Search

Your search keyword '"Laroche B"' showing total 149 results
Start Over Author "Laroche B"
149 results on '"Laroche B"'

5. Single seed microbiota: assembly and transmission from parent plant to seedling

Author

Matthieu Barret, Martial Briand, Laroche B, Brice Marolleau, Coralie Marais, Anne Préveaux, Marie Simonin, Guillaume Chesneau, Institut de Recherche en Horticulture et Semences (IRHS), Université d'Angers (UA)-AGROCAMPUS OUEST, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Mathématiques et Informatique Appliquées du Génome à l'Environnement [Jouy-En-Josas] (MaIAGE), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Dynamiques de populations multi-échelles pour des systèmes physiologiques (MUSCA), Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Physiologie de la reproduction et des comportements [Nouzilly] (PRC), Institut Français du Cheval et de l'Equitation [Saumur]-Université de Tours-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Français du Cheval et de l'Equitation [Saumur]-Université de Tours-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Mathématiques et Informatique Appliquées du Génome à l'Environnement [Jouy-En-Josas] (MaIAGE), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), ANR-17-CE20-0009,SEEDS,SuccEssion des Ensembles microbiens lors du Développement des Semences(2017), Institut Français du Cheval et de l'Equitation [Saumur]-Université de Tours (UT)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Français du Cheval et de l'Equitation [Saumur]-Université de Tours (UT)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Mathématiques et Informatique Appliquées du Génome à l'Environnement [Jouy-En-Josas] (MaIAGE), Université d'Angers (UA)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-INSTITUT AGRO Agrocampus Ouest, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Institut Français du Cheval et de l'Equitation [Saumur] (IFCE)-Université de Tours (UT)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Français du Cheval et de l'Equitation [Saumur] (IFCE)-Université de Tours (UT)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Mathématiques et Informatique Appliquées du Génome à l'Environnement [Jouy-En-Josas] (MaIAGE), Université d'Angers (UA)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro Rennes Angers, and ANR-20-PCPA-0009,SUCSEED,Stop the Use of pestiCides in seeds(2020)

Subjects
[SDV]Life Sciences [q-bio], Ecological succession, Microbiology, law.invention, seedling transmission, Crop, 03 medical and health sciences, law, Abundance (ecology), Virology, Botany, microbiota, 030304 developmental biology, 0303 health sciences, biology, 030306 microbiology, fungi, food and beverages, biology.organism_classification, Plant development, Transmission (mechanics), Taxon, Seedling, community assembly, neutral models, selection, seed
Abstract

The datasets supporting the conclusions of this article are available in the European Nucleotide Archive under the accession number PRJEB45079.; International audience; The seed acts as the primary inoculum source for the plant microbiota. Understanding the processes involved in its assembly and dynamics during germination and seedling emergence has the potential to allow for the improvement of crop establishment. Changes in the bacterial community structure were tracked in 1,000 individual seeds that were collected throughout seed developments of beans and radishes. Seeds were associated with a dominant bacterial taxon that represented more than 75% of all reads. The identity of this taxon was highly variable between the plants and within the seeds of the same plant. We identified selection as the main ecological process governing the succession of dominant taxa during seed filling and maturation. In a second step, we evaluated the seedling transmission of seed-borne taxa in 160 individual plants. While the initial bacterial abundance on seeds was not a good predictor of seedling transmission, the identities of the seed-borne taxa modified the phenotypes of seedlings. Overall, this work revealed that individual seeds are colonized by a few bacterial taxa of highly variable identity, which appears to be important for the early stages of plant development.

Published
2021
Full Text
View/download PDF

8. Airborne gamma-ray spectrometry

Author

Martelet, G, primary, Nehlig, P, additional, Arrouays, D, additional, Messner, F, additional, Tourlière, B, additional, Laroche, B, additional, Deparis, J, additional, Saby, N, additional, de Forges, A, additional, Jolivet, C, additional, and Ratié, C, additional

Published
2014
Full Text
View/download PDF

10. A Multi-Scale Epidemic Model of Salmonella infection with Heterogeneous Shedding

Author

Labarthe, S., Laroche, B., Nguyen, T. N. T., Polizzi, B., Patout, F., Ribot, M., & Stegmaier, T.

Abstract

Salmonella strains colonize the digestive tract of farm livestock, such as chickens or pigs, without affecting them, and potentially infect food products, representing a threat for human health ranging from food poisoning to typhoid fever. It has been shown that the ability to excrete the pathogen in the environment and contaminate other animals is variable. This heterogeneity in pathogen carriage and shedding results from interactions between the host’s immune response, the pathogen and the commensal intestinal microbiota. In this paper we propose a novel generic multiscale modeling framework of heterogeneous pathogen transmission in an animal population. At the intra-host level, the model describes the interaction between the commensal microbiota, the pathogen and the inflammatory response. Random fluctuations in the ecological dynamics of the individual microbiota and transmission at between-host scale are added to obtain a drift-diffusion PDE model of the pathogen distribution at the population level. The model is further extended to represent transmission between several populations. The asymptotic behavior as well as the impact of control strategies including cleaning and antimicrobial administration are investigated through numerical simulation. MOMIRPPC MOMIR PPC

Published
2020

11. Involvement of phospholipase D in mineralization process in cultured osteoblasts

Author

Abdallah, D., Skafi, N., Hamade, E., Reibel, S., Vitale, N., El Jamal, A., Bougault, C., Laroche, B., Vico, L., Badran, B., Hussein, N., Magne, D., Buchet, R., Brizuela, L, Mebarek, S., ICBMS, UMR5246, Métabolisme, Enzymes et Mécanismes Moléculaires (MEM²), Institut de Chimie et Biochimie Moléculaires et Supramoléculaires (ICBMS), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut de Chimie du CNRS (INC)-École Supérieure Chimie Physique Électronique de Lyon-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), and Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut de Chimie du CNRS (INC)-École Supérieure Chimie Physique Électronique de Lyon-Centre National de la Recherche Scientifique (CNRS)

Subjects
[SDV.BBM.BC]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Biochemistry [q-bio.BM], [SDV.BBM.BC] Life Sciences [q-bio]/Biochemistry, Molecular Biology/Biochemistry [q-bio.BM], ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
2019

12. Parameter estimation for dynamical systems using an FDA approach

Author

Brunel, Nicolas, Goujot, D, Labarthe, S, Laroche, B., Mathématiques et Informatique Appliquées du Génome à l'Environnement [Jouy-En-Josas] (MaIAGE), Institut National de la Recherche Agronomique (INRA), Université Paris Saclay (COmUE), Laboratoire de Mathématiques et Modélisation d'Evry (LaMME), Centre National de la Recherche Scientifique (CNRS)-ENSIIE-Université d'Évry-Val-d'Essonne (UEVE)-Institut National de la Recherche Agronomique (INRA), Ingénierie, Procédés, Aliments (GENIAL), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, ENSIIE-Université d'Évry-Val-d'Essonne (UEVE)-Institut National de la Recherche Agronomique (INRA)-Centre National de la Recherche Scientifique (CNRS), Institut National de la Recherche Agronomique (INRA)-Université d'Évry-Val-d'Essonne (UEVE)-ENSIIE-Centre National de la Recherche Scientifique (CNRS), Laroche, Béatrice, and ProdInra, Archive Ouverte

Subjects
[STAT.ME] Statistics [stat]/Methodology [stat.ME], [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], [INFO.INFO-MO] Computer Science [cs]/Modeling and Simulation, [MATH.MATH-NA] Mathematics [math]/Numerical Analysis [math.NA], [STAT.ME]Statistics [stat]/Methodology [stat.ME], [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, [STAT.ML] Statistics [stat]/Machine Learning [stat.ML], ComputingMilieux_MISCELLANEOUS, [MATH.MATH-NA]Mathematics [math]/Numerical Analysis [math.NA]
Abstract

Parameter estimation for dynamical systems using an FDA approach. International Conference of the ERCIM WG on Computational and Methodological Statistics (CMStatistics 2018)

Published
2018

13. Approaches to Styrenyl Building Blocks for the Synthesis of Polyene Xanthomonadin and its Analogues

Author

Madden, K.S., Laroche, B., David, S., Batsanov, A.S., Thompson, D., Knowles, J.P., and Whiting, A.

Subjects
0301 basic medicine, 010405 organic chemistry, Aryl, Organic Chemistry, F100, Sonogashira coupling, Halogenation, Ring (chemistry), Polyene, 01 natural sciences, Combinatorial chemistry, Coupling reaction, 0104 chemical sciences, 03 medical and health sciences, chemistry.chemical_compound, Hydroboration, 030104 developmental biology, chemistry, Structural isomer, Physical and Theoretical Chemistry
Abstract

A number of aryl building blocks for the synthesis of two xanthomonadin natural product pigments, as well as a related analogue, were accessed using a divergent hydroboration/bromoboration approach from a key alkynyl intermediate. A new approach towards substitution patterns around the ring was adopted following the isolation of an unexpected regioisomer from the bromination reaction. Potential coupling reactions onto these building blocks were explored, with a successful Sonogashira coupling performed on the key alkynyl intermediate, and with the key debrominated styrenyl boronate ester intermediate functionalised both by preliminary Suzuki–Miyaura coupling and by iododeboronation/Heck–Mizoroki coupling. Coupling reactions with brominated styrenyl intermediates proved much more challenging due to the instability of the intermediates to cross‐coupling, but some studies have shown promise.

Published
2018

14. Challenges in microbial ecology: building predictive understanding of community function and dynamics

Author

Massachusetts Institute of Technology. Department of Civil and Environmental Engineering, Cordero Sanchez, Otto X., Widder, Stefanie, Allen, Rosalind J, Pfeiffer, Thomas, Curtis, Thomas P, Wiuf, Carsten, Sloan, William T, Cordero, Otto X, Brown, Sam P, Momeni, Babak, Shou, Wenying, Kettle, Helen, Flint, Harry J, Haas, Andreas F, Laroche, B?atrice, Kreft, Jan-Ulrich, Rainey, Paul B, Freilich, Shiri, Schuster, Stefan, Milferstedt, Kim, van der Meer, Jan R, Huisman, Jef, Free, Andrew, Picioreanu, Cristian, Quince, Christopher, Klapper, Isaac, Labarthe, Simon, Smets, Barth F, Wang, Harris, Soyer, Orkun S, Grosskopf, Tobias, Massachusetts Institute of Technology. Department of Civil and Environmental Engineering, Cordero Sanchez, Otto X., Widder, Stefanie, Allen, Rosalind J, Pfeiffer, Thomas, Curtis, Thomas P, Wiuf, Carsten, Sloan, William T, Cordero, Otto X, Brown, Sam P, Momeni, Babak, Shou, Wenying, Kettle, Helen, Flint, Harry J, Haas, Andreas F, Laroche, B?atrice, Kreft, Jan-Ulrich, Rainey, Paul B, Freilich, Shiri, Schuster, Stefan, Milferstedt, Kim, van der Meer, Jan R, Huisman, Jef, Free, Andrew, Picioreanu, Cristian, Quince, Christopher, Klapper, Isaac, Labarthe, Simon, Smets, Barth F, Wang, Harris, Soyer, Orkun S, and Grosskopf, Tobias

Abstract

The importance of microbial communities (MCs) cannot be overstated. MCs underpin the biogeochemical cycles of the earth’s soil, oceans and the atmosphere, and perform ecosystem functions that impact plants, animals and humans. Yet our ability to predict and manage the function of these highly complex, dynamically changing communities is limited. Building predictive models that link MC composition to function is a key emerging challenge in microbial ecology. Here, we argue that addressing this challenge requires close coordination of experimental data collection and method development with mathematical model building. We discuss specific examples where model–experiment integration has already resulted in important insights into MC function and structure. We also highlight key research questions that still demand better integration of experiments and models. We argue that such integration is needed to achieve significant progress in our understanding of MC dynamics and function, and we make specific practical suggestions as to how this could be achieved., United States. Army Research Office (W911NF-14-1-0445)

Published
2017

16. Challenges in microbial ecology: Building predictive understanding of community function and dynamics

Author

Widder, S, Allen, RJ, Pfeiffer, T, Curtis, TP, Wiuf, C, Sloan, WT, Cordero, OX, Brown, SP, Momeni, B, Shou, W, Kettle, H, Flint, HJ, Hass, AF, Laroche, B, Kreft, JU, Rainey, PB, Freilich, S, Shuster, S, Milferstedt, K, Van der Meer, JR, Grosskopf, T, Huisman, J, Free, A, Picioreanu, C, Quince, C, Klapper, I, Labarthe, S, Smets, B, Wang, H, Allison, SD, Chong, J, Lagomarsion, MC, Croze, OA, Hamelin, J, Harmand, J, Hoyle, R, Hwa, TT, Jin, Q, Johson, DR, Lorenzo, VD, Mobilia, M, Murphy, B, Peaudecerf, F, Prosser, JI, Quinn, RA, Ralser, M, Smith, AG, Steyer, JP, Swainston, N, Tarnita, CE, Trably, E, Warren, PB, Wilmes, P, Soyer, O, CUBE, Department of Microbiology and Ecosystem Science, Medizinische Universität Wien = Medical University of Vienna, SUPA, School of Physics and Astronomy, University of Edinburgh, New Zealand Institute for Advanced Study, School of Civil Engineering and Geosciences, University of Newcastle upon Tyne, Department of Mathematical Sciences [Copenhagen], Faculty of Science [Copenhagen], University of Copenhagen = Københavns Universitet (KU)-University of Copenhagen = Københavns Universitet (KU), Infrastructure and Environment Research Division, School of Engineering, University of Glasgow, Department of Civil and Environmental Engineering [Cambridge, USA] (CEE), Massachusetts Institute of Technology (MIT), Centre for Immunity, Infection and Evolution, School of Biological Sciences, Department of Biology, Boston College (BC), Fred Hutchinson Cancer Research Center [Seattle] (FHCRC), Division of Basic Sciences, Biomathematics and Statistics Scotland, Rowett Institute of Nutrition and Health, University of Aberdeen, Biology Department [San Diego], San Diego State University (SDSU), Mathématiques et Informatique Appliquées du Génome à l'Environnement [Jouy-En-Josas] (MaIAGE), Institut National de la Recherche Agronomique (INRA), School of Biosciences, University of Birmingham, Newe Ya’ar Research Center, Agricultural Research Organization, Department of Bioinformatics, Friedrich-Schiller-Universität Jena, Laboratoire de Biotechnologie de l'Environnement [Narbonne] (LBE), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA), Department of Fundamental Microbiology [Lausanne], Université de Lausanne (UNIL), School of Life Sciences, University of Warwick, Department of Aquatic Microbiology, University of Amsterdam, Institute of Quantitative Biology, Biochemistry and Biotechnology, School of Biological Science, Department of Biotechnology, Delft University of Technology (TU Delft), Warwick Medical School, University of Warwick [Coventry], Department of Mathematics, Temple University [Philadelphia], Pennsylvania Commonwealth System of Higher Education (PCSHE)-Pennsylvania Commonwealth System of Higher Education (PCSHE), Department of Environmental Engineering, Technical University of Denmark [Lyngby] (DTU), Department of Systems Biology, Columbia University [New York], Genomic Physics [LCQB] (LCQB-Gphi), Biologie Computationnelle et Quantitative = Laboratory of Computational and Quantitative Biology (LCQB), Institut de Biologie Paris Seine (IBPS), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Institut de Biologie Paris Seine (IBPS), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), Isaac Newton Institute of Mathematical Sciences, University of Copenhagen = Københavns Universitet (UCPH)-University of Copenhagen = Københavns Universitet (UCPH), Department of Civil and Environmental Engineering [Cambridge] (CEE), Friedrich-Schiller-Universität = Friedrich Schiller University Jena [Jena, Germany], Institut National de la Recherche Agronomique (INRA)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Université de Lausanne = University of Lausanne (UNIL), University of Amsterdam [Amsterdam] (UvA), Danmarks Tekniske Universitet = Technical University of Denmark (DTU), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut de Biologie Paris Seine (IBPS), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut de Biologie Paris Seine (IBPS), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), Widder, Stefanie [0000-0003-0733-5666], Brown, Sam P [0000-0003-1892-9275], Momeni, Babak [0000-0003-1271-5196], Kreft, Jan-Ulrich [0000-0002-2351-224X], Smets, Barth F [0000-0003-4119-6292], Soyer, Orkun S [0000-0002-9504-3796], Apollo - University of Cambridge Repository, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Aquatic Microbiology (IBED, FNWI), Massachusetts Institute of Technology. Department of Civil and Environmental Engineering, and Cordero Sanchez, Otto X.

Subjects
0301 basic medicine, microbial, Mini Review, Ecology (disciplines), media_common.quotation_subject, Air Microbiology, Biology, Microbiology, 03 medical and health sciences, Microbial ecology, challenge in microbial, predictive, ecology, challenge, Animals, Humans, Seawater, SDG 14 - Life Below Water, QA, Function (engineering), Ecosystem, Soil Microbiology, Ecology, Evolution, Behavior and Systematics, media_common, Structure (mathematical logic), Ecology, Microbiology and Parasitology, Models, Theoretical, 15. Life on land, Modélisation et simulation, Data science, Method development, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, Microbiologie et Parasitologie, QR, 030104 developmental biology, [SDV.MP]Life Sciences [q-bio]/Microbiology and Parasitology, 13. Climate action, Modeling and Simulation, Key (cryptography), Research questions, Model building
Abstract

The importance of microbial communities (MCs) cannot be overstated. MCs underpin the biogeochemical cycles of the earth’s soil, oceans and the atmosphere, and perform ecosystem functions that impact plants, animals and humans. Yet our ability to predict and manage the function of these highly complex, dynamically changing communities is limited. Building predictive models that link MC composition to function is a key emerging challenge in microbial ecology. Here, we argue that addressing this challenge requires close coordination of experimental data collection and method development with mathematical model building. We discuss specific examples where model–experiment integration has already resulted in important insights into MC function and structure. We also highlight key research questions that still demand better integration of experiments and models. We argue that such integration is needed to achieve significant progress in our understanding of MC dynamics and function, and we make specific practical suggestions as to how this could be achieved., United States. Army Research Office (W911NF-14-1-0445)

Published
2016
Full Text
View/download PDF

17. Challenges in microbial ecology: building predictive understanding of community function and dynamics

Author

Widder, S., Allen, R.J., Pfeiffer, T., Curtis, T.P., Wiuf, C., Sloan, W.T., Cordero, O.X., Brown, S.P., Momeni, B., Shou, W., Kettle, H., Flint, H.J., Haas, A.F., Laroche, B., Kreft, J.U., Rainey, P.B., Freilich, S., Schuster, S., Milferstedt, K., van der Meer, J.R., Groβkopf, T., Huisman, J., Free, A., Picioreanu, C., Quince, C., Klapper, I., Labarthe, S., Smets, B.F., Wang, H., Soyer, O.S., and Isaac Newton Institute Fellows

Abstract

The importance of microbial communities (MCs) cannot be overstated. MCs underpin the biogeochemical cycles of the earth's soil, oceans and the atmosphere, and perform ecosystem functions that impact plants, animals and humans. Yet our ability to predict and manage the function of these highly complex, dynamically changing communities is limited. Building predictive models that link MC composition to function is a key emerging challenge in microbial ecology. Here, we argue that addressing this challenge requires close coordination of experimental data collection and method development with mathematical model building. We discuss specific examples where model-experiment integration has already resulted in important insights into MC function and structure. We also highlight key research questions that still demand better integration of experiments and models. We argue that such integration is needed to achieve significant progress in our understanding of MC dynamics and function, and we make specific practical suggestions as to how this could be achieved.

Published
2016

18. Reduction of metabolic models by polygons optimization method applied to Bioethanol production with co-substrates

Author

Robles-Rodriguez, C. E., Bideaux, C., Sebastien Gaucel, Laroche, B., Gorret, N., Aceves-Lara, C. A., Laboratoire d'Ingénierie des Systèmes Biologiques et des Procédés (LISBP), Centre National de la Recherche Scientifique (CNRS)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de la Recherche Agronomique (INRA), Université Fédérale Toulouse Midi-Pyrénées, Université Toulouse III - Paul Sabatier (UT3), Institut National Polytechnique (Toulouse) (Toulouse INP), Génie et Microbiologie des Procédés Alimentaires (GMPA), AgroParisTech-Institut National de la Recherche Agronomique (INRA), Unité de recherche Mathématiques et Informatique Appliquées (MIA), Institut National de la Recherche Agronomique (INRA), Institut National de la Recherche Agronomique (INRA)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS), Université de Toulouse (UT), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, and Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Elementary Modes, Quantitative Biology::Molecular Networks, [SDV]Life Sciences [q-bio], co-substrate ethanol production, Optimization methods, Yield Analysis
Abstract

In literature metabolic stoichiometric matrix reduction is based on convex analysis by choosing the greatest triangle. This paper proposes a new methodology for the reduction of metabolic networks based on the concept of convex hull by optimization methods. Different polygons are tested to conjointly minimize the squared error (convex hull - experimental data) and maximize the convex hull area in order to reduce the set of metabolic reactions involved in the model. The advantage of this method relies on its ability to select different geometries in a simple manner with the knowledge of the elementary modes. A cybernetic model implementing the proposed optimization method is tested with data for bioethanol production by Saccharomyces cerevisiae growing on four substrates. Parameter estimation and model validation allow comparing the performance of the chosen polygons for reduction of metabolic pathways.

Published
2014

20. Some aspects of electrical conduction in granular systems of various dimensions

Author

Creyssels S. Dorbolo A. Merlen C. Laroche B. Castaing E. Falcon, M., Matière et Systèmes Complexes (MSC (UMR_7057)), and Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS)

Subjects
[PHYS.MECA.BIOM]Physics [physics]/Mechanics [physics]/Biomechanics [physics.med-ph]
Published
2007

22. Testing structural identifiability of non-linear drying model for rice

Author

Laroche, B., Courtois, Francis, UAR1203 DEPT MIA Département Mathématiques et Informatique Appliquées, Institut National de la Recherche Agronomique (INRA), Ingénierie Procédés Aliments (GENIAL), Institut National de la Recherche Agronomique (INRA)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-AgroParisTech-Conservatoire National des Arts et Métiers [CNAM] (CNAM), and Institut National de la Recherche Agronomique (INRA)-AgroParisTech-Conservatoire National des Arts et Métiers [CNAM] (CNAM)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)

Subjects
[SDV]Life Sciences [q-bio], food and beverages
Abstract

Testing structural identifiability of non-linear drying model for rice. 44. IEEE Conference on Decision and Control and European Control Conference

Published
2005

26. A multi-scale epidemic model of Salmonella infection with heterogeneous shedding∗

Author

Labarthe Simon, Laroche Béatrice, Nguyen Thi Nhu Thao, Polizzi Bastien, Patout Florian, Ribot Magali, and Stegmaier Tabea

Subjects
Applied mathematics. Quantitative methods, T57-57.97, Mathematics, QA1-939
Abstract

Salmonella strains colonize the digestive tract of farm livestock, such as chickens or pigs, without affecting them, and potentially infect food products, representing a threat for human health ranging from food poisoning to typhoid fever. It has been shown that the ability to excrete the pathogen in the environment and contaminate other animals is variable. This heterogeneity in pathogen carriage and shedding results from interactions between the host’s immune response, the pathogen and the commensal intestinal microbiota. In this paper we propose a novel generic multiscale modeling framework of heterogeneous pathogen transmission in an animal population. At the intra-host level, the model describes the interaction between the commensal microbiota, the pathogen and the inflammatory response. Random fluctuations in the ecological dynamics of the individual microbiota and transmission at between-host scale are added to obtain a drift-diffusion PDE model of the pathogen distribution at the population level. The model is further extended to represent transmission between several populations. The asymptotic behavior as well as the impact of control strategies including cleaning and antimicrobial administration are investigated through numerical simulation.

Published
2020
Full Text
View/download PDF

31. The assessment of disease aggressivity in stage D2 prostate cancer patients (review)

Author

Koutsilieris M, Laroche B, Thabet M, and Yves Fradet

Subjects
Gonadotropin-Releasing Hormone, Male, Neoplasms, Hormone-Dependent, Humans, Prostatic Neoplasms, Estrogens, Testosterone, Neoplasm Metastasis, Prognosis, Orchiectomy, Neoplasm Staging
Abstract

Suppression of androgen levels in blood of stage D2 prostate cancer patients has been the prominent treatment for advanced prostate cancer. However, the duration of hormone sensitivity of prostate tumor is variable. The type of initial response to hormonal treatment, the length of response and patient's survival are in direct association with disease aggressiveness. Recently, an arithmetic formula expressing disease aggressivity was computed using pretreatment values of prostatic acid phosphatase (P.A.P.), alkaline phosphatase (A.P.), degree of tumor differentiation and number of bone metastases. This aggressiveness score was related to disease response and patients outcome receiving hormonal treatments. The use of an arithmetic formula to express disease aggressivity could result in a subdivision of the disease. The identification of the subgroup of stage D2 patients destined not to benefit from hormonal manipulation could change the strategies employed up until today for the treatment of advanced prostate cancer.

Published
1990

39. Combined in situ micromechanical cantilever-based sensing and ellipsometry.

Author

Michel Godin, B. R., Olivier Laroche, B. R., Vincent Tabard-Cossa, B. R., L. Y. Beaulieu, B. R., Peter Grütter, B. R., and Williams, P. J.

Subjects
*MICROELECTROMECHANICAL systems, *DETECTORS, *ELLIPSOMETRY, *GOLD
Abstract

Two cantilever-based chemical sensors are reported. First, a differential cantilever-based chemical sensor capable of sensitive surface stress measurements has been designed and implemented. The system uses two commercially available atomic force microscope cantilevers. The surface of one cantilever is functionalized to make it receptive to specific target analytes, while the second, passivated cantilever is used as a reference by subtracting undesired signals, resulting from mechanical and thermal noise, from the signal of interest. The second system is a combined cantilever-based sensor and ellipsometer capable of simultaneous in situ surface stress and film thickness measurements. Both sensors, operated in static mode, achieve a cantilever deflection measurement sensitivity of 0.2 nm and a surface stress resolution of 5×10[sup -5] N/m. Molecular monolayer thicknesses are measured simultaneously with a 0.1 nm resolution. The real-time formation process of alkanethiol self-assembled monolayers on gold was investigated as a model system to characterize these instruments. © 2003 American Institute of Physics. [ABSTRACT FROM AUTHOR]

Published
2003
Full Text
View/download PDF

40. A mixture model for the dynamic of the gut mucus layer

Author

El Bouti Tamara, Goudon Thierry, Labarthe Simon, Laroche Béatrice, Polizzi Bastien, Rachah Amira, Ribot Magali, and Tesson Rémi

Subjects
Applied mathematics. Quantitative methods, T57-57.97, Mathematics, QA1-939
Abstract

We introduce a mixture model intended to describe the dynamics of the mucus layer that wraps the gut mucosa. This model takes into account the fluid mechanics of the gut content, the inhomogeneous rheology that depends on the fluid composition, and the main physiological mechanisms that ensure the homoeostasis of the mucus layer. Numerical simulations, based on a finite volume approach, prove the ability of the model to produce a stable steady-state mucus layer. We also perform a sensitivity analysis by using a meta-model based on polynomial chaos in order to identify the main parameters impacting the shape of the mucus layer. The effect of the interaction of the mucus with a population of bacteria is eventually discussed.

Published
2016
Full Text
View/download PDF

43. Abcès rénal et périrénal chez l'enfant

Author

Laroche, B, Homsy, Y, Perreault, G, and Laberge, I

Subjects
Male, bacterial infections and mycoses, Kidney, Prognosis, Abscess, Anti-Bacterial Agents, Radiography, Child, Preschool, Drainage, Humans, Female, Kidney Diseases, Child, Research Article
Abstract

One child with a pure perinephric abscess and three with renal abscesses, one of which had perinephric extension, are described. All presented with a long course of subacute infection leading to localizing symptoms or signs in the flank. The diagnosis was confirmed by radiologic examination. All the abscesses were surgically drained at various intervals after diagnosis, while the patients were receiving antibiotic therapy. Salvage of renal function was possible in all cases. A rational approach to the diagnosis and management of such abscesses is emphasized.

Published
1979

45. Total androgen ablation: Canadian experience

Author

Béland G, Elhilali M, Yves Fradet, Laroche B, Ew, Ramsey, Trachtenberg J, Pm, Venner, and Hd, Tewari

Subjects
Adult, Aged, 80 and over, Male, Double-Blind Method, Imidazoles, Humans, Prostatic Neoplasms, Androgen Antagonists, Middle Aged, Imidazolidines, Combined Modality Therapy, Orchiectomy, Aged
Abstract

A multicenter randomized, double-blind trial comparing total androgen blockade obtained by the use of castration with a pure anti-androgen (nilutamide) with simple castration was begun. One hundred and five patients received the combined treatment and 103 the orchiectomy plus placebo. Several features were used to evaluate the efficacy. Bone pain responded better to combined treatment at 6 months (P = 0.042). The number of favorable responses, as evaluated by the NPCP criteria, was 61% with simple castration and 78% with the combined treatment (P = 0.013). There was no statistically significant difference between the two groups in time to progression (logrank test P = 0.462) or survival (logrank test P = 0.137) despite an increase in median survival of 5.4 months. All other measures showed no difference between the two treatments. With total androgen blockade, 50% of the patients had disease progression at 1 year, and 45% were dead at 2 years. A review of the results of similar reported studies suggests no improvement or very modest improvement with total androgen blockade over testicular androgen ablation alone.

Catalog

Books, media, physical & digital resources
See catalog results