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14. Identification and in-silico characterization of splice-site variants from a large cardiogenetic national registry

Author

Rayani, Kaveh, Davies, Brianna, Cheung, Matthew, Comber, Drake, Roberts, Jason D., Tadros, Rafik, Green, Martin S., Healey, Jeffrey S., Simpson, Christopher S., Sanatani, Shubhayan, Steinberg, Christian, MacIntyre, Ciorsti, Angaran, Paul, Duff, Henry, Hamilton, Robert, Arbour, Laura, Leather, Richard, Seifer, Colette, Fournier, Anne, Atallah, Joseph, Kimber, Shane, Makanjee, Bhavanesh, Alqarawi, Wael, Cadrin-Tourigny, Julia, Joza, Jacqueline, Gardner, Martin, Talajic, Mario, Bagnall, Richard D., Krahn, Andrew D., and Laksman, Zachary W. M.

Published
2023
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20. The Prevalence and Characteristics of Arrhythmic Mitral Valve Prolapse in Patients With Unexplained Cardiac Arrest

Author

Alqarawi, Wael, Tadros, Rafik, Roberts, Jason D., Cheung, Christopher C., Green, Martin S., Burwash, Ian G., Steinberg, Christian, Healey, Jeffrey S., Khan, Habib, McIntyre, Ciorsti, Cadrin-Touringy, Julia, Laksman, Zachary W.M., Simpson, Christopher S., Sanatani, Shubhayan, Gardner, Martin, Angaran, Paul, Ilhan, Erkan, Talajic, Mario, Arbour, Laura, Leather, Richard, Seifer, Colette, Joza, Jacqueline, Lee, Felicity, Lau, Lawrence, Nair, Girish, Wells, George, and Krahn, Andrew D.

Published
2023
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23. Scientific AI in materials science: a path to a sustainable and scalable paradigm

Author

DeCost, Brian, Hattrick-Simpers, Jason, Trautt, Zachary, Kusne, Aaron, Campo, Eva, and Green, Martin

Subjects
Condensed Matter - Materials Science, Physics - Computational Physics
Abstract

Recently there has been an ever-increasing trend in the use of machine learning (ML) and artificial intelligence (AI) methods by the materials science, condensed matter physics, and chemistry communities. This perspective article identifies key scientific, technical, and social opportunities that the materials community must prioritize to consistently develop and leverage Scientific AI to provide a credible path towards the advancement of current materials-limited technologies. Here we highlight the intersections of these opportunities with a series of proposed paths forward. The opportunities are roughly sorted from scientific/technical (e.g., development of robust, physically meaningful multiscale material representations) to social (e.g., promoting an AI-ready workforce). The proposed paths forward range from developing new infrastructure and capabilities to deploying them in industry and academia. We provide a brief introduction to AI in materials science and engineering, followed by detailed discussions of each of the opportunities and paths forward.

Published
2020

29. An International Multicenter Evaluation of Inheritance Patterns, Arrhythmic Risks, and Underlying Mechanisms of CASQ2-Catecholaminergic Polymorphic Ventricular Tachycardia

Author

Ng, Kevin, Titus, Erron W, Lieve, Krystien V, Roston, Thomas M, Mazzanti, Andrea, Deiter, Frederick H, Denjoy, Isabelle, Ingles, Jodie, Till, Jan, Robyns, Tomas, Connors, Sean P, Steinberg, Christian, Abrams, Dominic J, Pang, Benjamin, Scheinman, Melvin M, Bos, J Martijn, Duffett, Stephen A, van der Werf, Christian, Maltret, Alice, Green, Martin S, Rutberg, Julie, Balaji, Seshadri, Cadrin-Tourigny, Julia, Orland, Kate M, Knight, Linda M, Brateng, Caitlin, Wu, Jeremy, Tang, Anthony S, Skanes, Allan C, Manlucu, Jaimie, Healey, Jeff S, January, Craig T, Krahn, Andrew D, Collins, Kathryn K, Maginot, Kathleen R, Fischbach, Peter, Etheridge, Susan P, Eckhardt, Lee L, Hamilton, Robert M, Ackerman, Michael J, Noguer, Ferran Rosés I, Semsarian, Christopher, Jura, Natalia, Leenhardt, Antoine, Gollob, Michael H, Priori, Silvia G, Sanatani, Shubhayan, Wilde, Arthur AM, Deo, Rahul C, and Roberts, Jason D

Subjects
Clinical Research, Cardiovascular, Genetics, Heart Disease, Aetiology, 2.1 Biological and endogenous factors, Calsequestrin, Female, Heterozygote, Homozygote, Humans, Male, Mutation, Missense, Risk Factors, Tachycardia, Ventricular, arrhythmias, cardiac, catecholaminergic polymorphic ventricular tachycardia, death, sudden, genetics, arrhythmias, cardiac, death, sudden, cardiac, Cardiorespiratory Medicine and Haematology, Clinical Sciences, Public Health and Health Services, Cardiovascular System & Hematology
Abstract

BackgroundGenetic variants in calsequestrin-2 (CASQ2) cause an autosomal recessive form of catecholaminergic polymorphic ventricular tachycardia (CPVT), although isolated reports have identified arrhythmic phenotypes among heterozygotes. Improved insight into the inheritance patterns, arrhythmic risks, and molecular mechanisms of CASQ2-CPVT was sought through an international multicenter collaboration.MethodsGenotype-phenotype segregation in CASQ2-CPVT families was assessed, and the impact of genotype on arrhythmic risk was evaluated using Cox regression models. Putative dominant CASQ2 missense variants and the established recessive CASQ2-p.R33Q variant were evaluated using oligomerization assays and their locations mapped to a recent CASQ2 filament structure.ResultsA total of 112 individuals, including 36 CPVT probands (24 homozygotes/compound heterozygotes and 12 heterozygotes) and 76 family members possessing at least 1 presumed pathogenic CASQ2 variant, were identified. Among CASQ2 homozygotes and compound heterozygotes, clinical penetrance was 97.1% and 26 of 34 (76.5%) individuals had experienced a potentially fatal arrhythmic event with a median age of onset of 7 years (95% CI, 6-11). Fifty-one of 66 CASQ2 heterozygous family members had undergone clinical evaluation, and 17 of 51 (33.3%) met diagnostic criteria for CPVT. Relative to CASQ2 heterozygotes, CASQ2 homozygote/compound heterozygote genotype status in probands was associated with a 3.2-fold (95% CI, 1.3-8.0; P=0.013) increased hazard of a composite of cardiac syncope, aborted cardiac arrest, and sudden cardiac death, but a 38.8-fold (95% CI, 5.6-269.1; P

Published
2020

40. Multifunctional coatings for solar module glass

Author

Song, Ning, primary, Chang, Nathan, additional, Gentle, Angus, additional, Zeng, Yiyu, additional, Jiang, Yajie, additional, Wu, Yanfang, additional, Deng, Shuo, additional, Cheng, Yuhao, additional, Huang, Jialiang, additional, Zhou, Zibo, additional, Keevers, Mark, additional, and Green, Martin A., additional

Published
2024
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46. Extraction of black hole coalescence waveforms from noisy data

Author

Green, Martin A. and Moffat, J. W.

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics, General Relativity and Quantum Cosmology
Abstract

We describe an independent analysis of LIGO data for black hole coalescence events. Gravitational wave strain waveforms are extracted directly from the data using a filtering method that exploits the observed or expected time-dependent frequency content. Statistical analysis of residual noise, after filtering out spectral peaks (and considering finite bandwidth), shows no evidence of non-Gaussian behaviour. There is also no evidence of anomalous causal correlation between noise signals at the Hanford and Livingston sites. The extracted waveforms are consistent with black hole coalescence template waveforms provided by LIGO. Simulated events, with known signals injected into real noise, are used to determine uncertainties due to residual noise and demonstrate that our results are unbiased. Conceptual and numerical differences between our RMS signal-to-noise ratios (SNRs) and the published matched-filter detection SNRs are discussed., Comment: 15 pages, 11 figures. Version accepted for publication

Published
2017
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47. The 2019 materials by design roadmap

Author

Alberi, Kirstin, Nardelli, Marco Buongiorno, Zakutayev, Andriy, Mitas, Lubos, Curtarolo, Stefano, Jain, Anubhav, Fornari, Marco, Marzari, Nicola, Takeuchi, Ichiro, Green, Martin L, Kanatzidis, Mercouri, Toney, Mike F, Butenko, Sergiy, Meredig, Bryce, Lany, Stephan, Kattner, Ursula, Davydov, Albert, Toberer, Eric S, Stevanovic, Vladan, Walsh, Aron, Park, Nam-Gyu, Aspuru-Guzik, Alán, Tabor, Daniel P, Nelson, Jenny, Murphy, James, Setlur, Anant, Gregoire, John, Li, Hong, Xiao, Ruijuan, Ludwig, Alfred, Martin, Lane W, Rappe, Andrew M, Wei, Su-Huai, and Perkins, John

Subjects
Engineering, Materials Engineering, Affordable and Clean Energy, Climate Action, density functional theory, materials genome initative, materials design, high-throughput methods, energy applications, Physical Sciences, Applied Physics, Physical sciences
Abstract

Advances in renewable and sustainable energy technologies critically depend on our ability to design and realize materials with optimal properties. Materials discovery and design efforts ideally involve close coupling between materials prediction, synthesis and characterization. The increased use of computational tools, the generation of materials databases, and advances in experimental methods have substantially accelerated these activities. It is therefore an opportune time to consider future prospects for materials by design approaches. The purpose of this Roadmap is to present an overview of the current state of computational materials prediction, synthesis and characterization approaches, materials design needs for various technologies, and future challenges and opportunities that must be addressed. The various perspectives cover topics on computational techniques, validation, materials databases, materials informatics, high-throughput combinatorial methods, advanced characterization approaches, and materials design issues in thermoelectrics, photovoltaics, solid state lighting, catalysts, batteries, metal alloys, complex oxides and transparent conducting materials. It is our hope that this Roadmap will guide researchers and funding agencies in identifying new prospects for materials design.

Published
2019

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