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2. Reciprocal Degradation of YME1L and OMA1 Adapts Mitochondrial Proteolytic Activity during Stress

Author

T. Kelly Rainbolt, Justine Lebeau, Cristina Puchades, and R. Luke Wiseman

Subjects
Biology (General), QH301-705.5
Abstract

The mitochondrial inner membrane proteases YME1L and OMA1 are critical regulators of essential mitochondrial functions, including inner membrane proteostasis maintenance and mitochondrial dynamics. Here, we show that YME1L and OMA1 are reciprocally degraded in response to distinct types of cellular stress. OMA1 is degraded through a YME1L-dependent mechanism in response to toxic insults that depolarize the mitochondrial membrane. Alternatively, insults that depolarize mitochondria and deplete cellular ATP stabilize active OMA1 and promote YME1L degradation. We show that the differential degradation of YME1L and OMA1 alters their proteolytic processing of the dynamin-like GTPase OPA1, a critical regulator of mitochondrial inner membrane morphology, which influences the recovery of tubular mitochondria following membrane-depolarization-induced fragmentation. Our results reveal the differential stress-induced degradation of YME1L and OMA1 as a mechanism for sensitively adapting mitochondrial inner membrane protease activity and function in response to distinct types of cellular insults.

Published
2016
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6. Masculinity and mental illness in and after men's intimate partner relationships

Author

John L. Oliffe, Mary T. Kelly, Gabriela Gonzalez Montaner, Zac E. Seidler, John S. Ogrodniczuk, and Simon M. Rice

Subjects
Masculinity, Men's mental illness, Men's relationships, Male depression, Men's divorce, Marital transitions, Public aspects of medicine, RA1-1270
Abstract

Male suicide continues to be a significant issue worldwide for which there are a myriad of social risk factors. Amongst these, distressed and/or disrupted (i.e., separation, divorce) intimate partner relationships are known to heighten men's mental illness and suicide risk. The current qualitative study offers novel insights to the connections between masculinity and mental illness in and after men's intimate partner relationships. Drawing from in-depth interviews with 47 Canadian and Australian men, three themes were inductively derived: 1) The trouble inside, 2) Breaking up and breaking down, and 3) Finding help. The ‘trouble inside’ results revealed relationship transitions wherein challenges to couple dynamics flowed from diverse life course events (conflict, illness, bereavement, co-parenting). Partnership transgressions (most often infidelity) also featured to heighten men's mental illness vulnerabilities and threaten the feasibility of the relationship. ‘Breaking up and breaking down’ chronicled participants' anxiety, depression and suicidality in the aftermath of their relationship ending. Herein, substance use and other maladaptive behaviours were used by men to blunt feelings and/or self-medicate mental illness. These strategies were ineffectual for moving on from blaming partners or grieving the loss of support and social connectedness provided by ex-partners. ‘Finding help’ included men's eventual self-help, uptake of informal assistance from friends and family, formal professional care services, and the use of facilitated male peer group resources. Norming the use of these diverse help resources were men's alignments to strength-based asset-building masculine ideals, wherein their help-seeking was bridged to, and reflective of their (albeit latent) commitment to better managing their mental health and future relationships. Highlighting the gendered dimensions of mental illness in men's intimate partner relationships, the current study also thoughtfully considers content and contexts for the delivery of tailored upstream suicide prevention programs focussed on men building better relationships.

Published
2022

7. The role of omega-3 polyunsaturated fatty acid supplementation in the management of type 2 diabetes mellitus: A narrative review

Author

Jaimon T Kelly, Elena S. George, Wolfgang Marx, Oana A. Tatucu-Babet, Catherine Itsiopoulos, Gina L. Trakman, Sarah Dash, Colleen J. Thomas, Laima Brazionis, and Hannah L Mayr

Subjects
medicine.medical_specialty, Endocrinology, Diabetes and Metabolism, 030209 endocrinology & metabolism, lcsh:TX341-641, 030204 cardiovascular system & hematology, Diabetic nephropathy, lcsh:Biochemistry, 03 medical and health sciences, Liver disease, 0302 clinical medicine, Diabetes mellitus, medicine, lcsh:QD415-436, Intensive care medicine, chemistry.chemical_classification, Nutrition and Dietetics, business.industry, Type 2 Diabetes Mellitus, medicine.disease, Mental health, Clinical trial, chemistry, Diabetes, Review, Omega-3, Diabetes complications, Nutrient supplementation, Animal studies, business, lcsh:Nutrition. Foods and food supply, Food Science, Polyunsaturated fatty acid
Abstract

Background: Type 2 Diabetes Mellitus (T2DM) poses a significant health and financial burden to individuals and healthcare systems. Omega-3 polyunsaturated fatty acids (PUFA) possess numerous properties (e.g. anti-inflammatory, anti-thrombotic, anti-lipidemic) that may be beneficial in the management of T2DM and its complications. Methods: In this narrative review, we discuss the potential mechanisms, clinical evidence-base, and practical considerations regarding the use of omega-3 PUFA supplementation for the management of glycaemic control and common comorbid conditions, including diabetic nephropathy and retinopathy, liver disease, cognition and mental health, and cardiometabolic disease. Results/conclusion: Omega-3 PUFA supplementation is generally well-tolerated and does not appear to be contraindicated for patients on anticoagulant therapy; however, uncertainty persists regarding the purity and stability of commercial omega-3 PUFA products. Despite promising animal studies, the current clinical evidence for the use of omega-3 supplementation for the management of T2DM and associated conditions is both limited and conflicting. Results from existing clinical trials do not support the use of omega-3 PUFA for glycaemic control and there are limited studies in T2DM populations to support the use of omega-3 PUFAs for associated complications of diabetes. Possible contributors to the conflicting evidence base are study design issues, such as inadequate intervention period, sample size, omega 3 supplement dose, variations in the EPA to DHA ratio and clinical heterogeneity among diabetic populations. Keywords: Omega-3, Diabetes, Diabetes complications, Nutrient supplementation, Review

Published
2018

8. An ensemble-based model of PM2.5 concentration across the contiguous United States with high spatiotemporal resolution

Author

Qian Di, Alexei Lyapustin, Petros Koutrakis, James T. Kelly, Joel Schwartz, Yujie Wang, Heresh Amini, Itai Kloog, Rachel F. Silvern, Loretta J. Mickley, Liuhua Shi, Christine Choirat, and M. Benjamin Sabath

Subjects
010504 meteorology & atmospheric sciences, 010501 environmental sciences, 01 natural sciences, Standard deviation, Article, Machine Learning, Air Pollution, Statistics, lcsh:Environmental sciences, 0105 earth and related environmental sciences, General Environmental Science, Mathematics, Geographic difference, lcsh:GE1-350, Air Pollutants, Models, Statistical, Ensemble forecasting, Artificial neural network, Generalized additive model, Elevation, United States, Random forest, Particulate Matter, Gradient boosting, Algorithms, Environmental Monitoring
Abstract

Various approaches have been proposed to model PM2.5 in the recent decade, with satellite-derived aerosol optical depth, land-use variables, chemical transport model predictions, and several meteorological variables as major predictor variables. Our study used an ensemble model that integrated multiple machine learning algorithms and predictor variables to estimate daily PM2.5 at a resolution of 1 km × 1 km across the contiguous United States. We used a generalized additive model that accounted for geographic difference to combine PM2.5 estimates from neural network, random forest, and gradient boosting. The three machine learning algorithms were based on multiple predictor variables, including satellite data, meteorological variables, land-use variables, elevation, chemical transport model predictions, several reanalysis datasets, and others. The model training results from 2000 to 2015 indicated good model performance with a 10-fold cross-validated R2 of 0.86 for daily PM2.5 predictions. For annual PM2.5 estimates, the cross-validated R2 was 0.89. Our model demonstrated good performance up to 60 μg/m3. Using trained PM2.5 model and predictor variables, we predicted daily PM2.5 from 2000 to 2015 at every 1 km × 1 km grid cell in the contiguous United States. We also used localized land-use variables within 1 km × 1 km grids to downscale PM2.5 predictions to 100 m × 100 m grid cells. To characterize uncertainty, we used meteorological variables, land-use variables, and elevation to model the monthly standard deviation of the difference between daily monitored and predicted PM2.5 for every 1 km × 1 km grid cell. This PM2.5 prediction dataset, including the downscaled and uncertainty predictions, allows epidemiologists to accurately estimate the adverse health effect of PM2.5. Compared with model performance of individual base learners, an ensemble model would achieve a better overall estimation. It is worth exploring other ensemble model formats to synthesize estimations from different models or from different groups to improve overall performance. Keywords: Fine particulate matter (PM2.5), Ensemble model, Neural network, Gradient boosting, Random forest

Published
2019

9. Intraoperative and Postoperative Complications With Arthroscopic Management of Femoroacetabular Impingement

Author

Bryan T. Kelly, Ryan A. Mlynarek, and Matthew S. Dooley

Subjects
medicine.medical_specialty, Neurovascular injury, business.industry, medicine, Hip arthroscopy, medicine.disease, business, Extravasation, Femoroacetabular impingement, Surgery, Femoral head avascular necrosis
Abstract

The indications for hip arthroscopy continue to expand, and the volume of procedures performed has increased exponentially since 2010. So too has the number of potential complications encountered because of the complexity of the operation and the steep learning curve. Potential complications can be wide ranging, from access-associated neurovascular injury to iatrogenic chondrolabral injury, postoperative fracture or instability, femoral head avascular necrosis, or intra- and postoperative cardiopulmonary compromise secondary to fluid extravasation. The goal of this chapter is to present the most common hip arthroscopy complications and strategies to prevent and treat those complications should they occur.

Published
2019
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11. Contributors

Author

Marta E. Aparicio-García, Joan L. Bottorff, Andreea C. Brabete, Joan C. Chrisler, Lucia Colodro-Conde, Isabel Cuéllar-Flores, Juan F. Díaz-Morales, Karen Goodall, Julie Hepworth, Joy L. Johnson, Mary T. Kelly, Nedra R. Lander, Rosa M. Limiñana-Gras, Chris McVittie, Danielle Nahon, Karen Newbigging, John L. Oliffe, Rosa M. Patró-Hernández, Jennan G. Read, Patricia P. Rieker, M. Pilar Sánchez-López, Londa Schiebinger, and Sabrina T. Wong

Published
2017
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12. Electrospray Ionization in Mass Spectrometry

Author

Ioan Marginean, Keqi Tang, Jason S. Page, and Ryan T. Kelly

Subjects
Desorption electrospray ionization, Matrix-assisted laser desorption electrospray ionization, Chemistry, Electrospray ionization, Extractive electrospray ionization, Analytical chemistry, Atmospheric-pressure chemical ionization, Direct electron ionization liquid chromatography–mass spectrometry interface, Mass spectrometry, Ambient ionization
Abstract

Since the pioneering work of Fenn et al. in the 1980s, electrospray ionization (ESI) has increasingly become the ionization technique of choice for molecular mass spectrometry (MS) in a broad range of chemical and biological applications because of its ability to create intact, multiply charged gas-phase ions and its ready coupling with MS. Highly charged droplets generated by electrospray are known to undergo asymmetric fission at the Rayleigh charge limit to form smaller droplets. The generation of gas-phase ions in ESI follows two possible mechanisms as described by Dole’s charged residue model (CRM) and Iribarne and Thomson’s ion evaporation model (IEM). Many currently available ESI–MS technologies highlighted in this article have been developed to improve ionization efficiency in ESI and ion transmission efficiency through the ESI–MS interface and to reduce bias and ion suppression in the ESI process. New technologies are continually emerging to make ESI–MS even more sensitive and quantitative.

Published
2017
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13. List of Contributors

Author

Mark J. Abzug, Cezmi A. Akdis, Katrina Allen, Leonard B. Bacharier, Mark Ballow, Ashton Bartholow, Allan Becker, Edward M. Behrens, Bruce G. Bender, M. Cecilia Berin, Brett P. Bielory, Leonard Bielory, S. Allan Bock, Mark Boguniewicz, Francisco A. Bonilla, A. Wesley Burks, Andrew Bush, Kenny H. Chan, Mirna Chehade, Anca M. Chiriac, Lisa Cicutto, Samuel A. Collins, Ronina A. Covar, Benjamin P. Davis, Fatma Dedeoglu, Pascal Demoly, Peyton A. Eggleston, Robert Eisenberg, Thomas A. Fleisher, Luz Fonacier, Deborah A. Gentile, James E. Gern, Marion Groetch, Susanne Halken, Robert G. Hamilton, Elysia M. Hollams, Steven M. Holland, John W. Holloway, Patrick G. Holt, Arne Høst, Daniel J. Jackson, Stacie M. Jones, Meyer Kattan, Brian T. Kelly, Kevin J. Kelly, Susan Kim, Donald B. Kohn, Howard M. Lederman, Heather K. Lehman, Robert F. Lemanske, Donald Y.M. Leung, Chris A. Liacouras, Andrew H. Liu, Gabrielle A. Lockett, Jonathan E. Markowitz, Fernando D. Martinez, Elizabeth C. Matsui, Bruce D. Mazer, Henry Milgrom, Amanda B. Muir, Harold S. Nelson, David P. Nichols, Luigi D. Notarangelo, Natalija Novak, Anna Nowak-Węgrzyn, Hans C. Oettgen, J. Tod Olin, Joao Bosco Oliveira, Hanneke (Joanne) N.G. Oude Elberink, Oscar Palomares, Wanda Phipatanakul, Nicole Pleskovic, Susan Prescott, Sergio D. Rosenzweig, Marc E. Rothenberg, Hugh A. Sampson, William J. Sheehan, Scott H. Sicherer, F. Estelle R. Simons, David P. Skoner, Joseph D. Spahn, Robert C. Strunk, Kathleen E. Sullivan, Robert P. Sundel, Stanley J. Szefler, Troy R. Torgerson, Erika von Mutius, Rudolph S. Wagner, Julie Wang, Richard W. Weber, Robert A. Wood, and Bruce L. Zuraw

Published
2016
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14. Latex Allergy

Author

Brian T. Kelly and Kevin J. Kelly

Subjects
medicine.medical_specialty, business.industry, Latex allergy, medicine, business, medicine.disease, Dermatology
Published
2016
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15. Search for a heavy gauge boson W' in the final state with an electron and large missing transverse energy in pp collisions at sort(s)=7TeV

Author

V. KHACHATRYAN, A. M. SIRUNYAN, A. TUMASYAN, W. ADAM, T. BERGAUER, M. DRAGICEVIC, J. ER . A. N(O, C. FABJAN, M. FRIEDL, R. FRUHWIRTH, V. M. GHETE, J. HAMMER, S. H. N(ANSEL, C. HARTL, M. HOCH, N. H. N(ORMANN, J. HRUBEC, M. JEITLER, G. KASIECZKA, W. KIESENHOFER, M. KRAMMER, D. LIKO, I. MIKULEC, M. PERNICKA, H. ROHRINGER, R. SCH N(OFBECK, J. STRAUSS, A. TAUROK, F. TEISCHINGER, W. WALTENBERGER, G. WALZEL, E. WIDL, C. E. WULZ, V. MOSSOLOV, N. SHUMEIKO, J. SUAREZ GONZALEZ, L. BENUCCI, L. CEARD, E. A. DE WOLF, X. JANSSEN, T. MAES, L. MUCIBELLO, S. OCHESANU, B. ROLAND, R. ROUGNY, M. SELVAGGI, H. VAN HAEVERMAET, P. VAN MECHELEN, N. VAN REMORTEL, V. ADLER, S. BEAUCERON, S. BLYWEERT, J. D. $B!G (BHONDT, O. DEVROEDE, A. KALOGEROPOULOS, J. MAES, M. MAES, S. TAVERNIER, W. VAN DONINCK, P. VAN MULDERS, I. VILLELLA, E. C. CHABERT, O. CHARAF, B. CLERBAUX, G. DE LENTDECKER, V. DERO, A. P. R. GAY, G. H. HAMMAD, T. HREUS, P. E. MARAGE, C. VANDER VELDE, P. VANLAER, J. WICKENS, S. COSTANTINI, M. GRUNEWALD, B. KLEIN, A. MARINOV, D. RYCKBOSCH, F. THYSSEN, M. TYTGAT, L. VANELDEREN, P. VERWILLIGEN, S. WALSH, N. ZAGANIDIS, S. BASEGMEZ, G. BRUNO, J. CAUDRON, J. DE FAVEREAU DE JENERET, C. DELAERE, P. DEMIN, D. FAVART, A. GIAMMANCO, G. GR N4EGOIRE, J. HOLLAR, V. LEMAITRE, O. MILITARU, S. OVYN, D. PAGANO, A. PIN, K. PIOTRZKOWSKI, L. QUERTENMONT, N. SCHUL, N. BELIY, T. CAEBERGS, E. DAUBIE, G. A. ALVES, D. DE JESUS DAMIAO, M. E. POL, M. H. G. SOUZA, W. CARVALHO, E. M. DA COSTA, C. DE OLIVEIRA MARTINS, S. FONSECA DE SOUZA, L. MUNDIM, H. NOGIMA, V. OGURI, J. M. OTALORA GOICOCHEA, W. L. PRADO DA SILVA, A. SANTORO, S. M. SILVA DO, AMARAL, A. SZNAJDER, F. TORRES DA SILVA DE ARAUJO, F. A. DIAS, M. A. F. DIAS, T. R. FERNANDEZ PEREZ TOMEI, E. M. GREGORES2, F. MARINHO, S. F. NOVAES, SANDRA S. PADULA, N. DARMENOV, L. DIMITROV, V. GENCHEV, P. IAYDJIEV, S. PIPEROV, M. RODOZOV, S. STOYKOVA, G. SULTANOV, V. TCHOLAKOV, R. TRAYANOV, I. VANKOV, M. DYULENDAROVA, R. HADJIISKA, V. KOZHUHAROV, L. LITOV, E. MARINOVA, M. MATEEV, B. PAVLOV, P. PETKOV, J. G. BIAN, G. M. CHEN, H. S. CHEN, C. H. JIANG, D. LIANG, S. LIANG, J. WANG, X. WANG, Z. WANG, M. YANG, J. ZANG, Z. ZHANG, Y. BAN, S. GUO, Z. HU, W. LI, Y. MAO, S. J. QIAN, H. TENG, B. ZHU, A. CABRERA, B. GOMEZ MORENO, A. A. OCAMPO RIOS, A. F. OSORIO OLIVEROS, J. C. SANABRIA, N. GODINOVIC, D. LELAS, K. LELAS, R. PLESTINA3, D. POLIC, I. PULJAK, Z. ANTUNOVIC, M. DZELALIJA, V. BRIGLJEVIC, S. DURIC, K. KADIJA, S. MOROVIC, A. ATTIKIS, R. FEREOS, M. GALANTI, J. MOUSA, C. NICOLAOU, F. PTOCHOS, P. A. RAZIS, H. RYKACZEWSKI, Y. ASSRAN, M. A. MAHMOUD, A. HEKTOR, M. KADASTIK, K. KANNIKE, M. M. N(UNTEL, M. RAIDAL, L. REBANE, V. AZZOLINI, P. EEROLA, S. CZELLAR, J. H. N(ARK N(ONEN, A. HEIKKINEN, V. KARIM N(AKI, R. KINNUNEN, J. KLEM, M. J. KORTELAINEN, T. LAMP N4EN, K. LASSILA PERINI, S. LEHTI, T. LIND N4EN, P. LUUKKA, T. M. N(AENP N(A N(A, E. TUOMINEN, J. TUOMINIEMI, E. TUOVINEN, D. UNGARO, L. WENDLAND, K. BANZUZI, A. KORPELA, T. TUUVA, D. SILLOU, M. BESANCON, M. DEJARDIN, D. DENEGRI, J. DESCAMPS, B. FABBRO, J. L. FAURE, F. FERRI, S. GANJOUR, F. X. GENTIT, A. GIVERNAUD, P. GRAS, G. HAMEL DE MONCHENAULT, P. JARRY, E. LOCCI, J. MALCLES, M. MARIONNEAU, L. MILLISCHER, J. RANDER, A. ROSOWSKY, D. ROUSSEAU, M. TITOV, P. VERRECCHIA, S. BAFFIONI, L. BIANCHINI, M. BLUJ, C. BROUTIN, P. BUSSON, C. CHARLOT, L. DOBRZYNSKI, R. GRANIER DE, CASSAGNAC, M. HAGUENAUER, P. MIN N4E, C. MIRONOV, C. OCHANDO, P. PAGANINI, D. SABES, R. SALERNO, Y. SIROIS, C. THIEBAUX, A. ZABI, J. L. AGRAM, A. BESSON, D. BLOCH, D. BODIN, J. M. BROM, M. CARDACI, E. CONTE, F. DROUHIN, C. FERRO, J. C. FONTAINE, D. GEL N4E, U. GOERLACH, S. GREDER, P. JUILLOT, M. KARIM, A. C. LE BIHAN, Y. MIKAMI, P. VAN HOVE, F. FASSI, D. MERCIER, C. BATY, N. BEAUPERE, M. BEDJIDIAN, O. BONDU, G. BOUDOUL, D. BOUMEDIENE, H. BRUN, N. CHANON, R. CHIERICI, D. CONTARDO, P. DEPASSE, H. EL MAMOUNI, A. FALKIEWICZ, J. FAY, S. GASCON, B. ILLE, T. KURCA, T. LE GRAND, M. LETHUILLIER, L. MIRABITO, S. PERRIES, V. SORDINI, S. TOSI, Y. TSCHUDI, P. VERDIER, H. XIAO, V. ROINISHVILI, G. ANAGNOSTOU, M. EDELHOFF, L. FELD, N. HERACLEOUS, O. HINDRICHS, R. JUSSEN, K. KLEIN, J. MERZ, N. MOHR, A. OSTAPCHUK, A. PERIEANU, F. RAUPACH, J. SAMMET, S. SCHAEL, D. SPRENGER, H. WEBER, M. WEBER, B. WITTMER, M. ATA, W. BENDER, M. ERDMANN, J. FRANGENHEIM, T. HEBBEKER, A. HINZMANN, K. HOEPFNER, C. HOF, T. KLIMKOVICH, D. KLINGEBIEL, P. KREUZER1, D. LANSKEY, C. MAGASS, M. MERSCHMEYER, A. MEYER, P. PAPACZ, H. PIETA, H. REITHLER, S. A. SCHMITZ, L. SONNENSCHEIN, J. STEGGEMANN, D. TEYSSIER, M. BONTENACKELS, M. DAVIDS, M. DUDA, G. FL N(UGGE, H. GEENEN, M. GIFFELS, W. HAJ AHMAD, D. HEYDHAUSEN, T. KRESS, Y. KUESSEL, A. LINN, A. NOWACK, L. PERCHALLA, O. POOTH, J. RENNEFELD, P. SAUERLAND, A. STAHL, M. THOMAS, D. TORNIER, M. H. ZOELLER, M. ALDAYA MARTIN, W. BEHRENHOFF, U. BEHRENS, M. BERGHOLZ, K. BORRAS, A. CAMPBELL, E. CASTRO, D. DAMMANN, G. ECKERLIN, A. FLOSSDORF, G. FLUCKE, A. GEISER, I. GLUSHKOV, J. HAUK, H. JUNG, M. KASEMANN, I. KATKOV, P. KATSAS, C. KLEINWORT, H. KLUGE, A. KNUTSSON, D. KR N( UCKER, E. KUZNETSOVA, W. LANGE, W. LOHMANN, R. MANKEL, M. MARIENFELD, I. A. MELZER PELLMANN, A. B. MEYER, J. MNICH, A. MUSSGILLER, J. OLZEM, A. PARENTI, A. RASPEREZA, A. RAVAL, R. SCHMIDT, T. SCHOERNER SADENIUS, N. SEN, M. STEIN, J. TOMASZEWSKA, D. VOLYANSKYY, R. WALSH, C. WISSING, C. AUTERMANN, S. BOBROVSKYI, J. DRAEGER, D. ECKSTEIN, H. ENDERLE, U. GEBBERT, K. KASCHUBE, G. KAUSSEN, R. KLANNER, B. MURA, S. NAUMANN EMME, F. NOWAK, N. PIETSCH, C. SANDER, H. SCHETTLER, P. SCHLEPER, M. SCHR N(ODER, T. SCHUM, J. SCHWANDT, A. K. SRIVASTAVA, H. STADIE, G. STEINBR N(UCK, J. THOMSEN, R. WOLF, J. BAUER, V. BUEGE, A. CAKIR, T. CHWALEK, D. DAEUWEL, W. DE BOER, A. DIERLAMM, G. DIRKES, M. FEINDT, J. GRUSCHKE, C. HACKSTEIN, F. HARTMANN, M. HEINRICH, H. HELD, K. H. HOFFMANN, S. HONC, T. KUHR, D. MARTSCHEI, S. MUELLER, T.H. M. N(ULLER, M. B. NEULAND, M. NIEGEL, O. OBERST, A. OEHLER, J. OTT, T. PEIFFER, D. PIPARO, G. QUAST, K. RABBERTZ, F. RATNIKOV, M. RENZ, A. SABELLEK, C. SAOUT, A. SCHEURER, P. SCHIEFERDECKER, F. P. SCHILLING, G. SCHOTT, H. J. SIMONIS, F. M. STOBER, D. TROENDLE, J. WAGNER KUHR, M. ZEISE, V. ZHUKOV, E. B. ZIEBARTH, G. DASKALAKIS, T. GERALIS, S. KESISOGLOU, A. KYRIAKIS, D. LOUKAS, I. MANOLAKOS, A. MARKOU, C. MARKOU, C. MAVROMMATIS, E. PETRAKOU, L. GOUSKOS, T. MERTZIMEKIS, A. PANAGIOTOU, I. EVANGELOU, P. KOKKAS, N. MANTHOS, I. PAPADOPOULOS, V. PATRAS, F. A. TRIANTIS, A. ARANYI, G. BENCZE, L. BOLDIZSAR, G. DEBRECZENI, C. HAJDU1, D. HORVATH, A. KAPUSI, K. KRAJCZAR1, A. LASZLO, F. SIKLER, G. VESZTERGOMBI1, N. BENI, J. MOLNAR, J. PALINKAS, Z. SZILLASI, V. VESZPREMI, P. RAICS, Z. L. TROCSANYI, B. UJVARI, S. BANSAL, S. B. BERI, V. BHATNAGAR, M. JINDAL, M. KAUR, J. M. KOHLI, M. Z. MEHTA, N. NISHU, L. K. SAINI, A. SHARMA, R. SHARMA, A. P. SINGH, J. B. SINGH, S. P. SINGH, S. AHUJA, S. BHATTACHARYA, S. CHAUHAN, B. C. CHOUDHARY, P. GUPTA, S. JAIN, A. KUMAR, R. K. SHIVPURI, R. K. CHOUDHURY, D. DUTTA, S. KAILAS, S. K. KATARIA, A. K. MOHANTY1, L. M. PANT, P. SHUKLA, P. SUGGISETTI, T. AZIZ, M. GUCHAIT12, A. GURTU, M. MAITY13, D. MAJUMDER, G. MAJUMDER, K. MAZUMDAR, G. B. MOHANTY, A. SAHA, K. SUDHAKAR, N. WICKRAMAGE, S. BANERJEE, S. DUGAD, N. K. MONDAL, H. ARFAEI, H. BAKHSHIANSOHI, S. M. ETESAMI, A. FAHIM, M. HASHEMI, A. JAFARI, M. KHAKZAD, A. MOHAMMADI, M. MOHAMMADI NAJAFABADI, S. PAKTINAT MEHDIABADI, B. SAFARZADEH, M. ZEINALI, M. ABBRESCIA, L. BARBONE, C. CALABRIA, A. COLALEO, D. CREANZA, N. DE FILIPPIS, M. DE, PALMA, A. DIMITROV, F. FEDELE, L. FIORE, G. IASELLI, L. LUSITO, G. MAGGI, M. MAGGI, N. MANNA, B. MARANGELLI, S. MY, S. NUZZO, G. A. PIERRO, A. POMPILI, G. PUGLIESE, F. ROMANO, G. ROSELLI, G. SELVAGGI, L. SILVESTRIS, R. TRENTADUE, S. TUPPUTI, G. ZITO, G. ABBIENDI, A. C. BENVENUTI, F. R. CAVALLO, G. M. DALLAVALLE, F. FABBRI, P. GIACOMELLI, M. GIUNTA, C. GRANDI, S. MARCELLINI, G. MASETTI, A. MONTANARI, F. ODORICI, A. PERROTTA, A. M. ROSSI, R. TRAVAGLINI, S. ALBERGO, G. 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DOMINGUEZ, M. EADS, J. KELLER, T. KELLY, I. KRAVCHENKO, J. LAZO-FLORES, C. LUNDSTEDT, H. MALBOUISSON, S. MALIK, G.R. SNOW, U. BAUR, A. GODSHALK, I. IASHVILI, A. KHARCHILAVA, K. SMITH, J. ZENNAMO, G. ALVERSON, E. BARBERIS, D. BAUMGARTEL, O. BOERIU, M. CHASCO, K. KAADZE, S. REUCROFT, J. SWAIN, D. WOOD, J. ZHANG, A. ANASTASSOV, A. KUBIK, N. ODELL, R.A. OFIERZYNSKI, B. POLLACK, A. POZDNYAKOV, S. STOYNEV, M. VELASCO, S.WON, L. ANTONELLI, D. BERRY, M. HILDRETH, C. JESSOP, D.J. KARMGARD, J. KOLB, T. KOLBERG, K. LANNON, W. LUO, S. LYNCH, N. MARINELLI, D.M. MORSE, T. PEARSON, R. RUCHTI, J. SLAUNWHITE, N. VALLS, J. WARCHOL, M.WAYNE, J. ZIEGLER, B. BYLSMA, L.S. DURKIN, J. GU, P. KILLEWALD, T.Y. LING, M. RODENBURG, G.WILLIAMS, N. ADAM, E. BERRY, P. ELMER, D. GERBAUDO, V. HALYO, P. HEBDA, A. HUNT, J. JONES, E. LAIRD, D. LOPES PEGNA, D. MARLOW, T. MEDVEDEVA, M. MOONEY, J. OLSEN, P. PIROU N4E, H. SAKA, D. STICKLAND, C. TULLY, J.S.WERNER, A. ZURANSKI, J.G. ACOSTA, X.T. HUANG, A. LOPEZ, H. MENDEZ, S. 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THOMAS, G. CERIZZA, M. HOLLINGSWORTH, S. SPANIER, Z.C. YANG, A. YORK, J. ASAADI, R. EUSEBI, J. GILMORE, A. GURROLA, T. KAMON, V. KHOTILOVICH, R. MONTALVO, C.N. NGUYEN, J. PIVARSKI, A. SAFONOV, S. SENGUPTA, A. TATARINOV, D. TOBACK, M.WEINBERGER, N. AKCHURIN, C. BARDAK, J. DAMGOV, C. JEONG, K. KOVITANGGOON, S.W. LEE, P. MANE, Y. ROH, A. SILL, I. VOLOBOUEV, R.WIGMANS, E. YAZGAN, E. APPELT, E. BROWNSON, D. ENGH, C. FLOREZ, W. GABELLA, W. JOHNS, P. KURT, C. MAGUIRE, A. MELO, P. SHELDON, J. VELKOVSKA, M.W. ARENTON, M. BALAZS, S. BOUTLE, M. BUEHLER, S. CONETTI, B. COX, B. FRANCIS, R. HIROSKY, A. LEDOVSKOY, C. LIN, C. NEU, T. PATEL, R. YOHAY, S. GOLLAPINNI, R. HARR, P.E. KARCHIN, V. LOGGINS, M. MATTSON, C. MILST`ENE, A. SAKHAROV, M. ANDERSON, M. BACHTIS, J.N. BELLINGER, D. CARLSMITH, S. DASU, J. EFRON, L. GRAY, K.S. GROGG, M. GROTHE, R. HALL-WILTON, M. HERNDON, P. KLABBERS, J. KLUKAS, A. LANARO, C. LAZARIDIS, J. LEONARD, J. LIU, D. LOMIDZE, R. LOVELESS, A. MOHAPATRA, W. PARKER, D. REEDER, I. ROSS, A. SAVIN, W.H. SMITH, J. SWANSON, M.WEINBERG, and L. BRIGLIADORI

Subjects
CMS, High Energy Physics::Phenomenology, High Energy Physics::Experiment, LHC, Nuclear Experiment, HEP
Abstract

A search for a heavy gauge boson W‧ has been conducted by the CMS experiment at the LHC in the decay channel with an electron and large transverse energy imbalance ETmiss, using proton-proton collision data corresponding to an integrated luminosity of 36 pb-1. No excess above standard model expectations is seen in the transverse mass distribution of the electron-ETmiss system. Assuming standard-model-like couplings and decay branching fractions, a W‧ boson with a mass less than 1.36 TeV/c2 is excluded at 95% confidence level.

Published
2011

16. Search for a heavy bottom-like quark in pp collisions at sqrt(s)=7TeV

Author

S. CHATRCHYAN, V. KHACHATRYAN, A. M. SIRUNYAN, A. TUMASYAN, W. ADAM, T. BERGAUER, M. DRAGICEVIC, J. ER O, C. FABJAN, M. FRIEDL, R. FRUHWIRTH, V. M. GHETE, J. HAMMER, S. H. AENSEL, C. HARTL, M. HOCH, N. H. OERMANN, J. HRUBEC, M. JEITLER, G. KASIECZKA, W. KIESENHOFER, M. KRAMMER, D. LIKO, I. MIKULEC, M. PERNICKA, H. ROHRINGER, R. SCH N(OFBECK, J. STRAUSS, A. TAUROK, F. TEISCHINGER, W. WALTENBERGER, G. WALZEL, E. WIDL, C. E. WULZ, V. MOSSOLOV, N. SHUMEIKO, J. SUAREZ GONZALEZ, L. BENUCCI, L. CEARD, E. A. DE WOLF, X. JANSSEN, T. MAES, L. MUCIBELLO, S. OCHESANU, B. ROLAND, R. ROUGNY, M. SELVAGGI, H. VAN HAEVERMAET, P. VAN MECHELEN, N. VAN REMORTEL, V. ADLER, S. BEAUCERON, S. BLYWEERT, J. D. $B!G (BHONDT, O. DEVROEDE, A. KALOGEROPOULOS, J. MAES, M. MAES, S. TAVERNIER, W. VAN DONINCK, P. VAN MULDERS, I. VILLELLA, E. C. CHABERT, O. CHARAF, B. CLERBAUX, G. DE LENTDECKER, V. DERO, A. P. R. GAY, G. H. HAMMAD, T. HREUS, P. E. MARAGE, C. VANDER VELDE, P. VANLAER, J. WICKENS, S. COSTANTINI, M. GRUNEWALD, B. KLEIN, A. MARINOV, D. RYCKBOSCH, F. THYSSEN, M. TYTGAT, L. VANELDEREN, P. VERWILLIGEN, S. WALSH, N. ZAGANIDIS, S. BASEGMEZ, G. BRUNO, J. CAUDRON, J. DE FAVEREAU DE JENERET, C. DELAERE, P. DEMIN, D. FAVART, A. GIAMMANCO, G. GR N4EGOIRE, J. HOLLAR, V. LEMAITRE, O. MILITARU, S. OVYN, D. PAGANO, A. PIN, K. PIOTRZKOWSKI, L. QUERTENMONT, N. SCHUL, N. BELIY, T. CAEBERGS, E. DAUBIE, G. A. ALVES, D. DE JESUS DAMIAO, M. E. POL, M. H. G. SOUZA, W. CARVALHO, E. M. DA COSTA, C. DE OLIVEIRA MARTINS, S. FONSECA DE SOUZA, L. MUNDIM, H. NOGIMA, V. OGURI, J. M. OTALORA GOICOCHEA, W. L. PRADO DA SILVA, A. SANTORO, S. M. SILVA DO, AMARAL, A. SZNAJDER, F. TORRES DA SILVA DE ARAUJO, F. A. DIAS, M. A. F. DIAS, T. R. FERNANDEZ PEREZ TOMEI, E. M. GREGORES2, F. MARINHO, S. F. NOVAES, SANDRA S. PADULA, N. DARMENOV, L. DIMITROV, V. GENCHEV, P. IAYDJIEV, S. PIPEROV, M. RODOZOV, S. STOYKOVA, G. SULTANOV, V. TCHOLAKOV, R. TRAYANOV, I. VANKOV, M. DYULENDAROVA, R. HADJIISKA, V. KOZHUHAROV, L. LITOV, E. 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LYKKEN, K. MAESHIMA, J.M. MARRAFFINO, D. MASON, P. MCBRIDE, T. MCCAULEY, T. MIAO, K. MISHRA, S. MRENNA, Y. MUSIENKO, C. NEWMAN-HOLMES, V. O $B!G (BDELL, S. POPESCU, R. PORDES, O. PROKOFYEV, N. SAOULIDOU, E. SEXTON-KENNEDY, S. SHARMA, A. SOHA, W.J. SPALDING, L. SPIEGEL, P. TAN, L. TAYLOR, S. TKACZYK, L. UPLEGGER, E.W. VAANDERING, R. VIDAL, J. WHITMORE, W. WU, F. YANG, F. YUMICEVA, J.C. YUN, D. ACOSTA, P. AVERY, D. BOURILKOV, M. CHEN, G.P. DI GIOVANNI, D. DOBUR, A. DROZDETSKIY, R.D. FIELD, M. FISHER, Y. FU, I.K. FURIC, J. GARTNER, S. GOLDBERG, B. KIM, S. KLIMENKO, J. KONIGSBERG, A. KORYTOV, K. KOTOV, A. KROPIVNITSKAYA, T. KYPREOS, K. MATCHEV, G. MITSELMAKHER, L. MUNIZ, Y. PAKHOTIN, M. PETTERSON, C. PRESCOTT, R. REMINGTON, M. SCHMITT, B. SCURLOCK, P. SELLERS, M. SNOWBALL, D.WANG, J. YELTON, M. ZAKARIA, C. CERON, V. GAULTNEY, L. KRAMER, L.M. LEBOLO, S. LINN, P. MARKOWITZ, G. MARTINEZ, D. MESA, J.L. RODRIGUEZ, T. ADAMS, A. ASKEW, J. BOCHENEK, J. CHEN, B. DIAMOND, S.V. GLEYZER, J. HAAS, S. HAGOPIAN, V. HAGOPIAN, M. JENKINS, K.F. JOHNSON, H. PROSPER, S. SEKMEN, V. VEERARAGHAVAN, M.M. BAARMAND, B. DORNEY, S. GURAGAIN, M. HOHLMANN, H. KALAKHETY, R. RALICH, I. VODOPIYANOV, M.R. ADAMS, I.M. ANGHEL, L. APANASEVICH, Y. BAI, V.E. BAZTERRA, R.R. BETTS, J. CALLNER, R. CAVANAUGH, C. DRAGOIU, E.J. GARCIA-SOLIS, C.E. GERBER, D.J. HOFMAN, S. KHALATYAN, F. LACROIX, C. O $B!G (BBRIEN, E. SHABALINA, C. SILVESTRE, A. SMORON, D. STROM, N. VARELAS, U. AKGUN, E.A. ALBAYRAK, B. BILKI, K. CANKOCAK, W. CLARIDA, F. DURU, C.K. LAE, E. MCCLIMENT, J.-P. MERLO, H. MERMERKAYA, A. MESTVIRISHVILI, A. MOELLER, J. NACHTMAN, C.R. NEWSOM, E. NORBECK, J. OLSON, Y. ONEL, F. OZOK, S. SEN, J.WETZEL, T. YETKIN, K. YI, B.A. BARNETT, B. BLUMENFELD, A. BONATO, C. ESKEW, D. FEHLING, G. GIURGIU, A.V. GRITSAN, Z.J. GUO, G. HU, P. MAKSIMOVIC, S. RAPPOCCIO, M. SWARTZ, N.V. TRAN, A. WHITBECK, P. BARINGER, A. BEAN, G. BENELLI, O. GRACHOV, M. MURRAY, D. NOONAN, V. RADICCI, S. SANDERS, J.S.WOOD, V. ZHUKOVA, D. BANDURIN, T. BOLTON, I. CHAKABERIA, A. IVANOV, M. MAKOUSKI, Y. MARAVIN, S. SHRESTHA, I. SVINTRADZE, Z.WAN, J. GRONBERG, D. LANGE, D. WRIGHT, A. BADEN, M. BOUTEMEUR, S.C. ENO, D. FERENCEK, J.A. GOMEZ, N.J. HADLEY, R.G. KELLOGG, M. KIRN, Y. LU, A.C. MIGNEREY, K. ROSSATO, P. RUMERIO, F. SANTANASTASIO, A. SKUJA, J. TEMPLE, M.B. TONJES, S.C. TONWAR, E. TWEDT, B. ALVER, G. BAUER, J. BENDAVID, W. BUSZA, E. BUTZ, I.A. CALI, M. CHAN, V. DUTTA, P. EVERAERTS, G. GOMEZ CEBALLOS, M. GONCHAROV, K.A. HAHN, P. HARRIS, Y. KIM, M. KLUTE, Y.-J. LEE, C. LOIZIDES, P.D. LUCKEY, T. MA, S. NAHN, C. PAUS, C. ROLAND, G. ROLAND, M. RUDOLPH, G.S.F. STEPHANS, K. SUMOROK, K. SUNG, E.A. WENGER, B. WYSLOUCH, S. XIE, Y. YILMAZ, A.S. YOON, M. ZANETTI, P. COLE, S.I. COOPER, P. CUSHMAN, B. DAHMES, A. DE BENEDETTI, P.R. DUDERO, G. FRANZONI, J. HAUPT, K. KLAPOETKE, Y. KUBOTA, J. MANS, V. REKOVIC, R. RUSACK, M. SASSEVILLE, A. SINGOVSKY, L.M. CREMALDI, R. GODANG, R. KROEGER, L. PERERA, R. RAHMAT, D.A. SANDERS, D. SUMMERS, K. BLOOM, S. BOSE, J. BUTT, D.R. CLAES, A. DOMINGUEZ, M. EADS, J. KELLER, T. KELLY, I. KRAVCHENKO, J. LAZO-FLORES, C. LUNDSTEDT, H. MALBOUISSON, S. MALIK, G.R. SNOW, U. BAUR, A. GODSHALK, I. IASHVILI, A. KHARCHILAVA, K. SMITH, J. ZENNAMO, G. ALVERSON, E. BARBERIS, D. BAUMGARTEL, O. BOERIU, M. CHASCO, K. KAADZE, S. REUCROFT, J. SWAIN, D. WOOD, J. ZHANG, A. ANASTASSOV, A. KUBIK, N. ODELL, R.A. OFIERZYNSKI, B. POLLACK, A. POZDNYAKOV, S. STOYNEV, M. VELASCO, S.WON, L. ANTONELLI, D. BERRY, M. HILDRETH, C. JESSOP, D.J. KARMGARD, J. KOLB, T. KOLBERG, K. LANNON, W. LUO, S. LYNCH, N. MARINELLI, D.M. MORSE, T. PEARSON, R. RUCHTI, J. SLAUNWHITE, N. VALLS, J. WARCHOL, M.WAYNE, J. ZIEGLER, B. BYLSMA, L.S. DURKIN, J. GU, P. KILLEWALD, T.Y. LING, M. RODENBURG, G.WILLIAMS, N. ADAM, E. BERRY, P. ELMER, D. GERBAUDO, V. HALYO, P. HEBDA, A. HUNT, J. JONES, E. LAIRD, D. LOPES PEGNA, D. MARLOW, T. MEDVEDEVA, M. MOONEY, J. OLSEN, P. PIROU N4E, H. SAKA, D. STICKLAND, C. TULLY, J.S.WERNER, A. ZURANSKI, J.G. ACOSTA, X.T. HUANG, A. LOPEZ, H. MENDEZ, S. OLIVEROS, J.E. RAMIREZ VARGAS, A. ZATSERKLYANIY, E. ALAGOZ, V.E. BARNES, G. BOLLA, L. BORRELLO, D. BORTOLETTO, A. EVERETT, A.F. GARFINKEL, Z. GECSE, L. GUTAY, M. JONES, O. KOYBASI, A.T. LAASANEN, N. LEONARDO, C. LIU, V. MAROUSSOV, M. MEIER, P. MERKEL, D.H. MILLER, N. NEUMEISTER, K. POTAMIANOS, I. SHIPSEY, D. SILVERS, A. SVYATKOVSKIY, H.D. YOO, J. ZABLOCKI, Y. ZHENG, P. JINDAL, N. PARASHAR, C. BOULAHOUACHE, V. CUPLOV, K.M. ECKLUND, F.J.M. GEURTS, J.H. LIU, J. MORALES, B.P. PADLEY, R. REDJIMI, J. ROBERTS, J. ZABEL, B. BETCHART, A. BODEK, Y.S. CHUNG, P. DE BARBARO, R. DEMINA, Y. ESHAQ, H. FLACHER, A. GARCIA, BELLIDO, P. GOLDENZWEIG, Y. GOTRA, J. HAN, A. HAREL, D.C. MINER, D. ORBAKER, G. PETRILLO, D. VISHNEVSKIY, M. ZIELINSKI, A. BHATTI, L. DEMORTIER, K. GOULIANOS, G. LUNGU, C. MESROPIAN, M. YAN, O. ATRAMENTOV, A. BARKER, D. DUGGAN, Y. GERSHTEIN, R. GRAY, E. HALKIADAKIS, D. HIDAS, D. HITS, A. LATH, S. PANWALKAR, R. PATEL, A. RICHARDS, K. ROSE, S. SCHNETZER, S. SOMALWAR, R. STONE, S. THOMAS, G. CERIZZA, M. HOLLINGSWORTH, S. SPANIER, Z.C. YANG, A. YORK, J. ASAADI, R. EUSEBI, J. GILMORE, A. GURROLA, T. KAMON, V. KHOTILOVICH, R. MONTALVO, C.N. NGUYEN, J. PIVARSKI, A. SAFONOV, S. SENGUPTA, A. TATARINOV, D. TOBACK, M.WEINBERGER, N. AKCHURIN, C. BARDAK, J. DAMGOV, C. JEONG, K. KOVITANGGOON, S.W. LEE, P. MANE, Y. ROH, A. SILL, I. VOLOBOUEV, R.WIGMANS, E. YAZGAN, E. APPELT, E. BROWNSON, D. ENGH, C. FLOREZ, W. GABELLA, W. JOHNS, P. KURT, C. MAGUIRE, A. MELO, P. SHELDON, J. VELKOVSKA, M.W. ARENTON, M. BALAZS, S. BOUTLE, M. BUEHLER, S. CONETTI, B. COX, B. FRANCIS, R. HIROSKY, A. LEDOVSKOY, C. LIN, C. NEU, T. PATEL, R. YOHAY, S. GOLLAPINNI, R. HARR, P.E. KARCHIN, V. LOGGINS, M. MATTSON, C. MILST`ENE, A. SAKHAROV, M. ANDERSON, M. BACHTIS, J.N. BELLINGER, D. CARLSMITH, S. DASU, J. EFRON, L. GRAY, K.S. GROGG, M. GROTHE, R. HALL-WILTON, M. HERNDON, P. KLABBERS, J. KLUKAS, A. LANARO, C. LAZARIDIS, J. LEONARD, J. LIU, D. LOMIDZE, R. LOVELESS, A. MOHAPATRA, W. PARKER, D. REEDER, I. ROSS, A. SAVIN, W.H. SMITH, J. SWANSON, M.WEINBERG, and L. BRIGLIADORI

Subjects
HADRON-HADRON SCATTERING, CMS, MUON, LHC, ELECTRON
Abstract

A search for pair-produced bottom-like quarks in pp collisions at √s = 7 TeV is conducted with the CMS experiment at the LHC. The decay b →tW is considered in this search. The b¯b →tW−¯tW+ process can be identified by the distinctive signature of trileptons and same-sign dileptons. With a data sample corresponding to an integrated luminosity of 34 pb^−1, no excess above the standard model background predictions is observed and a b quark with a mass between 255 and 361 GeV/c2 is excluded at the 95% confidence level.

Published
2011

17. The Study of Deep-Sea Cephalopods

Author

Michael Vecchione, Heather Judkins, Kathrin S. R. Bolstad, José Eduardo A. R. Marian, Heather E. Braid, Uwe Piatkowski, Aaron B. Evans, Amanda Reid, José C. Xavier, Jesse T. Kelly, José Angel Alvarez Perez, Dirk Fuchs, Ryuta Nakajima, and Henk Jan T. Hoving

Subjects
0106 biological sciences, Ecology, Range (biology), 010604 marine biology & hydrobiology, media_common.quotation_subject, Biogeography, Longevity, Significant part, Biology, biology.organism_classification, 010603 evolutionary biology, 01 natural sciences, Tropical ecology, Deep sea, Cephalopod, 14. Life underwater, media_common, Trophic level
Abstract

"Deep-sea" cephalopods are here defined as cephalopods that spend a significant part of their life cycles outside the euphotic zone. In this chapter, the state of knowledge in several aspects of deep-sea cephalopod research are summarized, including information sources for these animals, diversity and general biogeography and life cycles, including reproduction. Recommendations are made for addressing some of the remaining knowledge deficiencies using a variety of traditional and more recently developed methods. The types of oceanic gear that are suitable for collecting cephalopod specimens and images are reviewed. Many groups of deep-sea cephalopods require taxonomic reviews, ideally based on both morphological and molecular characters. Museum collections play a vital role in these revisions, and novel (molecular) techniques may facilitate new use of old museum specimens. Fundamental life-cycle parameters remain unknown for many species; techniques developed for neritic species that could potentially be applied to deep-sea cephalopods are discussed. Reproductive tactics and strategies in deep-sea cephalopods are very diverse and call for comparative evolutionary and experimental studies, but even in the twenty-first century, mature individuals are still unknown for many species. New insights into diet and trophic position have begun to reveal a more diverse range of feeding strategies than the typically voracious predatory lifestyle known for many cephalopods. Regular standardized deep-sea cephalopod surveys are necessary to provide insight into temporal changes in oceanic cephalopod populations and to forecast, verify and monitor the impacts of global marine changes and human impacts on these populations.

Published
2014
Full Text
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18. A search for excited leptons in pp collisions at √s = 7 TeV

Author

S. Chatrchyan, V. Khachatryan, A.M. Sirunyan, A. Tumasyan, W. Adam, T. Bergauer, M. Dragicevic, J. Erö, C. Fabjan, M. Friedl, R. Frühwirth, V.M. Ghete, J. Hammer, S. Hänsel, M. Hoch, N. Hörmann, J. Hrubec, M. Jeitler, W. Kiesenhofer, M. Krammer, D. Liko, I. Mikulec, M. Pernicka, H. Rohringer, R. Schöfbeck, J. Strauss, A. Taurok, F. Teischinger, P. Wagner, W. Waltenberger, G. Walzel, E. Widl, C.-E. Wulz, V. Mossolov, N. Shumeiko, J. Suarez Gonzalez, S. Bansal, L. Benucci, E.A. De Wolf, X. Janssen, J. Maes, T. Maes, L. Mucibello, S. Ochesanu, B. Roland, R. Rougny, M. Selvaggi, H. Van Haevermaet, P. Van Mechelen, N. Van Remortel, F. Blekman, S. Blyweert, J. DʼHondt, O. Devroede, R. Gonzalez Suarez, A. Kalogeropoulos, M. Maes, W. Van Doninck, P. Van Mulders, G.P. Van Onsem, I. Villella, O. Charaf, B. Clerbaux, G. De Lentdecker, V. Dero, A.P.R. Gay, G.H. Hammad, T. Hreus, P.E. Marage, L. Thomas, C. Vander Velde, P. Vanlaer, V. Adler, A. Cimmino, S. Costantini, M. Grunewald, B. Klein, J. Lellouch, A. Marinov, J. Mccartin, D. Ryckbosch, F. Thyssen, M. Tytgat, L. Vanelderen, P. Verwilligen, S. Walsh, N. Zaganidis, S. Basegmez, G. Bruno, J. Caudron, L. Ceard, E. Cortina Gil, J. De Favereau De Jeneret, C. Delaere, D. Favart, A. Giammanco, G. Grégoire, J. Hollar, V. Lemaitre, J. Liao, O. Militaru, C. Nuttens, S. Ovyn, D. Pagano, A. Pin, K. Piotrzkowski, N. Schul, N. Beliy, T. Caebergs, E. Daubie, G.A. Alves, L. Brito, D. De Jesus Damiao, M.E. Pol, M.H.G. Souza, W.L. Aldá Júnior, W. Carvalho, E.M. Da Costa, C. De Oliveira Martins, S. Fonseca De Souza, L. Mundim, H. Nogima, V. Oguri, W.L. Prado Da Silva, A. Santoro, S.M. Silva Do Amaral, A. Sznajder, C.A. Bernardes, F.A. Dias, T.R. Fernandez Perez Tomei, E.M. Gregores, C. Lagana, F. Marinho, P.G. Mercadante, S.F. Novaes, Sandra S. Padula, N. Darmenov, V. Genchev, P. Iaydjiev, S. Piperov, M. Rodozov, S. Stoykova, G. Sultanov, V. Tcholakov, R. Trayanov, A. Dimitrov, R. Hadjiiska, A. Karadzhinova, V. Kozhuharov, L. Litov, M. Mateev, B. Pavlov, P. Petkov, J.G. Bian, G.M. Chen, H.S. Chen, C.H. Jiang, D. Liang, S. Liang, X. Meng, J. Tao, J. Wang, X. Wang, Z. Wang, H. Xiao, M. Xu, J. Zang, Z. Zhang, Y. Ban, S. Guo, Y. Guo, W. Li, Y. Mao, S.J. Qian, H. Teng, B. Zhu, W. Zou, A. Cabrera, B. Gomez Moreno, A.A. Ocampo Rios, A.F. Osorio Oliveros, J.C. Sanabria, N. Godinovic, D. Lelas, K. Lelas, R. Plestina, D. Polic, I. Puljak, Z. Antunovic, M. Dzelalija, V. Brigljevic, S. Duric, K. Kadija, S. Morovic, A. Attikis, M. Galanti, J. Mousa, C. Nicolaou, F. Ptochos, P.A. Razis, M. Finger, Y. Assran, S. Khalil, M.A. Mahmoud, A. Hektor, M. Kadastik, M. Müntel, M. Raidal, L. Rebane, A. Tiko, V. Azzolini, P. Eerola, G. Fedi, S. Czellar, J. Härkönen, A. Heikkinen, V. Karimäki, R. Kinnunen, M.J. Kortelainen, T. Lampén, K. Lassila-Perini, S. Lehti, T. Lindén, P. Luukka, T. Mäenpää, E. Tuominen, J. Tuominiemi, E. Tuovinen, D. Ungaro, L. Wendland, K. Banzuzi, A. Karjalainen, A. Korpela, T. Tuuva, D. Sillou, M. Besancon, S. Choudhury, M. Dejardin, D. Denegri, B. Fabbro, J.L. Faure, F. Ferri, S. Ganjour, F.X. Gentit, A. Givernaud, P. Gras, G. Hamel de Monchenault, P. Jarry, E. Locci, J. Malcles, M. Marionneau, L. Millischer, J. Rander, A. Rosowsky, I. Shreyber, M. Titov, P. Verrecchia, S. Baffioni, F. Beaudette, L. Benhabib, L. Bianchini, M. Bluj, C. Broutin, P. Busson, C. Charlot, T. Dahms, L. Dobrzynski, S. Elgammal, R. Granier de Cassagnac, M. Haguenauer, P. Miné, C. Mironov, C. Ochando, P. Paganini, D. Sabes, R. Salerno, Y. Sirois, C. Thiebaux, B. Wyslouch, A. Zabi, J.-L. Agram, J. Andrea, D. Bloch, D. Bodin, J.-M. Brom, M. Cardaci, E.C. Chabert, C. Collard, E. Conte, F. Drouhin, C. Ferro, J.-C. Fontaine, D. Gelé, U. Goerlach, S. Greder, P. Juillot, M. Karim, A.-C. Le Bihan, Y. Mikami, P. Van Hove, F. Fassi, D. Mercier, C. Baty, S. Beauceron, N. Beaupere, M. Bedjidian, O. Bondu, G. Boudoul, D. Boumediene, H. Brun, J. Chasserat, R. Chierici, D. Contardo, P. Depasse, H. El Mamouni, J. Fay, S. Gascon, B. Ille, T. Kurca, T. Le Grand, M. Lethuillier, L. Mirabito, S. Perries, V. Sordini, S. Tosi, Y. Tschudi, P. Verdier, D. Lomidze, G. Anagnostou, S. Beranek, M. Edelhoff, L. Feld, N. Heracleous, O. Hindrichs, R. Jussen, K. Klein, J. Merz, N. Mohr, A. Ostapchuk, A. Perieanu, F. Raupach, J. Sammet, S. Schael, D. Sprenger, H. Weber, M. Weber, B. Wittmer, M. Ata, E. Dietz-Laursonn, M. Erdmann, T. Hebbeker, A. Hinzmann, K. Hoepfner, T. Klimkovich, D. Klingebiel, P. Kreuzer, D. Lanske, J. Lingemann, C. Magass, M. Merschmeyer, A. Meyer, P. Papacz, H. Pieta, H. Reithler, S.A. Schmitz, L. Sonnenschein, J. Steggemann, D. Teyssier, M. Bontenackels, M. Davids, M. Duda, G. Flügge, H. Geenen, M. Giffels, W. Haj Ahmad, D. Heydhausen, F. Hoehle, B. Kargoll, T. Kress, Y. Kuessel, A. Linn, A. Nowack, L. Perchalla, O. Pooth, J. Rennefeld, P. Sauerland, A. Stahl, M. Thomas, D. Tornier, M.H. Zoeller, M. Aldaya Martin, W. Behrenhoff, U. Behrens, M. Bergholz, A. Bethani, K. Borras, A. Cakir, A. Campbell, E. Castro, D. Dammann, G. Eckerlin, D. Eckstein, A. Flossdorf, G. Flucke, A. Geiser, J. Hauk, H. Jung, M. Kasemann, I. Katkov, P. Katsas, C. Kleinwort, H. Kluge, A. Knutsson, M. Krämer, D. Krücker, E. Kuznetsova, W. Lange, W. Lohmann, R. Mankel, M. Marienfeld, I.-A. Melzer-Pellmann, A.B. Meyer, J. Mnich, A. Mussgiller, J. Olzem, A. Petrukhin, D. Pitzl, A. Raspereza, A. Raval, M. Rosin, R. Schmidt, T. Schoerner-Sadenius, N. Sen, A. Spiridonov, M. Stein, J. Tomaszewska, R. Walsh, C. Wissing, C. Autermann, V. Blobel, S. Bobrovskyi, J. Draeger, H. Enderle, U. Gebbert, M. Görner, K. Kaschube, G. Kaussen, H. Kirschenmann, R. Klanner, J. Lange, B. Mura, S. Naumann-Emme, F. Nowak, N. Pietsch, C. Sander, H. Schettler, P. Schleper, E. Schlieckau, M. Schröder, T. Schum, J. Schwandt, H. Stadie, G. Steinbrück, J. Thomsen, C. Barth, J. Bauer, J. Berger, V. Buege, T. Chwalek, W. De Boer, A. Dierlamm, G. Dirkes, M. Feindt, J. Gruschke, C. Hackstein, F. Hartmann, M. Heinrich, H. Held, K.H. Hoffmann, S. 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McBride, T. Miao, K. Mishra, S. Mrenna, Y. Musienko, C. Newman-Holmes, V. OʼDell, R. Pordes, O. Prokofyev, N. Saoulidou, E. Sexton-Kennedy, S. Sharma, W.J. Spalding, L. Spiegel, P. Tan, L. Taylor, S. Tkaczyk, L. Uplegger, E.W. Vaandering, R. Vidal, J. Whitmore, W. Wu, F. Yang, F. Yumiceva, J.C. Yun, D. Acosta, P. Avery, D. Bourilkov, M. Chen, S. Das, M. De Gruttola, G.P. Di Giovanni, D. Dobur, A. Drozdetskiy, R.D. Field, M. Fisher, Y. Fu, I.K. Furic, J. Gartner, B. Kim, J. Konigsberg, A. Korytov, A. Kropivnitskaya, T. Kypreos, K. Matchev, G. Mitselmakher, L. Muniz, C. Prescott, R. Remington, A. Rinkevicius, M. Schmitt, B. Scurlock, P. Sellers, N. Skhirtladze, M. Snowball, D. Wang, J. Yelton, M. Zakaria, V. Gaultney, L. Kramer, L.M. Lebolo, S. Linn, P. Markowitz, G. Martinez, J.L. Rodriguez, T. Adams, A. Askew, J. Bochenek, J. Chen, B. Diamond, S.V. Gleyzer, J. Haas, S. Hagopian, V. Hagopian, M. Jenkins, K.F. Johnson, H. Prosper, L. Quertenmont, S. Sekmen, V. Veeraraghavan, M.M. Baarmand, B. Dorney, S. Guragain, M. Hohlmann, H. Kalakhety, R. Ralich, I. Vodopiyanov, M.R. Adams, I.M. Anghel, L. Apanasevich, Y. Bai, V.E. Bazterra, R.R. Betts, J. Callner, R. Cavanaugh, C. Dragoiu, L. Gauthier, C.E. Gerber, D.J. Hofman, S. Khalatyan, G.J. Kunde, F. Lacroix, M. Malek, C. OʼBrien, C. Silkworth, C. Silvestre, A. Smoron, D. Strom, N. Varelas, U. Akgun, E.A. Albayrak, B. Bilki, W. Clarida, F. Duru, C.K. Lae, E. McCliment, J.-P. Merlo, H. Mermerkaya, A. Mestvirishvili, A. Moeller, J. Nachtman, C.R. Newsom, E. Norbeck, J. Olson, Y. Onel, F. Ozok, S. Sen, J. Wetzel, T. Yetkin, K. Yi, B.A. Barnett, B. Blumenfeld, A. Bonato, C. Eskew, D. Fehling, G. Giurgiu, A.V. Gritsan, Z.J. Guo, G. Hu, P. Maksimovic, S. Rappoccio, M. Swartz, N.V. Tran, A. Whitbeck, P. Baringer, A. Bean, G. Benelli, O. Grachov, R.P. Kenny Iii, M. Murray, D. Noonan, S. Sanders, J.S. Wood, V. Zhukova, A.F. Barfuss, T. Bolton, I. Chakaberia, A. Ivanov, M. Makouski, Y. Maravin, S. Shrestha, I. Svintradze, Z. Wan, J. Gronberg, D. Lange, D. Wright, A. Baden, M. Boutemeur, S.C. Eno, D. Ferencek, J.A. Gomez, N.J. Hadley, R.G. Kellogg, M. Kirn, Y. Lu, A.C. Mignerey, K. Rossato, P. Rumerio, F. Santanastasio, A. Skuja, J. Temple, M.B. Tonjes, S.C. Tonwar, E. Twedt, B. Alver, G. Bauer, J. Bendavid, W. Busza, E. Butz, I.A. Cali, M. Chan, V. Dutta, P. Everaerts, G. Gomez Ceballos, M. Goncharov, K.A. Hahn, P. Harris, Y. Kim, M. Klute, Y.-J. Lee, C. Loizides, P.D. Luckey, T. Ma, S. Nahn, C. Paus, D. Ralph, C. Roland, G. Roland, M. Rudolph, G.S.F. Stephans, F. Stöckli, K. Sumorok, K. Sung, E.A. Wenger, R. Wolf, S. Xie, M. Yang, Y. Yilmaz, A.S. Yoon, M. Zanetti, S.I. Cooper, P. Cushman, B. Dahmes, A. De Benedetti, P.R. Dudero, G. Franzoni, J. Haupt, K. Klapoetke, Y. Kubota, J. Mans, N. Pastika, V. Rekovic, R. Rusack, M. Sasseville, A. Singovsky, N. Tambe, L.M. Cremaldi, R. Godang, R. Kroeger, L. Perera, R. Rahmat, D.A. Sanders, D. Summers, K. Bloom, S. Bose, J. Butt, D.R. Claes, A. Dominguez, M. Eads, J. Keller, T. Kelly, I. Kravchenko, J. Lazo-Flores, H. Malbouisson, S. Malik, G.R. Snow, U. Baur, A. Godshalk, I. Iashvili, A. Kharchilava, S.P. Shipkowski, K. Smith, J. Zennamo, G. Alverson, E. Barberis, D. Baumgartel, O. Boeriu, M. Chasco, S. Reucroft, J. Swain, D. Trocino, D. Wood, J. Zhang, A. Anastassov, A. Kubik, N. Odell, R.A. Ofierzynski, B. Pollack, A. Pozdnyakov, S. Stoynev, M. Velasco, S. Won, L. Antonelli, D. Berry, A. Brinkerhoff, M. Hildreth, C. Jessop, D.J. Karmgard, J. Kolb, T. Kolberg, K. Lannon, W. Luo, S. Lynch, N. Marinelli, D.M. Morse, T. Pearson, R. Ruchti, J. Slaunwhite, N. Valls, M. Wayne, J. Ziegler, B. Bylsma, L.S. Durkin, J. Gu, C. Hill, P. Killewald, K. Kotov, T.Y. Ling, M. Rodenburg, G. Williams, N. Adam, E. Berry, P. Elmer, D. Gerbaudo, V. Halyo, P. Hebda, A. Hunt, J. Jones, E. Laird, D. Lopes Pegna, D. Marlow, T. Medvedeva, M. Mooney, J. Olsen, P. Piroué, X. Quan, B. Safdi, H. Saka, D. Stickland, C. Tully, J.S. Werner, A. Zuranski, J.G. Acosta, X.T. Huang, A. Lopez, H. Mendez, S. Oliveros, J.E. Ramirez Vargas, A. Zatserklyaniy, E. Alagoz, V.E. Barnes, G. Bolla, L. Borrello, D. Bortoletto, M. De Mattia, A. Everett, A.F. Garfinkel, L. Gutay, Z. Hu, M. Jones, O. Koybasi, M. Kress, A.T. Laasanen, N. Leonardo, C. Liu, V. Maroussov, P. Merkel, D.H. Miller, N. Neumeister, I. Shipsey, D. Silvers, A. Svyatkovskiy, H.D. Yoo, J. Zablocki, Y. Zheng, P. Jindal, N. Parashar, C. Boulahouache, K.M. Ecklund, F.J.M. Geurts, B.P. Padley, R. Redjimi, J. Roberts, J. Zabel, B. Betchart, A. Bodek, Y.S. Chung, R. Covarelli, P. de Barbaro, R. Demina, Y. Eshaq, H. Flacher, A. Garcia-Bellido, P. Goldenzweig, Y. Gotra, J. Han, A. Harel, D.C. Miner, D. Orbaker, G. Petrillo, W. Sakumoto, D. Vishnevskiy, M. Zielinski, A. Bhatti, R. Ciesielski, L. Demortier, K. Goulianos, G. Lungu, C. Mesropian, O. Atramentov, A. Barker, D. Duggan, Y. Gershtein, R. Gray, E. Halkiadakis, D. Hidas, D. Hits, A. Lath, S. Panwalkar, R. Patel, K. Rose, S. Schnetzer, S. Somalwar, R. Stone, S. Thomas, G. Cerizza, M. Hollingsworth, S. Spanier, Z.C. Yang, A. York, R. Eusebi, W. Flanagan, J. Gilmore, A. Gurrola, T. Kamon, V. Khotilovich, R. Montalvo, I. Osipenkov, Y. Pakhotin, J. Pivarski, A. Safonov, S. Sengupta, A. Tatarinov, D. Toback, M. Weinberger, N. Akchurin, C. Bardak, J. Damgov, C. Jeong, K. Kovitanggoon, S.W. Lee, T. Libeiro, P. Mane, Y. Roh, A. Sill, I. Volobouev, R. Wigmans, E. Yazgan, E. Appelt, E. Brownson, D. Engh, C. Florez, W. Gabella, M. Issah, W. Johns, P. Kurt, C. Maguire, A. Melo, P. Sheldon, B. Snook, S. Tuo, J. Velkovska, M.W. Arenton, M. Balazs, S. Boutle, B. Cox, B. Francis, R. Hirosky, A. Ledovskoy, C. Lin, C. Neu, R. Yohay, S. Gollapinni, R. Harr, P.E. Karchin, P. Lamichhane, M. Mattson, C. Milstène, A. Sakharov, M. Anderson, M. Bachtis, J.N. Bellinger, D. Carlsmith, S. Dasu, J. Efron, L. Gray, K.S. Grogg, M. Grothe, R. Hall-Wilton, M. Herndon, A. Hervé, P. Klabbers, J. Klukas, A. Lanaro, C. Lazaridis, J. Leonard, R. Loveless, A. Mohapatra, F. Palmonari, D. Reeder, I. Ross, A. Savin, W.H. Smith, J. Swanson, M. Weinberg, Massachusetts Institute of Technology. Department of Physics, Massachusetts Institute of Technology. Laboratory for Nuclear Science, Massachusetts Institute of Technology. School of Science, Alver, B., Bauer, Gerry P., Bendavid, Joshua L., Busza, Wit, Butz, Erik M., Cali, Ivan Amos, Chan, M., Dutta, V., Everaerts, Pieter Bruno Bart, Gomez-Ceballos, Guillelmo, Goncharov, Maxim, Hahn, Kristian Allan, Harris, P., Kim, Y., Klute, Markus, Lee, Y.-J., Li, W., Loizides, C., Luckey, P. David, Jr., Ma, Teng, Nahn, Steven, Paus, Christoph M. E., Ralph, Duncan Kelley, Roland, Christof E., Roland, Gunther M., Rudolph, Matthew Scott, Stephans, George S. F., Stockli, Fabian, Sumorok, Konstanty C., Sung, Kevin Kai Hong, Wenger, Edward Allen, Wolf, R., Xie, S., Yang, M., Yilmaz, Yetkin, Yoon, A. S., Zanetti, Marco, Wyslouch, Boleslaw, UAM. Departamento de Física Teórica, Universidad de Cantabria, and Çukurova Üniversitesi, Fen-Edebiyat Fakültesi, Fizik Bölümü

Subjects
Physics, Particle physics, Nuclear and High Energy Physics, Muon, 010308 nuclear & particles physics, CMS, physics, Electroweak interaction, High Energy Physics::Phenomenology, Física, Context (language use), HERA, Lambda, 01 natural sciences, Nuclear physics, Standard Model (mathematical formulation), Excited state, 0103 physical sciences, High Energy Physics::Experiment, 010306 general physics, Lepton
Abstract

A search for excited leptons is carried out with the CMS detector at the LHC, using 36 pb[superscript −1] of pp collision data recorded at √s = 7 TeV. The search is performed for associated production of a lepton and an oppositely charged excited lepton pp→ℓℓ*, followed by the decay ℓ*→ℓγ, resulting in the ℓℓγ final state, where ℓ=e, μ. No excess of events above the standard model expectation is observed. Interpreting the findings in the context of ℓ* production through four-fermion contact interactions and subsequent decay via electroweak processes, first upper limits are reported for ℓ* production at this collision energy. The exclusion region in the compositeness scale Λ and excited lepton mass M[subscript ℓ*] parameter space is extended beyond previously established limits. For Λ=M[subscript ℓ*], excited lepton masses are excluded below 1070 [GeV over c[superscript 2]] for e* and 1090 [GeV over c[superscript 2]] for μ* at the 95% confidence level., United States. Dept. of Energy, National Science Foundation (U.S.)

Published
2011

20. Contributors

Author

Davis Baird, Prasanta S. Bandyopadhyay, Deborah Bennett, José M. Bernardo, Jeffrey D. Blume, Robert J. Boik, Arijit Chakrabarti, Richard Charnigo, Steve Cherry, Abhijit Dasgupta, A. Philip Dawid, Michael Dickson, David L. Dowe, Kenny Easwaran, Roberto Festa, Malcolm R. Forster, Jayanta K. Ghosh, Sander Greenland, Mark C. Greenwood, Jason Grossman, Peter D. Grünwald, Alan Hájek, Gilbert Harman, Joel Harper, James Hawthorne, Colin Howson, Kevin T. Kelly, Sanjeev Kulkarni, Subhash R. Lele, Deborah G. Mayo, Johnnie Moore, John D. Norton, C.K. Raju, Jan-Willem Romeijn, Steven de Rooij, Elliott Sober, Aris Spanos, Peter Spirtes, Cidambi Srinivasan, Daniel Steel, Mark L. Taper, Choh Man Teng, C. Andy Tsao, Susan Vineberg, Paul Weirich, Gregory Wheeler, Jon Williamson, and Sandy L. Zabell

Published
2011
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21. List of Contributors

Author

J. Mack Aldridge, Champ L. Baker, Nikolaos V. Bardakos, Paul E. Beaulé, Martin Beck, Michel P.J. v/d Bekerom, Benoit Benoit, Karen K. Briggs, Robert L. Buly, Denise Chan, Lorenzo Childress, John C. Clohisy, Ronald E. Delanois, Octavia Devon, Michael Dienst, Keelan R. Enseki, Teresa M. Ferguson, Reinhold Ganz, Michael B. Gerhardt, James A. Goulet, Carlos A. Guanche, Daniël Haverkamp, Marcia A. Horner, Victor M. Ilizaliturri, Jon A. Jacobson, David Kahan, Bryan T. Kelly, Vikas Khanduja, Mininder S. Kocher, Jason Koh, David A. Kuppersmith, Christopher M. Larson, Jo-Ann Lee, Michael Leunig, Kartik Logishetty, Dean G. Lorich, Travis Maak, Tallal Charles Mamisch, Aditya V. Maheshwari, David R. Marker, René K. Marti, Hal David Martin, RobRoy L. Martin, Joseph C. McCarthy, Mike S. McGrath, Morteza Meftah, William C. Meyers, Nick G. Mohtadi, Michael A. Mont, Ryan M. Nunley, M. Elizabeth Pedersen, Murat Pekmezci, Aaron Perdue, Marc J. Philippon, Mario Quesada, Amar S. Ranawat, Anil S. Ranawat, Chitranjan S. Ranawat, Margaret M. Rich, Hassan Sadri, Marc R. Safran, Thomas G. Sampson, Perry L. Schoenecker, Karl F. Schultz, Jon K. Sekiya, Michael K. Shindle, Klaus A. Siebenrock, Moritz Tannast, Mehul M. Taylor, James R. Urbaniak, Zackary D. Vaughn, Richard N. Villar, James E. Voos, Stuart L. Weinstein, Stefan Werlen, Yi-Meng Yen, and Chad T. Zehms

Published
2011
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23. CONTRIBUTORS

Author

Robert Afra, Answorth A. Allen, David W. Altchek, Olufemi R. Ayeni, Champ L. Baker, Asheesh Bedi, S. Josh Bell, Jay Boughanem, Sepp Braun, Karen K. Briggs, Daniel D. Buss, J.W. Thomas Byrd, Edwin R. Cadet, Keith W. Chan, Mark S. Cohen, Steven B. Cohen, Dana C. Covey, Michael J. DeFranco, Christopher B. Dewing, Christopher C. Dodson, Eric W. Edmonds, James C. Esch, Douglas A. Evans, Gregory C. Fanelli, Edward Feliciano, Nicholas A. Ferran, Anthony Festa, Larry D. Field, Donald C. Fithian, Brian T. Fitzgerald, Freddie H. Fu, Justin J. Gent, Neil Ghodadra, Scott D. Gillogly, Christopher D. Harner, Eric P. Hofmeister, Keith G. Holley, Nils Horn, Brian D. Johnson, Warren R. Kadrmas, Bryan T. Kelly, Christopher K. Kepler, W. Ben Kibler, Mininder S. Kocher, Robert B. Kohen, Phillip R. Langer, Lance E. LeClere, Marvin Y. Lo, John D. MacGillivray, Nicola Maffulli, Michael T. Mazurek, Augustus D. Mazzocca, L. Pearce McCarty, Bruce S. Miller, Peter J. Millett, Timothy S. Mologne, Kenneth R. Morse, Kenneth J. Mroczek, Volker Musahl, Shane J. Nho, Robert P. Nirschl, Elifho Obopilwe, Francesco Oliva, Kraig Pepper, Charles J. Petit, Christopher Pokabla, Matthew T. Provencher, Arun J. Ramappa, R. Judd Robins, Ben C. Robinson, William G. Rodkey, Anthony A. Romeo, Michael J. Salata, Nelson S. Saldua, Jon K. Sekiya, Wei Shen, Seth L. Sherman, Alexander Y. Shin, Bertil Smith, Daniel J. Solomon, J. Richard Steadman, C. Christopher Stroud, Joseph J. Stuart, Michael A. Terry, Brian C. Toolan, Geoffrey S. Van Thiel, Stephen C. Weber, Riley J. Williams, Edward M. Wojtys, Andrew B. Wolff, Yi-Meng Yen, and Scot A. Youngblood

Published
2010
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25. List of Contributors

Author

John F. Ahrens, Philip A. Banks, V. Michael Barringer, James F. Brady, Leslie D. Bray, Charles B. Breckenridge, Robert S. Bretzlaff, David C. Bridges, Orvin C. Burnside, Gerald A. Carlson, Beth Carroll, Allan J. Cessna, B.R. Christensen, G. Euel Coats, Dennis Cooper, Cheryl Dando, Malcolm D. Devine, J. Charles Eldridge, Clyde L. Elmore, Richard S. Fawcett, Leonard P. Gianessi, Thomas J. Gould, Dennis S. Hackett, Nina E. Heard, John F. Hebblethwaite, Walter Heri, Eugene R. Hill, Robert A. Kahrs, Steven T. Kelly, Ellery L. Knake, William C. Koskinen, David A. Laird, Arthur H. Lange, Homer M. LeBaron, Raphi T. Mandelbaum, Kendra M. Marut, Janis E. McFarland, Marshal D. McGlamery, Richard A. McLaughlin, Gustav Müller, James B. Nabors, Michael Newton, Charles A. Norwood, James H. Orson, Thomas Parshley, Frank Pfister, David R. Pike, David L. Regehr, Edward P. Richard, Michael J. Sadowsky, Lance T. Santo, Elisabeth A. Scribner, Shiv D. Sharma, Amit Shukla, Robert L. Sielken, Bruce J. Simoneaux, Megh Singh, Dudley T. Smith, Keith R. Solomon, Carol N. Somody, James T. Stevens, Darrell D. Sumner, Arpad Z. Szarka, James M. Taylor, E. Michael Thurman, Dennis P. Tierney, Achim Trebst, Ciriaco Valdez-Flores, Lawrence P. Wackett, Christoph Werner, Lawrence T. Wetzel, Jere White, and Robert A. Yokley

Published
2008
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27. CONTRIBUTORS

Author

Samson Abramsky, Pieter Adriaans, F. Alexander Bais, Alexandru Baltag, Johan van Benthem, Margaret A. Boden, John Collier, Keith Devlin, Hans van Ditmarsch, Fred Dretske, J. Michael Dunn, J. Doyne Farmer, Luciano Floridi, Peter Grünwald, Peter Harremoēs, Hans Kamp, Kevin T. Kelly, John McCarthy, Maricarmen Martinez, Lawrence S. Moss, Duska Rosenberg, Hans Rott, Martin Stokhof, Flemming Topsøe, Paul Vitányi, and Bernard Walliser

Published
2008
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28. Mononuclear Iron Carbonyls without Hydrocarbon Ligands

Author

Anna T. Kelly, Kenton H. Whitmire, and Cristina Hofmann

Subjects
chemistry.chemical_classification, Hydride, Isocyanide, Inorganic chemistry, Iodide, chemistry.chemical_element, Redox, chemistry.chemical_compound, chemistry, Bromide, Polymer chemistry, Amine gas treating, Selenium, Phosphine
Abstract

This chapter is dedicated to mononuclear iron carbonyls without hydrocarbon ligands. Beginning with the simple mononuclear iron carbonyls, iron carbonyl hydrides and iron carbonyls with group 13, 14, and 15 ligands are treated in detail. In addition, the oxygen, sulfur, selenium, and tellurium donor ligands are discussed. The chapter also covers the fluoride, chloride, bromide, and iodide complexes.

Published
2007
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29. Recent Advances in Inflammatory and Immunological Diseases: Focus on Arthritis Therapy

Author

J.S. Skotnicki, R. Newton, J. Trzaskos, R. Magolda, and T. Kelly

Subjects
medicine.medical_specialty, Arthritis therapy, Mechanism (biology), business.industry, Rheumatoid arthritis, Immunology, medicine, Drug approval, Arthritis, Immunological diseases, medicine.disease, business, Intensive care medicine
Abstract

For several decades new treatments for inflammatory and immunological diseases have remained elusive. In the past 15 years, several new drugs with novel mechanisms have been approved. This chapter outlines the new understandings that have emerged along with some new mechanisms that offer powerful relief for arthritis patients. This chapter did not aim to be a comprehensive list of every new mechanism being pursued for inflammatory and immunological diseases. Rather it overviews research efforts that have resulted in drug approvals and/or new mechanisms that offer new promising opportunities to treat rheumatoid arthritis and osteoarthritis.

Published
2007
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31. [31] Properties of animal ceramide glycanases

Author

Subhash Basu, Manju Basu, Mark A. Girzadas, Patrick T. Kelly, and Zhixiong Li

Subjects
Ceramide, biology, Sphingosine, Endoglycosidase, chemistry.chemical_compound, medicine.anatomical_structure, chemistry, Biochemistry, Apoptosis, Lactation, Second messenger system, biology.protein, medicine, Signal transduction, Sphingomyelin
Abstract

Publisher Summary Ceramide glycanase (CGase), the endoglycosidase that cleaves various glycosphingolipids in a one-step process liberating ceramide and the corresponding oligosaccharides, is characterized from different annelids, bacteria, and mollusk. The same CGase activity has also been detected and purified from mammalian sources. The discovery of the mammalian CGases raises the possibility of glycosphingolipids (GSLs) being involved in the signal transduction and/or apoptotic processes as a secondary messenger. The ceramide and its breakdown product sphingosine that is very much in the limelight of apoptosis and signal transduction cascades have so far been predicted to come from the breakdown of the major membrane lipid sphingomyelin by the action of sphingomyelinase. Substantial CGase activities have been observed in different mammalian organs, although the highest activity was found in the mammary tissues. A parallel between CGase activity and the gestation as well as lactation has also been observed. However, the significance of this observation has not been established as it was for bovine mammary glucosidase. CGase activities in the kidneys and the mammary tissues of newborn rats parallel that found in the lactating mammary tissue. This chapter discusses the study of CGase characterized from mammalian sources.

Published
2000
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33. MATHEMATICAL FOUNDATIONS

Author

Clark Glymour, Kevin T. Kelly, Richard Scheines, and Peter Spirtes

Subjects
Discrete mathematics, Algebraic identity, Latent variable, Tetrad, Graph, Mathematics
Abstract

Publisher Summary This chapter discusses the mathematical foundations of the TETRAD program. The TETRAD program uses a modification of a well-known path finding algorithm to compute the tetrad equations a model implies. It first calculates the open paths and the treks between each pair of variables in the graph. The program then calculates, for each pair u,v of measured variables, the trek sum for that pair. The products of the trek sums are then compared to determine whether or not they constitute an algebraic identity. If they do, the model implies the corresponding tetrad equation. To determine the sets of suggested trek additions to an initial model, the program proceeds through all foursomes of measured variables. For each foursome, it locates an appropriate subgraph of the initial model and determines whether or not to recommend the addition of a trek between any pair of variables in the subgraph. The program's recommendations do not distinguish between directed edges connecting two measured variables in either direction or the introduction of a new error variable connected to two measured variables. The program distinguishes, however, between these sorts of modifications and the addition of a directed edge between a latent variable and a measured variable.

Published
1987
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34. THE CASE AGAINST CAUSAL MODELING

Author

Clark Glymour, Richard Scheines, Kevin T. Kelly, and Peter Spirtes

Subjects
Causal relations, Causal inference, Causal theory of knowledge, Behavioural sciences, Experimental data, Latent variable, Causal decision theory, Psychology, Epistemology, Causal model
Abstract

This chapter presents the case against causal modeling in artificial intelligence and nonexperimental science. Causal modeling of nonexperimental data has been controversial since its beginning, and it is no less controversial today. The controversies are very philosophical, and they involve fundamental differences about what makes for science. The social and behavioral sciences are inevitably compared with the natural sciences, and disputes about methodology in social and behavioral science are often disputes about what it is that has made the natural sciences so successful in understanding, predicting, and controlling the physical world. The chapter discusses the following criticisms of causal modeling: (1) causal modeling involves a mistaken, or incoherent, conception of causal relations; (2) theories with latent variables should be rejected on methodological or semantic grounds; (3) only experimental data can contribute to the knowledge of causal relationships; (4) those who advocate causal models do not, and presumably cannot, make a case for the assumptions of their models; and (5) linear causal models should be rejected because they are always literally false.

Published
1987
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35. A BRIEF HISTORY OF HEURISTIC SEARCH IN APPLIED STATISTICS

Author

Richard Scheines, Peter Spirtes, Clark Glymour, and Kevin T. Kelly

Subjects
Common cause and special cause, Statistics, Statistical model, Computational problem, Tetrad, Sample mean and sample covariance, Action (physics), General algorithm, Mathematics, Causal model
Abstract

This chapter presents a brief history of heuristic search in applied statistics. Analogous arguments apply for any larger number of measured variables. If there are n measured variables, then the number of possible tetrad equations among these variables is three times the number of ways of choosing four from n. If the correlations are because of the action of one common cause, then all of the tetrad equations must hold regardless of the values of the linear coefficients; conversely, if all of the tetrad equations hold, then the correlations can be generated by a one-factor model with appropriate values of the linear coefficients. Spearman, Kelley, Holzinger, and others had good ideas about scientific explanation and about the way to search for good explanations of nonexperimental data. The idea was not entirely lost and persists here and there in recent work in statistical modeling. It never came to fruition was because of computational problems, not to statistical difficulties. There was lacking of a general algorithm that would permit the rapid calculation of the tetrad equations or other constraints robustly implied by any linear causal model and would compare those constraints with the constraints approximately satisfied by the sample covariance matrix.

Published
1987
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36. CAUSAL AND STATISTICAL MODELS

Author

Clark Glymour, Peter Spirtes, Kevin T. Kelly, and Richard Scheines

Subjects
Statistical assumption, Linear model, Econometrics, Statistical model, Causal structure, Statistical theory, Tetrad, Mathematics, Statistical hypothesis testing, Causal model
Abstract

This chapter discusses casual and statistical models of the TETRAD program in artificial intelligence. Most linear models in the social and behavioral sciences have a causal interpretation, and often the causal claims within a statistical model are the principal concern and point of the investigation. Linear causal models include a set of equations relating the variables of the model, a set of stochastic assumptions about the probability distributions of those variables, jointly and individually, and a set of causal relationships among the variables. The strategy behind the TETRAD program is to abstract the causal structure from a statistical model, ignoring the equations and most of the statistical assumptions. The theoretical basis of the TETRAD program consists of a number of new theorems about the connection between directed graphs and statistical properties of models. The TETRAD program, however, does not do statistical estimation of parameters and performs only statistical tests for vanishing tetrad differences and vanishing partial correlations. It performs no tests of models or of the significance of parameter estimates. The use of statistical tests has a decision theoretic foundation in Neyman–Pearson theory, and the most devoted adherents of the tradition reject any theory comparisons that are not framed as hypothesis tests.

Published
1987
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37. THE PROBLEMS OF SCIENCE WITHOUT EXPERIMENTS

Author

Kevin T. Kelly, Clark Glymour, Peter Spirtes, and Richard Scheines

Subjects
Work (electrical), Management science, Computer science, media_common.quotation_subject, Public policy, Statistical model, Normality, Test (assessment), media_common
Abstract

This chapter discusses the problems of nonexperimental science associated with artificial intelligence. Nonexperimental statistical studies are the only way to get information that helps to decide questions of health or public policy. In many statistical studies in the social sciences, equations may be written down and references cited, but little or no justification is given for the assumptions that are made, the hypotheses put forward are not tested, and no predictions of any consequence are derived. It is important to recognize the difference between practice and principle. If much of the quantitative work that aims to extract causal conclusions from nonexperimental data is not very convincing, then that might be either because of reasons of principle that cannot be overcome or because of individual lapses that can be overcome. The notion of applying artificial intelligence techniques to the problems of statistical model specification is not really novel. Several programs have been developed or proposed that apply artificial intelligence techniques to statistical modeling. Systems have been developed to test automatically the adequacy of common modeling assumptions, such as linearity and normality, and to suggest transformations of variables or other changes where these assumptions fail.

Published
1987
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38. WHAT TETRAD CAN DO

Author

Richard Scheines, Clark Glymour, Peter Spirtes, and Kevin T. Kelly

Subjects
Class (set theory), media_common.quotation_subject, Linear model, Applied mathematics, Directed graph, Variance (accounting), Tetrad, Linear combination, Conformity, Algorithm, Generator (mathematics), Mathematics, media_common
Abstract

This chapter discusses the strengths and limitations of the TETRAD program. It presents a problem using simulated data of TETRAD model. On real data sets, TETRAD sometimes yields results that suggest that no linear model will adequately explain the data. In other cases, the program gives results that suggest that too many linear models will explain the data. In a study explained in the chapter data was generated using SYSTAT BASIC. The simulated data was produced for the exogenous variables with a pseudo-random-number generator distributed normally with mean 0 and variance 1. All other variables were linear combinations of their respective immediate ancestors in the directed graph of the model. The linear coefficients were also chosen at random, although their values for each particular model are non-stochastic constants. Kohn's Class and Conformity describes several large survey studies that investigate the relationships between social class, attitudes, and personality structure.

Published
1987
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39. [52] Role of autophosphorylation in regulating calmodulin-dependent protein kinases

Author

Paul T. Kelly and Shirish Shenolikar

Subjects
Biochemistry, biology, Ca2+/calmodulin-dependent protein kinase, Cyclin-dependent kinase 2, Autophosphorylation, biology.protein, ASK1, Mitogen-activated protein kinase kinase, Phosphorylase kinase, MAP2K7, MAPK14, Cell biology
Abstract

Publisher Summary This chapter examines the role of autophosphorylation in the potential regulation of protein kinases. Many of the Ca 2+ -mediated processes are coupled to calmodulin, a ubiquitous Ca 2+ -binding protein, and specific protein kinases whose activities are regulated in a Ca 2+ /calmodulin-dependent manner. Many of these protein kinases undergo autophosphorylation—that is, the holoenzyme catalyzes the phosphorylation of one or more amino acids within its own structure through an intramolecular reaction. Moreover, autophosphorylation is an important event in regulating kinase function(s) in vivo. Phosphorylase kinase, myosin light chain kinase (MLCK), and multifunctional calmodulin-dependent protein kinase are regulated by Ca 2+ /calmodulin and display autophosphorylation. The analysis of the function of specific subunits of phosphorylase kinase is also outlined. The determinants of substrate specificity of phosphorylase kinase are elucidated by kinetic studies, using synthetic peptides corresponding to the site phosphorylated in phosphorylase B as substrates. Autophosphorylation of multiple sites also plays a useful role in controlling MLCK activity of by modulating the reversal by protein phosphatases.

Published
1987
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40. THE USE OF MUTANT MYELOMA CELLS TO EXPLORE THE PRODUCTION OF IMMUNOGLOBULINS

Author

L. Frank, T. Kelly, S. Weitzman, David H. Margulies, Barbara K. Birshtein, J.-L. Preud'homme, S. L. Morrison, B. Dharmgrongartama, Matthew D. Scharff, and W. M. Kuehl

Subjects
High rate, chemistry.chemical_compound, Glycosylation, chemistry, biology, Mutant, biology.protein, Mutagenesis (molecular biology technique), Secretion, Antibody, Primary sequence, Molecular biology
Abstract

A method has been developed for identifying and quantifying mouse myeloma cells which have undergone mutations in immunoglobulin production. Such variants arise at a very high rate both spontaneously and with mutagenesis. Mutagenesis results in primary sequence variants which are blocked in a variety of steps in the synthesis, assembly, glycosylation, and secretion of H chains. These mutants can be used to investigate the production and genetic control of immunoglobulins.

Published
1975
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41. SIMULATION STUDIES

Author

Richard Scheines, Peter Spirtes, Clark Glymour, and Kevin T. Kelly

Subjects
Data set, Computer science, Sample size determination, Linear model, Graph (abstract data type), Enhanced Data Rates for GSM Evolution, Multiple edges, Tetrad, Algorithm
Abstract

Publisher Summary This chapter presents the simulation studies of generated data. It presents how TETRAD handles the problems in which data were generated from a known linear model. When multiple edges are missing the TETRAD program will, if the initial model is correct and the sample sizes are large, often reduce the number of alternative models to a handful, including the correct model, but it will not usually produce the correct model uniquely. The TETRAD strategy for solving these problems is easy. The common skeletal graph is given and the data set is given to TETRAD. The chapter presents the cases in which TETRAD was able to give a unique and correct answer because the modeling assumptions were strictly realized, the sample size was large, and only one additional edge or correlated error was required. In the cases in which several edges or correlated errors must be inferred from an initial model and data, the program will not usually fix upon a single alternative, but it will serve to dramatically reduce the possibilities.

Published
1987
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42. CASE STUDIES

Author

Peter Spirtes, Richard Scheines, Clark Glymour, and Kevin T. Kelly

Subjects
Empirical data, business.industry, Computer science, ComputerApplications_COMPUTERSINOTHERSYSTEMS, Artificial intelligence, Latent variable, Machine learning, computer.software_genre, business, Tetrad, computer, Test (assessment)
Abstract

Publisher Summary This chapter describes the application of TETRAD to a number of cases. It discusses the data sets that are empirical rather than simulated. For empirical data, TETRAD can help find plausible models that have been overlooked by unaided workers and that do well on a chi-square test. TETRAD can help search for alternative skeletal models. In particular, it can help the user to decide whether or not latent variables ought to be introduced to account for the data. Assuming an initial model, TETRAD can help the user discriminate between correlated errors and direct connections between variables and can even discriminate between alternative causal directions for direct connections. The program makes it easy to take advantage of the user's prior knowledge in the search for adequate models. In fact, effective use of the program requires such knowledge to eliminate hypotheses that do well by TETRAD's criteria but make no causal sense.

Published
1987
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43. OBJECTIONS TO DISCOVERY BY COMPUTER

Author

Peter Spirtes, Richard Scheines, Kevin T. Kelly, and Clark Glymour

Subjects
Computer science, business.industry, Process (engineering), media_common.quotation_subject, Bayesian probability, Inference, Statistical model, Data science, Bayesian statistics, Prior probability, Artificial intelligence, Simplicity, business, Explanatory power, media_common
Abstract

This chapter discusses the objections to the discovery of artificial intelligence programs by computer. It presents the objections that apply to some forms of computer discovery or computer-aided search for theories: (1) people have various kinds of special knowledge that computers do not have, and that knowledge makes people better at discovery than computers can possibly be; (2) discovery is a form of inference and inference should proceed in accordance with the requirements of Bayesian statistics and Bayesian epistemology, but computer programs that aid in scientific discovery often do not work on strict Bayesian principles; and (3) offering computer programs that are intended to aid in the process of discovery is like playing with fire. Artificial intelligence programs for discovering statistical models are really carrying out decision procedures, but they do not act like rational Bayesian agents. Even if a program has carried out Bayesian calculations, whatever prior probability distribution and utilities the program uses may not be shared by human researchers. From a Bayesian perspective, the TETRAD program is a device for investigating a part of the catch-all hypothesis and for locating within it specific alternatives that give the evidence a reasonable likelihood and that have the virtues of simplicity and explanatory power.

Published
1987
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45. Theory Discovery and the Hypothesis Language

Author

Kevin T. Kelly

Subjects
Philosophy, Range (mathematics), Computer science, business.industry, Artificial intelligence, Computational linguistics, business, FOS: Philosophy, ethics and religion
Abstract

This paper develops a precise framework in which to compare the discovery problems determined by a wide range of distinct hypothesis languages. Twelve theorems are presented which provide a comprehensive picture of the solvability of these problems according to four intuitively motivated criteria of scientific success.

Published
1988
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46. RUNNING TETRAD

Author

Clark Glymour, Kevin T. Kelly, Richard Scheines, and Peter Spirtes

Subjects
Theoretical computer science, Coprocessor, Simple (abstract algebra), Computer science, Graph (abstract data type), Directed graph, Covariance, Chip, Tetrad
Abstract

Publisher Summary This chapter discusses the uses of TETRAD program. TETRAD is an interactive command-driven program that is simple to use. The chapter describes the format of input files, presents a description of each commands, some transcripts of actual TETRAD sessions, and also describes the types of errors that can occur. It discusses two versions of TETRAD. One of these versions, TETRAD, is designed for computers that do not have an 8087 coprocessor chip; the other version, TET87, is designed for computers that do have this chip. The two programs act in an identical fashion, except that TET87 is considerably faster than TETRAD. The TETRAD program requires both a directed graph and correlation or covariance data for the measured variables in that graph. The file from which TETRAD reads the correlation of covariance data is called the covariance file. The file from which TETRAD reads the directed graph will be called the graph file. In both the covariance file and in the directed graph, variables must have names.

Published
1987
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47. THE STRUCTURE AND METHOD OF TETRAD

Author

Richard Scheines, Kevin T. Kelly, Clark Glymour, and Peter Spirtes

Subjects
Data set, Structure (mathematical logic), Set (abstract data type), Mode (statistics), Econometrics, Directed graph, Covariance, Tetrad, Algorithm, Mathematics, Causal model
Abstract

This chapter describes the structure and method of the TETRAD program. The TETRAD program helps to search for good models of correlation or covariance data. The chapter focuses on the methodological principles behind TETRAD, how they are realized in the program, and how these principles can be used with the program in searching for good causal models. It presents the output the program provides, describes some of the calculations that go into the output, and illustrates how the output can be used to find good causal models. The TETRAD program operates in two modes. One mode provides the user with information to conduct a breadth-first search, one edge at a time. The other, more automatic mode, conducts a search that does not necessarily proceed one edge at a time, but that may consider several simultaneous additions to an initial model. The two modes can be used together. TETRAD requires the user to provide the computer with a directed graph, representing an initial causal model, and a set of variances and co variances, or correlations, for the measured variables in the model. Given a data set and an initial model, the TETRAD program will compute all of the tetrad equations implied by the model.

Published
1987
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48. Regulation of Ca2+/Calmodulin-Dependent Protein Kinase II By Autophosphorylation/Dephosphorylation

Author

Shirish Shenolikar, Ronald Licktelg, and Paul T. Kelly

Subjects
Dephosphorylation, Synapsin I, Biochemistry, Kinase, Ca2+/calmodulin-dependent protein kinase, Phosphatase, Autophosphorylation, Phosphorylation, Biology, Cell biology, MAPK14
Abstract

Publisher Summary This chapter elaborates regulation of Ca 2+ /calmodulin-dependent protein kinase II by autophosphorylation/dephosphorylation. In many neural tissues and tissues innervated by them, intracellular changes in Ca 2+ concentrations are known to regulate processes underlying cell contractility, cytoskeletal functions and cell–cell communication. In forebrain, CaM-KII is present in cytosolic and particulate cellular compartments. The effects of autophosphorylation on CaM-KII activity toward the subsequent phosphorylation of synapsin I was assessed by a sequential two-stage (I and II) assay. When autophosphorylated CaM-KII was incubated with protein phosphatase type-1 (PrP-1) for 20 minutes at 30°C, 80% and 85% of the 32 P associated with the 50 and 60 kDa subunits of CaM-KII were removed, respectively. It was indicated that the in vitro autophosphorylation of CaM-KII produces an enzyme whose properties are quite different from that of the unphosphorylated kinase. Ca 2+ /CaM-dependent form of cytosolic CaM-KII can be regenerated from the autophosphorylated kinase by dephosphorylation with purified type-1 protein phosphatase. Recent experiments indicate that the phosphorylation of CaM-KII does occur in brain tissue slices differences between in situ vs. in vitro phosphorylation properties have been observed.

Published
1987
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49. USING TETRAD WITH EQS AND LISREL

Author

Clark Glymour, Peter Spirtes, Kevin T. Kelly, and Richard Scheines

Subjects
Structure (mathematical logic), media_common.quotation_subject, Statistics, Econometrics, Range (statistics), Directed graph, Tetrad, LISREL, Structural equation modeling, Normality, Mathematics, Causal model, media_common
Abstract

This chapter presents the discovery of casual structure using TETRAD with EQS and LISREL. The LISREL and EQS programs are among the most widely used computer packages for estimating and testing structural equation models. EQS is always, and LISREL is usually, straightforward to use on models found with TETRAD. The appearance of the LISREL programs made available full information regarding maximum likelihood estimation and testing. Under the assumption of normality, the program made possible the estimation and testing of a great range of structural equation models. The LISREL programs impose certain classifications of variables, however, and these classifications and the restrictions that accompany them can sometimes cause difficulties in representing causal models. The LISREL program permits the user to specify covariances and variances among the phi variables, the psi variables, the theta delta variables, and the theta epsilon variables. In directed graph representations of a causal model, covariances of these sorts are represented as undirected or bidirected edges connecting error variables. On the other hand, the EQS program is considerably easier to use than LISREL, but the PC version can often take 15 minutes or more for a single run. If one wants to estimate and test 20 models, 5 hours might be required.

Published
1987
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50. Segond Fractures Can Be Identified With Excellent Accuracy Utilizing Deep Learning on Anteroposterior Knee Radiographs

Author

Jacob F. Oeding, M.S., Ayoosh Pareek, M.D., Kyle N. Kunze, M.D., Benedict U. Nwachukwu, M.D., M.B.A., Harry G. Greditzer, IV, M.D., Christopher L. Camp, M.D., Bryan T. Kelly, M.D., Andrew D. Pearle, M.D., Anil S. Ranawat, M.D., and Riley J. Williams, III, M.D.

Subjects
Sports medicine, RC1200-1245
Abstract

Purpose: To develop a deep learning model for the detection of Segond fractures on anteroposterior (AP) knee radiographs and to compare model performance to that of trained human experts. Methods: AP knee radiographs were retrieved from the Hospital for Special Surgery ACL Registry, which enrolled patients between 2009 and 2013. All images corresponded to patients who underwent anterior cruciate ligament reconstruction by 1 of 23 surgeons included in the registry data. Images were categorized into 1 of 2 classes based on radiographic evidence of a Segond fracture and manually annotated. Seventy percent of the images were used to populate the training set, while 20% and 10% were reserved for the validation and test sets, respectively. Images from the test set were used to compare model performance to that of expert human observers, including an orthopaedic surgery sports medicine fellow and a fellowship-trained orthopaedic sports medicine surgeon with over 10 years of experience. Results: A total of 324 AP knee radiographs were retrieved, of which 34 (10.4%) images demonstrated evidence of a Segond fracture. The overall mean average precision (mAP) was 0.985, and this was maintained on the Segond fracture class (mAP = 0.978, precision = 0.844, recall = 1). The model demonstrated 100% accuracy with perfect sensitivity and specificity when applied to the independent testing set and the ability to meet or exceed human sensitivity and specificity in all cases. Compared to an orthopaedic surgery sports medicine fellow, the model required 0.3% of the total time needed to evaluate and classify images in the independent test set. Conclusions: A deep learning model was developed and internally validated for Segond fracture detection on AP radiographs and demonstrated perfect accuracy, sensitivity, and specificity on a small test set of radiographs with and without Segond fractures. The model demonstrated superior performance compared with expert human observers. Clinical Relevance: Deep learning can be used for automated Segond fracture identification on radiographs, leading to improved diagnosis of easily missed concomitant injuries, including lateral meniscus tears. Automated identification of Segond fractures can also enable large-scale studies on the incidence and clinical significance of these fractures, which may lead to improved management and outcomes for patients with knee injuries.

Published
2024
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