Search

Your search keyword '"Fergus To"' showing total 46,091 results
Start Over Author "Fergus To"
46,091 results on '"Fergus To"'

251. Real-Time Predictive Maintenance using Autoencoder Reconstruction and Anomaly Detection

Author

Givnan, Sean, Chalmers, Carl, Fergus, Paul, Ortega, Sandra, and Whalley, Tom

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

Rotary machine breakdown detection systems are outdated and dependent upon routine testing to discover faults. This is costly and often reactive in nature. Real-time monitoring offers a solution for detecting faults without the need for manual observation. However, manual interpretation for threshold anomaly detection is often subjective and varies between industrial experts. This approach is ridged and prone to a large number of false positives. To address this issue, we propose a Machine Learning (ML) approach to model normal working operation and detect anomalies. The approach extracts key features from signals representing known normal operation to model machine behaviour and automatically identify anomalies. The ML learns generalisations and generates thresholds based on fault severity. This provides engineers with a traffic light system were green is normal behaviour, amber is worrying and red signifies a machine fault. This scale allows engineers to undertake early intervention measures at the appropriate time. The approach is evaluated on windowed real machine sensor data to observe normal and abnormal behaviour. The results demonstrate that it is possible to detect anomalies within the amber range and raise alarms before machine failure.

Published
2021

252. The KLEVER survey: Nitrogen abundances at $z\sim$2 and probing the existence of a fundamental nitrogen relation

Author

Hayden-Pawson, Connor, Curti, Mirko, Maiolino, Roberto, Cirasuolo, Michele, Belfiore, Francesco, Cappellari, Michele, Concas, Alice, Cresci, Giovanni, Cullen, Fergus, Kobayashi, Chiaki, Mannucci, Filippo, Marconi, Alessandro, Meneghetti, Massimo, Mercurio, Amata, Peng, Yingjie, Swinbank, Mark, and Vincenzo, Fiorenzo

Subjects
Astrophysics - Astrophysics of Galaxies
Abstract

We present a comparison of the nitrogen-to-oxygen ratio (N/O) in 37 high-redshift galaxies at $z\sim$2 taken from the KMOS Lensed Emission Lines and VElocity Review (KLEVER) Survey with a comparison sample of local galaxies, taken from the Sloan Digital Sky Survey (SDSS). The KLEVER sample shows only a mild enrichment in N/O of $+$0.1 dex when compared to local galaxies at a given gas-phase metallicity (O/H), but shows a depletion in N/O of $-$0.36 dex when compared at a fixed stellar mass (M$_*$). We find a strong anti-correlation in local galaxies between N/O and SFR in the M$_*$-N/O plane, similar to the anti-correlation between O/H and SFR found in the mass-metallicity relation (MZR). We use this anti-correlation to construct a fundamental nitrogen relation (FNR), analogous to the fundamental metallicity relation (FMR). We find that KLEVER galaxies are consistent with both the FMR and the FNR. This suggests that the depletion of N/O in high-$z$ galaxies when considered at a fixed M$_*$ is driven by the redshift-evolution of the mass-metallicity relation in combination with a near redshift-invariant N/O-O/H relation. Furthermore, the existence of an fundamental nitrogen relation suggests that the mechanisms governing the fundamental metallicity relation must be probed by not only O/H, but also N/O, suggesting pure-pristine gas inflows are not the primary driver of the FMR, and other properties such as variations in galaxy age and star formation efficiency must be important., Comment: 23 pages, 11 figures, Accepted for publication in Monthly Notices of the Royal Astronomical Society

Published
2021
Full Text
View/download PDF

253. A Bayesian Inference Framework for Gamma-Ray Burst Afterglow Properties

Author

Lin, En-Tzu, Hayes, Fergus, Lamb, Gavin P., Heng, Ik Siong, Kong, Albert K. H., Williams, Michael J., Saha, Surojit, and Veitch, John

Subjects
Astrophysics - High Energy Astrophysical Phenomena
Abstract

In the field of multi-messenger astronomy, Bayesian inference is commonly adopted to compare the compatibility of models given the observed data. However, to describe a physical system like neutron star mergers and their associated gamma-ray burst (GRB) events, usually more than ten physical parameters are incorporated in the model. With such a complex model, likelihood evaluation for each Monte Carlo sampling point becomes a massive task and requires a significant amount of computational power. In this work, we perform quick parameter estimation on simulated GRB X-ray light curves using an interpolated physical GRB model. This is achieved by generating a grid of GRB afterglow light curves across the parameter space and replacing the likelihood with a simple interpolation function in the high-dimensional grid that stores all light curves. This framework, compared to the original method, leads to a $\sim$90$\times$ speedup per likelihood estimation. It will allow us to explore different jet models and enable fast model comparison in the future., Comment: 9 pages, 4 figures, accepted to the special issue of Universe, "Waiting for GODOT -- Present and Future of Multi-Messenger Astronomy"

Published
2021
Full Text
View/download PDF

254. The MOSDEF Survey: Implications of the Lack of Evolution in the Dust Attenuation-Mass Relation to z~2

Author

Shapley, Alice E., Sanders, Ryan L., Salim, Samir, Reddy, Naveen A., Kriek, Mariska, Mobasher, Bahram, Coil, Alison, Siana, Brian, Price, Sedona H., Shivaei, Irene, Dunlop, James S., McLure, Ross J., and Cullen, Fergus

Subjects
Astrophysics - Astrophysics of Galaxies
Abstract

We investigate the relationship between dust attenuation and stellar mass ($M_*$) in star-forming galaxies over cosmic time. For this analysis, we compare measurements from the MOSFIRE Deep Evolution Field (MOSDEF) survey at $z\sim2.3$ and the Sloan Digital Sky Survey (SDSS) at $z\sim0$, augmenting the latter optical dataset with both UV Galaxy Evolution Explorer (GALEX) and mid-infrared Wide-field Infrared Survey Explorer (WISE) photometry from the GALEX-SDSS-WISE Catalog. We quantify dust attenuation using both spectroscopic measurements of H$\alpha$ and H$\beta$ emission lines, and photometric measurements of the rest-UV stellar continuum. The H$\alpha$/H$\beta$ ratio is used to determine the magnitude of attenuation at the wavelength of H$\alpha$, $A_{{\rm H}\alpha}$. Rest-UV colors and spectral-energy-distribution fitting are used to estimate $A_{1600}$, the magnitude of attenuation at a rest wavelength of 1600\AA. As in previous work, we find a lack of significant evolution in the relation between dust attenuation and $M_*$ over the redshift range $z\sim0$ to $z\sim2.3$. Folding in the latest estimates of the evolution of $M_{{\rm dust}}$, $({M_{{\rm dust}}}/{M_{{\rm gas}}})$, and gas surface density at fixed $M_*$, we find that the expected $M_{{\rm dust}}$ and dust mass surface density are both significantly higher at $z\sim2.3$ than at $z\sim0$. These differences appear at odds with the lack of evolution in dust attenuation. To explain the striking constancy in attenuation vs. $M_*$, it is essential to determine the relationship between metallicity and $({M_{{\rm dust}}}/{M_{{\rm gas}}})$, the dust mass absorption coefficient, and dust geometry, and the evolution of these relations and quantities from $z\sim0$ to $z\sim2.3$., Comment: 14 pages, 4 figures, accepted to ApJ

Published
2021
Full Text
View/download PDF

255. Metal-enriched halo gas across galaxy overdensities over the last 10 billion years

Author

Dutta, Rajeshwari, Fumagalli, Michele, Fossati, Matteo, Bielby, Richard M., Stott, John P., Lofthouse, Emma K., Cantalupo, Sebastiano, Cullen, Fergus, Crain, Robert A., Tripp, Todd M., Prochaska, J. Xavier, Battaia, Fabrizio Arrigoni, Burchett, Joseph N., Fynbo, Johan P. U., Murphy, Michael T., Schaye, Joop, Tejos, Nicolas, and Theuns, Tom

Subjects
Astrophysics - Astrophysics of Galaxies, Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract

We present a study of metal-enriched halo gas traced by MgII and CIV absorption at z<2 in the MUSE Analysis of Gas around Galaxies survey and the Quasar Sightline and Galaxy Evolution survey. Using these large and complete galaxy surveys in quasar fields, we study the dependence of the metal distribution on galaxy properties and overdensities, out to physical projected separations of 750 kpc. We find that the cool, low-ionization gas is significantly affected by the environment across the full redshift range probed, with ~2-3 times more prevalent and stronger MgII absorption in higher overdensity group environments and in regions with greater overall stellar mass and star formation rates. Complementary to these results, we have further investigated the more highly ionized gas as traced by CIV absorption, and found that it is likely to be more extended than the MgII gas, with ~2 times higher covering fraction at a given distance. We find that the strength and covering fraction of CIV absorption show less significant dependence on galaxy properties and environment than the MgII absorption, but more massive and star-forming galaxies nevertheless also show ~2 times higher incidence of CIV absorption. The incidence of MgII and CIV absorption within the virial radius shows a tentative increase with redshift, being higher by a factor of ~1.5 and ~4, respectively, at z>1. It is clear from our results that environmental processes have a significant impact on the distribution of metals around galaxies and need to be fully accounted for when analyzing correlations between gaseous haloes and galaxy properties., Comment: 27 pages; accepted for publication in MNRAS

Published
2021
Full Text
View/download PDF

256. A quantum algorithm for gravitational wave matched filtering

Author

Gao, Sijia, Hayes, Fergus, Croke, Sarah, Messenger, Chris, and Veitch, John

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

Quantum computational devices, currently under development, have the potential to accelerate data analysis techniques beyond the ability of any classical algorithm. We propose the application of a quantum algorithm for the detection of unknown signals in noisy data. We apply Grover's algorithm to matched-filtering, a signal processing technique that compares data to a number of candidate signal templates. In comparison to the classical method, this provides a speed-up proportional to the square-root of the number of templates, which would make possible otherwise intractable searches. We demonstrate both a proof-of-principle quantum circuit implementation, and a simulation of the algorithm's application to the detection of the first gravitational wave signal GW150914. We discuss the time complexity and space requirements of our algorithm as well as its implications for the currently computationally-limited searches for continuous gravitational waves., Comment: 24 pages, 19 Figures

Published
2021

257. Inclination estimates from off-axis GRB afterglow modelling

Author

Lamb, Gavin P, Fernández, Joseph J, Hayes, Fergus, Kong, Albert K H, Lin, En-Tzu, Tanvir, Nial R, Hendry, Martin, Heng, Ik Siong, Saha, Surojit, and Veitch, John

Subjects
Astrophysics - High Energy Astrophysical Phenomena
Abstract

For gravitational wave (GW) detected neutron star mergers, one of the leading candidates for electromagnetic (EM) counterparts is the afterglow from an ultra-relativistic jet. Where this afterglow is observed, it will likely be viewed off-axis, such as the afterglow following GW170817/GRB 170817A. The temporal behaviour of an off-axis observed GRB afterglow can be used to reveal the lateral jet structure, and statistical model fits can put constraints on the various model free-parameters. Amongst these parameters is the inclination of the system to the line of sight. Along with the GW detection, the afterglow modelling provides the best constraint on the inclination to the line-of-sight and can improve the estimates of cosmological parameters e.g. the Hubble constant, from GW-EM events. However, modelling of the afterglow depends on the assumed jet structure and, often overlooked, the effects of lateral spreading. Here we show how the inclusion of lateral spreading in the afterglow models can affect the estimated inclination of GW-EM events., Comment: 14 pages, 5 figures, Accepted to the special issue of Universe, "Waiting for GODOT -- Present and Future of Multi-Messenger Astronomy"

Published
2021
Full Text
View/download PDF

258. Effect of blood pressure-lowering agents on microvascular function in people with small vessel diseases (TREAT-SVDs): a multicentre, open-label, randomised, crossover trial

Author

Kopczak, Anna, Stringer, Michael S., van den Brink, Hilde, Kerkhofs, Danielle, Blair, Gordon W., van Dinther, Maud, Arteaga Reyes, Carmen, Jaime Garcia, Daniela, Onkenhout, Laurien, Wartolowska, Karolina A., Thrippleton, Michael J., Kampaite, Agniete, Duering, Marco, Staals, Julie, Lesnik-Oberstein, Saskia, Muir, Keith, Middeke, Martin, Norrving, Bo, Bousser, Marie-Germaine, Mansmann, Ulrich, Rothwell, Peter M., Doubal, Fergus N., van Oostenbrugge, Robert, Biessels, Geert Jan, Webb, Alastair J.S., Wardlaw, Joanna, Dichgans, Martin, André, Elisabeth, Kääb, Stefan, Anders, Hans-Joachim, Hack, Remco, Kaffe, Maria, Dewenter, Anna, Malik, Rainer, Stringer, Michael S, Blair, Gordon W, Reyes, Carmen Arteaga, Garcia, Daniela Jaime, Wartolowska, Karolina A, Thrippleton, Michael J, Muir, Keith W, Rothwell, Peter M, Doubal, Fergus N, Webb, Alastair J S, and Wardlaw, Joanna M

Published
2023
Full Text
View/download PDF

260. Simulation Study of Surveillance Strategies for Faster Detection of Novel SARS-CoV-2 Variants

Author

Patel, Selina, Cumming, Fergus, Mayers, Carl, Charlet, Andre, and Riley, Steven

Subjects
Identification and classification, Evaluation, Methods, SARS-CoV-2 variants -- Identification and classification, Sentinel surveillance -- Methods, Medical research, Public health administration -- Evaluation, Medicine, Experimental, Sentinel health events -- Methods
Abstract

In the current phase of the COVID-19 pandemic, waves of SARS-CoV-2 infection are driven by novel variants and their sublineages, which continue to cause illness and death with potential to [...]

Published
2023
Full Text
View/download PDF

264. Neoplasia risk in patients with Lynch syndrome treated with immune checkpoint blockade

Author

Harrold, Emily C., Foote, Michael B., Rousseau, Benoit, Walch, Henry, Kemel, Yelena, Richards, Allison L., Keane, Fergus, Cercek, Andrea, Yaeger, Rona, Rathkopf, Dana, Segal, Neil H., Patel, Zalak, Maio, Anna, Borio, Matilde, O’Reilly, Eileen M., Reidy, Diane, Desai, Avni, Janjigian, Yelena Y., Murciano-Goroff, Yonina R., Carlo, Maria I., Latham, Alicia, Liu, Ying L., Walsh, Michael F., Ilson, David, Rosenberg, Jonathan E., Markowitz, Arnold J., Weiser, Martin R., Rossi, Anthony M., Vanderbilt, Chad, Mandelker, Diana, Bandlamudi, Chaitanya, Offit, Kenneth, Berger, Michael F., Solit, David B., Saltz, Leonard, Shia, Jinru, Diaz, Jr., Luis A., and Stadler, Zsofia K.

Published
2023
Full Text
View/download PDF

265. Safety, tolerability, viral kinetics, and immune correlates of protection in healthy, seropositive UK adults inoculated with SARS-CoV-2: a single-centre, open-label, phase 1 controlled human infection study

Author

Alparaque, Maricel, Anid, Liisa, Barnes, Eleanor, Benamore, Rachel, Bharti, Neha, Patel, Bhumika, Burns, Adrian, Byard, Nicholas, Conway, Oliver, Cooper, Cushla, Crowther, Charlotte, Dunachie, Susanna J, Johnstone, Trudi, Jose, Jyolsna, Luciw, Michael, Mujadidi, Yama, Nehiweze, Aiseosa, Nyamunda, Sibongile, Orobiyi-Rieba, Maria, Parvelikudy, Bindu, Platt, Abigail, Pswarayi, Dzikamayi, Quaddy, Jack, Samuel, Binnie Elizabeth, Sette, Alessandro, Sodipo, Victoria, Srijith, Preethu, Stone, Helen, Turner, Cheryl, Valmores, Mary Ann, Voaides, Alexandru, Vuddamalay, Gavindren, Jackson, Susan, Marshall, Julia L, Mawer, Andrew, Lopez-Ramon, Raquel, Harris, Stephanie A, Satti, Iman, Hughes, Eileen, Preston-Jones, Hannah, Cabrera Puig, Ingrid, Longet, Stephanie, Tipton, Tom, Laidlaw, Stephen, Doherty, Rebecca Powell, Morrison, Hazel, Mitchell, Robert, Tanner, Rachel, Ateere, Alberta, Stylianou, Elena, Wu, Meng-San, Fredsgaard-Jones, Timothy P W, Breuer, Judith, Rapeport, Garth, Ferreira, Vanessa M, Gleeson, Fergus, Pollard, Andrew J, Carroll, Miles, Catchpole, Andrew, Chiu, Christopher, and McShane, Helen

Published
2024
Full Text
View/download PDF

268. Cardiopulmonary exercise variables and their association with postoperative morbidity and mortality after major oesophagogastric cancer surgery—a multicentre observational study

Author

West, Malcolm A., Rahman, Saqib, Jack, Sandy, Grocott, Michael P.W., Levett, Denny Z.H., Rashid, Yasir, Griffiths, John, Ezra, Martin, Ayres, Lyndsay, Neville-Webbe, Helen, Javed, Muhammad Shafiq, Shrotri, Milind, Khan, Iftikhar, Whitmore, David, Prabhu, Pradeep, Timbrell, David, Allen, Sophie, Packham, Andrew O., Sharpe, David, Anderson, Helen, Minto, Gary, McAleer, Samuel, McPhail, Stuart, Alasmar, Mohamed, Hartley, Robert A., Sultan, Javed, Grace, Ben, Underwood, Timothy J., Byrne, James, Noble, Fergus, Kelly, Jamie, Ansell, Gillian, and Edwards, Mark

Published
2024
Full Text
View/download PDF

269. Early trajectories of virological and immunological biomarkers and clinical outcomes in patients admitted to hospital for COVID-19: an international, prospective cohort study

Author

Sahner, David, Tierney, John, Vogel, Susan E., Herpin, Betsey R., Smolskis, Mary C., McKay, Laura A., Cahill, Kelly, Crew, Page, Sardana, Ratna, Raim, Sharon Segal, Hensely, Lisa, Lorenzo, Johsua, Mock, Rebecca, Zuckerman, Judith, Atri, Negin, Miller, Mark, Vallee, David, Chung, Lucy, Kang, Nayon, Barrett, Kevin, Adam, Stacey J., Read, Sarah, Draghia-Akli, Ruxandra, Currier, Judy, Hughes, Eric, Harrigan, Rachel H., Amos, Laura, Carlsen, Amy, Carter, Anita, Collins, Gary, Davis, Bionca, Denning, Eileen, DuChene, Alain, Eckroth, Kate, Engen, Nicole, Frase, Alex, Gandits, Greg, Grund, Birgit, Harrison, Merrie, Hurlbut, Nancy, Kaiser, Payton, Koopmeiners, Joseph, Larson, Gregg, Meger, Sue, Mistry, Shweta Sharma, Murray, Thomas, Nelson, Ray, Quan, Kien, Quan, Siu Fun, Reilly, Cavan, Siegel, Lianne, Thompson, Greg, Vock, David, Walski, Jamie, Gelijns, Annetine C., Moskowitz, Alan J., Bagiella, Emilia, Moquete, Ellen, O'Sullivan, Karen, Marks, Mary E., Accardi, Evan, Kinzel, Emily, Burris, Sarah, Bedoya, Gabriela, Gupta, Lola, Overbey, Jessica R., Santos, Milerva, Gillinov, Marc A., Miller, Marissa A., Taddei-Peters, Wendy C., Fenton, Kathleen, Sandkovsky, Uriel, Gottlieb, Robert L., Mack, Michael, Berhe, Mezgebe, Haley, Clinton, Dishner, Emma, Bettacchi, Christopher, Golden, Kevin, Duhaime, Erin, Ryan, Madison, Tallmadge, Catherine, Estrada, Lorie, Jones, Felecia, Villa, Samatha, Wang, Samatha, Robert, Raven, Coleman, Tanquinisha, Clariday, Laura, Baker, Rebecca, Hurutado-Rodriguez, Mariana, Iram, Nazia, Fresnedo, Michelle, Davis, Allyson, Leonard, Kiara, Ramierez, Noelia, Thammavong, Jon, Duque, Krizia, Turner, Emma, Fisher, Tammy, Robinson, Dianna, Ransom, Desirae, Maldonado, Nicholas, Lusk, Erica, Killian, Aaron, Palacious, Adriana, Solis, Edilia, Jerrow, Janet, Watts, Matthew, Whitacre, Heather, Cothran, Elizabeth, Smith, Peter K., Barkauskas, Christina E., Vekstein, Andrew M., Ko, Emily R., Dreyer, Grace R., Stafford, Neil, Brooks, Megan, Der, Tatyana, Witte, Marie, Gamarallage, Ruwan, Franzone, John, Ivey, Noel, Lumsden, Rebecca H., Mosaly, Nilima, Mourad, Ahmaad, Holland, Thomas L., Motta, Mary, Lane, Kathleen, McGowan, Lauren M., Stout, Jennifer, Aloor, Heather, Bragg, Kennesha M., Toledo, Barvina, McLendon-Arvik, Beth, Bussadori, Barbara, Hollister, Beth A., Griffin, Michelle, Giangiacomo, Dana M., Rodriguez, Vicente, Bokhart, Gordon, Eichman, Sharon M., Parrino, Patrick E., Spindel, Stephen, Bansal, Aditya, Baumgarten, Katherine, Hand, Johnathan, Vonderhaar, Derek, Nossaman, Bobby, Sylvia Laudun, Ames, DeAnna, Broussard, Shane, Hernandez, Nilmo, Isaac, Geralyn, Dinh, Huan, Zheng, Yiling, Tran, Sonny, McDaniel, Hunter, Crovetto, Nicolle, Perin, Emerson, Costello, Briana, Manian, Prasad, Sohail, M. Rizwan, Postalian, Alexander, Hinsu, Punit, Watson, Carolyn, Chen, James, Fink, Melyssa, Sturgis, Lydia, Walker, Kim, Mahon, Kim, Parenti, Jennifer, Kappenman, Casey, Knight, Aryn, Sturek, Jeffrey M., Barros, Andrew, Enfield, Kyle B., Kadl, Alexandra, Green, China J., Simon, Rachel M., Fox, Ashley, Thornton, Kara, Adams, Amy, Badhwar, Vinay, Sharma, Sunil, Peppers, Briana, McCarthy, Paul, Krupica, Troy, Sarwari, Arif, Reece, Rebecca, Fornaresico, Lisa, Glaze, Chad, Evans, Raquel, Di, Fang, Carlson, Shawn, Aucremanne, Tanja, Tennant, Connie, Sutton, Lisa Giblin, Buterbaugh, Sabrina, Williams, Roger, Bunner, Robin, Traverse, Jay H., Rhame, Frank, Huelster, Joshua, Kethireddy, Rajesh, Davies, Irena, Salamanca, Julianne, Majeski, Christine, Skelton, Paige, Zarambo, Maria, Sarafolean, Andrea, Bowdish, Michael E., Borok, Zea, Wald-Dickler, Noah, Hutcheon, Douglass, Towfighi, Amytis, Lee, Mary, Lewis, Meghan R., Spellberg, Brad, Sher, Linda, Sharma, Aniket, Olds, Anna P., Justino, Chris, Loxano, Edward, Romero, Chris, Leong, Janet, Rodina, Valentina, Quesada, Christine, Hamilton, Luke, Escobar, Jose, Leshnower, Brad, Bender, William, Sharifpour, Milad, Miller, Jeffrey, Farrington, Woodrow, Baio, Kim T., McBride, Mary, Fielding, Michele, Mathewson, Sonya, Porte, Kristina, Maton, Missy, Ponder, Chari, Haley, Elisabeth, Spainhour, Christine, Rogers, Susan, Tyler, Derrick, Madathil, Ronson J., Rabin, Joseph, Levine, Andrea, Saharia, Kapil, Tabatabai, Ali, Lau, Christine, Gammie, James S., Peguero, Maya-Loren, McKernan, Kimberly, Audette, Mathew, Fleischmann, Emily, Akbari, Kreshta, Lee, Myounghee, Chi, Andrew, Salehi, Hanna, Pariser, Alan, Nyguyen, Phuong Tran, Moore, Jessica, Gee, Adrienne, Vincent, Shelika, Zuckerman, Richard A., Iribarne, Alexander, Metzler, Sara, Shipman, Samantha, Johnson, Haley, Newton, Crystallee, Parr, Doug, Miller, Leslie, Schelle, Beth, McLean, Sherry, Rothbaum, Howard R., Alvarez, Michael S., Kalan, Shivam P., Germann, Heather H., Hendershot, Jennifer, Moroney, Karen, Herring, Karen, Cook, Sharri, Paul, Pam, Walker-Ignasiak, Rebecca, North, Crystal, Oldmixon, Cathryn, Ringwood, Nancy, Muzikansky, Ariela, Morse, Richard, Fitzgerald, Laura, Morin, Haley D., Brower, Roy G., Reineck, Lora A., Bienstock, Karen, Steingrub, Jay H., Hou, Peter K., Steingrub, Jay S., Tidswell, Mark A., Kozikowski, Lori-Ann, Kardos, Cynthia, DeSouza, Leslie, Romain, Sarah, Thornton-Thompson, Sherell, Talmor, Daniel, Shapiro, Nathan, Andromidas, Konstantinos, Banner-Goodspeed, Valerie, Bolstad, Michael, Boyle, Katherine L., Cabrera, Payton, deVilla, Arnaldo, Ellis, Joshua C., Grafals, Ana, Hayes, Sharon, Higgins, Conor, Kurt, Lisa, Kurtzman, Nicholas, Redman, Kimberly, Rosseto, Elinita, Scaffidi, Douglas, Filbin, Michael R., Hibbert, Kathryn A., Parry, Blair, Margolin, Justin, Hillis, Brooklynn, Hamer, Rhonda, Brait, Kelsey, Beakes, Caroline, McKaig, Brenna, Kugener, Eleonore, Jones, Alan E., Galbraith, James, Nandi, Utsav, Peacock, Rebekah, Hendey, Gregory, Kangelaris, Kirsten, Ashktorab, Kimia, Gropper, Rachel, Agrawal, Anika, Yee, Kimberley J., Jauregui, Alejandra E., Zhuo, Hanjing, Almasri, Eyad, Fayed, Mohamed, Hubel, Kinsley A., Hughes, Alyssa R., Garcia, Rebekah L., Lim, George W., Chang, Steven Y., Lin, Michael Y., Vargas, Julia, Sihota, Hena, Beutler, Rebecca, Agarwal, Trisha, Wilson, Jennifer G., Vojnik, Rosemary, Perez, Cynthia, McDowell, Jordan H., Roque, Jonasel, Wang, Henry, Huebinger, Ryan M., Patel, Bela, Vidales, Elizabeth, Albertson, Timothy, Hardy, Erin, Harper, Richart, Moss, Marc A., Baduashvili, Amiran, Chauhan, Lakshmi, Douin, David J., Martinez, Flora, Finck, Lani L., Bastman, Jill, Howell, Michelle, Higgins, Carrie, McKeehan, Jeffrey, Finigan, Jay, Stubenrauch, Peter, Janssen, William J., Griesmer, Christine, VerBurg, Olivia, Hyzy, Robert C., Park, Pauline K., Nelson, Kristine, McSparron, Jake I., Co, Ivan N., Wang, Bonnie R., Jimenez, Jose, Olbrich, Norman, McDonough, Kelli, Jia, Shijing, Hanna, Sinan, Gong, Michelle N., Richardson, Lynne D., Nair, Rahul, Lopez, Brenda, Amosu, Omowunmi, Offor, Obiageli, Tzehaie, Hiwet, Nkemdirim, William, Boujid, Sabah, Mosier, Jarrod M., Hypes, Cameron, Campbell, Elizabeth Salvagio, Bixby, Billie, Gilson, Boris, Lopez, Anitza, Bime, Christian, Parthasarathy, Sairam, Cano, Ariana M., Hite, R. Duncan, Terndrup, Thomas E., Wiedemann, Herbert P., Hudock, Kristin, Tanzeem, Hammad, More, Harshada, Martinkovic, Jamie, Sellers, Susan, Houston, Judy, Burns, Mary, Kiran, Simra, Roads, Tammy, Kennedy, Sarah, Duggal, Abhijit, Thiruchelvam, Nirosshan, Ashok, Kiran, King, Alexander H., Mehkri, Omar, Dugar, Siddharth, Sahoo, Debasis, Yealy, Donald M., Angus, Derek C., Weissman, Alexandra J., Vita, Tina M., Berryman, Emily, Hough, Catherine L., Khan, Akram, Krol, Olivia F., Mills, Emmanuel, Kinjal, Mistry, Briceno, Genesis, Reddy, Raju, Hubel, Kinsley, Jouzestani, Milad K., McDougal, Madeline, Deshmukh, Rupali, Johnston, Nicholas J., Robinson, Bryce H., Gundel, Staphanie J., Katsandres, Sarah C., Chen, Peter, Torbati, Sam S., Parimon, Tanyalak, Caudill, Antonina, Mattison, Brittany, Jackman, Susan E., Chen, Po-En, Bayoumi, Emad, Ojukwu, Cristabelle, Fine, Devin, Weissberg, Gwendolyn, Isip, Katherine, Choi-Kuaea, Yunhee, Mehdikhani, Shaunt, Dar, Tahir B., Fleury Augustin, Nsole Biteghe, Tran, Dana, Dukov, Jennifer Emilow, Matusov, Yuri, Choe, June, Hindoyan, Niree A., Wynter, Timothy, Pascual, Ethan, Clapham, Gregg J., Herrera, Lisa, Caudill, Antonia, O’Mahony, D. Shane, Nyatsatsang, Sonam T., Wilson, David M., Wallick, Julie A., Duven, Alexandria M., Fletcher, Dakota D., Miller, Chadwick, Files, D. Clark, Gibbs, Kevin W., Flores, Lori S., LaRose, Mary E., Landreth, Leigha D., Palacios, D. Rafael, Parks, Lisa, Hicks, Madeline, Goodwin, Andrew J., Kilb, Edward F., Lematty, Caitlan T., Patti, Kerilyn, Grady, Abigail, Rasberry, April, Morris, Peter E., Sturgill, Jamie L., Cassity, Evan P., Dhar, Sanjay, Montgomery-Yates, Ashley A., Pasha, Sarah N., Mayer, Kirby P., Pharm.D., Brittany Bissel, Trott, Terren, Rehman, Shahnaz, de Wit, Marjolein, Mason, Jessica, Bledsoe, Joseph, Knowlton, Kirk U., Brown, Samuel, Lanspa, Michael, Leither, Lindsey, Pelton, Ithan, Armbruster, Brent P., Montgomery, Quinn, Kumar, Naresh, Fergus, Melissa, Imel, Karah, Palmer, Ghazal, Webb, Brandon, Klippel, Carolyn, Jensen, Hannah, Duckworth, Sarah, Gray, Andrew, Burke, Tyler, Knox, Dan, Lumpkin, Jenna, Aston, Valerie T., Applegate, Darrin, Serezlic, Erna, Brown, Katie, Merril, Mardee, Harris, Estelle S., Middleton, Elizabeth A., Barrios, Macy A.G., Greer, Jorden, Schmidt, Amber D., Webb, Melissa K., Paine, Roert, Callahan, Sean J., Waddoups, Lindsey J., Yamane, Misty B., Self, Wesley H., Rice, Todd W., Casey, Jonathan D., Johnson, Jakea, Gray, Christopher, Hays, Margaret, Roth, Megan, Menon, Vidya, Kasubhai, Moiz, Pillai, Anjana, Daniel, Jean, Sittler, Daniel, Kanna, Balavenkatesh, Jilani, Nargis, Amaro, Francisco, Santana, Jessica, Lyakovestsky, Aleksandr, Madhoun, Issa, Desroches, Louis Marie, Amadon, Nicole, Bahr, Alaa, Ezzat, Imaan, Guerrero, Maryanne, Padilla, Joane, Fullmer, Jessie, Singh, Inderpreet, Ali Shah, Syed Hamad, Narang, Rajeev, Mock, Polly, Shadle, Melissa, Hernandez, Brenda, Welch, Kevin, Payne, Andrea, Ertl, Gabriela, Canario, Daniel, Barrientos, Isabel, Goss, Danielle, DeVries, Mattie, Folowosele, Ibidolapo, Garner, Dorothy, Gomez, Mariana, Price, Justin, Bansal, Ekta, Wong, Jim, Faulhaber, Jason, Fazili, Tasaduq, Yeary, Brian, Ndolo, Ruth, Bryant, Christina, Smigeil, Bridgette, Robinson, Philip, Najjar, Rana, Jones, Patrice, Nguyen, Julie, Chin, Christina, Taha, Hassan, Najm, Salah, Smith, Christopher, Moore, Jason, Nassar, Talal, Gallinger, Nick, Christian, Amy, Mauer, D’Amber, Phipps, Ashley, Waters, Michael, Zepeda, Karla, Coslet, Jordan, Landazuri, Rosalynn, Pineda, Jacob, Uribe, Nicole, Garcia, Jose Ruiz, Barbabosa, Cecilia, Sandler, Kaitlyn, Overcash, J. Scott, Marquez, Adrienna, Chu, Hanh, Lee, Kia, Quillin, Kimberly, Garcia, Andrea, Lew, Pauline, Rogers, Ralph, Shehadeh, Fadi, Mylona, Evangelia K., Kaczynski, Matthew, Tran, Quynh-Lam, Benitez, Gregorio, Mishra, Biswajit, Felix, Lewis Oscar, Vafea, Maria Tsikala, Atalla, Eleftheria, Davies, Robin, Hedili, Salma, Monkeberg, Maria Andrea, Tabler, Sandra, Harrington, Britt, Meegada, Sreenath, Koripalli, Venkata Sandeep, Muddana, Prithvi, Jain, Lakshay, Undavalli, Chaitanya, Kavya, Parasa, Ibiwoye, Mofoluwaso, Akilo, Hameed, Lovette, Bryce D., Wylie, Jamie-Crystal, Smith, Diana M., Poon, Kenneth, Eckardt, Paula, Heysu, Rubio-Gomez, Sundararaman, Nithya, Alaby, Doris, Sareli, Candice, Sánchez, Adriana, Popielski, Laura, Kambo, Amy, Viens, Kimberley, Turner, Melissa, Vjecha, Michael J., Weintrob, Amy, Brar, Indira, Markowitz, Norman, Pastor, Erika, Corpuz, Roweena, Alangaden, George, McKinnon, John, Ramesh, Mayur, Herc, Erica, Yared, Nicholas, Lanfranco, Odaliz Abreu, Rivers, Emanuel, Swiderek, Jennifer, Gupta, Ariella Hodari, Pabla, Pardeep, Eliya, Sonia, Jazrawi, Jehan, Delor, Jeremy, Desai, Mona, Cook, Aaron, Jaehne, Anja Kathrina, Gill, Jasreen Kaur, Renaud, Sheri, Sarveswaran, Siva, Gardner, Edward, Scott, James, Bianchini, Monica, Melvin, Casey, Kim, Gina, Wyles, David, Kamis, Kevin, Miller, Rachel, Douglas, Ivor, Haukoos, Jason, Hicks, Carrie, Lazarte, Susana, Marines-Price, Rubria, Osuji, Alice, Agbor Agbor, Barbine Tchamba, Petersen, Tianna, Kamel, Dena, Hansen, Laura, Garcia, Angie, Cha, Christine, Mozaffari, Azadeh, Hernandez, Rosa, Cutrell, James, Kim, Mina, DellaValle, Natalie, Gonzales, Sonia, Somboonwit, Charurut, Oxner, Asa, Guerra, Lucy, Hayes, Michael, Nguyen, Thi, Tran, Thanh, Pinto, Avenette, Hatlen, Timothy, Anderson, Betty, Zepeda-Gutierrez, Ana, Martin, Dannae, Temblador, Cindi, Cuenca, Avon, Tanoviceanu, Roxanne, Prieto, Martha, Guerrero, Mario, Daar, Eric, Correa, Ramiro, Hartnell, Gabe, Wortmann, Glenn, Doshi, Saumil, Moriarty, Theresa, Gonzales, Melissa, Garman, Kristin, Baker, Jason V., Frosch, Anne, Goldsmith, Rachael, Driver, Brian, Frank, Christine, Leviton, Tzivia, Prekker, Matthew, Jibrell, Hodan, Lo, Melanie, Klaphake, Jonathan, Mackedanz, Shari, Ngo, Linh, Garcia-Myers, Kelly, Kunisaki, Ken M., Wendt, Chris, Melzer, Anne, Wetherbee, Erin, Drekonja, Dimitri, Pragman, Alexa, Hamel, Aimee, Thielen, Abbie, Hassler, Miranda, Walquist, Mary, Augenbraun, Michael, George, Jensen, Demeo, Lynette, Mishko, Motria, Thomas, Lorraine, Tatem, Luis, Dehovitz, Jack, Abassi, Mahsa, Leuck, Anne-Marie, Rao, Via, Pullen, Matthew, Luke, Darlette, LaBar, Derek, Christiansen, Theresa, Howard, Diondra, Biswas, Kousick, Harrington, Cristin, Garcia, Amanda, Bremer, Tammy, Burke, Tara, Koker, Brittany, Davis-Karim, Anne, Pittman, David, Vasudeva, Shikha S., Johnstone, Jaylynn R., Agnetti, Kate, Davis, Ruby, Trautner, Barbara, Hines-Munson, Casey, Van, John, Dillon, Laura, Wang, Yiqun, Nagy-Agren, Stephanie, Vasudeva, Shikha, Ochalek, Tracy, Caldwell, Erin, Humerickhouse, Edward, Boone, David, McGraw, William, Looney, David J., Mehta, Sanjay R., Johns, Scott Thompson, St. John, Melissa, Raceles, Jacqueline, Sear, Emily, Funk, Stephen, Cesarini, Rosa, Fang, Michelle, Nicalo, Keith, Drake, Wonder, Jones, Beatrice, Holtman, Teresa, Nguyen, Hien H., Maniar, Archana, Johnson, Eric A., Nguyen, Lam, Tran, Michelle T., Barrett, Thomas W., Johnston, Tera, Huggins, John T., Beiko, Tatsiana Y., Hughes, Heather Y., McManigle, William C., Tanner, Nichole T., Washburn, Ronald G., Ardelt, Magdalena, Tuohy, Patricia A., Mixson, Jennifer L., Hinton, Charles G., Thornley, Nicola, Allen, Heather, Elam, Shannon, Boatman, Barry, Baber, Brittany J., Ryant, Rudell, Roller, Brentin, Nguyen, Chinh, Mikail, Amani Morgan, Research, Marivic Hansen, Lichtenberger, Paola, Baracco, Gio, Ramos, Carol, Bjork, Lauren, Sueiro, Melyssa, Tien, Phyllis, Freasier, Heather, Buck, Theresa, Nekach, Hafida, Holodniy, Mark, Chary, Aarthi, Lu, Kan, Peters, Theresa, Lopez, Jessica, Tan, Susanna Yu, Lee, Robert H., Asghar, Aliya, Karyn Isip, Tasadduq Karim, Le, Katherine, Nguyen, Thao, Wong, Shinn, Raben, Dorthe, Murray, Daniel D., Jensen, Tomas O., Peters, Lars, Aagaard, Bitten, Nielsen, Charlotte B., Krapp, Katharina, Nykjær, Bente Rosdahl, Olsson, Christina, Kanne, Katja Lisa, Grevsen, Anne Louise, Joensen, Zillah Maria, Bruun, Tina, Bojesen, Ane, Woldbye, Frederik, Normand, Nick E., Esman, Frederik V.L., Benfield, Thomas, Clausen, Clara Lundetoft, Hovmand, Nichlas, Israelsen, Simone Bastrup, Iversen, Katrine, Leding, Caecilie, Pedersen, Karen Brorup, Thorlacius-Ussing, Louise, Tinggaard, Michaela, Tingsgard, Sandra, Krohn-Dehli, Louise, Pedersen, Dorthe, Villadsen, Signe, Staehr Jensen, Jens-Ulrik, Overgaard, Rikke, Rastoder, Ema, Heerfordt, Christian, Hedsund, Caroline, Ronn, Christian Phillip, Kamstrup, Peter Thobias, Hogsberg, Dorthe Sandbaek, Bergsoe, Christina, Søborg, Christian, Hissabu, Nuria M.S., Arp, Bodil C., Ostergaard, Lars, Staerke, Nina Breinholt, Yehdego, Yordanos, Sondergaard, Ane, Johansen, Isik S., Pedersen, Andreas Arnholdt, Knudtzen, Fredrikke C., Larsen, Lykke, Hertz, Mathias A., Fabricius, Thilde, Holden, Inge K., Lindvig, Susan O., Helleberg, Marie, Gerstoft, Jan, Kirk, Ole, Jensen, Tomas Ostergaard, Madsen, Birgitte Lindegaard, Pedersen, Thomas Ingemann, Harboe, Zitta Barrella, Roge, Birgit Thorup, Hansen, Thomas Michael, Glesner, Matilde Kanstrup, Lofberg, Sandra Valborg, Nielsen, Ariella Denize, Leicht von Huth, Sebastian, Nielsen, Henrik, Thisted, Rikke Krog, Petersen, Kristine Toft, Juhl, Maria Ruwald, Podlekareva, Daria, Johnsen, Stine, Andreassen, Helle Frost, Pedersen, Lars, Clara Ellinor Lindnér, Cecilia Ebba, Wiese, Lothar, Knudsen, Lene Surland, Skrøder Nytofte, Nikolaj Julian, Havmøller, Signe Ravn, Expósito, Maria, Badillo, José, Martínez, Ana, Abad, Elena, Chamorro, Ana, Figuerola, Ariadna, Mateu, Lourdes, España, Sergio, Lucero, Maria Constanza, Santos, José Ramón, Lladós, Gemma, Lopez, Cristina, Carabias, Lydia, Molina-Morant, Daniel, Loste, Cora, Bracke, Carmen, Siles, Adrian, Fernández-Cruz, Eduardo, Di Natale, Marisa, Padure, Sergiu, Gomez, Jimena, Ausin, Cristina, Cervilla, Eva, Balastegui, Héctor, Sainz, Carmen Rodríguez, Lopez, Paco, Carbone, Javier, Escobar, Mariam, Balerdi, Leire, Legarda, Almudena, Roldan, Montserrat, Letona, Laura, Muñoz, José, Camprubí, Daniel, Arribas, Jose R., Sánchez, Rocio Montejano, Díaz-Pollán, Beatriz, Stewart, Stefan Mark, Garcia, Irene, Borobia, Alberto, Mora-Rillo, Marta, Estrada, Vicente, Cabello, Noemi, Nuñez-Orantos, M.J., Sagastagoitia, I., Homen, J.R., Orviz, E., Montalvá, Adrián Sánchez, Espinosa-Pereiro, Juan, Bosch-Nicolau, Pau, Salvador, Fernando, Burgos, Joaquin, Morales-Rull, Jose Luis, Moreno Pena, Anna Maria, Acosta, Cristina, Solé-Felip, Cristina, Horcajada, Juan P., Sendra, Elena, Castañeda, Silvia, López-Montesinos, Inmaculada, Gómez-Junyent, Joan, Gonzáles, Carlota Gudiol, Cuervo, Guilermo, Pujol, Miquel, Carratalà, Jordi, Videla, Sebastià, Günthard, Huldrych, Braun, Dominique L., West, Emily, M’Rabeth-Bensalah, Khadija, Eichinger, Mareile L., Grüttner-Durmaz, Manuela, Grube, Christina, Zink, Veronika, pharmacist, Goes pharmacist, Josefine, Fätkenheuer, Gerd, Malin, Jakob J., Tsertsvadze, Tengiz, Abutidze, Akaki, Chkhartishvili, Nikoloz, Metchurtchlishvili, Revaz, Endeladze, Marina, Paciorek, Marcin, Bursa, Dominik, Krogulec, Dominika, Pulik, Piotr, Ignatowska, Anna, Horban, Andrzej, Bakowska, Elzbieta, Kowaska, Justyna, Bednarska, Agnieszka, Jurek, Natalia, Skrzat-Klapaczynska, Agata, Bienkowski, Carlo, Hackiewicz, Malgorzata, Makowiecki, Michal, Platowski, Antoni, Fishchuk, Roman, Kobrynska, Olena, Levandovska, Khrystyna, Kirieieva, Ivanna, Kuziuk, Mykhailo, Naucler, Pontus, Perlhamre, Emma, Mazouch, Lotta, Kelleher, Anthony, Polizzotto, Mark, Carey, Catherine, Chang, Christina C., Hough, Sally, Virachit, Sophie, Davidson, Sarah, Bice, Daniel J., Ognenovska, Katherine, Cabrera, Gesalit, Flynn, Ruth, Young, Barnaby E., Chia, Po Ying, Lee, Tau Hong, Lin, Ray J., Lye, David C., Ong, Sean W.X., Puah, Ser Hon, Yeo, Tsin Wen, Diong, Shiau Hui, Ongko, Juwinda, Yeo, He Ping, Eriobu, Nnakelu, Kwaghe, Vivian, Zaiyad, Habib, Idoko, Godwin, Uche, Blessing, Selvamuthu, Poongulali, Kumarasamy, Nagalingeswaran, Beulah, Faith Ester, Govindarajan, Narayan, Mariyappan, Kowsalya, Losso, Marcelo H., Abela, Cecilia, Moretto, Renzo, Belloc, Carlos G., Ludueña, Jael, Amar, Josefina, Toibaro, Javier, Macias, Laura Moreno, Fernandez, Lucia, Frare, Pablo S., Chaio, Sebastian R., Pachioli, Valeria, Timpano, Stella M., Sanchez, Marisa del Lujan, de Paz Sierra, Mariana, Stanek, Vanina, Belloso, Waldo, Cilenti, Flavia L., Valentini, Ricardo N., Stryjewski, Martin E., Locatelli, Nicolas, Soler Riera, Maria C., Salgado, Clara, Baeck, Ines M., Di Castelnuovo, Valentina, Zarza, Stella M., Hudson, Fleur, Parmar, Mahesh K.B., Goodman, Anna L., Dphil, Badrock, Jonathan, Gregory, Adam, Goodall, Katharine, Harris, Nicola, Wyncoll, James, Bhagani, S., Rodger, A., Luntiel, A., Patterson, C., Morales, J., Witele, E., Preston, A.-M., Nandani, A., Price, D.A., Hanrath, Aiden, Nell, Jeremy, Patel, Bijal, Hays, Carole, Jones, Geraldine, Davidson, Jade, Bawa, T., Mathews, M., Mazzella, A., Bisnauthsing, K., Aguilar-Jimenez, L., Borchini, F., Hammett, S., Touloumi, Giota, Pantazis, Nikos, Gioukari, Vicky, Souliou, Tania, Antoniadou, A., Protopapas, K., Kavatha, D., Grigoropoulou, S., Oikonomopoulo, C., Moschopoulos, C., Koulouris, N.G., Tzimopoulos, K., Koromilias, A., Argyraki, K., Lourida, P., Bakakos, P., Kalomenidis, I., Vlachakos, V., Barmparessou, Z., Balis, E., Zakynthinos, S., Sigala, I., Gianniou, N., Dima, E., Magkouta, S., Synolaki, E., Konstanta, S., Vlachou, M., Stathopoulou, P., Panagopoulos, P., Petrakis, V., Papazoglou, D., Tompaidou, E., Isaakidou, E., Poulakou, G., Rapti, V., Leontis, K., Nitsotolis, T., Athanasiou, K., Syrigos, K., Kyriakoulis, K., Trontzas, I., Arfara-Melanini, M., Kolonis, V., Kityo, Cissy, Mugerwa, Henry, Kiweewa, Francis, Kimuli, Ivan, Lukaakome, Joseph, Nsereko, Christoher, Lubega, Gloria, Kibirige, Moses, Nakahima, William, Wangi, Deus, Aguti, Evelyne, Generous, Lilian, Massa, Rosemary, Nalaki, Margaret, Magala, Felix, Nabaggala, Phiona Kaweesi, Kidega, Robert, Faith, Oryem Daizy, Florence, Apio, Emmanuel, Ocung, Beacham, Mugoonyi Paul, Geoffrey, Amone, Nakiboneka, Dridah, Apiyo, Paska, Kirenga, Bruce, Atukunda, Angella, Muttamba, Winters, Remmy, Kyeyume, Segawa, Ivan, Pheona, Nsubuga, Kigere, David, Mbabazi, Queen Lailah, Boersalino, Ledra, Nyakoolo, Grace, Fred, Aniongo, Alupo, Alice, Ebong, Doryn, Monday, Edson, Nalubwama, Ritah Norah, Kainja, Milton, Ambrose, Munu, Kwehayo, Vanon, Nalubega, Mary Grace, Ongoli, Augustine, Obbo, Stephen, Sebudde, Nicholus, Alaba, Jeniffer, Magombe, Geoffrey, Tino, Harriet, Obonya, Emmanuel, Lutaakome, Joseph, Kitonsa, Jonathan, Onyango, Martin, Naboth, Tukamwesiga, Naluyinda, Hadijah, Nanyunja, Regina, Irene, Muttiibwa, Jane, Biira, Wimfred, Kyobejja, Leonard, Ssemazzi, Deus, Tkiinomuhisha, Babra, Namasaba, Taire, Paul, Nabankema, Evelyn, Ogavu, Joseph, Mugerwa, Oscar, Okoth, Ivan, Mwebaze, Raymond, Mugabi, Timothy, Makhoba, Anthony, Arikiriza, Phiona, Theresa, Nabuuma, Nakayima, Hope, Frank, Kisuule, Ramgi, Patrícia, Pereira, Kássia, Osinusi, Anu, Cao, Huyen, Klekotka, Paul, Price, Karen, Nirula, Ajay, Osei, Suzette, Tipple, Craig, Wills, Angela, Peppercorn, Amanda, Watson, Helen, Gupta, Rajesh, Alexander, Elizabeth, Mogalian, Erik, Lin, Leo, Ding, Xiao, Margolis, David, Yan, Li, Girardet, Jean-Luc, Ma, Ji, Hong, Zhi, Zhu, Quing, Seegobin, Seth, Gibbs, Michael, Latchman, Mickel, Hasior, Katarzyna, Bouquet, Jerome, Wei, Jianxin, Streicher, Katie, Schmelzer, Albert, Brooks, Dennis, Butcher, Jonny, Tonev, Dimitar, Arbetter, Douglas, Damstetter, Philippe, Legenne, Philippe, Stumpp, Michael, Goncalves, Susana, Ramanathan, Krishnan, Chandra, Richa, Baseler, Beth, Teitelbaum, Marc, Schechner, Adam, Holley, H. Preston, Jankelevich, Shirley, Becker, Nancy, Dolney, Suzanne, Hissey, Debbie, Simpson, Shelly, Kim, Mi Ha, Beeler, Joy, Harmon, Liam, Asomah, Mabel, Jato, Yvonne, Stottlemyer, April, Tang, Olivia, Vanderpuye, Sharon, Yeon, Lindsey, Buehn, Molly, Eccard-Koons, Vanessa, Frary, Sadie, MacDonald, Leah, Cash, Jennifer, Hoopengardner, Lisa, Linton, Jessica, Schaffhauser, Marylu, Nelson, Michaela, Spinelli-Nadzam, Mary, Proffitt, Calvin, Lee, Christopher, Engel, Theresa, Fontaine, Laura, Osborne, C.K., Hohn, Matt, Galcik, Michael, Thompson, DeeDee, Kopka, Stacey, Shelley, Denise M., Mendez, Gregg, Brown, Shawn, Albert, Sara, Balde, Abby, Baracz, Michelle, Bielica, Mona, Billouin-Frazier, Shere, Choudary, Jay, Dixon, Mary, Eyler, Carolyn, Frye, Leanne, Gertz, Jensen, Giebeig, Lisa, Gulati, Neelam, Hankinson, Liz, Hogarty, Debi, Huber, Lynda, Krauss, Gary, Lake, Eileen, Manandhar, Meryan, Rudzinski, Erin, Sandrus, Jen, Suders, Connie, Natarajan, Ven, Rupert, Adam W., Baseler, Michael, Lynam, Danielle, Imamichi, Tom, Laverdure, Sylvain, McCormack, Ashley, Paudel, Sharada, Cook, Kyndal, Haupt, Kendra, Khan, Ayub, Hazen, Allison, Badralmaa, Yunden, Smith, Kenneth, Patel, Bhakti, Kubernac, Amanda, Kubernac, Robert, Hoover, Marie L., Solomon, Courtney, Rashid, Marium, Murphy, Joseph, Brown, Craig, DuChateau, Nadine, Ellis, Sadie, Flosi, Adam, Fox, Lisa, Johnson, Les, Nelson, Rich, Stojanovic, Jelena, Treagus, Amy, Wenner, Christine, Williams, Richard, Jensen, Tomas O, Murray, Thomas A, Grandits, Greg A, Jain, Mamta K, Shaw-Saliba, Kathryn, Matthay, Michael A, Baker, Jason V, Dewar, Robin L, Goodman, Anna L, Hatlen, Timothy J, Highbarger, Helene C, Lallemand, Perrine, Leshnower, Bradley G, Looney, David, Moschopoulos, Charalampos D, Murray, Daniel D, Mylonakis, Eleftherios, Rehman, M Tauseef, Rupert, Adam, Stevens, Randy, Turville, Stuart, Wick, Katherine, Lundgren, Jens, and Ko, Emily R

Published
2024
Full Text
View/download PDF

273. Fundamental configuration optimisation of aircraft with electric propulsion

Author

Flanagan, Fergus and Jarrett, Jerome

Subjects
Aircraft, Electric Propulsion
Abstract

Electric propulsion has the potential to induce a paradigm shift in how aircraft perform, provided the same shift occurs in their design. Improvements to conventional aircraft have reduced CO2 emissions per passenger kilometre by, on average, 3.8% per year since 1960, but these incremental improvements to conventional designs are not sufficient to meet the ambitious goals for reductions in the environmental impact of air transport. To meet these goals, radically different architectures and propulsion systems are required. These radical architectures are by definition difficult to design due to the lack of historical data to enable configuration choices, normally enabled by prior knowledge of performance trends for various configurations. Without the configuration established, optimisers cannot be used due to the small integer variable choices, such as number and position of propulsors, embedded in configuration design. This challenge is solved by developing a optimisation scheme for hybrid-electric aircraft that can deal with the small value integer variables that derail conventional schemes. This scheme is applied to conventional aircraft, for conventional missions, and replicates the expected performance and design of these aircraft. Then, the scheme is applied to missions currently infeasible with conventional aircraft. First of these missions was a STOL aircraft with 500 km range carrying 10 people. Application of this optimisation scheme produced an aircraft capable of a 241 m take-off run, cruise range of 500 km at 200 kt, burning only 287 kg of fuel. Second of these missions, a STOL survey aircraft designed to operate in the High Himalayas, an aircraft that bridges the capability gap between the remote access of helicopters and the endurance of fixed wing aircraft, coupling a 250 m balanced field length with a 1200 km range whilst burning only 217 kg of Jet-A. This work shows how the unlocking of this integer variable problem is critical to evaluating the true potential of electric propulsion architectures and how they shift and morph conventional design space maps, and unlock performance previously unavailable to both conventional designs and conventionally configured aircraft with electric propulsion.

Published
2022
Full Text
View/download PDF

274. Complexity methods for understanding global health governance, financing and delivery arrangements : from system-wide dynamics to neglected tropical disease control in Uganda

Author

Fergus, Cristin Alexis

Subjects
H Social Sciences (General), RA0421 Public health. Hygiene. Preventive Medicine
Abstract

This thesis aims to understand how methodological and conceptual approaches to complexity in quantitative analysis can improve evidence and decision-making, specifically for schistosomiasis control in Uganda and more broadly within global health. Engaging directly with the complexity through methodological choices provided new insights into policies and practices in global health. In Paper 1, I provided an overview of actors and power dynamics in global health, by describing the changing landscape of global health actors as it relates to relative shifts in power over time. This is accomplished by capturing the emergent, dynamic network structure of development aid for health in the period encompassing the 'MDG era', between 1990 and 2015. This paper was published in the Journal of Health Policy and Planning (https://doi.org/10.1093/heapol/czac025). Paper 2 aimed to develop evidence for decision-making in response to the needs of policymakers and practitioners, with a focus on schistosomiasis transmission and control activities in Uganda. This was accomplished by (1) capturing the perspectives of national and sub-national decision-makers on schistosomiasis transmission using participatory modelling, and (2) using the participatory modelling outputs to inform mathematical model simulations in response to the evidence needs. The implementation of this approach challenged the balance of power between international and domestic actors in the development of evidence and decisions regarding the delivery of global health interventions. This paper was published in BMJ Global Health (http://dx.doi.org/10.1136/bmjgh-2021-007113). Paper 3 used the outcomes of the participatory systems mapping workshops and individual-based simulations to guide the scope and content of economic evaluations of schistosomiasis interventions. The results indicated that the most cost-effective scenario is a system of implementation reliant on volunteers from within communities and donated drugs. As anticipated, when all else is held equal, including these costs result in lower cost-effectiveness ratios relative to other interventions. Further, the results bring into question the purpose of continuing interventions which are not predicted to achieve the desired targets within the 30-year time horizon. This paper highlighted potential opportunities for schistosomiasis intervention design and implementation which is more aligned with the aims of equitable, country-led sustainable development. Paper 4 shifted the focus within the discussion of evidence for decision-making in global health to consider one particular type, peer-reviewed publications, which is most often considered as 'best practice' in evidence-based decision-making. A systematic review captured the network of authors who had published on MDA. These results constituted the sampling frame for a remote survey to elucidate perspectives on their roles in policy and practice related to MDA. The findings highlighted the ongoing structural disparities in research leadership and found broad concern about opportunities and about disconnects that limit engagement between researchers and decision-makers for use of primary research in policy and decision-making processes. Paper 4 was published in the Journal of Public Health Policy (https://doi.org/10.1057/s41271-021-00294-x). Broadly speaking, the papers in this thesis have shown that while reductionist, linear perspectives may be part of the reason for the continuation of ineffectual policies and practices, the confluence of politics, power relations, and economies in the context of a complex system of actors and processes also plays a significant role with regards to policy and practice decision making. This was observed in relation to schistosomiasis in Uganda and more broadly in global health at the system level. This thesis uses language and methods common in health sciences to communicate critiques in a way that can be engaged with by health policy-makers, practitioners, and many public health researchers. Finally, this thesis showed the possibilities for using network-based and computational models for understanding complexity within the global health 'system'.

Published
2022
Full Text
View/download PDF

275. Crystallisation and confinement of mesoscale active matter

Author

Moore, Fergus J., Liverpool, Tanniemola, and Gersen, Henkjan

Subjects
Physics, Active Matter, Soft Matter, Active Brownian Particles, Colloids, Janus Particles, Gels
Abstract

Active matter systems are comprised of bodies that consume energy from the environment for self-propulsion and as a result are driven out-of-equilibrium. These systems encom- pass a range of lengthscales and describe both living and artificial systems. Moreover, the motion and collective interactions of these active bodies lead to an ensemble of rich and complex behaviours not seen in equilibrium systems. On mesoscopic lengthscales (nm to μm), the prominent active systems are bacterial swimmers and active colloidal particles. However, as a result of the experimental challenges involved, there have been relatively few studies of mesoscale active matter systems in 3D. In this thesis, we aim to address two poorly understood areas of active systems in 3D: the crystallisation of active Brownian spheres, and the behaviour of active matter in 3D non-trivial confinement. Regarding crystallisation, we perform Brownian dynamics simulations to explore nucleation and polymorph selection in crystals of mono-disperse active spheres. We find two activity de- pendant crystallisation regimes: spinodal, and nucleation and growth. In the former we observe an increase in the crystal growth rate due to the accelerated dynamics in the melt, and in the latter we find that activity inhibits nucleation. Remarkably, activity also has a strong effect on polymorph selection. While the passive system crystallises in an equimolar mixture of FCC and HCP, active particles progressively favour the FCC phase at higher activity. The transport of mesoscale active matter occurs naturally in complex environments that are often crowded, random and irregular. We explore structural and dynamical properties of 3D active particles in random environments with a two-pronged approach. First we conduct Brownian dynamics simulations of active Brownian spheres in two random environments. We find that the confinement increases the heterogeneity of the dynamics, with new populations of absorbed and localised particles appearing close to the obstacles. This heterogeneity has a profound impact on the motility induced phase separation exhibited by the particles at high activity, ranging from growth in random disorder to a bicontinuous phase separation in porous environments. Finally we approach this same problem through experiment and present the first 3D active colloidal system in non-trivial confinement. The system is comprised of active Janus particles driven via an external field, propelling through the porous network surrounding a colloidal gel. This system is imaged in real-time with confocal microscopy and the particle dynamics are extracted with particle tracking methods.

Published
2022

277. Predicting the future risk of lung cancer: development, and internal and external validation of the CanPredict (lung) model in 19·67 million people and evaluation of model performance against seven other risk prediction models

Author

Gleeson, Fergus, Baldwin, David, Batchkala, George, Buchanan, James, Burchardt, Judith, Chakraborty, Rohan, Chana, Ravi, Chen, Yan, Coupland, Carol, Crichton, Charles, Davies, Jim, Devaraj, Anand, Fan, Mengran, Hippisley-Cox, Julia, Koleva-Kolarova, Rositsa, Lee, Richard, Liao, Weiqi, Nair, Arjun, Pickup, Lyndsey, Powell, Anne, Rittscher, Jens, Shadmaan, Amied, Shanmugam, Kandavel, Stokes, Elizabeth, Verrill, Clare, Watkins, Johnathan, Wordsworth, Sarah, Coupland, Carol A C, Baldwin, David R, and Gleeson, Fergus V

Published
2023
Full Text
View/download PDF

278. Incorporating Target-Specific Pharmacophoric Information into Deep Generative Models for Fragment Elaboration

Author

Hadfield, Thomas E, Imrie, Fergus, Merritt, Andy, Birchall, Kristian, and Deane, Charlotte M

Subjects
Medicinal and Biomolecular Chemistry, Chemical Sciences, Ligands, Proteins, Theoretical and Computational Chemistry, Computation Theory and Mathematics, Medicinal & Biomolecular Chemistry, Medicinal and biomolecular chemistry, Theoretical and computational chemistry
Abstract

Despite recent interest in deep generative models for scaffold elaboration, their applicability to fragment-to-lead campaigns has so far been limited. This is primarily due to their inability to account for local protein structure or a user's design hypothesis. We propose a novel method for fragment elaboration, STRIFE, that overcomes these issues. STRIFE takes as input fragment hotspot maps (FHMs) extracted from a protein target and processes them to provide meaningful and interpretable structural information to its generative model, which in turn is able to rapidly generate elaborations with complementary pharmacophores to the protein. In a large-scale evaluation, STRIFE outperforms existing, structure-unaware, fragment elaboration methods in proposing highly ligand-efficient elaborations. In addition to automatically extracting pharmacophoric information from a protein target's FHM, STRIFE optionally allows the user to specify their own design hypotheses.

Published
2022

279. Correction: Polygenic risk modeling for prediction of epithelial ovarian cancer risk

Author

Dareng, Eileen O, Tyrer, Jonathan P, Barnes, Daniel R, Jones, Michelle R, Yang, Xin, Aben, Katja KH, Adank, Muriel A, Agata, Simona, Andrulis, Irene L, Anton-Culver, Hoda, Antonenkova, Natalia N, Aravantinos, Gerasimos, Arun, Banu K, Augustinsson, Annelie, Balmaña, Judith, Bandera, Elisa V, Barkardottir, Rosa B, Barrowdale, Daniel, Beckmann, Matthias W, Beeghly-Fadiel, Alicia, Benitez, Javier, Bermisheva, Marina, Bernardini, Marcus Q, Bjorge, Line, Black, Amanda, Bogdanova, Natalia V, Bonanni, Bernardo, Borg, Ake, Brenton, James D, Budzilowska, Agnieszka, Butzow, Ralf, Buys, Saundra S, Cai, Hui, Caligo, Maria A, Campbell, Ian, Cannioto, Rikki, Cassingham, Hayley, Chang-Claude, Jenny, Chanock, Stephen J, Chen, Kexin, Chiew, Yoke-Eng, Chung, Wendy K, Claes, Kathleen BM, Colonna, Sarah, Cook, Linda S, Couch, Fergus J, Daly, Mary B, Dao, Fanny, Davies, Eleanor, de la Hoya, Miguel, de Putter, Robin, Dennis, Joe, DePersia, Allison, Devilee, Peter, Diez, Orland, Ding, Yuan Chun, Doherty, Jennifer A, Domchek, Susan M, Dörk, Thilo, du Bois, Andreas, Dürst, Matthias, Eccles, Diana M, Eliassen, Heather A, Engel, Christoph, Evans, Gareth D, Fasching, Peter A, Flanagan, James M, Fortner, Renée T, Machackova, Eva, Friedman, Eitan, Ganz, Patricia A, Garber, Judy, Gensini, Francesca, Giles, Graham G, Glendon, Gord, Godwin, Andrew K, Goodman, Marc T, Greene, Mark H, Gronwald, Jacek, Hahnen, Eric, Haiman, Christopher A, Håkansson, Niclas, Hamann, Ute, Hansen, Thomas VO, Harris, Holly R, Hartman, Mikael, Heitz, Florian, Hildebrandt, Michelle AT, Høgdall, Estrid, Høgdall, Claus K, Hopper, John L, Huang, Ruea-Yea, Huff, Chad, Hulick, Peter J, Huntsman, David G, Imyanitov, Evgeny N, Isaacs, Claudine, Jakubowska, Anna, James, Paul A, and Janavicius, Ramunas

Subjects
GEMO Study Collaborators, GC-HBOC Study Collaborators, EMBRACE Collaborators, OPAL Study Group, AOCS Group, KConFab Investigators, HEBON Investigators, OCAC Consortium, CIMBA Consortium, Genetics, Clinical Sciences, Genetics & Heredity
Published
2022

280. eP160: Bilateral oophorectomy and the risk of breast cancer in women with a pathogenic variant in BRCA1: A reappraisal

Author

Kotsopoulos, joanne, Lubinski, Jan, Gronwald, Jacek, Menkiszak, Janusz, McCuaig, Jeanna, Metcalfe, Kelly, Foulkes, William, Neuhausen, Susan, Sun, Sophie, Karlan, Beth, Eisen, Andrea, Tung, Nadine, Olopade, Olufunmilayo, Couch, Fergus, Huzarski, Tomasz, Senter, Leigha, Bordeleau, Louise, Singer, Christian, Eng, Charis, Fruscio, Robert, Pal, Tuya, Sun, Ping, and Narod, Steven

Subjects
Biological Sciences, Genetics, Clinical Sciences, Genetics & Heredity
Published
2022

281. Polygenic risk modeling for prediction of epithelial ovarian cancer risk

Author

Dareng, Eileen O, Tyrer, Jonathan P, Barnes, Daniel R, Jones, Michelle R, Yang, Xin, Aben, Katja KH, Adank, Muriel A, Agata, Simona, Andrulis, Irene L, Anton-Culver, Hoda, Antonenkova, Natalia N, Aravantinos, Gerasimos, Arun, Banu K, Augustinsson, Annelie, Balmaña, Judith, Bandera, Elisa V, Barkardottir, Rosa B, Barrowdale, Daniel, Beckmann, Matthias W, Beeghly-Fadiel, Alicia, Benitez, Javier, Bermisheva, Marina, Bernardini, Marcus Q, Bjorge, Line, Black, Amanda, Bogdanova, Natalia V, Bonanni, Bernardo, Borg, Ake, Brenton, James D, Budzilowska, Agnieszka, Butzow, Ralf, Buys, Saundra S, Cai, Hui, Caligo, Maria A, Campbell, Ian, Cannioto, Rikki, Cassingham, Hayley, Chang-Claude, Jenny, Chanock, Stephen J, Chen, Kexin, Chiew, Yoke-Eng, Chung, Wendy K, Claes, Kathleen BM, Colonna, Sarah, Cook, Linda S, Couch, Fergus J, Daly, Mary B, Dao, Fanny, Davies, Eleanor, de la Hoya, Miguel, de Putter, Robin, Dennis, Joe, DePersia, Allison, Devilee, Peter, Diez, Orland, Ding, Yuan Chun, Doherty, Jennifer A, Domchek, Susan M, Dörk, Thilo, du Bois, Andreas, Dürst, Matthias, Eccles, Diana M, Eliassen, Heather A, Engel, Christoph, Evans, Gareth D, Fasching, Peter A, Flanagan, James M, Fortner, Renée T, Machackova, Eva, Friedman, Eitan, Ganz, Patricia A, Garber, Judy, Gensini, Francesca, Giles, Graham G, Glendon, Gord, Godwin, Andrew K, Goodman, Marc T, Greene, Mark H, Gronwald, Jacek, Hahnen, Eric, Haiman, Christopher A, Håkansson, Niclas, Hamann, Ute, Hansen, Thomas VO, Harris, Holly R, Hartman, Mikael, Heitz, Florian, Hildebrandt, Michelle AT, Høgdall, Estrid, Høgdall, Claus K, Hopper, John L, Huang, Ruea-Yea, Huff, Chad, Hulick, Peter J, Huntsman, David G, Imyanitov, Evgeny N, Isaacs, Claudine, Jakubowska, Anna, James, Paul A, and Janavicius, Ramunas

Subjects
Prevention, Cancer, Ovarian Cancer, Rare Diseases, Good Health and Well Being, Bayes Theorem, Breast Neoplasms, Carcinoma, Ovarian Epithelial, Female, Genetic Predisposition to Disease, Humans, Male, Ovarian Neoplasms, Polymorphism, Single Nucleotide, Prospective Studies, Risk Factors, GEMO Study Collaborators, GC-HBOC Study Collaborators, EMBRACE Collaborators, OPAL Study Group, AOCS Group, KConFab Investigators, HEBON Investigators, OCAC Consortium, CIMBA Consortium, Genetics, Clinical Sciences, Genetics & Heredity
Abstract

Polygenic risk scores (PRS) for epithelial ovarian cancer (EOC) have the potential to improve risk stratification. Joint estimation of Single Nucleotide Polymorphism (SNP) effects in models could improve predictive performance over standard approaches of PRS construction. Here, we implemented computationally efficient, penalized, logistic regression models (lasso, elastic net, stepwise) to individual level genotype data and a Bayesian framework with continuous shrinkage, "select and shrink for summary statistics" (S4), to summary level data for epithelial non-mucinous ovarian cancer risk prediction. We developed the models in a dataset consisting of 23,564 non-mucinous EOC cases and 40,138 controls participating in the Ovarian Cancer Association Consortium (OCAC) and validated the best models in three populations of different ancestries: prospective data from 198,101 women of European ancestries; 7,669 women of East Asian ancestries; 1,072 women of African ancestries, and in 18,915 BRCA1 and 12,337 BRCA2 pathogenic variant carriers of European ancestries. In the external validation data, the model with the strongest association for non-mucinous EOC risk derived from the OCAC model development data was the S4 model (27,240 SNPs) with odds ratios (OR) of 1.38 (95% CI: 1.28-1.48, AUC: 0.588) per unit standard deviation, in women of European ancestries; 1.14 (95% CI: 1.08-1.19, AUC: 0.538) in women of East Asian ancestries; 1.38 (95% CI: 1.21-1.58, AUC: 0.593) in women of African ancestries; hazard ratios of 1.36 (95% CI: 1.29-1.43, AUC: 0.592) in BRCA1 pathogenic variant carriers and 1.49 (95% CI: 1.35-1.64, AUC: 0.624) in BRCA2 pathogenic variant carriers. Incorporation of the S4 PRS in risk prediction models for ovarian cancer may have clinical utility in ovarian cancer prevention programs.

Published
2022

282. Bayesian Analysis of Quasar Lightcurves with a Running Optimal Average: New Time Delay Measurements of COSMOGRAIL Gravitationally Lensed Quasars

Author

Donnan, Fergus R., Horne, Keith, and Santisteban, Juan V. Hernández

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - Astrophysics of Galaxies
Abstract

We present a new method of modelling time-series data based on the running optimal average (ROA). By identifying the effective number of parameters for the ROA model, in terms of the shape and width of its window function and the times and accuracies of the data, we enable a Bayesian analysis, optimising the ROA width, along with other model parameters, by minimising the Bayesian Information Criterion (BIC) and sampling joint posterior parameter distributions using MCMC methods. For analysis of quasar lightcurves, our implementation of ROA modelling can measure time delays among lightcurves at different wavelengths or from different images of a lensed quasar and, in future work, be used to inter-calibrate lightcurve data from different telescopes and estimate the shape and thus the power-density spectrum of the lightcurve. Our noise model implements a robust treatment of outliers and error-bar adjustments to account for additional variance or poorly-quantified uncertainties. Tests with simulated data validate the parameter uncertainty estimates. We compare ROA delay measurements with results from cross-correlation and from JAVELIN, which models lightcurves with a prior on the power-density spectrum. We analyse published COSMOGRAIL lightcurves of multi-lensed quasar lightcurves and present the resulting measurements of the inter-image time delays and detection of microlensing effects., Comment: Accepted for publication in MNRAS

Published
2021
Full Text
View/download PDF

283. Crystallisation and Polymorph Selection in Active Brownian Particles

Author

Moore, Fergus J., Royall, C. Patrick, Liverpool, Tanniemola B., and Russo, John

Subjects
Condensed Matter - Soft Condensed Matter
Abstract

We explore crystallisation and polymorph selection in active Brownian particles with numerical simulation. In agreement with previous work [Wysocki $\textit{et al.}$ $\textit{Europhys. Lett.}$, $\textbf{105}$ 48004 (2014)], we find that crystallisation is suppressed by activity and occurs at higher densities with increasing $P\'{e}clet$ number ($Pe$). While the nucleation rate decreases with increasing activity, the crystal growth rate increases due to the accelerated dynamics in the melt. As a result of this competition we observe the transition from a nucleation and growth regime at high $Pe$ to "spinodal nucleation" at low $Pe$. Unlike the case of passive hard spheres, where preference for FCC over HCP polymorphs is weak, activity causes the annealing of HCP stacking faults, thus strongly favouring the FCC symmetry at high $Pe$. When freezing occurs more slowly, in the nucleation and growth regime, this tendency is much reduced and we see a trend towards the passive case of little preference for either polymorph., Comment: 9 pages, 6 figures

Published
2021

284. Mastering Visual Continuous Control: Improved Data-Augmented Reinforcement Learning

Author

Yarats, Denis, Fergus, Rob, Lazaric, Alessandro, and Pinto, Lerrel

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

We present DrQ-v2, a model-free reinforcement learning (RL) algorithm for visual continuous control. DrQ-v2 builds on DrQ, an off-policy actor-critic approach that uses data augmentation to learn directly from pixels. We introduce several improvements that yield state-of-the-art results on the DeepMind Control Suite. Notably, DrQ-v2 is able to solve complex humanoid locomotion tasks directly from pixel observations, previously unattained by model-free RL. DrQ-v2 is conceptually simple, easy to implement, and provides significantly better computational footprint compared to prior work, with the majority of tasks taking just 8 hours to train on a single GPU. Finally, we publicly release DrQ-v2's implementation to provide RL practitioners with a strong and computationally efficient baseline.

Published
2021

285. Imitation by Predicting Observations

Author

Jaegle, Andrew, Sulsky, Yury, Ahuja, Arun, Bruce, Jake, Fergus, Rob, and Wayne, Greg

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

Imitation learning enables agents to reuse and adapt the hard-won expertise of others, offering a solution to several key challenges in learning behavior. Although it is easy to observe behavior in the real-world, the underlying actions may not be accessible. We present a new method for imitation solely from observations that achieves comparable performance to experts on challenging continuous control tasks while also exhibiting robustness in the presence of observations unrelated to the task. Our method, which we call FORM (for "Future Observation Reward Model") is derived from an inverse RL objective and imitates using a model of expert behavior learned by generative modelling of the expert's observations, without needing ground truth actions. We show that FORM performs comparably to a strong baseline IRL method (GAIL) on the DeepMind Control Suite benchmark, while outperforming GAIL in the presence of task-irrelevant features., Comment: ICML 2021

Published
2021

286. Kernel Identification Through Transformers

Author

Simpson, Fergus, Davies, Ian, Lalchand, Vidhi, Vullo, Alessandro, Durrande, Nicolas, and Rasmussen, Carl

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

Kernel selection plays a central role in determining the performance of Gaussian Process (GP) models, as the chosen kernel determines both the inductive biases and prior support of functions under the GP prior. This work addresses the challenge of constructing custom kernel functions for high-dimensional GP regression models. Drawing inspiration from recent progress in deep learning, we introduce a novel approach named KITT: Kernel Identification Through Transformers. KITT exploits a transformer-based architecture to generate kernel recommendations in under 0.1 seconds, which is several orders of magnitude faster than conventional kernel search algorithms. We train our model using synthetic data generated from priors over a vocabulary of known kernels. By exploiting the nature of the self-attention mechanism, KITT is able to process datasets with inputs of arbitrary dimension. We demonstrate that kernels chosen by KITT yield strong performance over a diverse collection of regression benchmarks., Comment: To appear in Neural Information Processing Systems (NeurIPS) 2021

Published
2021

287. Matrix Product State Pre-Training for Quantum Machine Learning

Author

Dborin, James, Barratt, Fergus, Wimalaweera, Vinul, Wright, Lewis, and Green, Andrew G.

Subjects
Quantum Physics
Abstract

Hybrid Quantum-Classical algorithms are a promising candidate for developing uses for NISQ devices. In particular, Parametrised Quantum Circuits (PQCs) paired with classical optimizers have been used as a basis for quantum chemistry and quantum optimization problems. Training PQCs relies on methods to overcome the fact that the gradients of PQCs vanish exponentially in the size of the circuits used. Tensor network methods are being increasingly used as a classical machine learning tool, as well as a tool for studying quantum systems. We introduce a circuit pre-training method based on matrix product state machine learning methods, and demonstrate that it accelerates training of PQCs for both supervised learning, energy minimization, and combinatorial optimization., Comment: v2: Added short comparison to entanglement devised barren plateau mitigation - relevant paper missed in first submission

Published
2021

295. The evolution of the mass-metallicity relations from the VANDELS survey and the GAEA Semi-Analytic model

Author

Fontanot, Fabio, Calabrò, Antonello, Talia, Margherita, Mannucci, Filippo, Castellano, Marco, Cresci, Giovanni, De Lucia, Gabriella, Gallazzi, Anna, Hirschmann, Michaela, Pentericci, Laura, Xie, Lizhi, Amorin, Ricardo, Bolzonella, Micol, Bongiorno, Angela, Cucciati, Olga, Cullen, Fergus, Fynbo, Johan P. U., Hathi, Nimish, Hibon, Pascale, McLure, Ross J., and Pozzetti, Lucia

Subjects
Astrophysics - Astrophysics of Galaxies, Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract

In this work, we study the evolution of the mass-metallicity relations (MZRs) as predicted by the GAlaxy Evolution and Assembly (GAEA) semi-analytic model. We contrast these predictions with recent results from the VANDELS survey, that allows us to expand the accessible redshift range for the stellar MZR up to $z\sim3.5$. We complement our study by considering the evolution of the gas-phase MZR in the same redshift range. We show that GAEA is able to reproduce the observed evolution of the $z<3.5$ gas-phase MZR and $z<0.7$ stellar MZR, while it overpredicts the stellar metallicity at $z\sim3.5$. Furthermore, GAEA also reproduces the so-called fundamental metallicity relation (FMR) between gas-phase metallicity, stellar mass and star formation rate (SFR). In particular, the gas-phase FMR in GAEA is already in place at $z\sim5$ and shows almost no evolution at lower redshift. GAEA predicts the existence of a stellar FMR, that is, however, characterized by a relevant redshift evolution, although its shape follows closely the gas-phase FMR. We also report additional unsolved tensions between model and data: the overall normalization of the predicted MZR agrees with observations only within $\sim$0.1 dex; the largest discrepancies are seen at $z\sim3.5$ where models tend to slightly overpredict observed metallicities; the slope of the predicted MZR at fixed SFR is too steep below a few ${\rm M}_\odot\ {\rm yr}^{-1}$. Finally, we provide model predictions for the evolution of the MZRs at higher redshifts, that would be useful in the context of future surveys, like those that will be performed with JWST., Comment: 12 pages, 8 figures, MNRAS accepted, replaced to match the published version

Published
2021
Full Text
View/download PDF

296. Offline Reinforcement Learning with Fisher Divergence Critic Regularization

Author

Kostrikov, Ilya, Tompson, Jonathan, Fergus, Rob, and Nachum, Ofir

Subjects
Computer Science - Machine Learning
Abstract

Many modern approaches to offline Reinforcement Learning (RL) utilize behavior regularization, typically augmenting a model-free actor critic algorithm with a penalty measuring divergence of the policy from the offline data. In this work, we propose an alternative approach to encouraging the learned policy to stay close to the data, namely parameterizing the critic as the log-behavior-policy, which generated the offline data, plus a state-action value offset term, which can be learned using a neural network. Behavior regularization then corresponds to an appropriate regularizer on the offset term. We propose using a gradient penalty regularizer for the offset term and demonstrate its equivalence to Fisher divergence regularization, suggesting connections to the score matching and generative energy-based model literature. We thus term our resulting algorithm Fisher-BRC (Behavior Regularized Critic). On standard offline RL benchmarks, Fisher-BRC achieves both improved performance and faster convergence over existing state-of-the-art methods.

Published
2021

297. Modelling Animal Biodiversity Using Acoustic Monitoring and Deep Learning

Author

Chalmers, C., Fergus, P., Wich, S., and Longmore, S. N.

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

For centuries researchers have used sound to monitor and study wildlife. Traditionally, conservationists have identified species by ear; however, it is now common to deploy audio recording technology to monitor animal and ecosystem sounds. Animals use sound for communication, mating, navigation and territorial defence. Animal sounds provide valuable information and help conservationists to quantify biodiversity. Acoustic monitoring has grown in popularity due to the availability of diverse sensor types which include camera traps, portable acoustic sensors, passive acoustic sensors, and even smartphones. Passive acoustic sensors are easy to deploy and can be left running for long durations to provide insights on habitat and the sounds made by animals and illegal activity. While this technology brings enormous benefits, the amount of data that is generated makes processing a time-consuming process for conservationists. Consequently, there is interest among conservationists to automatically process acoustic data to help speed up biodiversity assessments. Processing these large data sources and extracting relevant sounds from background noise introduces significant challenges. In this paper we outline an approach for achieving this using state of the art in machine learning to automatically extract features from time-series audio signals and modelling deep learning models to classify different bird species based on the sounds they make. The acquired bird songs are processed using mel-frequency cepstrum (MFC) to extract features which are later classified using a multilayer perceptron (MLP). Our proposed method achieved promising results with 0.74 sensitivity, 0.92 specificity and an accuracy of 0.74.

Published
2021

298. The Minecraft Kernel: Modelling correlated Gaussian Processes in the Fourier domain

Author

Simpson, Fergus, Boukouvalas, Alexis, Cadek, Vaclav, Sarkans, Elvijs, and Durrande, Nicolas

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

In the univariate setting, using the kernel spectral representation is an appealing approach for generating stationary covariance functions. However, performing the same task for multiple-output Gaussian processes is substantially more challenging. We demonstrate that current approaches to modelling cross-covariances with a spectral mixture kernel possess a critical blind spot. For a given pair of processes, the cross-covariance is not reproducible across the full range of permitted correlations, aside from the special case where their spectral densities are of identical shape. We present a solution to this issue by replacing the conventional Gaussian components of a spectral mixture with block components of finite bandwidth (i.e. rectangular step functions). The proposed family of kernel represents the first multi-output generalisation of the spectral mixture kernel that can approximate any stationary multi-output kernel to arbitrary precision.

Published
2021

300. Intelligence, educational attainment, and brain structure in those at familial high‐risk for schizophrenia or bipolar disorder

Author

Zwarte, Sonja MC, Brouwer, Rachel M, Agartz, Ingrid, Alda, Martin, Alonso‐Lana, Silvia, Bearden, Carrie E, Bertolino, Alessandro, Bonvino, Aurora, Bramon, Elvira, Buimer, Elizabeth EL, Cahn, Wiepke, Canales‐Rodríguez, Erick J, Cannon, Dara M, Cannon, Tyrone D, Caseras, Xavier, Castro‐Fornieles, Josefina, Chen, Qiang, Chung, Yoonho, De la Serna, Elena, Bonnin, Caterina Mar, Demro, Caroline, Di Giorgio, Annabella, Doucet, Gaelle E, Eker, Mehmet Cagdas, Erk, Susanne, Fatjó‐Vilas, Mar, Fears, Scott C, Foley, Sonya F, Frangou, Sophia, Fullerton, Janice M, Glahn, David C, Goghari, Vina M, Goikolea, Jose M, Goldman, Aaron L, Gonul, Ali Saffet, Gruber, Oliver, Hajek, Tomas, Hawkins, Emma L, Heinz, Andreas, Ongun, Ceren Hidiroglu, Hillegers, Manon HJ, Houenou, Josselin, Pol, Hilleke E Hulshoff, Hultman, Christina M, Ingvar, Martin, Johansson, Viktoria, Jönsson, Erik G, Kane, Fergus, Kempton, Matthew J, Koenis, Marinka MG, Kopecek, Miloslav, Krämer, Bernd, Lawrie, Stephen M, Lenroot, Rhoshel K, Marcelis, Machteld, Mattay, Venkata S, McDonald, Colm, Meyer‐Lindenberg, Andreas, Michielse, Stijn, Mitchell, Philip B, Moreno, Dolores, Murray, Robin M, Mwangi, Benson, Nabulsi, Leila, Newport, Jason, Olman, Cheryl A, Os, Jim, Overs, Bronwyn J, Ozerdem, Aysegul, Pergola, Giulio, Picchioni, Marco M, Piguet, Camille, Pomarol‐Clotet, Edith, Radua, Joaquim, Ramsay, Ian S, Richter, Anja, Roberts, Gloria, Salvador, Raymond, Aydogan, Aybala Saricicek, Sarró, Salvador, Schofield, Peter R, Simsek, Esma M, Simsek, Fatma, Soares, Jair C, Sponheim, Scott R, Sugranyes, Gisela, Toulopoulou, Timothea, Tronchin, Giulia, Vieta, Eduard, Walter, Henrik, Weinberger, Daniel R, Whalley, Heather C, Wu, Mon‐Ju, Yalin, Nefize, Andreassen, Ole A, Ching, Christopher RK, Thomopoulos, Sophia I, Erp, Theo GM, Jahanshad, Neda, and Thompson, Paul M

Subjects
Biological Psychology, Biomedical and Clinical Sciences, Psychology, Brain Disorders, Bipolar Disorder, Clinical Research, Serious Mental Illness, Neurosciences, Schizophrenia, Mental Health, Aetiology, 2.3 Psychological, social and economic factors, 2.1 Biological and endogenous factors, Mental health, Cognitive Dysfunction, Educational Status, Family, Genetic Predisposition to Disease, Humans, Intelligence, Magnetic Resonance Imaging, Neuroimaging, bipolar disorder, education, intelligence, neuroimaging, relatives, schizophrenia, Cognitive Sciences, Experimental Psychology, Biological psychology, Cognitive and computational psychology
Abstract

First-degree relatives of patients diagnosed with schizophrenia (SZ-FDRs) show similar patterns of brain abnormalities and cognitive alterations to patients, albeit with smaller effect sizes. First-degree relatives of patients diagnosed with bipolar disorder (BD-FDRs) show divergent patterns; on average, intracranial volume is larger compared to controls, and findings on cognitive alterations in BD-FDRs are inconsistent. Here, we performed a meta-analysis of global and regional brain measures (cortical and subcortical), current IQ, and educational attainment in 5,795 individuals (1,103 SZ-FDRs, 867 BD-FDRs, 2,190 controls, 942 schizophrenia patients, 693 bipolar patients) from 36 schizophrenia and/or bipolar disorder family cohorts, with standardized methods. Compared to controls, SZ-FDRs showed a pattern of widespread thinner cortex, while BD-FDRs had widespread larger cortical surface area. IQ was lower in SZ-FDRs (d = -0.42, p = 3 × 10-5 ), with weak evidence of IQ reductions among BD-FDRs (d = -0.23, p = .045). Both relative groups had similar educational attainment compared to controls. When adjusting for IQ or educational attainment, the group-effects on brain measures changed, albeit modestly. Changes were in the expected direction, with less pronounced brain abnormalities in SZ-FDRs and more pronounced effects in BD-FDRs. To conclude, SZ-FDRs and BD-FDRs show a differential pattern of structural brain abnormalities. In contrast, both had lower IQ scores and similar school achievements compared to controls. Given that brain differences between SZ-FDRs and BD-FDRs remain after adjusting for IQ or educational attainment, we suggest that differential brain developmental processes underlying predisposition for schizophrenia or bipolar disorder are likely independent of general cognitive impairment.

Published
2022

Catalog

Books, media, physical & digital resources
See catalog results