexts around risk prediction model inferences and evaluate their acceptability. Finally, we study the benefits of contextual explanations by building an end-to-end AI pipeline including data cohorting, AI risk modeling, post-hoc model explanations, and prototyped a visual dashboard to present the combined insights from different context dimensions and data sources, while predicting and identifying the drivers of risk of Chronic Kidney Disease - a common type-2 diabetes comorbidity. All of these steps were performed in engagement with medical experts, including a final evaluation of the dashboard results by an expert medical panel. We show that LLMs, in particular BERT and SciBERT, can be readily deployed to extract some relevant explanations to support clinical usage. To understand the value-add of the contextual explanations, the expert panel evaluated these regarding actionable insights in the relevant clinical setting. Overall, our paper is one of the first end-to-end analyses identifying the feasibility and benefits of contextual explanations in a real-world clinical use case.

Published
2023
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4. Distillation to Enhance the Portability of Risk Models Across Institutions with Large Patient Claims Database

Author

Nyemba, Steve, Yan, Chao, Zhang, Ziqi, Rajmane, Amol, Meyer, Pablo, Chakraborty, Prithwish, and Malin, Bradley

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

Artificial intelligence, and particularly machine learning (ML), is increasingly developed and deployed to support healthcare in a variety of settings. However, clinical decision support (CDS) technologies based on ML need to be portable if they are to be adopted on a broad scale. In this respect, models developed at one institution should be reusable at another. Yet there are numerous examples of portability failure, particularly due to naive application of ML models. Portability failure can lead to suboptimal care and medical errors, which ultimately could prevent the adoption of ML-based CDS in practice. One specific healthcare challenge that could benefit from enhanced portability is the prediction of 30-day readmission risk. Research to date has shown that deep learning models can be effective at modeling such risk. In this work, we investigate the practicality of model portability through a cross-site evaluation of readmission prediction models. To do so, we apply a recurrent neural network, augmented with self-attention and blended with expert features, to build readmission prediction models for two independent large scale claims datasets. We further present a novel transfer learning technique that adapts the well-known method of born-again network (BAN) training. Our experiments show that direct application of ML models trained at one institution and tested at another institution perform worse than models trained and tested at the same institution. We further show that the transfer learning approach based on the BAN produces models that are better than those trained on just a single institution's data. Notably, this improvement is consistent across both sites and occurs after a single retraining, which illustrates the potential for a cheap and general model transfer mechanism of readmission risk prediction.

Published
2022

5. Crowd-sourced machine learning prediction of long COVID using data from the National COVID Cohort Collaborative

Author

Aggarwal, Ataes, Agor, Joseph, Al-Amery, Amera, Aminu, Oluwatobiloba, Anand, Adit, Antonescu, Corneliu, Arora, Mehak, Asaduzzaman, Sayed, Asmussen, Tanner, Baghbanzadeh, Mahdi, Baker, Frazier, Bangert, Bridget, Bekhet, Laila, Bhattacharya, Biplab, Blase, Jenny, Butzin-Dozier, Zachary, Caffo, Brian, Chang, Hao, Chen, Zeyuan, Chen, Jiandong, Chiang, Jeffrey, Cho, Peter, Cockrell, Robert, Combs, Parker, Coyle, Jeremy, Crosby, Ciara, Dai, Zongyu, Dai, Ran, Danesharasteh, Anseh, Yildirim, Elif, Delong, Grant, Demilt, Ryan, Deng, Kaiwen, Dey, Sanjoy, Dhamdhere, Rohan, Dickson, Andrew, Dijour, Phoebe, Dinh, Dong, Dixon, Richard, Domi, Albi, Dutta, Souradeep, Elizondo, Mirna, Ertem, Zeynep, Feuerwerker, Solomon, Fliss, Danica, Fowler, Jennifer, Fu, Sunyang, Gardner, Kelly, Getty, Neil, Ghalwash, Mohamed, Gloster, Logan, Greer, Phil, Guan, Yuanfang, Ham, Colby, Hanoudi, Samer, Harper, Jeremy, Hendrix, Nathaniel, Hershkovich, Leeor, Hightower, Jake, Hu, Junjie, Huang, Jiani, Huang, Yu, Huang, Tongtong, Hur, Junguk, Isgut, Monica, Ismail, Hamid, Izmirlian, Grant, Jang, Kuk, Jemiyo, Christianah, Jeong, Hayoung, Ji, Yunwen, Ji, Xiayan, Jiang, Ming, Jiang, Sihang, Jiang, Xiaoqian, Jiang, Yuye, Johnson, Akin, Analyst, Zach, Kapse, Saarthak, Kartoun, Uri, KC, Dukka, Fard, Zahra, Kosfeld, Tim, Krichevsky, Spencer, Kuo, Mike, Larie, Dale, Lederer, Lauren, Leng, Shan, Li, Ziyang, Li, Hongyang, Li, Haodong, Li, Jianfu, Li, Tiantian, Liang, Xinwen, Liang, Hengyue, Liu, Feifan, Liu, Daniel, Luo, Gang, Munia, Tamanna, Madduri, Ravi, Madhira, Vithal, Mani, Shivali, Mansourifard, Farzaneh, Matson, Robert, Mertens, Andrew, Metsis, Vangelis, Meyer, Pablo, Mikhailova, Catherine, Miller, Dante, Milo, Christopher, Mitchell, Elliot, Modanwal, Gourav, Moore, Ronald, Morgenthaler, David, Musal, Rasim, Naik, Mayur, Nalawade, Vinit, Narain, Rohan, Narendrula, Saideep, Obiri, Alena, Okawa, Satoshi, Okechukwu, Chima, Olorunnisola, Toluwanimi, Ossowski, Tim, Parekh, Harsh, Park, Jean, Patel, Saaya, Patterson, Jason, Paul, Chetan, Peng, Le, Perkins, Diana, Pokharel, Suresh, Poplavskiy, Dmytro, Pryor, Zach, Pungitore, Sarah, Qin, Hong, Rababa, Salahaldeen, Rahman, Mahbubur, Rahmani, Elior, Rahnavard, Gholamali, Raihan, Md, Rajendran, Suraj, Ravichandran, Sarangan, Reddy, Chandan, Reyes, Abel, Roghanizad, Ali, Rouffa, Sean, Ruan, Xiaoyang, Saha, Arpita, Sawant, Sahil, Schiaffino, Melody, Seira, Diego, Sengupta, Saurav, Shalaev, Ruslan, Shetty, Gaurav, Shi, Seraphina, Shinguyen, Linh, Singh, Karnika, Sinha, Soumya, Socia, Damien, Stalians, Halen, Stavropoulos, Charalambos, Strube, Jan, Subramanian, Devika, Sun, Jiehuan, Sun, Ju, Sun, Chengkun, Sundararajan, Prathic, Talebi, Salmonn, Tawiah, Edward, Tesic, Jelena, Thiess, Mikaela, Tian, Raymond, Torre-Healy; Ming-Tse Tsai, Luke, Tyus, David, Vardhan, Madhurima, Velingker, Neelay, Walzer, Benjamin, Walzer, Jacob, Wang, Junda, Wang, Lu, Wang, Will, Wang, Jonathan, Wang, Yisen, Weatherly, Chad, Wu, Fanyou, Wu, Yifeng, Wu, Yinjun, Xia, Fangfang, Yan, Hao, Yang, Zhichao, Ye, Biao, Yin, Rui, Yin, Changyu, Yoo, Yun, You, Albert, Yu, June, Zanaj, Martin, Zaiman, Zachary, Zhang, Kai, Zhang, Xiaoyi, Zhang, Tianmai, Zhao, Zixuan, Zhi, Degui, Zhong, Yishan, Zhou, Huixue, Zhou, Andrea, Zhu, Yuanda, Zhu, Yitan, Zhu, Sophie, Adams, Meredith, Alexander, Caleb, Amor, Benjamin, Anzalone, Alfred, Bates, Benjamin, Beasley, Will, Bennett, Tellen, Bissell, Mark, Boudreau, Eilis, Bozzette, Samuel, Bradwell, Katie, Bramante, Carolyn, Brown, Don, Burgoon, Penny, Buse, John, Callahan, Tiffany, Cato, Kenrick, Chapman, Scott, Chute, Christopher, Clark, Jaylyn, Clark, Marshall, Cooper, Will, Cottrell, Lesley, Crowley, Karen, Deacy, Mariam, Dillon, Christopher, Eichmann, David, Emmett, Mary, Erwin-Cohen, Rebecca, Francis, Patricia, French, Evan, Fuentes, Rafael, Gabriel, Davera, Gagnier, Joel, Garbarini, Nicole, Ge, Jin, Gersing, Kenneth, Girvin, Andrew, Gordon, Valery, Graves, Alexis, Guinney, Justin, Haendel, Melissa, Hayanga, J.W., Hendricks, Brian, Hernandez, Wenndy, Hill, Elaine, Hillegass, William, Hong, Stephanie, Housman, Dan, Hurley, Robert, Islam, Jessica, Jawa, Randeep, Johnson, Steve, Kamaleswaran, Rishi, Kibbe, Warren, Koraishy, Farrukh, Kostka, Kristin, Kurilla, Michael, Lee, Adam, Lehmann, Harold, Liu, Hongfang, Loomba, Johanna, Madlock-Brown; Sandeep Mallipattu, Charisse, Manna, Amin, Mariona, Federico, Marti, Emily, Martin, Greg, Mathew, Jomol, Mazzotti, Diego, McMurry, Julie, Mehta, Hemalkumar, Michael, Sam, Miller, Robert, Misquitta, Leonie, Moffitt, Richard, Morris, Michele, Murray, Kimberly, Northington, Lavance, O’Neil, Shawn, Olex, Amy, Palchuk, Matvey, Patel, Brijesh, Patel, Rena, Payne, Philip, Pfaff, Emily, Pincavitch, Jami, Portilla, Lili, Prior, Fred, Pyarajan, Saiju, Pyles, Lee, Qureshi, Nabeel, Robinson, Peter, Rutter, Joni, Sadan, Ofer, Safdar, Nasia, Saha, Amit, Saltz, Joel, Saltz, Mary, Schmitt, Clare, Setoguchi, Soko, Sharafeldin, Noha, Sharathkumar, Anjali, Sheikh, Usman, Sidky, Hythem, Sokos, George, Southerland, Andrew, Spratt, Heidi, Starren, Justin, Subbian, Vignesh, Suver, Christine, Takemoto, Cliff, Temple-O'Connor, Meredith, Topaloglu, Umit, Vedula, Satyanarayana, Walden, Anita, Walters, Kellie, Ward-Caviness, Cavin, Wilcox, Adam, Wilkins, Ken, Williams, Andrew, Wu, Chunlei, Zampino, Elizabeth, Zhang, Xiaohan, Zhu, Richard, Bergquist, Timothy, Tariq, Adbul, Philips, Rachael V., Pirracchio, Romain, van der Laan, Mark, Colford, John M., Jr., Hubbard, Alan, Gao, Jifan, Chen, Guanhua, Stein, Adam, Long, Qi, Holmes, John, Mowery, Danielle, Wong, Eric, Parekh, Ravi, Getzen, Emily, and Blase, Jennifer

Published
2024
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6. Disease Progression Modeling Workbench 360

Author

Suryanarayanan, Parthasarathy, Chakraborty, Prithwish, Madan, Piyush, Bore, Kibichii, Ogallo, William, Chandra, Rachita, Ghalwash, Mohamed, Buleje, Italo, Remy, Sekou, Mahatma, Shilpa, Meyer, Pablo, and Hu, Jianying

Subjects
Computer Science - Machine Learning, Computer Science - Human-Computer Interaction
Abstract

In this work we introduce Disease Progression Modeling workbench 360 (DPM360) opensource clinical informatics framework for collaborative research and delivery of healthcare AI. DPM360, when fully developed, will manage the entire modeling life cycle, from data analysis (e.g., cohort identification) to machine learning algorithm development and prototyping. DPM360 augments the advantages of data model standardization and tooling (OMOP-CDM, Athena, ATLAS) provided by the widely-adopted OHDSI initiative with a powerful machine learning training framework, and a mechanism for rapid prototyping through automatic deployment of models as containerized services to a cloud environment., Comment: Submitted to OHDSI Collaborator Showcase, 2021 (https://www.ohdsi.org/2021-collaborator-showcase)

Published
2021

9. Recent Smell Loss Is the Best Predictor of COVID-19 Among Individuals With Recent Respiratory Symptoms.

Author

Gerkin, Richard, Ohla, Kathrin, Veldhuizen, Maria, Joseph, Paule, Kelly, Christine, Bakke, Alyssa, Steele, Kimberley, Farruggia, Michael, Pellegrino, Robert, Pepino, Marta, Bouysset, Cédric, Soler, Graciela, Pereda-Loth, Veronica, Dibattista, Michele, Cooper, Keiland, Croijmans, Ilja, Di Pizio, Antonella, Ozdener, Mehmet, Fjaeldstad, Alexander, Lin, Cailu, Sandell, Mari, Singh, Preet, Brindha, V, Olsson, Shannon, Saraiva, Luis, Ahuja, Gaurav, Alwashahi, Mohammed, Bhutani, Surabhi, DErrico, Anna, Fornazieri, Marco, Golebiowski, Jérôme, Dar Hwang, Liang, Öztürk, Lina, Roura, Eugeni, Spinelli, Sara, Whitcroft, Katherine, Faraji, Farhoud, Fischmeister, Florian, Heinbockel, Thomas, Hsieh, Julien, Huart, Caroline, Konstantinidis, Iordanis, Menini, Anna, Morini, Gabriella, Olofsson, Jonas, Philpott, Carl, Pierron, Denis, Shields, Vonnie, Voznessenskaya, Vera, Albayay, Javier, Altundag, Aytug, Bensafi, Moustafa, Bock, María, Calcinoni, Orietta, Fredborg, William, Laudamiel, Christophe, Lim, Juyun, Lundström, Johan, Macchi, Alberto, Meyer, Pablo, Moein, Shima, Santamaría, Enrique, Sengupta, Debarka, Rohlfs Dominguez, Paloma, Yanik, Hüseyin, Hummel, Thomas, Hayes, John, Reed, Danielle, Niv, Masha, Munger, Steven, and Parma, Valentina

Subjects
anosmia, chemosensory, coronavirus, hyposmia, olfactory, prediction, Adult, Anosmia, COVID-19, Cross-Sectional Studies, Female, Humans, Male, Middle Aged, Prognosis, SARS-CoV-2, Self Report, Smell
Abstract

In a preregistered, cross-sectional study, we investigated whether olfactory loss is a reliable predictor of COVID-19 using a crowdsourced questionnaire in 23 languages to assess symptoms in individuals self-reporting recent respiratory illness. We quantified changes in chemosensory abilities during the course of the respiratory illness using 0-100 visual analog scales (VAS) for participants reporting a positive (C19+; n = 4148) or negative (C19-; n = 546) COVID-19 laboratory test outcome. Logistic regression models identified univariate and multivariate predictors of COVID-19 status and post-COVID-19 olfactory recovery. Both C19+ and C19- groups exhibited smell loss, but it was significantly larger in C19+ participants (mean ± SD, C19+: -82.5 ± 27.2 points; C19-: -59.8 ± 37.7). Smell loss during illness was the best predictor of COVID-19 in both univariate and multivariate models (ROC AUC = 0.72). Additional variables provide negligible model improvement. VAS ratings of smell loss were more predictive than binary chemosensory yes/no-questions or other cardinal symptoms (e.g., fever). Olfactory recovery within 40 days of respiratory symptom onset was reported for ~50% of participants and was best predicted by time since respiratory symptom onset. We find that quantified smell loss is the best predictor of COVID-19 amongst those with symptoms of respiratory illness. To aid clinicians and contact tracers in identifying individuals with a high likelihood of having COVID-19, we propose a novel 0-10 scale to screen for recent olfactory loss, the ODoR-19. We find that numeric ratings ≤2 indicate high odds of symptomatic COVID-19 (4 < OR < 10). Once independently validated, this tool could be deployed when viral lab tests are impractical or unavailable.

Published
2021

10. 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

13. Reducing overfitting in challenge-based competitions

Author

Neto, Elias Chaibub, Hoff, Bruce R, Bare, Chris, Bot, Brian M, Yu, Thomas, Magravite, Lara, Trister, Andrew D, Norman, Thea, Meyer, Pablo, Saez-Rodrigues, Julio, Costello, James C, Guinney, Justin, and Stolovitzky, Gustavo

Subjects
Statistics - Applications
Abstract

Over-fitting is a dreaded foe in challenge-based competitions. Because participants rely on public leaderboards to evaluate and refine their models, there is always the danger they might over-fit to the holdout data supporting the leaderboard. The recently published Ladder algorithm aims to address this problem by preventing the participants from exploiting willingly or inadvertently minor fluctuations in public leaderboard scores during model refinement. In this paper, we report a vulnerability of the Ladder that induces severe over-fitting of the leaderboard when the sample size is small. To circumvent this attack, we propose a variation of the Ladder that releases a bootstrapped estimate of the public leaderboard score instead of providing participants with a direct measure of performance. We also extend the scope of the Ladder to arbitrary performance metrics by relying on a more broadly applicable testing procedure based on the Bayesian bootstrap. Our method makes it possible to use a leaderboard, with the technical and social advantages that it provides, even in cases where data is scant.

Published
2016

14. Benchmarked approaches for reconstruction of in vitro cell lineages and in silico models of C. elegans and M. musculus developmental trees

Author

Gong, Wuming, Granados, Alejandro A., Hu, Jingyuan, Jones, Matthew G., Raz, Ofir, Salvador-Martínez, Irepan, Zhang, Hanrui, Chow, Ke-Huan K., Kwak, Il-Youp, Retkute, Renata, Prusokiene, Alisa, Prusokas, Augustinas, Khodaverdian, Alex, Zhang, Richard, Rao, Suhas, Wang, Robert, Rennert, Phil, Saipradeep, Vangala G., Sivadasan, Naveen, Rao, Aditya, Joseph, Thomas, Srinivasan, Rajgopal, Peng, Jiajie, Han, Lu, Shang, Xuequn, Garry, Daniel J., Yu, Thomas, Chung, Verena, Mason, Michael, Liu, Zhandong, Guan, Yuanfang, Yosef, Nir, Shendure, Jay, Telford, Maximilian J., Shapiro, Ehud, Elowitz, Michael B., and Meyer, Pablo

Published
2021
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16. 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

17. Explanation Ontology: A general-purpose, semantic representation for supporting user-centered explanations.

Author

Chari, Shruthi, Seneviratne, Oshani, Ghalwash, Mohamed, Shirai, Sola, Gruen, Daniel M., Meyer, Pablo, Chakraborty, Prithwish, and McGuinness, Deborah L.

Subjects
KNOWLEDGE representation (Information theory), ARTIFICIAL intelligence, TRUST, ONTOLOGY, EXPLANATION
Abstract

In the past decade, trustworthy Artificial Intelligence (AI) has emerged as a focus for the AI community to ensure better adoption of AI models, and explainable AI is a cornerstone in this area. Over the years, the focus has shifted from building transparent AI methods to making recommendations on how to make black-box or opaque machine learning models and their results more understandable by experts and non-expert users. In our previous work, to address the goal of supporting user-centered explanations that make model recommendations more explainable, we developed an Explanation Ontology (EO). The EO is a general-purpose representation that was designed to help system designers connect explanations to their underlying data and knowledge. This paper addresses the apparent need for improved interoperability to support a wider range of use cases. We expand the EO, mainly in the system attributes contributing to explanations, by introducing new classes and properties to support a broader range of state-of-the-art explainer models. We present the expanded ontology model, highlighting the classes and properties that are important to model a larger set of fifteen literature-backed explanation types that are supported within the expanded EO. We build on these explanation type descriptions to show how to utilize the EO model to represent explanations in five use cases spanning the domains of finance, food, and healthcare. We include competency questions that evaluate the EO's capabilities to provide guidance for system designers on how to apply our ontology to their own use cases. This guidance includes allowing system designers to query the EO directly and providing them exemplar queries to explore content in the EO represented use cases. We have released this significantly expanded version of the Explanation Ontology at https://purl.org/heals/eo and updated our resource website, https://tetherless-world.github.io/explanation-ontology , with supporting documentation. Overall, through the EO model, we aim to help system designers be better informed about explanations and support these explanations that can be composed, given their systems' outputs from various AI models, including a mix of machine learning, logical and explainer models, and different types of data and knowledge available to their systems. [ABSTRACT FROM AUTHOR]

Published
2024
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19. PLANTAS DE INTERESSE PARA A CONSERVAÇÃO NA PORÇÃO SUPERIOR DA BACIA DO RIO SANTO ANTÔNIO - LESTE DO ESPINHAÇO MERIDIONAL

Author

Meyer, Pablo Burkowski, primary, Quaresma, Aline Silva, additional, Oliveira, Caetano Troncoso, additional, Giorni, Victor Teixeira, additional, Jales, Laís Ferreira, additional, Rocha, Maria José Reis da, additional, Brina, Ana Elisa, additional, Damasceno, Alexandre Gomes, additional, Anastacio, Ana Cristina Silva Amoroso, additional, and Mendes, Marília Silva, additional

Published
2020
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20. UNIDADES DE CONSERVAÇÃO URBANAS: TRABALHO INTEGRADO PARA CONCILIAR PRESERVAÇÃO E OCUPAÇÃO HUMANA DO TERRITÓRIO

Author

Brina, Ana Elisa, primary, Ramos, Diego Petrocchi, additional, Silva, Douglas Henrique da, additional, Barbosa, Elaine Ferreira, additional, Ferraz, Gabriel Guerra, additional, Pena, Kalil Felix, additional, Jales, Laís Ferreira, additional, Lima, Márcio Alonso, additional, Mendes, Marilia Silva, additional, Fonseca, Mônica Tavares da, additional, Meyer, Pablo Burkowski, additional, Moreira, Patrícia da Fátima, additional, Cançado, Vanessa Lucena, additional, and Moura, Vitor Marcos Aguiar de, additional

Published
2020
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21. 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

22. Leveraging crowdsourcing to accelerate global health solutions

Author

Davis, Sage, Button-Simons, Katrina, Bensellak, Taoufik, Ahsen, Eren Mehmet, Checkley, Lisa, Foster, Gabriel J., Su, Xinzhuan, Moussa, Ahmed, Mapiye, Darlington, Khoo, Sok Kean, Nosten, Francois, Anderson, Timothy J. C., Vendrely, Katelyn, Bletz, Julie, Yu, Thomas, Panji, Sumir, Ghouila, Amel, Mulder, Nicola, Norman, Thea, Kern, Steven, Meyer, Pablo, Stolovitzky, Gustavo, Ferdig, Michael T., and Siwo, Geoffrey H.

Published
2019
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25. Interpretable Stratification for Chronic Kidney Disease Progression Based on Time to Event Analysis

Author

Ghalwash, Mohamed, Koseki, Akira, Iwamori, Toshiya, Kudo, Michiharu, and Meyer, Pablo

Subjects
Articles
Abstract

In Chronic Kidney Disease (CKD), kidneys are damaged and lose their ability to filter blood, leading to a plethora of health consequences that end up in dialysis. Despite its prevalence, CKD goes often undetected at early stages. In order to better understand disease progression, we stratified patients with CKD by considering the time to dialysis from diagnosis of early CKD (stages 1 or 2). To achieve this, we first reduced the number of clinical features in a predictive time-to-dialysis model and identified the top important features on a cohort of ∼ 40, 000 CKD patients. The extracted features were used to stratify a subpopulation of 3, 522 patients that showed anemia and were prescribed for cardiovascular-related drugs and progressed faster to dialysis. On the other side, clustering patients using conventional clustering methods based on their clinical features did not allow such clear interpretation to identify the main factors for leading fast progression to dialysis. To our knowledge this is the first study extracting interpretable features for stratifying a cohort of early CKD patients using time-to-event analysis which could help prevention and the development of new treatments.

Published
2023

27. Evaluation and optimization of sequence-based gene regulatory deep learning models

Author

Rafi, Abdul Muntakim, primary, Nogina, Daria, additional, Penzar, Dmitry, additional, Lee, Dohoon, additional, Lee, Danyeong, additional, Kim, Nayeon, additional, Kim, Sangyeup, additional, Kim, Dohyeon, additional, Shin, Yeojin, additional, Kwak, Il-Youp, additional, Meshcheryakov, Georgy, additional, Lando, Andrey, additional, Zinkevich, Arsenii, additional, Kim, Byeong-Chan, additional, Lee, Juhyun, additional, Kang, Taein, additional, Vaishnav, Eeshit Dhaval, additional, Yadollahpour, Payman, additional, DREAM Challenge Consortium, Random Promoter, additional, Kim, Sun, additional, Albrecht, Jake, additional, Regev, Aviv, additional, Gong, Wuming, additional, Kulakovskiy, Ivan V., additional, Meyer, Pablo, additional, and de Boer, Carl, additional

Published
2023
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28. Informing clinical assessment by contextualizing post-hoc explanations of risk prediction models in type-2 diabetes

Author

Chari, Shruthi, primary, Acharya, Prasant, additional, Gruen, Daniel M., additional, Zhang, Olivia, additional, Eyigoz, Elif K., additional, Ghalwash, Mohamed, additional, Seneviratne, Oshani, additional, Saiz, Fernando Suarez, additional, Meyer, Pablo, additional, Chakraborty, Prithwish, additional, and McGuinness, Deborah L., additional

Published
2023
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30. The novelty Mezilaurus conceicionensis (Lauraceae) and a key to the species of the genus from Brazilian Southeastern

Author

Alves, Flávio Macedo, Meyer, Pablo Burkowski, and Assis, Leandro Cézanne De Souza

Subjects
Tracheophyta, Magnoliopsida, Lauraceae, Laurales, Biodiversity, Plant Science, Plantae, Ecology, Evolution, Behavior and Systematics, Taxonomy
Abstract

A new species, Mezilaurus conceicionensis (Lauraceae), from Southeastern Brazil, was discovered during fieldwork at Serra da Ferrugem, State of Minas Gerais. This new species is described and illustrated in this study, being distinguished from the remaining species of Mezilaurus species mostly by its globoid fruits. An identification key of the species of Mezilaurus from Southeastern Brazil is presented.

Published
2022
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32. Predicting/characterizing chemosensory changes in COVID+ vs. COVID- using natural language processing of multinational survey data

Author

Walsh-Messinger, Julie, Meyer, Pablo, Parma, Valentina, Farruggia, Michael, and Pellegrino, Robert

Subjects
Medicine and Health Sciences, Social and Behavioral Sciences
Abstract

COVID-19 is associated with notable decrements in chemosensation. Notably, other upper respiratory infections (URIs) are also associated with decrements in chemosensory ability, though their pathophysiology and extent likely differ from that of COVID-19. Here, we seek to determine whether self-reported text descriptors of chemosensory ability can predict COVID+ versus COVID- status, and then characterize the semantic architecture of these predictive features. Finally, we seek to use the text descriptors provided to better characterize qualitative loss in smell and chemesthesis. We will answer the following questions: 1. What is the sensitivity and specificity of applying natural language processing tools to self-reported text descriptors of chemosensory ability for predicting COVID+ vs. COVID-? 2. What are the characteristics of the semantic architecture of predictive self-reported text descriptions of chemosensory-related COVID+ vs COVID- diagnosis? 3. What are the qualitative characteristics of anosmia, hyposmia, parosmia, and phantosmia associated with COVID+?

Published
2022
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34. 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
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35. Mezilaurus conceicionensis L. C. S. Assis, P. B. Meyer & F. M. Alves 2022, sp. nov

Author

Alves, Flávio Macedo, Meyer, Pablo Burkowski, and Assis, Leandro Cézanne De Souza

Subjects
Tracheophyta, Magnoliopsida, Lauraceae, Mezilaurus conceicionensis, Laurales, Mezilaurus, Biodiversity, Plantae, Taxonomy
Abstract

Mezilaurus conceicionensis L.C.S.Assis, P.B.Meyer & F.M.Alves, sp. nov. (Figs 1–2). Type:— BRAZIL. Minas Gerais: Conceição do Mato Dentro, Fazenda Santa Cruz, Propriedade da Vale, 1025 m, 19°03’25”S, 43°24’11”W, 20 February 2019 (fl., fr.), P.B. Meyer & N.L. de Paula 3803 (holotype, BHCB 203295!; isotype, RB!). Diagnosis:— Mezilaurus conceicionensis is similar in floral structure to Mezilaurus revolutifolia, however, it differs by having a shrubby habit, the branches distally puberulous, stamens connate and globoid fruits. Shrubs to small trees, 1.5–5 m tall. Branches with rhythmic growth; branchlets cylindrical, grayish to dark-brown, distally puberulous to glabrescent, with scars of senescent leaves and conspicuous lenticels. Apical buds totally covered by sericeous and translucent light-brown hairs. Leaves alternate to sub-opposite, clustered at the apex of the branchlets; petioles 0.6–1.8 cm long, 15–20% of the leaf length, plane to prominent at the adaxial surface, glabrous to subglabrous; blades 1.8–10 cm × 0.9–4.2 cm, obovate to oblanceolate, base attenuate, margins revolute, apex emarginate, rounded, obtuse or rarely mucronate, adaxial and abaxial surfaces glabrous to glabrescent, primary, secondary, and tertiary veins prominent at both surfaces, secondary veins brochidodromous, diverging at 35–75° to the primary vein, domatia absent. Cataphylls present, totally covered by sericeous and translucent light-brown hairs. Inflorescences 3.5– 6 cm long, dibotrya, lateral branchlets umbeliform, raquis cerose, partially covered by tomentose indument and lightcream hairs. Flowers 3–6 mm long, globose to sub-globose; pedicels 2.5–4.5 mm long, glabrous; hypanthium ca. 1 × 1.5 mm, cotiliform, inner and outer surfaces glabrous; tepals 0.2–0.6 × 0.5–1 mm, shorter than the hypanthium, ca. 1 / 3 of the flower length, subequal, outer tepals slightly shorter and wider than the inner ones, outer tepals depressed-ovate, glabrous to glabrescent, inner tepals broadly ovate to depressed-ovate, glabrous to glabrescent; stamens 1–1.1 mm long, connate, inserted at anthesis, filaments 0.5–0.6 mm long, rectangular, partially to totally covered by tomentose and opaque indument, anthers slightly deltate, glabrous, rarely papillose, locelli apical; pistil ca. 1.2 × 1.0 mm, ovary ca. 0.8 mm long, narrowly ellipsoid, glabrous. Style ca. 0.4 mm long, ca. 1 / 3 of the pistil length, glabrous. Fruits 1.8–3 cm, globose to subglobose, glaucous. Cupules ca. 1 × 4 mm, plane. Paratypes:— BRAZIL. Minas Gerais: Conceição do Mato Dentro, 1043 m, 19º02’47”S, 43º23’19”W, 16 February 2016 (fl.), E. Tameirão-Neto & C.V. Vidal 5431 (BHCB!, RB!); 19º00’03”S, 43º24’05”W, 14 August 2016 (fl., fr.), E. Tameirão-Neto & C.V. Vidal 5112 (BHCB!, RB!); 1005 m, 19º33’99”S, 43º23’13”W, 24 July 2014 (fl.), C.V. Vidal 1258 (BHCB!, RB!); 1010 m, 19º34’98”S, 43º23’70”W, 11 December 2014 (fl.), C.V. Vidal 1321 (BHCB!, RB!); Fazenda Santa Cruz, Propriedade da Vale, 1010 m, 19°03’04”S, 43°23’12”W, 20 February 2019 (fl., fr.), P.B. Meyer & N.L. Paula 3775 (BHCB!); 1014 m, 19°03’03”S, 43°23’12”W, 20 February 2019 (fl., fr.), P.B. Meyer & N.L. Paula 3776 (BHCB!); 1015 m, 19°03’03”S, 43°23’13”W, 20 February 2019 (fl., fr.), P.B. Meyer & N.L. Paula 3777 (BHCB!); 1018 m, 19°03’03”S, 43°23’13”W, 20 February 2019 (fl., fr.), P.B. Meyer & N.L. Paula 3778 (BHCB!); 1018 m, 19°03’03”S, 43°23’13”W, 20 February 2019 (fl., fr.), P.B. Meyer & N.L. Paula 3779 (BHCB!); 1006 m, 19°03’04”S, 43°23’11”W, 20 February 2019 (fl., fr.), P.B. Meyer & N.L. Paula 3780 (BHCB!); 1003 m, 19°03’04”S, 43°23’11”W, 20 February 2019 (fl., fr.), P.B. Meyer & N.L. Paula 3781 (BHCB!); 1001 m, 19°03’05”S, 43°23’11”W, 20 February 2019 (fl., fr.), P.B. Meyer & N.L. Paula 3782 (BHCB!); 993 m, 19°03’05”S, 43°23’10”W, 20 February 2019 (fl., fr.), P.B. Meyer & N.L. Paula 3783 (BHCB!); 983 m, 19°03’05”S, 43°23’07”W, 20 February 2019 (fl., fr.), P.B. Meyer & N.L. Paula 3784 (BHCB!); 1014 m, 19°03’03”S, 43°23’11”W, 20 February 2019 (fl., fr.), P.B. Meyer & N.L. Paula 3785 (BHCB!); 1015 m, 19°03’03”S, 43°23’12”W, 20 February 2019 (fl., fr.), P.B. Meyer & N.L. Paula 3786 (BHCB!); 1014 m, 19°03’03”S, 43°23’12”W, 20 February 2019 (fl., fr.), P.B. Meyer & N.L. Paula 3787 (BHCB!); 1015 m, 19°03’03”S, 43°23’13”W, 20 February 2019 (fl., fr.), P.B. Meyer & N.L. Paula 3788 (BHCB!); 1016 m, 19°03’03”S, 43°23’13”W, 20 February 2019 (fl., fr.), P.B. Meyer & N.L. Paula 3789 (BHCB!); 1042 m, 19°03’20”S, 43°23’16”W, 20 February 2019 (fl., fr.), P.B. Meyer & N.L. Paula 3790 (BHCB!); 1041 m, 19°03’03”S, 43°24’17”W, 20 February 2019 (fl., fr.), P.B. Meyer & N.L. Paula 3791 (BHCB!); 1044 m, 19°03’21”S, 43°24’16”W, 20 February 2019 (fl., fr.), P.B. Meyer & N.L. Paula 3792 (BHCB!); 1045 m, 19°03’21”S, 43°24’16”W, 20 February 2019 (fl., fr.), P.B. Meyer & N.L. Paula 3793 (BHCB!); 1046 m, 19°03’21”S, 43°24’16”W, 20 February 2019 (fl., fr.), P.B. Meyer & N.L. Paula 3794 (BHCB!); 1036 m, 19°03’22”S, 43°24’15”W, 20 February 2019 (fl., fr.), P.B. Meyer & N.L. Paula 3795 (BHCB!); 1033 m, 19°03’23”S, 43°24’15”W, 20 February 2019 (fl., fr.), P.B. Meyer & N.L. Paula 3796 (BHCB!); 1034 m, 19°03’23”S, 43°24’14”W, 20 February 2019 (fl., fr.), P.B. Meyer & N.L. Paula 3797 (BHCB!); 1035 m, 19°03’24”S, 43°24’13”W, 20 February 2019 (fl., fr.), P.B. Meyer & N.L. Paula 3798 (BHCB!); 1036 m, 19°03’24”S, 43°24’13”W, 20 February 2019 (fl., fr.), P.B. Meyer & N.L. Paula 3799 (BHCB!); 1030 m, 19°03’24”S, 43°24’12”W, 20 February 2019 (fl., fr.), P.B. Meyer & N.L. Paula 3800 (BHCB!); 1026 m, 19°03’25”S, 43°24’12”W, 20 February 2019 (fl., fr.), P.B. Meyer & N.L. Paula 3801 (BHCB!); 1024 m, 19°03’25”S, 43°24’11”W, 20 February 2019 (fl., fr.), P.B. Meyer & N.L. Paula 3802 (BHCB!); 1027 m, 19°03’25”S, 43°24’11”W, 20 February 2019 (fl., fr.), P.B. Meyer & N.L. Paula 3804 (BHCB!). 19º30’11”S, 43º23’04”W, 16 August 2012 (fl., fr.), E. Tameirão-Neto & C.V. Vidal 5156 (BHCB!, RB); Ibid., 1007 m, 19º05’13”S, 43º38’69”W, 30 July 2017 (fl.), P.B. Meyer 3062 (BHCB!, CGMS!, K!, MO!, RB!); Ibid., Serra da Ferrugem, 19º00’03”S, 43º23’51”W, 27 July 2017 (fl., fr.), A.S. Quaresma & C.T. Oliveira 854 (BHCB!, HEPH!, RB!). Distribution and ecology:— Mezilaurus conceicionensis is endemic to the campos rupestres over canga couraçada in the municipality of Conceição do Mato Dentro, State of Minas Gerais, Brazil (Fig. 5a of Assunção-Silva & Assis [2021] as Mezilaurus sp. nov 1), between 980–1050 m, associated to genus Vellozia Vandelli (1788: 32), a typical genus of campos rupestres (Fig 1). Phenology: — During fieldwork, the flowering of M. conceicionensis was continuously observed from August 2018 to July 2019, without interruptions, with peaks being recorded in the months of October 2018 and March 2019. Fruiting was also observed throughout this period, with peaks mostly from greater amounts of fruit being recorded between the months of May and July 2019. Etymology: — The epithet pays tribute to the type locality, the municipality of Conceição do Mato Dentro, MG, Brazil, located at the base of Serra da Ferrugem. Conservation status:— Mezilaurus conceicionensis is restricted to the Serra da Ferrugem mountain range (also called “Serra da Serpentina”), municipality of Conceição do Mato Dentro, State of Minas Gerais, Brazil. So far, it is known only from five subpopulations in this mountain range. According to the IUCN (IUCN 2012, 2016), this species can be tentatively be classified as Critically Endangered - CR (A4c;B1ab(iii) + 2ab(iii)). This classification is based on its small AOO, estimated in 0.49 km ², small EOO, calculate in 21.927 km ², and a continuing and probably not reversible reduction in its population size, based on the decline of this AOO, EOO and habitat quality. Mezilaurus conceicionensis is distinct from the remaining species of the genus by its shrubby habit, leaves with both surfaces glabrous to glabrescent, flowers clustered (umbel-like) along the lateral branches of the inflorescences, flowers globose, glabrous to glabrescent, tepals ca. 1 / 3 the flower length, shorter than the hypanthium, stamens connate and inserted, and globoid fruits. Mezilaurus conceicionensis can be confused with the collections of M. revolutifolia F.M.Alves & P.L.R.Moraes in Alves et al. (2011: 506), but the latter is characterized by its tepals as long as the hypanthium, ca. 1 / 2 the flower length to slightly larger, exserted and free stamens, and ellipsoid to obovoid fruits. Mezilaurus conceicionensis can also be confused with M. crassiramea (Meissner 1864: 117) Taubert ex Mez (1892: 112), also found in campos rupestres from the State of Minas Gerais. However, in M. crassiramea, the leaves are abaxially hirsute, villous or lanate, petioles are 1–10% of the leaf length, lanate to puberulous, the lateral branch of the inflorescences is racemiform, the stamens are exserted and free, and the fruits are ca. 3.1 × 2.8 cm, ellipsoid to obovoid. A key to the species of Mezilaurus from Southeastern Brazil is presented below., Published as part of Alves, Flávio Macedo, Meyer, Pablo Burkowski & Assis, Leandro Cézanne De Souza, 2022, The novelty Mezilaurus conceicionensis (Lauraceae) and a key to the species of the genus from Brazilian Southeastern, pp. 185-192 in Phytotaxa 541 (2) on pages 186-190, DOI: 10.11646/phytotaxa.541.2.8, http://zenodo.org/record/6388781, {"references":["Assuncao-Silva, C. C. & Assis, L. C. S. (2021) Areas of endemism of Lauraceae: new insights on the biogeographic regionalization of the Espinhaco Range, Brazil. Cladistics. [in press] https: // doi. org / 10.1111 / cla. 12481","Vandelli, D. (1788) Florae Lusitanicae et Brasiliensis specimen ... Typographia Academico-Regia, Coimbra, 96 pp. https: // dx. doi. org / 10.5962 / bhl. title. 51092","IUCN (2012) The IUCN Red List Categories and Criteria: version 3.1. Second edition. IUCN Red List Unit, Cambridge U. K. Available from: http: // www. iucnredlist. org (accessed 10 May 2011).","IUCN (2016) Guidelines for using the IUCN red list categories and criteria. version 12. Prepared by the standards and petitions subcommittee. IUCN Species Survival Commission. IUCN, Gland, Switzerland and Cambridge, UK. [http: // www. iucnredlist. org / documents / RedListGuidelines. pdf]","Alves, F. M., Souza, V. C. & Moraes, P. L. R. de (2011) Mezilaurus revolutifolia (Lauraceae), a new species from Brazilian Atlantic forest. Kew Bulletin 66: 505 - 509. https: // doi. org / 10.1007 / s 12225 - 011 - 9302 - z","Meissner, C. F. (1864) Lauraceae. In: Candolle, A. L. P. P. de (Ed.) Prodromus Systematis Naturalis Regni Vegetabilis, vol. 15, p. 1. Sumptibus Victoris Masson et filii, Paris, pp. 1 - 260. https: // doi. org / 10.5962 / bhl. title. 286","Mez, C. (1892) Spicilegium Laureanum. Arbeiten aus dem Konigl. Botanischen Garten zu Breslau 1: 71 - 166."]}

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2022
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36. Mezilaurus Alves & Meyer & Assis 2022

Author

Alves, Flávio Macedo, Meyer, Pablo Burkowski, and Assis, Leandro Cézanne De Souza

Subjects
Tracheophyta, Magnoliopsida, Lauraceae, Laurales, Mezilaurus, Biodiversity, Plantae, Taxonomy
Abstract

Key to the Brazilian Southeast species of Mezilaurus 1. Leaves with erect indument abaxially; inflorescences in racemes with lateral branches raceme-like; bracteoles persistent; Brazilian Cerrado or Atlantic Forest; Minas Gerais and Espírito Santo states............................................................. Mezilaurus crassiramea - Leaves glabrous or glabrescent abaxially; inflorescences in racemes with lateral branches umbel-like; bracteoles deciduous; Atlantic Forest; Espírito Santo, Minas Gerais or Rio de Janeiro states..............................................................................................2 2. Globose flowers; globoid fruits; Vegetation on rocky outcrops (campo rupestre); Minas Gerais... Mezilaurus conceicionensis - Ellipsoid flowers; ellipsoid fruits; Atlantic Forest; Espírito Santo and Rio de Janeiro......................................................................3 3. Leaves narrow-elliptical or oblanceolate, 3–8.4 times longer than wide; Atlantic Forest; Rio de Janeiro......................................................................................................................................... Mezilaurus navalium (Allemão 1847: 265) Taub. ex Mez (1892: 112) - Leaves elliptic or obovate, 1.7–2.8 times longer than wide; Atlantic Forest; Espírito Santo............................................................4 4. Pedicelate flowers; filaments glabrous.............................................................. Mezilaurus glabriantha Alves & Souza (2012: 257) - Sessile flowers; filaments densely pubescent............. Mezilaurus sessiliflora P.L.R.Moraes & Vergne in Moraes et al. (2017: 153), Published as part of Alves, Flávio Macedo, Meyer, Pablo Burkowski & Assis, Leandro Cézanne De Souza, 2022, The novelty Mezilaurus conceicionensis (Lauraceae) and a key to the species of the genus from Brazilian Southeastern, pp. 185-192 in Phytotaxa 541 (2) on page 190, DOI: 10.11646/phytotaxa.541.2.8, http://zenodo.org/record/6388781, {"references":["Allemao, F. F. (1847) Laurinea. Silvia dos Arsenaes (Gen. Nov.). Silvia (Gen. Nov.). Navalium (Sp. Nov.). Nome vulgar: Tapinhoan. Archivo Medico Brasileiro 3: 265 - 268.","Mez, C. (1892) Spicilegium Laureanum. Arbeiten aus dem Konigl. Botanischen Garten zu Breslau 1: 71 - 166.","Alves, F. M. & Souza, V. C. (2012) Two new species of Mezilaurus (Lauraceae) from Brazil. Brittonia 64: 257 - 262. https: // doi. org / 10.1007 / s 12228 - 011 - 9228 - 7","Moraes, P. L. R., Vergne, M. C. & van der Werff, H. (2017) Three new species of Lauraceae from the Atlantic rainforest of Brazil. Phytotaxa 316: 149 - 160. https: // doi. org / 10.11646 / phytotaxa. 316.2.4"]}

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2022
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38. Figure 1 from: Augsten M, Meyer PB, Freitas LB, Batista JAN, Stehmann JR (2022) Nicotiana gandarela (Solanaceae), a new species of ‘tobacco’ highly endangered from the Quadrilátero Ferrífero in Brazil. PhytoKeys 190: 113-129. https://doi.org/10.3897/phytokeys.190.76111

Author

Augsten, Mariana, primary, Meyer, Pablo Burkowski, additional, Freitas, Loreta B., additional, Batista, João A. N., additional, and Stehmann, João Renato, additional

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2022
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39. Supplementary material 2 from: Augsten M, Meyer PB, Freitas LB, Batista JAN, Stehmann JR (2022) Nicotiana gandarela (Solanaceae), a new species of ‘tobacco’ highly endangered from the Quadrilátero Ferrífero in Brazil. PhytoKeys 190: 113-129. https://doi.org/10.3897/phytokeys.190.76111

Author

Augsten, Mariana, primary, Meyer, Pablo Burkowski, additional, Freitas, Loreta B., additional, Batista, João A. N., additional, and Stehmann, João Renato, additional

Published
2022
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40. Figure 3 from: Augsten M, Meyer PB, Freitas LB, Batista JAN, Stehmann JR (2022) Nicotiana gandarela (Solanaceae), a new species of ‘tobacco’ highly endangered from the Quadrilátero Ferrífero in Brazil. PhytoKeys 190: 113-129. https://doi.org/10.3897/phytokeys.190.76111

Author

Augsten, Mariana, primary, Meyer, Pablo Burkowski, additional, Freitas, Loreta B., additional, Batista, João A. N., additional, and Stehmann, João Renato, additional

Published
2022
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41. Figure 2 from: Augsten M, Meyer PB, Freitas LB, Batista JAN, Stehmann JR (2022) Nicotiana gandarela (Solanaceae), a new species of ‘tobacco’ highly endangered from the Quadrilátero Ferrífero in Brazil. PhytoKeys 190: 113-129. https://doi.org/10.3897/phytokeys.190.76111

Author

Augsten, Mariana, primary, Meyer, Pablo Burkowski, additional, Freitas, Loreta B., additional, Batista, João A. N., additional, and Stehmann, João Renato, additional

Published
2022
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42. Supplementary material 1 from: Augsten M, Meyer PB, Freitas LB, Batista JAN, Stehmann JR (2022) Nicotiana gandarela (Solanaceae), a new species of ‘tobacco’ highly endangered from the Quadrilátero Ferrífero in Brazil. PhytoKeys 190: 113-129. https://doi.org/10.3897/phytokeys.190.76111

Author

Augsten, Mariana, primary, Meyer, Pablo Burkowski, additional, Freitas, Loreta B., additional, Batista, João A. N., additional, and Stehmann, João Renato, additional

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2022
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43. Supplementary material 3 from: Augsten M, Meyer PB, Freitas LB, Batista JAN, Stehmann JR (2022) Nicotiana gandarela (Solanaceae), a new species of ‘tobacco’ highly endangered from the Quadrilátero Ferrífero in Brazil. PhytoKeys 190: 113-129. https://doi.org/10.3897/phytokeys.190.76111

Author

Augsten, Mariana, primary, Meyer, Pablo Burkowski, additional, Freitas, Loreta B., additional, Batista, João A. N., additional, and Stehmann, João Renato, additional

Published
2022
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