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Tian D, Chakos A, Hirst L, Chung S, Yan T. M15 Surgery for Type A Intramural Haematoma: A Systematic Review of Clinical Outcomes. Heart Lung Circ 2021. [DOI: 10.1016/j.hlc.2021.03.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Chung S, Candelaria D, Gallagher R. Women’s Health-related Quality of Life Substantially Improves With Tailored Cardiac Rehabilitation: A Systematic Review and Meta-analysis. Heart Lung Circ 2021. [DOI: 10.1016/j.hlc.2021.06.374] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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Abbott R, Abbott TD, Abraham S, Acernese F, Ackley K, Adams C, Adhikari RX, Adya VB, Affeldt C, Agathos M, Agatsuma K, Aggarwal N, Aguiar OD, Aich A, Aiello L, Ain A, Ajith P, Akcay S, Allen G, Allocca A, Altin PA, Amato A, Anand S, Ananyeva A, Anderson SB, Anderson WG, Angelova SV, Ansoldi S, Antier S, Appert S, Arai K, Araya MC, Areeda JS, Arène M, Arnaud N, Aronson SM, Arun KG, Asali Y, Ascenzi S, Ashton G, Aston SM, Astone P, Aubin F, Aufmuth P, AultONeal K, Austin C, Avendano V, Babak S, Bacon P, Badaracco F, Bader MKM, Bae S, Baer AM, Baird J, Baldaccini F, Ballardin G, Ballmer SW, Bals A, Balsamo A, Baltus G, Banagiri S, Bankar D, Bankar RS, Barayoga JC, Barbieri C, Barish BC, Barker D, Barkett K, Barneo P, Barone F, Barr B, Barsotti L, Barsuglia M, Barta D, Bartlett J, Bartos I, Bassiri R, Basti A, Bawaj M, Bayley JC, Bazzan M, Bécsy B, Bejger M, Belahcene I, Bell AS, Beniwal D, Benjamin MG, Bentley JD, Bergamin F, Berger BK, Bergmann G, Bernuzzi S, Berry CPL, Bersanetti D, Bertolini A, Betzwieser J, Bhandare R, Bhandari AV, Bidler J, Biggs E, Bilenko IA, Billingsley G, Birney R, Birnholtz O, Biscans S, Bischi M, Biscoveanu S, Bisht A, Bissenbayeva G, Bitossi M, Bizouard MA, Blackburn JK, Blackman J, Blair CD, Blair DG, Blair RM, Bobba F, Bode N, Boer M, Boetzel Y, Bogaert G, Bondu F, Bonilla E, Bonnand R, Booker P, Boom BA, Bork R, Boschi V, Bose S, Bossilkov V, Bosveld J, Bouffanais Y, Bozzi A, Bradaschia C, Brady PR, Bramley A, Branchesi M, Brau JE, Breschi M, Briant T, Briggs JH, Brighenti F, Brillet A, Brinkmann M, Brockill P, Brooks AF, Brooks J, Brown DD, Brunett S, Bruno G, Bruntz R, Buikema A, Bulik T, Bulten HJ, Buonanno A, Buscicchio R, Buskulic D, Byer RL, Cabero M, Cadonati L, Cagnoli G, Cahillane C, Calderón Bustillo J, Callaghan JD, Callister TA, Calloni E, Camp JB, Canepa M, Cannon KC, Cao H, Cao J, Carapella G, Carbognani F, Caride S, Carney MF, Carullo G, Casanueva Diaz J, Casentini C, Castañeda J, Caudill S, Cavaglià M, Cavalier F, Cavalieri R, Cella G, Cerdá-Durán P, Cesarini E, Chaibi O, Chakravarti K, Chan C, Chan M, Chandra K, Chao S, Charlton P, Chase EA, Chassande-Mottin E, Chatterjee D, Chaturvedi M, Chatziioannou K, Chen HY, Chen X, Chen Y, Cheng HP, Cheong CK, Chia HY, Chiadini F, Chierici R, Chincarini A, Chiummo A, Cho G, Cho HS, Cho M, Christensen N, Chu Q, Chua S, Chung KW, Chung S, Ciani G, Ciecielag P, Cieślar M, Ciobanu AA, Ciolfi R, Cipriano F, Cirone A, Clara F, Clark JA, Clearwater P, Clesse S, Cleva F, Coccia E, Cohadon PF, Cohen D, Colleoni M, Collette CG, Collins C, Colpi M, Constancio M, Conti L, Cooper SJ, Corban P, Corbitt TR, Cordero-Carrión I, Corezzi S, Corley KR, Cornish N, Corre D, Corsi A, Cortese S, Costa CA, Cotesta R, Coughlin MW, Coughlin SB, Coulon JP, Countryman ST, Couvares P, Covas PB, Coward DM, Cowart MJ, Coyne DC, Coyne R, Creighton JDE, Creighton TD, Cripe J, Croquette M, Crowder SG, Cudell JR, Cullen TJ, Cumming A, Cummings R, Cunningham L, Cuoco E, Curylo M, Canton TD, Dálya G, Dana A, Daneshgaran-Bajastani LM, D'Angelo B, Danilishin SL, D'Antonio S, Danzmann K, Darsow-Fromm C, Dasgupta A, Datrier LEH, Dattilo V, Dave I, Davier M, Davies GS, Davis D, Daw EJ, DeBra D, Deenadayalan M, Degallaix J, De Laurentis M, Deléglise S, Delfavero M, De Lillo N, Del Pozzo W, DeMarchi LM, D'Emilio V, Demos N, Dent T, De Pietri R, De Rosa R, De Rossi C, DeSalvo R, de Varona O, Dhurandhar S, Díaz MC, Diaz-Ortiz M, Dietrich T, Di Fiore L, Di Fronzo C, Di Giorgio C, Di Giovanni F, Di Giovanni M, Di Girolamo T, Di Lieto A, Ding B, Di Pace S, Di Palma I, Di Renzo F, Divakarla AK, Dmitriev A, Doctor Z, Donovan F, Dooley KL, Doravari S, Dorrington I, Downes TP, Drago M, Driggers JC, Du Z, Ducoin JG, Dupej P, Durante O, D'Urso D, Dwyer SE, Easter PJ, Eddolls G, Edelman B, Edo TB, Edy O, Effler A, Ehrens P, Eichholz J, Eikenberry SS, Eisenmann M, Eisenstein RA, Ejlli A, Errico L, Essick RC, Estelles H, Estevez D, Etienne ZB, Etzel T, Evans M, Evans TM, Ewing BE, Fafone V, Fairhurst S, Fan X, Farinon S, Farr B, Farr WM, Fauchon-Jones EJ, Favata M, Fays M, Fazio M, Feicht J, Fejer MM, Feng F, Fenyvesi E, Ferguson DL, Fernandez-Galiana A, Ferrante I, Ferreira EC, Ferreira TA, Fidecaro F, Fiori I, Fiorucci D, Fishbach M, Fisher RP, Fittipaldi R, Fitz-Axen M, Fiumara V, Flaminio R, Floden E, Flynn E, Fong H, Font JA, Forsyth PWF, Fournier JD, Frasca S, Frasconi F, Frei Z, Freise A, Frey R, Frey V, Fritschel P, Frolov VV, Fronzè G, Fulda P, Fyffe M, Gabbard HA, Gadre BU, Gaebel SM, Gair JR, Galaudage S, Ganapathy D, Ganguly A, Gaonkar SG, García-Quirós C, Garufi F, Gateley B, Gaudio S, Gayathri V, Gemme G, Genin E, Gennai A, George D, George J, Gergely L, Ghonge S, Ghosh A, Ghosh A, Ghosh S, Giacomazzo B, Giaime JA, Giardina KD, Gibson DR, Gier C, Gill K, Glanzer J, Gniesmer J, Godwin P, Goetz E, Goetz R, Gohlke N, Goncharov B, González G, Gopakumar A, Gossan SE, Gosselin M, Gouaty R, Grace B, Grado A, Granata M, Grant A, Gras S, Grassia P, Gray C, Gray R, Greco G, Green AC, Green R, Gretarsson EM, Griggs HL, Grignani G, Grimaldi A, Grimm SJ, Grote H, Grunewald S, Gruning P, Guidi GM, Guimaraes AR, Guixé G, Gulati HK, Guo Y, Gupta A, Gupta A, Gupta P, Gustafson EK, Gustafson R, Haegel L, Halim O, Hall ED, Hamilton EZ, Hammond G, Haney M, Hanke MM, Hanks J, Hanna C, Hannam MD, Hannuksela OA, Hansen TJ, Hanson J, Harder T, Hardwick T, Haris K, Harms J, Harry GM, Harry IW, Hasskew RK, Haster CJ, Haughian K, Hayes FJ, Healy J, Heidmann A, Heintze MC, Heinze J, Heitmann H, Hellman F, Hello P, Hemming G, Hendry M, Heng IS, Hennes E, Hennig J, Heurs M, Hild S, Hinderer T, Hoback SY, Hochheim S, Hofgard E, Hofman D, Holgado AM, Holland NA, Holt K, Holz DE, Hopkins P, Horst C, Hough J, Howell EJ, Hoy CG, Huang Y, Hübner MT, Huerta EA, Huet D, Hughey B, Hui V, Husa S, Huttner SH, Huxford R, Huynh-Dinh T, Idzkowski B, Iess A, Inchauspe H, Ingram C, Intini G, Isac JM, Isi M, Iyer BR, Jacqmin T, Jadhav SJ, Jadhav SP, James AL, Jani K, Janthalur NN, Jaranowski P, Jariwala D, Jaume R, Jenkins AC, Jiang J, Johns GR, Johnson-McDaniel NK, Jones AW, Jones DI, Jones JD, Jones P, Jones R, Jonker RJG, Ju L, Junker J, Kalaghatgi CV, Kalogera V, Kamai B, Kandhasamy S, Kang G, Kanner JB, Kapadia SJ, Karki S, Kashyap R, Kasprzack M, Kastaun W, Katsanevas S, Katsavounidis E, Katzman W, Kaufer S, Kawabe K, Kéfélian F, Keitel D, Keivani A, Kennedy R, Key JS, Khadka S, Khalili FY, Khan I, Khan S, Khan ZA, Khazanov EA, Khetan N, Khursheed M, Kijbunchoo N, Kim C, Kim GJ, Kim JC, Kim K, Kim W, Kim WS, Kim YM, Kimball C, King PJ, Kinley-Hanlon M, Kirchhoff R, Kissel JS, Kleybolte L, Klimenko S, Knowles TD, Knyazev E, Koch P, Koehlenbeck SM, Koekoek G, Koley S, Kondrashov V, Kontos A, Koper N, Korobko M, Korth WZ, Kovalam M, Kozak DB, Kringel V, Krishnendu NV, Królak A, Krupinski N, Kuehn G, Kumar A, Kumar P, Kumar R, Kumar R, Kumar S, Kuo L, Kutynia A, Lackey BD, Laghi D, Lalande E, Lam TL, Lamberts A, Landry M, Lane BB, Lang RN, Lange J, Lantz B, Lanza RK, La Rosa I, Lartaux-Vollard A, Lasky PD, Laxen M, Lazzarini A, Lazzaro C, Leaci P, Leavey S, Lecoeuche YK, Lee CH, Lee HM, Lee HW, Lee J, Lee K, Lehmann J, Leroy N, Letendre N, Levin Y, Li AKY, Li J, Li K, Li TGF, Li X, Linde F, Linker SD, Linley JN, Littenberg TB, Liu J, Liu X, Llorens-Monteagudo M, Lo RKL, Lockwood A, London LT, Longo A, Lorenzini M, Loriette V, Lormand M, Losurdo G, Lough JD, Lousto CO, Lovelace G, Lück H, Lumaca D, Lundgren AP, Ma Y, Macas R, Macfoy S, MacInnis M, Macleod DM, MacMillan IAO, Macquet A, Magaña Hernandez I, Magaña-Sandoval F, Magee RM, Majorana E, Maksimovic I, Malik A, Man N, Mandic V, Mangano V, Mansell GL, Manske M, Mantovani M, Mapelli M, Marchesoni F, Marion F, Márka S, Márka Z, Markakis C, Markosyan AS, Markowitz A, Maros E, Marquina A, Marsat S, Martelli F, Martin IW, Martin RM, Martinez V, Martynov DV, Masalehdan H, Mason K, Massera E, Masserot A, Massinger TJ, Masso-Reid M, Mastrogiovanni S, Matas A, Matichard F, Mavalvala N, Maynard E, McCann JJ, McCarthy R, McClelland DE, McCormick S, McCuller L, McGuire SC, McIsaac C, McIver J, McManus DJ, McRae T, McWilliams ST, Meacher D, Meadors GD, Mehmet M, Mehta AK, Mejuto Villa E, Melatos A, Mendell G, Mercer RA, Mereni L, Merfeld K, Merilh EL, Merritt JD, Merzougui M, Meshkov S, Messenger C, Messick C, Metzdorff R, Meyers PM, Meylahn F, Mhaske A, Miani A, Miao H, Michaloliakos I, Michel C, Middleton H, Milano L, Miller AL, Millhouse M, Mills JC, Milotti E, Milovich-Goff MC, Minazzoli O, Minenkov Y, Mishkin A, Mishra C, Mistry T, Mitra S, Mitrofanov VP, Mitselmakher G, Mittleman R, Mo G, Mogushi K, Mohapatra SRP, Mohite SR, Molina-Ruiz M, Mondin M, Montani M, Moore CJ, Moraru D, Morawski F, Moreno G, Morisaki S, Mours B, Mow-Lowry CM, Mozzon S, Muciaccia F, Mukherjee A, Mukherjee D, Mukherjee S, Mukherjee S, Mukund N, Mullavey A, Munch J, Muñiz EA, Murray PG, Nagar A, Nardecchia I, Naticchioni L, Nayak RK, Neil BF, Neilson J, Nelemans G, Nelson TJN, Nery M, Neunzert A, Ng KY, Ng S, Nguyen C, Nguyen P, Nichols D, Nichols SA, Nissanke S, Nitz A, Nocera F, Noh M, North C, Nothard D, Nuttall LK, Oberling J, O'Brien BD, Oganesyan G, Ogin GH, Oh JJ, Oh SH, Ohme F, Ohta H, Okada MA, Oliver M, Olivetto C, Oppermann P, Oram RJ, O'Reilly B, Ormiston RG, Ortega LF, O'Shaughnessy R, Ossokine S, Osthelder C, Ottaway DJ, Overmier H, Owen BJ, Pace AE, Pagano G, Page MA, Pagliaroli G, Pai A, Pai SA, Palamos JR, Palashov O, Palomba C, Pan H, Panda PK, Pang PTH, Pankow C, Pannarale F, Pant BC, Paoletti F, Paoli A, Parida A, Parker W, Pascucci D, Pasqualetti A, Passaquieti R, Passuello D, Patricelli B, Payne E, Pearlstone BL, Pechsiri TC, Pedersen AJ, Pedraza M, Pele A, Penn S, Perego A, Perez CJ, Périgois C, Perreca A, Perriès S, Petermann J, Pfeiffer HP, Phelps M, Phukon KS, Piccinni OJ, Pichot M, Piendibene M, Piergiovanni F, Pierro V, Pillant G, Pinard L, Pinto IM, Piotrzkowski K, Pirello M, Pitkin M, Plastino W, Poggiani R, Pong DYT, Ponrathnam S, Popolizio P, Porter EK, Powell J, Prajapati AK, Prasai K, Prasanna R, Pratten G, Prestegard T, Principe M, Prodi GA, Prokhorov L, Punturo M, Puppo P, Pürrer M, Qi H, Quetschke V, Quinonez PJ, Raab FJ, Raaijmakers G, Radkins H, Radulesco N, Raffai P, Rafferty H, Raja S, Rajan C, Rajbhandari B, Rakhmanov M, Ramirez KE, Ramos-Buades A, Rana J, Rao K, Rapagnani P, Raymond V, Razzano M, Read J, Regimbau T, Rei L, Reid S, Reitze DH, Rettegno P, Ricci F, Richardson CJ, Richardson JW, Ricker PM, Riemenschneider G, Riles K, Rizzo M, Robertson NA, Robinet F, Rocchi A, Rodriguez-Soto RD, Rolland L, Rollins JG, Roma VJ, Romanelli M, Romano R, Romel CL, Romero-Shaw IM, Romie JH, Rose CA, Rose D, Rose K, Rosińska D, Rosofsky SG, Ross MP, Rowan S, Rowlinson SJ, Roy PK, Roy S, Roy S, Ruggi P, Rutins G, Ryan K, Sachdev S, Sadecki T, Sakellariadou M, Salafia OS, Salconi L, Saleem M, Salemi F, Samajdar A, Sanchez EJ, Sanchez LE, Sanchis-Gual N, Sanders JR, Santiago KA, Santos E, Sarin N, Sassolas B, Sathyaprakash BS, Sauter O, Savage RL, Savant V, Sawant D, Sayah S, Schaetzl D, Schale P, Scheel M, Scheuer J, Schmidt P, Schnabel R, Schofield RMS, Schönbeck A, Schreiber E, Schulte BW, Schutz BF, Schwarm O, Schwartz E, Scott J, Scott SM, Seidel E, Sellers D, Sengupta AS, Sennett N, Sentenac D, Sequino V, Sergeev A, Setyawati Y, Shaddock DA, Shaffer T, Sharifi S, Shahriar MS, Sharma A, Sharma P, Shawhan P, Shen H, Shikauchi M, Shink R, Shoemaker DH, Shoemaker DM, Shukla K, ShyamSundar S, Siellez K, Sieniawska M, Sigg D, Singer LP, Singh D, Singh N, Singha A, Singhal A, Sintes AM, Sipala V, Skliris V, Slagmolen BJJ, Slaven-Blair TJ, Smetana J, Smith JR, Smith RJE, Somala S, Son EJ, Soni S, Sorazu B, Sordini V, Sorrentino F, Souradeep T, Sowell E, Spencer AP, Spera M, Srivastava AK, Srivastava V, Staats K, Stachie C, Standke M, Steer DA, Steinke M, Steinlechner J, Steinlechner S, Steinmeyer D, Stevenson S, Stocks D, Stops DJ, Stover M, Strain KA, Stratta G, Strunk A, Sturani R, Stuver AL, Sudhagar S, Sudhir V, Summerscales TZ, Sun L, Sunil S, Sur A, Suresh J, Sutton PJ, Swinkels BL, Szczepańczyk MJ, Tacca M, Tait SC, Talbot C, Tanasijczuk AJ, Tanner DB, Tao D, Tápai M, Tapia A, Tapia San Martin EN, Tasson JD, Taylor R, Tenorio R, Terkowski L, Thirugnanasambandam MP, Thomas M, Thomas P, Thompson JE, Thondapu SR, Thorne KA, Thrane E, Tinsman CL, Saravanan TR, Tiwari S, Tiwari S, Tiwari V, Toland K, Tonelli M, Tornasi Z, Torres-Forné A, Torrie CI, Tosta E Melo I, Töyrä D, Travasso F, Traylor G, Tringali MC, Tripathee A, Trovato A, Trudeau RJ, Tsang KW, Tse M, Tso R, Tsukada L, Tsuna D, Tsutsui T, Turconi M, Ubhi AS, Udall R, Ueno K, Ugolini D, Unnikrishnan CS, Urban AL, Usman SA, Utina AC, Vahlbruch H, Vajente G, Valdes G, Valentini M, van Bakel N, van Beuzekom M, van den Brand JFJ, Van Den Broeck C, Vander-Hyde DC, van der Schaaf L, Van Heijningen JV, van Veggel AA, Vardaro M, Varma V, Vass S, Vasúth M, Vecchio A, Vedovato G, Veitch J, Veitch PJ, Venkateswara K, Venugopalan G, Verkindt D, Veske D, Vetrano F, Viceré A, Viets AD, Vinciguerra S, Vine DJ, Vinet JY, Vitale S, Vivanco FH, Vo T, Vocca H, Vorvick C, Vyatchanin SP, Wade AR, Wade LE, Wade M, Walet R, Walker M, Wallace GS, Wallace L, Walsh S, Wang JZ, Wang S, Wang WH, Ward RL, Warden ZA, Warner J, Was M, Watchi J, Weaver B, Wei LW, Weinert M, Weinstein AJ, Weiss R, Wellmann F, Wen L, Weßels P, Westhouse JW, Wette K, Whelan JT, Whiting BF, Whittle C, Wilken DM, Williams D, Willis JL, Willke B, Winkler W, Wipf CC, Wittel H, Woan G, Woehler J, Wofford JK, Wong ICF, Wright JL, Wu DS, Wysocki DM, Xiao L, Yamamoto H, Yang L, Yang Y, Yang Z, Yap MJ, Yazback M, Yeeles DW, Yu H, Yu H, Yuen SHR, Zadrożny AK, Zadrożny A, Zanolin M, Zelenova T, Zendri JP, Zevin M, Zhang J, Zhang L, Zhang T, Zhao C, Zhao G, Zhou M, Zhou Z, Zhu XJ, Zimmerman AB, Zucker ME, Zweizig J. GW190521: A Binary Black Hole Merger with a Total Mass of 150 M_{⊙}. PHYSICAL REVIEW LETTERS 2020; 125:101102. [PMID: 32955328 DOI: 10.1103/physrevlett.125.101102] [Citation(s) in RCA: 73] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/30/2020] [Revised: 06/19/2020] [Accepted: 07/09/2020] [Indexed: 06/08/2023]
Abstract
On May 21, 2019 at 03:02:29 UTC Advanced LIGO and Advanced Virgo observed a short duration gravitational-wave signal, GW190521, with a three-detector network signal-to-noise ratio of 14.7, and an estimated false-alarm rate of 1 in 4900 yr using a search sensitive to generic transients. If GW190521 is from a quasicircular binary inspiral, then the detected signal is consistent with the merger of two black holes with masses of 85_{-14}^{+21} M_{⊙} and 66_{-18}^{+17} M_{⊙} (90% credible intervals). We infer that the primary black hole mass lies within the gap produced by (pulsational) pair-instability supernova processes, with only a 0.32% probability of being below 65 M_{⊙}. We calculate the mass of the remnant to be 142_{-16}^{+28} M_{⊙}, which can be considered an intermediate mass black hole (IMBH). The luminosity distance of the source is 5.3_{-2.6}^{+2.4} Gpc, corresponding to a redshift of 0.82_{-0.34}^{+0.28}. The inferred rate of mergers similar to GW190521 is 0.13_{-0.11}^{+0.30} Gpc^{-3} yr^{-1}.
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Chung S, Storey P, Shepherd TM, Lui YW. MR Susceptibility Imaging with a Short TE (MR-SISET): A Clinically Feasible Technique to Resolve Thalamic Nuclei. AJNR Am J Neuroradiol 2020; 41:1629-1631. [PMID: 32675340 DOI: 10.3174/ajnr.a6683] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2019] [Accepted: 05/11/2020] [Indexed: 11/07/2022]
Abstract
The thalamus consists of several functionally distinct nuclei, some of which serve as targets for functional neurosurgery. Visualization of such nuclei is a major challenge due to their low signal contrast on conventional imaging. We introduce MR susceptibility imaging with a short TE, leveraging susceptibility differences among thalamic nuclei, to automatically delineate 15 thalamic subregions. The technique has the potential to enable direct targeting of thalamic nuclei for functional neurosurgical guidance.
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Bergman M, Abdul-Ghani M, DeFronzo RA, Manco M, Sesti G, Fiorentino TV, Ceriello A, Rhee M, Phillips LS, Chung S, Cravalho C, Jagannathan R, Monnier L, Colette C, Owens D, Bianchi C, Del Prato S, Monteiro MP, Neves JS, Medina JL, Macedo MP, Ribeiro RT, Filipe Raposo J, Dorcely B, Ibrahim N, Buysschaert M. Review of methods for detecting glycemic disorders. Diabetes Res Clin Pract 2020; 165:108233. [PMID: 32497744 PMCID: PMC7977482 DOI: 10.1016/j.diabres.2020.108233] [Citation(s) in RCA: 96] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/19/2020] [Accepted: 05/19/2020] [Indexed: 02/07/2023]
Abstract
Prediabetes (intermediate hyperglycemia) consists of two abnormalities, impaired fasting glucose (IFG) and impaired glucose tolerance (IGT) detected by a standardized 75-gram oral glucose tolerance test (OGTT). Individuals with isolated IGT or combined IFG and IGT have increased risk for developing type 2 diabetes (T2D) and cardiovascular disease (CVD). Diagnosing prediabetes early and accurately is critical in order to refer high-risk individuals for intensive lifestyle modification. However, there is currently no international consensus for diagnosing prediabetes with HbA1c or glucose measurements based upon American Diabetes Association (ADA) and the World Health Organization (WHO) criteria that identify different populations at risk for progressing to diabetes. Various caveats affecting the accuracy of interpreting the HbA1c including genetics complicate this further. This review describes established methods for detecting glucose disorders based upon glucose and HbA1c parameters as well as novel approaches including the 1-hour plasma glucose (1-h PG), glucose challenge test (GCT), shape of the glucose curve, genetics, continuous glucose monitoring (CGM), measures of insulin secretion and sensitivity, metabolomics, and ancillary tools such as fructosamine, glycated albumin (GA), 1,5- anhydroglucitol (1,5-AG). Of the approaches considered, the 1-h PG has considerable potential as a biomarker for detecting glucose disorders if confirmed by additional data including health economic analysis. Whether the 1-h OGTT is superior to genetics and omics in providing greater precision for individualized treatment requires further investigation. These methods will need to demonstrate substantially superiority to simpler tools for detecting glucose disorders to justify their cost and complexity.
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Noh G, Nam J, Chung S, Kim K, Lee R. P-349 Clinical significance of lymph node radio to predict prognosis in colon cancer. Ann Oncol 2020. [DOI: 10.1016/j.annonc.2020.04.431] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022] Open
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Milley L, Boring J, Courville A, Gallagher I, Guo J, Howard R, Raisinger K, Rozga I, Schick A, Stagliano M, Torres S, Yang S, Chung S, Hall K. Postprandial Responses to Isocaloric Low-Carbohydrate vs Low-Fat Meals After 2 Weeks of Inpatient Ad libitum Feeding. Curr Dev Nutr 2020. [DOI: 10.1093/cdn/nzaa049_039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Abstract
Objectives
To explore postprandial responses to isocaloric meals after ∼2 weeks on an ad libitum Low Carbohydrate (LC) diet vs. a Low Fat (LF) diet.
Methods
16 healthy volunteers without diabetes were admitted to the NIH Clinical Center and randomized to consume a LC or LF diet for 2 weeks immediately followed by 2 weeks of the alternate diet. The LC diet was composed of ∼75% fat, ∼10% carbohydrate, and ∼15% protein; the LF diet was ∼75% carbohydrate, ∼10% fat, and ∼15% protein. Daily meals and snacks were matched for presented calories and participants were instructed to consume as much or as little as desired. On day 13 of each diet after an 8 hour fast, participants consumed a liquid meal containing 30% of energy requirements with a macronutrient composition corresponding to the prevailing diet. Blood was drawn at 0, 10, 20, 30, 60, 90, 120, 180, 240, 300, 360 minutes post meal consumption. Plasma concentrations of glucose, lactate, insulin, c-peptide, free fatty acids, and triglycerides were measured.
Results
7 females and 9 males with an age of (mean ± SE) 28.7 ±1.7 y and BMI of 27.5 ± 1.5 kg/m2 completed the study. During the LC diet, baseline levels of triglycerides and lactate were significantly lower (−33.5 ± 9.1 mg/dl; P = 0.003, −0.18 ± 0.05 mM; P = 0.002, respectively) and glucose, insulin, and c-peptide also tended to be lower (−3.7 ± 2.0 mg/dl; P = 0.09, −2.2 ± 1.2 µU/ml; P = 0.08, −0.35 ± 0.17 ng/ml; P = 0.06, respectively) whereas free fatty acids were significantly higher (0.28 ± 0.06 mM; P = 0.0005) compared to the LF diet. Average postprandial levels of glucose, lactate, insulin, and c-peptide were significantly lower following the LC meal (−11 ± 3 mg/dl; P = 0.003, −0.88 ± 0.06 mM; P < 0.0001, −35 ± 9 µU/ml; P = 0.002, −2.5 ± 0.3 ng/ml; P < 0.0001, respectively) whereas free fatty acids and triglycerides were significantly higher (0.52 ± 0.03 mM; P < 0.0001, 32 ± 12 mg/dl; P = 0.03, respectively) compared to the LF meal.
Conclusions
Following a ∼2 week adaptation period to ad libitum LC vs. LF diets, isocaloric meals resulted in substantial differences in postprandial glucose, lactate, insulin, c-peptide, free fatty acids, and triglycerides.
Funding Sources
Intramural Research Program of the NIDDK.
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Gallagher I, Boring J, Courville A, Guo J, Howard R, Milley L, Raisinger K, Rozga I, Schick A, Stagliano M, Torres S, Yang S, Chung S, Hall K. Ad Libitum Energy Intake Differences Between a Plant-Based, Low-Fat and an Animal-Based, Low-Carbohydrate Diet: An Inpatient Randomized Crossover Study. Curr Dev Nutr 2020. [DOI: 10.1093/cdn/nzaa049_019] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Abstract
Objectives
To investigate differences in ad libitum energy intake when inpatient adults were exposed to diets with equal calories and protein but varied widely in the ratio of carbohydrate to fat.
Methods
16 adults without diabetes were admitted to the Metabolic Clinical Research Unit in the NIH Clinical Center for four continuous weeks and were randomized to receive either a plant-based, low-fat (LF) diet or an animal-based, low-carbohydrate (LC) diet for two weeks, followed by the alternate diet for two weeks. The LF diet was ∼75% carbohydrate and ∼10% fat, whereas the LC was ∼10% carbohydrate and ∼75% fat. The LF diet had ∼4-fold more fiber and was ∼60% of the energy density of the LC diet. Both diets were matched for protein and the presented calories were double each subject's maintenance energy requirements, as calculated from their measured resting energy expenditure multiplied by 1.6. Participants received three daily meals, had continuous access to snacks, and were instructed to eat as much or as little as they wanted. Leftovers were weighed to determine food intake. ProNutra software was used to calculate energy and nutrient intake.
Results
The study enrolled 9 men and 7 women with an age (mean ± SE) of 29 ± 1.7 years and body mass index (BMI) of 27.5 ± 1.5. During exposure to the LF diet, participants consumed 726 ± 84 kcal/d less than during the LC diet (P < 0.0001). The composition of the food intake closely matched the presented diets, with the LF diet consumption of 75.2 ± 0.2% carbohydrate, 10.7 ± 0.2% fat, and 14.0 ± 0.3% protein whereas the LC diet consumption was 9.9 ± 0.1% carbohydrate, 74.5 ± 0.2% fat, and 15.6 ± 0.2% protein. During the second week of the LC diet, when ketosis had been fully established (capillary β-hydroxybutyrate = 1.4 ± 0.08 mM), intake decreased by 295 ± 52 kcal/d compared to the first week (P < 0.0001) whereas during the second week of the LF diet intake was not significantly changed (−51 ± 52 kcal/d; P = 0.33). Nevertheless, energy intake remained 611 ± 68 kcal/d lower during the second week of the LF diet than the LC diet (P < 0.0001).
Conclusions
Exposure to the LF diet resulted in significantly lower ad libitum energy intake compared to the LC diet, potentially due to its lower energy density and greater fiber content.
Funding Sources
Intramural Research Program of the National Institute of Diabetes and Digestive and Kidney Diseases.
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Schick A, Boring J, Courville A, Gallagher I, Guo J, Howard R, Milley L, Raisinger K, Rozga I, Stagliano M, Torres S, Yang S, Chung S, Hall K. Effects of Ad Libitum Low Carbohydrate Versus Low Fat Diets on Body Weight and Fat Mass. Curr Dev Nutr 2020. [DOI: 10.1093/cdn/nzaa049_051] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Abstract
Objectives
To describe the effects of ad libitum low-fat (LF) and low-carbohydrate (LC) diets on body weight and fat mass.
Methods
Sixteen adults without diabetes spent 29 continuous days residing at the Metabolic Clinical Research Unit of the NIH Clinical Center where they were fed ad libitum either an animal-based, LC diet (75% fat, 10% carbohydrates, 15% protein) or a plant-based, LF diet (75% carbohydrates, 10% fat, 15% protein). Participants were randomly assigned to one diet for the first phase of the study (14 days), after which they were switched to the other diet for the remainder of the study. Participants were given three meals daily and were provided with additional snacks amounting to 200% of their daily energy requirements as determined by their resting energy expenditure multiplied by 1.6. Subjects were told that this was not a weight loss study and were not informed about the primary study aim. They were instructed to eat as much or as little as they desired. Total body weight and fat mass were measured using a calibrated scale and dual-energy X-ray absorptiometry, respectively. Subjects were blinded to their data and wore loose-fitting scrubs to avoid any feedback regarding changes in the fit of their clothing.
Results
Subjects included 7 women and 9 men, with an age of (mean ± SE) 29 ± 1.7 years and BMI of 27.5 ± 1.5 at baseline. Participants lost weight on both diets, with the LC diet resulting in 1.34 ± 0.31 kg of weight loss (P = 0.0006) and the LF diet resulting in 1.09 ± 0.31 kg of weight loss (P = 0.003) which was not significantly different from the LC diet (P = 0.58). However, participants lost 0.6 ± 0.17 kg of body fat on the LF diet (P = 0.002) but the LC diet did not result in significant body fat loss (0.04 ± 0.17 kg; P = 0.8) and the difference in body fat loss between the diets was statistically significant (P = 0.03).
Conclusions
While participants lost similar amounts of weight on both diets, only the LF diet led to significant body fat loss. Early weight loss with a LC diet does not necessarily reflect a similar state of negative energy balance as compared with a LF diet.
Funding Sources
Intramural Research Program of the National Institutes of Diabetes and Digestive and Kidney Diseases.
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Maurer J, Covarrubias I, Baik J, Weber F, Chung S. 0076 The Role of Preoptic Area GABAergic Axonal Projections to Tuberomammillary Nucleus in Sleep Homeostasis. Sleep 2020. [DOI: 10.1093/sleep/zsaa056.074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Abstract
Introduction
Sleep deprivation has profound widespread physiological effects including cognitive impairment, compromised immune system function and increased risk of cardiovascular disease. The preoptic area (POA) of the hypothalamus contains sleep-active GABAergic neurons that respond to sleep homeostasis. We have shown that activation of POA GABAergic axons innervating the tuberomammillary nucleus (TMN, GABAergicPOA ->TMN) are critical for sleep regulation but it is unknown if these projections modulate sleep homeostasis.
Methods
To monitor in vivo neural activity of GABAergicPOA ->TMN projection neurons during sleep deprivation and rebound, fiber photometry was used. GAD2-Cre mice (n=6) were injected with AAV-DIO-GCaMP6S into the POA and an optic fiber was implanted into the TMN. An electroencephalogram (EEG) and electromyography (EMG) implant was mounted upon the skull to identify brain states. Calcium activity was measured for six hours starting at ZT4. Each mouse was recorded for three days to establish baseline sleep calcium activity with at least two days between sessions. During sleep deprivation sessions, an experimenter sleep deprived each mouse starting at ZT0 for six hours by gently brushing the animal with a small paintbrush to maintain wakefulness and minimize the stress to the animal.
Results
During baseline sleep recordings, GABAergicPOA ->TMN projection neurons are most active during sleep (NREM and REM) which is maintained until wake onset. As sleep pressure increases, GABAergicPOA ->TMN projection neurons display gradual increase in neural activity compared to time-matched points during baseline sleep recordings. Once mice were permitted to enter sleep rebound, GABAergicPOA ->TMN projection neurons gradually displayed decreased activity as sleep pressure eased.
Conclusion
GABAergicPOA ->TMN projection neurons show a strong increase in activity to drive homeostatic sleep need during periods of increased sleep pressure but subside once this pressure is reduced.
Support
This work is supported by NIH grant R01-NS-110865.
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Antila H, Kwak I, Covarrubias I, Baik J, Hong J, Stucynski J, Weber F, Chung S. 0154 Role of Noradrenergic Projection to the Preoptic Area in Regulation of Arousal. Sleep 2020. [DOI: 10.1093/sleep/zsaa056.152] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Abstract
Introduction
Locus coeruleus (LC) is a noradrenergic nucleus in the brainstem involved in the regulation of attention, arousal, mood and sensory gating. LC projects to multiple brain regions and recent development of novel systems neuroscience tools allows the dissection of projection-specific LC function in more detail. One of the regions with noradrenergic projection is the preoptic area of the hypothalamus (POA). POA has been shown to contain neurons that are important for regulating sleep, and we have examined the function of the LC projection to the POA in sleep and arousal.
Methods
We used optogenetics, chemogenetics, fiber photometry and in vivo electrophysiology to study the function of LC noradrenergic projection to the POA.
Results
Norepinephrine release in the POA fluctuates with brain state changes indicating that the LC to POA projection may be involved in regulating sleep and arousal. Optogenetic stimulation of LC fibers in the POA promotes wakefulness. Furthermore, optogenetic stimulation of the LC fibers in the POA modulates the activity of sleep- and wake-active neurons.
Conclusion
We have identified the role of the LC noradrenergic projection to the POA in the regulation of brain states. Stimulation of the LC fibers in the POA promotes wakefulness and modulates the activity dynamics of sleep- and wake-active neurons in the POA. Our results provide more detailed information about the role of this specific projection, which has been known to exist for a long time, but with insufficient in vivo evidence of its precise function.
Support
Sigrid Juselius foundation, Alfred P. Sloan Research Fellowship in Neuroscience, The Whitehall foundation grant, McCabe Fund Award, NARSAD Young Investigator Award.
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Stucynski J, Schott A, Baik J, Hong J, Weber F, Chung S. 0074 Inhibitory Neurons in the Dorsomedial Medulla Promote REM Sleep. Sleep 2020. [DOI: 10.1093/sleep/zsaa056.072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Abstract
Introduction
The neural circuits controlling rapid eye movement (REM) sleep, and in particular the role of the medulla in regulating this brain state, remains an active area of study. Previous electrophysiological recordings in the dorsomedial medulla (DM) and electrical stimulation experiments suggested an important role of this area in the control of REM sleep. However the identity of the involved neurons and their precise role in REM sleep regulation are still unclear.
Methods
The properties of DM GAD2 neurons in mice were investigated through stereotaxic injection of CRE-dependent viruses in conjunction with implantation of electrodes for electroencephalogram (EEG) and electromyogram (EMG) recordings and optic fibers. Experiments included in vivo calcium imaging (fiber photometry) across sleep and wake states, optogenetic stimulation of cell bodies, chemogenetic excitation and suppression (DREADDs), and connectivity mapping using viral tracing and optogenetics.
Results
Imaging the calcium activity of DM GAD2 neurons in vivo indicates that these neurons are most active during REM sleep. Optogenetic stimulation of DM GAD2 neurons reliably triggered transitions into REM sleep from NREM sleep. Consistent with this, chemogenetic activation of DM GAD2 neurons increased the amount of REM sleep while inhibition suppressed its occurrence and enhanced NREM sleep. Anatomical tracing revealed that DM GAD2 neurons project to several areas involved in sleep / wake regulation including the wake-promoting locus coeruleus (LC) and the REM sleep-suppressing ventrolateral periaquaductal gray (vlPAG). Optogenetic activation of axonal projections from DM to LC, and DM to vlPAG was sufficient to induce REM sleep.
Conclusion
These experiments demonstrate that DM inhibitory neurons expressing GAD2 powerfully promote initiation of REM sleep in mice. These findings further characterize the dorsomedial medulla as a critical structure involved in REM sleep regulation and inform future investigations of the REM sleep circuitry.
Support
R01 HL149133
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Schott A, Baik J, Chung S, Weber F. 0071 A Medullary Circuit Controlling REM Sleep. Sleep 2020. [DOI: 10.1093/sleep/zsaa056.069] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Abstract
Introduction
Rapid eye movement (REM) sleep is a distinct brain state known for its association with vivid dreaming in humans, though it is also crucial for other mental processes such as memory consolidation and emotion regulation. REM sleep is punctuated by phasic neurophysiological events known as pontine (P)-waves, which are thought to contribute to the cognitive functions of REM sleep. However, little is known about the neural circuits regulating these P-waves, or those responsible for initiating REM sleep itself. Here, we show that a yet unstudied population of medullary neurons expressing corticotropin-releasing-hormone (CRH) are important for controlling both the induction of REM sleep and its phasic events.
Methods
To measure the endogenous activity of CRH+ neurons in the dorsomedial medulla (dmM), we injected the calcium indicator GCaMP6 in the dmM of CRH-Cre mice. To optogenetically manipulate dmM CRH+ neuron activity, we delivered either an excitatory (ChR2) or inhibitory (iC++) opsin to the dmM of CRH-Cre mice. To record P-waves, we implanted microelectrodes to record local field potentials in the subcoeruleus region of the pons.
Results
Fiber photometry recordings showed that dmM CRH+ neurons are selectively active during REM sleep, and optogenetic stimulation and inhibition of this population is sufficient to promote and reduce REM sleep, respectively. Additionally, dmM CRH+ neuron activity is correlated with P-waves in the pons, and optogenetic activation of dmM CRH+ cells reliably triggers P-waves during REM sleep. Finally, histological examination of fluorescently labeled dmM CRH+ axons revealed strong projections to several pontine areas involved in P-wave generation as well as modulation of the theta rhythm during REM sleep.
Conclusion
Our results suggest that dmM CRH+ neurons are involved in controlling REM sleep initiation as well as phasic events within REM sleep. These neurons thus constitute an important component of the brainstem circuitry regulating REM sleep.
Support
National Institutes of Health (R01 HL149133)
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Kim KW, Chung S, Lee SY, Yoon SS, Kang HR. Successful Infusion of Obinutuzumab by Desensitization: A Case of Anaphylactic Shock During Desensitization. J Investig Allergol Clin Immunol 2020; 30:457-459. [PMID: 32376522 DOI: 10.18176/jiaci.0581] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
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Baykal AP, Parks EJ, Shamburek R, Chung S, Syed-Abdul MM, Muniyappa R, Cochran E, Startzell M, Gharib AM, Ouwerkerk R, Walter PJ, Brown RJ. OR17-01 Leptin Decreases De Novo Lipogenesis in Lipodystrophic Patients. J Endocr Soc 2020. [PMCID: PMC7207974 DOI: 10.1210/jendso/bvaa046.035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Abstract
De novo lipogenesis (DNL) plays a role in the development of hepatic steatosis and non-alcoholic fatty liver disease (NAFLD). In rodent models of both health and lipodystrophy (LD), leptin decreases DNL. In human patients with LD, reduced adipose tissue results in adipokine deficiencies, including lower plasma leptin, which contributes to insulin resistance, dyslipidemia and ectopic accumulation of triglycerides (TG). The mechanisms by which leptin regulates serum and hepatic-TG are not well elucidated. Studying patients with LD before and after leptin therapy provides an important clinical model for understanding leptin’s effect on DNL. We hypothesized that leptin treatment in lipodystrophic patients would decrease DNL by decreasing insulin resistance and glycemia, resulting in reduced circulating and hepatic-TG. Leptin-naïve patients with LD (n=11) were treated with recombinant leptin (metreleptin) for 6 months. All measurements were performed after an 8–12 hr fast. The % of TG in TG-rich lipoproteins (TRLP-TG) derived from DNL (% DNL) was measured using body water labeling (oral D2O) of TG and mass spectrometry analysis. Absolute DNL was calculated as the product of TRLP-TG and % DNL. HbA1c and serum-TG were measured biochemically, hepatic-TG by MRI, and total body and hepatic insulin sensitivity measured during a hyperinsulinemic-euglycemic clamp. DNL decreased after metreleptin: % DNL from 22.8±6.8 to 9.1±5.1% (p=0.0008) and absolute DNL from 54.2±32.1 to 8.6±6.5 mg/dl (p=0.003). TRLP-TG decreased from (median [interquartile range]) 160 [107, 280] to 98 [66, 147] mg/dl (p=0.01). Total body and hepatic insulin sensitivity increased from 3.7 [3.0, 7.3] to 8.4 [5.1,10.6] mg/kgFFM/min (p=0.03) and from 61.0 [48.5, 69.3] to 84.7 [75.2, 107.6] % (p =0.01), respectively. HbA1c decreased from 8.6±1.8 to 7.1±1.4% (p=0.04), hepatic-TG decreased from 17.6±11.9 to 10.3±9.1% (p=0.02), and serum-TG from 386 [216, 686] to 223 [118, 497] mg/dl (p=0.06). DNL correlated negatively with insulin sensitivity both before (r=-0.73, p=0.03) and after (r=-0.85, p=0.004) metreleptin. DNL correlated positively with hepatic-TG before (r=0.70 p=0.03) and tended to correlate after metreleptin (r=0.65, p=0.06). The change in DNL correlated with change in serum-TG (r=0.77, p=0.04) but not the change in hepatic-TG (p=0.80). We show here for the first time that 6 months of metreleptin treatment in humans with LD decreased DNL by 84% and was associated with reductions in glycemia and improved peripheral and hepatic insulin sensitivity. These data indicate a strong link between metreleptin’s effects to increase clearance of blood glucose by peripheral tissues and reduce hepatic carbohydrate flux, resulting in DNL reductions. This led to lowered hepatic steatosis and dyslipidemia and suggests treatments that target multi-organ insulin resistance may lead to decreased NAFLD and cardiovascular risk.
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Wu N, Phang J, Park J, Shen Y, Huang Z, Zorin M, Jastrzebski S, Fevry T, Katsnelson J, Kim E, Wolfson S, Parikh U, Gaddam S, Lin LLY, Ho K, Weinstein JD, Reig B, Gao Y, Toth H, Pysarenko K, Lewin A, Lee J, Airola K, Mema E, Chung S, Hwang E, Samreen N, Kim SG, Heacock L, Moy L, Cho K, Geras KJ. Deep Neural Networks Improve Radiologists' Performance in Breast Cancer Screening. IEEE TRANSACTIONS ON MEDICAL IMAGING 2020; 39:1184-1194. [PMID: 31603772 PMCID: PMC7427471 DOI: 10.1109/tmi.2019.2945514] [Citation(s) in RCA: 208] [Impact Index Per Article: 52.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/19/2023]
Abstract
We present a deep convolutional neural network for breast cancer screening exam classification, trained, and evaluated on over 200000 exams (over 1000000 images). Our network achieves an AUC of 0.895 in predicting the presence of cancer in the breast, when tested on the screening population. We attribute the high accuracy to a few technical advances. 1) Our network's novel two-stage architecture and training procedure, which allows us to use a high-capacity patch-level network to learn from pixel-level labels alongside a network learning from macroscopic breast-level labels. 2) A custom ResNet-based network used as a building block of our model, whose balance of depth and width is optimized for high-resolution medical images. 3) Pretraining the network on screening BI-RADS classification, a related task with more noisy labels. 4) Combining multiple input views in an optimal way among a number of possible choices. To validate our model, we conducted a reader study with 14 readers, each reading 720 screening mammogram exams, and show that our model is as accurate as experienced radiologists when presented with the same data. We also show that a hybrid model, averaging the probability of malignancy predicted by a radiologist with a prediction of our neural network, is more accurate than either of the two separately. To further understand our results, we conduct a thorough analysis of our network's performance on different subpopulations of the screening population, the model's design, training procedure, errors, and properties of its internal representations. Our best models are publicly available at https://github.com/nyukat/breast_cancer_classifier.
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Courville AB, Yang SB, Andrus S, Hayat N, Kuemmerle A, Leahy E, Briker S, Zambell K, Chung S, Sumner AE. Body adiposity measured by bioelectrical impedance is an alternative to dual-energy x-ray absorptiometry in black Africans: The Africans in America Study. Nutrition 2020; 74:110733. [PMID: 32179385 DOI: 10.1016/j.nut.2020.110733] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2019] [Revised: 01/02/2020] [Accepted: 01/09/2020] [Indexed: 12/17/2022]
Abstract
OBJECTIVES The prevalence of cardiometabolic disease has risen in Africa and parallels the obesity epidemic. To assess cardiometabolic risk, body composition measurements by dual-energy X-ray absorptiometry (DXA) are ideal. In communities with limited resources, alternative measures may be useful but have not been compared extensively in black Africans. Therefore, the aim of this study was to identify alternative methods of body composition assessment, such as body adiposity index (BAI) and bioelectrical impedance analysis (BIA), for use in African-born blacks. METHODS This was a cross-sectional study with African-born blacks. BAI and five BIA predictive equations (using variations of height, weight, age, sex, and impedance) were compared with DXA to estimate percent fat. Participants were 266 African-born blacks (39 ± 10 y, body mass index 28 ± 4 kg/m2, and 68% men) living in metropolitan Washington DC. Equivalence (90% confidence interval, -3 to 3), concordance, and Bland-Altman analyses (bias <2%, R2 closest to zero) compared BAI or BIA predictive equations to DXA as the criterion method. RESULTS DXA percent fat was 27.2% ± 5.5% and 40.3% ± 6.9% in men and women, respectively. BAI underestimated percent fat in men (bias: 1.88 ± 4.71, R2 = 0.25, P < 0.001) and women (bias: 6.47 ± 4.94, R2 = 0.08, P = 0.01). Of the five BIA predictive equations, the equation reported by Sun et al. had the best agreement with DXA percent fat for men (bias: -0.91 ± 3.67, R2 = 0.02, P = 0.05) and women (bias: -0.92 ± 4.02, R2 = 0.003, P = 0.58). Percent fat from the Sun et al. equation best agreed with DXA percent fat. CONCLUSION BIA with the Sun et al. predictive equation was the best alternative to DXA for body fat assessment in African-born blacks.
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Chang J, Guo X, Rao V, Gromisch E, Chung S, Kluger H, Cha C, Gorelick F, Testani J, Safirstein R, Crowley S, Peixoto A, Desir G. Identification of Two Forms of Human Plasma Renalase, and Their Association With All-Cause Mortality. Kidney Int Rep 2020; 5:362-368. [PMID: 32154458 PMCID: PMC7056858 DOI: 10.1016/j.ekir.2019.12.002] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2019] [Revised: 11/15/2019] [Accepted: 12/06/2019] [Indexed: 01/13/2023] Open
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Ahmed S, Chung S, Sohail N, Qazi IA, Justin A. Application of cell entrapping beads for Quorum Quenching technique in submerged membrane bioreactor. WATER SCIENCE AND TECHNOLOGY : A JOURNAL OF THE INTERNATIONAL ASSOCIATION ON WATER POLLUTION RESEARCH 2020; 81:744-752. [PMID: 32460277 DOI: 10.2166/wst.2020.149] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Biofouling is unwanted accumulation of microbial population on the membrane surface which limits the use of membrane bioreactor (MBR) in the market. Disruption of the biofilm formation by Quorum Quenching (QQ) by using cell entrapping beads (CEBs) is an approach with great potential to control membrane biofouling as the beads used provide not only mitigating effect on biofilm formation, by interfering Quorum Sensing, but also physical forces to detach the biofilm from the membrane surface. This research aimed to develop QQ-CEB with locally available chemicals in Pakistan and its application to evaluate the QQ effect together with physical and chemical cleaning. Various CEBs were made of different mixtures of sodium alginate and polyvinyl alcohol (PVA) and their quality was tested considering physical and biological aspects. Rhodococcus sp. BH4 and Pseudomonas putida were entrapped in the CEBs and then introduced in MBR as one of biofouling control methods along with standard backwash and chemical backwash. The CEBs made of specific concentration of PVA were proven to be more durable and helpful in mitigating biofouling as compared to that of sodium alginate. An MBR operated with PVA-alginate QQ CEBs together with chemical backwash showed the best performance without deterioration of effluent quality.
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Abbott BP, Abbott R, Abbott TD, Abraham S, Acernese F, Ackley K, Adams C, Adya VB, Affeldt C, Agathos M, Agatsuma K, Aggarwal N, Aguiar OD, Aiello L, Ain A, Ajith P, Akutsu T, Allen G, Allocca A, Aloy MA, Altin PA, Amato A, Ananyeva A, Anderson SB, Anderson WG, Ando M, Angelova SV, Antier S, Appert S, Arai K, Arai K, Arai Y, Araki S, Araya A, Araya MC, Areeda JS, Arène M, Aritomi N, Arnaud N, Arun KG, Ascenzi S, Ashton G, Aso Y, Aston SM, Astone P, Aubin F, Aufmuth P, AultONeal K, Austin C, Avendano V, Avila-Alvarez A, Babak S, Bacon P, Badaracco F, Bader MKM, Bae SW, Bae YB, Baiotti L, Bajpai R, Baker PT, Baldaccini F, Ballardin G, Ballmer SW, Banagiri S, Barayoga JC, Barclay SE, Barish BC, Barker D, Barkett K, Barnum S, Barone F, Barr B, Barsotti L, Barsuglia M, Barta D, Bartlett J, Barton MA, Bartos I, Bassiri R, Basti A, Bawaj M, Bayley JC, Bazzan M, Bécsy B, Bejger M, Belahcene I, Bell AS, Beniwal D, Berger BK, Bergmann G, Bernuzzi S, Bero JJ, Berry CPL, Bersanetti D, Bertolini A, Betzwieser J, Bhandare R, Bidler J, Bilenko IA, Bilgili SA, Billingsley G, Birch J, Birney R, Birnholtz O, Biscans S, Biscoveanu S, Bisht A, Bitossi M, Bizouard MA, Blackburn JK, Blair CD, Blair DG, Blair RM, Bloemen S, Bode N, Boer M, Boetzel Y, Bogaert G, Bondu F, Bonilla E, Bonnand R, Booker P, Boom BA, Booth CD, Bork R, Boschi V, Bose S, Bossie K, Bossilkov V, Bosveld J, Bouffanais Y, Bozzi A, Bradaschia C, Brady PR, Bramley A, Branchesi M, Brau JE, Briant T, Briggs JH, Brighenti F, Brillet A, Brinkmann M, Brisson V, Brockill P, Brooks AF, Brown DA, Brown DD, Brunett S, Buikema A, Bulik T, Bulten HJ, Buonanno A, Buskulic D, Buy C, Byer RL, Cabero M, Cadonati L, Cagnoli G, Cahillane C, Bustillo JC, Callister TA, Calloni E, Camp JB, Campbell WA, Canepa M, Cannon K, Cannon KC, Cao H, Cao J, Capocasa E, Carbognani F, Caride S, Carney MF, Carullo G, Diaz JC, Casentini C, Caudill S, Cavaglià M, Cavalier F, Cavalieri R, Cella G, Cerdá-Durán P, Cerretani G, Cesarini E, Chaibi O, Chakravarti K, Chamberlin SJ, Chan M, Chan ML, Chao S, Charlton P, Chase EA, Chassande-Mottin E, Chatterjee D, Chaturvedi M, Chatziioannou K, Cheeseboro BD, Chen CS, Chen HY, Chen KH, Chen X, Chen Y, Chen YR, Cheng HP, Cheong CK, Chia HY, Chincarini A, Chiummo A, Cho G, Cho HS, Cho M, Christensen N, Chu HY, Chu Q, Chu YK, Chua S, Chung KW, Chung S, Ciani G, Ciobanu AA, Ciolfi R, Cipriano F, Cirone A, Clara F, Clark JA, Clearwater P, Cleva F, Cocchieri C, Coccia E, Cohadon PF, Cohen D, Colgan R, Colleoni M, Collette CG, Collins C, Cominsky LR, Constancio M, Conti L, Cooper SJ, Corban P, Corbitt TR, Cordero-Carrión I, Corley KR, Cornish N, Corsi A, Cortese S, Costa CA, Cotesta R, Coughlin MW, Coughlin SB, Coulon JP, Countryman ST, Couvares P, Covas PB, Cowan EE, Coward DM, Cowart MJ, Coyne DC, Coyne R, Creighton JDE, Creighton TD, Cripe J, Croquette M, Crowder SG, Cullen TJ, Cumming A, Cunningham L, Cuoco E, Canton TD, Dálya G, Danilishin SL, D’Antonio S, Danzmann K, Dasgupta A, Da Silva Costa CF, Datrier LEH, Dattilo V, Dave I, Davier M, Davis D, Daw EJ, DeBra D, Deenadayalan M, Degallaix J, De Laurentis M, Deléglise S, Pozzo WD, DeMarchi LM, Demos N, Dent T, De Pietri R, Derby J, De Rosa R, De Rossi C, DeSalvo R, de Varona O, Dhurandhar S, Díaz MC, Dietrich T, Fiore LD, Giovanni MD, Girolamo TD, Lieto AD, Ding B, Pace SD, Palma ID, Renzo FD, Dmitriev A, Doctor Z, Doi K, Donovan F, Dooley KL, Doravari S, Dorrington I, Downes TP, Drago M, Driggers JC, Du Z, Ducoin JG, Dupej P, Dwyer SE, Easter PJ, Edo TB, Edwards MC, Effler A, Eguchi S, Ehrens P, Eichholz J, Eikenberry SS, Eisenmann M, Eisenstein RA, Enomoto Y, Essick RC, Estelles H, Estevez D, Etienne ZB, Etzel T, Evans M, Evans TM, Fafone V, Fair H, Fairhurst S, Fan X, Farinon S, Farr B, Farr WM, Fauchon-Jones EJ, Favata M, Fays M, Fazio M, Fee C, Feicht J, Fejer MM, Feng F, Fernandez-Galiana A, Ferrante I, Ferreira EC, Ferreira TA, Ferrini F, Fidecaro F, Fiori I, Fiorucci D, Fishbach M, Fisher RP, Fishner JM, Fitz-Axen M, Flaminio R, Fletcher M, Flynn E, Fong H, Font JA, Forsyth PWF, Fournier JD, Frasca S, Frasconi F, Frei Z, Freise A, Frey R, Frey V, Fritschel P, Frolov VV, Fujii Y, Fukunaga M, Fukushima M, Fulda P, Fyffe M, Gabbard HA, Gadre BU, Gaebel SM, Gair JR, Gammaitoni L, Ganija MR, Gaonkar SG, Garcia A, García-Quirós C, Garufi F, Gateley B, Gaudio S, Gaur G, Gayathri V, Ge GG, Gemme G, Genin E, Gennai A, George D, George J, Gergely L, Germain V, Ghonge S, Ghosh A, Ghosh A, Ghosh S, Giacomazzo B, Giaime JA, Giardina KD, Giazotto A, Gill K, Giordano G, Glover L, Godwin P, Goetz E, Goetz R, Goncharov B, González G, Castro JMG, Gopakumar A, Gorodetsky ML, Gossan SE, Gosselin M, Gouaty R, Grado A, Graef C, Granata M, Grant A, Gras S, Grassia P, Gray C, Gray R, Greco G, Green AC, Green R, Gretarsson EM, Groot P, Grote H, Grunewald S, Gruning P, Guidi GM, Gulati HK, Guo Y, Gupta A, Gupta MK, Gustafson EK, Gustafson R, Haegel L, Hagiwara A, Haino S, Halim O, Hall BR, Hall ED, Hamilton EZ, Hammond G, Haney M, Hanke MM, Hanks J, Hanna C, Hannam MD, Hannuksela OA, Hanson J, Hardwick T, Haris K, Harms J, Harry GM, Harry IW, Hasegawa K, Haster CJ, Haughian K, Hayakawa H, Hayama K, Hayes FJ, Healy J, Heidmann A, Heintze MC, Heitmann H, Hello P, Hemming G, Hendry M, Heng IS, Hennig J, Heptonstall AW, Heurs M, Hild S, Himemoto Y, Hinderer T, Hiranuma Y, Hirata N, Hirose E, Hoak D, Hochheim S, Hofman D, Holgado AM, Holland NA, Holt K, Holz DE, Hong Z, Hopkins P, Horst C, Hough J, Howell EJ, Hoy CG, Hreibi A, Hsieh BH, Huang GZ, Huang PW, Huang YJ, Huerta EA, Huet D, Hughey B, Hulko M, Husa S, Huttner SH, Huynh-Dinh T, Idzkowski B, Iess A, Ikenoue B, Imam S, Inayoshi K, Ingram C, Inoue Y, Inta R, Intini G, Ioka K, Irwin B, Isa HN, Isac JM, Isi M, Itoh Y, Iyer BR, Izumi K, Jacqmin T, Jadhav SJ, Jani K, Janthalur NN, Jaranowski P, Jenkins AC, Jiang J, Johnson DS, Jones AW, Jones DI, Jones R, Jonker RJG, Ju L, Jung K, Jung P, Junker J, Kajita T, Kalaghatgi CV, Kalogera V, Kamai B, Kamiizumi M, Kanda N, Kandhasamy S, Kang GW, Kanner JB, Kapadia SJ, Karki S, Karvinen KS, Kashyap R, Kasprzack M, Katsanevas S, Katsavounidis E, Katzman W, Kaufer S, Kawabe K, Kawaguchi K, Kawai N, Kawasaki T, Keerthana NV, Kéfélian F, Keitel D, Kennedy R, Key JS, Khalili FY, Khan H, Khan I, Khan S, Khan Z, Khazanov EA, Khursheed M, Kijbunchoo N, Kim C, Kim C, Kim JC, Kim J, Kim K, Kim W, Kim WS, Kim YM, Kimball C, Kimura N, King EJ, King PJ, Kinley-Hanlon M, Kirchhoff R, Kissel JS, Kita N, Kitazawa H, Kleybolte L, Klika JH, Klimenko S, Knowles TD, Knyazev E, Koch P, Koehlenbeck SM, Koekoek G, Kojima Y, Kokeyama K, Koley S, Komori K, Kondrashov V, Kong AKH, Kontos A, Koper N, Korobko M, Korth WZ, Kotake K, Kowalska I, Kozak DB, Kozakai C, Kozu R, Kringel V, Krishnendu N, Królak A, Kuehn G, Kumar A, Kumar P, Kumar R, Kumar R, Kumar S, Kume J, Kuo CM, Kuo HS, Kuo L, Kuroyanagi S, Kusayanagi K, Kutynia A, Kwak K, Kwang S, Lackey BD, Lai KH, Lam TL, Landry M, Lane BB, Lang RN, Lange J, Lantz B, Lanza RK, Lartaux-Vollard A, Lasky PD, Laxen M, Lazzarini A, Lazzaro C, Leaci P, Leavey S, Lecoeuche YK, Lee CH, Lee HK, Lee HM, Lee HW, Lee J, Lee K, Lee RK, Lehmann J, Lenon A, Leonardi M, Leroy N, Letendre N, Levin Y, Li J, Li KJL, Li TGF, Li X, Lin CY, Lin F, Lin FL, Lin LCC, Linde F, Linker SD, Littenberg TB, Liu GC, Liu J, Liu X, Lo RKL, Lockerbie NA, London LT, Longo A, Lorenzini M, Loriette V, Lormand M, Losurdo G, Lough JD, Lousto CO, Lovelace G, Lower ME, Lück H, Lumaca D, Lundgren AP, Luo LW, Lynch R, Ma Y, Macas R, Macfoy S, MacInnis M, Macleod DM, Macquet A, Magaña-Sandoval F, Zertuche LM, Magee RM, Majorana E, Maksimovic I, Malik A, Man N, Mandic V, Mangano V, Mansell GL, Manske M, Mantovani M, Marchesoni F, Marchio M, Marion F, Márka S, Márka Z, Markakis C, Markosyan AS, Markowitz A, Maros E, Marquina A, Marsat S, Martelli F, Martin IW, Martin RM, Martynov DV, Mason K, Massera E, Masserot A, Massinger TJ, Masso-Reid M, Mastrogiovanni S, Matas A, Matichard F, Matone L, Mavalvala N, Mazumder N, McCann JJ, McCarthy R, McClelland DE, McCormick S, McCuller L, McGuire SC, McIver J, McManus DJ, McRae T, McWilliams ST, Meacher D, Meadors GD, Mehmet M, Mehta AK, Meidam J, Melatos A, Mendell G, Mercer RA, Mereni L, Merilh EL, Merzougui M, Meshkov S, Messenger C, Messick C, Metzdorff R, Meyers PM, Miao H, Michel C, Michimura Y, Middleton H, Mikhailov EE, Milano L, Miller AL, Miller A, Millhouse M, Mills JC, Milovich-Goff MC, Minazzoli O, Minenkov Y, Mio N, Mishkin A, Mishra C, Mistry T, Mitra S, Mitrofanov VP, Mitselmakher G, Mittleman R, Miyakawa O, Miyamoto A, Miyazaki Y, Miyo K, Miyoki S, Mo G, Moffa D, Mogushi K, Mohapatra SRP, Montani M, Moore CJ, Moraru D, Moreno G, Morisaki S, Moriwaki Y, Mours B, Mow-Lowry CM, Mukherjee A, Mukherjee D, Mukherjee S, Mukund N, Mullavey A, Munch J, Muñiz EA, Muratore M, Murray PG, Nagano K, Nagano S, Nagar A, Nakamura K, Nakano H, Nakano M, Nakashima R, Nardecchia I, Narikawa T, Naticchioni L, Nayak RK, Negishi R, Neilson J, Nelemans G, Nelson TJN, Nery M, Neunzert A, Ng KY, Ng S, Nguyen P, Ni WT, Nichols D, Nishizawa A, Nissanke S, Nocera F, North C, Nuttall LK, Obergaulinger M, Oberling J, O’Brien BD, Obuchi Y, O’Dea GD, Ogaki W, Ogin GH, Oh JJ, Oh SH, Ohashi M, Ohishi N, Ohkawa M, Ohme F, Ohta H, Okada MA, Okutomi K, Oliver M, Oohara K, Ooi CP, Oppermann P, Oram RJ, O’Reilly B, Ormiston RG, Ortega LF, O’Shaughnessy R, Oshino S, Ossokine S, Ottaway DJ, Overmier H, Owen BJ, Pace AE, Pagano G, Page MA, Pai A, Pai SA, Palamos JR, Palashov O, Palomba C, Pal-Singh A, Pan HW, Pan KC, Pang B, Pang HF, Pang PTH, Pankow C, Pannarale F, Pant BC, Paoletti F, Paoli A, Papa MA, Parida A, Park J, Parker W, Pascucci D, Pasqualetti A, Passaquieti R, Passuello D, Patil M, Patricelli B, Pearlstone BL, Pedersen C, Pedraza M, Pedurand R, Pele A, Arellano FEP, Penn S, Perez CJ, Perreca A, Pfeiffer HP, Phelps M, Phukon KS, Piccinni OJ, Pichot M, Piergiovanni F, Pillant G, Pinard L, Pinto I, Pirello M, Pitkin M, Poggiani R, Pong DYT, Ponrathnam S, Popolizio P, Porter EK, Powell J, Prajapati AK, Prasad J, Prasai K, Prasanna R, Pratten G, Prestegard T, Privitera S, Prodi GA, Prokhorov LG, Puncken O, Punturo M, Puppo P, Pürrer M, Qi H, Quetschke V, Quinonez PJ, Quintero EA, Quitzow-James R, Raab FJ, Radkins H, Radulescu N, Raffai P, Raja S, Rajan C, Rajbhandari B, Rakhmanov M, Ramirez KE, Ramos-Buades A, Rana J, Rao K, Rapagnani P, Raymond V, Razzano M, Read J, Regimbau T, Rei L, Reid S, Reitze DH, Ren W, Ricci F, Richardson CJ, Richardson JW, Ricker PM, Riles K, Rizzo M, Robertson NA, Robie R, Robinet F, Rocchi A, Rolland L, Rollins JG, Roma VJ, Romanelli M, Romano R, Romel CL, Romie JH, Rose K, Rosińska D, Rosofsky SG, Ross MP, Rowan S, Rüdiger A, Ruggi P, Rutins G, Ryan K, Sachdev S, Sadecki T, Sago N, Saito S, Saito Y, Sakai K, Sakai Y, Sakamoto H, Sakellariadou M, Sakuno Y, Salconi L, Saleem M, Samajdar A, Sammut L, Sanchez EJ, Sanchez LE, Sanchis-Gual N, Sandberg V, Sanders JR, Santiago KA, Sarin N, Sassolas B, Sathyaprakash BS, Sato S, Sato T, Sauter O, Savage RL, Sawada T, Schale P, Scheel M, Scheuer J, Schmidt P, Schnabel R, Schofield RMS, Schönbeck A, Schreiber E, Schulte BW, Schutz BF, Schwalbe SG, Scott J, Scott SM, Seidel E, Sekiguchi T, Sekiguchi Y, Sellers D, Sengupta AS, Sennett N, Sentenac D, Sequino V, Sergeev A, Setyawati Y, Shaddock DA, Shaffer T, Shahriar MS, Shaner MB, Shao L, Sharma P, Shawhan P, Shen H, Shibagaki S, Shimizu R, Shimoda T, Shimode K, Shink R, Shinkai H, Shishido T, Shoda A, Shoemaker DH, Shoemaker DM, ShyamSundar S, Siellez K, Sieniawska M, Sigg D, Silva AD, Singer LP, Singh N, Singhal A, Sintes AM, Sitmukhambetov S, Skliris V, Slagmolen BJJ, Slaven-Blair TJ, Smith JR, Smith RJE, Somala S, Somiya K, Son EJ, Sorazu B, Sorrentino F, Sotani H, Souradeep T, Sowell E, Spencer AP, Srivastava AK, Srivastava V, Staats K, Stachie C, Standke M, Steer DA, Steinke M, Steinlechner J, Steinlechner S, Steinmeyer D, Stevenson SP, Stocks D, Stone R, Stops DJ, Strain KA, Stratta G, Strigin SE, Strunk A, Sturani R, Stuver AL, Sudhir V, Sugimoto R, Summerscales TZ, Sun L, Sunil S, Suresh J, Sutton PJ, Suzuki T, Suzuki T, Swinkels BL, Szczepańczyk MJ, Tacca M, Tagoshi H, Tait SC, Takahashi H, Takahashi R, Takamori A, Takano S, Takeda H, Takeda M, Talbot C, Talukder D, Tanaka H, Tanaka K, Tanaka K, Tanaka T, Tanaka T, Tanioka S, Tanner DB, Tápai M, Martin ENTS, Taracchini A, Tasson JD, Taylor R, Telada S, Thies F, Thomas M, Thomas P, Thondapu SR, Thorne KA, Thrane E, Tiwari S, Tiwari S, Tiwari V, Toland K, Tomaru T, Tomigami Y, Tomura T, Tonelli M, Tornasi Z, Torres-Forné A, Torrie CI, Töyrä D, Travasso F, Traylor G, Tringali MC, Trovato A, Trozzo L, Trudeau R, Tsang KW, Tsang TTL, Tse M, Tso R, Tsubono K, Tsuchida S, Tsukada L, Tsuna D, Tsuzuki T, Tuyenbayev D, Uchikata N, Uchiyama T, Ueda A, Uehara T, Ueno K, Ueshima G, Ugolini D, Unnikrishnan CS, Uraguchi F, Urban AL, Ushiba T, Usman SA, Vahlbruch H, Vajente G, Valdes G, Bakel NV, Beuzekom MV, Brand JFJVD, Broeck CVD, Vander-Hyde DC, Schaaf LVD, Heijningen JVV, Putten MHPMV, Veggel AAV, Vardaro M, Varma V, Vass S, Vasúth M, Vecchio A, Vedovato G, Veitch J, Veitch PJ, Venkateswara K, Venugopalan G, Verkindt D, Vetrano F, Viceré A, Viets AD, Vine DJ, Vinet JY, Vitale S, Vivanco FH, Vo T, Vocca H, Vorvick C, Vyatchanin SP, Wade AR, Wade LE, Wade M, Walet R, Walker M, Wallace L, Walsh S, Wang G, Wang H, Wang J, Wang JZ, Wang WH, Wang YF, Ward RL, Warden ZA, Warner J, Was M, Watchi J, Weaver B, Wei LW, Weinert M, Weinstein AJ, Weiss R, Wellmann F, Wen L, Wessel EK, Weßels P, Westhouse JW, Wette K, Whelan JT, Whiting BF, Whittle C, Wilken DM, Williams D, Williamson AR, Willis JL, Willke B, Wimmer MH, Winkler W, Wipf CC, Wittel H, Woan G, Woehler J, Wofford JK, Worden J, Wright JL, Wu CM, Wu DS, Wu HC, Wu SR, Wysocki DM, Xiao L, Xu WR, Yamada T, Yamamoto H, Yamamoto K, Yamamoto K, Yamamoto T, Yancey CC, Yang L, Yap MJ, Yazback M, Yeeles DW, Yokogawa K, Yokoyama J, Yokozawa T, Yoshioka T, Yu H, Yu H, Yuen SHR, Yuzurihara H, Yvert M, Zadrożny AK, Zanolin M, Zeidler S, Zelenova T, Zendri JP, Zevin M, Zhang J, Zhang L, Zhang T, Zhao C, Zhao Y, Zhou M, Zhou Z, Zhu XJ, Zhu ZH, Zimmerman AB, Zucker ME, Zweizig J. Prospects for observing and localizing gravitational-wave transients with Advanced LIGO, Advanced Virgo and KAGRA. LIVING REVIEWS IN RELATIVITY 2020; 23:3. [PMID: 33015351 PMCID: PMC7520625 DOI: 10.1007/s41114-020-00026-9] [Citation(s) in RCA: 49] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/01/2019] [Accepted: 05/27/2020] [Indexed: 05/05/2023]
Abstract
We present our current best estimate of the plausible observing scenarios for the Advanced LIGO, Advanced Virgo and KAGRA gravitational-wave detectors over the next several years, with the intention of providing information to facilitate planning for multi-messenger astronomy with gravitational waves. We estimate the sensitivity of the network to transient gravitational-wave signals for the third (O3), fourth (O4) and fifth observing (O5) runs, including the planned upgrades of the Advanced LIGO and Advanced Virgo detectors. We study the capability of the network to determine the sky location of the source for gravitational-wave signals from the inspiral of binary systems of compact objects, that is binary neutron star, neutron star-black hole, and binary black hole systems. The ability to localize the sources is given as a sky-area probability, luminosity distance, and comoving volume. The median sky localization area (90% credible region) is expected to be a few hundreds of square degrees for all types of binary systems during O3 with the Advanced LIGO and Virgo (HLV) network. The median sky localization area will improve to a few tens of square degrees during O4 with the Advanced LIGO, Virgo, and KAGRA (HLVK) network. During O3, the median localization volume (90% credible region) is expected to be on the order of 10 5 , 10 6 , 10 7 Mpc 3 for binary neutron star, neutron star-black hole, and binary black hole systems, respectively. The localization volume in O4 is expected to be about a factor two smaller than in O3. We predict a detection count of 1 - 1 + 12 ( 10 - 10 + 52 ) for binary neutron star mergers, of 0 - 0 + 19 ( 1 - 1 + 91 ) for neutron star-black hole mergers, and 17 - 11 + 22 ( 79 - 44 + 89 ) for binary black hole mergers in a one-calendar-year observing run of the HLV network during O3 (HLVK network during O4). We evaluate sensitivity and localization expectations for unmodeled signal searches, including the search for intermediate mass black hole binary mergers.
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Lee J, Chung S, Youn S, Kim C, Yeo S. The influence of cognitive emotion regulation strategies on depressive symptoms in breast cancer patients. Sleep Med 2019. [DOI: 10.1016/j.sleep.2019.11.606] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Suh S, Oh H, Han S, Hong Y, Chung S. Development and validation of the semi-structured nightmare disorder interview. Sleep Med 2019. [DOI: 10.1016/j.sleep.2019.11.1020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Falasinnu T, Chaichian Y, Li J, Chung S, Waitzfelder BE, Fortmann SP, Palaniappan L, Simard JF. Does SLE widen or narrow race/ethnic disparities in the risk of five co-morbid conditions? Evidence from a community-based outpatient care system. Lupus 2019; 28:1619-1627. [PMID: 31660790 DOI: 10.1177/0961203319884646] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
OBJECTIVE The heterogeneous spectrum of systemic lupus erythematosus (SLE) often presents with secondary complications such as cardiovascular disease (CVD), infections and neoplasms. Our study assessed whether the presence of SLE independently increases or reduces the disparities, accounting for the already higher risk of these outcomes among racial/ethnic minority groups without SLE. METHODS We defined a cohort using electronic health records data (2005-2016) from a mixed-payer community-based outpatient setting in California serving patients of diverse racial/ethnic backgrounds. The eligible population included adult patients with SLE and matched non-SLE patients (≥18 years old). SLE was the primary exposure. The following outcomes were identified: pneumonia, other infections, CVD and neoplasms. For each racial/ethnic group, we calculated the proportion of incident co-morbidities by SLE exposure, followed by logistic regression for each outcome with SLE as the exposure. We evaluated interaction on the additive and multiplicative scales by calculating the relative excess risk due to interaction and estimating the cross-product term in each model. RESULTS We identified 1036 SLE cases and 8875 controls. The incidence for all outcomes was higher among the SLE exposed. We found little difference in the odds of the outcomes associated with SLE across racial/ethnic groups, even after multivariable adjustment. This finding was consistent on the multiplicative and additive scales. CONCLUSION We demonstrated that SLE status does not independently confer substantial interaction or heterogeneity by race/ethnicity toward the risk of pneumonia, other infections, CVD or neoplasms. Further studies in larger datasets are necessary to validate this novel finding.
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Abbott BP, Abbott R, Abbott TD, Abraham S, Acernese F, Ackley K, Adams C, Adhikari RX, Adya VB, Affeldt C, Agathos M, Agatsuma K, Aggarwal N, Aguiar OD, Aiello L, Ain A, Ajith P, Allen G, Allocca A, Aloy MA, Altin PA, Amato A, Anand S, Ananyeva A, Anderson SB, Anderson WG, Angelova SV, Antier S, Appert S, Arai K, Araya MC, Areeda JS, Arène M, Arnaud N, Aronson SM, Arun KG, Ascenzi S, Ashton G, Aston SM, Astone P, Aubin F, Aufmuth P, AultONeal K, Austin C, Avendano V, Avila-Alvarez A, Babak S, Bacon P, Badaracco F, Bader MKM, Bae S, Baird J, Baker PT, Baldaccini F, Ballardin G, Ballmer SW, Bals A, Banagiri S, Barayoga JC, Barbieri C, Barclay SE, Barish BC, Barker D, Barkett K, Barnum S, Barone F, Barr B, Barsotti L, Barsuglia M, Barta D, Bartlett J, Bartos I, Bassiri R, Basti A, Bawaj M, Bayley JC, Bazzan M, Bécsy B, Bejger M, Belahcene I, Bell AS, Beniwal D, Benjamin MG, Berger BK, Bergmann G, Bernuzzi S, Berry CPL, Bersanetti D, Bertolini A, Betzwieser J, Bhandare R, Bidler J, Biggs E, Bilenko IA, Bilgili SA, Billingsley G, Birney R, Birnholtz O, Biscans S, Bischi M, Biscoveanu S, Bisht A, Bitossi M, Bizouard MA, Blackburn JK, Blackman J, Blair CD, Blair DG, Blair RM, Bloemen S, Bobba F, Bode N, Boer M, Boetzel Y, Bogaert G, Bondu F, Bonnand R, Booker P, Boom BA, Bork R, Boschi V, Bose S, Bossilkov V, Bosveld J, Bouffanais Y, Bozzi A, Bradaschia C, Brady PR, Bramley A, Branchesi M, Brau JE, Breschi M, Briant T, Briggs JH, Brighenti F, Brillet A, Brinkmann M, Brockill P, Brooks AF, Brooks J, Brown DD, Brunett S, Buikema A, Bulik T, Bulten HJ, Buonanno A, Buskulic D, Buy C, Byer RL, Cabero M, Cadonati L, Cagnoli G, Cahillane C, Calderón Bustillo J, Callister TA, Calloni E, Camp JB, Campbell WA, Cannon KC, Cao H, Cao J, Carapella G, Carbognani F, Caride S, Carney MF, Carullo G, Casanueva Diaz J, Casentini C, Caudill S, Cavaglià M, Cavalier F, Cavalieri R, Cella G, Cerdá-Durán P, Cesarini E, Chaibi O, Chakravarti K, Chamberlin SJ, Chan M, Chao S, Charlton P, Chase EA, 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R, Fletcher M, Floden E, Flynn E, Fong H, Font JA, Forsyth PWF, Fournier JD, Vivanco FH, Frasca S, Frasconi F, Frei Z, Freise A, Frey R, Frey V, Fritschel P, Frolov VV, Fronzè G, Fulda P, Fyffe M, Gabbard HA, Gadre BU, Gaebel SM, Gair JR, Gammaitoni L, Gaonkar SG, García-Quirós C, Garufi F, Gateley B, Gaudio S, Gaur G, Gayathri V, Gemme G, Genin E, Gennai A, George D, George J, Gergely L, Ghonge S, Ghosh A, Ghosh A, Ghosh S, Giacomazzo B, Giaime JA, Giardina KD, Gibson DR, Gill K, Glover L, Gniesmer J, Godwin P, Goetz E, Goetz R, Goncharov B, González G, Gonzalez Castro JM, Gopakumar A, Gossan SE, Gosselin M, Gouaty R, Grace B, Grado A, Granata M, Grant A, Gras S, Grassia P, Gray C, Gray R, Greco G, Green AC, Green R, Gretarsson EM, Grimaldi A, Grimm SJ, Groot P, Grote H, Grunewald S, Gruning P, Guidi GM, Gulati HK, Guo Y, Gupta A, Gupta A, Gupta P, Gustafson EK, Gustafson R, Haegel L, Halim O, Hall BR, Hall ED, Hamilton EZ, Hammond G, Haney M, Hanke MM, Hanks J, Hanna C, Hannam MD, 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ME, Zweizig J, Shandera S. Search for Subsolar Mass Ultracompact Binaries in Advanced LIGO's Second Observing Run. PHYSICAL REVIEW LETTERS 2019; 123:161102. [PMID: 31702344 DOI: 10.1103/physrevlett.123.161102] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/25/2019] [Indexed: 06/10/2023]
Abstract
We present a search for subsolar mass ultracompact objects in data obtained during Advanced LIGO's second observing run. In contrast to a previous search of Advanced LIGO data from the first observing run, this search includes the effects of component spin on the gravitational waveform. We identify no viable gravitational-wave candidates consistent with subsolar mass ultracompact binaries with at least one component between 0.2 M_{⊙}-1.0 M_{⊙}. We use the null result to constrain the binary merger rate of (0.2 M_{⊙}, 0.2 M_{⊙}) binaries to be less than 3.7×10^{5} Gpc^{-3} yr^{-1} and the binary merger rate of (1.0 M_{⊙}, 1.0 M_{⊙}) binaries to be less than 5.2×10^{3} Gpc^{-3} yr^{-1}. Subsolar mass ultracompact objects are not expected to form via known stellar evolution channels, though it has been suggested that primordial density fluctuations or particle dark matter with cooling mechanisms and/or nuclear interactions could form black holes with subsolar masses. Assuming a particular primordial black hole (PBH) formation model, we constrain a population of merging 0.2 M_{⊙} black holes to account for less than 16% of the dark matter density and a population of merging 1.0 M_{⊙} black holes to account for less than 2% of the dark matter density. We discuss how constraints on the merger rate and dark matter fraction may be extended to arbitrary black hole population models that predict subsolar mass binaries.
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Seo S, Lee A, Kim S, Chung S, Lee S. 204 TRPV3 is highly activated in keratinocytes from patients with atopic dermatitis and contributes to warmth-evoked pruritogens release and itch behaviors. J Invest Dermatol 2019. [DOI: 10.1016/j.jid.2019.07.205] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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