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486 results on '"CONNOLLY, JOHN"'

2. High-dimensional multiple imputation (HDMI) for partially observed confounders including natural language processing-derived auxiliary covariates

Author

Weberpals, Janick, Shaw, Pamela A., Lin, Kueiyu Joshua, Wyss, Richard, Plasek, Joseph M, Zhou, Li, Ngan, Kerry, DeRamus, Thomas, Raman, Sudha R., Hammill, Bradley G., Lee, Hana, Toh, Sengwee, Connolly, John G., Dandreo, Kimberly J., Tian, Fang, Liu, Wei, Li, Jie, Hernández-Muñoz, José J., Schneeweiss, Sebastian, and Desai, Rishi J.

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

Multiple imputation (MI) models can be improved by including auxiliary covariates (AC), but their performance in high-dimensional data is not well understood. We aimed to develop and compare high-dimensional MI (HDMI) approaches using structured and natural language processing (NLP)-derived AC in studies with partially observed confounders. We conducted a plasmode simulation study using data from opioid vs. non-steroidal anti-inflammatory drug (NSAID) initiators (X) with observed serum creatinine labs (Z2) and time-to-acute kidney injury as outcome. We simulated 100 cohorts with a null treatment effect, including X, Z2, atrial fibrillation (U), and 13 other investigator-derived confounders (Z1) in the outcome generation. We then imposed missingness (MZ2) on 50% of Z2 measurements as a function of Z2 and U and created different HDMI candidate AC using structured and NLP-derived features. We mimicked scenarios where U was unobserved by omitting it from all AC candidate sets. Using LASSO, we data-adaptively selected HDMI covariates associated with Z2 and MZ2 for MI, and with U to include in propensity score models. The treatment effect was estimated following propensity score matching in MI datasets and we benchmarked HDMI approaches against a baseline imputation and complete case analysis with Z1 only. HDMI using claims data showed the lowest bias (0.072). Combining claims and sentence embeddings led to an improvement in the efficiency displaying the lowest root-mean-squared-error (0.173) and coverage (94%). NLP-derived AC alone did not perform better than baseline MI. HDMI approaches may decrease bias in studies with partially observed confounders where missingness depends on unobserved factors.

Published
2024

3. Considerations for first field trials of low-threshold gene drive for malaria vector control

Author

Connolly, John B, Burt, Austin, Christophides, George, Diabate, Abdoulaye, Habtewold, Tibebu, Hancock, Penelope A, James, Anthony A, Kayondo, Jonathan K, Lwetoijera, Dickson Wilson, Manjurano, Alphaxard, McKemey, Andrew R, Santos, Michael R, Windbichler, Nikolai, and Randazzo, Filippo

Subjects
Medical Microbiology, Biomedical and Clinical Sciences, Clinical Sciences, Malaria, Vector-Borne Diseases, Biotechnology, Genetics, Rare Diseases, Infectious Diseases, 3.2 Interventions to alter physical and biological environmental risks, Prevention of disease and conditions, and promotion of well-being, Infection, Good Health and Well Being, Mosquito Control, Mosquito Vectors, Animals, Anopheles, Gene Drive Technology, Adaptive trial design, Africa, Cluster randomized controlled trial, Genetic efficacy, Entomological efficacy, Epidemiological efficacy, Mosquito, Population modification, Population suppression, Spillover, Microbiology, Public Health and Health Services, Tropical Medicine, Medical microbiology, Public health
Abstract

Sustainable reductions in African malaria transmission require innovative tools for mosquito control. One proposal involves the use of low-threshold gene drive in Anopheles vector species, where a 'causal pathway' would be initiated by (i) the release of a gene drive system in target mosquito vector species, leading to (ii) its transmission to subsequent generations, (iii) its increase in frequency and spread in target mosquito populations, (iv) its simultaneous propagation of a linked genetic trait aimed at reducing vectorial capacity for Plasmodium, and (v) reduced vectorial capacity for parasites in target mosquito populations as the gene drive system reaches fixation in target mosquito populations, causing (vi) decreased malaria incidence and prevalence. Here the scope, objectives, trial design elements, and approaches to monitoring for initial field releases of such gene dive systems are considered, informed by the successful implementation of field trials of biological control agents, as well as other vector control tools, including insecticides, Wolbachia, larvicides, and attractive-toxic sugar bait systems. Specific research questions to be addressed in initial gene drive field trials are identified, and adaptive trial design is explored as a potentially constructive and flexible approach to facilitate testing of the causal pathway. A fundamental question for decision-makers for the first field trials will be whether there should be a selective focus on earlier points of the pathway, such as genetic efficacy via measurement of the increase in frequency and spread of the gene drive system in target populations, or on wider interrogation of the entire pathway including entomological and epidemiological efficacy. How and when epidemiological efficacy will eventually be assessed will be an essential consideration before decisions on any field trial protocols are finalized and implemented, regardless of whether initial field trials focus exclusively on the measurement of genetic efficacy, or on broader aspects of the causal pathway. Statistical and modelling tools are currently under active development and will inform such decisions on initial trial design, locations, and endpoints. Collectively, the considerations here advance the realization of developer ambitions for the first field trials of low-threshold gene drive for malaria vector control within the next 5 years.

Published
2024

20. Mapping and monitoring peatland conditions from global to field scale

Author

Minasny, Budiman, Adetsu, Diana Vigah, Aitkenhead, Matt, Artz, Rebekka R. E., Baggaley, Nikki, Barthelmes, Alexandra, Beucher, Amélie, Caron, Jean, Conchedda, Giulia, Connolly, John, Deragon, Raphaël, Evans, Chris, Fadnes, Kjetil, Fiantis, Dian, Gagkas, Zisis, Gilet, Louis, Gimona, Alessandro, Glatzel, Stephan, Greve, Mogens H., Habib, Wahaj, Hergoualc’h, Kristell, Hermansen, Cecilie, Kidd, Darren B., Koganti, Triven, Kopansky, Dianna, Large, David J., Larmola, Tuula, Lilly, Allan, Liu, Haojie, Marcus, Matthew, Middleton, Maarit, Morrison, Keith, Petersen, Rasmus Jes, Quaife, Tristan, Rochefort, Line, Rudiyanto, Toca, Linda, Tubiello, Francesco N., Weber, Peter Lystbæk, Weldon, Simon, Widyatmanti, Wirastuti, Williamson, Jenny, and Zak, Dominik

Published
2024
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21. Estimation of the non-linear parameter in Generalised Diversity-Interactions models is unaffected by change in structure of the interaction terms

Author

Vishwakarma, Rishabh, Byrne, Laura, Connolly, John, Moral, Rafael de Andrade, and Brophy, Caroline

Subjects
Statistics - Applications, Statistics - Computation
Abstract

Research over the past few decades has assumed the richness (number of species) to be the primary driver of the biodiversity and ecosystem function (BEF) relationship. However, biodiversity is multi-dimensional, and richness alone does not capture all its attributes. Diversity-Interactions modelling is a regression-based framework that models the biodiversity and ecosystem function relationship by incorporating species-specific effects along with the interactions between species. The species interactions in a Diversity-Interactions model can take several different forms ranging in complexity from a single interaction term (assuming all pairs of species interact in the same way) to many interaction terms (e.g. assuming a separate interaction for all pairs of species). The specification of the interactions may also include a non-linear parameter ({\theta}) as an exponent to the product of the species proportions to allow for non-linear relationship between the response and species interactions, giving rise to Generalized Diversity-Interactions modelling. The structure of the interaction terms describes the underlying biological processes and thus the selection of a correct structure is important. The inclusion of {\theta} introduces complexity to the model selection process: we could (a) select the interaction structure first by assuming {\theta}=1 and then estimate {\theta}, or (b) estimate {\theta} first and then select the appropriate interaction structure by fixing {\theta}, or (c) test for {\theta} and its inclusion for each interaction structure. It is also unknown whether {\theta} is robust to changes in the underlying interaction structure. Using a simulation study, we test the robustness of {\theta} and compare multiple model selection approaches to identify an optimal and computationally efficient model selection procedure for Generalized Diversity-Interactions models., Comment: For code to reproduce the analysis see https://github.com/rishvish/Theta-Simulation-Study

Published
2023

22. Going beyond richness: Modelling the BEF relationship using species identity, evenness, richness and species interactions via the DImodels R package

Author

Moral, Rafael A., Vishwakarma, Rishabh, Connolly, John, Byrne, Laura, Hurley, Catherine, Finn, John A., and Brophy, Caroline

Subjects
Quantitative Biology - Populations and Evolution, Statistics - Methodology
Abstract

BEF studies aim to understand how ecosystems respond to a gradient of species diversity. Diversity-Interactions (DI) models are suitable for analysing the BEF relationship. These models relate an ecosystem function response of a community to the identity of the species in the community, their evenness (proportions) and interactions. The number of species in the community (richness) is also implicitly modelled through this approach. It is common in BEF studies to model an ecosystem function as a function of richness; while this can uncover trends in the BEF relationship, by definition, species diversity is much broader than richness alone, and important patterns in the BEF relationship may remain hidden. In this paper, we introduce the DImodels R package for implementing DI models. We also compare DI models to traditional modelling approaches to highlight the advantages of using a multi-dimensional definition of species diversity. We show that using DI models can lead to considerably improved model fit over other methods; it does this by incorporating variation due to the multiple facets of species diversity. Predicting from a DI model is not limited to the study design points, the model can extrapolate to predict for any species composition and proportions (assuming there is sufficient coverage of this space in the study design). Expressing the BEF relationship as a function of richness alone can be useful to capture overall trends. However, collapsing the multiple dimensions of species diversity to a single dimension (such as richness) can result in valuable ecological information being lost. DI modelling provides a framework to test the multiple components of species diversity in the BEF relationship. It facilitates uncovering a deeper ecological understanding of the BEF relationship and can lead to enhanced inference.

Published
2022

23. Selection, optimization and validation of ten chronic disease polygenic risk scores for clinical implementation in diverse US populations

Author

Lennon, Niall J., Kottyan, Leah C., Kachulis, Christopher, Abul-Husn, Noura S., Arias, Josh, Belbin, Gillian, Below, Jennifer E., Berndt, Sonja I., Chung, Wendy K., Cimino, James J., Clayton, Ellen Wright, Connolly, John J., Crosslin, David R., Dikilitas, Ozan, Velez Edwards, Digna R., Feng, QiPing, Fisher, Marissa, Freimuth, Robert R., Ge, Tian, Glessner, Joseph T., Gordon, Adam S., Patterson, Candace, Hakonarson, Hakon, Harden, Maegan, Harr, Margaret, Hirschhorn, Joel N., Hoggart, Clive, Hsu, Li, Irvin, Marguerite R., Jarvik, Gail P., Karlson, Elizabeth W., Khan, Atlas, Khera, Amit, Kiryluk, Krzysztof, Kullo, Iftikhar, Larkin, Katie, Limdi, Nita, Linder, Jodell E., Loos, Ruth J. F., Luo, Yuan, Malolepsza, Edyta, Manolio, Teri A., Martin, Lisa J., McCarthy, Li, McNally, Elizabeth M., Meigs, James B., Mersha, Tesfaye B., Mosley, Jonathan D., Musick, Anjene, Namjou, Bahram, Pai, Nihal, Pesce, Lorenzo L., Peters, Ulrike, Peterson, Josh F., Prows, Cynthia A., Puckelwartz, Megan J., Rehm, Heidi L., Roden, Dan M., Rosenthal, Elisabeth A., Rowley, Robb, Sawicki, Konrad Teodor, Schaid, Daniel J., Smit, Roelof A. J., Smith, Johanna L., Smoller, Jordan W., Thomas, Minta, Tiwari, Hemant, Toledo, Diana M., Vaitinadin, Nataraja Sarma, Veenstra, David, Walunas, Theresa L., Wang, Zhe, Wei, Wei-Qi, Weng, Chunhua, Wiesner, Georgia L., Yin, Xianyong, and Kenny, Eimear E.

Published
2024
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26. Quantifying the phenome-wide disease burden of obesity using electronic health records and genomics.

Author

Robinson, Jamie, Carroll, Robert, Bastarache, Lisa, Chen, Qingxia, Pirruccello, James, Mou, Zongyang, Wei, Wei-Qi, Connolly, John, Mentch, Frank, Crane, Paul, Hebbring, Scott, Crosslin, David, Gordon, Adam, Rosenthal, Elisabeth, Stanaway, Ian, Hayes, M, Wei, Wei, Petukhova, Lynn, Namjou-Khales, Bahram, Zhang, Ge, Safarova, Mayya, Walton, Nephi, Still, Christopher, Bottinger, Erwin, Loos, Ruth, Murphy, Shawn, Jackson, Gretchen, Abumrad, Naji, Kullo, Iftikhar, Jarvik, Gail, Larson, Eric, Weng, Chunhua, Roden, Dan, Khera, Amit, and Denny, Joshua

Subjects
Humans, Phenomics, Electronic Health Records, Genome-Wide Association Study, Diabetes Mellitus, Type 2, Polymorphism, Single Nucleotide, Genomics, Genetic Predisposition to Disease, Obesity, Phenotype, Cost of Illness
Abstract

OBJECTIVE: High BMI is associated with many comorbidities and mortality. This study aimed to elucidate the overall clinical risk of obesity using a genome- and phenome-wide approach. METHODS: This study performed a phenome-wide association study of BMI using a clinical cohort of 736,726 adults. This was followed by genetic association studies using two separate cohorts: one consisting of 65,174 adults in the Electronic Medical Records and Genomics (eMERGE) Network and another with 405,432 participants in the UK Biobank. RESULTS: Class 3 obesity was associated with 433 phenotypes, representing 59.3% of all billing codes in individuals with severe obesity. A genome-wide polygenic risk score for BMI, accounting for 7.5% of variance in BMI, was associated with 296 clinical diseases, including strong associations with type 2 diabetes, sleep apnea, hypertension, and chronic liver disease. In all three cohorts, 199 phenotypes were associated with class 3 obesity and polygenic risk for obesity, including novel associations such as increased risk of renal failure, venous insufficiency, and gastroesophageal reflux. CONCLUSIONS: This combined genomic and phenomic systematic approach demonstrated that obesity has a strong genetic predisposition and is associated with a considerable burden of disease across all disease classes.

Published
2022

27. Recommendations for environmental risk assessment of gene drive applications for malaria vector control

Author

Connolly, John B, Mumford, John D, Glandorf, Debora CM, Hartley, Sarah, Lewis, Owen T, Evans, Sam Weiss, Turner, Geoff, Beech, Camilla, Sykes, Naima, Coulibaly, Mamadou B, Romeis, Jörg, Teem, John L, Tonui, Willy, Lovett, Brian, Mankad, Aditi, Mnzava, Abraham, Fuchs, Silke, Hackett, Talya D, Landis, Wayne G, Marshall, John M, and Aboagye-Antwi, Fred

Subjects
Rare Diseases, Gene Therapy, Vector-Borne Diseases, Infectious Diseases, Malaria, Prevention, Genetics, 2.2 Factors relating to the physical environment, Aetiology, Infection, Good Health and Well Being, Animals, Anopheles, Gene Drive Technology, Mosquito Control, Mosquito Vectors, Risk Assessment, Gene drive, Population suppression, Vector control, Environmental risk assessment, Engagement, Modelling, Ecological risk, Microbiology, Medical Microbiology, Public Health and Health Services, Tropical Medicine
Abstract

Building on an exercise that identified potential harms from simulated investigational releases of a population suppression gene drive for malaria vector control, a series of online workshops identified nine recommendations to advance future environmental risk assessment of gene drive applications.

Published
2022

31. Single-cell landscape of functionally cured chronic hepatitis B patients reveals activation of innate and altered CD4-CTL-driven adaptive immunity

Author

Narmada, Balakrishnan Chakrapani, Khakpoor, Atefeh, Shirgaonkar, Niranjan, Narayanan, Sriram, Aw, Pauline Poh Kim, Singh, Malay, Ong, Kok Haur, Owino, Collins Oduor, Ng, Jane Wei Ting, Yew, Hui Chuing, Binte Mohamed Nasir, Nu Soibah, Au, Veonice Bijin, Sng, Reina, Kaliaperumal, Nivashini, Khine, Htet Htet Toe Wai, di Tocco, Francesca Casuscelli, Masayuki, Otsuka, Naikar, Shamita, Ng, Hui Xin, Chia, Su Li, Seah, Cindy Xin Yi, Alnawaz, Myra HJ., Wai, Chris Lee Yoon, Tay, Amy Yuh Ling, Mangat, Kamarjit Singh, Chew, Valerie, Yu, Weimiao, Connolly, John Edward, Periyasamy, Giridharan, Plissonnier, Marie-Laure, Levrero, Massimo, Lim, Seng Gee, and DasGupta, Ramanuj

Published
2024
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32. Managing differential performance of polygenic risk scores across groups: Real-world experience of the eMERGE Network

Author

Lewis, Anna C.F., Chisholm, Rex L., Connolly, John J., Esplin, Edward D., Glessner, Joe, Gordon, Adam, Green, Robert C., Hakonarson, Hakon, Harr, Margaret, Holm, Ingrid A., Jarvik, Gail P., Karlson, Elizabeth, Kenny, Eimear E., Kottyan, Leah, Lennon, Niall, Linder, Jodell E., Luo, Yuan, Martin, Lisa J., Perez, Emma, Puckelwartz, Megan J., Rasmussen-Torvik, Laura J., Sabatello, Maya, Sharp, Richard R., Smoller, Jordan W., Sterling, Rene, Terek, Shannon, Wei, Wei-Qi, and Fullerton, Stephanie M.

Published
2024
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33. Author Correction: Genetic variation in the human leukocyte antigen region confers susceptibility to Clostridioides difficile infection

Author

Ferar, Kathleen, Hall, Taryn O., Crawford, Dana C., Rowley, Robb, Satterfield, Benjamin A., Li, Rongling, Gragert, Loren, Karlson, Elizabeth W., de Andrade, Mariza, Kullo, Iftikhar J., McCarty, Catherine A., Kho, Abel, Hayes, M. Geoffrey, Ritchie, Marylyn D., Crane, Paul K., Mirel, Daniel B., Carlson, Christopher, Connolly, John J., Hakonarson, Hakon, Crenshaw, Andrew T., Carrell, David, Luo, Yuan, Dikilitas, Ozan, Denny, Joshua C., Jarvik, Gail P., and Crosslin, David R.

Published
2023
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34. Genetic variation in the human leukocyte antigen region confers susceptibility to Clostridioides difficile infection

Author

Ferar, Kathleen, Hall, Taryn O., Crawford, Dana C., Rowley, Robb, Satterfield, Benjamin A., Li, Rongling, Gragert, Loren, Karlson, Elizabeth W., de Andrade, Mariza, Kullo, Iftikhar J., McCarty, Catherine A., Kho, Abel, Hayes, M. Geoffrey, Ritchie, Marylyn D., Crane, Paul K., Mirel, Daniel B., Carlson, Christopher, Connolly, John J., Hakonarson, Hakon, Crenshaw, Andrew T., Carrell, David, Luo, Yuan, Dikilitas, Ozan, Denny, Joshua C., Jarvik, Gail P., and Crosslin, David R.

Published
2023
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44. Prospective, multi-site study of healthcare utilization after actionable monogenic findings from clinical sequencing

Author

Linder, Jodell E., Tao, Ran, Chung, Wendy K., Kiryluk, Krzysztof, Liu, Cong, Weng, Chunhua, Connolly, John J., Hakonarson, Hakon, Harr, Margaret, Leppig, Kathleen A., Jarvik, Gail P., Veenstra, David L., Aufox, Sharon, Chisholm, Rex L., Gordon, Adam S., Hoell, Christin, Rasmussen-Torvik, Laura J., Smith, Maureen E., Holm, Ingrid A., Miller, Erin M., Prows, Cynthia A., Elskeally, Omar, Kullo, Iftikhar J., Lee, Christopher, Jose, Sheethal, Manolio, Teri A., Rowley, Robb, Padi-Adjirackor, Nana Addo, Wilmayani, Ni Ketut, City, Brittany, Wei, Wei-Qi, Wiesner, Georgia L., Rahm, Alanna Kulchak, Williams, Janet L., Williams, Marc S., and Peterson, Josh F.

Published
2023
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45. Atypical hemolytic uremic syndrome in the era of terminal complement inhibition: an observational cohort study

Author

Holt, Richard, Jones, Caroline, Ashman, Neil, Fan, Stanley, Forbes, Suzanne, Rajakariar, Ravindra, De Freitas, Declan, Magee, Colm, Hanko, Jennifer, Milford, David, Muorah, Mordi, Howie, Alexander J., Baharani, Jyoti, Dasgupta, Indranil, Roberts, Russell, MacDiarmaid-Gordon, Adam, Fry, Andrew, Torpey, Nicholas, Waldron, Mary, Awan, Atif, Raftery, Tara, Ratcliffe, Laura, Vilar, Enric, Newman, Joel, Seviar, Dale, Marks, Stephen D., Rees, Lesley, Veligratli, Faidra, Waters, Aoife, Chowdhury, Paramit, Pattison, James, Booth, Caroline, Williams, Nicole, Corbett, Richard, Amin, Beena, Pickering, Matthew, Bramham, Kate, Al-Hamed, Mohamed, Hill, Anita, Mooney, Andrew, Finlay, Eric, Newnham, Amanda, Yadav, Pallavi, Kaur, Amrit, Plant, Nicholas, Shenoy, Mohan, Choong, Lye Wai, Helps, Aileen, McLemon, Robert, Muniraju, Thalakunte, Khan, Izhar, Kidder, Dana, Padmanabhan, Neal, Cousland, Zoe, Price, Jonathan, Taylor, Alison, Jennings, Claudine, Ahmad, Sohail, Mead, Paul, Aslam, Mona, Bebb, Charlotte, Byrne, Catherine, Ferraro, Alastair, Christian, Martin, Evans, Jonathan, Kim, Jon Jin, Lunn, Andrew, Mallik, Meeta, Mason, Phil, Mole, David, Craze, Janet, Sangala, Nicholas, Uniacke, Mark, McGregor, Ellon, Lambie, Stewart, Bhandary, Nitin, Flossmann, Oliver, Mohteshamzadeh, Mobin, Abeyaratne, Asanga, Bingham, Coralie, Clissold, Rhian, Smyth, Lucy, Connolly, John, Reynolds, Ben, Neary, John, Bell, Joanne, Roxburgh, Sarah, Popoola, Joyce, Donne, Rosie, Fardon, Nicholas, Khwaja, Arif, Siddiqi, Shareen, Kumar, Gurinder, Kardasz, Steve, Moore, Iain, Tez, Didem, Willows, Jamie, Davison, Rachel, Harty, John, Wong, William, Williams, Andrew J., Gale, Daniel, Coward, Richard, Inward, Carol, Mraz, Martin, Ayub, Waqar, Short, Andrew, Lappin, David, Baines, Richard, Bommayya, Girish, Houlberg, Kris, Saif, Imran, Gilbert, Rodney, Griffin, Sian, Smith, Graham, Van Der Voort, Judith, Chandar, Jayanthi, Freundlich, Michael, McCarroll, Frank, Wong, Germaine, Laboi, Paul, Molyneux, Rebekah, Brocklebank, Vicky, Walsh, Patrick R., Smith-Jackson, Kate, Hallam, Thomas M., Marchbank, Kevin J., Wilson, Valerie, Bigirumurame, Theophile, Dutt, Tina, Montgomery, Emma K., Malina, Michal, Wong, Edwin K. S., Johnson, Sally, Sheerin, Neil S., and Kavanagh, David

Published
2023
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47. Education and electronic medical records and genomics network, challenges, and lessons learned from a large-scale clinical trial using polygenic risk scores

Author

Connolly, John J., Berner, Eta S., Smith, Maureen, Levy, Samuel, Terek, Shannon, Harr, Margaret, Karavite, Dean, Suckiel, Sabrina, Holm, Ingrid A., Dufendach, Kevin, Nelson, Catrina, Khan, Atlas, Chisholm, Rex L., Allworth, Aimee, Wei, Wei-Qi, Bland, Harris T., Clayton, Ellen Wright, Soper, Emily R., Linder, Jodell E., Limdi, Nita A., Miller, Alexandra, Nigbur, Scott, Bangash, Hana, Hamed, Marwan, Sherafati, Alborz, Lewis, Anna C.F., Perez, Emma, Orlando, Lori A., Rakhra-Burris, Tejinder K., Al-Dulaimi, Mustafa, Cifric, Selma, Scherr, Courtney Lynam, Wynn, Julia, Hakonarson, Hakon, and Sabatello, Maya

Published
2023
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49. First-in-Human Study to Evaluate Safety, Tolerability, Pharmacokinetics, and Pharmacodynamics of a Rapidly Developed SARS-CoV-2 Therapeutic Antibody, AOD01, in Healthy Adults

Author

Prativadibhayankaram, Venkateshan S., Lee, Lawrence Soon-U, Lye, David, Xiaoying, Xu, Nellore, Ranjani, Pendharkar, Vishal, Hentze, Hannes, Guan, Siyu, Ayers, Benjamin J., Seah, Shirley G. K., Chye, De Hoe, Talib, Najwa S. N., Kaliaperumal, Nivashini, Ong, Wei Yee, Wong, Zi Xin, Au, Veonice B., Alok, Anshula, Connolly, John E., Boyd-Kirkup, Jerome D., Ingram, Piers J., Hanson, Brendon J., Ethirajulu, Kantharaj, O’Connell, Damian, and Chan, Conrad E. Z.

Published
2022
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