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3,966 results on '"Miller, Gary"'

1. High-Resolution Mass Spectrometry for Human Exposomics: Expanding Chemical Space Coverage

Author

Lai, Yunjia, Koelmel, Jeremy P, Walker, Douglas I, Price, Elliott J, Papazian, Stefano, Manz, Katherine E, Castilla-Fernández, Delia, Bowden, John A, Nikiforov, Vladimir, David, Arthur, Bessonneau, Vincent, Amer, Bashar, Seethapathy, Suresh, Hu, Xin, Lin, Elizabeth Z, Jbebli, Akrem, McNeil, Brooklynn R, Barupal, Dinesh, Cerasa, Marina, Xie, Hongyu, Kalia, Vrinda, Nandakumar, Renu, Singh, Randolph, Tian, Zhenyu, Gao, Peng, Zhao, Yujia, Froment, Jean, Rostkowski, Pawel, Dubey, Saurabh, Coufalíková, Kateřina, Seličová, Hana, Hecht, Helge, Liu, Sheng, Udhani, Hanisha H, Restituito, Sophie, Tchou-Wong, Kam-Meng, Lu, Kun, Martin, Jonathan W, Warth, Benedikt, Pollitt, Krystal J Godri, Klánová, Jana, Fiehn, Oliver, Metz, Thomas O, Pennell, Kurt D, Jones, Dean P, and Miller, Gary W

Subjects
Medical Biochemistry and Metabolomics, Analytical Chemistry, Biomedical and Clinical Sciences, Chemical Sciences, Bioengineering, 2.5 Research design and methodologies (aetiology), Generic health relevance, Good Health and Well Being, Humans, Mass Spectrometry, Exposome, Metabolomics, Proteomics, Environmental Exposure, exposome, toxicants, high-resolutionmass spectrometry, chromatography, non-targetedanalysis, environmentalexposures, chemical space, metabolomics, environmental exposures, high-resolution mass spectrometry, non-targeted analysis, Environmental Sciences
Abstract

In the modern "omics" era, measurement of the human exposome is a critical missing link between genetic drivers and disease outcomes. High-resolution mass spectrometry (HRMS), routinely used in proteomics and metabolomics, has emerged as a leading technology to broadly profile chemical exposure agents and related biomolecules for accurate mass measurement, high sensitivity, rapid data acquisition, and increased resolution of chemical space. Non-targeted approaches are increasingly accessible, supporting a shift from conventional hypothesis-driven, quantitation-centric targeted analyses toward data-driven, hypothesis-generating chemical exposome-wide profiling. However, HRMS-based exposomics encounters unique challenges. New analytical and computational infrastructures are needed to expand the analysis coverage through streamlined, scalable, and harmonized workflows and data pipelines that permit longitudinal chemical exposome tracking, retrospective validation, and multi-omics integration for meaningful health-oriented inferences. In this article, we survey the literature on state-of-the-art HRMS-based technologies, review current analytical workflows and informatic pipelines, and provide an up-to-date reference on exposomic approaches for chemists, toxicologists, epidemiologists, care providers, and stakeholders in health sciences and medicine. We propose efforts to benchmark fit-for-purpose platforms for expanding coverage of chemical space, including gas/liquid chromatography-HRMS (GC-HRMS and LC-HRMS), and discuss opportunities, challenges, and strategies to advance the burgeoning field of the exposome.

Published
2024

2. The Role of Stakeholders: Implications for Continuous Improvement in Principal Preparation

Author

Arzu, Ethel M., Agan, Tia, Miller, Gary J., and Badgett, Kevin

Abstract

Research conducted in partnership with the Wallace Foundation and the Education Development Center revealed the importance of school principals on the success of student achievement and attendance, as well as on teacher efficacy and retention. The implications of this research reveal the importance of stronger and more focused principal preparation programs. Therefore, the Wallace Foundation established a continuous improvement process that engages teams of program faculty and stakeholders in conducting evidence-based assessments of program quality. In this article, three programs document their experiences with the self-study process. Readers will gain a deeper appreciation for the collaborative role that stakeholders play in shaping and improving the quality of programs in higher education institutions.

Published
2023

3. Decoding the Molecular Universe -- Workshop Report

Author

Metz, Thomas O., Adkins, Joshua N., Armentrout, Peter B., Chain, Patrick, Chu, Fanny, Corley, Courtney D, Cort, John R., Denis, Elizabeth, Drell, Daniel, Duncan, Katherine R., Ewing, Robert G., Fernandez, Facundo M., Fiehn, Oliver, Garg, Neha, Grimme, Stefan, Henry, Christopher, Hettich, Robert L., Kind, Tobias, Linington, Roger G., Miller, Gary W., Northen, Trent, Overdahl, Kirsten, Patrinos, Ari, Raftery, Daniel, Rigor, Paul, Smith, Richard D., Sobus, Jon, Teeguarden, Justin, Vertes, Akos, Waters, Katrina, Webb-Robertson, Bobbie-Jo, Williams, Antony, and Wishart, David

Subjects
Quantitative Biology - Biomolecules
Abstract

On August 9-10, 2023, a workshop was convened at the Pacific Northwest National Laboratory (PNNL) in Richland, WA that brought together a group of internationally recognized experts in metabolomics, natural products discovery, chemical ecology, chemical and biological threat assessment, cheminformatics, computational chemistry, cloud computing, artificial intelligence, and novel technology development. These experts were invited to assess the value and feasibility of a grand-scale project to create new technologies that would allow the identification and quantification of all small molecules, or to decode the molecular universe. The Decoding the Molecular Universe project would extend and complement the success of the Human Genome Project by developing new capabilities and technologies to measure small molecules (defined as non-protein, non-polymer molecules less than 1500 Daltons) of any origin and generated in biological systems or produced abiotically. Workshop attendees 1) explored what new understanding of biological and environmental systems could be revealed through the lens of small molecules; 2) characterized the similarities in current needs and technical challenges between each science or mission area for unambiguous and comprehensive determination of the composition and quantities of small molecules of any sample; 3) determined the extent to which technologies or methods currently exist for unambiguously and comprehensively determining the small molecule composition of any sample and in a reasonable time; and 4) identified the attributes of the ideal technology or approach for universal small molecule measurement and identification. The workshop concluded with a discussion of how a project of this scale could be undertaken, possible thrusts for the project, early proof-of-principle applications, and similar efforts upon which the project could be modeled.

Published
2023

6. Spectral Clustering on Large Datasets: When Does it Work? Theory from Continuous Clustering and Density Cheeger-Buser

Author

Chu, Timothy, Miller, Gary, and Walkington, Noel

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Data Structures and Algorithms, Mathematics - Functional Analysis
Abstract

Spectral clustering is one of the most popular clustering algorithms that has stood the test of time. It is simple to describe, can be implemented using standard linear algebra, and often finds better clusters than traditional clustering algorithms like $k$-means and $k$-centers. The foundational algorithm for two-way spectral clustering, by Shi and Malik, creates a geometric graph from data and finds a spectral cut of the graph. In modern machine learning, many data sets are modeled as a large number of points drawn from a probability density function. Little is known about when spectral clustering works in this setting -- and when it doesn't. Past researchers justified spectral clustering by appealing to the graph Cheeger inequality (which states that the spectral cut of a graph approximates the ``Normalized Cut''), but this justification is known to break down on large data sets. We provide theoretically-informed intuition about spectral clustering on large data sets drawn from probability densities, by proving when a continuous form of spectral clustering considered by past researchers (the unweighted spectral cut of a probability density) finds good clusters of the underlying density itself. Our work suggests that Shi-Malik spectral clustering works well on data drawn from mixtures of Laplace distributions, and works poorly on data drawn from certain other densities, such as a density we call the `square-root trough'. Our core theorem proves that weighted spectral cuts have low weighted isoperimetry for all probability densities. Our key tool is a new Cheeger-Buser inequality for all probability densities, including discontinuous ones.

Published
2023

11. Semaglutide and cardiovascular outcomes in patients with obesity and prevalent heart failure: a prespecified analysis of the SELECT trial

Author

Abe, Mitsunori, Abhaichand, Rajpal K, Abhayaratna, Walter P, Abhyankar, Atul, Abidin, Imran B Zainal, Abou Assi, Hiba, Accini Mendoza, Jose L, Adas, Mine, Agaiby, John M, Agarwal, Devendra K, Agha, Maher, Ahmed, Azazuddin, Ahtiainen, Petteri, Aigner, Elmar, Ajay, Naik, Ali, Norsiah, Al-Karadsheh, Amer, Allison, Roy, Allison, Dale C, Alpenidze, Diana, Altuntas, Yuksel, Al-Zoebi, Ayham, Ambuj, Roy, Amerena, John, Anderson, Robert J, Ando, Toshiaki, Andrews, Robert, Antonova, Elizaveta, Appel, Karl-Friedrich, Arantes, Flávia B, Araz, Mustafa, Arbel, Yaron, Arenas León, José L, Argyrakopoulou, Georgia, Ariani, Mehrdad, Arias Mendoza, Maria A, Arif, Ahmed A, Arneja, Jaspal, Aroda, Vanita R, Aronne, Louis J, Arstall, Margaret, Asamoah, Njaimeh, Asanin, Milika, Audish, Hanid, Avram, Rodica, Badat, Aysha, Badiu, Corin V, Bakdash, Wa'el, Bakiner, Okan S, Bandezi, Vuyokazi N, Bang, Liew H, Bansal, Sandeep, Baranyai, Marietta, Barbarash, Olga, Barber, Mark, Barnum, Otis, Barone Rochette, Gilles, Bashkin, Amir, Baum, Seth, Bays, Harold E, Bazzoni Ruiz, Alberto E, Beckowski, Maciej, Beerachee, Yaswin, Bellary, Srikanth, Belousova, Lidia, Berk, Martin, Bernstein, Marc, Berra, Cesare, Beshay, Isaac, Bhagwat, Ajit, Bhan, Arti, Biggs, William C, Billings, Liana, Bitar, Fahed, Block, Bradley, Bo, Simona, Bogdanski, Pawel, Bolshakova, Olga O, Boshchenko, Alla A, Bosworth, Hayden, Botero Lopez, Rodrigo, Bôttcher, Morten, Bourgeois, Ronald, Brautigam, Donald, Breton, Cristian F, Broadley, Andrew, Brockmyre, Andrew P, Brodie, Steven K, Bucci, Marco, Budincevic, Hrvoje, Budoff, Matthew J, Buffman, Barry, Buljubasic, Nediljka, Buranapin, Supawan, Burgess, Lesley, Burguera, Bartolomé, Buriakovska, Olena, Buscemi, Silvio, Busch, Robert, Buse, John B, Buynak, Robert, Byrne, Maria, Caceaune, Elena, Cadena Bonfanti, Alberto J, Calinescu, Cornell V, Call, Robert S, Canecki Varzic, Silvija, Cannon, Kevin, Capehorn, Matt, Cariou, Bertrand, Carr, Jeffrey, Carrillo-Jimenez, Rodolfo, Casas, Marcelo, Castro, Almudena, Celik, Ahmet, Cercato, Cintia, Cermak, Ondrej, Cha, James Y, Chacon, Carolina, Chaicha-Brom, Tira, Chandra, Sandeep, Chettibi, Mohamed, Chevts, Julia, Christopher, Johann, Chrustowski, Witold, Cif, Adriana, Clark, Rebecca, Clark, Wayne, Clifford, Piers, Coetzee, Kathleen, Cogni, Giulia, Colao, Anna Maria, Colquhoun, David M, Concha, Mauricio, Condit, Jonathan, Constance, Christian, Constantin, Ciprian, Constantinescu, Silviana, Corbett, Clive, Cornett, George M, Correia, Marcelo, Cortinovis, Fiorenzo, Cosma, Dana, Creely, Steven, Cross, David, Curtis, Brian, Czochra, Wojciech, Daboul, Nizar Y, Dagdelen, Selcuk, D'agostino, Ronald, Dang, Cuong, Datta, Sudip, Davuluri, Ashwini K, Dawood, Saleem Y, De Jong, Douwe M, De La Cuesta, Carmen, De Los Rios Ibarra, Manuel O, De Pablo, Carmen, De Pauw, Michel, Dela Llana, Alexander, Delibasic, Maja, Delic-Brkljacic, Diana, Demicheli, Thibaud, Denger, Ralf J, Desai, Devang, Desai, Piyush, Desouza, Cyrus V, Dicker, Dror, Djenic, Nemanja, Dobson, Simon, Doi, Masayuki, Doran, Jesse A, Dorman, Reinhart, Dotta, Francesco, Dukes, Carl E, Duronto, Ernesto, Durst, Ronen, Dvoryashina, Irina V, Ebrahim, Iftikhar O, Eggebrecht, Holger, Egstrup, Kenneth, Ekinci, Elif I, Eliasson, Björn, Eliasson, Ken, Enache, Georgiana, Enculescu, Dan, English, Patrick, Ermakova, Polina, Ershova, Olga, Ezaki, Hirotaka, Ezhov, Marat, Farias, Eduardo, Farias, Javier M, Farsky, Pedro S, Ferreira, Daniel, Filteau, Pierre, Finneran, Matthew P, Folkens, Eric M, Fonseca, Alberto G, Fonseca, Luisa, Fordan, Steven, Fourie, Nyda, França, Sara, Franco, Denise R, Franek, Edward, Friedman, Keith, Frittitta, Lucia, Froer, Michael, Fuckar, Krunoslav, Fujii, Kenshi, Fujita, Ryoko, Fukushima, Yasushi, Fulat, Mohamed, Fulwani, Mahesh, Gajos, Grzegorz, Galyavich, Albert, Gambill, Michael L, Gandotra, Dheeraj, Winston, Gandy, Jr., Garcia Hernandez, Pedro A, García Reza, Raymundo, Garg, Naveen, Garg, Sandeep, Garvey, William T, Garza, Juan C, Gatta-Cherifi, Blandine, Gelev, Valeri, Geller, Steven A, Geohas, Jeffrey G, Georgiev, Borislav, Ghazi, Adline, Gilbert, Matthew P, Gilinskaya, Olga, Gislason, Gunnar, Gogas Yavuz, Dilek, González Albarrán, Olga, Gordeev, Ivan G, Gorton, Sidney C, Goudev, Assen, Gretland Valderhaug, Tone, Groenemeijer, Bjorn, Gul, Ibrahim, Gullestad, Lars, Gurieva, Irina, Guseva, Galina N, Hagenow, Andreas, Haluzik, Martin, Halvorsen, Sigrun, Hammoudi, Naima, Hanaoka, Keiichi, Hancu, Nicolae, Hanusch, Ursula, Harris, Kathleen, Harris, Barry, Hartleib, Michael, Hartman, Aaron N, Hata, Yoshiki, Heimer, Brian, Herman, Lee, Herzog, William, Hewitt, Eric, Heymer, Peter, Hiremath, Shirish, Hjelmesaeth, Joeran, Høgalmen, Rasmus Geir, Høivik, Hans Olav, Holmer, Helene, Horoshko, Olha, Houser, Patricia M, Hove, Jens D, Hsieh, I-Chang, Hulot, Jean-Sébastien, Hussein, Zanariah, Ilashchuk, Tetiana, Ilveskoski, Erkki, Ipatko, Irina, Iranmanesh, Ali, Isawa, Tsuyoshi, Issa, Moises, Iteld, Bruce, Iwasawa, Takamasa, Jabbar, Danish, Jackson, Richard A, Jackson-Voyzey, Ewart, Jacob, Stephan, Jaffrani, Naseem A, Jardula, Michael F, Jastreboff, Ania, Jensen, Svend E, Jerkins, Terri, Jimenez-Ramos, Silvia A, Jitendra Pal Singh, Sawhney, Johnson, Wallace, Joyce, John M, Jozefowska, Malgorzata, Jugnundan, Prakash, Jungmair, Wolfgang, Jurowiecki, Jaroslaw, Kadokami, Toshiaki, Kahali, Dhiman, Kahrmann, Gerd, Kaiser, Sergio E, Kalmucki, Piotr, Kanadasi, Mehmet, Kandath, David, Kania, Grzegorz, Kannan, J, Kapp, Cornelia, Karczmarczyk, Agnieszka, Kartalis, Athanasios, Kaser, Susanne, Kasim, Sazzli Shahlan, Kastelic, Richard, Kato, Toshiaki, Katova, Tzvetana, Kaul, Upendra, Kautzky-Willer, Alexandra, Kawanishi, Masahiro, Kayikcioglu, Meral, Kazakova, Elena E, Keeling, Philip, Kempe, Hans-Peter, Kereiakes, Dean J, Kerneis, Mathieu, Keski-Opas, Tiina, Khadra, Suhail, Khaisheva, Larisa, Kharakhulakh, Marina, Khlevchuk, Tatiana, Khoo, Jeffrey, Kiatchoosakun, Songsak, Kinoshita, Noriyuki, Kinoshita, Masaharu, Kitamura, Ryoji, Kiyosue, Arihiro, Klavina, Irina, Klein, Eric J, Klimsa, Zdenek, Klonoff, David, Klug, Eric, Kobalava, Zhanna, Kodera, Satoshi, Koga, Tokushi, Kokkinos, Alexander, Koleckar, Pavel, Könyves, László, Koren, Michael J, Kormann, Adrian P, Kostner, Karam, Kreutzmann, Kristin, Krishinan, Saravanan, Krishnasamy, Sathya S, Krivosheeva, Inga, Kruljac, Ivan, Kubicki, Ted, Kuchar, Ladislav, Kujawiak, Monika, Kunishige, Hideyuki, Kurtinecz, Melinda, Kurtz Lisboa, Hugo R, Kushnir, Mykola, Kyyak, Yulian, Lace, Arija, Lakka, Timo, Lalic, Nebojsa, Lalic, Katarina, Lambadiari, Vaia, Lanaras, Leonidas, Lang, Chim, Langlois, Marie-France, Lash, Joseph, Latkovskis, Gustavs, Lau, David, Lazcano Soto, José Roberto, Le Roux, Carel, Ledesma, Gilbert N, Lee, Li Yuan, Lee, Thung-Lip, Lee, Kelvin, Lehrke, Michael, Leite, Silmara O, Leksycka, Agata, Lenzmeier, Thomas, Leonetti, Frida, Leonidova, Viktoriia, Lepor, Norman, Leung, Melissa, Levchenko, Olena, Levins, Peter, Levy, Louis J, Lewis, Matthew, Liberopoulos, Evangelos, Liberty, Idit, Lindholm, Carl-Johan, Lingvay, Ildiko, Linhart, Ales, Liu, Ming-En, Liu, Jenny, Lofton, Holly, Logemann, Timothy, Lombaard, Johannes J, Lombard, Landman, Lorraine, Richard, Lovell, Charles F, Ludvik, Bernhard, Lukaszewicz, Monika, Lupkovics, Géza, Lupovitch, Steven, Lupu, Sirona, Lynch, Mary, Lysak, Zoreslava, Lysenko, Tatyana A, Maeda, Hajime, Maeda, Itaru, Mæng, Michael, Mahajan, Ajay U, Maher, Vincent, Maia, Lilia N, Makotoko, Ellen M, Malavazos, Alexis, Malecha, Jan, Malicherova, Emilia, Manita, Mamoru, Mannucci, Edoardo, Mareev, Viacheslav, Marin, Liliana, Markova, Tatiana, Marso, Steven P, Martens, F.M.A.C., Martinez, Cuper, Martinez Cano, Carlos A, Martins, Cristina, Masmiquel Comas, Luis, Matsumoto, Takashi, Mcdonald, Kenneth, Mcgowan, Barbara, Mcgrew, Frank, Mclean, Barry K, Mcpherson, David D, Merino Torres, Juan Francisco, Meyers, Peter, Meyhöfer, Sebastian, Mezquita Raya, Pedro, Milanova, Maria, Milicic, Davor, Miller, Gary, Mills, Richard E, Mîndrescu, Nicoleta M, Mingrone, Geltrude, Minkova, Dotska A, Mirani, Marco, Miras, Alexander, Mistodie, Cristina V, Mitomo, Satoru, Mittal, Sanjay, Miyake, Taiji, Miyamoto, Naomasa, Molony, David, Monteiro, Pedro, Mooe, Thomas, Moosa, Naeem, Morales Portillo, Cristobal, Morales Villegas, Enrique C, Morawski, Emily J, Morbey, Claire, Morin, Robert P, Morisaki, Kuniaki, Morosanu, Magdalena, Mosenzon, Ofri, Mostovoy, Yuriy, Munir, Iqbal, Muratori, Fabrizio, Murray, Ryan, Murthy, Avinash, Myint, Min, Myshanych, Galyna, Nafornita, Valerica, Nagano, Takuya, Nair, Sunil, Nakhle, Samer N, Natsuaki, Masahiro, Nayak, Bindu M, Nibouche, Djamel Eddine, Nicholls, Stephen, Nicolau, José C, Nicolescu, Georgiana, Nierop, Peter, Niskanen, Leo, Ntaios, George, Nygård, Ottar Kjell, Oaks, Joshua B, Obrezan, Andrey, O'donnell, Philip, Oguri, Mitsutoshi, Oguzhan, Abdurrahman, Oh, Fumiki, Ohsugi, Mitsuru, Okada, Yoshio, Okayama, Hideki, Onaca, Adriana, Onaka, Haruhiko, Oneil, Patrick, Ong, Tiong Kiam, Ong, Stephen, Ono, Yasuhiro, Opsahl, Paul J, Ostrowska, Lucyna, Oviedo, Alejandra, Ozdogan, Oner, Ozpelit, Ebru, Pagkalos, Emmanouil, Pagotto, Uberto, Páll, Dénes, Pandey, Amritanshu- Shekhar, Parkhomenko, Oleksandr, Parvathareddy, Krishna Malakondareddy, Patel, Minesh B, Patsilinakos, Sotirios, Paul, Neil, Pedersen, Sue, Pereira, Isabel, Pereira, Edward Scott, Perez Terns, Paula, Perez-Vargas, Elba A, Pergaeva, Yulia, Perkelvald, Alexander, Peskov, Andrey B, Peter, Jonathan, Peters, Karina, Petit, Catherine, Petrov, Ivo, Philis-Tsimikas, Athena, Pietilä, Mikko, Pinto, Fausto, Piros, Annamária, Piyayotai, Dilok, Platonov, Dmitriy, Poirier, Paul, Pop, Lavinia, Popa, Bogdan, Pop-Busui, Rodica, Poremba, John, Porto, Alejandro, Postadzhiyan, Arman, Pothineni, Ramesh B, Potu, Ranganatha P, Powell, Talessa, Prafulla, Kerkar G, Prager, Rudolf, Prakova-Teneva, Zhulieta R, Pratley, Richard E, Price, Hermione, Pulka, Grazyna, Pullman, John, Punt, Zelda E, Purighalla, Raman S, Purnell, Peter, Qureshi, Mansoor, Rabasa-Lhoret, Remi, Raikhel, Marina A, Rancane, Gita, Randeva, Harpal, Rasouli, Neda, Reurean Pintilei, Delia V, Reyes, Ciro R, Rezgale, Inga, Rice, Eva, Riley, Thaddeus H, Risser, Joseph A, Ristic, Arsen, Rivas Fernández, Margarita, Robbins, David, Robitaille, Yves, Rodbard, Helena W, Rodriguez Plazas, Jaime A, Römer, T.J., Rosen, Glenn, Rosman, Dr Azhari, Rossi, Paulo, Rudenko, Leonid, Ruffin, Omari, Ruhani, Anwar Irawan, Runev, Nikolay, Ruyatkin, Dmitriy, Ruzic, Alen, Ryabov, Vyacheslav V, Rydén, Lars, Saggar, Suraj, Sakamoto, Tomohiro, Salter, Tim, Samal, Aditya K, Samoilova, Yulia, Sanabria, Hugo D, Sancak, Seda, Sangrigoli, Renee, Sansanayudh, Nakarin, Santini, Ferruccio, Saraiva, José F, Sardinov, Ruslan, Sargeant, William, Sari, Ramazan, Sathananthan, Airani, Sathyapalan, Thozhukat, Sato, Atsushi, Sauter, Joachim, Sbraccia, Paolo, Schaap, J., Schaum, Thomas, Schiele, François, Scott, John, Segal Lieberman, Gabriella, Segner, Alexander, Senior, Roxy, Sergeeva-Kondrachenko, Marina Y, Serota, Harvey, Serusclat, Pierre, Sethi, Rishi, Shah, Manoj K, Shah, Neerav, Shalaev, Sergey, Sharma, Raj, Sharma, Sumeet, Shaydyuk, Oksana, Shea, Heidi C, Shechter, Michael, Shehadeh, Naim, Shirazi, Mitra, Shlesinger, Yshay, Shneker, Ayham, Shutemova, Elena, Siasos, Gerasimos, Siddiqui, Imran A, Sidey, Jennifer, Sigal, Felix, Sime, Iveta, Singh, Narendra, Siraj, Elias, Sivalingam, Kanagaratnam, Skoczylas, Grzegorz, Smith, Stephen K, Smolenskaya, Olga, Snyder, Brian, Sofer, Yael, Sofley, C.W., Solano, Royce, Sonmez, Yusuf A, Sorokin, Maxim, Soto González, Alfonso, Sotolongo, Carlos, Soufer, Joseph, Soyluk Selcukbiricik, Ozlem, Spaic, Tamara, Spriggs, Douglas, Sreenan, Seamus, Stahl, Hans-Detlev, Stamatelopoulos, Kimon, Stanislavchuk, Mykola, Stankovic, Goran, Stasek, Josef, Steg, Gabriel, Steindorf, Joerg, Stephan, Dominique, Stewart, John, Still, Christopher, St-Maurice, Francois, Stogowska-Nikiciuk, Barbara, Stoker, Jeff, Stokic, Edita, Strzelecka, Anna, Sturm, Kerstin, Sueyoshi, Atsushi, Sugiura, Toshiyuki, Sultan, Senan, Suplotova, Lyudmila A, Suwanagool, Arisara, Suwanwalaikorn, Sompongse, Sveklina, Tatiana, Swanson, Neil, Swart, Henk, Swenson, Bradley P, Szyprowska, Ewa, Tait, Graeme, Takács, Róbert, Takeuchi, Yuzo, Tamirisa, Aparna, Tanaka, Hideki, Tatovic, Danijela, Tellier, Guy, Teragawa, Hiroki, Teterovska, Dace, Thomas, Nihal, Thuan, Jean-Francois, Tinahones, Francisco, Tisheva-Gospodinova, Snezhanka, Toarba, Cristina, Todoriuk, Liudmyla, Tokmakova, Mariya, Tonstad, Serena, Toplak, Hermann, Tran, Henry, Tripathy, Devjit, Trusau, Aliaksandr, Tsabedze, Nqoba, Tsougos, Elias, Tsoukas, George M, Tuccinardi, Dario, Tuna, Mazhar M, Turatti, Luiz A, Tziomalos, Konstantinos, Udommongkol, Chesda, Ueda, Osamu, Ukkola, Olavi, Unubol, Mustafa, Urbach, Dorothea, Urina Triana, Miguel A, Usdan, Lisa, Vaidya, Bijay, Vale, Noah, Vallieres, Gerald, Van Beek, Andre P, Van De Borne, Philippe, Van Der Walt, Eugene, Van Der Zwaan, C., Van Nieuwenhuizen, Elane, Van Zyl, Louis, Vanduynhoven, Philippe, Varghese, Kiron, Vasileva, Svetla P, Vassilev, Dobrin, Vathesatogkit, Prin, Velychko, Valentyna, Vercammen, Chris, Verges, Bruno, Verma, Subodh, Verwerft, Jan, Vesela, Alica, Veselovskaya, Nadezhda G, Vettor, Roberto, Veze, Irina, Vijan, Vinod, Vijayaraghavan, Ram, Villarino, Adriana, Vincent, Royce, Vinogradova, Oksana, Vishlitzky, Victor, Vlad, Adrian, Vladu, Ionela Mihaela, Vo, Anthony, Von Engelhardt, Charlotte, Von Münchhausen, Candy, Vorobyeva, Olga, Vossenberg, T., Vrolix, Mathias, Vukicevic, Marjana, Vyshnyvetskyy, Ivan, Wadvalla, Shahid, Wagner, Jan, Wakeling, John, Wallace, James, Wan Mohamed, Wan Mohd Izani, Wander, Gurpreet S, Ward, Kathleen, Warren, Mark L, Watanabe, Atsuyuki, Weber, Bruce, Weintraub, Howard, Weisnagel, John, Welker, James, Wendisch, Ulrich, Wenocur, Howard S, Wierum, Craig, Wilding, John, William, Maged, Wilson, Pete, Wilson, Jonathan P, Wong, Yuk-Ki, Wongcharoen, Wanwarang, Wozniak, Iwona, Wu, Chau-Chung, Wyatt, Nell, Wynne, Alan, Yamaguchi, Hiroshi, Yamasaki, Masahiro, Yazici, Dilek, Yeh, Hung-I, Yotov, Yoto, Yuan, Qingyang, Zacher, Jeffrey, Zagrebelnaya, Olga, Zaidman, Cesar J, Zalevskaya, Alsu, Zarich, Stuart, Zatelli, Maria Chiara, Zeller, Helga, Zhdanova, Elena A, Zornitzki, Taiba, Zrazhevskiy, Konstantin, Zykov, Mikhail, Lincoff, A Michael, Ryan, Donna H, Colhoun, Helen M, Deanfield, John E, Emerson, Scott S, Kahn, Steven E, Kushner, Robert F, Plutzky, Jorge, Brown-Frandsen, Kirstine, Hovingh, G Kees, Hardt-Lindberg, Soren, Tornøe, Christoffer W, Deanfield, John, Scirica, Benjamin M, Ryan, Donna, Kosiborod, Mikhail N, Hardt-Lindberg, Søren, Frenkel, Ofir, Weeke, Peter E, Rasmussen, Søren, Lang, Chim C, and Urina-Triana, Miguel

Published
2024
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15. Teacher Perceptions of Skills, Knowledge, and Resources Needed to Promote Social and Emotional Learning in Rural Classrooms

Author

Zolkoski, Staci M., Aguilera, Samantha Estrada, West, Erin M., Miller, Gary J., Holm, Jessica M., Sass, Sarah M., and Stocks, Eric L.

Abstract

The incorporation of social and emotional learning (SEL) in schools has been shown to improve academic and psychological health of students. Research has been limited regarding implementation of SEL programs in rural communities, where student needs are heightened. The current study examined factors that could impact teachers' intentions to be early adopters of a SEL curriculum in a rural community. Seventy-six teachers provided self-report data regarding perceptions of professional strengths, school climate, school resources for student support, ability to educate diverse students, ability to teach specific SEL domains, and intentions to be an early adopter of a SEL program. Present results indicated positive perceptions of school climate, one's ability to teach diverse students, and one's ability to teach self-management skills positively predicted intentions to be an early adopter of a SEL curriculum. Implications for rural schools are explored and recommendations for adoption of SEL curricula in rural schools are provided.

Published
2020

16. Teacher Retention in a Rural East Texas School District

Author

Miller, Gary J.

Abstract

This qualitative study investigated what factors kept teachers committed to teaching in one rural east Texas school district despite the perceived educational inequalities of rural districts compared to suburban and urban districts. When it comes to teacher retention, rural school leaders cite school environment and community lifestyle compatibility as the primary reasons for staying. Despite the considerable number of rural school districts throughout the United States, research continues to largely ignore the unique relationships found in rural locales. Rural school districts offer many advantages over their suburban and urban counterparts; it is a matter of identifying what is important to rural teachers and attending to those needs. In an effort to improve teacher retention, the first-year superintendent of a small rural school district in east Texas wanted to know why some teachers stayed while others left. To examine the affect of rurality on teacher retention, 98 teachers responded to an online questionnaire on what kept them in the district, followed by 10 teachers randomly selected from the group for an in-depth face-to-face interview to probe further into their reasons for staying. Using a progressive refinement two-cycle coding technique, several themes emerged explaining their reasons for staying. If rural school district leaders can better understand the reasons why teachers remain in rural districts, then they can improve their retention efforts. The study concludes with a review of recommended strategies that rural school district leaders can implement to incentive teachers to stay.

Published
2020

17. Lipopolysaccharide-binding protein and future Parkinson’s disease risk: a European prospective cohort

Author

Zhao, Yujia, Walker, Douglas I., Lill, Christina M., Bloem, Bastiaan R., Darweesh, Sirwan K. L., Pinto-Pacheco, Brismar, McNeil, Brooklyn, Miller, Gary W., Heath, Alicia K., Frissen, Myrthe, Petrova, Dafina, Sánchez, Maria-Jose, Chirlaque, María-Dolores, Guevara, Marcela, Zibetti, Maurizio, Panico, Salvatore, Middleton, Lefkos, Katzke, Verena, Kaaks, Rudolf, Riboli, Elio, Masala, Giovanna, Sieri, Sabina, Zamora-Ros, Raul, Amiano, Pilar, Jenab, Mazda, Peters, Susan, and Vermeulen, Roel

Published
2023
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18. The Interplay of Environmental Exposures and Mental Health: Setting an Agenda

Author

Reuben, Aaron, Manczak, Erika M, Cabrera, Laura Y, Alegria, Margarita, Bucher, Meghan L, Freeman, Emily C, Miller, Gary W, Solomon, Gina M, and Perry, Melissa J

Subjects
Climate-Related Exposures and Conditions, Prevention, Behavioral and Social Science, Mental Health, Mental health, Good Health and Well Being, Environmental Exposure, Environmental Health, Humans, Environmental Sciences, Medical and Health Sciences, Toxicology
Abstract

BackgroundTo date, health-effects research on environmental stressors has rarely focused on behavioral and mental health outcomes. That lack of research is beginning to change. Science and policy experts in the environmental and behavioral health sciences are coming together to explore converging evidence on the relationship-harmful or beneficial-between environmental factors and mental health.ObjectivesTo organize evidence and catalyze new findings, the National Academy of Sciences, Engineering, and Medicine (NASEM) hosted a workshop 2-3 February 2021 on the interplay of environmental exposures and mental health outcomes.MethodsThis commentary provides a nonsystematic, expert-guided conceptual review and interdisciplinary perspective on the convergence of environmental and mental health, drawing from hypotheses, findings, and research gaps presented and discussed at the workshop. Featured is an overview of what is known about the intersection of the environment and mental health, focusing on the effects of neurotoxic pollutants, threats related to climate change, and the importance of health promoting environments, such as urban green spaces.DiscussionWe describe what can be gained by bridging environmental and psychological research disciplines and present a synthesis of what is needed to advance interdisciplinary investigations. We also consider the implications of the current evidence for a) foundational knowledge of the etiology of mental health and illness, b) toxicant policy and regulation, c) definitions of climate adaptation and community resilience, d) interventions targeting marginalized communities, and e) the future of research training and funding. We include a call to action for environmental and mental health researchers, focusing on the environmental contributions to mental health to unlock primary prevention strategies at the population level and open equitable paths for preventing mental disorders and achieving optimal mental health for all. https://doi.org/10.1289/EHP9889.

Published
2022

21. The osteoarthritis prevention study (TOPS) - A randomized controlled trial of diet and exercise to prevent Knee Osteoarthritis: Design and rationale

Author

Messier, Stephen P., Callahan, Leigh F., Losina, Elena, Mihalko, Shannon L., Guermazi, Ali, Ip, Edward, Miller, Gary D., Katz, Jeffrey N., Loeser, Richard F., Pietrosimone, Brian G., Soto, Sandra, Cook, James L., Newman, Jovita J., DeVita, Paul, Spindler, Kurt P., Runhaar, Jos, Armitano-Lago, Cortney, Duong, Vicky, Selzer, Faith, Hill, Ryan, Love, Monica, Beavers, Daniel P., Saldana, Santiago, Stoker, Aaron M., Rice, Paige E., and Hunter, David J.

Published
2024
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24. Metric Transforms and Low Rank Matrices via Representation Theory of the Real Hyperrectangle

Author

Alman, Josh, Chu, Timothy, Miller, Gary, Narayanan, Shyam, Sellke, Mark, and Song, Zhao

Subjects
Computer Science - Computational Geometry, Computer Science - Machine Learning, Mathematics - Metric Geometry
Abstract

In this paper, we develop a new technique which we call representation theory of the real hyperrectangle, which describes how to compute the eigenvectors and eigenvalues of certain matrices arising from hyperrectangles. We show that these matrices arise naturally when analyzing a number of different algorithmic tasks such as kernel methods, neural network training, natural language processing, and the design of algorithms using the polynomial method. We then use our new technique along with these connections to prove several new structural results in these areas, including: $\bullet$ A function is a positive definite Manhattan kernel if and only if it is a completely monotone function. These kernels are widely used across machine learning; one example is the Laplace kernel which is widely used in machine learning for chemistry. $\bullet$ A function transforms Manhattan distances to Manhattan distances if and only if it is a Bernstein function. This completes the theory of Manhattan to Manhattan metric transforms initiated by Assouad in 1980. $\bullet$ A function applied entry-wise to any square matrix of rank $r$ always results in a matrix of rank $< 2^{r-1}$ if and only if it is a polynomial of sufficiently low degree. This gives a converse to a key lemma used by the polynomial method in algorithm design. Our work includes a sophisticated combination of techniques from different fields, including metric embeddings, the polynomial method, and group representation theory.

Published
2020

50. Weighted Cheeger and Buser Inequalities, with Applications to Clustering and Cutting Probability Densities

Author

Chu, Timothy, Miller, Gary L., Walkington, Noel J., and Wang, Alex L.

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

In this paper, we show how sparse or isoperimetric cuts of a probability density function relate to Cheeger cuts of its principal eigenfunction, for appropriate definitions of `sparse cut' and `principal eigenfunction'. We construct these appropriate definitions of sparse cut and principal eigenfunction in the probability density setting. Then, we prove Cheeger and Buser type inequalities similar to those for the normalized graph Laplacian of Alon-Milman. We demonstrate that no such inequalities hold for most prior definitions of sparse cut and principal eigenfunction. We apply this result to generate novel algorithms for cutting probability densities and clustering data, including a principled variant of spectral clustering.

Published
2020

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