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686 results on '"Earles A"'

1. Ascertainment of Helicobacter pylori Infection and Eradication Treatment Using a Nationwide Electronic Health Record Database

Author

Shailja C. Shah, Rohan Gupta, Ranier Bustamante, Mark Lamm, Hanin Yassin, Ashley Earles, Adriana Hung, Alese Halvorson, Robert Greevy, Samir Gupta, Joshua Demb, Lin Liu, and Christianne L. Roumie

Subjects
Gastric Neoplasm, Gastrointestinal Diseases, Peptic Ulcer Disease, Infectious Disease, Diseases of the digestive system. Gastroenterology, RC799-869
Abstract

Background and Aims: There are limited contemporary population-based data on Helicobacter pylori epidemiology and outcomes in the United States. Our primary aim was to create a validated cohort of veterans with H pylori testing or treatment using Veterans Health Administration data. Methods: Using Veterans Health Administration structured and unstructured data, we developed and validated 4 algorithms for H pylori infection (3 algorithms) and treatment status (1 algorithm). During the development phase, we iteratively modified each algorithm based on a manual review of random sets of electronic health records (reference standard). The a priori validation goal was to achieve a one-sided 95% confidence lower bound (LB) for positive predictive value (PPV) and/or negative predictive value (NPV) >90%. We applied the Bonferroni correction when both PPV and NPV were relevant. Results: For H pylori infection, we achieved 99.0% PPV (LB = 94.6%) and 100% NPV (LB = 96.4%) for discriminating H pylori positive vs negative status using structured (ie, laboratory tests) and 95% PPV (LB = 90.3%) and 97.9% NPV (LB = 93.9%) using unstructured (ie, histopathology reports) data. Diagnostic codes achieved 98% PPV (LB = 93.0%) for H pylori diagnosis. The treatment algorithm was composed of multiple antimicrobial combinations and overall achieved ≥98% PPV (LB = 93.0%) for H pylori treatment, except for amoxicillin/levofloxacin (PPV

Published
2024
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2. Simulation of Automatically Annotated Visible and Multi-/Hyperspectral Images Using the Helios 3D Plant and Radiative Transfer Modeling Framework

Author

Tong Lei, Jan Graefe, Ismael K. Mayanja, Mason Earles, and Brian N. Bailey

Subjects
Plant culture, SB1-1110, Genetics, QH426-470, Botany, QK1-989
Abstract

Deep learning and multimodal remote and proximal sensing are widely used for analyzing plant and crop traits, but many of these deep learning models are supervised and necessitate reference datasets with image annotations. Acquiring these datasets often demands experiments that are both labor-intensive and time-consuming. Furthermore, extracting traits from remote sensing data beyond simple geometric features remains a challenge. To address these challenges, we proposed a radiative transfer modeling framework based on the Helios 3-dimensional (3D) plant modeling software designed for plant remote and proximal sensing image simulation. The framework has the capability to simulate RGB, multi-/hyperspectral, thermal, and depth cameras, and produce associated plant images with fully resolved reference labels such as plant physical traits, leaf chemical concentrations, and leaf physiological traits. Helios offers a simulated environment that enables generation of 3D geometric models of plants and soil with random variation, and specification or simulation of their properties and function. This approach differs from traditional computer graphics rendering by explicitly modeling radiation transfer physics, which provides a critical link to underlying plant biophysical processes. Results indicate that the framework is capable of generating high-quality, labeled synthetic plant images under given lighting scenarios, which can lessen or remove the need for manually collected and annotated data. Two example applications are presented that demonstrate the feasibility of using the model to enable unsupervised learning by training deep learning models exclusively with simulated images and performing prediction tasks using real images.

Published
2024
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6. Novel algorithms for high-resolution prediction of canopy evapotranspiration in grapevine

Author

Matthew Jenkins, Autumn Mannsfeld, Shayla Nikzad, Jean-Jacques Lambert, Konrad Miller, Mark Burns, J. Mason Earles, and David Block

Subjects
evapotranspiration, modelling, high resolution irrigation, mass balance, convective mass transfer, grapevine, Agriculture, Botany, QK1-989
Abstract

Developing low-cost technology for custom water delivery to individual or small groups of plants is a critical next step to advance precision irrigation. Current systems for estimating evapotranspiration (ET), or plant water use, work on the scale of a full vineyard (e.g., 3–5 acres) or the scale of a single vine, but at a cost that prohibits monitoring past a small number of representative vines. To develop and evaluate low-cost ET sensors for individual grapevines, we used three head-pruned Zinfandel vines in pots and placed them on load cells to collect continuous weights indicative of actual ET. We mounted research-grade sensors for humidity, temperature, and wind speed on each vine and saved data at 2-minute intervals during three growing seasons. We developed three models based on first principles (Convective Mass Transfer or Mass Balance approaches) or simple correlations to predict actual single-plant ET from these data. We present here the results of a multi-year trial at the UC-Davis RMI vineyard to illustrate the performance of each of the models for ET estimation. Relative model performance was assessed by comparing model predictions to ground truth data provided by measurements from load cells–including assessments of estimated instantaneous ET rate, estimated cumulative water use over a one-hour window surrounding solar noon, and estimated cumulative water use over a full 24-hour period. The three algorithms developed consistently performed well, with single vine ET rate predictions showing a strong linear relationship with ground truth (range in r2 over three seasons CMT r2 = 0.61–0.86; MB r2 = 0.07–0.91; EM r2 = 0.57–0.92). The MB approach, which includes two measurements of relative humidity and temperature, was the most variable, likely due to the impact of sensor placement. In all seasons, we also examined the trend in the plant scaling factor found in each model, deemed As, which, based on model theory, is a function of vine size. Taken together, these results suggest that high-resolution irrigation (HRI) models are a promising new method for ET estimation at the single plant level.

Published
2023
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11. Models of justice evoked in published scientific studies of plastic pollution

Author

Max Liboiron, Rui Liu, Elise Earles, and Imari Walker-Franklin

Subjects
plastic pollution, models of justice, value-based science, endocrine disrupting compounds, chemical pollution, plastic additives, Education, Science
Abstract

An exponentially growing body of international research engages with plastic pollution using different ideas on the right ways to frame, research, and intervene in the problem. The premise of this study is that all scientists work with understandings of what is right and wrong and why that is (models of justice) in their research, even when it is not explicitly stated, reflected upon, or a conscious part of the discussion. We surveyed 755 published articles on marine debris and plastic chemical additives and found that all evoked at least one model of justice, and often more. The most routinely used models included: developmental justice, distributive justice, and procedural justice. More rarely, we found appeals to environment-first justice and Indigenous sovereignty. While occasionally these multiple models worked synergistically, more often they conflicted. Our findings ground a call for fellow researchers to use a more intentional and systematic approach to evoking models of justice in our work. Our goal is to offer descriptions and insights about models of justice that are already being deployed to increase the sophistication of the ethical and normative orientations of our research and our fields, both in plastic pollution sciences and beyond.

Published
2023
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14. Impact of diabetes on the management and outcomes in atrial fibrillation: an analysis from the ESC-EHRA EORP-AF Long-Term General Registry

Author

Boriani, G., Lip, G.Y.H., Tavazzi, L., Maggioni, A.P., Dan, G.-A., Potpara, T., Nabauer, M., Marin, F., Kalarus, Z., Goda, A., Mairesse, G., Shalganov, T., Antoniades, L., Taborsky, M., Riahi, S., Muda, P., García Bolao, I., Piot, O., Etsadashvili, K., Simantirakis, E., Haim, M., Azhari, A., Najafian, J., Santini, M., Mirrakhimov, E., Kulzida, K.A., Erglis, A., Poposka, L., Burg, M., Crijns, H., Erküner, Ö., Atar, D., Lenarczyk, R., Martins Oliveira, M., Shah, D., Serdechnaya, E., Diker, E., Lane, D., Zëra, E., Ekmekçiu, U., Paparisto, V., Tase, M., Gjergo, H., Dragoti, J., Ciutea, M., Ahadi, N., el Husseini, Z., Raepers, M., Leroy, J., Haushan, P., Jourdan, A., Lepiece, C., Desteghe, L., Vijgen, J., Koopman, P., Van Genechten, G., Heidbuchel, H., Boussy, T., De Coninck, M., Van Eeckhoutte, H., Bouckaert, N., Friart, A., Boreux, J., Arend, C., Evrard, P., Stefan, L., Hoffer, E., Herzet, J., Massoz, M., Celentano, C., Sprynger, M., Pierard, L., Melon, P., Van Hauwaert, B., Kuppens, C., Faes, D., Van Lier, D., Van Dorpe, A., Gerardy, A., Deceuninck, O., Xhaet, O., Dormal, F., Ballant, E., Blommaert, D., Yakova, D., Hristov, M., Yncheva, T., Stancheva, N., Tisheva, S., Tokmakova, M., Nikolov, F., Gencheva, D., Kunev, B., Stoyanov, M., Marchov, D., Gelev, V., Traykov, V., Kisheva, A., Tsvyatkov, H., Shtereva, R., Bakalska-Georgieva, S., Slavcheva, S., Yotov, Y., Kubíčková, M., Marni Joensen, A., Gammelmark, A., Hvilsted Rasmussen, L., Dinesen, P., Krogh Venø, S., Sorensen, B., Korsgaard, A., Andersen, K., Fragtrup Hellum, C., Svenningsen, A., Nyvad, O., Wiggers, P., May, O., Aarup, A., Graversen, B., Jensen, L., Andersen, M., Svejgaard, M., Vester, S., Hansen, S., Lynggaard, V., Ciudad, M., Vettus, R., Maestre, A., Castaño, S., Cheggour, S., Poulard, J., Mouquet, V., Leparrée, S., Bouet, J., Taieb, J., Doucy, A., Duquenne, H., Furber, A., Dupuis, J., Rautureau, J., Font, M., Damiano, P., Lacrimini, M., Abalea, J., Boismal, S., Menez, T., Mansourati, J., Range, G., Gorka, H., Laure, C., Vassalière, C., Elbaz, N., Lellouche, N., Djouadi, K., Roubille, F., Dietz, D., Davy, J., Granier, M., Winum, P., Leperchois-Jacquey, C., Kassim, H., Marijon, E., Le Heuzey, J., Fedida, J., Maupain, C., Himbert, C., Gandjbakhch, E., Hidden-Lucet, F., Duthoit, G., Badenco, N., Chastre, T., Waintraub, X., Oudihat, M., Lacoste, J., Stephan, C., Bader, H., Delarche, N., Giry, L., Arnaud, D., Lopez, C., Boury, F., Brunello, I., Lefèvre, M., Mingam, R., Haissaguerre, M., Le Bidan, M., Pavin, D., Le Moal, V., Leclercq, C., Beitar, T., Martel, I., Schmid, A., Sadki, N., Romeyer-Bouchard, C., Da Costa, A., Arnault, I., Boyer, M., Piat, C., Lozance, N., Nastevska, S., Doneva, A., Fortomaroska Milevska, B., Sheshoski, B., Petroska, K., Taneska, N., Bakrecheski, N., Lazarovska, K., Jovevska, S., Ristovski, V., Antovski, A., Lazarova, E., Kotlar, I., Taleski, J., Kedev, S., Zlatanovik, N., Jordanova, S., Bajraktarova Proseva, T., Doncovska, S., Maisuradze, D., Esakia, A., Sagirashvili, E., Lartsuliani, K., Natelashvili, N., Gumberidze, N., Gvenetadze, R., Gotonelia, N., Kuridze, N., Papiashvili, G., Menabde, I., Glöggler, S., Napp, A., Lebherz, C., Romero, H., Schmitz, K., Berger, M., Zink, M., Köster, S., Sachse, J., Vonderhagen, E., Soiron, G., Mischke, K., Reith, R., Schneider, M., Rieker, W., Boscher, D., Taschareck, A., Beer, A., Oster, D., Ritter, O., Adamczewski, J., Walter, S., Frommhold, A., Luckner, E., Richter, J., Schellner, M., Landgraf, S., Bartholome, S., Naumann, R., Schoeler, J., Westermeier, D., William, F., Wilhelm, K., Maerkl, M., Oekinghaus, R., Denart, M., Kriete, M., Tebbe, U., Scheibner, T., Gruber, M., Gerlach, A., Beckendorf, C., Anneken, L., Arnold, M., Lengerer, S., Bal, Z., Uecker, C., Förtsch, H., Fechner, S., Mages, V., Martens, E., Methe, H., Schmidt, T., Schaeffer, B., Hoffmann, B., Moser, J., Heitmann, K., Willems, S., Klaus, C., Lange, I., Durak, M., Esen, E., Mibach, F., Mibach, H., Utech, A., Gabelmann, M., Stumm, R., Ländle, V., Gartner, C., Goerg, C., Kaul, N., Messer, S., Burkhardt, D., Sander, C., Orthen, R., Kaes, S., Baumer, A., Dodos, F., Barth, A., Schaeffer, G., Gaertner, J., Winkler, J., Fahrig, A., Aring, J., Wenzel, I., Steiner, S., Kliesch, A., Kratz, E., Winter, K., Schneider, P., Haag, A., Mutscher, I., Bosch, R., Taggeselle, J., Meixner, S., Schnabel, A., Shamalla, A., Hötz, H., Korinth, A., Rheinert, C., Mehltretter, G., Schön, B., Schön, N., Starflinger, A., Englmann, E., Baytok, G., Laschinger, T., Ritscher, G., Gerth, A., Dechering, D., Eckardt, L., Kuhlmann, M., Proskynitopoulos, N., Brunn, J., Foth, K., Axthelm, C., Hohensee, H., Eberhard, K., Turbanisch, S., Hassler, N., Koestler, A., Stenzel, G., Kschiwan, D., Schwefer, M., Neiner, S., Hettwer, S., Haeussler-Schuchardt, M., Degenhardt, R., Sennhenn, S., Brendel, M., Stoehr, A., Widjaja, W., Loehndorf, S., Logemann, A., Hoskamp, J., Grundt, J., Block, M., Ulrych, R., Reithmeier, A., Panagopoulos, V., Martignani, C., Bernucci, D., Fantecchi, E., Diemberger, I., Ziacchi, M., Biffi, M., Cimaglia, P., Frisoni, J., Giannini, I., Boni, S., Fumagalli, S., Pupo, S., Di Chiara, A., Mirone, P., Pesce, F., Zoccali, C., Malavasi, V.L., Mussagaliyeva, A., Ahyt, B., Salihova, Z., Koshum-Bayeva, K., Kerimkulova, A., Bairamukova, A., Lurina, B., Zuzans, R., Jegere, S., Mintale, I., Kupics, K., Jubele, K., Kalejs, O., Vanhear, K., Cachia, M., Abela, E., Warwicker, S., Tabone, T., Xuereb, R., Asanovic, D., Drakalovic, D., Vukmirovic, M., Pavlovic, N., Music, L., Bulatovic, N., Boskovic, A., Uiterwaal, H., Bijsterveld, N., De Groot, J., Neefs, J., van den Berg, N., Piersma, F., Wilde, A., Hagens, V., Van Es, J., Van Opstal, J., Van Rennes, B., Verheij, H., Breukers, W., Tjeerdsma, G., Nijmeijer, R., Wegink, D., Binnema, R., Said, S., Philippens, S., van Doorn, W., Szili-Torok, T., Bhagwandien, R., Janse, P., Muskens, A., van Eck, M., Gevers, R., van der Ven, N., Duygun, A., Rahel, B., Meeder, J., Vold, A., Holst Hansen, C., Engset, I., Dyduch-Fejklowicz, B., Koba, E., Cichocka, M., Sokal, A., Kubicius, A., Pruchniewicz, E., Kowalik-Sztylc, A., Czapla, W., Mróz, I., Kozlowski, M., Pawlowski, T., Tendera, M., Winiarska-Filipek, A., Fidyk, A., Slowikowski, A., Haberka, M., Lachor-Broda, M., Biedron, M., Gasior, Z., Kołodziej, M., Janion, M., Gorczyca-Michta, I., Wozakowska-Kaplon, B., Stasiak, M., Jakubowski, P., Ciurus, T., Drozdz, J., Simiera, M., Zajac, P., Wcislo, T., Zycinski, P., Kasprzak, J., Olejnik, A., Harc-Dyl, E., Miarka, J., Pasieka, M., Ziemińska-Łuć, M., Bujak, W., Śliwiński, A., Grech, A., Morka, J., Petrykowska, K., Prasał, M., Hordyński, G., Feusette, P., Lipski, P., Wester, A., Streb, W., Romanek, J., Woźniak, P., Chlebuś, M., Szafarz, P., Stanik, W., Zakrzewski, M., Kaźmierczak, J., Przybylska, A., Skorek, E., Błaszczyk, H., Stępień, M., Szabowski, S., Krysiak, W., Szymańska, M., Karasiński, J., Blicharz, J., Skura, M., Hałas, K., Michalczyk, L., Orski, Z., Krzyżanowski, K., Skrobowski, A., Zieliński, L., Tomaszewska-Kiecana, M., Dłużniewski, M., Kiliszek, M., Peller, M., Budnik, M., Balsam, P., Opolski, G., Tymińska, A., Ozierański, K., Wancerz, A., Borowiec, A., Majos, E., Dabrowski, R., Szwed, H., Musialik-Lydka, A., Leopold-Jadczyk, A., Jedrzejczyk-Patej, E., Koziel, M., Mazurek, M., Krzemien-Wolska, K., Starosta, P., Nowalany-Kozielska, E., Orzechowska, A., Szpot, M., Staszel, M., Almeida, S., Pereira, H., Brandão Alves, L., Miranda, R., Ribeiro, L., Costa, F., Morgado, F., Carmo, P., Galvao Santos, P., Bernardo, R., Adragão, P., Ferreira da Silva, G., Peres, M., Alves, M., Leal, M., Cordeiro, A., Magalhães, P., Fontes, P., Leão, S., Delgado, A., Costa, A., Marmelo, B., Rodrigues, B., Moreira, D., Santos, J., Santos, L., Terchet, A., Darabantiu, D., Mercea, S., Turcin Halka, V., Pop Moldovan, A., Gabor, A., Doka, B., Catanescu, G., Rus, H., Oboroceanu, L., Bobescu, E., Popescu, R., Dan, A., Buzea, A., Daha, I., Dan, G., Neuhoff, I., Baluta, M., Ploesteanu, R., Dumitrache, N., Vintila, M., Daraban, A., Japie, C., Badila, E., Tewelde, H., Hostiuc, M., Frunza, S., Tintea, E., Bartos, D., Ciobanu, A., Popescu, I., Toma, N., Gherghinescu, C., Cretu, D., Patrascu, N., Stoicescu, C., Udroiu, C., Bicescu, G., Vintila, V., Vinereanu, D., Cinteza, M., Rimbas, R., Grecu, M., Cozma, A., Boros, F., Ille, M., Tica, O., Tor, R., Corina, A., Jeewooth, A., Maria, B., Georgiana, C., Natalia, C., Alin, D., Dinu-Andrei, D., Livia, M., Daniela, R., Larisa, R., Umaar, S., Tamara, T., Ioachim Popescu, M., Nistor, D., Sus, I., Coborosanu, O., Alina-Ramona, N., Dan, R., Petrescu, L., Ionescu, G., Vacarescu, C., Goanta, E., Mangea, M., Ionac, A., Mornos, C., Cozma, D., Pescariu, S., Solodovnicova, E., Soldatova, I., Shutova, J., Tjuleneva, L., Zubova, T., Uskov, V., Obukhov, D., Rusanova, G., Isakova, N., Odinsova, S., Arhipova, T., Kazakevich, E., Zavyalova, O., Novikova, T., Riabaia, I., Zhigalov, S., Drozdova, E., Luchkina, I., Monogarova, Y., Hegya, D., Rodionova, L., Nevzorova, V., Lusanova, O., Arandjelovic, A., Toncev, D., Vukmirovic, L., Radisavljevic, M., Milanov, M., Sekularac, N., Zdravkovic, M., Hinic, S., Dimkovic, S., Acimovic, T., Saric, J., Radovanovic, S., Kocijancic, A., Obrenovic-Kircanski, B., Kalimanovska Ostric, D., Simic, D., Jovanovic, I., Petrovic, I., Polovina, M., Vukicevic, M., Tomasevic, M., Mujovic, N., Radivojevic, N., Petrovic, O., Aleksandric, S., Kovacevic, V., Mijatovic, Z., Ivanovic, B., Tesic, M., Ristic, A., Vujisic-Tesic, B., Nedeljkovic, M., Karadzic, A., Uscumlic, A., Prodanovic, M., Zlatar, M., Asanin, M., Bisenic, B., Vasic, V., Popovic, Z., Djikic, D., Sipic, M., Peric, V., Dejanovic, B., Milosevic, N., Backovic, S., Stevanovic, A., Andric, A., Pencic, B., Pavlovic-Kleut, M., Celic, V., Pavlovic, M., Petrovic, M., Vuleta, M., Petrovic, N., Simovic, S., Savovic, Z., Milanov, S., Davidovic, G., Iric-Cupic, V., Djordjevic, D., Damjanovic, M., Zdravkovic, S., Topic, V., Stanojevic, D., Randjelovic, M., Jankovic-Tomasevic, R., Atanaskovic, V., Antic, S., Simonovic, D., Stojanovic, M., Stojanovic, S., Mitic, V., Ilic, V., Petrovic, D., Deljanin Ilic, M., Ilic, S., Stoickov, V., Markovic, S., Mijatovic, A., Tanasic, D., Radakovic, G., Peranovic, J., Panic-Jelic, N., Vujadinovic, O., Pajic, P., Bekic, S., Kovacevic, S., García Fernandez, A., Perez Cabeza, A., Anguita, M., Tercedor Sanchez, L., Mau, E., Loayssa, J., Ayarra, M., Carpintero, M., Roldán Rabadan, I., Gil Ortega, M., Tello Montoliu, A., Orenes Piñero, E., Manzano Fernández, S., Marín, F., Romero Aniorte, A., Veliz Martínez, A., Quintana Giner, M., Ballesteros, G., Palacio, M., Alcalde, O., García-Bolao, I., Bertomeu Gonzalez, V., Otero-Raviña, F., García Seara, J., Gonzalez Juanatey, J., Dayal, N., Maziarski, P., Gentil-Baron, P., Koç, M., Onrat, E., Dural, I.E., Yilmaz, K., Özin, B., Tan Kurklu, S., Atmaca, Y., Canpolat, U., Tokgozoglu, L., Dolu, A.K., Demirtas, B., Sahin, D., Ozcan Celebi, O., Gagirci, G., Turk, U.O., Ari, H., Polat, N., Toprak, N., Sucu, M., Akin Serdar, O., Taha Alper, A., Kepez, A., Yuksel, Y., Uzunselvi, A., Yuksel, S., Sahin, M., Kayapinar, O., Ozcan, T., Kaya, H., Yilmaz, M.B., Kutlu, M., Demir, M., Gibbs, C., Kaminskiene, S., Bryce, M., Skinner, A., Belcher, G., Hunt, J., Stancombe, L., Holbrook, B., Peters, C., Tettersell, S., Shantsila, A., Senoo, K., Proietti, M., Russell, K., Domingos, P., Hussain, S., Partridge, J., Haynes, R., Bahadur, S., Brown, R., McMahon, S., McDonald, J., Balachandran, K., Singh, R., Garg, S., Desai, H., Davies, K., Goddard, W., Galasko, G., Rahman, I., Chua, Y., Payne, O., Preston, S., Brennan, O., Pedley, L., Whiteside, C., Dickinson, C., Brown, J., Jones, K., Benham, L., Brady, R., Buchanan, L., Ashton, A., Crowther, H., Fairlamb, H., Thornthwaite, S., Relph, C., McSkeane, A., Poultney, U., Kelsall, N., Rice, P., Wilson, T., Wrigley, M., Kaba, R., Patel, T., Young, E., Law, J., Runnett, C., Thomas, H., McKie, H., Fuller, J., Pick, S., Sharp, A., Hunt, A., Thorpe, K., Hardman, C., Cusack, E., Adams, L., Hough, M., Keenan, S., Bowring, A., Watts, J., Zaman, J., Goffin, K., Nutt, H., Beerachee, Y., Featherstone, J., Mills, C., Pearson, J., Stephenson, L., Grant, S., Wilson, A., Hawksworth, C., Alam, I., Robinson, M., Ryan, S., Egdell, R., Gibson, E., Holland, M., Leonard, D., Mishra, B., Ahmad, S., Randall, H., Hill, J., Reid, L., George, M., McKinley, S., Brockway, L., Milligan, W., Sobolewska, J., Muir, J., Tuckis, L., Winstanley, L., Jacob, P., Kaye, S., Morby, L., Jan, A., Sewell, T., Boos, C., Wadams, B., Cope, C., Jefferey, P., Andrews, N., Getty, A., Suttling, A., Turner, C., Hudson, K., Austin, R., Howe, S., Iqbal, R., Gandhi, N., Brophy, K., Mirza, P., Willard, E., Collins, S., Ndlovu, N., Subkovas, E., Karthikeyan, V., Waggett, L., Wood, A., Bolger, A., Stockport, J., Evans, L., Harman, E., Starling, J., Williams, L., Saul, V., Sinha, M., Bell, L., Tudgay, S., Kemp, S., Frost, L., Ingram, T., Loughlin, A., Adams, C., Adams, M., Hurford, F., Owen, C., Miller, C., Donaldson, D., Tivenan, H., Button, H., Nasser, A., Jhagra, O., Stidolph, B., Brown, C., Livingstone, C., Duffy, M., Madgwick, P., Roberts, P., Greenwood, E., Fletcher, L., Beveridge, M., Earles, S., McKenzie, D., Beacock, D., Dayer, M., Seddon, M., Greenwell, D., Luxton, F., Venn, F., Mills, H., Rewbury, J., James, K., Roberts, K., Tonks, L., Felmeden, D., Taggu, W., Summerhayes, A., Hughes, D., Sutton, J., Felmeden, L., Khan, M., Walker, E., Norris, L., O'Donohoe, L., Mozid, A., Dymond, H., Lloyd-Jones, H., Saunders, G., Simmons, D., Coles, D., Cotterill, D., Beech, S., Kidd, S., Wrigley, B., Petkar, S., Smallwood, A., Jones, R., Radford, E., Milgate, S., Metherell, S., Cottam, V., Buckley, C., Broadley, A., Wood, D., Allison, J., Rennie, K., Balian, L., Howard, L., Pippard, L., Board, S., Pitt-Kerby, T., Ding, Wern Yew, Kotalczyk, Agnieszka, Boriani, Giuseppe, Marin, Francisco, Blomström-Lundqvist, Carina, Potpara, Tatjana S., Fauchier, Laurent, and Lip, Gregory.Y.H.

Published
2022
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19. Cardiac troponins and adverse outcomes in European patients with atrial fibrillation: A report from the ESC-EHRA EORP atrial fibrillation general long-term registry

Author

Boriani, G., Lip, G.Y.H., Tavazzi, L., Maggioni, A.P., Dan, G-A., Potpara, T., Nabauer, M., Marin, F., Kalarus, Z., Fauchier, L., Goda, A., Mairesse, G., Shalganov, T., Antoniades, L., Taborsky, M., Riahi, S., Muda, P., García Bolao, I., Piot, O., Etsadashvili, K., Haim, M., Azhari, A., Najafian, J., Santini, M., Mirrakhimov, E., Kulzida, K., Erglis, A., Poposka, L., Burg, M.R., Crijns, H., Erküner, Ö., Atar, D., Lenarczyk, R., Martins Oliveira, M., Shah, D., Serdechnaya, E., Diker, E., Zëra, E., Ekmekçiu, U., Paparisto, V., Tase, M., Gjergo, H., Dragoti, J., Ciutea, M., Ahadi, N., el Husseini, Z., Raepers, M., Leroy, J., Haushan, P., Jourdan, A., Lepiece, C., Desteghe, L., Vijgen, J., Koopman, P., Van Genechten, G., Heidbuchel, H., Boussy, T., De Coninck, M., Van Eeckhoutte, H., Bouckaert, N., Friart, A., Boreux, J., Arend, C., Evrard, P., Stefan, L., Hoffer, E., Herzet, J., Massoz, M., Celentano, C., Sprynger, M., Pierard, L., Melon, P., Van Hauwaert, B., Kuppens, C., Faes, D., Van Lier, D., Van Dorpe, A., Gerardy, A., Deceuninck, O., Xhaet, O., Dormal, F., Ballant, E., Blommaert, D., Yakova, D., Hristov, M., Yncheva, T., Stancheva, N., Tisheva, S., Tokmakova, M., Nikolov, F., Gencheva, D., Kunev, B., Stoyanov, M., Marchov, D., Gelev, V., Traykov, V., Kisheva, A., Tsvyatkov, H., Shtereva, R., Bakalska-Georgieva, S., Slavcheva, S., Yotov, Y., Kubíčková, M., Marni Joensen, A., Gammelmark, A., Hvilsted Rasmussen, L., Dinesen, P., Krogh Venø, S., Sorensen, B., Korsgaard, A., Andersen, K., Fragtrup Hellum, C., Svenningsen, A., Nyvad, O., Wiggers, P., May, O., Aarup, A., Graversen, B., Jensen, L., Andersen, M., Svejgaard, M., Vester, S., Hansen, S., Lynggaard, V., Ciudad, M., Vettus, R., Maestre, A., Castaño, S., Cheggour, S., Poulard, J., Mouquet, V., Leparrée, S., Bouet, J., Taieb, J., Doucy, A., Duquenne, H., Furber, A., Dupuis, J., Rautureau, J., Font, M., Damiano, P., Lacrimini, M., Abalea, J., Boismal, S., Menez, T., Mansourati, J., Range, G., Gorka, H., Laure, C., Vassalière, C., Elbaz, N., Lellouche, N., Djouadi, K., Roubille, F., Dietz, D., Davy, J., Granier, M., Winum, P., Leperchois-Jacquey, C., Kassim, H., Marijon, E., Le Heuzey, J., Fedida, J., Maupain, C., Himbert, C., Gandjbakhch, E., Hidden-Lucet, F., Duthoit, G., Badenco, N., Chastre, T., Waintraub, X., Oudihat, M., Lacoste, J., Stephan, C., Bader, H., Delarche, N., Giry, L., Arnaud, D., Lopez, C., Boury, F., Brunello, I., Lefèvre, M., Mingam, R., Haissaguerre, M., Le Bidan, M., Pavin, D., Le Moal, V., Leclercq, C., Beitar, T., Martel, I., Schmid, A., Sadki, N., Romeyer-Bouchard, C., Da Costa, A., Arnault, I., Boyer, M., Piat, C., Lozance, N., Nastevska, S., Doneva, A., Fortomaroska Milevska, B., Sheshoski, B., Petroska, K., Taneska, N., Bakrecheski, N., Lazarovska, K., Jovevska, S., Ristovski, V., Antovski, A., Lazarova, E., Kotlar, I., Taleski, J., Kedev, S., Zlatanovik, N., Jordanova, S., Bajraktarova Proseva, T., Doncovska, S., Maisuradze, D., Esakia, A., Sagirashvili, E., Lartsuliani, K., Natelashvili, N., Gumberidze, N., Gvenetadze, R., Gotonelia, N., Kuridze, N., Papiashvili, G., Menabde, I., Glöggler, S., Napp, A., Lebherz, C., Romero, H., Schmitz, K., Berger, M., Zink, M., Köster, S., Sachse, J., Vonderhagen, E., Soiron, G., Mischke, K., Reith, R., Schneider, M., Rieker, W., Boscher, D., Taschareck, A., Beer, A., Oster, D., Ritter, O., Adamczewski, J., Walter, S., Frommhold, A., Luckner, E., Richter, J., Schellner, M., Landgraf, S., Bartholome, S., Naumann, R., Schoeler, J., Westermeier, D., William, F., Wilhelm, K., Maerkl, M., Oekinghaus, R., Denart, M., Kriete, M., Tebbe, U., Scheibner, T., Gruber, M., Gerlach, A., Beckendorf, C., Anneken, L., Arnold, M., Lengerer, S., Bal, Z., Uecker, C., Förtsch, H., Fechner, S., Mages, V., Martens, E., Methe, H., Schmidt, T., Schaeffer, B., Hoffmann, B., Moser, J., Heitmann, K., Willems, S., Klaus, C., Lange, I., Durak, M., Esen, E., Mibach, F., Mibach, H., Utech, A., Gabelmann, M., Stumm, R., Ländle, V., Gartner, C., Goerg, C., Kaul, N., Messer, S., Burkhardt, D., Sander, C., Orthen, R., Kaes, S., Baumer, A., Dodos, F., Barth, A., Schaeffer, G., Gaertner, J., Winkler, J., Fahrig, A., Aring, J., Wenzel, I., Steiner, S., Kliesch, A., Kratz, E., Winter, K., Schneider, P., Haag, A., Mutscher, I., Bosch, R., Taggeselle, J., Meixner, S., Schnabel, A., Shamalla, A., Hötz, H., Korinth, A., Rheinert, C., Mehltretter, G., Schön, B., Schön, N., Starflinger, A., Englmann, E., Baytok, G., Laschinger, T., Ritscher, G., Gerth, A., Dechering, D., Eckardt, L., Kuhlmann, M., Proskynitopoulos, N., Brunn, J., Foth, K., Axthelm, C., Hohensee, H., Eberhard, K., Turbanisch, S., Hassler, N., Koestler, A., Stenzel, G., Kschiwan, D., Schwefer, M., Neiner, S., Hettwer, S., Haeussler-Schuchardt, M., Degenhardt, R., Sennhenn, S., Brendel, M., Stoehr, A., Widjaja, W., Loehndorf, S., Logemann, A., Hoskamp, J., Grundt, J., Block, M., Ulrych, R., Reithmeier, A., Panagopoulos, V., Martignani, C., Bernucci, D., Fantecchi, E., Diemberger, I., Ziacchi, M., Biffi, M., Cimaglia, P., Frisoni, J., Giannini, I., Boni, S., Fumagalli, S., Pupo, S., Di Chiara, A., Mirone, P., Pesce, F., Zoccali, C., Malavasi, V.L., Mussagaliyeva, A., Ahyt, B., Salihova, Z., Koshum-Bayeva, K., Kerimkulova, A., Bairamukova, A., Lurina, B., Zuzans, R., Jegere, S., Mintale, I., Kupics, K., Jubele, K., Kalejs, O., Vanhear, K., Burg, M., Cachia, M., Abela, E., Warwicker, S., Tabone, T., Xuereb, R., Asanovic, D., Drakalovic, D., Vukmirovic, M., Pavlovic, N., Music, L., Bulatovic, N., Boskovic, A., Uiterwaal, H., Bijsterveld, N., De Groot, J., Neefs, J., van den Berg, N., Piersma, F., Wilde, A., Hagens, V., Van Es, J., Van Opstal, J., Van Rennes, B., Verheij, H., Breukers, W., Tjeerdsma, G., Nijmeijer, R., Wegink, D., Binnema, R., Said, S., Philippens, S., van Doorn, W., Szili-Torok, T., Bhagwandien, R., Janse, P., Muskens, A., van Eck, M., Gevers, R., van der Ven, N., Duygun, A., Rahel, B., Meeder, J., Vold, A., Holst Hansen, C., Engset, I., Dyduch-Fejklowicz, B., Koba, E., Cichocka, M., Sokal, A., Kubicius, A., Pruchniewicz, E., Kowalik-Sztylc, A., Czapla, W., Mróz, I., Kozlowski, M., Pawlowski, T., Tendera, M., Winiarska-Filipek, A., Fidyk, A., Slowikowski, A., Haberka, M., Lachor-Broda, M., Biedron, M., Gasior, Z., Kołodziej, M., Janion, M., Gorczyca-Michta, I., Wozakowska-Kaplon, B., Stasiak, M., Jakubowski, P., Ciurus, T., Drozdz, J., Simiera, M., Zajac, P., Wcislo, T., Zycinski, P., Kasprzak, J., Olejnik, A., Harc-Dyl, E., Miarka, J., Pasieka, M., Ziemińska-Łuć, M., Bujak, W., Śliwiński, A., Grech, A., Morka, J., Petrykowska, K., Prasał, M., Hordyński, G., Feusette, P., Lipski, P., Wester, A., Streb, W., Romanek, J., Woźniak, P., Chlebuś, M., Szafarz, P., Stanik, W., Zakrzewski, M., Kaźmierczak, J., Przybylska, A., Skorek, E., Błaszczyk, H., Stępień, M., Szabowski, S., Krysiak, W., Szymańska, M., Karasiński, J., Blicharz, J., Skura, M., Hałas, K., Michalczyk, L., Orski, Z., Krzyżanowski, K., Skrobowski, A., Zieliński, L., Tomaszewska-Kiecana, M., Dłużniewski, M., Kiliszek, M., Peller, M., Budnik, M., Balsam, P., Opolski, G., Tymińska, A., Ozierański, K., Wancerz, A., Borowiec, A., Majos, E., Dabrowski, R., Szwed, H., Musialik-Lydka, A., Leopold-Jadczyk, A., Jedrzejczyk-Patej, E., Koziel, M., Mazurek, M., Krzemien-Wolska, K., Starosta, P., Nowalany-Kozielska, E., Orzechowska, A., Szpot, M., Staszel, M., Almeida, S., Pereira, H., Brandão Alves, L., Miranda, R., Ribeiro, L., Costa, F., Morgado, F., Carmo, P., Galvao Santos, P., Bernardo, R., Adragão, P., Ferreira da Silva, G., Peres, M., Alves, M., Leal, M., Cordeiro, A., Magalhães, P., Fontes, P., Leão, S., Delgado, A., Costa, A., Marmelo, B., Rodrigues, B., Moreira, D., Santos, J., Santos, L., Terchet, A., Darabantiu, D., Mercea, S., Turcin Halka, V., Pop Moldovan, A., Gabor, A., Doka, B., Catanescu, G., Rus, H., Oboroceanu, L., Bobescu, E., Popescu, R., Dan, A., Buzea, A., Daha, I., Dan, G., Neuhoff, I., Baluta, M., Ploesteanu, R., Dumitrache, N., Vintila, M., Daraban, A., Japie, C., Badila, E., Tewelde, H., Hostiuc, M., Frunza, S., Tintea, E., Bartos, D., Ciobanu, A., Popescu, I., Toma, N., Gherghinescu, C., Cretu, D., Patrascu, N., Stoicescu, C., Udroiu, C., Bicescu, G., Vintila, V., Vinereanu, D., Cinteza, M., Rimbas, R., Grecu, M., Cozma, A., Boros, F., Ille, M., Tica, O., Tor, R., Corina, A., Jeewooth, A., Maria, B., Georgiana, C., Natalia, C., Alin, D., Dinu-Andrei, D., Livia, M., Daniela, R., Larisa, R., Umaar, S., Tamara, T., Ioachim Popescu, M., Nistor, D., Sus, I., Coborosanu, O., Alina-Ramona, N., Dan, R., Petrescu, L., Ionescu, G., Vacarescu, C., Goanta, E., Mangea, M., Ionac, A., Mornos, C., Cozma, D., Pescariu, S., Solodovnicova, E., Soldatova, I., Shutova, J., Tjuleneva, L., Zubova, T., Uskov, V., Obukhov, D., Rusanova, G., Isakova, N., Odinsova, S., Arhipova, T., Kazakevich, E., Zavyalova, O., Novikova, T., Riabaia, I., Zhigalov, S., Drozdova, E., Luchkina, I., Monogarova, Y., Hegya, D., Rodionova, L., Nevzorova, V., Lusanova, O., Arandjelovic, A., Toncev, D., Vukmirovic, L., Radisavljevic, M., Milanov, M., Sekularac, N., Zdravkovic, M., Hinic, S., Dimkovic, S., Acimovic, T., Saric, J., Radovanovic, S., Kocijancic, A., Obrenovic-Kircanski, B., Kalimanovska Ostric, D., Simic, D., Jovanovic, I., Petrovic, I., Polovina, M., Vukicevic, M., Tomasevic, M., Mujovic, N., Radivojevic, N., Petrovic, O., Aleksandric, S., Kovacevic, V., Mijatovic, Z., Ivanovic, B., Tesic, M., Ristic, A., Vujisic-Tesic, B., Nedeljkovic, M., Karadzic, A., Uscumlic, A., Prodanovic, M., Zlatar, M., Asanin, M., Bisenic, B., Vasic, V., Popovic, Z., Djikic, D., Sipic, M., Peric, V., Dejanovic, B., Milosevic, N., Backovic, S., Stevanovic, A., Andric, A., Pencic, B., Pavlovic-Kleut, M., Celic, V., Pavlovic, M., Petrovic, M., Vuleta, M., Petrovic, N., Simovic, S., Savovic, Z., Milanov, S., Davidovic, G., Iric-Cupic, V., Djordjevic, D., Damjanovic, M., Zdravkovic, S., Topic, V., Stanojevic, D., Randjelovic, M., Jankovic-Tomasevic, R., Atanaskovic, V., Antic, S., Simonovic, D., Stojanovic, M., Stojanovic, S., Mitic, V., Ilic, V., Petrovic, D., Deljanin Ilic, M., Ilic, S., Stoickov, V., Markovic, S., Mijatovic, A., Tanasic, D., Radakovic, G., Peranovic, J., Panic-Jelic, N., Vujadinovic, O., Pajic, P., Bekic, S., Kovacevic, S., García Fernandez, A., Perez Cabeza, A., Anguita, M., Tercedor Sanchez, L., Mau, E., Loayssa, J., Ayarra, M., Carpintero, M., Roldán Rabadan, I., Gil Ortega, M., Tello Montoliu, A., Orenes Piñero, E., Manzano Fernández, S., Marín, F., Romero Aniorte, A., Veliz Martínez, A., Quintana Giner, M., Ballesteros, G., Palacio, M., Alcalde, O., García-Bolao, I., Bertomeu Gonzalez, V., Otero-Raviña, F., García Seara, J., Gonzalez Juanatey, J., Dayal, N., Maziarski, P., Gentil-Baron, P., Koç, M., Onrat, E., Dural, I.E., Yilmaz, K., Özin, B., Tan Kurklu, S., Atmaca, Y., Canpolat, U., Tokgozoglu, L., Dolu, A.K., Demirtas, B., Sahin, D., Ozcan Celebi, O., Gagirci, G., Turk, U.O., Ari, H., Polat, N., Toprak, N., Sucu, M., Akin Serdar, O., Taha Alper, A., Kepez, A., Yuksel, Y., Uzunselvi, A., Yuksel, S., Sahin, M., Kayapinar, O., Ozcan, T., Kaya, H., Yilmaz, M.B., Kutlu, M., Demir, M., Gibbs, C., Kaminskiene, S., Bryce, M., Skinner, A., Belcher, G., Hunt, J., Stancombe, L., Holbrook, B., Peters, C., Tettersell, S., Shantsila, A., Lane, D., Senoo, K., Proietti, M., Russell, K., Domingos, P., Hussain, S., Partridge, J., Haynes, R., Bahadur, S., Brown, R., McMahon, S., McDonald, J., Balachandran, K., Singh, R., Garg, S., Desai, H., Davies, K., Goddard, W., Galasko, G., Rahman, I., Chua, Y., Payne, O., Preston, S., Brennan, O., Pedley, L., Whiteside, C., Dickinson, C., Brown, J., Jones, K., Benham, L., Brady, R., Buchanan, L., Ashton, A., Crowther, H., Fairlamb, H., Thornthwaite, S., Relph, C., McSkeane, A., Poultney, U., Kelsall, N., Rice, P., Wilson, T., Wrigley, M., Kaba, R., Patel, T., Young, E., Law, J., Runnett, C., Thomas, H., McKie, H., Fuller, J., Pick, S., Sharp, A., Hunt, A., Thorpe, K., Hardman, C., Cusack, E., Adams, L., Hough, M., Keenan, S., Bowring, A., Watts, J., Zaman, J., Goffin, K., Nutt, H., Beerachee, Y., Featherstone, J., Mills, C., Pearson, J., Stephenson, L., Grant, S., Wilson, A., Hawksworth, C., Alam, I., Robinson, M., Ryan, S., Egdell, R., Gibson, E., Holland, M., Leonard, D., Mishra, B., Ahmad, S., Randall, H., Hill, J., Reid, L., George, M., McKinley, S., Brockway, L., Milligan, W., Sobolewska, J., Muir, J., Tuckis, L., Winstanley, L., Jacob, P., Kaye, S., Morby, L., Jan, A., Sewell, T., Boos, C., Wadams, B., Cope, C., Jefferey, P., Andrews, N., Getty, A., Suttling, A., Turner, C., Hudson, K., Austin, R., Howe, S., Iqbal, R., Gandhi, N., Brophy, K., Mirza, P., Willard, E., Collins, S., Ndlovu, N., Subkovas, E., Karthikeyan, V., Waggett, L., Wood, A., Bolger, A., Stockport, J., Evans, L., Harman, E., Starling, J., Williams, L., Saul, V., Sinha, M., Bell, L., Tudgay, S., Kemp, S., Frost, L., Ingram, T., Loughlin, A., Adams, C., Adams, M., Hurford, F., Owen, C., Miller, C., Donaldson, D., Tivenan, H., Button, H., Nasser, A., Jhagra, O., Stidolph, B., Brown, C., Livingstone, C., Duffy, M., Madgwick, P., Roberts, P., Greenwood, E., Fletcher, L., Beveridge, M., Earles, S., McKenzie, D., Beacock, D., Dayer, M., Seddon, M., Greenwell, D., Luxton, F., Venn, F., Mills, H., Rewbury, J., James, K., Roberts, K., Tonks, L., Felmeden, D., Taggu, W., Summerhayes, A., Hughes, D., Sutton, J., Felmeden, L., Khan, M., Walker, E., Norris, L., O'Donohoe, L., Mozid, A., Dymond, H., Lloyd-Jones, H., Saunders, G., Simmons, D., Coles, D., Cotterill, D., Beech, S., Kidd, S., Wrigley, B., Petkar, S., Smallwood, A., Jones, R., Radford, E., Milgate, S., Metherell, S., Cottam, V., Buckley, C., Broadley, A., Wood, D., Allison, J., Rennie, K., Balian, L., Howard, L., Pippard, L., Board, S., Pitt-Kerby, T., Vitolo, Marco, Malavasi, Vincenzo L., Proietti, Marco, Diemberger, Igor, Fauchier, Laurent, Marin, Francisco, Nabauer, Michael, Potpara, Tatjana S., Dan, Gheorghe-Andrei, Kalarus, Zbigniew, Tavazzi, Luigi, Maggioni, Aldo Pietro, Lane, Deirdre A., Lip, Gregory Y.H., and Boriani, Giuseppe

Published
2022
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21. Reverse-Taper Mid-Infrared Quantum Cascade Lasers for Coherent Power Scaling

Author

Jae Ha Ryu, Benjamin Knipfer, Jeremy D. Kirch, Robert A. Marsland, Dan Botez, Tom Earles, Chris Galstad, Morgan Turville-Heitz, Chris Sigler, Axel Stromberg, Yan-Ting Sun, Sebastian Lourdudoss, and Luke J. Mawst

Subjects
Quantum cascade lasers, beam steering, semiconductor growth, Applied optics. Photonics, TA1501-1820, Optics. Light, QC350-467
Abstract

We present a reverse-taper quantum cascade laser (QCL) emitting at 4.6 μm, a novel-geometry device that can scale the output power while maintaining good beam quality. Buried-ridge waveguides with tapered and straight regions were formed by ICP etching and HVPE regrowth – the tapered region scales the output power, while the emitting facet is located at the narrow-end taper section, which provides mode filtering by suppressing high-order spatial modes. Beam profiles were observed under quasi-continuous-wave (QCW)/CW operation and beam quality (M2) measurements along with beam-stability measurements were performed – a small degree of collimated-beam centroid movement (2 values close to 1 up to ∼1 W QCW power. Devices of shorter cavity lengths were also investigated, indicating that the output power scales with the core-region volume but results in a small increase in angular deviation.

Published
2022
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22. Standardizing and Centralizing Datasets for Efficient Training of Agricultural Deep Learning Models

Author

Amogh Joshi, Dario Guevara, and Mason Earles

Subjects
Plant culture, SB1-1110, Genetics, QH426-470, Botany, QK1-989
Abstract

In recent years, deep learning models have become the standard for agricultural computer vision. Such models are typically fine-tuned to agricultural tasks using model weights that were originally fit to more general, non-agricultural datasets. This lack of agriculture-specific fine-tuning potentially increases training time and resource use, and decreases model performance, leading to an overall decrease in data efficiency. To overcome this limitation, we collect a wide range of existing public datasets for 3 distinct tasks, standardize them, and construct standard training and evaluation pipelines, providing us with a set of benchmarks and pretrained models. We then conduct a number of experiments using methods that are commonly used in deep learning tasks but unexplored in their domain-specific applications for agriculture. Our experiments guide us in developing a number of approaches to improve data efficiency when training agricultural deep learning models, without large-scale modifications to existing pipelines. Our results demonstrate that even slight training modifications, such as using agricultural pretrained model weights, or adopting specific spatial augmentations into data processing pipelines, can considerably boost model performance and result in shorter convergence time, saving training resources. Furthermore, we find that even models trained on low-quality annotations can produce comparable levels of performance to their high-quality equivalents, suggesting that datasets with poor annotations can still be used for training, expanding the pool of currently available datasets. Our methods are broadly applicable throughout agricultural deep learning and present high potential for substantial data efficiency improvements.

Published
2023
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25. A workflow for segmenting soil and plant X-ray computed tomography images with deep learning in Google’s Colaboratory

Author

Devin A. Rippner, Pranav V. Raja, J. Mason Earles, Mina Momayyezi, Alexander Buchko, Fiona V. Duong, Elizabeth J. Forrestel, Dilworth Y. Parkinson, Kenneth A. Shackel, Jeffrey L. Neyhart, and Andrew J. McElrone

Subjects
X-ray computed tomography, deep learning, machine learning and AI, soil science, plant science, soil aggregate analysis, Plant culture, SB1-1110
Abstract

X-ray micro-computed tomography (X-ray μCT) has enabled the characterization of the properties and processes that take place in plants and soils at the micron scale. Despite the widespread use of this advanced technique, major limitations in both hardware and software limit the speed and accuracy of image processing and data analysis. Recent advances in machine learning, specifically the application of convolutional neural networks to image analysis, have enabled rapid and accurate segmentation of image data. Yet, challenges remain in applying convolutional neural networks to the analysis of environmentally and agriculturally relevant images. Specifically, there is a disconnect between the computer scientists and engineers, who build these AI/ML tools, and the potential end users in agricultural research, who may be unsure of how to apply these tools in their work. Additionally, the computing resources required for training and applying deep learning models are unique, more common to computer gaming systems or graphics design work, than to traditional computational systems. To navigate these challenges, we developed a modular workflow for applying convolutional neural networks to X-ray μCT images, using low-cost resources in Google’s Colaboratory web application. Here we present the results of the workflow, illustrating how parameters can be optimized to achieve best results using example scans from walnut leaves, almond flower buds, and a soil aggregate. We expect that this framework will accelerate the adoption and use of emerging deep learning techniques within the plant and soil sciences.

Published
2022
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27. Prolonged inpatient stay after upfront total laryngectomy is associated with overall survival

Author

Daniel Jacobs, Samipya Kafle, Joseph Earles, Rahmatullah Rahmati, Saral Mehra, and Benjamin L. Judson

Subjects
inpatient length of stay, laryngeal neoplasm, NCDB, survival, total laryngectomy, Otorhinolaryngology, RF1-547, Surgery, RD1-811
Abstract

Abstract Objectives To investigate factors and complications associated with prolonged inpatient length of stay (LOS) in patients who receive total laryngectomy (TL), and to analyze its effect on short‐term and long‐term overall survival (OS). Methods The National Cancer Database (NCDB) was queried from 2004 to 2016 for patients with laryngeal cancer, who received TL within 60 days of diagnosis, and who had an inpatient LOS ≥1 night. Multivariable binary logistic regression and survival analyses on propensity score matched cohorts with Kaplan‐Meier analysis and extended Cox regression were utilized. Results Eight thousand two hundred and ninety‐eight patients from the NCDB were included. Median inpatient LOS was 8 days after TL (IQR: 7, 12). Prolonged LOS was defined as above the 75th percentile or 13 days or greater. On multivariable analysis, increasing patient age (OR 1.14 per 10 years, P = .003), female sex (OR 1.35, P

Published
2021
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28. Non-destructive Plant Biomass Monitoring With High Spatio-Temporal Resolution via Proximal RGB-D Imagery and End-to-End Deep Learning

Author

Nicolas Buxbaum, Johann Heinrich Lieth, and Mason Earles

Subjects
controlled environment agriculture, deep learning, biomass, monitoring, lettuce, computer vision, Plant culture, SB1-1110
Abstract

Plant breeders, scientists, and commercial producers commonly use growth rate as an integrated signal of crop productivity and stress. Plant growth monitoring is often done destructively via growth rate estimation by harvesting plants at different growth stages and simply weighing each individual plant. Within plant breeding and research applications, and more recently in commercial applications, non-destructive growth monitoring is done using computer vision to segment plants in images from the background, either in 2D or 3D, and relating these image-based features to destructive biomass measurements. Recent advancements in machine learning have improved image-based localization and detection of plants, but such techniques are not well suited to make biomass predictions when there is significant self-occlusion or occlusion from neighboring plants, such as those encountered under leafy green production in controlled environment agriculture. To enable prediction of plant biomass under occluded growing conditions, we develop an end-to-end deep learning approach that directly predicts lettuce plant biomass from color and depth image data as provided by a low cost and commercially available sensor. We test the performance of the proposed deep neural network for lettuce production, observing a mean prediction error of 7.3% on a comprehensive test dataset of 864 individuals and substantially outperforming previous work on plant biomass estimation. The modeling approach is robust to the busy and occluded scenes often found in commercial leafy green production and requires only measured mass values for training. We then demonstrate that this level of prediction accuracy allows for rapid, non-destructive detection of changes in biomass accumulation due to experimentally induced stress induction in as little as 2 days. Using this method growers may observe and react to changes in plant-environment interactions in near real time. Moreover, we expect that such a sensitive technique for non-destructive biomass estimation will enable novel research and breeding of improved productivity and yield in response to stress.

Published
2022
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31. The AIFS Institute: Building a better food system through AI.

Author

Tagkopoulos, Ilias, Earles, Mason J., Lemay, Danielle G., Liu, Xin, Nitin, Nitin, Smith, Aaron D., Zohdi, Tarek I., and Brown, Stephen F.

Subjects
ARTIFICIAL intelligence, INFRASTRUCTURE (Economics), AUTONOMOUS robots, PRECISION farming, CONNECTIVE tissues
Abstract

Our food system is complex, multifaceted, and in need of an upgrade. Population growth, climate change, and socioeconomic disparities are some of the challenges that create a systemic threat to its sustainability and capacity to address the needs of an evolving planet. The mission of the AI Institute of Next Generation Food Systems (AIFS) is to leverage the latest advances in AI to help create a more sustainable, efficient, nutritious, safe, and resilient food system. Instead of using AI in isolation, AIFS views it as the connective tissue that can bring together interconnected solutions from farm to fork. From guiding molecular breeding and building autonomous robots for precision agriculture, to predicting pathogen outbreaks and recommending personalized diets, AIFS projects aspire to pave the way for infrastructure and systems that empower practitioners to build the food system of the next generation. Workforce education, outreach, and ethical considerations related to the emergence of AI solutions in this sector are an integral part of AIFS with several collaborative activities aiming to foster an open dialogue and bringing closer students, trainees, teachers, producers, farmers, workers, policy makers, and other professionals. [ABSTRACT FROM AUTHOR]

Published
2024
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33. High-Power, High-Efficiency Red Laser Diode Structures Grown on GaAs and GaAsP Metamorphic Superlattices

Author

Steven Ruder, Tom Earles, Christian Galstad, Michael Klaus, Don Olson, and Luke J. Mawst

Subjects
diode lasers, visible lasers, metamorphic buffer, Applied optics. Photonics, TA1501-1820
Abstract

Three types of GaAsP metamorphic buffer layers, including linearly graded, step graded, and metamorphic superlattices, were compared for the purposes of virtual substrates for red laser diode heterostructures. Laser diodes were fabricated on GaAs substrates and relaxed GaAsP metamorphic superlattice virtual substrates. A laser diode structure with a tensile-strained quantum well on a standard miscut GaAs substrate achieved TM-polarized emission at a 638 nm wavelength with 45% peak power conversion efficiency (PCE) at a 880 mW continuous wave (CW) output power with T0 = 77 K and T1 = 266 K. An analogous laser diode structure with a compressively strained quantum well on the metamorphic superlattice emitted TE-polarized 639 nm light with 35.5% peak PCE at 880 mW CW with T0 = 90 K and T1 = 300 K.

Published
2022
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35. Why Are Verbs so Hard to Remember? Effects of Semantic Context on Memory for Verbs and Nouns

Author

Earles, Julie L. and Kersten, Alan W.

Abstract

Three experiments test the theory that verb meanings are more malleable than noun meanings in different semantic contexts, making a previously seen verb difficult to remember when it appears in a new semantic context. Experiment 1 revealed that changing the direct object noun in a transitive sentence reduced recognition of a previously seen verb, whereas changing the verb had little impact on noun recognition. Experiment 2 revealed that verbs exhibited context effects more similar to those shown by superordinate nouns rather than basic-level nouns. Experiment 3 demonstrated that the degree of meaning change in a target word resulting from changes in semantic context influenced the magnitude of context effects, but context effects remained larger for verbs than for nouns even when the degree of meaning change was similar for nouns and verbs. These results are discussed with respect to the imageability and grammatical roles played by nouns and verbs in a sentence.

Published
2017
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36. Reading Gender: A Feminist, Queer Approach to Children's Literature and Children's Discursive Agency

Author

Earles, Jennifer

Abstract

Children's literature helps young people make sense of gender. However, while books offer children the imaginative ability to create their own worlds, normative gender can manifest in characters and stories. The study described in this article draws upon "disruptive" storytimes with 114 preschool children, interviews with 20 parents and staff, and observations at 20 preschools. Employing a feminist, queer approach, I develop two derivative books that switch a boy-hero for a girl and vice versa. These books are read to children in educational settings. This method interrogates gendered characters and stories' attachments to such concepts as love, acceptance, bodily agency, and adventure. Results show how children interact with these characters and stories and how they use categorisation and narrative construction to make sense of gender. I focus on the gender discourses at play and moments of childhood discursive agency. Themes include literature, doing and being, positioning, bodies, and feminist tales.

Published
2017
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38. Digitally deconstructing leaves in 3D using X‐ray microcomputed tomography and machine learning

Author

Guillaume Théroux‐Rancourt, Matthew R. Jenkins, Craig R. Brodersen, Andrew McElrone, Elisabeth J. Forrestel, and J. Mason Earles

Subjects
image segmentation, microCT, plant leaf internal anatomy, plant phenotyping, random forest classification, Biology (General), QH301-705.5, Botany, QK1-989
Abstract

Premise X‐ray microcomputed tomography (microCT) can be used to measure 3D leaf internal anatomy, providing a holistic view of tissue organization. Previously, the substantial time needed for segmenting multiple tissues limited this technique to small data sets, restricting its utility for phenotyping experiments and limiting our confidence in the inferences of these studies due to low replication numbers. Methods and Results We present a Python codebase for random forest machine learning segmentation and 3D leaf anatomical trait quantification that dramatically reduces the time required to process single‐leaf microCT scans into detailed segmentations. By training the model on each scan using six hand‐segmented image slices out of >1500 in the full leaf scan, it achieves >90% accuracy in background and tissue segmentation. Conclusions Overall, this 3D segmentation and quantification pipeline can reduce one of the major barriers to using microCT imaging in high‐throughput plant phenotyping.

Published
2020
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47. Conversations to Transform Geometry Class

Author

Szydlik, Jennifer Earles, Parrott, Amy, and Belnap, Jason Knight

Abstract

Classroom culture is negotiated and established through both conversations and practices. Traditionally, teachers and researchers have focused primarily on the individual and social construction of mathematical content--that is, students' conceptual understanding and procedural skills--through mathematical actions and practices. This article shares three explicit discussions about the nature of mathematics: (1) Mathematical Objects Exist Only in Our Minds: The authors put this question to their geometry students on the first day of class; (2) Definitions Matter to Mathematicians. They Matter a Lot!: Definitions are crucial to mathematicians. They provide a means of conveying ideal mental objects, and they give precise conditions for classifying objects and making arguments about those objects. The authors have found that students learn to value and understand definitions better if they regularly engage in cultural conversations in which they are asked to use a definition to classify a set of objects and then have the opportunity to discuss the very nature of definitions; and (3) Examples--Unless You Can Examine Them All--Do Not Make A Mathematical Proof: During the first weeks of geometry classes, the authors discuss the Euclidean geometry theorem that the sum of the angles in a triangle is a straight angle. They typically first spend ten to fifteen minutes exploring representations of various triangles. In small groups, students may cut out triangles and then fold the triangle so that the vertices of the triangles meet at a single point, or they may tear off the angles containing the vertices and arrange them so that the three angles are adjacent to one another. They may also draw several triangle representations and measure the angles with protractors.

Published
2016

49. Mathematical Conversations to Transform Algebra Class

Author

Szydlik, Jennifer Earles

Abstract

Classroom culture is established through both conversations and practices. Traditionally in mathematics class, the focus is primarily on the latter; that is, students are shown what "doing mathematics" looks like, and then asked that they try it themselves. This article discusses three mathematical conversations that help bring covert--and often ineffectual--meanings into the light and build understanding as a community. This conversational process, sometimes called "interrogating meaning," allows students to make explicit their assumptions about how, when, and for what purpose a mathematical notation, representation, or term is used (Rosebery et al. 2005). In other words, these conversations can help students recognize the strengths and weaknesses of their own interpretations and give them agency in changing them. Further, these conversations allow teachers a window into students' thinking.

Published
2015

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