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1. Multi-fidelity Hamiltonian Monte Carlo

Author

Patel, Dhruv V., Lee, Jonghyun, Farthing, Matthew W., Kitanidis, Peter K., and Darve, Eric F.

Subjects
Computer Science - Computational Engineering, Finance, and Science, Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

Numerous applications in biology, statistics, science, and engineering require generating samples from high-dimensional probability distributions. In recent years, the Hamiltonian Monte Carlo (HMC) method has emerged as a state-of-the-art Markov chain Monte Carlo technique, exploiting the shape of such high-dimensional target distributions to efficiently generate samples. Despite its impressive empirical success and increasing popularity, its wide-scale adoption remains limited due to the high computational cost of gradient calculation. Moreover, applying this method is impossible when the gradient of the posterior cannot be computed (for example, with black-box simulators). To overcome these challenges, we propose a novel two-stage Hamiltonian Monte Carlo algorithm with a surrogate model. In this multi-fidelity algorithm, the acceptance probability is computed in the first stage via a standard HMC proposal using an inexpensive differentiable surrogate model, and if the proposal is accepted, the posterior is evaluated in the second stage using the high-fidelity (HF) numerical solver. Splitting the standard HMC algorithm into these two stages allows for approximating the gradient of the posterior efficiently, while producing accurate posterior samples by using HF numerical solvers in the second stage. We demonstrate the effectiveness of this algorithm for a range of problems, including linear and nonlinear Bayesian inverse problems with in-silico data and experimental data. The proposed algorithm is shown to seamlessly integrate with various low-fidelity and HF models, priors, and datasets. Remarkably, our proposed method outperforms the traditional HMC algorithm in both computational and statistical efficiency by several orders of magnitude, all while retaining or improving the accuracy in computed posterior statistics.

Published
2024

2. Health Professional Student Placements and Workforce Location Outcomes: Protocol of an Observational Cohort Study

Author

Campbell, Narelle, Farthing, Annie, Witt, Susan, Anderson, Jessie, Lenthall, Sue, Moore, Leigh, and Rissel, Chris

Subjects
Medicine, Computer applications to medicine. Medical informatics, R858-859.7
Abstract

BackgroundThe successful recruitment and retention of health professionals to rural and remote areas of Australia is a health policy priority. Nursing or allied health professional students’ learning placements in the Northern Territory (NT) of Australia, most of which is considered remote, may influence rural or remote work location decisions. ObjectiveThe aim of this study is to determine where allied health professionals and nurses who have had a student placement in the NT of Australia end up practicing. MethodsThis research is an observational cohort study, with data collection occurring at baseline and then repeated annually over 10 years (ie, 2017-2018 to 2029). The baseline data collection includes a demographic profile of allied health and nursing students and their evaluations of their NT placements using a nationally consistent questionnaire (ie, the Student Satisfaction Survey). The Work Location Survey, which will be administered annually, will track work location and the influences on work location decisions. ResultsThis study will generate unique data on the remote and rural work locations of nursing and allied health professional students who had a placement in the NT of Australia. It will be able to determine what are the most important characteristics of those who take up remote and rural employment, even if outside of the NT, and to identify barriers to remote employment. ConclusionsThis study will add knowledge to the literature regarding rates of allied health and nursing professionals working in remote or rural settings following remote or rural learning placements. The results will be of interest to government and remote health workforce planners. Trial RegistrationAustralian New Zealand Clinical Trials Registry (ANZCTR) ACTRN12620000797976; https://www.anzctr.org.au/ACTRN12620000797976.aspx International Registered Report Identifier (IRRID)PRR1-10.2196/21832

Published
2021
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5. Blending Bathymetry: Combination of image-derived parametric approximations and celerity data sets for nearshore bathymetry estimation

Author

Lee, Jonghyun, DeVore, Katherine, Hesser, Tyler, Bak, A. Spicer, Brodie, Katherine, Bruder, Brittany, and Farthing, Matthew

Subjects
Physics - Atmospheric and Oceanic Physics, Physics - Data Analysis, Statistics and Probability, Physics - Geophysics
Abstract

Estimation of nearshore bathymetry is important for accurate prediction of nearshore wave conditions. However, direct data collection is expensive and time-consuming while accurate airborne lidar-based survey is limited by breaking waves and decreased light penetration affected by water turbidity. Instead, tower-based platforms or Unmanned Aircraft System (UAS) can provide indirect video-based observations. The video-based time-series imagery provides wave celerity information and time-averaged (timex) or variance enhanced (var) images identify persistent regions of wave breaking. In this work, we propose a rapid and improved bathymetry estimation method that takes advantage of image-derived wave celerity and a first-order bathymetry estimate from Parameter Beach Tool (PBT), software that fits parameterized sandbar and slope forms to the timex or var images. Two different sources of the data, PBT and wave celerity, are combined or blended optimally based on their assumed accuracy in a statistical framework. The PBT-derived bathymetry serves as "prior" coarse-scale background information and then is updated and corrected with the imagery-derived wave data through the dispersion relationship, which results in a better bathymetry estimate that is consistent with imagery-based wave data. To illustrate the accuracy of our proposed method, imagery data sets collected in 2017 at the US Army EDRC's Field Research Facility in Duck, NC under different weather and wave height conditions are tested. Estimated bathymetry profiles are remarkably close to the direct survey data. The computational time for the estimation from PBT-based bathymetry and imagery-derived wave celerity is only about five minutes on a free Google Cloud node with one CPU core. These promising results indicate the feasibility of reliable real-time bathymetry imaging during a single flight of UAS., Comment: 21 pages, 14 figures, preprints

Published
2023

6. On Their Own Terms: How Cocalera Organizing Expanded Indigenous Women’s Rights in Bolivia

Author

Linda Farthing and Thomas Grisaffi

Subjects
Bolivia, coca, Bartolina Sisa, Chachawarmi, intersectionality, interseccionalidad, Latin America. Spanish America, F1201-3799, Social Sciences
Abstract

A key element in the historically unprecedented advances in indigenous women’s political representation under Bolivia’s Evo Morales’s administration (2006–2019) was the influence that women coca growers played in the rural women’s indigenous organization known as the Bartolinas. Driven in no small measure by their resistance to the US-financed War on Drugs in the Chapare region, the cocaleras became both Bolivia’s strongest indigenous women’s organization and its most dedicated advocates for indigenous women’s rights. This article contends that intersectionality—of gender, class, and indigenous identities—is at the heart of understanding indigenous women’s transformation from “helpers” of a male-dominated peasant union to government ministers in the space of ten years. Not only did they effectively deploy chachawarmi, the Andean concept of gender complementarity, to advance their rights in a way consistent with their cultural identity and political loyalties, but they also benefited from the gains of a predominantly urban middle-class feminist movement even though they formally rejected the feminist movement’s composition and perceived orientation.

Published
2024
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7. Towards Research-Based Organizational Structures in Mathematics Tutoring Centres

Author

Cameron Byerley, Carolyn Johns, Deborah Moore-Russo, Brian Rickard, Carolyn James, Melissa Mills, Behailu Mammo, Janet Oien, Linda Burks, William Heasom, Melissa Ferreira, Cynthia Farthing, and Daniel Moritz

Abstract

Undergraduate mathematics tutoring centres are prevalent in many countries; however, there is limited research-based evidence on effective organizational structures for these centres. In this study, we consider two research questions. First, how can the quantitative and qualitative data from 10 mathematics tutoring centres be organized for research purposes? Second, what hypotheses do expert mathematics tutoring centre leaders generate about characteristics of effective centres given data from a sample of ten centres? We collected quantitative data from over 26,000 students taking mathematics courses at ten institutions. Data collected included college entrance exam scores, high school grade point average, number of student visits to the centre per eligible student and course letter grade. We used exploratory data analysis to look for relationships between visits to the tutoring centre, student grades and other variables. Qualitative centre characteristics that were considered include: specialist-generalist tutoring system, tutoring capacity, physical layout, relationships between tutors and mathematics instructors and extent of tutor training. We used the Delphi process to generate testable hypotheses from the data, such as the following: (1) The more courses a tutor is responsible for tutoring the more likely it is that the tutor will struggle to answer student questions, when the difficulty level of the courses is roughly the same. (2) Centres with more specialized tutor models have more visits per student than centres with generalized tutor models. The preceding two hypotheses, along with the other generated hypotheses, have been identified by the experts participating in this study as plausible based on professional experience, exploratory data analysis and inferences based on prior research on tutoring. This study has not rigorously shown the validity of these hypotheses; rather it lays the groundwork for future investigations to determine what combination of features characterize an effective tutoring centre.

Published
2024
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9. Differentiable modelling to unify machine learning and physical models for geosciences

Author

Shen, Chaopeng, Appling, Alison P, Gentine, Pierre, Bandai, Toshiyuki, Gupta, Hoshin, Tartakovsky, Alexandre, Baity-Jesi, Marco, Fenicia, Fabrizio, Kifer, Daniel, Li, Li, Liu, Xiaofeng, Ren, Wei, Zheng, Yi, Harman, Ciaran J, Clark, Martyn, Farthing, Matthew, Feng, Dapeng, Kumar, Praveen, Aboelyazeed, Doaa, Rahmani, Farshid, Song, Yalan, Beck, Hylke E, Bindas, Tadd, Dwivedi, Dipankar, Fang, Kuai, Höge, Marvin, Rackauckas, Chris, Mohanty, Binayak, Roy, Tirthankar, Xu, Chonggang, and Lawson, Kathryn

Subjects
Information and Computing Sciences, Machine Learning, Networking and Information Technology R&D (NITRD), Machine Learning and Artificial Intelligence, Generic health relevance
Abstract

Process-based modelling offers interpretability and physical consistency in many domains of geosciences but struggles to leverage large datasets efficiently. Machine-learning methods, especially deep networks, have strong predictive skills yet are unable to answer specific scientific questions. In this Perspective, we explore differentiable modelling as a pathway to dissolve the perceived barrier between process-based modelling and machine learning in the geosciences and demonstrate its potential with examples from hydrological modelling. ‘Differentiable’ refers to accurately and efficiently calculating gradients with respect to model variables or parameters, enabling the discovery of high-dimensional unknown relationships. Differentiable modelling involves connecting (flexible amounts of) prior physical knowledge to neural networks, pushing the boundary of physics-informed machine learning. It offers better interpretability, generalizability, and extrapolation capabilities than purely data-driven machine learning, achieving a similar level of accuracy while requiring less training data. Additionally, the performance and efficiency of differentiable models scale well with increasing data volumes. Under data-scarce scenarios, differentiable models have outperformed machine-learning models in producing short-term dynamics and decadal-scale trends owing to the imposed physical constraints. Differentiable modelling approaches are primed to enable geoscientists to ask questions, test hypotheses, and discover unrecognized physical relationships. Future work should address computational challenges, reduce uncertainty, and verify the physical significance of outputs.

Published
2023

10. Towards a responsible machine learning approach to identify forced labor in fisheries

Author

Joo, Rocío, McDonald, Gavin, Miller, Nathan, Kroodsma, David, Farthing, Courtney, Belhabib, Dyhia, and Hochberg, Timothy

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

Many fishing vessels use forced labor, but identifying vessels that engage in this practice is challenging because few are regularly inspected. We developed a positive-unlabeled learning algorithm using vessel characteristics and movement patterns to estimate an upper bound of the number of positive cases of forced labor, with the goal of helping make accurate, responsible, and fair decisions. 89% of the reported cases of forced labor were correctly classified as positive (recall) while 98% of the vessels certified as having decent working conditions were correctly classified as negative. The recall was high for vessels from different regions using different gears, except for trawlers. We found that as much as ~28% of vessels may operate using forced labor, with the fraction much higher in squid jiggers and longlines. This model could inform risk-based port inspections as part of a broader monitoring, control, and surveillance regime to reduce forced labor. * Translated versions of the English title and abstract are available in five languages in S1 Text: Spanish, French, Simplified Chinese, Traditional Chinese, and Indonesian., Comment: 24 pages, 2 figures, 2 supp files

Published
2023

11. Differentiable modeling to unify machine learning and physical models and advance Geosciences

Author

Shen, Chaopeng, Appling, Alison P., Gentine, Pierre, Bandai, Toshiyuki, Gupta, Hoshin, Tartakovsky, Alexandre, Baity-Jesi, Marco, Fenicia, Fabrizio, Kifer, Daniel, Li, Li, Liu, Xiaofeng, Ren, Wei, Zheng, Yi, Harman, Ciaran J., Clark, Martyn, Farthing, Matthew, Feng, Dapeng, Kumar, Praveen, Aboelyazeed, Doaa, Rahmani, Farshid, Beck, Hylke E., Bindas, Tadd, Dwivedi, Dipankar, Fang, Kuai, Höge, Marvin, Rackauckas, Chris, Roy, Tirthankar, Xu, Chonggang, Mohanty, Binayak, and Lawson, Kathryn

Subjects
Computer Science - Machine Learning, Computer Science - Computational Engineering, Finance, and Science, Physics - Atmospheric and Oceanic Physics, Physics - Geophysics
Abstract

Process-Based Modeling (PBM) and Machine Learning (ML) are often perceived as distinct paradigms in the geosciences. Here we present differentiable geoscientific modeling as a powerful pathway toward dissolving the perceived barrier between them and ushering in a paradigm shift. For decades, PBM offered benefits in interpretability and physical consistency but struggled to efficiently leverage large datasets. ML methods, especially deep networks, presented strong predictive skills yet lacked the ability to answer specific scientific questions. While various methods have been proposed for ML-physics integration, an important underlying theme -- differentiable modeling -- is not sufficiently recognized. Here we outline the concepts, applicability, and significance of differentiable geoscientific modeling (DG). "Differentiable" refers to accurately and efficiently calculating gradients with respect to model variables, critically enabling the learning of high-dimensional unknown relationships. DG refers to a range of methods connecting varying amounts of prior knowledge to neural networks and training them together, capturing a different scope than physics-guided machine learning and emphasizing first principles. Preliminary evidence suggests DG offers better interpretability and causality than ML, improved generalizability and extrapolation capability, and strong potential for knowledge discovery, while approaching the performance of purely data-driven ML. DG models require less training data while scaling favorably in performance and efficiency with increasing amounts of data. With DG, geoscientists may be better able to frame and investigate questions, test hypotheses, and discover unrecognized linkages.

Published
2023
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14. Online Privacy, Young People, and Datafication: Different Perceptions About Online Privacy Across Antigua & Barbuda, Australia, Ghana, and Slovenia

Author

Rys Farthing, Katja Koren Ošljak, Teki Akuetteh, Kadian Camacho, Genevieve Smith-Nunes, and Jun Zhao

Subjects
Communication. Mass media, P87-96
Abstract

Children and young people’s online privacy is increasingly challenged by the datafication of the digital world, and this is an increasingly important area of policy concern. Understanding what young people understand online privacy to be, and what they want done to protect it, is key to creating effective and rights-realizing policy responses. This article explores young people’s perceptions across four countries and finds they have nuanced understandings about online privacy and clear, robust ideas about how to improve it. Context mattered, and their online privacy concerns and ideal protections were often informed by their socio-political context and awareness of and trust in datafication.

Published
2024
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19. Variational encoder geostatistical analysis (VEGAS) with an application to large scale riverine bathymetry

Author

Forghani, Mojtaba, Qian, Yizhou, Lee, Jonghyun, Farthing, Matthew, Hesser, Tyler, Kitanidis, Peter K., and Darve, Eric F.

Subjects
Computer Science - Machine Learning, Physics - Geophysics
Abstract

Estimation of riverbed profiles, also known as bathymetry, plays a vital role in many applications, such as safe and efficient inland navigation, prediction of bank erosion, land subsidence, and flood risk management. The high cost and complex logistics of direct bathymetry surveys, i.e., depth imaging, have encouraged the use of indirect measurements such as surface flow velocities. However, estimating high-resolution bathymetry from indirect measurements is an inverse problem that can be computationally challenging. Here, we propose a reduced-order model (ROM) based approach that utilizes a variational autoencoder (VAE), a type of deep neural network with a narrow layer in the middle, to compress bathymetry and flow velocity information and accelerate bathymetry inverse problems from flow velocity measurements. In our application, the shallow-water equations (SWE) with appropriate boundary conditions (BCs), e.g., the discharge and/or the free surface elevation, constitute the forward problem, to predict flow velocity. Then, ROMs of the SWEs are constructed on a nonlinear manifold of low dimensionality through a variational encoder. Estimation with uncertainty quantification (UQ) is performed on the low-dimensional latent space in a Bayesian setting. We have tested our inversion approach on a one-mile reach of the Savannah River, GA, USA. Once the neural network is trained (offline stage), the proposed technique can perform the inversion operation orders of magnitude faster than traditional inversion methods that are commonly based on linear projections, such as principal component analysis (PCA), or the principal component geostatistical approach (PCGA). Furthermore, tests show that the algorithm can estimate the bathymetry with good accuracy even with sparse flow velocity measurements.

Published
2021
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20. Deep learning-based fast solver of the shallow water equations

Author

Forghani, Mojtaba, Qian, Yizhou, Lee, Jonghyun, Farthing, Matthew W., Hesser, Tyler, Kitanidis, Peter K., and Darve, Eric F.

Subjects
Computer Science - Machine Learning, Physics - Fluid Dynamics
Abstract

Fast and reliable prediction of river flow velocities is important in many applications, including flood risk management. The shallow water equations (SWEs) are commonly used for this purpose. However, traditional numerical solvers of the SWEs are computationally expensive and require high-resolution riverbed profile measurement (bathymetry). In this work, we propose a two-stage process in which, first, using the principal component geostatistical approach (PCGA) we estimate the probability density function of the bathymetry from flow velocity measurements, and then use machine learning (ML) algorithms to obtain a fast solver for the SWEs. The fast solver uses realizations from the posterior bathymetry distribution and takes as input the prescribed range of BCs. The first stage allows us to predict flow velocities without direct measurement of the bathymetry. Furthermore, we augment the bathymetry posterior distribution to a more general class of distributions before providing them as inputs to ML algorithm in the second stage. This allows the solver to incorporate future direct bathymetry measurements into the flow velocity prediction for improved accuracy, even if the bathymetry changes over time compared to its original indirect estimation. We propose and benchmark three different solvers, referred to as PCA-DNN (principal component analysis-deep neural network), SE (supervised encoder), and SVE (supervised variational encoder), and validate them on the Savannah river, Augusta, GA. Our results show that the fast solvers are capable of predicting flow velocities for different bathymetry and BCs with good accuracy, at a computational cost that is significantly lower than the cost of solving the full boundary value problem with traditional methods., Comment: arXiv admin note: substantial text overlap with arXiv:2012.02620

Published
2021

22. Evaluation of the Clinical Significance of Assessing Previous Gambling Problems Before Initiating Antipsychotic Treatment: An Audit

Author

Evelyn Farthing

Subjects
Psychiatry, RC435-571
Abstract

Aims This audit aims to address the critical link between antipsychotics and impulsive behaviors, particularly pathological gambling, by emphasizing the importance of assessing patients' gambling history before initiating antipsychotic treatment. The focus is on patients under the care of the Bolton Early Intervention in Psychosis (EIT) service, with the aim of meeting the standard set by NICE guidelines, ensuring that 100% of patients started on antipsychotics are asked about their previous gambling history. Methods Data was collected from prescription and shared care protocol lists for patients prescribed antipsychotics in the last six months. The PARIS progress notes and clinical correspondence were then searched to determine if patients had been asked about gambling. Results The audit revealed a significant gap in the practice, with minimal adherence to NICE guidelines regarding assessing gambling history before prescribing antipsychotics. Out of 35 patients, only one was asked about gambling history. Conclusion The recommendations for improvement include incorporating a gambling prompt into the medical review proforma, educating the team about the importance of this assessment, and adding the Problem Gambling Severity Index to the initial review by EIT.

Published
2024
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23. Diagnosis and Management of Daptomycin-Induced Acute Eosinophilic Pneumonia: A Case Report

Author

Fawzy Elbarbry, Kate Farthing, and Shannon Arguello

Subjects
Daptomycin, Acute Eosinophilic Pneumonia, Diagnosis, Management, Pharmacy and materia medica, RS1-441
Abstract

Daptomycin is a cyclic lipopeptide antibiotic that is indicated for the treatment of complicated skin infections and bacteremia caused by gram positive organisms. Acute eosinophilic pneumonia (AEP) is a rare, but serious adverse effect of daptomycin and caused by accumulation of eosinophils in the lung tissues, and can lead to respiratory failure. Early diagnosis and management of this condition is crucial to avoid severe complications, including death. Herein, we report a case of an elderly man who presented with signs and symptoms of AEP within two weeks of initiation of daptomycin for the treatment of MRSA bacteremia. The patient showed significant clinical improvement and decline in eosinophils upon discontinuation of daptomycin and starting a 5-day steroid course. Acute eosinophilic pneumonia should be kept in mind as a possible, although rare, adverse effect of daptomycin. Early recognition can be established through typical symptoms, eosinophilia, and chest X-ray showing pulmonary infiltrate. Rapid discontinuation of daptomycin with/without steroid therapy and supportive care usually results in significant clinical recover.

Published
2024
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24. Study design and rationale for TEENS+REACH: Evaluating ripple effects of a family-based lifestyle intervention to untreated family members

Author

Kristal Lyn Brown, Jessica Gokee LaRose, Hollie A. Raynor, Amy A. Gorin, Laura M. Thornton, Sarah Farthing, Kristina Tatum, and Melanie K. Bean

Subjects
Family-based lifestyle intervention, Ripple effects, Weight loss, Adolescence, Obesity, Medicine (General), R5-920
Abstract

Background: Understanding the effects of family-based lifestyle intervention beyond the treated adolescent is important, given that obesity is a familial disease and there are likely bidirectional relations between an adolescent's treatment success and broader household changes. However, it is unknown if recommended household-wide changes are adopted or if untreated family members experience weight-related benefits. Methods: TEENS + REACH leverages our ongoing randomized clinical trial of TEENS+, a family-based lifestyle intervention for adolescents with obesity, to determine: 1) if household-wide changes to the shared home environment are implemented, 2) if ripple effects to untreated family members are observed, and 3) whether these changes are predictive of adolescents’ weight management success. TEENS + REACH will expand trial assessments to include comprehensive assessments of the shared home feeding, weight, and physical activity environment of the target adolescents. Specifically, we will enroll untreated children (8–17yrs) and caregivers living in the same household as the target parent/adolescent dyad (N = 60 families). At 0, 2, 4 (primary endpoint), and 8-months, the target parent/adolescent dyad and other untreated children and caregivers in the home will complete anthropometric assessments. Discussion: Results will determine the familial reach of TEENS+ and reveal potential mediators of treatment response, which can inform future efforts to optimize family-based lifestyle interventions. Trial registration: TEENS + REACH was retrospectively registered in Clinicaltrials.gov March 22, 2023 (NCT05780970) as an observational study ancillary to the TEENS + clinical trial, registered February 22, 2019 (NCT03851796).

Published
2024
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25. Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Author

Cramer, Estee Y, Ray, Evan L, Lopez, Velma K, Bracher, Johannes, Brennen, Andrea, Rivadeneira, Alvaro J Castro, Gerding, Aaron, Gneiting, Tilmann, House, Katie H, Huang, Yuxin, Jayawardena, Dasuni, Kanji, Abdul H, Khandelwal, Ayush, Le, Khoa, Mühlemann, Anja, Niemi, Jarad, Shah, Apurv, Stark, Ariane, Wang, Yijin, Wattanachit, Nutcha, Zorn, Martha W, Gu, Youyang, Jain, Sansiddh, Bannur, Nayana, Deva, Ayush, Kulkarni, Mihir, Merugu, Srujana, Raval, Alpan, Shingi, Siddhant, Tiwari, Avtansh, White, Jerome, Abernethy, Neil F, Woody, Spencer, Dahan, Maytal, Fox, Spencer, Gaither, Kelly, Lachmann, Michael, Meyers, Lauren Ancel, Scott, James G, Tec, Mauricio, Srivastava, Ajitesh, George, Glover E, Cegan, Jeffrey C, Dettwiller, Ian D, England, William P, Farthing, Matthew W, Hunter, Robert H, Lafferty, Brandon, Linkov, Igor, Mayo, Michael L, Parno, Matthew D, Rowland, Michael A, Trump, Benjamin D, Zhang-James, Yanli, Chen, Samuel, Faraone, Stephen V, Hess, Jonathan, Morley, Christopher P, Salekin, Asif, Wang, Dongliang, Corsetti, Sabrina M, Baer, Thomas M, Eisenberg, Marisa C, Falb, Karl, Huang, Yitao, Martin, Emily T, McCauley, Ella, Myers, Robert L, Schwarz, Tom, Sheldon, Daniel, Gibson, Graham Casey, Yu, Rose, Gao, Liyao, Ma, Yian, Wu, Dongxia, Yan, Xifeng, Jin, Xiaoyong, Wang, Yu-Xiang, Chen, YangQuan, Guo, Lihong, Zhao, Yanting, Gu, Quanquan, Chen, Jinghui, Wang, Lingxiao, Xu, Pan, Zhang, Weitong, Zou, Difan, Biegel, Hannah, Lega, Joceline, McConnell, Steve, Nagraj, VP, Guertin, Stephanie L, Hulme-Lowe, Christopher, Turner, Stephen D, Shi, Yunfeng, Ban, Xuegang, Walraven, Robert, Hong, Qi-Jun, Kong, Stanley, and van de Walle, Axel

Subjects
Bioengineering, Good Health and Well Being, COVID-19, Data Accuracy, Forecasting, Humans, Pandemics, Probability, Public Health, United States, forecasting, ensemble forecast, model evaluation
Abstract

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks.

Published
2022

26. Data-driven reduced order modeling of environmental hydrodynamics using deep autoencoders and neural ODEs

Author

Dutta, Sourav, Rivera-Casillas, Peter, Cecil, Orie M., Farthing, Matthew W., Perracchione, Emma, and Putti, Mario

Subjects
Computer Science - Machine Learning
Abstract

Model reduction for fluid flow simulation continues to be of great interest across a number of scientific and engineering fields. In a previous work [arXiv:2104.13962], we explored the use of Neural Ordinary Differential Equations (NODE) as a non-intrusive method for propagating the latent-space dynamics in reduced order models. Here, we investigate employing deep autoencoders for discovering the reduced basis representation, the dynamics of which are then approximated by NODE. The ability of deep autoencoders to represent the latent-space is compared to the traditional proper orthogonal decomposition (POD) approach, again in conjunction with NODE for capturing the dynamics. Additionally, we compare their behavior with two classical non-intrusive methods based on POD and radial basis function interpolation as well as dynamic mode decomposition. The test problems we consider include incompressible flow around a cylinder as well as a real-world application of shallow water hydrodynamics in an estuarine system. Our findings indicate that deep autoencoders can leverage nonlinear manifold learning to achieve a highly efficient compression of spatial information and define a latent-space that appears to be more suitable for capturing the temporal dynamics through the NODE framework., Comment: 16 pages, 7 figures, To Appear in the proceedings of the IXth International Conference on Computational Methods for Coupled Problems in Science and Engineering (COUPLED PROBLEMS 2021), 14-16 June, 2021. arXiv admin note: substantial text overlap with arXiv:2104.13962

Published
2021

27. Immunogenicity of third dose COVID-19 vaccine strategies in patients who are immunocompromised with suboptimal immunity following two doses (OCTAVE-DUO): an open-label, multicentre, randomised, controlled, phase 3 trial

Author

Beesley, Richard, Churchill, Vicky, Insch, Elspeth, Loughton, Holly, MacDonald, Eilean, Lax, Siân, Lowe, Faye, Magwaro, Sophia, Gradwell, Mark, Kinsella, Francesca, Rolfe, Hayley, McIntyre, Stacey, Mortimer, Paige, Al-Taei, Saly, Tadros, Susan, Workman, Sarita, Arnott, Maxine, Brock, James, Melville, Andrew, Najm, Aurelie, Rutherford, Matthew, Sunzini, Flavia, Herman, Lou S, Hobbs, Agnieszka, Ragno, Martina, Wu, Mary Y, Selby, Rachael, Clay, Jennifer, Hutchison, Clare, Lown, Robert, Lwin, May N, Meardon, Naomi, Kelleher, Peter, Lightstone, Liz, Walters, Thomas, Denyer, Jayne, Ibrahim, Rahima, Gleeson, Sarah, Martin, Paul, McAdoo, Stephen, Baker, Helena, Horswill, Sarah, Parungao, Nina, Saich, Stephen, Cullinane, James, Irwin, Sophie, Klenerman, Paul, Marjot, Thomas, Chakraverty, Ronjon, Holroyd, Christopher, Kavi, Janki, Trown, Doreen, Babbage, Gavin, Chackathayil, Julia, Faria, Patricia, Ingham, Karen, Miah, Murad, Miranda, Mauro, O'Reilly, Nicola, Smith, Callie, Driver, Kimberley, Gauntlett, Kaylee, Farthing, Andrew, Rundell, Suzann, Smith, Emily, Tong, Andrew, Woolcock, Kieran, Hanke, Daniel, Laidlaw, Stephen, Malik, Zainab, Nguyen, Dung, Provine, Nicholas, Tipton, Tom, Walker, Victoria, Goodyear, Carl S, Patel, Amit, Barnes, Eleanor, Willicombe, Michelle, Siebert, Stefan, de Silva, Thushan I, Snowden, John A, Lim, Sean H, Bowden, Sarah J, Billingham, Lucinda, Richter, Alex, Carroll, Miles, Carr, Edward J, Beale, Rupert, Rea, Daniel, Parry, Helen, Pirrie, Sarah, Lim, Zixiang, Satsangi, Jack, Dunachie, Susanna J, Cook, Gordon, Miller, Paul, Basu, Neil, Gilmour, Ashley, Hodgkins, Anne-Marie, Evans, Lili, Hughes, Ana, Longet, Stephanie, Meacham, Georgina, Yong, Kwee L, A'Hearne, Matthew J, Koh, Mickey B C, Burns, Siobhan O, Orchard, Kim, Paterson, Caron, McIlroy, Graham, Murray, Sam M, Thomson, Tina, Dimitriadis, Stavros, Goulston, Lyndsey, Miller, Samantha, Keillor, Victoria, Prendecki, Maria, Thomas, David, Kirkham, Amanda, McInnes, Iain B, and Kearns, Pamela

Published
2024
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31. The Leaky Pipeline in Emergency Medicine: Understanding Factors Pushing Women Away and Informing Interventions

Author

Klekowski, Nicole, Balgord, Sarah, Diaz, Rosemarie, Farthing, Alex, Escolero, Sylvia, DeCloux, Koryanne, Burkhardt, John, Abir, Mahshid, Haggins, Adrianne, and Hopson, Laura

Abstract

Learning Objectives: Understand the environmental factors which influence selection of Emergency Medicine as a specialty by women medical students.Background: Women represent 28.3% of EM physicians. There is now gender parity in US medical schools, but women applicants to EM ranges 33-37%. Prior research does not explain these gender differences. There are known differences in resident experiences and assessments based on gender.Objectives: We sought to explore how clinical experiences and perceptions of the specialty influence selection of EM by women.Methods: Using purposive and convenience sampling to represent diverse learning environments, we conducted semi-structured interviews of men and women US senior medical students who considered EM as a specialty. Interviews were transcribed, de-identified, and coded using constant comparative analysis until saturation. We conducted thematic analysis using a constructivist approach and grounded theory. Reflexivity and credibility activities were performed.Results: 25 students from 11 geographically diverse schools completed interviews. 68% (17/25) were women. The majority (21/25) expressed commitment to EM. Four main themes were identified: 1. EM culture was perceived as exclusionary; 2. Beliefs about attributes of EM physicians and the specialty were influenced by gender; 3. Distressing patient encounters and physician/staff behaviors negatively affected students; and 4. Access to mentors, representation and exposure to EM affected interest. Table 1.Conclusions: The EM gender differential is affected by societal gender roles and an environment that rewards traditional masculine traits. Conflict with behavioral norms may hinder women forming their professional identity as an emergency physician. Potential interventions include recognizing the gendered perception of the field; establishing early, longitudinal mentoring and engagement with the specialty; and building a supportive culture to overcome mistreatment concerns. As for limitations, students hold multiple intersecting identities, and this study primarily focused on gender.

Published
2022

32. Neural Ordinary Differential Equations for Data-Driven Reduced Order Modeling of Environmental Hydrodynamics

Author

Dutta, Sourav, Rivera-Casillas, Peter, and Farthing, Matthew W.

Subjects
Computer Science - Machine Learning, Physics - Computational Physics
Abstract

Model reduction for fluid flow simulation continues to be of great interest across a number of scientific and engineering fields. Here, we explore the use of Neural Ordinary Differential Equations, a recently introduced family of continuous-depth, differentiable networks (Chen et al 2018), as a way to propagate latent-space dynamics in reduced order models. We compare their behavior with two classical non-intrusive methods based on proper orthogonal decomposition and radial basis function interpolation as well as dynamic mode decomposition. The test problems we consider include incompressible flow around a cylinder as well as real-world applications of shallow water hydrodynamics in riverine and estuarine systems. Our findings indicate that Neural ODEs provide an elegant framework for stable and accurate evolution of latent-space dynamics with a promising potential of extrapolatory predictions. However, in order to facilitate their widespread adoption for large-scale systems, significant effort needs to be directed at accelerating their training times. This will enable a more comprehensive exploration of the hyperparameter space for building generalizable Neural ODE approximations over a wide range of system dynamics., Comment: 10 pages, 6 figures, To Appear in Proceedings of AAAI 2021 Spring Symposium on Combining Artificial Intelligence and Machine Learning with Physics Sciences, March 22-24, 2021

Published
2021

35. Estimating Nearshore Morphological Change through Ensemble Optimal Interpolation with Altimetric Data

Author

Matthew P. Geheran, Katherine R. DeVore, Matthew W. Farthing, A. Spicer Bak, Katherine L. Brodie, Tyler J. Hesser, and Patrick J. Dickhudt

Subjects
data assimilation, bathymetry estimation, altimetry, Naval architecture. Shipbuilding. Marine engineering, VM1-989, Oceanography, GC1-1581
Abstract

Nearshore bathymetry changes on scales of hours to months in ways that strongly impact coastal processes. However, even at the best-monitored sites, surveys are typically not conducted with sufficient frequency to capture important changes such as sandbar migration. As a result, nearshore models often rely on outdated bathymetric boundary conditions, which may introduce significant errors. In this study, we investigate ensemble optimal interpolation (EnOI) as a method to update survey-derived bathymetry with altimetric measurements that are spatially sparse but have high temporal availability. We present the results of two synthetic examples and two field data experiments that demonstrate the ability of the method to accurately track morphological change between surveys. The method reduces the RMSE relative to a static bathymetry (corresponding to the day before the first assimilation step) by 23% to 68%. When compared with an estimate linearly interpolated between survey-derived bathymetries, the EnOI analysis reduces the RMSE by 19% to 47% in three out of the four experiments.

Published
2024
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38. The Rapid Evaluation of COVID-19 Vaccination in Emergency Departments for Underserved Patients Study

Author

Rodriguez, Robert M, Torres, Jesus R, Chang, Anna Marie, Haggins, Adrianne N, Eucker, Stephanie A, O’Laughlin, Kelli N, Anderson, Erik, Miller, Daniel G, Wilkerson, R Gentry, Caldwell, Martina, Lim, Stephen C, Raja, Ali S, Baumann, Brigitte M, Graterol, Joseph, Eswaran, Vidya, Chinnock, Brian, Investigators, The REVVED UP, Nichol, Graham, Parry, Blair A, Hunt, Alaina, Kelly, Morgan, Taira, Breena R, Pham, Michael, Tiao, Joshua, Lasko, Kyra, Aivale, Mayuri, Farthing, Alex, Byl, Nicole, Chan, Virginia, Anaya, Nancy, Wong, Angela H, Chadalawada, Bhanu, and Tupetz, Anna

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Immunization, Prevention, Emergency Care, Clinical Research, Vaccine Related, Health Services, Prevention of disease and conditions, and promotion of well-being, 3.4 Vaccines, Generic health relevance, Good Health and Well Being, COVID-19, COVID-19 Vaccines, Cross-Sectional Studies, Emergency Service, Hospital, Female, Health Services Accessibility, Humans, Male, Surveys and Questionnaires, United States, Vaccination, Vaccination Refusal, Vulnerable Populations, REVVED UP Investigators, Emergency & Critical Care Medicine, Clinical sciences
Abstract

Study objectiveEmergency departments (EDs) often serve vulnerable populations who may lack primary care and have suffered disproportionate COVID-19 pandemic effects. Comparing patients having and lacking a regular source of medical care and other ED patient characteristics, we assessed COVID-19 vaccine hesitancy, reasons for not wanting the vaccine, perceived access to vaccine sites, and willingness to get the vaccine as part of ED care.MethodsThis was a cross-sectional survey conducted from December 10, 2020, to March 7, 2021, at 15 safety net US EDs. Primary outcomes were COVID-19 vaccine hesitancy, reasons for vaccine hesitancy, and sites (including EDs) for potential COVID-19 vaccine receipt.ResultsOf 2,575 patients approached, 2,301 (89.4%) participated. Of the 18.4% of respondents who lacked a regular source of medical care, 65% used the ED as their usual source of health care. The overall rate of vaccine hesitancy was 39%; the range among the 15 sites was 28% to 58%. Respondents who lacked a regular source of medical care were more commonly vaccine hesitant than those who had a regular source of medical care (47% versus 38%, 9% difference, 95% confidence interval 4% to 14%). Other characteristics associated with greater vaccine hesitancy were younger age, female sex, Black race, Latinx ethnicity, and not having received an influenza vaccine in the past 5 years. Of the 61% who would accept a COVID-19 vaccine, 21% stated that they lacked a primary physician or clinic at which to receive it; the vast majority (95%) of these respondents would accept the COVID-19 vaccine as part of their care in the ED.ConclusionED patients who lack a regular source of medical care are particularly hesitant regarding COVID-19 vaccination. Most COVID-19 vaccine acceptors would accept it as part of their care in the ED. EDs may play pivotal roles in COVID-19 vaccine messaging and delivery to highly vulnerable populations.

Published
2021

39. Immunomodulatory therapy in children with paediatric inflammatory multisystem syndrome temporally associated with SARS-CoV-2 (PIMS-TS, MIS-C; RECOVERY): a randomised, controlled, open-label, platform trial

Author

Faust, Saul N, Haynes, Richard, Jones, Christine E, Staplin, Natalie, Whittaker, Elizabeth, Jaki, Thomas, Juszczak, Ed, Spata, Enti, Wan, Mandy, Bamford, Alasdair, Dimitri, Paul, Finn, Adam, Furness, John, Ramanan, Athimalaipet V, Gale, Christopher, Cathie, Katrina, Drysdale, Simon, Bernatoniene, Jolanta, Murray, Clare, Roehr, Charles C, Fleming, Paul, Riordan, Andrew, Bandi, Srini, Jyothish, Deepthi, Evans, Jennifer, Emonts, Marieke, Kelly, Dominic, Pathan, Nazima, Davies, Patrick, Hague, Rosie, Pollock, Louisa, Semple, Malcolm G, Peto, Leon, Baillie, J Kenneth, Buch, Maya, Jeffery, Katie, Knight, Marian, Lim, Wei Shen, Montgomery, Alan, Mukherjee, Aparna, Mumford, Andrew, Rowan, Kathryn, Thwaites, Guy, Mafham, Marion, Emberson, Jonathan, Landray, Martin J, Horby, Peter W, Chappell, Lucy, Day, Jeremy, Dung, DV, Quang, NN, Binh, Prof, Burhan, E, Alisjahbana, B, Koirala, J, Basnet, S, Kestelyn, E, Basnyat, B, Gyanwali, P, Hamers, RL, Sandercock, P, Darbyshire, J, DeMets, D, Fowler, R, Lalloo, D, Munavvar, M, Roberts, I, Warris, A, Wittes, J, Craddock-Bamford, A, Barton, J, Basoglu, A, Brown, R, Brudlo, W, Denis, E, Fletcher, L, Howard, S, Musini, S, Taylor, K, Cui, G, Goodenough, B, King, A, Lay, M, Murray, D, Stevens, W, Wallendszus, K, Welsh, R, Crichton, C, Davies, J, Goldacre, R, Harper, C, Knight, F, Nunn, M, Salih, H, Welch, J, Campbell, M, Pessoa-Amorim, G, Zayed, M, Wiles, J, Bagley, G, Cameron, S, Chamberlain, S, Farrell, B, Freeman, H, Kennedy, A, Whitehouse, A, Wilkinson, S, Wood, C, Reith, C, Davies, K, Halls, H, Holland, L, Truell, R, Wilson, K, Lingwood, Emma, Howie, L, Lunn, M, Rodgers, Penelope, Amuasi, J, Baird, K, Bao, T, Bittaye, M, Bonney, J, D'Alessandro, U, Dhimal, M, Huyen, T, Jagne, A, Karkey, A, Maiga, O, Matey, E, Nadjm, B, Nel, J, Pant, S, Puspatriani, K, Rahardjani, M, Raijal, S, Rees, H, Rimainar, A, Roberts, C, Rocca, A, Shrestha, S, Usuf, E, Vidaillic, C, Wulandari, F, Alexander, A, Amezaga, M, Armah, C, Asghar, A, Aubrey, P, Barker-Williams, K, Barnard, A, Bartholomew, J, Beaumont-Jewell, D, Beddows, S, Beety, J, Birch, C, Boyle, P, Brend, M, Brookes, M, Callens, C, Campbell, D, Campbell, H, Cate, H, Chambers, E, Chapman, K, Coe, A, Collins, J, Crawshaw, S, Das, S, De Soyza, A, Denton, E, Dicks, P, Dolman, M, Donaldson, K, Dowden, L, Dowling, S, Drake, C, Duckles-Leech, H, Duffy, H, Gerdes, L, Gibbons, K, Gill, L, Gilmour, K, Goodwin, J, Graham, J, Green, C, Hall, C, Hall, A, Hampshire, K, Hanson, J, Harman, T, Harper, H, Harrison, S, Hicks, J, Hodgson, H, Hough, M, Jackson, S, Johnson, G, Kirk, J, Lemoine, N, Levett, C, Lock, S, Lockett, T, Logan, M, Lomme, K, Luo, J, McMillan, N, Membrey, H, Mguni, N, Minton, J, Monaghan, H, Murphy, S, Muzengi, N, Naz, M, Nickson, I, Odam, M, Ohia, U, Okell, E, Oliver, A, Pearson, K, Pine, K, Pleass, R, Porter, D, Porter, L, Potter, A, Poustie, V, Pratt, S, Price, A, Priest, A, Rodgers, A, Rook, C, Ryan, P, Sam, S, Sharratt, P, Sheffield, J, Shenton, J, Shirley, K, Skinner, T, Slade, H, Sutu, M, Taylor, J, Thomas, E, Tinkler, H, Unsworth, A, Usher, R, van't Hoff, W, Walker, S, Walters, J, Walton, E, Warmington, J, Wayman, E, Williams, P, Williamson, J, Williamson, D, Yelnoorkar, F, Knight, M, Hodson, K, Pavord, S, Williamson, C, Bowman, L, Chen, F, Clarke, R, Goonasekara, M, Herrington, W, Judge, P, Ng, S, Preiss, D, Sammons, E, Zhu, D, Arkley, C, Bowker, P, Bruce, J, Byrne-Watts, V, Chapman, G, Coleman, G, Denwood, T, Gray, J, Harrison, D, Mather, N, Pinches, H, Rees, A, Rees, S, Turner, G, Abani, O, Abbas, A, Abbas, F, Abbas, J, Abbas, K, Abbas, M, Abbasi, S, Abbass, H, Abbott, A, Abdallah, N, Abdelaziz, A, Abdelfattah, M, Abdelqader, B, Abdul, A, Abdul, B, Abdul, S, Abdul Rasheed, A, Abdulakeem, A, Abdul-Kadir, R, Abdullah, A, Abdulmumeen, A, Abdul-Raheem, R, Abdulshukkoor, N, Abdusamad, K, Abed El Khaleq, Y, Abedalla, M, Abeer Ul Amna, A, Abel, L, Abernethy, K, Abeywickrema, M, Abhinaya, C, Aboaba, A, Aboagye-Odei, A, Aboelela, H, Abo-Leyah, H, Abouelela, K, Abou-Haggar, A, Abouibrahim, M, Abousamra, A, Abouzaid, M, Abraham, M, Abraham, T, Abraheem, A, Abrams, J, Abrams, R, Abu, H, Abu-Arafeh, A, Abubacker, S, Abung, A, Abusamra, Y, Aceampong, Y, Achara, A, Acharya, D, Acheampong, S, Acheampong, P, Acheson, J, Achieng, S, Acosta, A, Acquah, R, Acton, C, Adabie-Ankrah, J, Adair, P, Adam, F, Adam, M, Adamali, H, Adamczyk, M, Adams, C, Adams, D, Adams, K, Adams, L, Adams, N, Adams, R, Adams, T, Adamu-Ikeme, L, Adati, K, Adcock, K, Addo, A, Adeagbo, O, Adebiyi, A, Adedeji, O, Adegeye, Y, Adegoke, K, Adell, V, Adenwalla, S, Adeoye, F, Adesemoye, O, Adewunmi, E, Adeyanju, A, Adeyemi, J, Adeyemo, T, Adhikary, R, Aditya, A, Adkins, G, Adnan, A, Aeron-Thomas, J, Affleck, D, Afnan, C, Afridi, M, Aftab, Z, Agarwal, M, Agbeko, R, Agbo, C, Aggarwal, S, Aghababaie, A, Aguilar Jimenez, L, Ahamed Sadiq, S, Ahammed Nazeer, M, Ahmed, A, Ahmed, B, Ahmed, F, Ahmed, H, Ahmed, I, Ahmed, K, Ahmed, L, Ahmad, M, Ahmed, N, Ahmed, O, Ahmed, R, Ahmad, S, Ahmed, S H, Ahmed Ali, R, Ahmed Mohamud, B, Ahmer, S, Ahonia, A, Aiken, C, Ail, D, Ainsworth, M, Aissa, M, Aitken, L, Ajay, B, Ajibode, A, Ajmi, A, Akhtar, N, Akili, S, Akinbiyi, B, Akindolie, O, Akinfenwa, Y, Akinkugbe, O, Akinpelu, I, Akram, M, Aktinade, O, Akudi, U, Al Aaraj, A, Al Balushi, A, Al Dakhola, M, Al Swaifi, A, Al-Abadi, E, Alabi, A, Aladangady, N, Alafifi, M, Alam, A, Alam, S, Al-Asadi, A, Alatzoglou, K, Albert, P, Albon, L, Alcala, A, Alcorn, G, Alcorn, S, Aldana, A, Alderdice, D, Aldesouki, A, Aldouri, R, Aldridge, J, Aldridge, N, Alegria, A, Alexander, C, Alexander, J, Alexander, P, Al-Fori, J, Alghazawi, L, Alhabsha, O, Al-Hakim, B, Alhameed, R, Al-Hayali, M, Al-Hity, S, Ali, A, Ali, F, Ali, J, Ali, M, Ali, N, Ali, O, Ali, R, Ali, S, Aliberti, E, Alin, J, Alina, A, Alipustain, A, Aliyuda, F, Alizaedeh, K, Al-Jibury, M, Al-Juboori, S, Al-Khalil, M, Alkhudhayri, A, Alkhusheh, M, Allan, F, Allan, N, Allanson, A, Allcock, R, Allen, E, Allen, J, Allen, K, Allen, L, Allen, P, Allen, R, Allen, S, Allen, T, Alli, A, Allison, K, Allman, B, Allsup, D, Allsop, H, Allsop, L, Almahroos, A, Al-Moasseb, H, Al-Obaidi, M, Alomari, L, Al-Rabahi, A, Al-Ramadhani, B, Al-Saadi, Z, Al-Sammarraie, R, Alshaer, I, Al-Shahi Salman, R, Al-Shamkhani, W, Alsheikh, F, Al-Sheklly, B, Altaf, S, Alty, A, Alvarez, M, Alvarez Corral, M, Alveyn, E, Alzetani, M, Amamou, S, Amar, N, Ambalavanan, S, Ambrogetti, R, Ambrose, C, Ameen, A, Ames, K, Amin, A, Amin, K, Amin, S, Amin, T, Amit, B, Amjad, A, Amjad, N, Amosun, V, Amsal, M, Amsha, K, Amutio Martin, N, Amy, P, Anada, A, Anand, A, Anandappa, S, Anantapatnaikuni, S, Anderson, E, Anderson, J, Anderson, L, Anderson, M, Anderson, N, Anderson, R, Anderson, S, Anderson, W, Andreou, P, Andrews, A, Andrews, J, Aneke, K, Ang, A, Ang, W, Angel, T, Angela, A, Angelini, P, Anguvaa, L, Anichtchik, O, Anim-Somuah, M, Aniruddhan, K, Annett, J, Anning, L, Anstey, P, Anstey, R, Anthony, A, Anthony-Pillai, A, Antill, P, Antonnia, Z, Anu, V, Anwar, M, Apetri, E, Apostolopoulos, A, Appleby, S, Appleyard, D, Aquino, M, Araba, B, Aransiola, S, Araujo, M, Archer, A, Archer, D, Archer, S, Arcoria, D, Ardley, C, Arias, A, Aribike, O, Arimoto, R, Armata, I, Armistead, J, Armitage, A, Armstrong, C, Armstrong, M, Armstrong, S, Armstrong, W, Armtrong, P, Arndt, H, Arnison-Newgass, C, Arnold, D, Arnold, R, Arnott, A, Arora, D, Arora, R, Arora, K, Arora, P, Arter, A, Arumaithurai, A, Arya, A, Arya, R, Asandei, D, Asghar, M, Ashab, M, Ashbrook-Raby, C, Ashby, H, Ashcroft, J, Ashcroft, S, Asher, G, Ashfak, Z, Ashfaq, A, Ashish, A, Ashley, D, Ashman-Flavell, S, Ashok, S, Ashour, A, Ashraf, S, Ashraf, M, Ashraq, M, Ashton, D, Ashton, S, Ashworth, A, Ashworth, F, Ashworth, R, Aslam, A, Aslam, I, Aslam, S, Aslett, L, Asogan, H, Asrar, A, Assaf, O, Astin-Chamberlain, R, Athorne, D, Atkins, B, Atkins, C, Atkins, S, Atkinson, J, Atkinson, V, Atomode, A, Atraskiewicz, B, Attia, A, Attubato, E, Attwood, M, Auer, Z, Aujayeb, A, Aung, A, Aung, H, Aung, K, Aung, N, Aung, Y, Aung, Z, Austin, E, Austin, K, Auwal, A, Avari, M, Avery, M, Aveyard, N, Avis, J, Aviss, G, Avram, C, Avram, P, Awadelkareem, A, Awadzi, G, Awaly, M, Awan, A, Awoseyila, A, Aya, A, Ayaz, E, Ayers, A, Azam, J, Azeem, A, Azharuddin, M, Aziz, A, Aziz, G, Aziz, I, Aziz, N, Azkoul, A, Azman Shah, A, Azzopardi, G, Azzoug, H, Babatunde, F, Babi, M, Babiker, B, Babington, G, Babirecki, M, Babores, M, Babs-Osibodu, A, Bacciarelli, S, Bachar, R, Bachour, M, Bacon, G, Bacon, J, Badal, B, Bader, M, Badhan, G, Badhrinarayanan, S, Bae, J, Baggaley, A, Baggott, A, Bagmane, D, Bagshaw, L, Bahadori, K, Bailey, A, Bailey, J, Bailey, K, Bailey, L, Bailey, M, Bailey, P, Bailey, S, Baillie, H, Baillie, J, Bain, J, Bains, V, Baird, D, Baird, E, Baird, S, Baird, T, Baird, Y, Bajandouh, A, Baker, D, Baker, E, Baker, J, Baker, K, Baker, M, Baker, R, Baker, T, Baker, V, Bakere, H, Bakerly, N, Baker-Moffatt, M, Bakhai, A, Bakhtiar, N, Bakoulas, P, Bakthavatsalam, D, Balachandran, N, Balan, A, Balasingam, P, Balaskas, T, Balasubramania, M, Balatoni, N, Balcombe, A, Baldwin, A, Baldwin, C, Baldwin, D, Baldwin, F, Baldwin-Jones, R, Bale, N, Balfour, J, Ball, R, Ball, M, Ballard, K, Balluz, I, Balmforth, C, Balogh, E, Baltmr, A, Baluwala, A, Bambridge, G, Bamford, P, Bamford, A, Bamgboye, A, Bancroft, H, Bancroft, E, Banda, J, Bandaru, K, Bandla, N, Bandyopadhyam, S, Banerjee, A, Banerjee, R, Bani-Saad, O, Banks, H, Banks, L, Banks, P, Bann, C, Bannister, H, Bannister, O, Banton, L, Baptist, M, Baqai, T, Baral, A, Baramova, D, Barbe, R, Barbon, E, Barbosa, M, Barbour, J, Barclay, A, Barclay, C, Bardsley, G, Bareford, S, Bari, S, Barker, A, Barker, D, Barker, E, Barker, H, Barker, J, Barker, L, Barker, O, Barkha, S, Barla, J, Barlow, G, Barlow, R, Barlow, V, Barnacle, J, Barnes, D, Barnes, N, Barnes, R, Barnes, T, Barnetson, C, Barnett, A, Barnett-Vanes, A, Barnsley, W, Barr, A, Barr, C, Barr, D, Barr, J, Barratt, N, Barratt, S, Barrera, M, Barrett, A, Barrett, F, Barrett, J, Barrett, S, Barrow, E, Bartlett, C, Bartlett, G, Bartlett, J, Bartlett, L, Bartley, S, Bartolmeu-Pires, S, Barton, A, Barton, G, Barton, L, Barton, R, Baruah, R, Baryschpolec, S, Bashir, H, Bashyal, A, Basker, B, Basran, A, Bassett, G, Bassett, J, Bassford, C, Bassoy, B, Bastion, V, Basumatary, A, Basvi, P, Bataduwaarachchi, V, Bate, T, Bateman, H, Bateman, K, Bateman, V, Bates, E, Bates, H, Bates, M, Bates, S, Batham, S, Batista, A, Batla, A, Batra, D, Batty, A, Batty, H, Batty, T, Baum, M, Baumbe, R, Bautista, C, Bawa, T, Bawa, F, Bawani, F, Bax, S, Baxter, M, Baxter, N, Baxter, Z, Bayes, H, Bayo, L-A, Bazari, F, Bazaz, R, Bazli, A, Beacham, L, Beadles, W, Beadon, K, Beak, P, Beale, A, Beard, K, Beardsal, K, Bearpark, J, Beasley, A, Beattie, S, Beaumont, K, Beaver, T, Beavis, S, Beazley, C, Beck, S, Beckett, V, Beckitt, R, Beckley, S, Beddall, H, Beeby, D, Beeby, S, Beech, G, Beecroft, M, Beer, S, Beer, N, Bega, G, Begg, A, Begg, S, Beghini, S, Begum, A, Begum, S, Behan, 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D, Sykes, E, Sylvester, L, Symington, D, Symon, D, Syndercombe, A, Syrimi, Z, Syson, J, Szabo, G, Szabó, D, Szakmany, T, Szarazova, N, Szekely, M, Szekeres, A, Szeto, M, Szymiczek, K, Tadros, M, Tageldin, A, Tague, L, Tahir, H, Tahir, M, Tai, M, Tait, J, Takyi, A, Talbot, P, Talbot -Smith, A, Talbot-Ponsonby, J, Tallent, R, Tallon, B, Tan, A, Tan, B, Tan, H, Tan, J, Tan, K, Tan, W, Tana, A, Tanner, A, Tanney, C, Tanqueray, T, Tanton, E, Tanzil-Al-Imran, T, Tarft, H, Taribagil, P, Tarin, O, Tariq, S, Tarpey, D, Tarr, E, Tarrant, L, Tasiou, A, Tate, A, Tate, M, Tate, P, Tatham, K, Tavares, S, Tavoukjian, V, Taylor, A, Taylor, B, Taylor, C, Taylor, D, Taylor, E, Taylor, H, Taylor, L, Taylor, M, Taylor, N, Taylor, R, Taylor, S, Taylor, T, Taylor, V, Taylor-Siddons, M, Taynton, T, Te, A, Teasdale, F, Teasdale, J, Teasdale, K, Tebbutt, J, Tee, C, Teeluck, I, Tejero Moya, B, Tejwani, R, Telfer, A, Teli, V, Tempany, J, Temple, J, Temple, N, Tench, H, Teoh, Y, Tereszkowski-Kaminski, R, Terrett, L, Terry, L, Tetla, D, Tewari, S, Tewkesbury, D, Texeira, J, Tey, C, Thake, M, Thakker, C, Thakker, M, Thakrar, J, Thamu, B, Thatcher, H, Thayanandan, A, Thazhatheyil, K, Thein, E, Theocharidou, L, Thet, P, Thevarajah, K, Thevendra, M, Thiri Phoo, N, Thirlwall, Y, Thirumaran, M, Thomas, A, Thomas, C, Thomas, H, Thomas, J, Thomas, K, Thomas, L, Thomas, R, Thomas, S, Thomas, T, Thomas, V, Thomasson, K, Thomas-Turner, R, Thompson, C, Thompson, E, Thompson, F, Thompson, H, Thompson, J, Thompson, K, Thompson, L, Thompson, M, Thompson, O, Thompson, R, Thompson, Y, Thomson, N, Thorburn, P, Thorn, N, Thorne, C, Thorne, N, Thornton, A, Thornton, D, Thornton, J, Thornton, R, Thornton, S, Thornton, T, Thorpe, S, Thorpe, C, Thorpe, N, Thozthumparambil, P, Thrasyvoulou, L, Thraves, H, Thueux, G, Thu-Ta, P, Thwaiotes, V, Tiberi, S, Tieger, S, Tierney, C, Tighe, M, Tilbey, S, Till, C, Tiller, A, Tiller, H, Timerick, J, Timlick, E, Timmins, A, Timmis, A, Timms, H, Timoroksa, A, Tinashe, S, Tingley, S, Tinker, N, Tinkler, M, Tipper, J, Tirumalai Adisesh, A, Tivenan, H, Tluchowska, K, T-Michael, H, Todd, A, Todd, J, Todd, S, Toffoletti, O, Tohfa, M, Tohill, S, Tolson, M, Tomasova, N, Tomlin, S, Tomlins, S, Tomlinson, J, Tomlinson, K, Tonkin, J, Tonna, I, Toohey, C, Topham, K, Topping, M, Torokwa, A, Torrance, C, Touma, O, Tous Sampol, L, Tousis, R, Tout, M, Tovey, P, Towersey, G, Townley, J, Tozer, R, Tran, M, Tranter, H, Travers, J, Travill, C, Traynor, S, Trethowan, L, Treus Gude, E, Trevelyan, M, Trewick, N, Tridente, A, Triggs, S, Trim, F, Trimmings, A, Trinick, T, Trivedi, K, Troedson, S, Tropman, E, Trotter, A, Trous, S, Trower, H, Trowsdale Stannard, M, Trudgill, N, Truman, N, Truslove, M, Trussell, S, Trussell, T, Tsakiridou, K, Tsang, C, Tsang, P, Tsawayo, T, Tsilimpari, K, Tsinaslanidis, G, Tso, S, Tucker, S, Tucker, N, Tudor, D, Tufail, A, Tuff, J, Tuffney, J, Tully, R, Tunesi, G, Turbitt, K, Turel, R, Turgut, T, Turley, C, Turnbull, A, Turner, A, Turner, C, Turner, K, Turner, L, Turner, M, Turner, P, Turner, S, Turner, V, Turner-bone, I, Turney, S, Turvey, J, Tweed, C, Tweed, D, Twemlow, R, Twohey, E, Tyagi, B, Tyagi, V, Tyer, A, Tyler, A, Tyler, J, Tyzack, A, Tzavaras, P, Tzinieris, I, Uddin, A, Uddin, M, Uddin, R, Ugoji, J, Ukaegbu, E, Ul Haq, M, Ul Hassan, W, Ul-Haq, Z, Ullah, S, Um, J, Umaipalan, A, Umate, A, Umeadi, J, Umeh, A, Umeojiako, W, Ummat, B, Underhill, E, Underwood, C, Underwood, J, Uppal, V, Upson, G, Ur Rasool, M, Uriel, A, Urruela, S, Uru, H, Usher, J, Usher, M, Usher-Rea, A, Ustianowski, A, Uzu, H, Vaccari, L, Vaghela, U, Vaidya, A, Vail, D, Valecka, B, Valentine, J, Valeria, B, Vallabhaneni, P, Vallotton, N, Vamplew, L, Vamvakiti, E, Vamvakopoulos, J, van Bruggen, L, van de Venne, M, van der Meer, A, van der Stelt, N, van Koutrik, L, Van Loggerenberg, A, Vance-Daniel, J, Vancheeswaran, R, Vandeyoon, S, Vankayalapati, P, Vanmali, P, Vansomeren, C, Vara, S, Vardy, S, Varghese, A, Varghese, M, Varney, W, Varnier, G, Varouxaki, A, Varquez, R, Vasadi, V, Vassell, K, Vassell, O, Vasu, V, Vasudevan, V, Vatish, M, Vaughan, S, Vayalaman, H, Vayapooree, D, Vaz, C, Veale, N, Veerasamy, S, Velankar, S, Velauthar, L, Veli, N, Vella, N, Velugupati, A, Velusamy, A, Venables, I, Venditti, M, Venkataramakrishnan, R, Venn, R, Ventilacion, L, Vere, J, Veres, M, Vergnano, S, Verling, W, Verma, A, Vernall, R, Vernon, B, Vertue, M, Verueco, L, Verula, J, Vethanayagam, N, Vettikumaran, S, Veys, L, Vickers, C, Victor, S, Victoria, S, Vidler, J, Vijayakumar, B, Vijayaraghavan Nalini, V, Vilcinskaite, B, Vileito, A, Vilimiene, N, Vinall, L, Vinay, S, Vinayakarao, L, Vincent, O, Vincent, R, Virdee, P, Virgilio, E, Virk, A, Visentin, E, Vitaglione, M, Vithian, K, Vittoria, S, Vivekananthan, S, Vlad, E, Vlies, B, von Oven, L, Vooght, C, Vutipongsatorn, K, Vuylsteke, A, Vyras, E, W Hay, A, Wach, R, Wadams, B, Wadd, S, Waddington, N, Wade, P, Wadsley, J, Wadsworth, K, Wafa, S, Wagstaff, D, Wagstaff, L, Wahab, D, Wahbi, Z, Waheed Adigun, A, Waidyanatha, S, Waite, A, Wake, R, Wakefield, A, Wakeford, W, Wakinshaw, F, Waldeck, E, Walden, A, Walding, L, Waldron, A, Waldron, J, Wales, E, Wali, B, Walker, D, Walker, G, Walker, H, Walker, I, Walker, K, Walker, L, Walker, O, Walker, R, Wallace, G, Wallbutton, R, Wallen, J, Waller, A, Waller, R, Wallis, G, Wallis, L, Wallis, M, Walmsley, E, Walsh, D, Walsh, E, Walsh, L, Walstow, D, Walter, D, Walters, A, Walters, H, Walton, L, Walton, M, Walton, O, Walton, S, Wands, M, Wane, R, Wang, F, Wang, N, Wang, R, Wang, S, Warbrick, D, Warburton, S, Ward, C, Ward, D, Ward, E, Ward, H, Ward, J, Ward, L, Ward, N, Ward, R, Ward, T, Warden, S, Wardere, G, Wardle, S, Wardy, H, Waring, G, Waring, S, Warner, B, Warner, C, Warnock, L, Warran, S, Warren, J, Warren, L, Warren, R, Warren, Y, Warrender, D, Warren-Miell, H, Warwick, G, Wassall, H, Wasson, E, Wastson, E, Watchorn, H, Waterfall, H, Waters, A, Waters, D, Waterstone, M, Watkin, A, Watkins, C, Watkins, E, Watkins, K, Watkins, L, Watson, A, Watson, E, Watson, F, Watson, J, Watson, K, Watson, L, Watson, P, Watson, R, Watters, M, Watterson, D, Watts, D, Watts, J, Watts, M, Waugh, V, Wayman, M, Wazir, A, Weatherhead, M, Weatherly, N, Webb, C, Webb, H, Webb, K, Webb, S, Websdale, C, Webster, D, Webster, I, Webster, J, Webster, T, Wedlin, J, Wee, L, Weerakoon, R, Weerasinghe, T, Weeratunga, J, Weetman, M, Wei, S, Weichert, I, Welch, E, Welch, H, Welch, L, Welch, S, Welham, B, Weller, S, Wellings, L, Wells, B, Wellstead, S, Welsh, B, Welters, I, Welton, R, Wenn, V, Wentworth, L, Wesonga, J, Wesseldine, K, West, J, West, M, West, R, West, S, Western, L, Westhead, R, Weston, H, Westwood, A, Westwood, K, Westwood, S, Wetherill, B, Wheaver, S, Wheeler, H, Whelan, B, Whelband, M, Whileman, A, Whitcher, A, White, A, White, B, White, C, White, D, White, J, White, K, White, M, White, N, White, S, White, T, Whitehead, C, Whitehorn, K, Whitehouse, C, Whitehouse, T, Whiteley, J, Whiteley, L, Whiteley, S, Whitham, R, Whitlingum, G, Whitmore, D, Whittam, L, Whittington, A, Whittle, H, Whittle, R, Wiafe, E, Wiblin, L, Wickens, O, Widdrington, J, Wieboldt, J, Wieringa, H, Wiesender, C, Wiffen, L, Wight, A, Wignall, A, Wignall, C, Wilce, A, Wilcock, D, Wilcock, E, Wilcox, L, Wild, B, Wild, L, Wild, S, Wilde, M, Wilding, L, Wilding, P, Wildsmith, T, Wileman, J, Wiles, K, Wilhelmsen, E, Wiliams, T, Wilkie, J, Wilkin, D, Wilkins, H, Wilkins, J, Wilkins, S, Wilkinson, I, Wilkinson, L, Wilkinson, N, Wilkinson, T, Willetts, S, Williams, A, Williams, C, Williams, D, Williams, E, Williams, G, Williams, H, Williams, J, Williams, K, Williams, M, Williams, R, Williams, S, Williams, T, Williamson, A, Williamson, H, Williamson, R, Willis, E, Willis, H, Willis, J, Wills, L, Willsher, L, Willshire, C, Wilson, A, Wilson, B, Wilson, D, Willson, F, Wilson, I, Willson, J, Wilson, K-A, Wilson, L, Wilson, M, Wilson, T, Win, K, Win, M, Win, T, Win, W, Winckworth, L, Winder, L, Winder, P, Winearl, S, Winmill, H, Winn, S, Winpenny, C, Winslow, H, Winter, H, Winter, J, Winter-Goodwin, B, Winterton, J, Winwood, H, Wischhusen, J, Wisdom, S, Wise, M, Wiselka, M, Wiseman, R, Wiseman, S, Wishart, S, Wishlade, T, Witele, E, Withers, N, Wixted, D, Wodehouse, T, Wolf, W, Wolff, N, Wolffsohn, K, Wolf-Roberts, R, Wolodimeroff, E, Wolstencroft, A, Wong, A, Wong, C, Wong, C H, Wong, E, Wong, J, Wong, K, Wong, M, Wong, N, Wong, S, Wong, T, Wood, A, Wood, D, Wood, F, Wood, G, Wood, H, Wood, J, Wood, L, Wood, M, Wood, S, Wood, T, Woodall, K, Woodfield, R, Woodford, C, Woodford, E, Woodford, J, Woodhead, L, Woodhead, T, Woodland, P, Woodman, M, Woodmansey, S, Woods, C, Woods, J, Woods, K, Woods, S, Woodward, Z, Woolcock, M, Wooldridge, G, Woolf, R, Woollard, C, Woollen, L, Woolley, E, Woolley, J, Woosey, D, Wootton, D, Wootton, J, Worley, D, Worton, S, Wraight, J, Wray, M, Wren, K, Wren, L, Wrey Brown, C, Wright, C, Wright, D, Wright, F, Wright, H, Wright, I, Wright, L, Wright, R, Wright, S, Wright, T, Wroe, C, Wroe, H, Wu, H, Wu, P, Wubetu, J, Wulandari, R, Wurie, S, Wyatt, C, Wyn-Griffiths, F, Wynter, I, Xavier, B, Xhikola, A, Xia, B, Xia, Z, Y Ellis, T, Yacoba, E, Yakubi, M, Yan, M, Yanagisawa, Y, Yang, F, Yang, Y, Yanney, M, Yap, W, Yaqoob, N, Yasmin, S, Yates, B, Yates, D, Yates, E, Yates, H, Yates, M, Yates, T, Ye, J, Yearwood Martin, C, Yein, K, Yeoh, A, Yeung, C, Yew, P, Yewatkar, D, Ylquimiche Melly, L, Ynter, I, Yong, H, Yorke, J, Youens, J, Younes Ibrahim, A, Young, E, Young, G, Young, L, Yousafzar, A, Youssouf, S, Yousuf, A, Yu, C, Yuan, J, Yung, B, Yusef, D, Yusef, S, Yusuf, I, Zafar, A-M, Zagalo, S, Zaher, S, Zahoor, A, Zainab, M, Zak, T, Zaki, K, Zakir, N, Zalewska, K, Zamalloa, A, Zaman, M, Zaman, S, Zamikula, J, Zammit, L, Zammit-Mangion, M, Zawadzka, M, Zebracki, E, Zehnder, D, Zeidan, L, Zeinali, D, Zhang, J, Zhao, X, Zheng, D, Zia, M, Zibdeh, O, Zill-E-Huma, R, Zin, E, Zincone, E, Zindoga, G, Zinkin, E, Zinyemba, V, Zipitis, C, Zitter, L, Zmierczak, A, Zubikarai, G, Zubir, A, Zuhra, N, Zulaikha, R, Zulfikar, S, Zullo, C, and Zuriaga-Alvaro, A

Published
2024
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40. Automating An Environmental Heat Flux Simulation Workflow

Author

Mondragon, Tomas A., Patel, Reena R., Hunter, Robert Harrison, Kala, Raju, Farthing, Matthew, and White, Barry

Subjects
Climatic changes -- Influence, Algorithms -- Analysis, Simulation methods -- Evaluation, Company systems management, Algorithm, Workflow software, Science and technology
Abstract

A considerable amount of research has been performed by various groups with the goal of detecting objects of interest in various environments. These objects can be above ground, partially buried, or fully buried. One of the detection methods that has piqued the interest of researchers is Automated Target Recognition (ATR) algorithms that can pick out the locations of such objects from infrared (IR) sensor imagery once it was recognized that such objects absorb and emit heat at rates that distinguish them from their surrounding environments. In order to obtain training data for ATRs, one option is to gather large volumes of field data from many locations, but such efforts are resource and labor intensive. Another option is to generate synthetic training data that simulates heat flux in real environments. Several research groups in the past have implemented a combination of both, using field data to inform and verify simulation tools that model heat flux, energy absorption, and IR emission from various parts of the environment, as well developing simulation workflows that model real environments. Such workflows are often developed for the purpose of simulating a single environment. Our goal is to modify and generalize such a workflow so that simulation parameters and the location of object of interest can be changed by non-experts or automated processes. In addition; this study is also focused on developing an efficient methodology to simulate a large variety of scenarios to produce varied sets of synthetic training data for the ATRs. The HPC simulations involved in generating the training data for ATRs require the exchange of data between several high-fidelity physics models. Efficient techniques have been implemented to ensure that the inputs to each component are consistent and correct. A more generalized workflow has been developed that is able to simulate complex scenarios with new LiDAR data, weather data, vegetation placements, target placements, and as output produce synthetic IR imagery useful for training ATRs. The synthetic IR images are automatically tagged with bounding boxes so that minimum post processing is required when the images are used for training the ATRs. The synthetic IR imagery was passed on to researchers developing ATRs and used to train the algorithms. The parts of the workflow that are streamlined can now be executed with relative ease on a high-performance computing environment and components of the workflow can now be more easily updated and modified in the future. KEYWORDS: Automatic Target Recognition, Environmental Simulation, Sensor Simulation, High Performance Computation, Configuration and Input File Management, Synthetic Infrared Imagery, INTRODUCTION A considerable amount of research has been performed by various groups with the goal of detecting objects of interest in various environments. These objects can be above ground, partially [...]

Published
2023
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42. In vivo deuterium magnetic resonance imaging of xenografted tumors following systemic administration of deuterated water

Author

Jeffrey R. Brender, Julian C. Assmann, Don E. Farthing, Keita Saito, Shun Kishimoto, Kathrynne A. Warrick, Natella Maglakelidze, Terri L. Larus, Hellmut Merkle, Ronald E. Gress, Murali C. Krishna, and Nataliya P. Buxbaum

Subjects
Medicine, Science
Abstract

Abstract In vivo deuterated water (2H2O) labeling leads to deuterium (2H) incorporation into biomolecules of proliferating cells and provides the basis for its use in cell kinetics research. We hypothesized that rapidly proliferating cancer cells would become preferentially labeled with 2H and, therefore, could be visualized by deuterium magnetic resonance imaging (dMRI) following a brief period of in vivo systemic 2H2O administration. We initiated systemic 2H2O administration in two xenograft mouse models harboring either human colorectal, HT-29, or pancreatic, MiaPaCa-2, tumors and 2H2O level of ~ 8% in total body water (TBW). Three schemas of 2H2O administration were tested: (1) starting at tumor seeding and continuing for 7 days of in vivo growth with imaging on day 7, (2) starting at tumor seeding and continuing for 14 days of in vivo growth with imaging on day 14, and (3) initiation of labeling following a week of in vivo tumor growth and continuing until imaging was performed on day 14. Deuterium chemical shift imaging of the tumor bearing limb and contralateral control was performed on either day 7 of 14 after tumor seeding, as described. After 14 days of in vivo tumor growth and 7 days of systemic labeling with 2H2O, a clear deuterium contrast was demonstrated between the xenografts and normal tissue. Labeling in the second week after tumor implantation afforded the highest contrast between neoplastic and healthy tissue in both models. Systemic labeling with 2H2O can be used to create imaging contrast between tumor and healthy issue, providing a non-radioactive method for in vivo cancer imaging.

Published
2023
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43. The role of parents in behavioral treatment for adolescent obesity: design and rationale for the TEENS+ randomized clinical trial

Author

Melanie K. Bean, Jessica Gokee LaRose, Edmond P. Wickham, Hollie A. Raynor, Laura Caccavale, Ronald K. Evans, Laura M. Thornton, Sarah Farthing, Ashley Mendoza, and Suzanne E. Mazzeo

Subjects
Adolescent, Obesity, Parent, Randomized clinical trial, Public aspects of medicine, RA1-1270
Abstract

Abstract Background There is an urgent need for innovative approaches to adolescent obesity treatment, particularly among individuals from racially and ethnically marginalized backgrounds, who face increased risk of obesity and its associated morbidity and mortality. There is a particular dearth of research on the long-term efficacy of adolescent obesity treatments. Further, research and clinical practice guidelines consistently recommend parents’ inclusion in their adolescents’ obesity treatment, yet the most effective strategy to engage parents in adolescent obesity treatment remains unclear. Towards that end, this investigation will conduct a fully-powered, randomized clinical trial to examine the efficacy of two distinct approaches to involving parents in their adolescents’ obesity treatment. Methods Participants will be 210 12-16 year old adolescents (body mass index [BMI]≥85th percentile) and parents (BMI≥25 kg/m2) with overweight or obesity. Dyads will be randomized to one of two 4-month treatments: 1) TEENS+Parents as Coaches (PAC), engaging parents as helpers in their child’s weight management via parent skills training based on authoritative parenting, or 2) TEENS+Parent Weight Loss (PWL), engaging parents in their own behavioral weight management. All adolescents will participate in the TEENS+ protocol, which includes nutrition education with dietary goals, supervised physical activity, and behavioral support, and integrates motivational interviewing to enhance treatment engagement. Assessments of anthropometrics, dietary intake, physical activity, parenting and home environment variables will be completed at 0, 2, 4, 8, and 12 months with the primary endpoint at 12-month follow-up. Discussion Results of this investigation have the potential to significantly advance science in this area and ultimately inform clinical practice guidelines related to the role of parents in adolescent obesity treatment. Trial registration Clinicaltrials.gov NCT03851796. Registered: February 22, 2019.

Published
2023
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44. The effect of tart cherry juice compared to a sports drink on cycling exercise performance, substrate metabolism, and recovery.

Author

Ruirui Gao, Nicole Rapin, Justin W Andrushko, Jonathan P Farthing, Julianne Gordon, and Philip D Chilibeck

Subjects
Medicine, Science
Abstract

Tart cherries have low glycemic index, antioxidant and anti-inflammatory properties, and therefore may benefit performance and recovery from exercise. We determined the effects of consuming tart cherry juice versus a high-glycemic index sports drink on cycling performance, substrate oxidation, and recovery of low-frequency fatigue. Using a randomized, counter-balanced cross-over design, with one-month washout, 12 recreational cyclists (8 males and 4 females; 35±16y; VO2peak 38.2±7.4 ml/kg/min) consumed cherry juice or sports drink twice a day (300mL/d) for 4d before and 2d after exercise. On the exercise day, beverages (providing 1g/kg carbohydrate) were consumed 45min before 90min of cycling at 65%VO2peak, followed by a 10km time trial. Blood glucose, lactate, carbohydrate and fat oxidation, respiratory exchange ratio (RER), O2 cost of cycling, and rating of perceived exertion (RPE) were measured during the initial 90min of cycling. Muscle soreness, maximal voluntary contraction (MVC) and low-frequency fatigue were determined at baseline and after the time trial on the exercise day, and 30min after beverage consumption 24 and 48h later. There were no differences for time trial performance (17±3min cherry juice vs. 17±2min sports drink, p = 0.27) or any other measures between drink conditions. There were time main effects (p

Published
2024
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45. Application of deep learning to large scale riverine flow velocity estimation

Author

Forghani, Mojtaba, Qian, Yizhou, Lee, Jonghyun, Farthing, Matthew W., Hesser, Tyler, Kitanidis, Peter K., and Darve, Eric F.

Subjects
Computer Science - Machine Learning
Abstract

Fast and reliable prediction of riverine flow velocities is important in many applications, including flood risk management. The shallow water equations (SWEs) are commonly used for prediction of the flow velocities. However, accurate and fast prediction with standard SWE solvers is challenging in many cases. Traditional approaches are computationally expensive and require high-resolution riverbed profile measurement ( bathymetry) for accurate predictions. As a result, they are a poor fit in situations where they need to be evaluated repetitively due, for example, to varying boundary condition (BC), or when the bathymetry is not known with certainty. In this work, we propose a two-stage process that tackles these issues. First, using the principal component geostatistical approach (PCGA) we estimate the probability density function of the bathymetry from flow velocity measurements, and then we use multiple machine learning algorithms to obtain a fast solver of the SWEs, given augmented realizations from the posterior bathymetry distribution and the prescribed range of BCs. The first step allows us to predict flow velocities without direct measurement of the bathymetry. Furthermore, the augmentation of the distribution in the second stage allows incorporation of the additional bathymetry information into the flow velocity prediction for improved accuracy and generalization, even if the bathymetry changes over time. Here, we use three solvers, referred to as PCA-DNN (principal component analysis-deep neural network), SE (supervised encoder), and SVE (supervised variational encoder), and validate them on a reach of the Savannah river near Augusta, GA. Our results show that the fast solvers are capable of predicting flow velocities with good accuracy, at a computational cost that is significantly lower than the cost of solving the full boundary value problem with traditional methods.

Published
2020

46. Application of Deep Learning-based Interpolation Methods to Nearshore Bathymetry

Author

Qian, Yizhou, Forghani, Mojtaba, Lee, Jonghyun Harry, Farthing, Matthew, Hesser, Tyler, Kitanidis, Peter, and Darve, Eric

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

Nearshore bathymetry, the topography of the ocean floor in coastal zones, is vital for predicting the surf zone hydrodynamics and for route planning to avoid subsurface features. Hence, it is increasingly important for a wide variety of applications, including shipping operations, coastal management, and risk assessment. However, direct high resolution surveys of nearshore bathymetry are rarely performed due to budget constraints and logistical restrictions. Another option when only sparse observations are available is to use Gaussian Process regression (GPR), also called Kriging. But GPR has difficulties recognizing patterns with sharp gradients, like those found around sand bars and submerged objects, especially when observations are sparse. In this work, we present several deep learning-based techniques to estimate nearshore bathymetry with sparse, multi-scale measurements. We propose a Deep Neural Network (DNN) to compute posterior estimates of the nearshore bathymetry, as well as a conditional Generative Adversarial Network (cGAN) that samples from the posterior distribution. We train our neural networks based on synthetic data generated from nearshore surveys provided by the U.S.\ Army Corps of Engineer Field Research Facility (FRF) in Duck, North Carolina. We compare our methods with Kriging on real surveys as well as surveys with artificially added sharp gradients. Results show that direct estimation by DNN gives better predictions than Kriging in this application. We use bootstrapping with DNN for uncertainty quantification. We also propose a method, named DNN-Kriging, that combines deep learning with Kriging and shows further improvement of the posterior estimates.

Published
2020

47. A greedy non-intrusive reduced order model for shallow water equations

Author

Dutta, Sourav, Farthing, Matthew W., Perracchione, Emma, Savant, Gaurav, and Putti, Mario

Subjects
Physics - Computational Physics, Physics - Fluid Dynamics, 41A05, 65D05, 65M60
Abstract

In this work, we develop Non-Intrusive Reduced Order Models (NIROMs) that combine Proper Orthogonal Decomposition (POD) with a Radial Basis Function (RBF) interpolation method to construct efficient reduced order models for time-dependent problems arising in large scale environmental flow applications. The performance of the POD-RBF NIROM is compared with a traditional nonlinear POD (NPOD) model by evaluating the accuracy and robustness for test problems representative of riverine flows. Different greedy algorithms are studied in order to determine a near-optimal distribution of interpolation points for the RBF approximation. A new power-scaled residual greedy (psr-greedy) algorithm is proposed to address some of the primary drawbacks of the existing greedy approaches. The relative performances of these greedy algorithms are studied with numerical experiments using realistic two-dimensional (2D) shallow water flow applications involving coastal and riverine dynamics.

Published
2020
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