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3. Integrated genome-wide association, coexpression network, and expression single nucleotide polymorphism analysis identifies novel pathway in allergic rhinitis

Author

Burchard, Esteban, Bunyavanich, S, Schadt, EE, Himes, BE, Lasky-Su, J, Qiu, W, Lazarus, R, Ziniti, JP, Cohain, A, Linderman, M, and Torgerson, DG

Abstract

© 2014 Bunyavanich et al.; licensee BioMed Central Ltd.Background: Allergic rhinitis is a common disease whose genetic basis is incompletely explained. We report an integrated genomic analysis of allergic rhinitis. Methods. We performed genome wide associa

Published
2014

5. In the News

Author

Brownell, R. L., Andersen, S. B., Linderman, M., Hellier, C., Rich, S. M., Ninfo, A., and Mancuso, K.

Published
2009

9. Study of the Dependency on Magnetic Field and Bias Voltage of an AC-Biased TES Microcalorimeter

Author

Gottardi, L, Bruijn, M, denHartog, R, Hoevers, H, deKorte, P, vanderKuur, J, Linderman, M, Adams, J, Bailey, C, Bandler, S, Chervenak, J, Eckart, M, Finkbeiner, F, Kelley, R, Kilbourne, C, Porter, F, Sadlier, J, and Smith, S

Subjects
Electronics And Electrical Engineering
Abstract

At SRON we are studying the performance of a Goddard Space Flight Center single pixel TES microcalorimeter operated in an AC bias configuration. For x-ray photons at 6 keV the pixel shows an x-ray energy resolution Delta E(sub FWHM) = 3.7 eV, which is about a factor 2 worse than the energy resolution observed in an identical DC-biased pixel. In order to better understand the reasons for this discrepancy we characterized the detector as a function of temperature, bias working point and applied perpendicular magnetic field. A strong periodic dependency of the detector noise on the TES AC bias voltage is measured. We discuss the results in the framework of the recently observed weak-link behaviour of a TES microcalorimeter.

Published
2012
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11. Genetic Identification of a Common Collagen Disease in Puerto Ricans via Identity-by-Descent Mapping in a Health System

Author

Belbin, G.M., primary, Odgis, J., additional, Sorokin, E.P., additional, Yee, M-C., additional, Kohli, S., additional, Glicksberg, B.S., additional, Gignoux, C.R., additional, Wojcik, G.L., additional, Van Vleck, T., additional, Jeff, J.M., additional, Linderman, M., additional, Schurmann, C., additional, Ruderfer, D., additional, Cai, X., additional, Merkelson, A., additional, Justice, A.E., additional, Young, K.L., additional, Graff, M, additional, North, K.E., additional, Peter, U., additional, James, R., additional, Hindorff, L., additional, Kornreich, R., additional, Edelmann, L., additional, Gottesman, O., additional, Stahl, E.E.A., additional, Cho, J.H., additional, Loos, R.J.F., additional, Bottinger, E.P., additional, Nadkarni, G.N., additional, Abul-Husn, N. S., additional, and Kenny, E.E., additional

Published
2017
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12. Exome sequencing of extreme clopidogrel response phenotypes identifiesB4GALT2as a determinant of on-treatment platelet reactivity

Author

Scott, SA, primary, Collet, J-P, additional, Baber, U, additional, Yang, Y, additional, Peter, I, additional, Linderman, M, additional, Sload, J, additional, Qiao, W, additional, Kini, AS, additional, Sharma, SK, additional, Desnick, RJ, additional, Fuster, V, additional, Hajjar, RJ, additional, Montalescot, G, additional, and Hulot, J-S, additional

Published
2016
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13. Genetic fine mapping and genomic annotation defines causal mechanisms at type 2 diabetes susceptibility loci

Author

Gaulton, K. (Kyle), Ferreira, T. (Teresa), Lee, Y. (Yeji), Raimondo, A. (Anne), Mägi, R. (Reedik), Reschen, M.E. (Michael E.), Mahajan, A. (Anubha), Locke, A. (Adam), Rayner, N.W. (Nigel William), Robertson, N.R. (Neil), Scott, R.A. (Robert), Prokopenko, I. (Inga), Scott, L.J. (Laura), Green, T. (Todd), Sparsø, T. (Thomas), Thuillier, D. (Dorothee), Yengo, L. (Loic), Grallert, H. (Harald), Wahl, S. (Simone), Frånberg, M. (Mattias), Strawbridge, R.J. (Rona), Kestler, H. (Hans), Chheda, H. (Himanshu), Eisele, L. (Lewin), Gustafsson, S. (Stefan), Steinthorsdottir, V. (Valgerdur), Thorleifsson, G. (Gudmar), Qi, L. (Lu), Karssen, L.C. (Lennart), Leeuwen, E.M. (Elisa) van, Willems, S.M. (Sara), Li, M. (Man), Chen, H. (Han), Fuchsberger, C. (Christian), Kwan, P. (Phoenix), Ma, C. (Clement), Linderman, M. (Michael), Lu, Y. (Yingchang), Thomsen, S.K. (Soren K.), Rundle, J.K. (Jana K.), Beer, N.L. (Nicola L.), Bunt, M. (Martijn) van de, Chalisey, A. (Anil), Kang, H.M. (Hyun Min), Voight, B.F. (Benjamin), Abecasis, G.R. (Gonçalo), Almgren, P. (Peter), Baldassarre, D. (Damiano), Balkau, B. (Beverley), Benediktsson, R. (Rafn), Blüher, M. (Matthias), Boeing, H. (Heiner), Bonnycastle, L.L. (Lori), Bottinger, E.P. (Erwin P.), Burtt, N.P. (Noël), Carey, J. (Jason), Charpentier, G. (Guillaume), Chines, P.S. (Peter), Cornelis, M. (Marilyn), Couper, D.J. (David J.), Crenshaw, A. (Andrew), Dam, R.M. (Rob) van, Doney, A.S.F. (Alex), Dorkhan, M. (Mozhgan), Edkins, T. (Ted), Eriksson, J.G. (Johan G.), Esko, T. (Tõnu), Eury, E. (Elodie), Fadista, J. (João), Flannick, J. (Jason), Fontanillas, P. (Pierre), Fox, C.S. (Caroline), Franks, P.W. (Paul W.), Gertow, K. (Karl), Gieger, C. (Christian), Gigante, B. (Bruna), Gottesman, R.F. (Rebecca), Grant, G.B. (George), Grarup, N. (Niels), Groves, C.J. (Christopher J.), Hassinen, M. (Maija), Have, C.T. (Christian T.), Herder, C. (Christian), Holmen, O.L. (Oddgeir), Hreidarsson, A.B. (Astradur), Humphries, S.E. (Steve E.), Hunter, D.J. (David J.), Jackson, A.U. (Anne), Jonsson, A. (Anna), Jørgensen, M.E. (Marit E.), Jørgensen, T. (Torben), Kao, W.H.L. (Wen), Kerrison, N.D. (Nicola D.), Kinnunen, L. (Leena), Klopp, N. (Norman), Kong, A. (Augustine), Kovacs, P. (Peter), Kraft, P. (Peter), Kravic, J. (Jasmina), Langford, C. (Cordelia), Leander, K. (Karin), Liang, L. (Liming), Lichtner, P. (Peter), Lindgren, C.M. (Cecilia M.), Lindholm, B. (Bengt), Linneberg, A. (Allan), Liu, C.-T. (Ching-Ti), Lobbens, S. (Stéphane), Luan, J. (Jian'fan), Lyssenko, V. (Valeriya), Männistö, S. (Satu), McLeod, O. (Olga), Meyer, J. (Jobst), Mihailov, E. (Evelin), Mirza, G. (Ghazala), Mühleisen, T.W. (Thomas), Müller-Nurasyid, M. (Martina), Navarro, C. (Carmen), Nöthen, M.M. (Markus), Oskolkov, N.N. (Nikolay N.), Owen, K.R. (Katharine), Palli, D. (Domenico), Pechlivanis, S. (Sonali), Peltonen, L. (Leena Johanna), Perry, J.R.B. (John), Platou, C.P. (Carl), Roden, M. (Michael), Ruderfer, D. (Douglas), Rybin, D. (Denis), Van Der Schouw, Y.T. (Yvonne T.), Sennblad, B. (Bengt), Sigurosson, G. (Gunnar), Stancáková, A. (Alena), Steinbach, D., Storm, P. (Petter), Strauch, K. (Konstantin), Stringham, H.M. (Heather), Sun, Q., Thorand, B. (Barbara), Tikkanen, E. (Emmi), Tönjes, A. (Anke), Trakalo, J. (Joseph), Tremoli, E. (Elena), Tuomi, T. (Tiinamaija), Wennauer, R. (Roman), Wiltshire, S. (Steven), Wood, A.R. (Andrew), Zeggini, E. (Eleftheria), Dunham, I. (Ian), Birney, E. (Ewan), Pasquali, L. (Lorenzo), Ferrer, J. (Jorge), Loos, R.J.F. (Ruth), Dupuis, J. (Josée), Florez, J.C. (Jose), Boerwinkle, E.A. (Eric), Pankow, J.S. (James), Duijn, C.M. (Cornelia) van, Sijbrands, E.J.G. (Eric), Meigs, J.B. (James B.), Hu, F.B. (Frank), Thorsteinsdottir, U. (Unnur), Zwart, J-A. (John-Anker), Lakka, T.A. (Timo), Rauramaa, R. (Rainer), Stumvoll, M. (Michael), Pedersen, N.L. (Nancy L.), Lind, L. (Lars), Keinanen-Kiukaanniemi, S. (Sirkka), Korpi-Hyövälti, E. (Eeva), Saaristo, T. (Timo), Saltevo, J. (Juha), Kuusisto, J. (Johanna), Laakso, M. (Markku), Metspalu, A. (Andres), Erbel, R. (Raimund), Jöckel, K.-H. (Karl-Heinz), Moebus, S. (Susanne), Ripatti, S. (Samuli), Salomaa, V. (Veikko), Ingelsson, E. (Erik), Boehm, B.O. (Bernhard), Bergman, R.N. (Richard N.), Collins, F.S. (Francis S.), Mohlke, K.L. (Karen L.), Koistinen, H. (Heikki), Tuomilehto, J. (Jaakko), Hveem, K. (Kristian), Njølstad, I. (Inger), Deloukas, P. (Panagiotis), Donnelly, P.J. (Peter J.), Frayling, T.M. (Timothy), Hattersley, A.T. (Andrew), Faire, U. (Ulf) de, Hamsten, A. (Anders), Illig, T. (Thomas), Peters, A. (Annette), Cauchi, S. (Stephane), Sladek, R. (Rob), Froguel, P. (Philippe), Hansen, T. (Torben), Pedersen, O. (Oluf), Morris, A.D. (Andrew), Palmer, C.N.A. (Collin N. A.), Kathiresan, S. (Sekar), Melander, O. (Olle), Nilsson, P.M. (Peter M.), Groop, L. (Leif), Barroso, I.E. (Inês), Langenberg, C. (Claudia), Wareham, N.J. (Nicholas J.), O'Callaghan, C.A. (Christopher A.), Gloyn, A.L. (Anna), Altshuler, D. (David), Boehnke, M. (Michael), Teslovich, T.M. (Tanya M.), McCarthy, M.I. (Mark), Morris, A.P. (Andrew), Gaulton, K. (Kyle), Ferreira, T. (Teresa), Lee, Y. (Yeji), Raimondo, A. (Anne), Mägi, R. (Reedik), Reschen, M.E. (Michael E.), Mahajan, A. (Anubha), Locke, A. (Adam), Rayner, N.W. (Nigel William), Robertson, N.R. (Neil), Scott, R.A. (Robert), Prokopenko, I. (Inga), Scott, L.J. (Laura), Green, T. (Todd), Sparsø, T. (Thomas), Thuillier, D. (Dorothee), Yengo, L. (Loic), Grallert, H. (Harald), Wahl, S. (Simone), Frånberg, M. (Mattias), Strawbridge, R.J. (Rona), Kestler, H. (Hans), Chheda, H. (Himanshu), Eisele, L. (Lewin), Gustafsson, S. (Stefan), Steinthorsdottir, V. (Valgerdur), Thorleifsson, G. (Gudmar), Qi, L. (Lu), Karssen, L.C. (Lennart), Leeuwen, E.M. (Elisa) van, Willems, S.M. (Sara), Li, M. (Man), Chen, H. (Han), Fuchsberger, C. (Christian), Kwan, P. (Phoenix), Ma, C. (Clement), Linderman, M. (Michael), Lu, Y. (Yingchang), Thomsen, S.K. (Soren K.), Rundle, J.K. (Jana K.), Beer, N.L. (Nicola L.), Bunt, M. (Martijn) van de, Chalisey, A. (Anil), Kang, H.M. (Hyun Min), Voight, B.F. (Benjamin), Abecasis, G.R. (Gonçalo), Almgren, P. (Peter), Baldassarre, D. (Damiano), Balkau, B. (Beverley), Benediktsson, R. (Rafn), Blüher, M. (Matthias), Boeing, H. (Heiner), Bonnycastle, L.L. (Lori), Bottinger, E.P. (Erwin P.), Burtt, N.P. (Noël), Carey, J. (Jason), Charpentier, G. (Guillaume), Chines, P.S. (Peter), Cornelis, M. (Marilyn), Couper, D.J. (David J.), Crenshaw, A. (Andrew), Dam, R.M. (Rob) van, Doney, A.S.F. (Alex), Dorkhan, M. (Mozhgan), Edkins, T. (Ted), Eriksson, J.G. (Johan G.), Esko, T. (Tõnu), Eury, E. (Elodie), Fadista, J. (João), Flannick, J. (Jason), Fontanillas, P. (Pierre), Fox, C.S. (Caroline), Franks, P.W. (Paul W.), Gertow, K. (Karl), Gieger, C. (Christian), Gigante, B. (Bruna), Gottesman, R.F. (Rebecca), Grant, G.B. (George), Grarup, N. (Niels), Groves, C.J. (Christopher J.), Hassinen, M. (Maija), Have, C.T. (Christian T.), Herder, C. (Christian), Holmen, O.L. (Oddgeir), Hreidarsson, A.B. (Astradur), Humphries, S.E. (Steve E.), Hunter, D.J. (David J.), Jackson, A.U. (Anne), Jonsson, A. (Anna), Jørgensen, M.E. (Marit E.), Jørgensen, T. (Torben), Kao, W.H.L. (Wen), Kerrison, N.D. (Nicola D.), Kinnunen, L. (Leena), Klopp, N. (Norman), Kong, A. (Augustine), Kovacs, P. (Peter), Kraft, P. (Peter), Kravic, J. (Jasmina), Langford, C. (Cordelia), Leander, K. (Karin), Liang, L. (Liming), Lichtner, P. (Peter), Lindgren, C.M. (Cecilia M.), Lindholm, B. (Bengt), Linneberg, A. (Allan), Liu, C.-T. (Ching-Ti), Lobbens, S. (Stéphane), Luan, J. (Jian'fan), Lyssenko, V. (Valeriya), Männistö, S. (Satu), McLeod, O. (Olga), Meyer, J. (Jobst), Mihailov, E. (Evelin), Mirza, G. (Ghazala), Mühleisen, T.W. (Thomas), Müller-Nurasyid, M. (Martina), Navarro, C. (Carmen), Nöthen, M.M. (Markus), Oskolkov, N.N. (Nikolay N.), Owen, K.R. (Katharine), Palli, D. (Domenico), Pechlivanis, S. (Sonali), Peltonen, L. (Leena Johanna), Perry, J.R.B. (John), Platou, C.P. (Carl), Roden, M. (Michael), Ruderfer, D. (Douglas), Rybin, D. (Denis), Van Der Schouw, Y.T. (Yvonne T.), Sennblad, B. (Bengt), Sigurosson, G. (Gunnar), Stancáková, A. (Alena), Steinbach, D., Storm, P. (Petter), Strauch, K. (Konstantin), Stringham, H.M. (Heather), Sun, Q., Thorand, B. (Barbara), Tikkanen, E. (Emmi), Tönjes, A. (Anke), Trakalo, J. (Joseph), Tremoli, E. (Elena), Tuomi, T. (Tiinamaija), Wennauer, R. (Roman), Wiltshire, S. (Steven), Wood, A.R. (Andrew), Zeggini, E. (Eleftheria), Dunham, I. (Ian), Birney, E. (Ewan), Pasquali, L. (Lorenzo), Ferrer, J. (Jorge), Loos, R.J.F. (Ruth), Dupuis, J. (Josée), Florez, J.C. (Jose), Boerwinkle, E.A. (Eric), Pankow, J.S. (James), Duijn, C.M. (Cornelia) van, Sijbrands, E.J.G. (Eric), Meigs, J.B. (James B.), Hu, F.B. (Frank), Thorsteinsdottir, U. (Unnur), Zwart, J-A. (John-Anker), Lakka, T.A. (Timo), Rauramaa, R. (Rainer), Stumvoll, M. (Michael), Pedersen, N.L. (Nancy L.), Lind, L. (Lars), Keinanen-Kiukaanniemi, S. (Sirkka), Korpi-Hyövälti, E. (Eeva), Saaristo, T. (Timo), Saltevo, J. (Juha), Kuusisto, J. (Johanna), Laakso, M. (Markku), Metspalu, A. (Andres), Erbel, R. (Raimund), Jöckel, K.-H. (Karl-Heinz), Moebus, S. (Susanne), Ripatti, S. (Samuli), Salomaa, V. (Veikko), Ingelsson, E. (Erik), Boehm, B.O. (Bernhard), Bergman, R.N. (Richard N.), Collins, F.S. (Francis S.), Mohlke, K.L. (Karen L.), Koistinen, H. (Heikki), Tuomilehto, J. (Jaakko), Hveem, K. (Kristian), Njølstad, I. (Inger), Deloukas, P. (Panagiotis), Donnelly, P.J. (Peter J.), Frayling, T.M. (Timothy), Hattersley, A.T. (Andrew), Faire, U. (Ulf) de, Hamsten, A. (Anders), Illig, T. (Thomas), Peters, A. (Annette), Cauchi, S. (Stephane), Sladek, R. (Rob), Froguel, P. (Philippe), Hansen, T. (Torben), Pedersen, O. (Oluf), Morris, A.D. (Andrew), Palmer, C.N.A. (Collin N. A.), Kathiresan, S. (Sekar), Melander, O. (Olle), Nilsson, P.M. (Peter M.), Groop, L. (Leif), Barroso, I.E. (Inês), Langenberg, C. (Claudia), Wareham, N.J. (Nicholas J.), O'Callaghan, C.A. (Christopher A.), Gloyn, A.L. (Anna), Altshuler, D. (David), Boehnke, M. (Michael), Teslovich, T.M. (Tanya M.), McCarthy, M.I. (Mark), and Morris, A.P. (Andrew)

Abstract

We performed fine mapping of 39 established type 2 diabetes (T2D) loci in 27,206 cases and 57,574 controls of European ancestry. We identified 49 distinct association signals at these loci, including five mapping in or near KCNQ1. 'Credible sets' of the variants most likely to drive each distinct signal mapped predominantly to noncoding sequence, implying that association with T2D is mediated through gene regulation. Credible set variants were enriched for overlap with FOXA2 chromatin immunoprecipitation binding sites in human islet and liver cells, including at MTNR1B, where fine mapping implicated rs10830963 as driving T2D association. We confirmed that the T2D risk allele for this SNP increases FOXA2-bound enhancer activity in islet- and liver-derived cells. We observed allele-specific differences in NEUROD1 binding in islet-derived cells, consistent with evidence that the T2D risk allele increases islet MTNR1B expression. Our study demonstrates how integration of genetic and genomic information can define molecular mechanisms through which variants underlying association signals exert their effects on disease.

Published
2015
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14. Critical assessment of automated flow cytometry data analysis techniques

Author

Aghaeepour, N, Finak, G, Flowcap, Consortium, Dougall, D, Khodabakhshi, Ah, Mah, P, Obermoser, G, Spidlen, J, Taylor, I, Wuensch, Sa, Bramson, J, Eaves, C, Weng, Ap, Iii, Es, Ho, K, Kollmann, T, Rogers, W, De Rosa, S, Dalal, B, Azad, A, Pothen, A, Brandes, A, Bretschneider, H, Bruggner, R, Finck, R, Jia, R, Zimmerman, N, Linderman, M, Dill, D, Nolan, G, Chan, C, Khettabi, Fe, O'Neill, K, Chikina, M, Ge, Y, Sealfon, S, Sugár, I, Gupta, A, Shooshtari, P, Zare, H, De Jager PL, Jiang, M, Keilwagen, J, Maisog, Jm, Luta, G, Barbo, Aa, Májek, P, Vilček, J, Manninen, T, Huttunen, H, Ruusuvuori, P, Nykter, M, Mclachlan, Gj, Wang, K, Naim, I, Sharma, G, Nikolic, R, Pyne, S, Qian, Y, Qiu, P, Quinn, J, Roth, A, Dream, Consortium, Meyer, P, Stolovitzky, G, Saez Rodriguez, J, Norel, R, Bhattacharjee, M, Biehl, M, Bucher, P, Bunte, K, DI CAMILLO, Barbara, Sambo, Francesco, Sanavia, Tiziana, Trifoglio, Emanuele, Toffolo, GIANNA MARIA, Dimitrieva, S, Dreos, R, Ambrosini, G, Grau, J, Grosse, I, Posch, S, Guex, N, Kursa, M, Rudnicki, W, Liu, B, Maienschein Cline, M, Schneider, P, Seifert, M, Strickert, M, Vilar, Jm, Hoos, H, Mosmann, Tr, Brinkman, R, Gottardo, R, Scheuermann, Rh, Dialogue for Reverse Engineering Assessment and Methods (DREAM) Consortium, Guex, Nicolas, and Abrosini, Giovanna

Subjects
Computer science, Population, Immunology, Graft vs Host Disease, Image processing, Gating, computer.software_genre, Bioinformatics, Biochemistry, Sensitivity and Specificity, Biotechnology, Cell Biology, Molecular Biology, Article, Flow cytometry, 03 medical and health sciences, 0302 clinical medicine, Software, Computational biology and bioinformatics, Immunology, Cancer, Flow cytometry, medicine, Image Processing, Computer-Assisted, Animals, Cluster Analysis, Humans, education, 030304 developmental biology, Cancer, 0303 health sciences, Data processing, education.field_of_study, medicine.diagnostic_test, business.industry, Computational Biology, Reproducibility of Results, Computational biology and bioinformatics, Identification (information), Data Interpretation, Statistical, Data analysis, Leukocytes, Mononuclear, Data mining, Lymphoma, Large B-Cell, Diffuse, business, computer, Algorithms, West Nile Fever, 030215 immunology
Abstract

Traditional methods for flow cytometry (FCM) data processing rely on subjective manual gating. Recently, several groups have developed computational methods for identifying cell populations in multidimensional FCM data. The Flow Cytometry: Critical Assessment of Population Identification Methods (FlowCAP) challenges were established to compare the performance of these methods on two tasks: (i) mammalian cell population identification, to determine whether automated algorithms can reproduce expert manual gating and (ii) sample classification, to determine whether analysis pipelines can identify characteristics that correlate with external variables (such as clinical outcome). This analysis presents the results of the first FlowCAP challenges. Several methods performed well as compared to manual gating or external variables using statistical performance measures, which suggests that automated methods have reached a sufficient level of maturity and accuracy for reliable use in FCM data analysis.

Published
2013

15. Factors Associated With Failure Of Telaprevir- And Boceprevir-Based Hcv Triple Therapy

Author

Bichoupan, K., primary, Tandon, N., additional, Martel-Laferriere, V., additional, Sachs, D., additional, Ng, M., additional, Schonfeld, E.A., additional, Pappas, A., additional, Crismale, J., additional, Stivala, A., additional, Khaitova, V., additional, Gardenier, D., additional, Linderman, M., additional, Olson, W., additional, Perumalswami, P., additional, Schiano, T.D., additional, Odin, J.A., additional, Liu, L.U., additional, Dieterich, D.T., additional, and Branch, A.D., additional

Published
2014
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16. Perturbation and Pitch Normalization as Enhancements to Speaker Recognition

Author

AIR FORCE RESEARCH LAB ROME NY, Lawson, A, Linderman, M, Leonard, M, Stauffer, A, Pokines, B, Carlin, M, AIR FORCE RESEARCH LAB ROME NY, Lawson, A, Linderman, M, Leonard, M, Stauffer, A, Pokines, B, and Carlin, M

Abstract

This study proposes an approach to improving speaker recognition through the process of minute vocal tract length perturbation of training files, coupled with pitch normalization for both train and test data. The notion of perturbation as a method for improving the robustness of training data for supervised classification is taken from the field of optical character recognition, where distorting characters within a certain range has shown strong improvements across disparate conditions. This paper demonstrates that acoustic perturbation, in this case analysis, distortion, and resynthesis of vocal tract length for a given speaker, significantly improves speaker recognition when the resulting files are used to augment or replace the training data. A pitch length normalization technique is also discussed, which is combined with perturbation to improve open-set speaker recognition from an EER of 20% to 6.7%., See also ADM002314. IEEE International Conference on Acoustics, Speech and Signal Processing (34th) held in Taipei, Taiwan on 19-24 April 2009. U.S. Government or Federal Purpose Rights License, The original document contains color images.

Published
2009

17. A multilevel analysis of the impact of land use on interannual land-cover change in East Africa

Author

UCL - SC/GEO - Département de géologie et de géographie, Serneels, S., Linderman, M., Lambin, Eric, UCL - SC/GEO - Département de géologie et de géographie, Serneels, S., Linderman, M., and Lambin, Eric

Abstract

The aim of this study was to characterize the short-term land-cover change processes that were detected in Eastern Africa, based on a set of change metrics that allow for the quantification of interannual changes in vegetation productivity, changes in vegetation phenology and a combination of both. We tested to what extent land use, fire activity and livestock grazing modified the vegetation response to short-term rainfall variability in East Africa and how this is reflected in change metrics derived from MODerate Imaging Spectrometer (MODIS) time series of remote sensing data. We used a hierarchical approach to disentangle the contribution of human activities and climate variability to the patterns of short-term vegetation change in East Africa at different levels of organization. Our results clearly show that land use significantly influences the vegetation response to rainfall variability as measured by time series of MODIS data. Areas with different types of land use react in a different way to interannual climate variability, leading to different values of the change indices depending on the land use type. The impact of land use is more reflected in interannual variability of vegetation productivity and overall change in the vegetation, whereas changes in phenology are mainly driven by climate variability and affect most vegetation types in similar ways. Our multilevel approach led to improved models and clearly demonstrated that climate influence plays at a different scale than land use, fire and herbivore grazing. It helped us to understand dynamics within and between biomes in the study area and investigate the relative importance of different factors influencing short-term variability in change indices at different scales.

Published
2007

18. Categorization of land-cover change processes based on phenological indicators extracted from time series of vegetation index data

Author

UCL - SC/GEO - Département de géologie et de géographie, Lupo, F., Linderman, M., Vanacker, Veerle, Bartholome, E., Lambin, Eric, UCL - SC/GEO - Département de géologie et de géographie, Lupo, F., Linderman, M., Vanacker, Veerle, Bartholome, E., and Lambin, Eric

Abstract

A straightforward method for categorizing temporal patterns of land-cover change is presented. Two successive years of enhanced vegetation index (EVI) data derived from the Moderate Resolution Imagery Spectrometer (MODIS) were analysed. Five phenological indicators were extracted. Based on the interannual difference of each of the five indicators, indices of change in phenology were calculated. An unsupervised classification of these five indices of change applied to pixels characterized by a high change magnitude led to the identification of seven categories of land-cover change patterns. Thirty-one per cent of the change pixels could clearly be explained by a difference in only one or two phenological indicators, e. g. a shift in the start of the growing season or an interruption of the growing season due to floods. The remaining change pixels were explained by a combination of more than two indices of change. The output of this analysis is an allocation of change pixels to broad categories of land-cover change as a preliminary step for finer resolution analyses.

Published
2007

19. Exome sequencing of extreme clopidogrel response phenotypes identifies B4GALT2 as a determinant of on-treatment platelet reactivity.

Author

Scott, SA, Collet, J‐P, Baber, U, Yang, Y, Peter, I, Linderman, M, Sload, J, Qiao, W, Kini, AS, Sharma, SK, Desnick, RJ, Fuster, V, Hajjar, RJ, Montalescot, G, and Hulot, J‐S

Subjects
CLOPIDOGREL, EXOMES, NUCLEOTIDE sequencing, CORONARY disease, MEMBRANE glycoproteins, PATIENTS
Abstract

Interindividual variability in platelet aggregation is common among patients treated with clopidogrel and both high on-treatment platelet reactivity (HTPR) and low on-treatment platelet reactivity (LTPR) increase risks for adverse clinical outcomes. CYP2C19 influences clopidogrel response but only accounts for ∼12% of the variability in platelet reactivity. To identify novel variants implicated in on-treatment platelet reactivity, patients with coronary artery disease (CAD) with extreme pharmacodynamic responses to clopidogrel and wild-type CYP2C19 were subjected to exome sequencing. Candidate variants that clustered in the LTPR subgroup subsequently were genotyped across the discovery cohort ( n = 636). Importantly, carriers of B4GALT2 c.909C>T had lower on-treatment P2Y12 reaction units (PRUs; P = 0.0077) and residual platelet aggregation ( P = 0.0008) compared with noncarriers, which remained significant after adjusting for CYP2C19 and other clinical variables in both the discovery ( P = 0.0298) and replication ( n = 160; PRU: P = 0.0001) cohorts. B4GALT2 is a platelet-expressed galactosyltransferase, indicating that B4GALT2 c.909C>T may influence clopidogrel sensitivity through atypical cell-surface glycoprotein processing and platelet adhesion. [ABSTRACT FROM AUTHOR]

Published
2016
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20. Land-cover change and vegetation dynamics across Africa

Author

UCL, Linderman, M., Rowhani, Pedram, Benz, D., Serneels, S., Lambin, Eric, UCL, Linderman, M., Rowhani, Pedram, Benz, D., Serneels, S., and Lambin, Eric

Abstract

Using improved metrics and recently available MODerate resolution Imaging Spectrometer (MODIS) data, we examined the magnitude, extent, and nature of changes in photosynthetic activity and its timing across Sub-Saharan Africa. Changes in overall vegetation activity and shifts in its timing have considerable implications for a variety of processes including surface-atmosphere energy exchanges, terrestrial sources and sinks of carbon, the contribution of local evapotranspiration to the water cycle, disturbance regimes such as fires and pests, and the food security of societies using these ecosystems. While previous studies have examined broad-scale trends in phenology or provided more detailed estimates of land-cover conversion in the tropics, less is known of the year-to-year dynamics of vegetation. Here we quantified the overall changes in vegetation activity for each year between 2000 and 2004 and examined the proportion linked to differences in phenology and overall photosynthetic activity. In addition, we examined the nature of these changes in terms of frequency and duration, the proportion per ecosystem, identified areas of intensive change, and discuss the potential consequences of these changes. We found that most interannual change was not from shifts in timing or phenology, but rather largely due to differences in the amount of annual photosynthetic activity. In fact, there was as much as a 5% annual difference in vegetation activity across the continent. The changes were likely climate driven with particular vegetation types most susceptible to interannual change with high spatial and temporal variability found across the continent., Anglais

Published
2005

21. Impact of short-term rainfall fluctuation on interannual land cover change in sub-Saharan Africa

Author

UCL - SST/ELI/ELIC - Earth & Climate, Vanacker, Veerle, Linderman, M, Lupo, F, Flasse, S, Lambin, Eric, UCL - SST/ELI/ELIC - Earth & Climate, Vanacker, Veerle, Linderman, M, Lupo, F, Flasse, S, and Lambin, Eric

Abstract

Aim Interannual land cover change plays a significant role in food security, ecosystem processes, and regional and global climate modelling. Measuring the magnitude and location and understanding the driving factors of interannual land cover change are therefore of utmost importance to improve our understanding and prediction of these impacts and to better differentiate between natural and human causes of land cover change. Despite advances in quantifying the magnitude of land cover change, the interpretation of the observed land cover change in terms of climatic, ecological and anthropogenic processes still remains a complex issue. In this paper, we map land cover change across sub-Saharan Africa and examine the influences of rainfall fluctuations on interannual change. Location The analysis was applied to sub-Saharan Africa. Methods Ten-day rainfall estimates (RFE) obtained from National Oceanic and Atmospheric Administration's (NOAA) Climate Prediction Center (CPC) were used to extract information on inter and intra-annual rainfall fluctuations. The magnitude of land cover change was quantified based on the multitemporal change vector method measuring year-to-year differences in bidirectional reflectance distribution function (BRDF) corrected 16-day enhanced vegetation index (EVI) data from the Moderate Resolution Imaging Spectro-radiometer (MODIS). Statistical models were used to estimate the relationship between short-term rainfall variability and the magnitude of land cover change. The analysis was stratified first by physiognomic vegetation type and second by chorological data on species distribution to gain insights into spatial variations in response to short-term rainfall fluctuations. Results The magnitude of land cover change was significantly related to rainfall variability at the 5% level. Stratification considerably strengthened the relationship between the magnitude of change and rainfall variability. Explanatory power of the models ranged from R-2 =

Published
2005

23. Better than typing?

Author

Linderman, M.

Subjects
Electromyography -- Research, Penmanship -- Technology application, Computer input design -- Innovations, Science and technology, Input device, Technology application, Innovations, Research
Abstract

Keyboards, farewell! With a special glove and a couple of algorithms, computers can translate muscle activity, generated while writing, directly to typeface. Six volunteers donned a set of electrodes that [...]

Published
2009

43. Critical assessment of automated flow cytometry data analysis techniques

Author

Aghaeepour, Nima, Finak, Greg, Dougall, D., AH, Khodabakhshi, Mah, P., Obermoser, G., Spidlen, J., Taylor, I., SA, Wuensch, Bramson, J., Eaves, C., AP, Weng, ES, Iii, Ho, K., Kollmann, T., Rogers, W., De Rosa, S., Dalal, B., Azad, A., Pothen, A., Brandes, A., Bretschneider, H., Bruggner, R., Finck, R., Jia, R., Zimmerman, N., Linderman, M., Dill, D., Nolan, G., Chan, C., FE, Khettabi, O'Neill, K., Chikina, M., Ge, Y., Sealfon, S., Sugár, I., Gupta, A., Shooshtari, P., Zare, H., PL, De Jager, Jiang, M., Keilwagen, J., JM, Maisog, Luta, G., AA, Barbo, Májek, P., Vilček, J., Manninen, T., Huttunen, H., Ruusuvuori, P., Nykter, M., GJ, McLachlan, Wang, K., Naim, I., Sharma, G., Nikolic, R., Pyne, S., Qian, Y., Qiu, P., Quinn, J., Roth, A., Meyer, P., Stolovitzky, G., Saez-Rodriguez, J., Norel, R., Bhattacharjee, M., Biehl, M., Bucher, Philipp, Bunte, K., Di Camillo, B., Sambo, F., Sanavia, T., Trifoglio, E., Toffolo, G., Dimitrieva, Slavica, Dreos, René, Ambrosini, Giovanna, Grau, J., Grosse, I., Posch, S., Guex, N., Kursa, M., Rudnicki, W., Liu, B., Maienschein-Cline, M., Schneider, P., Seifert, M., Strickert, M., JM, Vilar, Hoos, Holger, Mosmann, Tim, Brinkman, Ryan, Gottardo, Raphael, and Scheuermann, Richard H.

Abstract

Traditional methods for flow cytometry (FCM) data processing rely on subjective manual gating. Recently, several groups have developed computational methods for identifying cell populations in multidimensional FCM data. The Flow Cytometry: Critical Assessment of Population Identification Methods (FlowCAP) challenges were established to compare the performance of these methods on two tasks: (i) mammalian cell population identification, to determine whether automated algorithms can reproduce expert manual gating and (ii) sample classification, to determine whether analysis pipelines can identify characteristics that correlate with external variables (such as clinical outcome). This analysis presents the results of the first FlowCAP challenges. Several methods performed well as compared to manual gating or external variables using statistical performance measures, which suggests that automated methods have reached a sufficient level of maturity and accuracy for reliable use in FCM data analysis.

49. Research design and baseline participant characteristics of the Resilient, Empowered, Active Living with Diabetes - Telehealth (REAL-T) Study: A randomized controlled trial for young adults with type 1 diabetes.

Author

Pyatak EA, Ali A, Khurana AR, Lee PJ, Sideris J, Fox S, Diaz J, Granados G, Blanchard J, McGuire R, Salazar Plascencia E, Salcedo-Rodriguez E, Flores-Garcia J, Linderman M, Taylor EE, Tapia V, Nnoli N, Sequeira PA, Freeby MJ, and Raymond JK

Subjects
Humans, Young Adult, Quality of Life, Pandemics, Research Design, Blood Glucose analysis, Diabetes Mellitus, Type 1 epidemiology, Diabetes Mellitus, Type 1 therapy, Telemedicine
Abstract

Background: Type 1 diabetes (T1D) is a chronic condition affecting nearly 1.9 million people in the United States. Young adults (YAs) with T1D face unique challenges in managing their condition, experiencing poorer health and well-being than other age groups. The current study is evaluating the Resilient, Empowered, Active Living (REAL) intervention, previously shown to improve glucose levels and quality of life among YAs with diabetes, using telehealth delivery (REAL-T) to expand reach and accessibility. This paper reports on the methodology and baseline participant characteristics of the REAL-T study., Methods: REAL-T is a two-arm randomized controlled trial that recruited 18-30 year olds with T1D via clinics and social media advertising. Data collection, which was adapted to be fully remote due to COVID-19, occurs every three months for one year. Participants receive either usual care or a 6-month telehealth occupational therapy intervention. The primary outcome is glycated hemoglobin (A1c); secondary outcomes include diabetes distress, quality of life, and continuous glucose monitor-derived measures., Results: The study enrolled a diverse sample of 209 YAs with T1D. Analysis of baseline data indicates equivalence between the intervention and control groups. Study participants have notably higher diabetes distress and poorer mental well-being than similar populations., Conclusion: The REAL-T study successfully adapted to remote implementation during the COVID-19 pandemic. By examining long-term outcomes, mediating pathways, and cost-effectiveness, the study will contribute knowledge of the impact of tailored interventions for YAs with T1D, designed to reduce disparities and improve health and well-being in this population., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023. Published by Elsevier Inc.)

Published
2023
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50. Object based classification of a riparian environment using ultra-high resolution imagery, hierarchical landcover structures, and image texture.

Author

Kutz K, Cook Z, and Linderman M

Subjects
Humans, Image Processing, Computer-Assisted, Iowa, Environmental Monitoring methods, Remote Sensing Technology methods
Abstract

Land cover mapping is an important part of resource management, planning, and economic predictions. Improvements in remote sensing, machine learning, image processing, and object based image analysis (OBIA) has made the process of identifying land cover types increasingly faster and reliable but these advances have not been able to utilize all of the information encompassed within ultra-high (sub-meter) resolution imagery. There have been few known attempts to try and maximize this detailed information in high resolution imagery using advanced textural components. Hierarchical land classes are also rarely used as an attribute within the machine learning step of object-based image analysis. In this study we try to circumnavigate the inherent problems associated with high resolution imagery by combining well researched data transformations that aid the OBIA process with a seldom used texture transformation in Geographic Object Based Image Analyses (GEOBIA/OBIA) known as the Gabor Transform and the hierarchal organization of landscapes. We will observe the difference made in segmentation and classification accuracy of a random forest classifier when we fuse a Gabor transformed image to a Normalized Difference Vegetation Index (NDVI), high resolution multi-spectral imagery (RGB and NIR) and Light Detection and Ranging (LiDAR) derived canopy height model (CHM) within a riparian area in Southeast Iowa, United States. Additionally, we will observe the effects on classification accuracy when adding multi-scale land cover data to objects. Both, the addition of hierarchical information and Gabor textural information, could aid the GEOBIA process in delineating and classifying the same objects that human experts would delineate within this riparian landscape., (© 2022. The Author(s).)

Published
2022
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