Littérature scientifique sur le sujet « Quantification des interactions »
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Articles de revues sur le sujet "Quantification des interactions":
Pérez, R. Navarro, J. E. Amaro et E. Ruiz Arriola. « Uncertainty quantification of effective nuclear interactions ». International Journal of Modern Physics E 25, no 05 (mai 2016) : 1641009. http://dx.doi.org/10.1142/s0218301316410093.
Muniategui, Ander, Rubén Nogales-Cadenas, Miguél Vázquez, Xabier L. Aranguren, Xabier Agirre, Aernout Luttun, Felipe Prosper, Alberto Pascual-Montano et Angel Rubio. « Quantification of miRNA-mRNA Interactions ». PLoS ONE 7, no 2 (14 février 2012) : e30766. http://dx.doi.org/10.1371/journal.pone.0030766.
Gonthier, Jérôme F., et Clémence Corminboeuf. « Quantification and Analysis of Intramolecular Interactions ». CHIMIA International Journal for Chemistry 68, no 4 (30 avril 2014) : 221–26. http://dx.doi.org/10.2533/chimia.2014.221.
Larijani, Banafshé, James Miles, Stephen G. Ward et Peter J. Parker. « Quantification of biomarker functionality predicts patient outcomes ». British Journal of Cancer 124, no 10 (15 mars 2021) : 1618–20. http://dx.doi.org/10.1038/s41416-021-01291-3.
Wolfe, Aaron J., Wei Si, Zhengqi Zhang, Adam R. Blanden, Yi-Ching Hsueh, Jack F. Gugel, Bach Pham et al. « Quantification of Membrane Protein-Detergent Complex Interactions ». Journal of Physical Chemistry B 121, no 44 (31 octobre 2017) : 10228–41. http://dx.doi.org/10.1021/acs.jpcb.7b08045.
Roy, Dipankar, et Raghavan B. Sunoj. « Quantification of Intramolecular Nonbonding Interactions in Organochalcogens ». Journal of Physical Chemistry A 110, no 17 (mai 2006) : 5942–47. http://dx.doi.org/10.1021/jp060218t.
Jucker, Barbara A., Alexander J. B. Zehnder et Hauke Harms. « Quantification of Polymer Interactions in Bacterial Adhesion ». Environmental Science & ; Technology 32, no 19 (octobre 1998) : 2909–15. http://dx.doi.org/10.1021/es980211s.
Schakenraad, J. M., J. H. Kuit, J. Arends, H. J. Busscher, J. Feijen et Ch R. H. Wildevuur. « In vivo quantification of cell-polymer interactions ». Biomaterials 8, no 3 (mai 1987) : 207–10. http://dx.doi.org/10.1016/0142-9612(87)90065-2.
Lazareno, Sebastian. « QUANTIFICATION OF RECEPTOR INTERACTIONS USING BINDING METHODS ». Journal of Receptors and Signal Transduction 21, no 2-3 (janvier 2001) : 139–65. http://dx.doi.org/10.1081/rrs-100107426.
Schneider, Hans-Jörg. « Quantification of noncovalent interactions – promises and problems ». New Journal of Chemistry 43, no 39 (2019) : 15498–512. http://dx.doi.org/10.1039/c9nj03325d.
Thèses sur le sujet "Quantification des interactions":
Varley, Lisa. « Intermolecular interactions : quantification and applications ». Thesis, University of Sheffield, 2012. http://etheses.whiterose.ac.uk/2739/.
Usman, Jauhr. « Quantification of affinity mediated cell/surface interactions ». Thesis, University of Bath, 1997. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.362307.
Merk, Daniel. « Uncertainties in the Quantification of Aerosol-Cloud Interactions ». Doctoral thesis, Universitätsbibliothek Leipzig, 2017. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-225523.
Pajor, Radoslaw. « Quantification of short term interactions between soil and fungi ». Thesis, Abertay University, 2012. https://rke.abertay.ac.uk/en/studentTheses/1fdaf041-ca50-4c70-ba7a-1859e07a11d5.
Prada, Jimenez de Cisneros Joaquin M. « Quantification of host-parasite interactions : sheep and their nematodes ». Thesis, University of Glasgow, 2014. http://theses.gla.ac.uk/6156/.
Lowden, Ben D. « A methodology for the quantification of outcrop permeability heterogeneities through probe permeametry ». Thesis, Imperial College London, 1993. http://hdl.handle.net/10044/1/7588.
Baksay, Sandra. « Etude des réseaux de pollinisation par métagénomique environnementale ». Thesis, Toulouse 3, 2020. http://www.theses.fr/2020TOU30322.
The global decline of plants and pollinators highlights the need to better understand the structural and functional characteristics of pollination networks. Studies have shown that it is possible to detect plant-pollinator interactions by identifying pollen in insect pollen loads using DNA metabarcoding and thus gain a more complete picture of interactions at both the species (plant and pollinator) and individual levels. However, the potential of this technique to quantify interactions is still under discussion. We have addressed this issue in two experiments in the laboratory and in the wild, in a meadow, under natural conditions. We found highly significant relationships between the number of DNA sequences of a plant species and (1) the number of its pollen grains in an experimental solution with known pollen abundances or in the pollen load of insects that forage freely in the meadow or (2) the number of visits it received. We identified methodological and biological biases that may reduce the quality of the relationship and thus of the quantification of the interactions, some of which can be reduced. However, amplification at 30-35 PCR cycles allows a good quantification for a quantity of pollen comparable to that found in insect pollen loads. In a third experiment, we used metabarcoding to study the response of pollinators and plant-pollinator networks to abundance variations of a massively flowering plant, Rhododendron ferrugineum, an emblematic species of the subalpine stage of the Pyrenees and the Alps that is locally very abundant. We showed that (1) although this species is attractive it never monopolizes pollinators, which continue to visit the community's rare species even in the densest heathland, (2) pollinators tend to be more specialized, and network specialization tends to increase with the decrease in floral resources although not all insect groups respond in the same way, and (3) most populations of pollinators are generalist but mostly focus their activity on a limited number of plant species and individuals are highly specialized. These results allow a better understanding of pollination service efficiency within very generalized networks. In summary, this work has shown that metabarcoding allows: (1) to correctly identify plants from pollen present in pollinators pollen load and thus to better understand the diet and behaviour of individuals, (2) to quantify plant-pollinator interactions fairly precisely, including at the individual level, and thus (3) to build more realistic pollination networks than with traditional methods. We also showed the potential of this method to better understand the structure and functioning of pollination networks and their responses to global changes
Seog, Joonil 1969. « Molecular mechanics of cartilage : quantification of GAG electrostatic interactions via high-resolution force spectroscopy ». Thesis, Massachusetts Institute of Technology, 2003. http://hdl.handle.net/1721.1/17641.
Includes bibliographical references (leaves 121-135).
Intermolecular repulsion forces between negatively charged glycosaminoglycan (CS-GAG) macromolecules are a major determinant of cartilage biomechanical properties. It is thought that the electrostatic component of the total intermolecular interaction is responsible for 50-75% of the equilibrium elastic modulus of cartilage in compression, while other forces (e.g., steric, hydration, van der Waals, etc.) may also play a role. To investigate these forces, radiolabeled CS-GAG polymer chains were chemically end-grafted to a planar surface to form model biomimetic polyelectrolyte "brush" layers whose environment was varied to mimic physiological conditions. The total intersurface force (<[or equal to] nN) between the CS-GAG brushes and chemically modified probe tips (SO₃⁻ and OH) was measured as a function of tip-substrate separation distance in aqueous solution using the technique of high-resolution force spectroscopy (HRFS). These experiments showed long-range, nonlinear, purely repulsive forces that decreased in magnitude and range with increasing ionic strength and decreasing pH. In order to estimate the contribution of the electrostatic component to the total intersurface force, the data were compared to a theoretical model of electrical double layer repulsion based on the Poisson-Boltzmann formulation. The CS-GAG brush layer was approximated as either a flat surface charge density or a smoothed volume of known fixed charge density and the probe tip was modeled as a smooth hemisphere of constant surface charge density.
(cont.) To further closely mimic physiological condition of the cartilage, the CS-GAG molecules were successfully attached to the AFM probe tip using electric field. The CS-GAG modified tip was characterized by measuring force at various environments and its parking density was also estimated using newly developed molecular level model. The measured force between CS-GAG modified tip and CS-GAG modified substrate showed a long-range interaction that significantly dependent on the ionic strength and pH, indicating the significant role of Coulombic interaction between CS-GAG layers. The equilibrium brush height measured using ellipsometry showed that CS-GAG behaves as an annealed polyelectrolyte that reached its maximum brush height around 0.1 M salt concentration. The equilibrium brush height was compared with the onset of the force increase to obtain further insight on the CS-GAG brush behavior during the force measurement.
by Joonil Seog.
Sc.D.
Padilla-Parra, Sergi. « Methodological development for the quantification of protein interactions in living cells using fluorescence microscopy ». Paris 7, 2009. http://www.theses.fr/2009PA077096.
This thesis presents different alternatives to the problem of detecting and quantifying protein interactions in vivo using different approaches like: i) Fluorescence Lifetime Imaging Microscopy (FLIM) applied to detect Fluorescence Resonance Energy Transfer (FRET), ii) Fluorescence Lifetime Correlation Spectroscopy (FCS), or iii) Fluorescence Recovery After Photobleaching (FRAP) coupled to-FLIM. A new methodological approach to analyze FRET-FLIM based on the study of the fraction of interacting donor (fɒ) is also presented. We introduce the new concept of a minimal fraction of donor molecules involved in FRET (mfɒ), coming from the mathematical minimization of fɒ. We find particular advantage in the use of mfɒ because it can be obtained without fitting procedures and is derived directly from FLIM data. We also propose a new FRET pair for quantitative analysis: mTFPl and Yellow Fluorescent Protein (YFP). This couple gives the highest fɒ values compared to the most commonly used Green Fluorescent Protein combined with a Red Acceptor (e. G. GFP-mCherry). We apply these new methodological developments to different biological examples: i) interaction between Amphiphysin 1 with N-WASp, ii) Rac activation and histone H4 acetylation. Finally we propose some interesting techniques to improve our results and the detection of protein interactions. The use of more powerful excitation sources, the combination of Total Internal Reflection Microscopy with Time-Gated FLIM using a continuum laser or the role of Super Resolution for protein interactions are some of them
Chapalain, Marion. « Dynamique des matières en suspension en mer côtière : caractérisation, quantification et interactions sédiments/matière organique ». Thesis, Brest, 2019. http://www.theses.fr/2019BRES0010/document.
The knowledge of suspended particulate matter (SPM) and turbidity dynamics in coastal waters is essential for studying marine ecosystems. Flocculation/deflocculation processes are crucial mechanisms controlling the dynamics of SPM physical characteristics and thus, the fate of these SPM in the environment. This PhD thesis focuses on the SPM characteristics and their dynamics in coastal waters, from tidal to annual scales, in response to hydrodynamic, hydrological and biological forcing that take place at the interface between estuaries and coastal seas. To this end, optical and acoustic sensors were deployed in situ through 6 field campaigns in 2016, in order to characterize and quantify SPM in the water column, near the mouth of the Seine estuary (France). A critical analysis of LISST-100X measurements in coastal waters is presented. The quantification of uncertainties on SPM concentration measurements is investigated: it highlights the crucial effect of salt retention, and the need for a minimum mass to filter, around 10 mg. A method for estimating an optimal filtration volume based on a reference turbidity measurement is proposed. High frequency measurements show that the dynamics of SPM and median diameter are controlled by the advection-flocculationsedimentation-resuspension cycle.These results allow to classify the factors controlling flocculation processes. Turbulence is identified as the main factor at the semi-diurnal and semi-lunar tidal scales, as the maximum median size of flocs decreases when the tidal currents intensify.At the seasonal scale, the variation of SPM characteristics (size, density, settling velocity) is correlated to the variability of the organic matter (OM) content: in particular, flocculation is enhanced by an increase of the particular OM fraction. The resulting larger and lesser dense flocs are also more resistant to the fragmentation induced by shear. This work also investigates the fractal approach applied to flocs. The fractal dimension variability, calculated by combining in situ data of SPM concentration and particle size distribution, can be associated to variations of the SPM composition, but can also result from uncertainties linked to instrument limitations. The latter are discussed in this PhD thesis. The seasonal variability of SPM characteristics is more pronounced offshore than at the mouth of the Seine estuary. From short-term observations in the Seine Bay and from long-term series in the Belgian coastal zone provided by the RBINS, optical turbidity and acoustic backscatter measurements are combined. They highlight an increase of the acoustic backscatter intensity when mean floc density
Livres sur le sujet "Quantification des interactions":
Timlin, Dennis, et Laj R. Ahuja, dir. Enhancing Understanding and Quantification of Soil-Root Growth Interactions. Madison, WI, USA : American Society of Agronomy and Soil Science Society of America, 2013. http://dx.doi.org/10.2134/advagricsystmodel4.
Hallman, Peter. Interactions of Degree and Quantification. BRILL, 2020.
Raydugin, Yuri G. Modern Risk Quantification in Complex Projects. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780198844334.001.0001.
Ahuja, Lajpat R., et Dennis Timlin. Enhancing Understanding and Quantification of Soil-Root Growth Interactions. Wiley & Sons, Limited, John, 2020.
Lidz, Jeffrey L. Quantification in Child Language. Sous la direction de Jeffrey L. Lidz, William Snyder et Joe Pater. Oxford University Press, 2016. http://dx.doi.org/10.1093/oxfordhb/9780199601264.013.21.
Krifka, Manfred. Quantification and Information Structure. Sous la direction de Caroline Féry et Shinichiro Ishihara. Oxford University Press, 2016. http://dx.doi.org/10.1093/oxfordhb/9780199642670.013.35.
Haiman, Christopher, et David J. Hunter. Genetic Epidemiology of Cancer. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190676827.003.0004.
Mayo, Julianne Susan. Fish-habitat interactions in a small freshwater lake and the evaluation of a visual census sampling method for the quantification of vertical, littoral zone habitat structure and fish distribution. 2003.
Advanced Computational Vibroacoustics Reducedorder Models And Uncertainty Quantification. Cambridge University Press, 2014.
Chapitres de livres sur le sujet "Quantification des interactions":
Achimova, Asya, Viviane Déprez et Julien Musolino. « Structural Asymmetry in Question/Quantifier Interactions ». Dans Linguistic and Cognitive Aspects of Quantification, 13–29. Cham : Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-91566-1_2.
Bartsch, F., M. L. Kang, S. T. Mees, J. Haier et P. Gassmann. « In Vivo Quantification of Metastatic Tumor Cell Adhesion in the Pulmonary Microvasculature ». Dans Cell-Cell Interactions, 89–101. Totowa, NJ : Humana Press, 2013. http://dx.doi.org/10.1007/978-1-62703-604-7_8.
Adiku, S. G. K., L. R. Ahuja, H. Ozier-Lafontaine, G. H. Dunn et L. Garcia. « Soil-Root Interactions in Mixed Plant Systems ». Dans Enhancing Understanding and Quantification of Soil-Root Growth Interactions, 245–72. Madison, WI, USA : American Society of Agronomy and Soil Science Society of America, 2015. http://dx.doi.org/10.2134/advagricsystmodel4.c11.
Dathe, Annette, Johannes A. Postma et Jonathan P. Lynch. « Modeling Resource Interactions Under Multiple Edaphic Stresses ». Dans Enhancing Understanding and Quantification of Soil-Root Growth Interactions, 273–94. Madison, WI, USA : American Society of Agronomy and Soil Science Society of America, 2015. http://dx.doi.org/10.2134/advagricsystmodel4.c12.
Gregory, Peter J., A. Glyn Bengough, Timothy S. George et Paul D. Hallett. « Rhizosphere Engineering by Plants : Quantifying Soil-Root Interactions ». Dans Enhancing Understanding and Quantification of Soil-Root Growth Interactions, 1–30. Madison, WI, USA : American Society of Agronomy and Soil Science Society of America, 2015. http://dx.doi.org/10.2134/advagricsystmodel4.c1.
Kronvang, B., R. Grant et A. L. Laubel. « Sediment and Phosphorus Export from a Lowland Catchment : Quantification of Sources ». Dans The Interactions Between Sediments and Water, 465–76. Dordrecht : Springer Netherlands, 1997. http://dx.doi.org/10.1007/978-94-011-5552-6_48.
Imai, Hideyuki, Daigo Izawa, Kiyotaka Yoshida et Yoshiharu Sato. « On Detecting Interactions in Hayashi’s Second Method of Quantification ». Dans Modeling Decisions for Artificial Intelligence, 205–14. Berlin, Heidelberg : Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-27774-3_20.
Zwart, Sara R., et Brandon J. Lewis. « Optimization of Detection and Quantification of Proteins on Membranes in Very High and Very Low Abundance Using Avidin and Streptavidin ». Dans Avidin-Biotin Interactions, 25–34. Totowa, NJ : Humana Press, 2008. http://dx.doi.org/10.1007/978-1-59745-579-4_3.
Baars, Woutijn J., et Charles E. Tinney. « Temporal and Spectral Quantification of the ‘Crackle’ Component in Supersonic Jet Noise ». Dans Fluid-Structure-Sound Interactions and Control, 205–10. Berlin, Heidelberg : Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40371-2_30.
Witt, Oliver, et Bernhard Westrich. « Quantification of erosion rates for undisturbed contaminated cohesive sediment cores by image analysis ». Dans The Interactions between Sediments and Water, 271–76. Dordrecht : Springer Netherlands, 2003. http://dx.doi.org/10.1007/978-94-017-3366-3_37.
Actes de conférences sur le sujet "Quantification des interactions":
Kallepalli, Akhil, et David B. James. « Quantification and influence of skin chromophores for remote detection of anemic conditions ». Dans Optical Interactions with Tissue and Cells XXXI, sous la direction de Bennett L. Ibey et Norbert Linz. SPIE, 2020. http://dx.doi.org/10.1117/12.2545784.
Fregonese, M. « Détection et quantification des phénomènes de fissuration assistée par l'environnement ». Dans PlastOx 2007 - Mécanismes et Mécanique des Interactions Plasticité - Environnement. Les Ulis, France : EDP Sciences, 2009. http://dx.doi.org/10.1051/ptox/2009015.
Loeven, G. J. A., J. A. S. Witteveen et H. Bijl. « (Student Paper) Efficient Uncertainty Quantification in Computational Fluid-Structure Interactions ». Dans 47th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference
14th AIAA/ASME/AHS Adaptive Structures Conference
7th. Reston, Virigina : American Institute of Aeronautics and Astronautics, 2006. http://dx.doi.org/10.2514/6.2006-1634.
FAWCETT, P., R. FUNK et N. KOMERATH. « Quantification of canard and wing interactions using spatial correlation velocimetry ». Dans 10th Applied Aerodynamics Conference. Reston, Virigina : American Institute of Aeronautics and Astronautics, 1992. http://dx.doi.org/10.2514/6.1992-2687.
Wang, Huayan, Shuai Shao, Vanlai Pham, Panju Shang, Cheng Zhong et Seungbae Park. « Quantification of Underfill Influence to Chip Packaging Interactions of WLCSP ». Dans ASME 2018 International Technical Conference and Exhibition on Packaging and Integration of Electronic and Photonic Microsystems. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/ipack2018-8257.
Bâce, Mihai, Sander Staal et Andreas Bulling. « Quantification of Users' Visual Attention During Everyday Mobile Device Interactions ». Dans CHI '20 : CHI Conference on Human Factors in Computing Systems. New York, NY, USA : ACM, 2020. http://dx.doi.org/10.1145/3313831.3376449.
Yu, Xiao, Richard L. Blackmon, Patricia Carabas-Hernendez, Ashley Fuller, Melissa A. Troester et Amy L. Oldenburg. « Quantification of mammary organoid toxicant response and mammary tissue motility using OCT fluctuation spectroscopy (Conference Presentation) ». Dans Optical Interactions with Tissue and Cells XXVII, sous la direction de E. Duco Jansen. SPIE, 2016. http://dx.doi.org/10.1117/12.2211461.
Hui, Fang, Yan Guo, Baoguo Li, Chunli Lv et Yuntao Ma. « Quantification of differences in root system architecture under maize/soybean interspecific interactions ». Dans 2018 6th International Symposium on Plant Growth Modeling, Simulation, Visualization and Applications (PMA). IEEE, 2018. http://dx.doi.org/10.1109/pma.2018.8611603.
Varon, Carolina, Dries Hendrikx, Juan Bolea, Pablo Laguna et Raquel Bailón. « Quantification of Linear and Nonlinear Cardiorespiratory Interactions under Autonomic Nervous System Blockade ». Dans 2019 Computing in Cardiology Conference. Computing in Cardiology, 2019. http://dx.doi.org/10.22489/cinc.2019.329.
Sakata, Mamiko. « Quantification of Multimodal Interactions as Open Communication in Manzai Duo-Comic Acts ». Dans 2017 International Conference on Culture and Computing (Culture and Computing). IEEE, 2017. http://dx.doi.org/10.1109/culture.and.computing.2017.46.
Rapports d'organisations sur le sujet "Quantification des interactions":
McAleer, Norma. A quantification and analysis of verbal interaction between clinician and client in a public school setting. Portland State University Library, janvier 2000. http://dx.doi.org/10.15760/etd.263.
McManus, Margaret A., Mark T. Stacey et John P. Ryan. Quantification of the Interacting Physical, Biological, Optical and Chemical Properties of Thin Layers in the Sea. Fort Belvoir, VA : Defense Technical Information Center, janvier 2009. http://dx.doi.org/10.21236/ada531014.
McManus, Margaret A., Mark T. Stacey et John P. Ryan. Quantification of the Interacting Physical, Biological, Optical and Chemical Properties of Thin Layers in the Sea. Fort Belvoir, VA : Defense Technical Information Center, septembre 2007. http://dx.doi.org/10.21236/ada574183.
Chefetz, Benny, Baoshan Xing, Leor Eshed-Williams, Tamara Polubesova et Jason Unrine. DOM affected behavior of manufactured nanoparticles in soil-plant system. United States Department of Agriculture, janvier 2016. http://dx.doi.org/10.32747/2016.7604286.bard.