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Painchaud V, Desrosiers P, Doyon N. The Determining Role of Covariances in Large Networks of Stochastic Neurons. Neural Comput 2024:1-42. [PMID: 38657971 DOI: 10.1162/neco_a_01656] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Accepted: 01/02/2024] [Indexed: 04/26/2024]
Abstract
Biological neural networks are notoriously hard to model due to their stochastic behavior and high dimensionality. We tackle this problem by constructing a dynamical model of both the expectations and covariances of the fractions of active and refractory neurons in the network's populations. We do so by describing the evolution of the states of individual neurons with a continuous-time Markov chain, from which we formally derive a low-dimensional dynamical system. This is done by solving a moment closure problem in a way that is compatible with the nonlinearity and boundedness of the activation function. Our dynamical system captures the behavior of the high-dimensional stochastic model even in cases where the mean-field approximation fails to do so. Taking into account the second-order moments modifies the solutions that would be obtained with the mean-field approximation and can lead to the appearance or disappearance of fixed points and limit cycles. We moreover perform numerical experiments where the mean-field approximation leads to periodically oscillating solutions, while the solutions of the second-order model can be interpreted as an average taken over many realizations of the stochastic model. Altogether, our results highlight the importance of including higher moments when studying stochastic networks and deepen our understanding of correlated neuronal activity.
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Affiliation(s)
- Vincent Painchaud
- Department of Mathematics and Statistics, McGill University, Montreal, Québec H3A 0B6, Canada
| | - Patrick Desrosiers
- Department of Physics, Engineering Physics, and Optics, Université Laval, Quebec City, Québec G1V 0A6, Canada
- CERVO Brain Research Center, Quebec City, Québec G1E 1T2, Canada
- Centre interdisciplinaire en modélisation mathématique de l'Université Laval, Quebec City, Québec G1V 0A6, Canada
| | - Nicolas Doyon
- Départment of Mathematics and Statistics, Université Laval, Quebec City, Québec G1V 0A6, Canada
- CERVO Brain Research Center, Quebec City, Québec G1E 1T2, Canada
- Centre interdisciplinaire en modélisation mathématique de l'Université Laval, Quebec City, Québec G1V 0A6, Canada
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2
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Lamontagne-Caron R, Desrosiers P, Potvin O, Doyon N, Duchesne S. Author Correction: Predicting cognitive decline in a low-dimensional representation of brain morphology. Sci Rep 2023; 13:20165. [PMID: 37978214 PMCID: PMC10656520 DOI: 10.1038/s41598-023-46972-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2023] Open
Affiliation(s)
- Rémi Lamontagne-Caron
- Département de médecine, Université Laval, Quebec, QC, G1V 0A6, Canada.
- Centre de recherche CERVO, Quebec, QC, G1J 2G3, Canada.
| | - Patrick Desrosiers
- Centre de recherche CERVO, Quebec, QC, G1J 2G3, Canada
- Centre interdisciplinaire en modélisation mathématique, Université Laval, Quebec, QC, G1V 0A6, Canada
- Département de physique, de génie physique et d'optique, Université Laval, Quebec, QC, G1V 0A6, Canada
| | | | - Nicolas Doyon
- Centre de recherche CERVO, Quebec, QC, G1J 2G3, Canada
- Centre interdisciplinaire en modélisation mathématique, Université Laval, Quebec, QC, G1V 0A6, Canada
- Département de mathématiques et de statistique, Université Laval, Quebec, QC, G1V 0A6, Canada
| | - Simon Duchesne
- Centre de recherche CERVO, Quebec, QC, G1J 2G3, Canada
- Département de radiologie et médecine nucléaire, Université Laval, Quebec, QC, G1V 0A6, Canada
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3
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Lamontagne-Caron R, Desrosiers P, Potvin O, Doyon N, Duchesne S. Predicting cognitive decline in a low-dimensional representation of brain morphology. Sci Rep 2023; 13:16793. [PMID: 37798311 PMCID: PMC10556003 DOI: 10.1038/s41598-023-43063-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Accepted: 09/19/2023] [Indexed: 10/07/2023] Open
Abstract
Identifying early signs of neurodegeneration due to Alzheimer's disease (AD) is a necessary first step towards preventing cognitive decline. Individual cortical thickness measures, available after processing anatomical magnetic resonance imaging (MRI), are sensitive markers of neurodegeneration. However, normal aging cortical decline and high inter-individual variability complicate the comparison and statistical determination of the impact of AD-related neurodegeneration on trajectories. In this paper, we computed trajectories in a 2D representation of a 62-dimensional manifold of individual cortical thickness measures. To compute this representation, we used a novel, nonlinear dimension reduction algorithm called Uniform Manifold Approximation and Projection (UMAP). We trained two embeddings, one on cortical thickness measurements of 6237 cognitively healthy participants aged 18-100 years old and the other on 233 mild cognitively impaired (MCI) and AD participants from the longitudinal database, the Alzheimer's Disease Neuroimaging Initiative database (ADNI). Each participant had multiple visits ([Formula: see text]), one year apart. The first embedding's principal axis was shown to be positively associated ([Formula: see text]) with participants' age. Data from ADNI is projected into these 2D spaces. After clustering the data, average trajectories between clusters were shown to be significantly different between MCI and AD subjects. Moreover, some clusters and trajectories between clusters were more prone to host AD subjects. This study was able to differentiate AD and MCI subjects based on their trajectory in a 2D space with an AUC of 0.80 with 10-fold cross-validation.
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Affiliation(s)
- Rémi Lamontagne-Caron
- Département de médecine, Université Laval, Quebec, QC, G1V 0A6, Canada.
- Centre de recherche CERVO, Quebec, QC, G1J 2G3, Canada.
| | - Patrick Desrosiers
- Centre de recherche CERVO, Quebec, QC, G1J 2G3, Canada
- Centre interdisciplinaire en modélisation mathématique, Université Laval, Quebec, QC, G1V 0A6, Canada
- Département de physique, de génie physique et d'optique, Université Laval, Quebec, QC, G1V 0A6, Canada
| | | | - Nicolas Doyon
- Centre de recherche CERVO, Quebec, QC, G1J 2G3, Canada
- Centre interdisciplinaire en modélisation mathématique, Université Laval, Quebec, QC, G1V 0A6, Canada
- Département de mathématiques et de statistique, Université Laval, Quebec, QC, G1V 0A6, Canada
| | - Simon Duchesne
- Centre de recherche CERVO, Quebec, QC, G1J 2G3, Canada
- Département de radiologie et médecine nucléaire, Université Laval, Quebec, QC, G1V 0A6, Canada
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Légaré A, Lemieux M, Desrosiers P, De Koninck P. Zebrafish brain atlases: a collective effort for a tiny vertebrate brain. Neurophotonics 2023; 10:044409. [PMID: 37786400 PMCID: PMC10541682 DOI: 10.1117/1.nph.10.4.044409] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Revised: 09/03/2023] [Accepted: 09/13/2023] [Indexed: 10/04/2023]
Abstract
In the past two decades, digital brain atlases have emerged as essential tools for sharing and integrating complex neuroscience datasets. Concurrently, the larval zebrafish has become a prominent vertebrate model offering a strategic compromise for brain size, complexity, transparency, optogenetic access, and behavior. We provide a brief overview of digital atlases recently developed for the larval zebrafish brain, intersecting neuroanatomical information, gene expression patterns, and connectivity. These atlases are becoming pivotal by centralizing large datasets while supporting the generation of circuit hypotheses as functional measurements can be registered into an atlas' standard coordinate system to interrogate its structural database. As challenges persist in mapping neural circuits and incorporating functional measurements into zebrafish atlases, we emphasize the importance of collaborative efforts and standardized protocols to expand these resources to crack the complex codes of neuronal activity guiding behavior in this tiny vertebrate brain.
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Affiliation(s)
| | - Mado Lemieux
- CERVO Brain Research Center, Québec, Québec, Canada
| | - Patrick Desrosiers
- CERVO Brain Research Center, Québec, Québec, Canada
- Université Laval, Department of Physics, Engineering Physics and Optics, Québec, Québec, Canada
| | - Paul De Koninck
- CERVO Brain Research Center, Québec, Québec, Canada
- Université Laval, Department of Biochemistry, Microbiology and Bio-informatics, Québec, Québec, Canada
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Désy B, Desrosiers P, Allard A. Dimension matters when modeling network communities in hyperbolic spaces. PNAS Nexus 2023; 2:pgad136. [PMID: 37181048 PMCID: PMC10167553 DOI: 10.1093/pnasnexus/pgad136] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Revised: 02/17/2023] [Accepted: 03/30/2023] [Indexed: 05/16/2023]
Abstract
Over the last decade, random hyperbolic graphs have proved successful in providing geometric explanations for many key properties of real-world networks, including strong clustering, high navigability, and heterogeneous degree distributions. These properties are ubiquitous in systems as varied as the internet, transportation, brain or epidemic networks, which are thus unified under the hyperbolic network interpretation on a surface of constant negative curvature. Although a few studies have shown that hyperbolic models can generate community structures, another salient feature observed in real networks, we argue that the current models are overlooking the choice of the latent space dimensionality that is required to adequately represent clustered networked data. We show that there is an important qualitative difference between the lowest-dimensional model and its higher-dimensional counterparts with respect to how similarity between nodes restricts connection probabilities. Since more dimensions also increase the number of nearest neighbors for angular clusters representing communities, considering only one more dimension allows us to generate more realistic and diverse community structures.
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Affiliation(s)
- Béatrice Désy
- School of Information Management, Victoria University of Wellington, Wellington 6140, New Zealand
- Antarctic Research Centre, Victoria University of Wellington, Wellington 6140, New Zealand
| | - Patrick Desrosiers
- Département de physique, de génie physique et d’optique, Université Laval, Québec, QC, Canada G1V 0A6
- Centre interdisciplinaire en modélisation mathématique, Université Laval, Québec, QC, Canada G1V 0A6
- Centre de recherche CERVO, Québec, QC, Canada G1J 2G3
| | - Antoine Allard
- Département de physique, de génie physique et d’optique, Université Laval, Québec, QC, Canada G1V 0A6
- Centre interdisciplinaire en modélisation mathématique, Université Laval, Québec, QC, Canada G1V 0A6
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6
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Vegué M, Thibeault V, Desrosiers P, Allard A. Dimension reduction of dynamics on modular and heterogeneous directed networks. PNAS Nexus 2023; 2:pgad150. [PMID: 37215634 PMCID: PMC10198746 DOI: 10.1093/pnasnexus/pgad150] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Revised: 02/17/2023] [Accepted: 04/12/2023] [Indexed: 05/24/2023]
Abstract
Dimension reduction is a common strategy to study nonlinear dynamical systems composed by a large number of variables. The goal is to find a smaller version of the system whose time evolution is easier to predict while preserving some of the key dynamical features of the original system. Finding such a reduced representation for complex systems is, however, a difficult task. We address this problem for dynamics on weighted directed networks, with special emphasis on modular and heterogeneous networks. We propose a two-step dimension-reduction method that takes into account the properties of the adjacency matrix. First, units are partitioned into groups of similar connectivity profiles. Each group is associated to an observable that is a weighted average of the nodes' activities within the group. Second, we derive a set of equations that must be fulfilled for these observables to properly represent the original system's behavior, together with a method for approximately solving them. The result is a reduced adjacency matrix and an approximate system of ODEs for the observables' evolution. We show that the reduced system can be used to predict some characteristic features of the complete dynamics for different types of connectivity structures, both synthetic and derived from real data, including neuronal, ecological, and social networks. Our formalism opens a way to a systematic comparison of the effect of various structural properties on the overall network dynamics. It can thus help to identify the main structural driving forces guiding the evolution of dynamical processes on networks.
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Affiliation(s)
- Marina Vegué
- Département de physique, de génie physique et d'optique, Université Laval, 2325 rue de l'Université, G1V 0A6 Québec, Canada
- Centre interdisciplinaire en modélisation mathématique, Université Laval, 2325 rue de l'Université, G1V 0A6 Québec, Canada
| | - Vincent Thibeault
- Département de physique, de génie physique et d'optique, Université Laval, 2325 rue de l'Université, G1V 0A6 Québec, Canada
- Centre interdisciplinaire en modélisation mathématique, Université Laval, 2325 rue de l'Université, G1V 0A6 Québec, Canada
| | - Patrick Desrosiers
- Département de physique, de génie physique et d'optique, Université Laval, 2325 rue de l'Université, G1V 0A6 Québec, Canada
- Centre interdisciplinaire en modélisation mathématique, Université Laval, 2325 rue de l'Université, G1V 0A6 Québec, Canada
- CERVO Brain Research Center, 2301 avenue d'Estimauville, G1E 1T2 Québec, Canada
| | - Antoine Allard
- Département de physique, de génie physique et d'optique, Université Laval, 2325 rue de l'Université, G1V 0A6 Québec, Canada
- Centre interdisciplinaire en modélisation mathématique, Université Laval, 2325 rue de l'Université, G1V 0A6 Québec, Canada
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7
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Painchaud V, Doyon N, Desrosiers P. Beyond Wilson-Cowan dynamics: oscillations and chaos without inhibition. Biol Cybern 2022; 116:527-543. [PMID: 36063212 PMCID: PMC9691500 DOI: 10.1007/s00422-022-00941-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Accepted: 07/15/2022] [Indexed: 06/15/2023]
Abstract
Fifty years ago, Wilson and Cowan developed a mathematical model to describe the activity of neural populations. In this seminal work, they divided the cells in three groups: active, sensitive and refractory, and obtained a dynamical system to describe the evolution of the average firing rates of the populations. In the present work, we investigate the impact of the often neglected refractory state and show that taking it into account can introduce new dynamics. Starting from a continuous-time Markov chain, we perform a rigorous derivation of a mean-field model that includes the refractory fractions of populations as dynamical variables. Then, we perform bifurcation analysis to explain the occurrence of periodic solutions in cases where the classical Wilson-Cowan does not predict oscillations. We also show that our mean-field model is able to predict chaotic behavior in the dynamics of networks with as little as two populations.
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Affiliation(s)
- Vincent Painchaud
- Department of Mathematics and Statistics, McGill University, Sherbrooke Street West, Montreal, QC H3A 0B6 Canada
| | - Nicolas Doyon
- Départment de Mathématiques et de Statistique, Université Laval, Avenue de la Médecine, Quebec City, QC G1V 0A6 Canada
- CERVO Brain Research Center, Avenue d’Estimauville, Quebec City, QC G1E 1T2 Canada
- Centre Interdisciplinaire en Modélisation Mathématique de l’Université Laval, Avenue de la Médecine, Quebec City, QC G1V 0A6 Canada
| | - Patrick Desrosiers
- Départment de Physique, de Génie Physique et d’Optique, Université Laval, Avenue de la Médecine, Quebec City, QC G1V 0A6 Canada
- CERVO Brain Research Center, Avenue d’Estimauville, Quebec City, QC G1E 1T2 Canada
- Centre Interdisciplinaire en Modélisation Mathématique de l’Université Laval, Avenue de la Médecine, Quebec City, QC G1V 0A6 Canada
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8
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Guilbert J, Légaré A, De Koninck P, Desrosiers P, Desjardins M. Toward an integrative neurovascular framework for studying brain networks. Neurophotonics 2022; 9:032211. [PMID: 35434179 PMCID: PMC8989057 DOI: 10.1117/1.nph.9.3.032211] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Accepted: 03/11/2022] [Indexed: 05/28/2023]
Abstract
Brain functional connectivity based on the measure of blood oxygen level-dependent (BOLD) functional magnetic resonance imaging (fMRI) signals has become one of the most widely used measurements in human neuroimaging. However, the nature of the functional networks revealed by BOLD fMRI can be ambiguous, as highlighted by a recent series of experiments that have suggested that typical resting-state networks can be replicated from purely vascular or physiologically driven BOLD signals. After going through a brief review of the key concepts of brain network analysis, we explore how the vascular and neuronal systems interact to give rise to the brain functional networks measured with BOLD fMRI. This leads us to emphasize a view of the vascular network not only as a confounding element in fMRI but also as a functionally relevant system that is entangled with the neuronal network. To study the vascular and neuronal underpinnings of BOLD functional connectivity, we consider a combination of methodological avenues based on multiscale and multimodal optical imaging in mice, used in combination with computational models that allow the integration of vascular information to explain functional connectivity.
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Affiliation(s)
- Jérémie Guilbert
- Université Laval, Department of Physics, Physical Engineering, and Optics, Québec, Canada
- Université Laval, Centre de recherche du CHU de Québec, Québec, Canada
| | - Antoine Légaré
- Université Laval, Department of Physics, Physical Engineering, and Optics, Québec, Canada
- Centre de recherche CERVO, Québec, Canada
- Université Laval, Department of Biochemistry, Microbiology, and Bioinformatics, Québec, Canada
| | - Paul De Koninck
- Centre de recherche CERVO, Québec, Canada
- Université Laval, Department of Biochemistry, Microbiology, and Bioinformatics, Québec, Canada
| | - Patrick Desrosiers
- Université Laval, Department of Physics, Physical Engineering, and Optics, Québec, Canada
- Centre de recherche CERVO, Québec, Canada
| | - Michèle Desjardins
- Université Laval, Department of Physics, Physical Engineering, and Optics, Québec, Canada
- Université Laval, Centre de recherche du CHU de Québec, Québec, Canada
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9
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Wang F, Bélanger E, Côté SL, Desrosiers P, Prescott SA, Côté DC, De Koninck Y. Sensory Afferents Use Different Coding Strategies for Heat and Cold. Cell Rep 2019; 23:2001-2013. [PMID: 29768200 DOI: 10.1016/j.celrep.2018.04.065] [Citation(s) in RCA: 56] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2017] [Revised: 02/22/2018] [Accepted: 04/13/2018] [Indexed: 11/24/2022] Open
Abstract
Primary afferents transduce environmental stimuli into electrical activity that is transmitted centrally to be decoded into corresponding sensations. However, it remains unknown how afferent populations encode different somatosensory inputs. To address this, we performed two-photon Ca2+ imaging from thousands of dorsal root ganglion (DRG) neurons in anesthetized mice while applying mechanical and thermal stimuli to hind paws. We found that approximately half of all neurons are polymodal and that heat and cold are encoded very differently. As temperature increases, more heating-sensitive neurons are activated, and most individual neurons respond more strongly, consistent with graded coding at population and single-neuron levels, respectively. In contrast, most cooling-sensitive neurons respond in an ungraded fashion, inconsistent with graded coding and suggesting combinatorial coding, based on which neurons are co-activated. Although individual neurons may respond to multiple stimuli, our results show that different stimuli activate distinct combinations of diversely tuned neurons, enabling rich population-level coding.
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Affiliation(s)
- Feng Wang
- CERVO Brain Research Centre, Québec Mental Health Institute, Quebec City, QC, Canada
| | - Erik Bélanger
- CERVO Brain Research Centre, Québec Mental Health Institute, Quebec City, QC, Canada; Center for Optics, Photonics and Lasers (COPL), Laval University, Quebec City, QC, Canada
| | - Sylvain L Côté
- CERVO Brain Research Centre, Québec Mental Health Institute, Quebec City, QC, Canada
| | - Patrick Desrosiers
- CERVO Brain Research Centre, Québec Mental Health Institute, Quebec City, QC, Canada; Department of Physics, Physical Engineering, and Optics, Laval University, Quebec City, QC, Canada
| | - Steven A Prescott
- Neurosciences and Mental Health, The Hospital for Sick Children, Toronto, ON, Canada; Department of Physiology and the Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, ON, Canada
| | - Daniel C Côté
- CERVO Brain Research Centre, Québec Mental Health Institute, Quebec City, QC, Canada; Center for Optics, Photonics and Lasers (COPL), Laval University, Quebec City, QC, Canada; Department of Physics, Physical Engineering, and Optics, Laval University, Quebec City, QC, Canada
| | - Yves De Koninck
- CERVO Brain Research Centre, Québec Mental Health Institute, Quebec City, QC, Canada; Center for Optics, Photonics and Lasers (COPL), Laval University, Quebec City, QC, Canada; Department of Psychiatry and Neuroscience, Laval University, Quebec City, QC, Canada.
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Redondo MJ, Geyer S, Steck AK, Sharp S, Wentworth JM, Weedon MN, Antinozzi P, Sosenko J, Atkinson M, Pugliese A, Oram RA, Antinozzi P, Atkinson M, Battaglia M, Becker D, Bingley P, Bosi E, Buckner J, Colman P, Gottlieb P, Herold K, Insel R, Kay T, Knip M, Marks J, Moran A, Palmer J, Peakman M, Philipson L, Pugliese A, Raskin P, Rodriguez H, Roep B, Russell W, Schatz D, Wherrett D, Wilson D, Winter W, Ziegler A, Benoist C, Blum J, Chase P, Clare-Salzler M, Clynes R, Eisenbarth G, Fathman C, Grave G, Hering B, Kaufman F, Leschek E, Mahon J, Nanto-Salonen K, Nepom G, Orban T, Parkman R, Pescovitz M, Peyman J, Roncarolo M, Simell O, Sherwin R, Siegelman M, Steck A, Thomas J, Trucco M, Wagner J, Greenbaum ,CJ, Bourcier K, Insel R, Krischer JP, Leschek E, Rafkin L, Spain L, Cowie C, Foulkes M, Krause-Steinrauf H, Lachin JM, Malozowski S, Peyman J, Ridge J, Savage P, Skyler JS, Zafonte SJ, Kenyon NS, Santiago I, Sosenko JM, Bundy B, Abbondondolo M, Adams T, Amado D, Asif I, Boonstra M, Bundy B, Burroughs C, Cuthbertson D, Deemer M, Eberhard C, Fiske S, Ford J, Garmeson J, Guillette H, Browning G, Coughenour T, Sulk M, Tsalikan E, Tansey M, Cabbage J, Dixit N, Pasha S, King M, Adcock K, Geyer S, Atterberry H, Fox L, Englert K, Mauras N, Permuy J, Sikes K, Berhe T, Guendling B, McLennan L, Paganessi L, Hays B, Murphy C, Draznin M, Kamboj M, Sheppard S, Lewis V, Coates L, Moore W, Babar G, Bedard J, Brenson-Hughes D, Henderson C, Cernich J, Clements M, Duprau R, Goodman S, Hester L, Huerta-Saenz L, Karmazin A, Letjen T, Raman S, Morin D, Henry M, Bestermann W, Morawski E, White J, Brockmyer A, Bays R, Campbell S, Stapleton A, Stone N, Donoho A, Everett H, Heyman K, Hensley H, Johnson M, Marshall C, Skirvin N, Taylor P, Williams R, Ray L, Wolverton C, Nickels D, Dothard C, Hsiao B, Speiser P, Pellizzari M, Bokor L, Izuora K, Abdelnour S, Cummings P, Paynor S, Leahy M, Riedl M, Shockley S, Karges C, Saad R, Briones T, Casella S, Herz C, Walsh K, Greening J, Hay F, Hunt S, Sikotra N, Simons L, Keaton N, Karounos D, Oremus R, Dye L, Myers L, Ballard D, Miers W, Sparks R, Thraikill K, Edwards K, Fowlkes J, Kinderman A, Kemp S, Morales A, Holland L, Johnson L, Paul P, Ghatak A, Phelen K, Leyland H, Henderson T, Brenner D, Law P, Oppenheimer E, Mamkin I, Moniz C, Clarson C, Lovell M, Peters A, Ruelas V, Borut D, Burt D, Jordan M, Leinbach A, Castilla S, Flores P, Ruiz M, Hanson L, Green-Blair J, Sheridan R, Wintergerst K, Pierce G, Omoruyi A, Foster M, Linton C, Kingery S, Lunsford A, Cervantes I, Parker T, Price P, Urben J, Doughty I, Haydock H, Parker V, Bergman P, Liu S, Duncum S, Rodda C, Thomas A, Ferry R, McCommon D, Cockroft J, Perelman A, Calendo R, Barrera C, Arce-Nunez E, Lloyd J, Martinez Y, De la Portilla M, Cardenas I, Garrido L, Villar M, Lorini R, Calandra E, D’Annuzio G, Perri K, Minuto N, Malloy J, Rebora C, Callegari R, Ali O, Kramer J, Auble B, Cabrera S, Donohoue P, Fiallo-Scharer R, Hessner M, Wolfgram P, Maddox K, Kansra A, Bettin N, McCuller R, Miller A, Accacha S, Corrigan J, Fiore E, Levine R, Mahoney T, Polychronakos C, Martin J, Gagne V, Starkman H, Fox M, Chin D, Melchionne F, Silverman L, Marshall I, Cerracchio L, Cruz J, Viswanathan A, Miller J, Wilson J, Chalew S, Valley S, Layburn S, Lala A, Clesi P, Genet M, Uwaifo G, Charron A, Allerton T, Milliot E, Cefalu W, Melendez-Ramirez L, Richards R, Alleyn C, Gustafson E, Lizanna M, Wahlen J, Aleiwe S, Hansen M, Wahlen H, Moore M, Levy C, Bonaccorso A, Rapaport R, Tomer Y, Chia D, Goldis M, Iazzetti L, Klein M, Levister C, Waldman L, Muller S, Wallach E, Regelmann M, Antal Z, Aranda M, Reynholds C, Leech N, Wake D, Owens C, Burns M, Wotherspoon J, Nguyen T, Murray A, Short K, Curry G, Kelsey S, Lawson J, Porter J, Stevens S, Thomson E, Winship S, Wynn L, O’Donnell R, Wiltshire E, Krebs J, Cresswell P, Faherty H, Ross C, Vinik A, Barlow P, Bourcier M, Nevoret M, Couper J, Oduah V, Beresford S, Thalagne N, Roper H, Gibbons J, Hill J, Balleaut S, Brennan C, 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Trunnel S, Transue D, Surhigh J, Bezzaire D, Moltz K, Zacharski E, Henske J, Desai S, Frizelis K, Khan F, Sjoberg R, Allen K, Manning P, Hendry G, Taylor B, Jones S, Couch R, Danchak R, Lieberman D, Strader W, Bencomo M, Bailey T, Bedolla L, Roldan C, Moudiotis C, Vaidya B, Anning C, Bunce S, Estcourt S, Folland E, Gordon E, Harrill C, Ireland J, Piper J, Scaife L, Sutton K, Wilkins S, Costelloe M, Palmer J, Casas L, Miller C, Burgard M, Erickson C, Hallanger-Johnson J, Clark P, Taylor W, Galgani J, Banerjee S, Banda C, McEowen D, Kinman R, Lafferty A, Gillett S, Nolan C, Pathak M, Sondrol L, Hjelle T, Hafner S, Kotrba J, Hendrickson R, Cemeroglu A, Symington T, Daniel M, Appiagyei-Dankah Y, Postellon D, Racine M, Kleis L, Barnes K, Godwin S, McCullough H, Shaheen K, Buck G, Noel L, Warren M, Weber S, Parker S, Gillespie I, Nelson B, Frost C, Amrhein J, Moreland E, Hayes A, Peggram J, Aisenberg J, Riordan M, Zasa J, Cummings E, Scott K, Pinto T, Mokashi A, McAssey K, Helden E, Hammond P, Dinning L, Rahman S, Ray S, Dimicri C, Guppy S, Nielsen H, Vogel C, Ariza C, Morales L, Chang Y, Gabbay R, Ambrocio L, Manley L, Nemery R, Charlton W, Smith P, Kerr L, Steindel-Kopp B, Alamaguer M, Tabisola-Nuesca E, Pendersen A, Larson N, Cooper-Olviver H, Chan D, Fitz-Patrick D, Carreira T, Park Y, Ruhaak R, Liljenquist D. A Type 1 Diabetes Genetic Risk Score Predicts Progression of Islet Autoimmunity and Development of Type 1 Diabetes in Individuals at Risk. Diabetes Care 2018; 41:1887-1894. [PMID: 30002199 PMCID: PMC6105323 DOI: 10.2337/dc18-0087] [Citation(s) in RCA: 86] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/11/2018] [Accepted: 06/06/2018] [Indexed: 02/03/2023]
Abstract
OBJECTIVE We tested the ability of a type 1 diabetes (T1D) genetic risk score (GRS) to predict progression of islet autoimmunity and T1D in at-risk individuals. RESEARCH DESIGN AND METHODS We studied the 1,244 TrialNet Pathway to Prevention study participants (T1D patients' relatives without diabetes and with one or more positive autoantibodies) who were genotyped with Illumina ImmunoChip (median [range] age at initial autoantibody determination 11.1 years [1.2-51.8], 48% male, 80.5% non-Hispanic white, median follow-up 5.4 years). Of 291 participants with a single positive autoantibody at screening, 157 converted to multiple autoantibody positivity and 55 developed diabetes. Of 953 participants with multiple positive autoantibodies at screening, 419 developed diabetes. We calculated the T1D GRS from 30 T1D-associated single nucleotide polymorphisms. We used multivariable Cox regression models, time-dependent receiver operating characteristic curves, and area under the curve (AUC) measures to evaluate prognostic utility of T1D GRS, age, sex, Diabetes Prevention Trial-Type 1 (DPT-1) Risk Score, positive autoantibody number or type, HLA DR3/DR4-DQ8 status, and race/ethnicity. We used recursive partitioning analyses to identify cut points in continuous variables. RESULTS Higher T1D GRS significantly increased the rate of progression to T1D adjusting for DPT-1 Risk Score, age, number of positive autoantibodies, sex, and ethnicity (hazard ratio [HR] 1.29 for a 0.05 increase, 95% CI 1.06-1.6; P = 0.011). Progression to T1D was best predicted by a combined model with GRS, number of positive autoantibodies, DPT-1 Risk Score, and age (7-year time-integrated AUC = 0.79, 5-year AUC = 0.73). Higher GRS was significantly associated with increased progression rate from single to multiple positive autoantibodies after adjusting for age, autoantibody type, ethnicity, and sex (HR 2.27 for GRS >0.295, 95% CI 1.47-3.51; P = 0.0002). CONCLUSIONS The T1D GRS independently predicts progression to T1D and improves prediction along T1D stages in autoantibody-positive relatives.
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Affiliation(s)
- Maria J. Redondo
- Texas Children’s Hospital, Baylor College of Medicine, Houston, TX
| | | | - Andrea K. Steck
- Barbara Davis Center for Childhood Diabetes, University of Colorado School of Medicine, Aurora, CO
| | - Seth Sharp
- Institute of Biomedical and Clinical Science, University of Exeter, Exeter, U.K
| | - John M. Wentworth
- Walter and Eliza Hall Institute of Medical Research and Royal Melbourne Hospital, Parkville, Victoria, Australia
| | - Michael N. Weedon
- Institute of Biomedical and Clinical Science, University of Exeter, Exeter, U.K
| | | | | | | | | | - Richard A. Oram
- Institute of Biomedical and Clinical Science, University of Exeter, Exeter, U.K
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Rubchinsky LL, Ahn S, Klijn W, Cumming B, Yates S, Karakasis V, Peyser A, Woodman M, Diaz-Pier S, Deraeve J, Vassena E, Alexander W, Beeman D, Kudela P, Boatman-Reich D, Anderson WS, Luque NR, Naveros F, Carrillo RR, Ros E, Arleo A, Huth J, Ichinose K, Park J, Kawai Y, Suzuki J, Mori H, Asada M, Oprisan SA, Dave AI, Babaie T, Robinson P, Tabas A, Andermann M, Rupp A, Balaguer-Ballester E, Lindén H, Christensen RK, Nakamura M, Barkat TR, Tosi Z, Beggs J, Lonardoni D, Boi F, Di Marco S, Maccione A, Berdondini L, Jędrzejewska-Szmek J, Dorman DB, Blackwell KT, Bauermeister C, Keren H, Braun J, Dornas JV, Mavritsaki E, Aldrovandi S, Bridger E, Lim S, Brunel N, Buchin A, Kerr CC, Chizhov A, Huberfeld G, Miles R, Gutkin B, Spencer MJ, Meffin H, Grayden DB, Burkitt AN, Davey CE, Tao L, Tiruvadi V, Ali R, Mayberg H, Butera R, Gunay C, Lamb D, Calabrese RL, Doloc-Mihu A, López-Madrona VJ, Matias FS, Pereda E, Mirasso CR, Canals S, Geminiani A, Pedrocchi A, D’Angelo E, Casellato C, Chauhan A, Soman K, Srinivasa Chakravarthy V, Muddapu VR, Chuang CC, Chen NY, Bayati M, Melchior J, Wiskott L, Azizi AH, Diba K, Cheng S, Smirnova EY, Yakimova EG, Chizhov AV, Chen NY, Shih CT, Florescu D, Coca D, Courtiol J, Jirsa VK, Covolan RJM, Teleńczuk B, Kempter R, Curio G, Destexhe A, Parker J, Klishko AN, Prilutsky BI, Cymbalyuk G, Franke F, Hierlemann A, da Silveira RA, Casali S, Masoli S, Rizza M, Rizza MF, Masoli S, Sun Y, Wong W, Farzan F, Blumberger DM, Daskalakis ZJ, Popovych S, Viswanathan S, Rosjat N, Grefkes C, Daun S, Gentiletti D, Suffczynski P, Gnatkovski V, De Curtis M, Lee H, Paik SB, Choi W, Jang J, Park Y, Song JH, Song M, Pallarés V, Gilson M, Kühn S, Insabato A, Deco G, Glomb K, Ponce-Alvarez A, Ritter P, Gilson M, Campo AT, Thiele A, Deeba F, Robinson PA, van Albada SJ, Rowley A, Hopkins M, Schmidt M, Stokes AB, Lester DR, Furber S, Diesmann M, Barri A, Wiechert MT, DiGregorio DA, Dimitrov AG, Vich C, Berg RW, Guillamon A, Ditlevsen S, Cazé RD, Girard B, Doncieux S, Doyon N, Boahen F, Desrosiers P, Laurence E, Doyon N, Dubé LJ, Eleonora R, Durstewitz D, Schmidt D, Mäki-Marttunen T, Krull F, Bettella F, Metzner C, Devor A, Djurovic S, Dale AM, Andreassen OA, Einevoll GT, Næss S, Ness TV, Halnes G, Halgren E, Halnes G, Mäki-Marttunen T, Pettersen KH, Andreassen OA, Sætra MJ, Hagen E, Schiffer A, Grzymisch A, Persike M, Ernst U, Harnack D, Ernst UA, Tomen N, Zucca S, Pasquale V, Pica G, Molano-Mazón M, Chiappalone M, Panzeri S, Fellin T, Oie KS, Boothe DL, Crone JC, Yu AB, Felton MA, Zulfiqar I, Moerel M, De Weerd P, Formisano E, Boothe DL, Crone JC, Felton MA, Oie K, Franaszczuk P, Diggelmann R, Fiscella M, Hierlemann A, Franke F, Guarino D, Antolík J, Davison AP, Frègnac Y, Etienne BX, Frohlich F, Lefebvre J, Marcos E, Mattia M, Genovesio A, Fedorov LA, Dijkstra TM, Sting L, Hock H, Giese MA, Buhry L, Langlet C, Giovannini F, Verbist C, Salvadé S, Giugliano M, Henderson JA, Wernecke H, Sándor B, Gros C, Voges N, Dabrovska P, Riehle A, Brochier T, Grün S, Gu Y, Gong P, Dumont G, Novikov NA, Gutkin BS, Tewatia P, Eriksson O, Kramer A, Santos J, Jauhiainen A, Kotaleski JH, Belić JJ, Kumar A, Kotaleski JH, Shimono M, Hatano N, Ahmad S, Cui Y, Hawkins J, Senk J, Korvasová K, Tetzlaff T, Helias M, Kühn T, Denker M, Mana P, Grün S, Dahmen D, Schuecker J, Goedeke S, Keup C, Goedeke S, Heuer K, Bakker R, Tiesinga P, Toro R, Qin W, Hadjinicolaou A, Grayden DB, Ibbotson MR, Kameneva T, Lytton WW, Mulugeta L, Drach A, Myers JG, Horner M, Vadigepalli R, Morrison T, Walton M, Steele M, Anthony Hunt C, Tam N, Amaducci R, Muñiz C, Reyes-Sánchez M, Rodríguez FB, Varona P, Cronin JT, Hennig MH, Iavarone E, Yi J, Shi Y, Zandt BJ, Van Geit W, Rössert C, Markram H, Hill S, O’Reilly C, Iavarone E, Shi Y, Perin R, Lu H, Zandt BJ, Bryson A, Rössert C, Hadrava M, Hlinka J, Hosaka R, Olenik M, Houghton C, Iannella N, Launey T, Kameneva T, Kotsakidis R, Meffin H, Soriano J, Kubo T, Inoue T, Kida H, Yamakawa T, Suzuki M, Ikeda K, Abbasi S, Hudson AE, Heck DH, Jaeger D, Lee J, Abbasi S, Janušonis S, Saggio ML, Spiegler A, Stacey WC, Bernard C, Lillo D, Bernard C, Petkoski S, Spiegler A, Drakesmith M, Jones DK, Zadeh AS, Kambhampati C, Karbowski J, Kaya ZG, Lakretz Y, Treves A, Li LW, Lizier J, Kerr CC, Masquelier T, Kheradpisheh SR, Kim H, Kim CS, Marakshina JA, Vartanov AV, Neklyudova AA, Kozlovskiy SA, Kiselnikov AA, Taniguchi K, Kitano K, Schmitt O, Lessmann F, Schwanke S, Eipert P, Meinhardt J, Beier J, Kadir K, Karnitzki A, Sellner L, Klünker AC, Kuch L, Ruß F, Jenssen J, Wree A, Sanz-Leon P, Knock SA, Chien SC, Maess B, Knösche TR, Cohen CC, Popovic MA, Klooster J, Kole MH, Roberts EA, Kopell NJ, Kepple D, Giaffar H, Rinberg D, Koulakov A, Forlim CG, Klock L, Bächle J, Stoll L, Giemsa P, Fuchs M, Schoofs N, Montag C, Gallinat J, Lee RX, Stephens GJ, Kuhn B, Tauffer L, Isope P, Inoue K, Ohmura Y, Yonekura S, Kuniyoshi Y, Jang HJ, Kwag J, de Kamps M, Lai YM, dos Santos F, Lam KP, Andras P, Imperatore J, Helms J, Tompa T, Lavin A, Inkpen FH, Ashby MC, Lepora NF, Shifman AR, Lewis JE, Zhang Z, Feng Y, Tetzlaff C, Kulvicius T, Li Y, Pena RFO, Bernardi D, Roque AC, Lindner B, Bernardi D, Vellmer S, Saudargiene A, Maninen T, Havela R, Linne ML, Powanwe A, Longtin A, Naveros F, Garrido JA, Graham JW, Dura-Bernal S, Angulo SL, Neymotin SA, Antic SD. 26th Annual Computational Neuroscience Meeting (CNS*2017): Part 2. BMC Neurosci 2017. [PMCID: PMC5592442 DOI: 10.1186/s12868-017-0371-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
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Young JG, Desrosiers P, Hébert-Dufresne L, Laurence E, Dubé LJ. Finite-size analysis of the detectability limit of the stochastic block model. Phys Rev E 2017; 95:062304. [PMID: 28709195 DOI: 10.1103/physreve.95.062304] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2016] [Indexed: 06/07/2023]
Abstract
It has been shown in recent years that the stochastic block model is sometimes undetectable in the sparse limit, i.e., that no algorithm can identify a partition correlated with the partition used to generate an instance, if the instance is sparse enough and infinitely large. In this contribution, we treat the finite case explicitly, using arguments drawn from information theory and statistics. We give a necessary condition for finite-size detectability in the general SBM. We then distinguish the concept of average detectability from the concept of instance-by-instance detectability and give explicit formulas for both definitions. Using these formulas, we prove that there exist large equivalence classes of parameters, where widely different network ensembles are equally detectable with respect to our definitions of detectability. In an extensive case study, we investigate the finite-size detectability of a simplified variant of the SBM, which encompasses a number of important models as special cases. These models include the symmetric SBM, the planted coloring model, and more exotic SBMs not previously studied. We conclude with three appendices, where we study the interplay of noise and detectability, establish a connection between our information-theoretic approach and random matrix theory, and provide proofs of some of the more technical results.
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Affiliation(s)
- Jean-Gabriel Young
- Département de Physique, de Génie Physique, et d'Optique, Université Laval, Québec (Québec), Canada G1V 0A6
| | - Patrick Desrosiers
- Département de Physique, de Génie Physique, et d'Optique, Université Laval, Québec (Québec), Canada G1V 0A6
- Centre de recherche de l'Institut universitaire en santé mentale de Québec, Québec (Québec), Canada G1J 2G3
| | | | - Edward Laurence
- Département de Physique, de Génie Physique, et d'Optique, Université Laval, Québec (Québec), Canada G1V 0A6
| | - Louis J Dubé
- Département de Physique, de Génie Physique, et d'Optique, Université Laval, Québec (Québec), Canada G1V 0A6
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Blondeau-Fournier O, Desrosiers P, Mathieu P. Supersymmetric Ruijsenaars-Schneider model. Phys Rev Lett 2015; 114:121602. [PMID: 25860733 DOI: 10.1103/physrevlett.114.121602] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/18/2014] [Indexed: 06/04/2023]
Abstract
An integrable supersymmetric generalization of the trigonometric Ruijsenaars-Schneider model is presented whose symmetry algebra includes the super Poincaré algebra. Moreover, its Hamiltonian is shown to be diagonalized by the recently introduced Macdonald superpolynomials. Somewhat surprisingly, the consistency of the scalar product forces the discreteness of the Hilbert space.
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Affiliation(s)
- O Blondeau-Fournier
- Département de physique, de génie physique et d'optique, Université Laval, Québec, Canada G1V 0A6
| | - P Desrosiers
- Département de physique, de génie physique et d'optique, Université Laval, Québec, Canada G1V 0A6
- CRIUSMQ, 2601 de la Canardière, Québec, Canada G1J 2G3
| | - P Mathieu
- Département de physique, de génie physique et d'optique, Université Laval, Québec, Canada G1V 0A6
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Fujii R, Fujita S, Waseda T, Oka Y, Takagi H, Tomizawa H, Sasagawa T, Makinoda S, Cavagna M, Braga DPAF, Figueira RCS, Aoki T, Maldonado LGL, Iaconelli A, Borges E, Prabhakar S, Dittrich R, Beckmann MW, Hoffmann I, Mueller A, Kjotrod S, Carlsen SM, Rasmussen PE, Holst-Larsen T, Mellembakken J, Thurin-Kjellberg A, Haapaniemi Kouru K, Morin Papunen L, Humaidan P, Sunde A, von During V, Pappalardo S, Valeri C, Crescenzi F, Manna C, Sallam HN, Polec A, Raki M, Tanbo T, Abyholm T, Fedorcsak P, Tabanelli C, Ferraretti AP, Feliciani E, Magli MC, Fasolino C, Gianaroli L, Wang T, Feng C, Song Y, Dong MY, Sheng JZ, Huang HF, Sayyah Melli M, Kazemi-shishvan M, Snajderova M, Zemkova D, Pechova M, Teslik L, Lanska V, Ketel I, Serne E, Stehouwer C, Korsen T, Hompes P, Smulders Y, Voorstemans L, Homburg R, Lambalk C, Bellver J, Martinez-Conejero JA, Pellicer A, Labarta E, Alama P, Melo MAB, Horcajadas JA, Agirregoitia N, Peralta L, Mendoza R, Exposito A, Matorras R, Agirregoitia E, Ajina M, Chaouache N, Gaddas M, Souissi A, Tabka Z, Saad A, Zaouali-Ajina M, Zbidi A, Eguchi N, Jinno M, Watanabe A, Hirohama J, Hatakeyama N, Choi YM, Kim JJ, Kim DH, Yoon SH, Ku SY, Kim SH, Kim JG, Lee KS, Moon SY, Hirohama J, Jinno M, Watanabe A, Eguchi N, Hatakeyama N, Jinno M, Watanabe A, Hirohama J, Eguchi N, Hatakeyama N, Xiong Y, Liang X, Li Y, Yang X, Wei L, Makinoda S, Tomizawa H, Fujita S, Takagi H, Oka Y, Waseda T, Sasagawa T, Fujii R, Utsunomiya T, Chu S, Li P, Akarsu S, Dirican EK, Akin KO, Kormaz C, Goktolga U, Ceyhan ST, Kara C, Nadamoto K, Tarui S, Ida M, Sugihara K, Haruki A, Hukuda A, Morimoto Y, Albu A, Albu D, Sandu L, Kong G, Cheung L, Lok I, Pinto A, Teixeira L, Figueiredo H, Pires I, Silva Carvalho JL, Pereira ML, Faut M, de Zuniga I, Colaci D, Barrios E, Oubina A, Terrado Gil G, Motta A, Colaci D, de Zuniga I, Horton M, Faut M, Sobral F, Gomez Pena M, Motta A, Gleicher N, Barad DH, Li YP, Zhao HC, Spaczynski RZ, Guzik P, Banaszewska B, Krauze T, Wykretowicz A, Wysocki H, Pawelczyk L, Sarikaya E, Gulerman C, Cicek N, Mollamahmutoglu L, Venetis CA, Kolibianakis EM, Toulis K, Goulis D, Loutradi K, Chatzimeletiou K, Papadimas I, Bontis I, Tarlatzis BC, Schultze-Mosgau A, Griesinger G, Schoepper B, Cordes T, Diedrich K, Al-Hasani S, Gomez R, Jovanovic V, Sauer CM, Shawber CJ, Sauer MV, Kitajewski J, Zimmermann RC, Bungum L, Jacobsson AK, Rosen F, Becker C, Andersen CY, Guner N, Giwercman A, Kiapekou E, Zapanti E, Boukelatou D, Mavreli T, Bletsa R, Stefanidis K, Drakakis P, Mastorakos G, Loutradis D, Malhotra N, Sharma V, Kumar S, Roy KK, Sharma JB, Ferraretti A, Gianaroli L, Magli MC, Crippa A, Stanghellini I, Robles F, Serdynska-Szuster M, Spaczynski RZ, Banaszewska B, Pawelczyk L, Kristensen SL, Ernst E, Toft G, Olsen SF, Bonde JP, Vested A, Ramlau-Hansen CH, Wang FF, Qu F, Ding GL, Huang HF, Gallot V, Genro V, Roux I, Scheffer JB, Frydman R, Fanchin R, Kanta Goswami S, Banerjee S, Chakravarty BN, Kabir SN, Seeber BE, Morandell E, Kurzthaler D, Wildt L, Dieplinger H, Tutuncu L, Bodur S, Dundar O, Ron - El R, Seger R, Komarovsky D, Kasterstein E, Komsky A, Maslansky B, Strassburger D, Ben-Ami I, Zhao XM, Ni RM, Lin L, Dong M, Tu CH, He ZH, Yang DZ, Karamalegos C, Polidoropoulos N, Papanikopoulos C, Stefanis P, Argyrou M, Doriza S, Sisi V, Moschopoulou M, Karagianni T, Mentorou C, Economou K, Davies S, Mastrominas M, Gougeon A, De Los Santos MJ, Garcia-Laez V, Martinez-Conejero JA, Horcajadas JA, Esteban F, Labarta E, Crespo J, Pellicer A, Li HWR, Anderson RA, Yeung WSB, Ho PC, Ng EHY, Yang HI, Lee KE, Seo SK, Kim HY, Cho SH, Choi YS, Lee BS, Park KH, Cho DJ, Hart R, Doherty D, Mori T, Hickey M, Sloboda D, Norman R, Huang RC, Beilin L, Freiesleben N, Lossl K, Johannsen TH, Loft A, Bangsboll S, Hougaard D, Friis-Hansen L, Christiansen M, Nyboe Andersen A, Thum MY, Abdalla H, Martinez-Salazar J, De la Fuente G, Kohls G, Pellicer A, Garcia Velasco JA, Yasmin E, Kukreja S, Barth J, Balen AH, Esra T, Var T, Citil A, Dogan M, Cicek N, Messini CI, Dafopoulos K, Chalvatzas N, Georgoulias P, Anifandis G, Messinis IE, Celik O, Hascalik S, Celik N, Sahin I, Aydin S, Hanna CW, Bretherick KL, Liu CC, Stephenson MD, Robinson WP, Louwers YV, Goodarzi MO, Taylor KD, Jones MR, Cui J, Kwon S, Chen YDI, Guo X, Stolk L, Uitterlinden AG, Laven JSE, Azziz R, Navaratnarajah R, Grun B, Sinclair J, Dafou D, Gayther S, Timms JF, Hardiman PJ, Ye Y, Wu R, Ou J, Kim SD, Jee BC, Lee JY, Suh CS, Kim SH, Jung JH, Moon SY, Opmeer BC, Broeze KA, Coppus SF, Collins JA, Den Hartog JE, Land JA, Van der Linden PJ, Marianowski P, Ng E, Van der Steeg JW, Steures P, Strandell A, Mol BW, Tarlatzi TB, Kyrou D, Mertzanidou A, Fatemi HM, Tarlatzis BC, Devroey P, Batenburg TE, Konig TE, Overbeek A, Hompes P, Schats R, Lambalk CB, Carone D, Vizziello G, Vitti A, Chiappetta R, Topcu HO, Yuksel B, Islimye M, Karakaya J, ozat M, Batioglu S, Kuchenbecker WK, Groen H, Bolster JH, van Asselt S, Wolffenbuettel BH, Land JA, Hoek A, Wu Y, Pan H, Chen X, Wang T, Huang H, Zavos A, Dafopoulos K, Georgoulias P, Messini CI, Verikouki C, Messinis IE, Van Os L, Vink-Ranti CQJ, Rijnders PM, Tucker KE, Jansen CAM, Lucco F, Pozzobon C, Lara E, Galliano D, Pellicer A, Ballesteros A, Ghoshdastidar B, Maity SP, Ghoshdastidar B, Ghoshdastidar S, Luna M, Vela G, Sandler B, Barritt J, Flisser ED, Copperman AB, Nogueira D, Prat L, Degoy J, Bonald F, Montagut J, Ghoshdastidar S, Maity S, Ghoshdastidar B, Chen S, Chen X, Luo C, Zhen H, Shi X, Wu F, Ni Y, Merdassi G, Chaker A, Kacem K, Benmeftah M, Fourati S, Wahabi D, Zhioua F, Zhioua A, Saini P, Saini A, Sugiyama R, Nakagawa K, Nishi Y, Jyuen H, Kuribayashi Y, Sugiyama R, Inoue M, Jancar N, Vrtacnik Bokal E, Virant-Klun I, Lee JH, Kim SG, Cha EM, Park IH, Lee KH, Dahdouh EM, Desrosiers P, St-Michel P, Villeneuve M, Fontaine JY, Granger L, Ramon O, Matorras R, Burgos J, Abanto E, Gonzalez M, Mugica J, Corcostegui B, Exposito A, Tal J, Ziskind G, Ohel G, Paltieli Y, Paz G, Lewit N, Sendel H, Khouri S, Calderon I, van Gelder P, Al-Inany HG, Antaki R, Dean N, Lapensee L, Racicot M, Menard S, Kadoch I, Meylaerts LJ, Dreesen L, Vandersteen M, Neumann C, Zollner U, Kato K, Segawa T, Kawachiya S, Okuno T, Kobayashi T, Takehara Y, Kato O, Jayaprakasan K, Nardo L, Hopkisson J, Campbell B, Raine-Fenning N. Posters * Reproductive Endocrinology (i.e. PCOS, Menarche, Menopause etc.). Hum Reprod 2010. [DOI: 10.1093/humrep/de.25.s1.438] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
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Desrosiers P, Carlson E, Chandler W, Chau H, Cong P, Doolen R, Freitag C, Lin S, Masui C, Wu E, Crevier T, Mullins D, Song L, Lou R, Zhan J, Tangkilisan A, Ung Q, Phan K. High throughput screening techniques for pre-formulation: salt selection and polymorph studies. Acta Crystallogr A 2002. [DOI: 10.1107/s0108767302085446] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
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Craig ID, Shum DT, Desrosiers P, McLeod C, Lefcoe MS, Paterson NA, Finley RJ, Woods B, Anderson RJ. Choriocarcinoma metastatic to the lung. A cytologic study with identification of human choriogonadotropin with an immunoperoxidase technique. Acta Cytol 1983; 27:647-50. [PMID: 6359796] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
A case of choriocarcinoma metastatic to the lung following a previous hydatidiform mole is presented. It was possible to make definitive identification of trophoblastic elements on a needle aspiration biopsy using an immunoperoxidase staining technique, thus avoiding diagnostic thoracotomy prior to therapeutic intervention. A method of immunoperoxidase staining of previously fixed and Papanicolaou-stained needle aspiration biopsy specimens is also described, and other uses of the immunoperoxidase technique on needle biopsy specimens are discussed.
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Desrosiers P, Sanchini E, Paré L. [Not Available]. Can Fam Physician 1983; 29:1785. [PMID: 21283419 PMCID: PMC2153922] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Subscribe] [Scholar Register] [Indexed: 05/30/2023]
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Craig ID, Desrosiers P, Lefcoe MS. Giant-cell carcinoma of the lung. A cytologic study. Acta Cytol 1983; 27:293-8. [PMID: 6575547] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
A case of giant-cell carcinoma of the lung, confirmed at autopsy, is presented. The cytologic features seen in sputum samples, bronchial washings and brushings and fine needle aspiration biopsy material as well as the histologic findings are described. The possible relationship to bronchioloalveolar carcinoma is discussed. The cytologic features of giant-cell carcinoma of the lung, when seen in the context of the clinical and radiologic setting, should allow the cytologic identification of the tumor prior to surgical intervention.
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Comtois G, Desrosiers P. The counselling group: rehabilitative tool for a psychiatric clientele. Can Ment Health 1982; 30:9-11. [PMID: 10255893] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 02/12/2023]
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Desrosiers P. French in CFP. Can Fam Physician 1979; 25:1434. [PMID: 20469302 PMCID: PMC2383242] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Subscribe] [Scholar Register] [Indexed: 05/29/2023]
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