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201. A near-term quantum algorithm for solving linear systems of equations based on the Woodbury identity

Author

O'Malley, Daniel, Henderson, Jessie M., Pelofske, Elijah, Greer, Sarah, Subasi, Yigit, Golden, John K., Lowrie, Robert, and Eidenbenz, Stephan

Subjects
Quantum Physics
Abstract

Quantum algorithms for solving linear systems of equations have generated excitement because of the potential speed-ups involved and the importance of solving linear equations in many applications. However, applying these algorithms can be challenging. The Harrow-Hassidim-Lloyd algorithm and improvements thereof require complex subroutines suitable for fault-tolerant hardware such as Hamiltonian simulation, making it ill-suited to current hardware. Variational algorithms, on the other hand, involve expensive optimization loops, which can be prone to barren plateaus and local optima. We describe a quantum algorithm for solving linear systems of equations that avoids these problems. Our algorithm is based on the Woodbury identity, which analytically describes the inverse of a matrix that is a low-rank modification of another (easily-invertible) matrix. This approach only utilizes basic quantum subroutines like the Hadamard test or the swap test, so it is well-suited to current hardware. There is no optimization loop, so barren plateaus and local optima do not present a problem. The low-rank aspect of the identity enables us to efficiently transfer information to and from the quantum computer. This approach can produce accurate results on current hardware. As evidence of this, we estimate an inner product involving the solution of a system of more than 16 million equations with 2% error using IBM's Auckland quantum computer. To our knowledge, no system of equations this large has previously been solved to this level of accuracy on a quantum computer.

Published
2022

202. Cleanformer: A multichannel array configuration-invariant neural enhancement frontend for ASR in smart speakers

Author

Caroselli, Joseph, Narayanan, Arun, Howard, Nathan, and O'Malley, Tom

Subjects
Electrical Engineering and Systems Science - Audio and Speech Processing, Computer Science - Sound
Abstract

This work introduces the Cleanformer, a streaming multichannel neural based enhancement frontend for automatic speech recognition (ASR). This model has a conformer-based architecture which takes as inputs a single channel each of raw and enhanced signals, and uses self-attention to derive a time-frequency mask. The enhanced input is generated by a multichannel adaptive noise cancellation algorithm known as Speech Cleaner, which makes use of noise context to derive its filter taps. The time-frequency mask is applied to the noisy input to produce enhanced output features for ASR. Detailed evaluations are presented with simulated and re-recorded datasets in speech-based and non-speech-based noise that show significant reduction in word error rate (WER) when using a large-scale state-of-the-art ASR model. It also will be shown to significantly outperform enhancement using a beamformer with ideal steering. The enhancement model is agnostic of the number of microphones and array configuration and, therefore, can be used with different microphone arrays without the need for retraining. It is demonstrated that performance improves with more microphones, up to 4, with each additional microphone providing a smaller marginal benefit. Specifically, for an SNR of -6dB, relative WER improvements of about 80\% are shown in both noise conditions., Comment: Accepted to ICASSP 2023

Published
2022

203. Constraining the $^{30}$P($p,\gamma)^{31}$S reaction rate in ONe novae via the weak, low-energy, $\beta$-delayed proton decay of $^{31}$Cl

Author

Budner, T., Friedman, M., Wrede, C., Brown, B. A., José, J., Pérez-Loureiro, D., Sun, L. J., Surbrook, J., Ayyad, Y., Bardayan, D. W., Chae, K., Chen, A. A., Chipps, K. A., Cortesi, M., Glassman, B., Hall, M. R., Janasik, M., Liang, J., O'Malley, P., Pollacco, E., Psaltis, A., Stomps, J., and Wheeler, T.

Subjects
Nuclear Experiment, Nuclear Theory
Abstract

The $^{30}$P$(p,\gamma)^{31}$S reaction plays an important role in understanding nucleosynthesis of $A\geq 30$ nuclides in oxygen-neon novae. The Gaseous Detector with Germanium Tagging was used to measure $^{31}$Cl $\beta$-delayed proton decay through the key $J^{\pi}=3/2^{+}$, 260-keV resonance. The intensity $I^{260}_{\beta p} = 8.3^{+1.2}_{-0.9} \times 10^{-6}$ represents the weakest $\beta$-delayed, charged-particle emission ever measured below 400 keV, resulting in a proton branching ratio of $\Gamma_p / \Gamma = 2.5^{+0.4}_{-0.3} \times 10^{-4}$. By combining this measurement with shell-model calculations for $\Gamma_{\gamma}$ and past work on other resonances, the total $^{30}$P$(p,\gamma)^{31}$S rate has been determined with reduced uncertainty. The new rate has been used in hydrodynamic simulations to model the composition of nova ejecta, leading to a concrete prediction of $^{30}$Si/$^{28}$Si excesses in presolar nova grains and the calibration of nuclear thermometers., Comment: 7 pages, 2 figures, accepted to Physical Review Letters on April 4, 2022

Published
2022
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204. Personal VAD 2.0: Optimizing Personal Voice Activity Detection for On-Device Speech Recognition

Author

Ding, Shaojin, Rikhye, Rajeev, Liang, Qiao, He, Yanzhang, Wang, Quan, Narayanan, Arun, O'Malley, Tom, and McGraw, Ian

Subjects
Electrical Engineering and Systems Science - Audio and Speech Processing, Computer Science - Machine Learning, Computer Science - Sound
Abstract

Personalization of on-device speech recognition (ASR) has seen explosive growth in recent years, largely due to the increasing popularity of personal assistant features on mobile devices and smart home speakers. In this work, we present Personal VAD 2.0, a personalized voice activity detector that detects the voice activity of a target speaker, as part of a streaming on-device ASR system. Although previous proof-of-concept studies have validated the effectiveness of Personal VAD, there are still several critical challenges to address before this model can be used in production: first, the quality must be satisfactory in both enrollment and enrollment-less scenarios; second, it should operate in a streaming fashion; and finally, the model size should be small enough to fit a limited latency and CPU/Memory budget. To meet the multi-faceted requirements, we propose a series of novel designs: 1) advanced speaker embedding modulation methods; 2) a new training paradigm to generalize to enrollment-less conditions; 3) architecture and runtime optimizations for latency and resource restrictions. Extensive experiments on a realistic speech recognition system demonstrated the state-of-the-art performance of our proposed method., Comment: Accepted by INTERSPEECH 2022

Published
2022

205. Light Response of Poly(ethylene 2,6-napthalate) to Neutrons

Author

Hackett, Brennan, deBoer, Richard, Efremenko, Yuri, Febbraro, Michael, Nattress, Jason, Bardayan, Dan, Boomershine, Chevelle, Brandenburg, Kristyn, Dede, Stefania, Derkin, Joseph, Fang, Ruoyu, Fritsch, Adam, Gula, August, Gyorgy, Gyurky, Hamad, Gula, Jones-Alberty, Yenuel, Kelmar, Beka, Manukyan, Khachatur, Matney, Miriam, McDonaugh, John, Moylan, Shane, O'Malley, Patrick, Shahina, Shahina, and Singh, Nisha

Subjects
Physics - Instrumentation and Detectors, High Energy Physics - Experiment, Nuclear Experiment
Abstract

There is increasing necessity for low background active materials as ton-scale, rare-event and cryogenic detectors are developed. Poly(ethylene-2,6-naphthalate) (PEN) has been considered for these applications because of its robust structural characteristics, and its scintillation light in the blue wavelength region. Radioluminescent properties of PEN have been measured to aid in the evaluation of this material. In this article we present a measurement of PEN's quenching factor using three different neutron sources; neutrons emitted from spontaneous fission in $^{252}$Cf, neutrons generated from a DD generator, and neutrons emitted from the $^{13}$C($\alpha$,n)$^{16}$O and the $^{7}$Li(p,n)$^{7}$Be nuclear reactions. The fission source used time-of-flight to determine the neutron energy, and the neutron energy from the nuclear reactions was defined using thin targets and reaction kinematics. The Birk's factor and scintillation efficiency were found to be $kB = 0.12 \pm 0.01$ mm MeV$^{-1}$ and $S = 1.31\pm0.09$ MeV$_{ee}$ MeV$^{-1}$ from a simultaneous analysis of the data obtained from the three different sources. With these parameters, it is possible to evaluate PEN as a viable material for large-scale, low background physics experiments., Comment: 20 pages, 14 figures

Published
2022

209. 'I Feel Quite Hopeful That My Future Is Still Going to Be Okay': 'Educational Aspirations during COVID-19'

Author

O'Malley, Blaise Y., Loomis, Colleen, Dimakos, Christina, Lamont, Sylvie L., Singh, Gurmakh, Pelletier, Janette, Christens, Brian D., Wright, Carrie, and Peters, Ray DeV

Abstract

The COVID-19 pandemic has radically altered how we learn, work, and live. This qualitative research aimed to study the effects of the COVID-19 pandemic on the educational and occupational aspirations of young Canadian adults. All close to 29 years of age, sixteen participants took part in one-on-one semi-structured interviews conducted through Zoom. Questions probed participants' hopes, dreams, and perceived obstacles regarding school and work. Coding was completed using the research software Dedoose. Thematic content analysis was performed using both deductive and inductive approaches. Three themes emerged: the benefits and drawbacks of working and learning from home; financial changes and concerns; and hope and optimism despite challenges posed by the pandemic. Working and learning from home were discussed by 88% of participants, making it the most prominent theme. Participants generally agreed that working and learning from home had many benefits, but some expressed concern about the quality of online education. In addition, the pandemic caused financial hardship for a few participants, forcing them to delay educational or occupational plans. However, the majority (75%) expressed positivity and hope for the future. Overall, although the timeline of some participants' educational or occupational plans changed, their aspirations largely remained the same.

Published
2021

210. Monitoring the Future National Survey Results on Drug Use, 1975-2020: Overview, Key Findings on Adolescent Drug Use

Author

University of Michigan, Institute for Social Research, Johnston, Lloyd D., Miech, Richard A., O'Malley, Patrick M., Bachman, Jerald G., Schulenberg, John E., and Patrick, Megan E.

Abstract

Monitoring the Future (MTF) is a long-term study of substance use and related factors among U.S. adolescents, college students, and adult high school graduates through age 60. It is conducted annually and supported by the National Institute on Drug Abuse. MTF findings identify emerging substance use problems, track substance use trends, publish many scientific findings, and help to inform policy and intervention strategies. The key findings regarding use of various substances by 8th, 10th, and 12th graders surveyed across the coterminous U.S. in 2020 are summarized. The year 2020 was an unusual year for the study in that data collection was halted earlier than usual, in March of that year, as a result of the emerging COVID-19 epidemic. This resulted in smaller samples being obtained that year. How the authors dealt with this disruption (as well as the disruption that occurred in the 2019 study, the transition from students using paper and pencil questionnaires to using electronic tablets) to the ongoing series is described. It should be noted that the 2020 data collection occurred early in 2020, so it does not cover most of the period of the pandemic, nor of its effects. [For "Monitoring the Future National Survey Results on Drug Use, 1975-2019: Overview, Key Findings on Adolescent Drug Use," see ED604018.]

Published
2021

211. Demographic Subgroup Trends among Young Adults in the Use of Various Licit and Illicit Drugs, 1988-2020. Monitoring the Future Occasional Paper Series. Paper 96

Author

University of Michigan, Institute for Social Research, Johnston, Lloyd D., Schulenberg, John E., Patrick, Megan E., O'Malley, Patrick M., Bachman, Jerald G., and Miech, Richard A.

Abstract

This occasional paper presents subgroup findings from the Monitoring the Future (MTF) study on levels of, and trends in, the use of a number of substances for nationally representative samples of high school graduates ages 19-30, "Monitoring the Future National Survey Results on Drug Use, 1975-2018. Volume II, College Students & Adults Ages 19-60" (see ED599071). The data have been gathered in a series of follow-up surveys of representative subsamples of high school seniors who were first surveyed in 12th grade as part of the MTF study. Data were first available for the 19-22 age group in 1980 and for the older age groups in subsequent years as the early cohorts progressed in age, as the tables and figures in this occasional paper indicate. The subgroup trends shown in the current occasional paper complement the last section of Chapter 5 (Trends in Drug Use in Early and Middle Adulthood) of that monograph by presenting the trend data for young adult subgroups in both graphic and tabular form. The results are described and discussed in Chapter 5, but the extensive set of tables and figures is provided here for the reader who wishes to view the figures and the underlying numerical values in tabular form. Three demographic dimensions are differentiated: gender, region of the country, and population density. Gender includes trends for males and females; region describes trends for each of the four major regions defined by the U.S. Census; and population density differentiates trends for five levels of community size. The Table of Contents and List of Figures in this occasional paper contain links to the content and figures. Following each figure is a table giving the numerical values associated with each trend line in that figure. [For the 1988-2019 Monitoring the Future Occasional Paper, see ED608244.]

Published
2021

212. Monitoring the Future National Survey Results: HIV/AIDS Risk & Protective Behaviors among Adults Ages 21 to 30 in the U.S., 2004-2020

Author

University of Michigan, Institute for Social Research, Johnston, Lloyd D., Schulenberg, John E., O'Malley, Patrick M., Patrick, Megan E., Miech, Richard A., and Bachman, Jerald G.

Abstract

Monitoring the Future (MTF) is a long-term study of American adolescents, college students, and adult high school graduates through age 60. The study is funded under a series of investigator-initiated, competing research grants from the National Institute on Drug Abuse and has been conducted annually by the University of Michigan's Institute for Social Research since 1975. The present monograph focuses on a range of behaviors--including certain forms of substance use--related to the spread of the human immunodeficiency virus (HIV), which is responsible for acquired immunodeficiency syndrome (AIDS). The population under study here includes high school graduates in the general population, ages 21-30. High school graduates who fall into this age range each year have been surveyed annually since 2004. HIV infection is clearly a serious public health concern. Worldwide, about 36.9 million people were living with AIDS at the end of 2017 (UNAIDS, 2018). In the United States, about 1.1 million people were living with diagnosed HIV infection as of 2019 (CDC, 2021), and 1 in 7 were unaware of their infection (CDC, 2021). The present monograph addresses some of the factors that may have been preventing greater progress against HIV/AIDS. The ages covered in this study contain the two age bands with the highest rates of newly diagnosed HIV infection in the United States: namely, ages 20-24 and 25-29. This monograph tracks key behaviors related to the spread of HIV/AIDS in the United States. In 2019, over 36,000 individuals became newly infected with HIV in the United States (CDC, 2021). MTF surveys assess both sexual risk behaviors and injection drug use (including needle sharing), which are two main sources of HIV infection. The present volume is the fourth monograph published this year in the annual series of reports. [For "Monitoring the Future National Survey Results: HIV/AIDS Risk & Protective Behaviors among Adults Ages 21 to 30 in the U.S., 2004-2019," see ED611887.]

Published
2021

213. Implementation of a Metacognitive Learning Strategies Session in an Online Asynchronous Human Anatomy and Physiology Course

Author

O'Malley, Chasity B., Ross, Kyla Turpin, Hull, Kerry, Hood, Suzanne, Page, Olivia, and Jensen, Murray

Abstract

A unique approach to improving student success in Human Anatomy and Physiology can be centered around teaching the students about metacognitive learning strategies. This perspective discusses the implementation of a metacognitive learning strategies session and highlights some of the outcomes that resulted from the session through exam wrapper responses. A one-hour session discussing metacognitive learning strategies was presented for the students through a pre-recorded video with embedded participation questions. Topics included methods for changing student studying habits to be more efficient and to retain a deeper level of understanding of the material. The session was also accompanied by exam wrappers to help the students to apply the concepts from the session.

Published
2021

214. Compassion Fatigue and Coping Mechanisms of Laboratory Animal Professionals from Europe, China, and Japan.

Author

O'Malley, Carly I, Moody, Carly M, Foster, Adrian, and Turner, Patricia V

Subjects
Agricultural, Veterinary and Food Sciences, Biological Sciences, Biomedical and Clinical Sciences, Behavioral and Social Science, Good Health and Well Being, Animals, Humans, Compassion Fatigue, Burnout, Professional, Japan, Surveys and Questionnaires, Adaptation, Psychological, China, Animals, Laboratory, Abbreviations, CF, compassion fatigue, CRO, contract research organization, EU, European Union, GP, general population, LAP, laboratory animal professionals, Agricultural and Veterinary Sciences, Medical and Health Sciences, Veterinary Sciences, Agricultural, veterinary and food sciences, Biological sciences, Biomedical and clinical sciences
Abstract

Laboratory animal professionals (LAP) may experience situations that contribute to compassion fatigue (CF). The goal of this research was to better understand CF in LAP in and across employment categories. Surveys were distributed through LAP organizational listservs in the European Union (EU), China, and Japan, and results were analyzed to identify CF prevalence, personal and work-related factors, coping mechanisms, and beneficial work-support programs. Independent χ²-tests compared personal and work-related factors and feelings of CF. Feelings of CF and coping mechanisms were compared with personality scores using independent sample t tests. There were 302 respondents from the EU, 39 from China, and 77 from Japan. Over half of respondents from the EU (52%) and China (56%) reported experiencing CF (52%), with fewer (32%) reporting CF in Japan. No major differences were found based on employer type. Personality scores were significantly related to feelings of CF and preferred coping mechanisms. Work-related factors that contributed to feelings of CF in over half of respondents included staffing levels, workplace relationships, and availability of programs geared to address CF. Across regions, talking to someone, physical activity, getting away from work, and self-care were effective coping mechanisms in over 50% of respondents. Fewer than 30% of respondents indicated that their place of employment had CF support programs, and even fewer (8% to 28%) indicated that these programs were helpful. The study results suggest that to be effective, employer CF programs for LAP should consider providing quiet places at work and programs for self-care, promoting physical and mental health and social support systems, and establishing opportunities to memorialize animals.

Published
2022

215. Expansion of the global RNA virome reveals diverse clades of bacteriophages

Author

Neri, Uri, Wolf, Yuri I, Roux, Simon, Camargo, Antonio Pedro, Lee, Benjamin, Kazlauskas, Darius, Chen, I Min, Ivanova, Natalia, Allen, Lisa Zeigler, Paez-Espino, David, Bryant, Donald A, Bhaya, Devaki, Consortium, RNA Virus Discovery, Narrowe, Adrienne B, Probst, Alexander J, Sczyrba, Alexander, Kohler, Annegret, Séguin, Armand, Shade, Ashley, Campbell, Barbara J, Lindahl, Björn D, Reese, Brandi Kiel, Roque, Breanna M, DeRito, Chris, Averill, Colin, Cullen, Daniel, Beck, David AC, Walsh, David A, Ward, David M, Wu, Dongying, Eloe-Fadrosh, Emiley, Brodie, Eoin L, Young, Erica B, Lilleskov, Erik A, Castillo, Federico J, Martin, Francis M, LeCleir, Gary R, Attwood, Graeme T, Cadillo-Quiroz, Hinsby, Simon, Holly M, Hewson, Ian, Grigoriev, Igor V, Tiedje, James M, Jansson, Janet K, Lee, Janey, VanderGheynst, Jean S, Dangl, Jeff, Bowman, Jeff S, Blanchard, Jeffrey L, Bowen, Jennifer L, Xu, Jiangbing, Banfield, Jillian F, Deming, Jody W, Kostka, Joel E, Gladden, John M, Rapp, Josephine Z, Sharpe, Joshua, McMahon, Katherine D, Treseder, Kathleen K, Bidle, Kay D, Wrighton, Kelly C, Thamatrakoln, Kimberlee, Nusslein, Klaus, Meredith, Laura K, Ramirez, Lucia, Buee, Marc, Huntemann, Marcel, Kalyuzhnaya, Marina G, Waldrop, Mark P, Sullivan, Matthew B, Schrenk, Matthew O, Hess, Matthias, Vega, Michael A, O’Malley, Michelle A, Medina, Monica, Gilbert, Naomi E, Delherbe, Nathalie, Mason, Olivia U, Dijkstra, Paul, Chuckran, Peter F, Baldrian, Petr, Constant, Philippe, Stepanauskas, Ramunas, Daly, Rebecca A, Lamendella, Regina, Gruninger, Robert J, McKay, Robert M, Hylander, Samuel, Lebeis, Sarah L, Esser, Sarah P, Acinas, Silvia G, Wilhelm, Steven S, Singer, Steven W, Tringe, Susannah S, Woyke, Tanja, Reddy, TBK, Bell, Terrence H, Mock, Thomas, McAllister, Tim, and Thiel, Vera

Subjects
Microbiology, Biological Sciences, Bioinformatics and Computational Biology, Infectious Diseases, Genetics, Microbiome, Biotechnology, Infection, Bacteriophages, DNA-Directed RNA Polymerases, Genome, Viral, Phylogeny, RNA, RNA Viruses, RNA-Dependent RNA Polymerase, Virome, RNA Virus Discovery Consortium, Bactriophage, Functional protein annotation, Metatranscriptomics, RNA Virus, RNA dependent RNA polymerase, Viral Ecology, Virus, Virus - Host prediction, viral phylogeny, Medical and Health Sciences, Developmental Biology, Biological sciences, Biomedical and clinical sciences
Abstract

High-throughput RNA sequencing offers broad opportunities to explore the Earth RNA virome. Mining 5,150 diverse metatranscriptomes uncovered >2.5 million RNA virus contigs. Analysis of >330,000 RNA-dependent RNA polymerases (RdRPs) shows that this expansion corresponds to a 5-fold increase of the known RNA virus diversity. Gene content analysis revealed multiple protein domains previously not found in RNA viruses and implicated in virus-host interactions. Extended RdRP phylogeny supports the monophyly of the five established phyla and reveals two putative additional bacteriophage phyla and numerous putative additional classes and orders. The dramatically expanded phylum Lenarviricota, consisting of bacterial and related eukaryotic viruses, now accounts for a third of the RNA virome. Identification of CRISPR spacer matches and bacteriolytic proteins suggests that subsets of picobirnaviruses and partitiviruses, previously associated with eukaryotes, infect prokaryotic hosts.

Published
2022

216. The landscape of Chlamydomonas histone H3 lysine 4 methylation reveals both constant features and dynamic changes during the diurnal cycle

Author

Strenkert, Daniela, Yildirim, Asli, Yan, Juying, Yoshinaga, Yuko, Pellegrini, Matteo, O'Malley, Ronan C, Merchant, Sabeeha S, and Umen, James G

Subjects
Biological Sciences, Bioinformatics and Computational Biology, Genetics, Human Genome, 1.1 Normal biological development and functioning, Underpinning research, Generic health relevance, Histones, Lysine, Chlamydomonas, RNA, Long Noncoding, DNA Methylation, Chromatin, Cytosine, meiotic genes, non-poly-adenylated transcripts, replication-dependent histones, DNA methylation, Biochemistry and Cell Biology, Plant Biology, Plant Biology & Botany, Biochemistry and cell biology, Plant biology
Abstract

Chromatin modifications are epigenetic regulatory features with major roles in various cellular events, yet they remain understudied in algae. We interrogated the genome-wide distribution pattern of mono- and trimethylated histone H3 lysine 4 (H3K4) using chromatin-immunoprecipitation followed by deep-sequencing (ChIP-seq) during key phases of the Chlamydomonas cell cycle: early G1 phase, Zeitgeber Time 1 (ZT1), when cells initiate biomass accumulation, S/M phase (ZT13) when cells are replicating DNA and undergoing mitosis, and late G0 phase (ZT23) when they are quiescent. Tri-methylated H3K4 was predominantly enriched at transcription start sites of the majority of protein coding genes (85%). The likelihood of a gene being marked by H3K4me3 correlated with it being transcribed at some point during the life cycle but not necessarily by continuous active transcription, as exemplified by early zygotic genes, which may remain transcriptionally dormant for thousands of generations between sexual cycles. The exceptions to this rule were around 120 loci, some of which encode non-poly-adenylated transcripts, such as small nuclear RNAs and replication-dependent histones that had H3K4me3 peaks only when they were being transcribed. Mono-methylated H3K4 was the default state for the vast majority of histones that were bound outside of transcription start sites and terminator regions of genes. A small fraction of the genome that was depleted of any H3 lysine 4 methylation was enriched for DNA cytosine methylation and the genes within these DNA methylation islands were poorly expressed. Besides marking protein coding genes, H3K4me3 ChIP-seq data served also as a annotation tool for validation of hundreds of long non-coding RNA genes.

Published
2022

221. Niraparib first-line maintenance therapy in patients with newly diagnosed advanced ovarian cancer: final overall survival results from the PRIMA/ENGOT-OV26/GOG-3012 trial

Author

Monk, B.J., Barretina-Ginesta, M.P., Pothuri, B., Vergote, I., Graybill, W., Mirza, M.R., McCormick, C.C., Lorusso, D., Moore, R.G., Freyer, G., O’Cearbhaill, R.E., Heitz, F., O’Malley, D.M., Redondo, A., Shahin, M.S., Vulsteke, C., Bradley, W.H., Haslund, C.A., Chase, D.M., Pisano, C., Holman, L.L., Pérez, M. J. Rubio, DiSilvestro, P., Gaba, L., Herzog, T.J., Bruchim, I., Compton, N., Shtessel, L., Malinowska, I.A., and González-Martín, A.

Published
2024
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223. Stratified analyses refine association between TLR7 rare variants and severe COVID-19

Author

Rimoldi, Valeria, Paraboschi, Elvezia M., Bandera, Alessandra, Peyvandi, Flora, Grasselli, Giacomo, Blasi, Francesco, Malvestiti, Francesco, Pelusi, Serena, Bianco, Cristiana, Miano, Lorenzo, Lombardi, Angela, Invernizzi, Pietro, Gerussi, Alessio, Citerio, Giuseppe, Biondi, Andrea, Valsecchi, Maria Grazia, Cazzaniga, Marina Elena, Foti, Giuseppe, Beretta, Ilaria, D'Angiò, Mariella, Bettini, Laura Rachele, Farré, Xavier, Iraola-Guzmán, Susana, Kogevinas, Manolis, Castaño-Vinyals, Gemma, Garcia-Etxebarria, Koldo, Nafria, Beatriz, D'Amato, Mauro, Palom, Adriana, Begg, Colin, Clohisey, Sara, Hinds, Charles, Horby, Peter, Knight, Julian, Ling, Lowell, Maslove, David, McAuley, Danny, Millar, Johnny, Montgomery, Hugh, Nichol, Alistair, Openshaw, Peter J.M., Pereira, Alexandre C., Ponting, Chris P., Rowan, Kathy, Semple, Malcolm G., Shankar-Hari, Manu, Summers, Charlotte, Walsh, Timothy, Baillie, J. Kenneth, Aravindan, Latha, Armstrong, Ruth, Biggs, Heather, Boz, Ceilia, Brown, Adam, Clark, Richard, Coutts, Audrey, Coyle, Judy, Cullum, Louise, Das, Sukamal, Day, Nicky, Donnelly, Lorna, Duncan, Esther, Fawkes, Angie, Fineran, Paul, Fourman, Max Head, Furlong, Anita, Furniss, James, Gallagher, Bernadette, Gilchrist, Tammy, Golightly, Ailsa, Griffiths, Fiona, Hafezi, Katarzyna, Hamilton, Debbie, Hendry, Ross, Law, Andy, Law, Dawn, Law, Rachel, Law, Sarah, Lidstone-Scott, Rebecca, Macgillivray, Louise, Maclean, Alan, Mal, Hanning, McCafferty, Sarah, Mcmaster, Ellie, Meikle, Jen, Moore, Shona C., Morrice, Kirstie, Murphy, Lee, Murphy, Sheena, Hellen, Mybaya, Oosthuyzen, Wilna, Zheng, Chenqing, Chen, Jiantao, Parkinson, Nick, Paterson, Trevor, Schon, Katherine, Stenhouse, Andrew, Das, Mihaela, Swets, Maaike, Szoor-McElhinney, Helen, Taneski, Filip, Turtle, Lance, Wackett, Tony, Ward, Mairi, Weaver, Jane, Wrobel, Nicola, Zechner, Marie, Arbane, Gill, Bociek, Aneta, Campos, Sara, Grau, Neus, Jones, Tim Owen, Lim, Rosario, Marotti, Martina, Ostermann, Marlies, Whitton, Christopher, Alldis, Zoe, Astin-Chamberlain, Raine, Bibi, Fatima, Biddle, Jack, Blow, Sarah, Bolton, Matthew, Borra, Catherine, Bowles, Ruth, Burton, Maudrian, Choudhury, Yasmin, Collier, David, Cox, Amber, Easthope, Amy, Ebano, Patrizia, Fotiadis, Stavros, Gurasashvili, Jana, Halls, Rosslyn, Hartridge, Pippa, Kallon, Delordson, Kassam, Jamila, Lancoma-Malcolm, Ivone, Matharu, Maninderpal, May, Peter, Mitchelmore, Oliver, Newman, Tabitha, Patel, Mital, Pheby, Jane, Pinzuti, Irene, Prime, Zoe, Prysyazhna, Oleksandra, Shiel, Julian, Taylor, Melanie, Tierney, Carey, Wood, Suzanne, Zak, Anne, Zongo, Olivier, Bonner, Stephen, Hugill, Keith, Jones, Jessica, Liggett, Steven, Headlam, Evie, Bandla, Nageswar, Gellamucho, Minnie, Davies, Michelle, Thompson, Christopher, Abdelrazik, Marwa, Bakthavatsalam, Dhanalakshmi, Elhassan, Munzir, Ganesan, Arunkumar, Haldeos, Anne, Moreno-Cuesta, Jeronimo, Purohit, Dharam, Vincent, Rachel, Xavier, Kugan, Rohit, Kumar, Alasdair, Frater, Saleem, Malik, David, Carter, Samuel, Jenkins, Lamond, Zoe, Alanna, Wall, Fernandez-Roman, Jaime, Hamilton, David O., Johnson, Emily, Johnston, Brian, Martinez, Maria Lopez, Mulla, Suleman, Shaw, David, Waite, Alicia A.C., Waugh, Victoria, Welters, Ingeborg D., Williams, Karen, Cavazza, Anna, Cockrell, Maeve, Corcoran, Eleanor, Depante, Maria, Finney, Clare, Jerome, Ellen, McPhail, Mark, Nayak, Monalisa, Noble, Harriet, O'Reilly, Kevin, Pappa, Evita, Saha, Rohit, Saha, Sian, Smith, John, Knighton, Abigail, Antcliffe, David, Banach, Dorota, Brett, Stephen, Coghlan, Phoebe, Fernandez, Ziortza, Gordon, Anthony, Rojo, Roceld, Arias, Sonia Sousa, Templeton, Maie, Meredith, Megan, Morris, Lucy, Ryan, Lucy, Clark, Amy, Sampson, Julia, Peters, Cecilia, Dent, Martin, Langley, Margaret, Ashraf, Saima, Wei, Shuying, Andrew, Angela, Bashyal, Archana, Davidson, Neil, Hutton, Paula, McKechnie, Stuart, Wilson, Jean, Baptista, David, Crowe, Rebecca, Fernandes, Rita, Herdman-Grant, Rosaleen, Joseph, Anna, O'Connor, Denise, Allen, Meryem, Loveridge, Adam, McKenley, India, Morino, Eriko, Naranjo, Andres, Simms, Richard, Sollesta, Kathryn, Swain, Andrew, Venkatesh, Harish, Khera, Jacyntha, Fox, Jonathan, Andrew, Gillian, Barclay, Lucy, Callaghan, Marie, Campbell, Rachael, Clark, Sarah, Hope, Dave, Marshall, Lucy, McCulloch, Corrienne, Briton, Kate, Singleton, Jo, Birch, Sohphie, Brimfield, Lutece, Daly, Zoe, Pogson, David, Rose, Steve, Battle, Ceri, Brinkworth, Elaine, Harford, Rachel, Murphy, Carl, Newey, Luke, Rees, Tabitha, Williams, Marie, Arnold, Sophie, Polgarova, Petra, Stroud, Katerina, Meaney, Eoghan, Jones, Megan, Ng, Anthony, Agrawal, Shruti, Pathan, Nazima, White, Deborah, Daubney, Esther, Elston, Kay, Grauslyte, Lina, Hussain, Musarat, Phull, Mandeep, Pogreban, Tatiana, Rosaroso, Lace, Salciute, Erika, Franke, George, Wong, Joanna, George, Aparna, Ortiz-Ruiz de Gordoa, Laura, Peasgood, Emily, Phillips, Claire, Bates, Michelle, Dasgin, Jo, Gill, Jaspret, Nilsson, Annette, Scriven, James, Delgado, Carlos Castro, Dawson, Deborah, Ding, Lijun, Durrant, Georgia, Ezeobu, Obiageri, Farnell-Ward, Sarah, Harrison, Abiola, Kanu, Rebecca, Leaver, Susannah, Maccacari, Elena, Manna, Soumendu, Saluzzio, Romina Pepermans, Queiroz, Joana, Samakomva, Tinashe, Sicat, Christine, Texeira, Joana, Da Gloria, Edna Fernandes, Lisboa, Ana, Rawlins, John, Mathew, Jisha, Kinch, Ashley, Hurt, William James, Shah, Nirav, Clark, Victoria, Thanasi, Maria, Yun, Nikki, Patel, Kamal, Bennett, Sara, Goodwin, Emma, Jackson, Matthew, Kent, Alissa, Tibke, Clare, Woodyatt, Wiesia, Zaki, Ahmed, Abraheem, Azmerelda, Bamford, Peter, Cawley, Kathryn, Dunmore, Charlie, Faulkner, Maria, Girach, Rumanah, Jeffrey, Helen, Jones, Rhianna, London, Emily, Nagra, Imrun, Nasir, Farah, Sainsbury, Hannah, Smedley, Clare, Patel, Tahera, Smith, Matthew, Chukkambotla, Srikanth, Kazi, Aayesha, Hartley, Janice, Dykes, Joseph, Hijazi, Muhammad, Keith, Sarah, Khan, Meherunnisa, Ryan-Smith, Janet, Springle, Philippa, Thomas, Jacqueline, Truman, Nick, Saad, Samuel, Coleman, Dabheoc, Fine, Christopher, Matt, Roseanna, Gay, Bethan, Dalziel, Jack, Ali, Syamlan, Goodchild, Drew, Harling, Rhiannan, Bhatterjee, Ravi, Goddard, Wendy, Davison, Chloe, Duberly, Stephen, Hargreaves, Jeanette, Bolton, Rachel, Davey, Miriam, Golden, David, Seaman, Rebecca, Cherian, Shiney, Cutler, Sean, Heron, Anne Emma, Roynon-Reed, Anna, Szakmany, Tamas, Williams, Gemma, Richards, Owen, Cheema, Yusuf, Brooke, Hollie, Buckley, Sarah, Suarez, Jose Cebrian, Charlesworth, Ruth, Hansson, Karen, Norris, John, Poole, Alice, Rose, Alastair, Sandhu, Rajdeep, Sloan, Brendan, Smithson, Elizabeth, Thirumaran, Muthu, Wagstaff, Veronica, Metcalfe, Alexandra, Brunton, Mark, Caterson, Jess, Coles, Holly, Frise, Matthew, Rai, Sabi Gurung, Jacques, Nicola, Keating, Liza, Tilney, Emma, Bartley, Shauna, Bhuie, Parminder, Gibson, Sian, Lyle, Amanda, McNeela, Fiona, Radhakrishnan, Jayachandran, Hughes, Alistair, Yates, Bryan, Reynolds, Jessica, Campbell, Helen, Thompsom, Maria, Dodds, Steve, Duffy, Stacey, Greer, Sandra, Shuker, Karen, Tridente, Ascanio, Khade, Reena, Sundar, Ashok, Tsinaslanidis, George, Birkinshaw, Isobel, Carter, Joseph, Howard, Kate, Ingham, Joanne, Joy, Rosie, Pearson, Harriet, Roche, Samantha, Scott, Zoe, Bancroft, Hollie, Bellamy, Mary, Carmody, Margaret, Daglish, Jacqueline, Moore, Faye, Rhodes, Joanne, Sangombe, Mirriam, Kadiri, Salma, Croft, Maria, White, Ian, Frost, Victoria, Aquino, Maia, Jha, Rajeev, Krishnamurthy, Vinodh, Lim, Lai, Lim, Li, Combes, Edward, Joefield, Teishel, Monnery, Sonja, Beech, Valerie, Trotman, Sallyanne, Almaden-Boyle, Christine, Austin, Pauline, Cabrelli, Louise, Cole, Stephen, Casey, Matt, Chapman, Susan, Whyte, Clare, Baird, Yolanda, Butler, Aaron, Chadbourn, Indra, Folkes, Linda, Fox, Heather, Gardner, Amy, Gomez, Raquel, Hobden, Gillian, Hodgson, Luke, King, Kirsten, Margarson, Michael, Martindale, Tim, Meadows, Emma, Raynard, Dana, Thirlwall, Yvette, Helm, David, Margalef, Jordi, Criste, Kristine, Cusack, Rebecca, Golder, Kim, Golding, Hannah, Jones, Oliver, Leggett, Samantha, Male, Michelle, Marani, Martyna, Prager, Kirsty, Williams, Toran, Roberts, Belinda, Salmon, Karen, Anderson, Peter, Archer, Katie, Austin, Karen, Davis, Caroline, Durie, Alison, Kelsall, Olivia, Thrush, Jessica, Vigurs, Charlie, Wild, Laura, Wood, Hannah-Louise, Tranter, Helen, Harrison, Alison, Cowley, Nicholas, McAlindon, Michael, Burtenshaw, Andrew, Digby, Stephen, Low, Emma, Morgan, Aled, Cother, Naiara, Rankin, Tobias, Clayton, Sarah, McCurdy, Alex, Ahmed, Cecilia, Baines, Balvinder, Clamp, Sarah, Colley, Julie, Haq, Risna, Hayes, Anne, Hulme, Jonathan, Hussain, Samia, Joseph, Sibet, Kumar, Rita, Maqsood, Zahira, Purewal, Manjit, Benham, Leonie, Bradshaw, Zena, Brown, Joanna, Caswell, Melanie, Cupitt, Jason, Melling, Sarah, Preston, Stephen, Slawson, Nicola, Stoddard, Emma, Warden, Scott, Deacon, Bethan, Lynch, Ceri, Pothecary, Carla, Roche, Lisa, Howe, Gwenllian Sera, Singh, Jayaprakash, Turner, Keri, Ellis, Hannah, Stroud, Natalie, Hunt, Jodie, Dearden, Joy, Dobson, Emma, Drummond, Andy, Mulcahy, Michelle, Munt, Sheila, O'Connor, Grainne, Philbin, Jennifer, Rishton, Chloe, Tully, Redmond, Winnard, Sarah, Cathcart, Susanne, Duffy, Katharine, Puxty, Alex, Puxty, Kathryn, Turner, Lynne, Ireland, Jane, Semple, Gary, Long, Kate, Whiteley, Simon, Wilby, Elizabeth, Ogg, Bethan, Cowton, Amanda, Kay, Andrea, Kent, Melanie, Potts, Kathryn, Wilkinson, Ami, Campbell, Suzanne, Brown, Ellen, Melville, Julie, Naisbitt, Jay, Joseph, Rosane, Lazo, Maria, Walton, Olivia, Neal, Alan, Alexander, Peter, Allen, Schvearn, Bradley-Potts, Joanne, Brantwood, Craig, Egan, Jasmine, Felton, Timothy, Padden, Grace, Ward, Luke, Moss, Stuart, Glasgow, Susannah, Abel, Lynn, Brett, Michael, Digby, Brian, Gemmell, Lisa, Hornsby, James, MacGoey, Patrick, O'Neil, Pauline, Price, Richard, Rodden, Natalie, Rooney, Kevin, Sundaram, Radha, Thomson, Nicola, Hopkins, Bridget, Thrasyvoulou, Laura, Willis, Heather, Clark, Martyn, Coulding, Martina, Jude, Edward, McCormick, Jacqueline, Mercer, Oliver, Potla, Darsh, Rehman, Hafiz, Savill, Heather, Turner, Victoria, Downes, Charlotte, Holding, Kathleen, Riches, Katie, Hilton, Mary, Hayman, Mel, Subramanian, Deepak, Daniel, Priya, Adanini, Oluronke, Bhatia, Nikhil, Msiska, Maines, Collins, Rebecca, Clement, Ian, Patel, Bijal, Gulati, A., Hays, Carole, Webster, K., Hudson, Anne, Webster, Andrea, Stephenson, Elaine, McCormack, Louise, Slater, Victoria, Nixon, Rachel, Hanson, Helen, Fearby, Maggie, Kelly, Sinead, Bridgett, Victoria, Robinson, Philip, Camsooksai, Julie, Humphrey, Charlotte, Jenkins, Sarah, Reschreiter, Henrik, Wadams, Beverley, Death, Yasmin, Bastion, Victoria, Clarke, Daphene, David, Beena, Kent, Harriet, Lorusso, Rachel, Lubimbi, Gamu, Murdoch, Sophie, Penacerrada, Melchizedek, Thomas, Alastair, Valentine, Jennifer, Vochin, Ana, Wulandari, Retno, Djeugam, Brice, Bell, Gillian, English, Katy, Katary, Amro, Wilcox, Louise, Bruce, Michelle, Connolly, Karen, Duncan, Tracy, T-Michael, Helen, Lindergard, Gabriella, Hey, Samuel, Fox, Claire, Alfonso, Jordan, Durrans, Laura Jayne, Guerin, Jacinta, Blackledge, Bethan, Harris, Jade, Hruska, Martin, Eltayeb, Ayaa, Lamb, Thomas, Hodgkiss, Tracey, Cooper, Lisa, Rothwell, Joanne, Allan, Angela, Anderson, Felicity, Kaye, Callum, Liew, Jade, Medhora, Jasmine, Scott, Teresa, Trumper, Erin, Botello, Adriana, Lankester, Liana, Nikitas, Nikitas, Wells, Colin, Stowe, Bethan, Spencer, Kayleigh, Brandwood, Craig, Smith, Lara, Birchall, Katie, Kolakaluri, Laurel, Baines, Deborah, Sukumaran, Anila, Apetri, Elena, Basikolo, Cathrine, Catlow, Laura, Charles, Bethan, Dark, Paul, Doonan, Reece, Harvey, Alice, Horner, Daniel, Knowles, Karen, Lee, Stephanie, Lomas, Diane, Lyons, Chloe, Marsden, Tracy, McLaughlan, Danielle, McMorrow, Liam, Pendlebury, Jessica, Perez, Jane, Poulaka, Maria, Proudfoot, Nicola, Slaughter, Melanie, Slevin, Kathryn, Thomas, Vicky, Walker, Danielle, Michael, Angiy, Collis, Matthew, Cosier, Tracey, Millen, Gemma, Richardson, Neil, Schumacher, Natasha, Weston, Heather, Rand, James, Baxter, Nicola, Henderson, Steven, Kennedy-Hay, Sophie, McParland, Christopher, Rooney, Laura, Sim, Malcolm, McCreath, Gordan, Akeroyd, Louise, Bano, Shereen, Bromley, Matt, Gurr, Lucy, Lawton, Tom, Morgan, James, Sellick, Kirsten, Warren, Deborah, Wilkinson, Brian, McGowan, Janet, Ledgard, Camilla, Stacey, Amelia, Pye, Kate, Bellwood, Ruth, Bentley, Michael, Bewley, Jeremy, Garland, Zoe, Grimmer, Lisa, Gumbrill, Bethany, Johnson, Rebekah, Sweet, Katie, Webster, Denise, Efford, Georgia, Convery, Karen, Fottrell-Gould, Deirdre, Hudig, Lisa, Keshet-Price, Jocelyn, Randell, Georgina, Stammers, Katie, Bokhari, Maria, Linnett, Vanessa, Lucas, Rachael, McCormick, Wendy, Ritzema, Jenny, Sanderson, Amanda, Wild, Helen, Rostron, Anthony, Roy, Alistair, Woods, Lindsey, Cornell, Sarah, Wakinshaw, Fiona, Rogerson, Kimberley, Jarmain, Jordan, Parker, Robert, Reddy, Amie, Turner-Bone, Ian, Wilding, Laura, Harding, Peter, Abernathy, Caroline, Foster, Louise, Gratrix, Andrew, Martinson, Vicky, Parkinson, Priyai, Stones, Elizabeth, Carbral-Ortega, Llucia, Bercades, Georgia, Brealey, David, Hass, Ingrid, MacCallum, Niall, Martir, Gladys, Raith, Eamon, Reyes, Anna, Smyth, Deborah, Zitter, Letizia, Benyon, Sarah, Marriott, Suzie, Park, Linda, Keenan, Samantha, Gordon, Elizabeth, Quinn, Helen, Baines, Kizzy, Cagova, Lenka, Fofano, Adama, Garner, Lucie, Holcombe, Helen, Mepham, Sue, Mitchell, Alice Michael, Mwaura, Lucy, Praman, Krithivasan, Vuylsteke, Alain, Zamikula, Julie, Purewal, Bally, Rivers, Vanessa, Bell, Stephanie, Blakemore, Hayley, Borislavova, Borislava, Faulkner, Beverley, Gendall, Emma, Goff, Elizabeth, Hayes, Kati, Thomas, Matt, Worner, Ruth, Smith, Kerry, Stephens, Deanna, Mew, Louise, Mwaura, Esther, Stewart, Richard, Williams, Felicity, Wren, Lynn, Sutherland, Sara-Beth, Bevan, Emily, Martin, Jane, Trodd, Dawn, Watson, Geoff, Brown, Caroline Wrey, Collins, Amy, Khaliq, Waqas, Gude, Estefania Treus, Akinkugbe, Olugbenga, Bamford, Alasdair, Beech, Emily, Belfield, Holly, Bell, Michael, Davies, Charlene, Jones, Gareth A.L., McHugh, Tara, Meghari, Hamza, O'Neill, Lauran, Peters, Mark J., Ray, Samiran, Tomas, Ana Luisa, Burn, Iona, Hambrook, Geraldine, Manso, Katarina, Penn, Ruth, Shanmugasundaram, Pradeep, Tebbutt, Julie, Thornton, Danielle, Cole, Jade, Davies, Rhys, Duffin, Donna, Hill, Helen, Player, Ben, Thomas, Emma, Williams, Angharad, Griffin, Denise, Muchenje, Nycola, Mupudzi, Mcdonald, Partridge, Richard, Conyngham, Jo-Anna, Thomas, Rachel, Wright, Mary, Corral, Maria Alvarez, Jacob, Reni, Jones, Cathy, Denmade, Craig, Beavis, Sarah, Dale, Katie, Gascoyne, Rachel, Hawes, Joanne, Pritchard, Kelly, Stevenson, Lesley, Whileman, Amanda, Doble, Patricia, Hutter, Joanne, Pawley, Corinne, Shovelton, Charmaine, Vaida, Marius, Butcher, Deborah, O'Sullivan, Susie, Butterworth-Cowin, Nicola, Ahmad, Norfaizan, Barker, Joann, Bauchmuller, Kris, Bird, Sarah, Cawthron, Kay, Harrington, Kate, Jackson, Yvonne, Kibutu, Faith, Lenagh, Becky, Masuko, Shamiso, Mills, Gary H., Raithatha, Ajay, Wiles, Matthew, Willson, Jayne, Newell, Helen, Lye, Alison, Nwafor, Lorenza, Jarman, Claire, Rowland-Jones, Sarah, Foote, David, Cole, Joby, Thompson, Roger, Watson, James, Hesseldon, Lisa, Macharia, Irene, Chetam, Luke, Smith, Jacqui, Ford, Amber, Anderson, Samantha, Birchall, Kathryn, Housley, Kay, Walker, Sara, Milner, Leanne, Hanratty, Helena, Trower, Helen, Phillips, Patrick, Oxspring, Simon, Donne, Ben, Jardine, Catherine, Williams, Dewi, Hay, Alasdair, Flanagan, Rebecca, Hughes, Gareth, Latham, Scott, McKenna, Emma, Anderson, Jennifer, Hull, Robert, Rhead, Kat, Cruz, Carina, Pattison, Natalie, Charnock, Rob, McFarland, Denise, Cosgrove, Denise, Ahmed, Ashar, Morris, Anna, Jakkula, Srinivas, Ali, Asifa, Brady, Megan, Dale, Sam, Dance, Annalisa, Gledhill, Lisa, Greig, Jill, Hanson, Kathryn, Holdroyd, Kelly, Home, Marie, Kelly, Diane, Kitson, Ross, Matapure, Lear, Melia, Deborah, Mellor, Samantha, Nortcliffe, Tonicha, Pinnell, Jez, Robinson, Matthew, Shaw, Lisa, Shaw, Ryan, Thomis, Lesley, Wilson, Alison, Wood, Tracy, Bayo, Lee-Ann, Merwaha, Ekta, Ishaq, Tahira, Hanley, Sarah, Hibbert, Meg, Tetla, Dariusz, Woodford, Chrsitopher, Durga, Latha, Kennard-Holden, Gareth, Branney, Debbie, Frankham, Jordan, Pitts, Sally, White, Nigel, Laha, Shondipon, Verlander, Mark, Williams, Alexandra, Altabaibeh, Abdelhakim, Alvaro, Ana, Gilbert, Kayleigh, Ma, Louise, Mostoles, Loreta, Parmar, Chetan, Simpson, Kathryn, Jetha, Champa, Booker, Lauren, Pratley, Anezka, Adams, Colene, Agasou, Anita, Arden, Tracie, Bowes, Amy, Boyle, Pauline, Beekes, Mandy, Button, Heather, Capps, Nigel, Carnahan, Mandy, Carter, Anne, Childs, Danielle, Donaldson, Denise, Hard, Kelly, Hurford, Fran, Hussain, Yasmin, Javaid, Ayesha, Jones, James, Jose, Sanal, Leigh, Michael, Martin, Terry, Millward, Helen, Motherwell, Nichola, Rikunenko, Rachel, Stickley, Jo, Summers, Julie, Ting, Louise, Tivenan, Helen, Tonks, Louise, Wilcox, Rebecca, Holland, Maureen, Keenan, Natalie, Lyons, Marc, Wassall, Helen, Marsh, Chris, Mahenthran, Mervin, Carter, Emma, Kong, Thomas, Blackman, Helen, Creagh-Brown, Ben, Donlon, Sinead, Michalak-Glinska, Natalia, Mtuwa, Sheila, Pristopan, Veronika, Salberg, Armorel, Smith, Eleanor, Stone, Sarah, Piercy, Charles, Verula, Jerik, Burda, Dorota, Montaser, Rugia, Harden, Lesley, Mayangao, Irving, Marriott, Cheryl, Bradley, Paul, Harris, Celia, Anderson, Susan, Andrews, Eleanor, Birch, Janine, Collins, Emma, Hammerton, Kate, O'Leary, Ryan, Clark, Michele, Purvis, Sarah, Barber, Russell, Hewitt, Claire, Hilldrith, Annette, Jackson-Lawrence, Karen, Shepardson, Sarah, Wills, Maryanne, Butler, Susan, Tavares, Silvia, Cunningham, Amy, Hindale, Julia, Arif, Sarwat, Bean, Sarah, Burt, Karen, Spivey, Michael, Demetriou, Carrie, Eckbad, Charlotte, Hierons, Sarah, Howie, Lucy, Mitchard, Sarah, Ramos, Lidia, Serrano-Ruiz, Alfredo, White, Katie, Kelly, Fiona, Cristiano, Daniele, Dormand, Natalie, Farzad, Zohreh, Gummadi, Mahitha, Liyanage, Kamal, Patel, Brijesh, Salmi, Sara, Sloane, Geraldine, Thwaites, Vicky, Varghese, Mathew, Zborowski, Anelise C., Allan, John, Geary, Tim, Houston, Gordon, Meikle, Alistair, O'Brien, Peter, Forsey, Miranda, Kaliappan, Agilan, Nicholson, Anne, Riches, Joanne, Vertue, Mark, Allan, Elizabeth, Darlington, Kate, Davies, Ffyon, Easton, Jack, Kumar, Sumit, Lean, Richard, Menzies, Daniel, Pugh, Richard, Qiu, Xinyi, Davies, Llinos, Williams, Hannah, Scanlon, Jeremy, Davies, Gwyneth, Mackay, Callum, Lewis, Joannne, Rees, Stephanie, Oblak, Metod, Popescu, Monica, Thankachen, Mini, Higham, Andrew, Simpson, Kerry, Craig, Jayne, Baruah, Rosie, Morris, Sheila, Ferguson, Susie, Shepherd, Amy, Prockter Moore, Luke Stephen, Vizcaychipi, Marcela Paola, Gomes de Almeida Martins, Laura, Carungcong, Jaime, Mohamed Ali, Inthakab Ali, Beaumont, Karen, Blunt, Mark, Coton, Zoe, Curgenven, Hollie, Elsaadany, Mohamed, Fernandes, Kay, Ally, Sameena Mohamed, Rangarajan, Harini, Sarathy, Varun, Selvanayagam, Sivarupan, Vedage, Dave, White, Matthew, Gill, Mandy, Paul, Paul, Ratnam, Valli, Shelton, Sarah, Wynter, Inez, Carmody, Siobhain, Page, Valerie Joan, Beith, Claire Marie, Black, Karen, Clements, Suzanne, Morrison, Alan, Strachan, Dominic, Taylor, Margaret, Clarkson, Michelle, D'Sylva, Stuart, Norman, Kathryn, Auld, Fiona, Donnachie, Joanne, Edmond, Ian, Prentice, Lynn, Runciman, Nikole, Salutous, Dario, Symon, Lesley, Todd, Anne, Turner, Patricia, Short, Abigail, Sweeney, Laura, Murdoch, Euan, Senaratne, Dhaneesha, Hill, Michaela, Kannan, Thogulava, Laura, Wild, Crawley, Rikki, Crew, Abigail, Cunningham, Mishell, Daniels, Allison, Harrison, Laura, Hope, Susan, Inweregbu, Ken, Jones, Sian, Lancaster, Nicola, Matthews, Jamie, Nicholson, Alice, Wray, Gemma, Langton, Helen, Prout, Rachel, Watters, Malcolm, Novis, Catherine, Barron, Anthony, Collins, Ciara, Kaul, Sundeep, Passmore, Heather, Prendergast, Claire, Reed, Anna, Rogers, Paula, Shokkar, Rajvinder, Woodruff, Meriel, Middleton, Hayley, Polgar, Oliver, Nolan, Claire, Mahay, Kanta, Collier, Dawn, Hormis, Anil, Maynard, Victoria, Graham, Cheryl, Walker, Rachel, Knights, Ellen, Price, Alicia, Thomas, Alice, Thorpe, Chris, Behan, Teresa, Burnett, Caroline, Hatton, Jonathan, Heeney, Elaine, Mitra, Atideb, Newton, Maria, Pollard, Rachel, Stead, Rachael, Amin, Vishal, Anastasescu, Elena, Anumakonda, Vikram, Karthik, Komala, Kausar, Rizwana, Reid, Karen, Smith, Jacqueline, Imeson-Wood, Janet, Skinner, Denise, Gaylard, Jane, Mullan, Dee, Newman, Julie, Brown, Alison, Crickmore, Vikki, Debreceni, Gabor, Wilkins, Joy, Nicol, Liz, Reece-Anthony, Rosie, Birt, Mark, Ghosh, Alison, Williams, Emma, Allen, Louise, Beranova, Eva, Crisp, Nikki, Deery, Joanne, Hazelton, Tracy, Knight, Alicia, Price, Carly, Tilbey, Sorrell, Turki, Salah, Turney, Sharon, Cooper, Joshua, Finch, Cheryl, Liderth, Sarah, Quinn, Alison, Waddington, Natalia, Coventry, Tina, Fowler, Susan, MacMahon, Michael, McGregor, Amanda, Cowley, Anne, Highgate, Judith, Gregory, Jane, O'Connell, Susan, Smith, Tim, Barberis, Luigi, Gopal, Shameer, Harris, Nichola, Lake, Victoria, Metherell, Stella, Radford, Elizabeth, Daniel, Amelia, Finn, Joanne, Saha, Rajnish, White, Nikki, Donnison, Phil, Trim, Fiona, Eapen, Beena, Birch, Jenny, Bough, Laura, Goodsell, Josie, Tutton, Rebecca, Williams, Patricia, Williams, Sarah, Winter-Goodwin, Barbara, Nichol, Ailstair, Brickell, Kathy, Smyth, Michelle, Murphy, Lorna, Coetzee, Samantha, Gales, Alistair, Otahal, Igor, Raj, Meena, Sell, Craig, Hilltout, Paula, Evitts, Jayne, Tyler, Amanda, Waldron, Joanne, Beesley, Kate, Board, Sarah, Kubisz-Pudelko, Agnieszka, Lewis, Alison, Perry, Jess, Pippard, Lucy, Wood, Di, Buckley, Clare, Barry, Peter, Flint, Neil, Rekha, Patel, Hales, Dawn, Bunni, Lara, Jennings, Claire, Latif, Monica, Marshall, Rebecca, Subramanian, Gayathri, McGuigan, Peter J., Wasson, Christopher, Finn, Stephanie, Green, Jackie, Collins, Erin, King, Bernadette, Campbell, Andy, Smuts, Sara, Duffield, Joseph, Smith, Oliver, Mallon, Lewis, Claire, Watkins, Botfield, Liam, Butler, Joanna, Dexter, Catherine, Fletcher, Jo, Garg, Atul, Kuravi, Aditya, Ranga, Poonam, Virgilio, Emma, Belagodu, Zakaula, Fuller, Bridget, Gherman, Anca, Olufuwa, Olumide, Paramsothy, Remi, Stuart, Carmel, Oakley, Naomi, Kamundi, Charlotte, Tyl, David, Collins, Katy, Silva, Pedro, Taylor, June, King, Laura, Coates, Charlotte, Crowley, Maria, Wakefield, Phillipa, Beadle, Jane, Johnson, Laura, Sargeant, Janet, Anderson, Madeleine, Brady, Ailbhe, Chan, Rebekah, Little, Jeff, McIvor, Shane, Prady, Helena, Whittle, Helen, Mathew, Bijoy, Attwood, Ben, Parsons, Penny, Ward, Geraldine, Bremmer, Pamela, Joe, West, Tracy, Baird, Jim, Ruddy, Davies, Ellie, Sathe, Sonia, Dennis, Catherine, McGregor, Alastair, Parris, Victoria, Srikaran, Sinduya, Sukha, Anisha, Clarke, Noreen, Whiteside, Jonathan, Mascarenhas, Mairi, Donaldson, Avril, Matheson, Joanna, Barrett, Fiona, O'Hara, Marianne, Okeefe, Laura, Bradley, Clare, Eastgate-Jackson, Christine, Filipe, Helder, Martin, Daniel, Maharajh, Amitaa, Garcia, Sara Mingo, Pakou, Glykeria, De Neef, Mark, Dent, Kathy, Horsley, Elizabeth, Akhtar, Muhmmad Nauman, Pearson, Sandra, Potoczna, Dorota, Spencer, Sue, Clapham, Melanie, Harper, Rosemary, Poultney, Una, Rice, Polly, Mutch, Rachel, Armstrong, Lisa, Bates, Hayley, Dooks, Emma, Farquhar, Fiona, Hairsine, Brigid, McParland, Chantal, Packham, Sophie, Bi, Rehana, Scholefield, Barney, Ashton, Lydia, George, Linsha, Twiss, Sophie, Wright, David, Chablani, Manish, Kirkby, Amy, Netherton, Kimberley, Davies, Kim, O'Brien, Linda, Omar, Zohra, Perkins, Emma, Lewis, Tracy, Sutherland, Isobel, Burns, Karen, Ben Chandler, Dr, Elliott, Kerry, Mallinson, Janine, Turnbull, Alison, Gondo, Prisca, Hadebe, Bernard, Kayani, Abdul, Masunda, Bridgett, Anderson, Taya, Hawcutt, Dan, O'Malley, Laura, Rad, Laura, Rogers, Naomi, Saunderson, Paula, Allison, Kathryn Sian, Afolabi, Deborah, Whitbread, Jennifer, Jones, Dawn, Dore, Rachael, Halkes, Matthew, Mercer, Pauline, Thornton, Lorraine, Dawson, Joy, Garrioch, Sweyn, Tolson, Melanie, Aldridge, Jonathan, Kapoor, Ritoo, Loader, David, Castle, Karen, Humphreys, Sally, Tampsett, Ruth, Mackintosh, Katherine, Ayers, Amanda, Harrison, Wendy, North, Julie, Allibone, Suzanne, Genetu, Roman, Kasipandian, Vidya, Patel, Amit, Mac, Ainhi, Murphy, Anthony, Mahjoob, Parisa, Nazari, Roonak, Worsley, Lucy, Fagan, Andrew, Bemand, Thomas, Black, Ethel, Dela Rosa, Arnold, Howle, Ryan, Jhanji, Shaman, Baikady, Ravishankar Rao, Tatham, Kate Colette, Thomas, Benjamin, Bell, Dina, Boyle, Rosalind, Douglas, Katie, Glass, Lynn, Lee, Emma, Lennon, Liz, Rattray, Austin, Taylor, Abigail, Hughes, Rachel Anne, Thomas, Helen, Rees, Alun, Duskova, Michaela, Phipps, Janet, Brooks, Suzanne, Edwards, Michelle, Quaid, Sheena, Watson, Ekaterina, Brayne, Adam, Fisher, Emma, Hunt, Jane, Jackson, Peter, Kaye, Duncan, Love, Nicholas, Parkin, Juliet, Tuckey, Victoria, Van Koutrik, Lynne, Carter, Sasha, Andrew, Benedict, Findlay, Louise, Adams, Katie, Service, Jen, Williams, Alison, Cheyne, Claire, Saunderson, Anne, Moultrie, Sam, Odam, Miranda, Hall, Kathryn, Mapfunde, Isheunesu, Willis, Charlotte, Lyon, Alex, Sri-Chandana, Chunda, Scherewode, Joslan, Stephenson, Lorraine, Marsh, Sarah, Hardy, John, Houlden, Henry, Moncur, Eleanor, Tariq, Ambreen, Tucci, Arianna, Hobrok, Maria, Loosley, Ronda, McGuinness, Heather, Tench, Helen, Wolf-Roberts, Rebecca, Irvine, Val, Shelley, Benjamin, Gorman, Claire, Gupta, Abhinav, Timlick, Elizabeth, Brady, Rebecca, Milligan, Barry, Bellini, Arianna, Bryant, Jade, Mayer, Anton, Pickard, Amy, Roe, Nicholas, Sowter, Jason, Howlett, Alex, Fidler, Katy, Tagliavini, Emma, Donnelly, Kevin, Boos, Jannik, van der Made, Caspar I., Ramakrishnan, Gayatri, Coughlan, Eamon, Asselta, Rosanna, Löscher, Britt-Sabina, Valenti, Luca V.C., de Cid, Rafael, Bujanda, Luis, Julià, Antonio, Pairo-Castineira, Erola, May, Sandra, Zametica, Berina, Heggemann, Julia, Albillos, Agustín, Banales, Jesus M., Barretina, Jordi, Blay, Natalia, Bonfanti, Paolo, Buti, Maria, Fernandez, Javier, Marsal, Sara, Prati, Daniele, Ronzoni, Luisa, Sacchi, Nicoletta, Schultze, Joachim L., Riess, Olaf, Franke, Andre, Rawlik, Konrad, Ellinghaus, David, Hoischen, Alexander, Schmidt, Axel, and Ludwig, Kerstin U.

Published
2024
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224. Patterns of initial ovarian cancer recurrence on niraparib maintenance monotherapy in patients with no baseline evidence of disease after first-line chemotherapy: An ad hoc subgroup analysis of PRIMA/ENGOT-OV26/GOG-3012

Author

Kamrava, Mitchell R., Gonzalez-Martin, Antonio, Pothuri, Bhavana, Vergote, Ignace, Graybill, Whitney, Mirza, Mansoor R., McCormick, Colleen, Lorusso, Domenica, Freyer, Gilles, O'Malley, David M., York, Whitney, Malinowska, Izabela A., and Monk, Bradley J.

Published
2024
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226. A Multicenter Evaluation of Treatment-associated Changes in Body Composition in Men With Germ Cell Tumors of the Testis: Implications for Adverse Events and Complications

Author

Buxton, Claire, Schmeusser, Benjamin N., Holt, Sarah K., Patil, Dattatraya, Phuong, Anthea, Chahine, Sophia, Marquardt, J. Peter, O’Malley, Ryan, Laidlaw, Grace, Schade, George R., Lin, Daniel W., Schweizer, Michael T., Yezefski, Todd, Yu, Evan Y., Montgomery, Bruce, Fintelmann, Florian J., Master, Viraj A., and Psutka, Sarah P.

Published
2024
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227. Tumor-associated mesenchymal stromal cells modulate macrophage phagocytosis in stromal-rich colorectal cancer via PD-1 signaling

Author

Leonard, Niamh A., Corry, Shania M., Reidy, Eileen, Egan, Hannah, O’Malley, Grace, Thompson, Kerry, McDermott, Emma, O’Neill, Aoise, Zakaria, Norashikin, Egan, Laurence J., Ritter, Thomas, Loessner, Daniela, Redmond, Keara, Sheehan, Margaret, Canney, Aoife, Hogan, Aisling M., Hynes, Sean O., Treacy, Oliver, Dunne, Philip D., and Ryan, Aideen E.

Published
2024
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232. Carbapenem-resistant Acinetobacter baumannii complex in the United States—An epidemiological and molecular description of isolates collected through the Emerging Infections Program, 2019

Author

Bulens, Sandra N., Campbell, Davina, McKay, Susannah L., Vlachos, Nicholas, Burgin, Alex, Burroughs, Mark, Padila, Jasmine, Grass, Julian E., Jacob, Jesse T., Smith, Gillian, Muleta, Daniel B., Maloney, Meghan, Macierowski, Bobbie, Wilson, Lucy E., Vaeth, Elisabeth, Lynfield, Ruth, O’Malley, Sean, Snippes Vagnone, Paula M., Dale, Jennifer, Janelle, Sarah J., Czaja, Christopher A., Johnson, Helen, Phipps, Erin C., Flores, Kristina G., Dumyati, Ghinwa, Tsay, Rebecca, Beldavs, Zintars G., Maureen Cassidy, P., Hall, Amanda, Walters, Maroya S., Guh, Alice Y., Magill, Shelley S., and Lutgring, Joseph D.

Published
2024
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233. Discharge to a Skilled Nursing Facility After Hip Fracture Results in Higher Rates of Periprosthetic Joint Infection

Author

Ghanem, Elie, McGwin, Gerald, Cichos, Kyle H., Boyd, Brandon, Crist, Brett, Keeney, Jim, Ewing, Michael, Sauer, Madeline A., Mears, Simon C., Stronach, Benjamin M., Stambough, Jeffrey B., Mueller, Joshua W., O’Malley, Michael, Patel, Stuti, Chen, Antonia F., Melnic, Christopher M., Jordan, Eric, Cao, Amanda, Hansen, Erik N., Sing, David, Bergin, Patrick F., Bhanat, Eldrin, Stayer, George W., Almand, Mariegene E., Yener, Ugur, Treu, Emily A., Frandsen, Jeffrey J., DeKeyser, Graham J., Blackburn, Brenna E., Archibeck, Michael J., Anderson, Lucas A., and Gililland, Jeremy M.

Published
2024
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234. Blind Optimism and Student Loan Debt.

Author

O'Malley, Thomas C. and Payne, Brian C.

Subjects
Student loans -- Laws, regulations and rules -- Research, College students -- Government finance -- Laws, regulations and rules -- Research, Collection (Accounting) -- Educational aspects -- Laws, regulations and rules -- Research, Debt management -- Educational aspects -- Laws, regulations and rules -- Research, Professional education -- Government finance -- Laws, regulations and rules -- Research, Optimism -- Educational aspects -- Analysis -- Research, Government regulation
Abstract

INTRODUCTION In recent decades, more Americans have enrolled in college to pursue the American dream. It is a widely held view in many countries that college degrees serve as catalysts [...], This paper analyses potential links between extreme optimism and student loan debt. Prior work finds extreme optimism to be associated with imprudent household savings and investment decisions. This paper explores whether these findings are relevant to student loan decisions. Using the most recent administration of the Survey of Consumer Finances (SCF), we present evidence that extreme optimists tend to be over-indebted with higher student loan balances, in absolute terms and relative to income. They are less likely to make payments on their student loan debt or reduce their loan balance. Extreme optimists are also more likely to take out loans for a degree they do not obtain and to enroll in income-based repayment plans.

Published
2023

235. A Collaborative Approach to National Food Code Adoption

Author

Wildey, Laura M., Laird, Terryn, Brown, Laura G., O'Malley, Elizabeth, Gridley-Smith, Chelsea L., Nutt, Elizabeth, Bacon, Brenda, and Dyjack, David T.

Subjects
Business-government relations, Food -- Safety and security measures, Food industry -- Safety and security measures -- Health aspects, Government regulation, Food and beverage production/distribution software, Environmental issues, Health
Abstract

The Food and Drug Administration Food Code is a set of science-based food safety guidelines that serve as the basis for jurisdictional food codes that regulate retail food service in the U.S. Uniform adoption of the Food Code has benefits for government, industry, and the public. Compliance with code provisions helps reduce risk of foodborne illness in retail establishments. Only 18 states, however, have adopted the most recent edition of the Food Code (Food and Drug Administration, 2023). The Retail Food Safety Regulatory Association Collaborative (Collaborative) has undertaken a multipronged approach that leverages the resources and strengths of its six organizations to support adoption of the Food Code. Through these approaches, the Collaborative aims to provide the tools, resources, and support needed to navigate the sometimes complex nature of adoption. This special report provides a summary of the different activities and approaches of the Collaborative to support national Food Code adoption., Introduction The vital work of retail food safety professionals happens in a rapidly evolving landscape of technological advancement, emerging trends, and globalization--a dynamic terrain that requires up-to-date, evidence-based regulations to [...]

Published
2023

237. Addressing quantum’s 'fine print' with efficient state preparation and information extraction for quantum algorithms and geologic fracture networks

Author

Jessie M. Henderson, John Kath, John K. Golden, Allon G. Percus, and Daniel O’Malley

Subjects
Medicine, Science
Abstract

Abstract Quantum algorithms provide an exponential speedup for solving certain classes of linear systems, including those that model geologic fracture flow. However, this revolutionary gain in efficiency does not come without difficulty. Quantum algorithms require that problems satisfy not only algorithm-specific constraints, but also application-specific ones. Otherwise, the quantum advantage carefully attained through algorithmic ingenuity can be entirely negated. Previous work addressing quantum algorithms for geologic fracture flow has illustrated core algorithmic approaches while incrementally removing assumptions. This work addresses two further requirements for solving geologic fracture flow systems with quantum algorithms: efficient system state preparation and efficient information extraction. Our approach to addressing each is consistent with an overall exponential speed-up.

Published
2024
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238. Virtual facilitation best practices and research priorities: a scoping review

Author

Asya Agulnik, Derrecka Boykin, Denalee M. O’Malley, Julia Price, Mia Yang, Mark McKone, Geoffrey Curran, and Mona J. Ritchie

Subjects
Implementation facilitation, Practice facilitation, Virtual facilitation, Implementation strategy, Scoping review, Medicine (General), R5-920
Abstract

Abstract Background Facilitation is an implementation strategy that supports the uptake of evidence-based practices. Recently, use of virtual facilitation (VF), or the application of facilitation using primarily video-based conferencing technologies, has become more common, especially since the COVID-19 pandemic. Thorough assessment of the literature on VF, however, is lacking. This scoping review aimed to identify and describe conceptual definitions of VF, evaluate the consistency of terminology, and recommend “best” practices for its use as an implementation strategy. Methods We conducted a scoping review to identify literature on VF following the PRISMA-ScR guidance. A search of PubMed, Embase, Web of Science, and CINAHL databases was conducted in June 2022 for English language articles published from January 2012 through May 2022 and repeated in May 2023 for articles published from January 2012 through April 2023. Identified articles, including studies and conference abstracts describing VF, were uploaded into Covidence and screened independently by two reviewers. Data extraction was done by two reviewers in Microsoft Excel; additionally, studies were evaluated based on the Proctor et al. (2013) reporting guidelines for specifying details of implementation strategies. Results The search strategy identified 19 articles. After abstract and full-text screening, eight studies described by 10 articles/abstracts were included in analysis. Best practices summarized across studies included (1) stakeholder engagement, (2) understanding the recipient’s organization, (3) facilitator training, (4) piloting, (5) evaluating facilitation, (6) use of group facilitation to encourage learning, and (7) integrating novel tools for virtual interaction. Three papers reported all or nearly all components of the Proctor et al. reporting guidelines; justification for use of VF was the most frequently omitted. Conclusions This scoping review evaluated available literature on use of VF as a primary implementation strategy and identified significant variability on how VF is reported, including inconsistent terminology, lack of details about how and why it was conducted, and limited adherence to published reporting guidelines. These inconsistencies impact generalizability of these methods by preventing replicability and full understanding of this emerging methodology. More work is needed to develop and evaluate best practices for effective VF to promote uptake of evidence-based interventions. Trial registration N/A.

Published
2024
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239. Assessing the Effect of Cervical Transcutaneous Spinal Stimulation With an Upper Limb Robotic Exoskeleton and Surface Electromyography

Author

Erin E. Mahan, Jeonghoon Oh, Elyse D. Z. Chase, Nathan B. Dunkelberger, Shane T. King, Dimitry Sayenko, and Marcia K. O'Malley

Subjects
Robotic rehabilitation, hybrid FES-robotic systems, neuromodulation, spinal cord injury, exoskeleton, Medical technology, R855-855.5, Therapeutics. Pharmacology, RM1-950
Abstract

Transcutaneous spinal stimulation (TSS) is a promising rehabilitative intervention to restore motor function and coordination for individuals with spinal cord injury (SCI). The effects of TSS are most commonly assessed by evaluating muscle response to stimulation using surface electromyography (sEMG). Given the increasing use of robotic devices to deliver therapy and the emerging potential of hybrid rehabilitation interventions that combine neuromodulation with robotic devices, there is an opportunity to leverage the on-board sensors of the robots to measure kinematic and torque changes of joints in the presence of stimulation. This paper explores the potential for robotic assessment of the effects of TSS delivered to the cervical spinal cord. We used a four degree-of-freedom exoskeleton to measure the torque response of upper limb (UL) joints during stimulation, while simultaneously recording sEMG. We analyzed joint torque and electromyography data generated during TSS delivered over individual sites of the cervical spinal cord in neurologically intact participants. We show that site-specific effects of TSS are manifested not only by modulation of the amplitude of spinally evoked motor potentials in UL muscles, but also by changes in torque generated by individual UL joints. We observed preferential resultant action of proximal muscles and joints with stimulation at the rostral site, and of proximal joints with rostral-lateral stimulation. Robotic assessment can be used to measure the effects of TSS, and could be integrated into complex control algorithms that govern the behavior of hybrid neuromodulation-robotic systems.

Published
2024
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240. Multi Degree of Freedom Hybrid FES and Robotic Control of the Upper Limb

Author

Nathan Dunkelberger, Skye A. Carlson, Jeffrey Berning, Eric M. Schearer, and Marcia K. O'Malley

Subjects
Functional electrical stimulation, upper limb, robotic exoskeleton, model predictive control, hybrid control, movement assistance, Medical technology, R855-855.5, Therapeutics. Pharmacology, RM1-950
Abstract

Individuals who have suffered a spinal cord injury often require assistance to complete daily activities, and for individuals with tetraplegia, recovery of upper-limb function is among their top priorities. Hybrid functional electrical stimulation (FES) and exoskeleton systems have emerged as a potential solution to provide upper limb movement assistance. These systems leverage the user’s own muscles via FES and provide additional movement support via an assistive exoskeleton. To date, these systems have focused on single joint movements, limiting their utility for the complex movements necessary for independence. In this paper, we extend our prior work on model predictive control (MPC) of hybrid FES-exo systems and present a multi degree of freedom (DOF) hybrid controller that uses the controller’s cost function to achieve desired behavior. In studies with neurologically intact individuals, the hybrid controller is compared to an exoskeleton acting alone for movement assistance scenarios incorporating multiple degrees-of-freedom of the limb to explore the potential for exoskeleton power consumption reduction and impacts on tracking accuracy. Additionally, each scenario is explored in simulation using the models required to generate the MPC formulation. The two DOF hybrid controller implementation saw reductions in power consumption and satisfactory trajectory tracking in both the physical and simulated systems. In the four DOF implementation, the experimental results showed minor improvements for some joints of the upper limb. In simulation, we observed comparable performance as in the two DOF implementation.

Published
2024
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241. Frequency Response from Aggregated V2G Chargers With Uncertain EV Connections

Author

O'Malley, Cormac, Badesa, Luis, Teng, Fei, and Strbac, Goran

Subjects
Electrical Engineering and Systems Science - Systems and Control
Abstract

Fast frequency response (FR) is highly effective at securing frequency dynamics after a generator outage in low inertia systems. Electric vehicles (EVs) equipped with vehicle to grid (V2G) chargers could offer an abundant source of FR in future. However, the uncertainty associated with V2G aggregation, driven by the uncertain number of connected EVs at the time of an outage, has not been fully understood and prevents its participation in the existing service provision framework. To tackle this limitation, this paper, for the first time, incorporates such uncertainty into system frequency dynamics, from which probabilistic nadir and steady state frequency requirements are enforced via a derived momeent-based distributionally-robust chance constraint. Field data from over 25,000 chargers is analysed to provide realistic parameters and connection forecasts to examine the value of FR from V2G chargers in annual operation of the the GB 2030 system. The case study demonstrates that uncertainty of EV connections can be effectively managed through the proposed scheduling framework, which results in annual savings of {\pounds}6,300 or 37.4 tCO2 per charger. The sensitivity of this value to renewable capacity and FR delays is explored, with V2G capacity shown to be a third as valuable as the same grid battery capacity.

Published
2022

242. Reduced order modeling for flow and transport problems with Barlow Twins self-supervised learning

Author

Kadeethum, Teeratorn, Ballarin, Francesco, O'Malley, Daniel, Choi, Youngsoo, Bouklas, Nikolaos, and Yoon, Hongkyu

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

We propose a unified data-driven reduced order model (ROM) that bridges the performance gap between linear and nonlinear manifold approaches. Deep learning ROM (DL-ROM) using deep-convolutional autoencoders (DC-AE) has been shown to capture nonlinear solution manifolds but fails to perform adequately when linear subspace approaches such as proper orthogonal decomposition (POD) would be optimal. Besides, most DL-ROM models rely on convolutional layers, which might limit its application to only a structured mesh. The proposed framework in this study relies on the combination of an autoencoder (AE) and Barlow Twins (BT) self-supervised learning, where BT maximizes the information content of the embedding with the latent space through a joint embedding architecture. Through a series of benchmark problems of natural convection in porous media, BT-AE performs better than the previous DL-ROM framework by providing comparable results to POD-based approaches for problems where the solution lies within a linear subspace as well as DL-ROM autoencoder-based techniques where the solution lies on a nonlinear manifold; consequently, bridges the gap between linear and nonlinear reduced manifolds. Furthermore, this BT-AE framework can operate on unstructured meshes, which provides flexibility in its application to standard numerical solvers, on-site measurements, experimental data, or a combination of these sources., Comment: arXiv admin note: text overlap with arXiv:2107.11460

Published
2022
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243. Machine Learning in Heterogeneous Porous Materials

Author

D'Elia, Marta, Deng, Hang, Fraces, Cedric, Garikipati, Krishna, Graham-Brady, Lori, Howard, Amanda, Karniadakis, George, Keshavarzzadeh, Vahid, Kirby, Robert M., Kutz, Nathan, Li, Chunhui, Liu, Xing, Lu, Hannah, Newell, Pania, O'Malley, Daniel, Prodanovic, Masa, Srinivasan, Gowri, Tartakovsky, Alexandre, Tartakovsky, Daniel M., Tchelepi, Hamdi, Vazic, Bozo, Viswanathan, Hari, Yoon, Hongkyu, and Zarzycki, Piotr

Subjects
Computer Science - Machine Learning, Condensed Matter - Materials Science
Abstract

The "Workshop on Machine learning in heterogeneous porous materials" brought together international scientific communities of applied mathematics, porous media, and material sciences with experts in the areas of heterogeneous materials, machine learning (ML) and applied mathematics to identify how ML can advance materials research. Within the scope of ML and materials research, the goal of the workshop was to discuss the state-of-the-art in each community, promote crosstalk and accelerate multi-disciplinary collaborative research, and identify challenges and opportunities. As the end result, four topic areas were identified: ML in predicting materials properties, and discovery and design of novel materials, ML in porous and fractured media and time-dependent phenomena, Multi-scale modeling in heterogeneous porous materials via ML, and Discovery of materials constitutive laws and new governing equations. This workshop was part of the AmeriMech Symposium series sponsored by the National Academies of Sciences, Engineering and Medicine and the U.S. National Committee on Theoretical and Applied Mechanics., Comment: The workshop link is: https://amerimech.mech.utah.edu

Published
2022

244. Numerical Evidence for Exponential Speed-up of QAOA over Unstructured Search for Approximate Constrained Optimization

Author

Golden, John, Bärtschi, Andreas, Eidenbenz, Stephan, and O'Malley, Daniel

Subjects
Quantum Physics, Computer Science - Data Structures and Algorithms
Abstract

Despite much recent work, the true promise and limitations of the Quantum Alternating Operator Ansatz (QAOA) are unclear. A critical question regarding QAOA is to what extent its performance scales with the input size of the problem instance, in particular the necessary growth in the number of QAOA rounds to reach a high approximation ratio. We present numerical evidence for an exponential speed-up of QAOA over Grover-style unstructured search in finding approximate solutions to constrained optimization problems. Our result provides a strong hint that QAOA is able to exploit the structure of an optimization problem and thus overcome the lower bound for unstructured search. To this end, we conduct a comprehensive numerical study on several Hamming-weight constrained optimization problems for which we include combinations of all standardly studied mixer and phase separator Hamiltonians (Ring mixer, Clique mixer, Objective Value phase separator) as well as quantum minimum-finding inspired Hamiltonians (Grover mixer, Threshold-based phase separator). We identify Clique-Objective-QAOA with an exponential speed-up over Grover-Threshold-QAOA and tie the latter's scaling to that of unstructured search, with all other QAOA combinations coming in at a distant third. Our result suggests that maximizing QAOA performance requires a judicious choice of mixer and phase separator, and should trigger further research into other QAOA variations.

Published
2022
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245. Neutron transfer reactions on the ground state and isomeric state of a 130Sn beam

Author

Jones, K. L., Bey, A., Burcher, S., Allmond, J. M., Galindo-Uribarri, A., Radford, D. C., Ahn, S., Ayres, A., Bardayan, 1 D. W., Cizewski, J. A., Ruiz, R. F. Garcia, Howard, M. E., Kozub, R. L., Liang, J. F., Manning, B., Matos, M., Nesaraja, C. D., O'Malley, P. D., Padilla-Rodal, E., Pain, S. D., Pittman, S. T., Ratkiewicz, A., Schmitt, K. T., Smith, M. S., Stracener, D. W., and Varner, R. L.

Subjects
Nuclear Experiment
Abstract

The structure of nuclei around the neutron-rich nucleus 132Sn is of particular interest due to the vicinity of the Z = 50 and N = 82 shell closures and the r-process nucleosynthetic path. Four states in 131Sn with a strong single-particle-like component have previously been studied via the (d,p) reaction, with limited excitation energy resolution. The 130Sn(9Be,8Be)131Sn and 130Sn(13C,12C)131Sn single-neutron transfer reactions were performed in inverse kinematics at the Holifield Radioactive Ion Beam Facility using particle-gamma coincidence spectroscopy. The uncertainties in the energies of the single-particle-like states have been reduced by more than an order of magnitude using the energies of gamma rays. The previous tentative Jpi values have been confirmed. Decays from high-spin states in 131Sn have been observed following transfer on the isomeric component of the 130Sn beam. The improved energies and confirmed spin-parities of the p-wave states important to the r-process lead to direct-semidirect cross-sections for neutron capture on the ground state of 130Sn at 30 keV that are in agreement with previous analyses. A similar assessment of the impact of neutron-transfer on the isomer would require significant nuclear structure and reaction theory input. There are few measurements of transfer reaction on isomers, and this is the first on an isomer in the 132Sn region., Comment: 7 pages, 4 figures

Published
2022
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246. Value of Fleet Vehicle Grid in Providing Transmission System Operator Services

Author

O'Malley, Cormac, Aunedi, Marko, Teng, Fei, and Strbac, Goran

Subjects
Electrical Engineering and Systems Science - Systems and Control
Abstract

In this paper a new aggregated model for electric vehicle (EV) fleets is presented that considers their daily and weekly usage patterns. A frequency-constrained stochastic unit commitment model is employed to optimally schedule EV charging and discharging as well as the provision of frequency response (FR) in an electricity system, while respecting the vehicles' energy requirements and driving schedules. Through case studies we demonstrate that an EV with vehicle to grid (V2G) capability can reduce system costs in a future GB electricity grid by up to {\pounds}12,000 per year, and reduce CO2 emissions by 60 tonnes per year, mainly due to reduced curtailment of wind power. The paper also quantifies the changes in the benefits of fleet V2G resulting from variations in FR delivery time, the penetration of wind or uptake of alternative flexibility providers. Finally , a battery degradation model dependent on an EV's state of charge is proposed and implemented in the stochastic scheduling problem. It enables significant degradation cost reductions of 16% with only a 0.4% reduction of an EV's system value.

Published
2022
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