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Статті в журналах з теми "Quantification interactions"

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Автор: Grafiati

Опубліковано: 22 червня 2022

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1

Pérez, R. Navarro, J. E. Amaro, and E. Ruiz Arriola. "Uncertainty quantification of effective nuclear interactions." International Journal of Modern Physics E 25, no. 05 (May 2016): 1641009. http://dx.doi.org/10.1142/s0218301316410093.

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We give a brief review on the development of phenomenological NN interactions and the corresponding quantification of statistical uncertainties. We look into the uncertainty of effective interactions broadly used in mean field calculations through the Skyrme parameters and effective field theory counterterms by estimating both statistical and systematic uncertainties stemming from the NN interaction. We also comment on the role played by different fitting strategies on the light of recent developments.

2

Muniategui, Ander, Rubén Nogales-Cadenas, Miguél Vázquez, Xabier L. Aranguren, Xabier Agirre, Aernout Luttun, Felipe Prosper, Alberto Pascual-Montano, and Angel Rubio. "Quantification of miRNA-mRNA Interactions." PLoS ONE 7, no. 2 (February 14, 2012): e30766. http://dx.doi.org/10.1371/journal.pone.0030766.

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3

Gonthier, Jérôme F., and Clémence Corminboeuf. "Quantification and Analysis of Intramolecular Interactions." CHIMIA International Journal for Chemistry 68, no. 4 (April 30, 2014): 221–26. http://dx.doi.org/10.2533/chimia.2014.221.

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4

Larijani, Banafshé, James Miles, Stephen G. Ward, and Peter J. Parker. "Quantification of biomarker functionality predicts patient outcomes." British Journal of Cancer 124, no. 10 (March 15, 2021): 1618–20. http://dx.doi.org/10.1038/s41416-021-01291-3.

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SummaryImplementation of a quantitative molecular imaging method (iFRET), which determines receptor–ligand interactions, has led to the finding that patients with a low extent of PD-1/PD-L1 interaction in metastatic NSCLC, and malignant melanoma, display significantly worsened overall survival compared to those with a high level of interaction.

5

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 (October 31, 2017): 10228–41. http://dx.doi.org/10.1021/acs.jpcb.7b08045.

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6

Roy, Dipankar, and Raghavan B. Sunoj. "Quantification of Intramolecular Nonbonding Interactions in Organochalcogens." Journal of Physical Chemistry A 110, no. 17 (May 2006): 5942–47. http://dx.doi.org/10.1021/jp060218t.

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7

Jucker, Barbara A., Alexander J. B. Zehnder, and Hauke Harms. "Quantification of Polymer Interactions in Bacterial Adhesion." Environmental Science & Technology 32, no. 19 (October 1998): 2909–15. http://dx.doi.org/10.1021/es980211s.

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8

Schakenraad, J. M., J. H. Kuit, J. Arends, H. J. Busscher, J. Feijen, and Ch R. H. Wildevuur. "In vivo quantification of cell-polymer interactions." Biomaterials 8, no. 3 (May 1987): 207–10. http://dx.doi.org/10.1016/0142-9612(87)90065-2.

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9

Lazareno, Sebastian. "QUANTIFICATION OF RECEPTOR INTERACTIONS USING BINDING METHODS." Journal of Receptors and Signal Transduction 21, no. 2-3 (January 2001): 139–65. http://dx.doi.org/10.1081/rrs-100107426.

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10

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.

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Quantification of noncovalent interactions is the key for the understanding of binding mechanisms, of biological systems, for the design of drugs, their delivery and for the design of receptors for separations, sensors, actuators, or smart materials.

11

Otzen, Daniel E., Alexander K. Buell, and Henrik Jensen. "Microfluidics and the quantification of biomolecular interactions." Current Opinion in Structural Biology 70 (October 2021): 8–15. http://dx.doi.org/10.1016/j.sbi.2021.02.006.

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12

Zeng, Qing Hua, Wen Xu, Ai Bing Yu, and Donald R. Paul. "Quantification of the Interface Interactions in Polymer Nanocomposites." Materials Science Forum 654-656 (June 2010): 2608–11. http://dx.doi.org/10.4028/www.scientific.net/msf.654-656.2608.

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Interfaces are important for many properties and applications of multiphase materials. This is particular true for particle-reinforced polymer composites, where the interfacial characteristics between particle and polymer play a crucial role in load transfer and mechanical properties. In polymer nanocomposites, the adhesion strength between particle and polymer matrix is a major factor in determining their mechanical properties. In this work, we present our recent study towards the quantification of the interaction strength at the interface of clay-based polymer nanocomposites by molecular dynamics simulation.

13

Gatlik-Landwojtowicz, Ewa, Päivi Äänismaa, and Anna Seelig. "Quantification and Characterization of P-Glycoprotein−Substrate Interactions." Biochemistry 45, no. 9 (March 2006): 3020–32. http://dx.doi.org/10.1021/bi051380+.

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14

Moya, Carlos, Óscar Iglesias, Xavier Batlle, and Amílcar Labarta. "Quantification of Dipolar Interactions in Fe3–xO4 Nanoparticles." Journal of Physical Chemistry C 119, no. 42 (October 13, 2015): 24142–48. http://dx.doi.org/10.1021/acs.jpcc.5b07516.

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15

Shen, Junjie, X. Jin Yang, and Andrea I. Schäfer. "Quantification of Hormone–Humic Acid Interactions in Nanofiltration." Environmental Science & Technology 46, no. 19 (September 13, 2012): 10597–604. http://dx.doi.org/10.1021/es301843s.

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16

Maynard, Stephanie A., Amy Gelmi, Stacey C. Skaalure, Isaac J. Pence, Charlotte Lee-Reeves, Julia E. Sero, Thomas E. Whittaker, and Molly M. Stevens. "Nanoscale Molecular Quantification of Stem Cell–Hydrogel Interactions." ACS Nano 14, no. 12 (November 20, 2020): 17321–32. http://dx.doi.org/10.1021/acsnano.0c07428.

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17

Jameson, David M., and Steven E. Seifried. "Quantification of Protein–Protein Interactions Using Fluorescence Polarization." Methods 19, no. 2 (October 1999): 222–33. http://dx.doi.org/10.1006/meth.1999.0853.

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18

Henry, Marc. "Nonempirical Quantification of Molecular Interactions in Supramolecular Assemblies." ChemPhysChem 3, no. 7 (July 15, 2002): 561–69. http://dx.doi.org/10.1002/1439-7641(20020715)3:7<561::aid-cphc561>3.0.co;2-e.

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19

Yan, Z., L. Guo, L. Hu, and J. Wang. "Specificity and affinity quantification of protein-protein interactions." Bioinformatics 29, no. 9 (March 7, 2013): 1127–33. http://dx.doi.org/10.1093/bioinformatics/btt121.

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20

Twomey, Alan, Rebekah Less, Kosaku Kurata, Hiroshi Takamatsu, and Alptekin Aksan. "In Situ Spectroscopic Quantification of Protein–Ice Interactions." Journal of Physical Chemistry B 117, no. 26 (June 20, 2013): 7889–97. http://dx.doi.org/10.1021/jp403267x.

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21

Hunter, Christopher A., Caroline M. R. Low, Jeremy G. Vinter, and Cristiano Zonta. "Quantification of Functional Group Interactions in Transition States." Journal of the American Chemical Society 125, no. 33 (August 2003): 9936–37. http://dx.doi.org/10.1021/ja034767d.

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22

Hallman, Peter. "Proportions in time: interactions of quantification and aspect." Natural Language Semantics 17, no. 1 (December 30, 2008): 29–61. http://dx.doi.org/10.1007/s11050-008-9038-y.

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23

Verstraeten, Gert. "Quantification of human–environment interactions in the past." Anthropocene 8 (December 2014): 1–5. http://dx.doi.org/10.1016/j.ancene.2015.06.002.

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24

Kaufmann, Tanja, Sébastien Herbert, Benjamin Hackl, Johanna Maria Besold, Christopher Schramek, Josef Gotzmann, Kareem Elsayad, and Dea Slade. "Direct measurement of protein–protein interactions by FLIM-FRET at UV laser-induced DNA damage sites in living cells." Nucleic Acids Research 48, no. 21 (October 14, 2020): e122-e122. http://dx.doi.org/10.1093/nar/gkaa859.

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Abstract Protein–protein interactions are essential to ensure timely and precise recruitment of chromatin remodellers and repair factors to DNA damage sites. Conventional analyses of protein–protein interactions at a population level may mask the complexity of interaction dynamics, highlighting the need for a method that enables quantification of DNA damage-dependent interactions at a single-cell level. To this end, we integrated a pulsed UV laser on a confocal fluorescence lifetime imaging (FLIM) microscope to induce localized DNA damage. To quantify protein–protein interactions in live cells, we measured Förster resonance energy transfer (FRET) between mEGFP- and mCherry-tagged proteins, based on the fluorescence lifetime reduction of the mEGFP donor protein. The UV-FLIM-FRET system offers a unique combination of real-time and single-cell quantification of DNA damage-dependent interactions, and can distinguish between direct protein–protein interactions, as opposed to those mediated by chromatin proximity. Using the UV-FLIM-FRET system, we show the dynamic changes in the interaction between poly(ADP-ribose) polymerase 1, amplified in liver cancer 1, X-ray repair cross-complementing protein 1 and tripartite motif containing 33 after DNA damage. This new set-up complements the toolset for studying DNA damage response by providing single-cell quantitative and dynamic information about protein–protein interactions at DNA damage sites.

25

Teuscher, Friedrich. "The quantification of Simpson’s paradox and other contributions to contingency table theory." PLOS ONE 17, no. 2 (February 24, 2022): e0262502. http://dx.doi.org/10.1371/journal.pone.0262502.

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The analysis of contingency tables is a powerful statistical tool used in experiments with categorical variables. This study improves parts of the theory underlying the use of contingency tables. Specifically, the linkage disequilibrium parameter as a measure of two-way interactions applied to three-way tables makes it possible to quantify Simpson’s paradox by a simple formula. With tests on three-way interactions, there is only one that determines whether the partial interactions of all variables agree or whether there is at least one variable whose partial interactions disagree. To date, there has been no test available that determines whether the partial interactions of a certain variable agree or disagree, and the presented work closes this gap. This work reveals the relation of the multiplicative and the additive measure of a three-way interaction. Another contribution addresses the question of which cells in a contingency table are fixed when the first- and second-order marginal totals are given. The proposed procedure not only detects fixed zero counts but also fixed positive counts. This impacts the determination of the degrees of freedom. Furthermore, limitations of methods that simulate contingency tables with given pairwise associations are addressed.

26

Gung, Benjamin W., Bright U. Emenike, Michael Lewis та Kristin Kirschbaum. "Quantification of CH⋅⋅⋅π Interactions: Implications on How Substituent Effects Influence Aromatic Interactions". Chemistry – A European Journal 16, № 41 (17 вересня 2010): 12357–62. http://dx.doi.org/10.1002/chem.201001362.

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27

Litvinov, Rustem I., Oleg V. Gorkun, Dennis K. Galanakis, Sergiy Yakovlev, Leonid Medved, Henry Shuman, and John W. Weisel. "Polymerization of fibrin: direct observation and quantification of individual B:b knob-hole interactions." Blood 109, no. 1 (August 29, 2006): 130–38. http://dx.doi.org/10.1182/blood-2006-07-033910.

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Abstract The polymerization of fibrin occurs primarily through interactions between N-terminal A- and B-knobs, which are exposed by the cleavage of fibrinopeptides A and B, respectively, and between corresponding a- and b-holes in the γ- and β-modules. Of the potential knob-hole interactions—A:a, B:b, A:b, and B:a—the first has been shown to be critical for fibrin formation, but the roles of the others have remained elusive. Using laser tweezers–based force spectroscopy, we observed and quantified individual B:b and A:b interactions. Both desA-fibrin with exposed A-knobs and desB-fibrin bearing B-knobs interacted with fragment D from the γD364H fibrinogen containing b-holes but no functional a-holes. The strength of single B:b interactions was found to be 15 to 20 pN, approximately 6-fold weaker than A:a interactions. B:b binding was abrogated by B-knob mimetic peptide, the (β15-66)2 fragment containing 2 B-knobs, and a monoclonal antibody against the β15-21 sequence. The interaction of desB-fibrin with fragment D containing a- and b-holes produced the same forces that were insensitive to A-knob mimetic peptide, suggesting that B:a interactions were absent. These results directly demonstrate for the first time B:b binding mediated by natural B-knobs exposed in a fibrin monomer.

28

Pattinson, Oliver, Dario Carugo, Fabrice Pierron, and Nicholas Evans. "Ultra-high speed quantification of cell strain during cell-microbubble interactions." Journal of the Acoustical Society of America 151, no. 4 (April 2022): A154. http://dx.doi.org/10.1121/10.0010950.

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Interactions between oscillating microbubbles and cells are of fundamental importance in understanding cell behaviour, including mechanotransduction, during therapeutic microbubble treatment. However, it is challenging to quantify cell deformation due to the short time domains at which microbubble-induced deformations occur. Developments in both ultra-high speed imaging and image processing may allow for quantification of cell strain at high temporal and spatial resolutions. Here, we tested the hypothesis that ultra-high speed imaging and digital image correlation could be used to measure and quantify microbubble-induced cell deformation. A hypervision HPV-X camera and a custom-designed, compact acoustic cell-culture device were used together to image interactions between DSPC-microbubbles and MG-63 cells at up to 5 × 106 fps, under ultrasound exposure at 1 MHz. Dynamic cell deformation was measured using digital image correlation with MatchID software. Microbubbles associated with MG63 cells in the acoustic device. Microbubble oscillation resulted in a peak deformation of 350 nm and strain of 5% on the cell during the bubble expansion phase, isolated locally to the point of interaction. These data show that cell deformation can be quantified dynamically during bubble-cell interactions, suggesting that mechanical properties, and potentially corresponding therapeutic effects, can be quantified at high-frequency strain rates.

29

McComas, Jennifer J., Timothy R. Vollmer, and Craig Kennedy. "DESCRIPTIVE ANALYSIS: QUANTIFICATION AND EXAMINATION OF BEHAVIOR—ENVIRONMENT INTERACTIONS." Journal of Applied Behavior Analysis 42, no. 2 (June 2009): 411–12. http://dx.doi.org/10.1901/jaba.2009.42-411.

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30

Hyonchol, Kim, Hideo Arakawa, Toshiya Osada, and Atsushi Ikai. "Quantification of fibronectin and cell surface interactions by AFM." Colloids and Surfaces B: Biointerfaces 25, no. 1 (May 2002): 33–43. http://dx.doi.org/10.1016/s0927-7765(01)00299-5.

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31

Keener, Laurelyn E., and Morris Levy. "Quantification Of Biomechanical Interactions Between Dressage Horse And Rider." Medicine & Science in Sports & Exercise 37, Supplement (May 2005): S121. http://dx.doi.org/10.1249/00005768-200505001-00618.

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32

Hieb, Aaron R., Sheena D'Arcy, Michael A. Kramer, Alison E. White, and Karolin Luger. "Fluorescence strategies for high-throughput quantification of protein interactions." Nucleic Acids Research 40, no. 5 (November 24, 2011): e33-e33. http://dx.doi.org/10.1093/nar/gkr1045.

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33

Starrett, Shelli Kay. "Quantification of nonlinear modal interactions using modal signal energies." Electric Power Systems Research 38, no. 3 (September 1996): 199–207. http://dx.doi.org/10.1016/s0378-7796(96)01085-1.

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34

Keener, Laurelyn E., and Morris Levy. "Quantification Of Biomechanical Interactions Between Dressage Horse And Rider." Medicine & Science in Sports & Exercise 37, Supplement (May 2005): S121. http://dx.doi.org/10.1097/00005768-200505001-00618.

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35

Le Bourvellec, C., and C. M. G. C. Renard. "Interactions between Polyphenols and Macromolecules: Quantification Methods and Mechanisms." Critical Reviews in Food Science and Nutrition 52, no. 3 (March 2012): 213–48. http://dx.doi.org/10.1080/10408398.2010.499808.

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36

Strauss, Marcel A., and Hermann A. Wegner. "Molecular Systems for the Quantification of London Dispersion Interactions." European Journal of Organic Chemistry 2019, no. 2-3 (October 25, 2018): 295–302. http://dx.doi.org/10.1002/ejoc.201800970.

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37

Haghighat, Anne-Cécile, and Stéphanie Seveau. "Quantification of host–microbe interactions by automated fluorescence microscopy." Journal of Immunological Methods 352, no. 1-2 (January 2010): 186–91. http://dx.doi.org/10.1016/j.jim.2009.11.013.

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38

Li, Qian, Thomas Becker, and Wolfgang Sand. "Quantification of cell-substratum interactions by atomic force microscopy." Colloids and Surfaces B: Biointerfaces 159 (November 2017): 639–43. http://dx.doi.org/10.1016/j.colsurfb.2017.08.023.

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39

Yeung, D., A. Gill, C. H. Maule, and R. J. Davies. "Detection and quantification of biomolecular interactions with optical biosensors." TrAC Trends in Analytical Chemistry 14, no. 2 (February 1995): 49–56. http://dx.doi.org/10.1016/0165-9936(95)91472-5.

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40

Kabir, Muammar M., David A. Saint, Eugene Nalivaiko, Derek Abbott, Andreas Voss, and Mathias Baumert. "Quantification of Cardiorespiratory Interactions Based on Joint Symbolic Dynamics." Annals of Biomedical Engineering 39, no. 10 (May 27, 2011): 2604–14. http://dx.doi.org/10.1007/s10439-011-0332-3.

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41

Li, Yingjie, Liwei Zhang, Jiangxiao Qiu, Junping Yan, Luwen Wan, Pengtao Wang, Ningke Hu, Wei Cheng, and Bojie Fu. "Spatially explicit quantification of the interactions among ecosystem services." Landscape Ecology 32, no. 6 (May 10, 2017): 1181–99. http://dx.doi.org/10.1007/s10980-017-0527-6.

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42

Barron, Daniel S., Stephen Heisig, Carla Agurto, Raquel Norel, Brittany Quagan, Albert Powers, Michael L. Birnbaum, Todd Constable, Guillermo Cecchi, and John H. Krystal. "Feasibility Analysis of Phenotype Quantification from Unstructured Clinical Interactions." Computational Psychiatry 6, no. 1 (January 11, 2022): 1. http://dx.doi.org/10.5334/cpsy.78.

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43

Cenci, Simone, and Serguei Saavedra. "Uncertainty quantification of the effects of biotic interactions on community dynamics from nonlinear time-series data." Journal of The Royal Society Interface 15, no. 147 (October 2018): 20180695. http://dx.doi.org/10.1098/rsif.2018.0695.

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Biotic interactions are expected to play a major role in shaping the dynamics of ecological systems. Yet, quantifying the effects of biotic interactions has been challenging due to a lack of appropriate methods to extract accurate measurements of interaction parameters from experimental data. One of the main limitations of existing methods is that the parameters inferred from noisy, sparsely sampled, nonlinear data are seldom uniquely identifiable. That is, many different parameters can be compatible with the same dataset and can generalize to independent data equally well. Hence, it is difficult to justify conclusive assertions about the effect of biotic interactions without information about their associated uncertainty. Here, we develop an ensemble method based on model averaging to quantify the uncertainty associated with the effect of biotic interactions on community dynamics from non-equilibrium ecological time-series data. Our method is able to detect the most informative time intervals for each biotic interaction within a multivariate time series and can be easily adapted to different regression schemes. Overall, this novel approach can be used to associate a time-dependent uncertainty with the effect of biotic interactions. Moreover, because we quantify uncertainty with minimal assumptions about the data-generating process, our approach can be applied to any data for which interactions among variables strongly affect the overall dynamics of the system.

44

Carnerero, J. M., A. Sánchez-Coronilla, E. I. Martín, A. Jimenez-Ruiz, and R. Prado-Gotor. "Quantification of nucleobases/gold nanoparticles interactions: energetics of the interactions through apparent binding constants determination." Physical Chemistry Chemical Physics 19, no. 33 (2017): 22121–28. http://dx.doi.org/10.1039/c7cp03692b.

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45

Lira-Palma, Danitza, Karolyn González-Rosales, Ramón D. Castillo, Rosario Spencer, and Andrés Fresno. "Categorical Cross-Recurrence Quantification Analysis Applied to Communicative Interaction during Ainsworth’s Strange Situation." Complexity 2018 (November 1, 2018): 1–15. http://dx.doi.org/10.1155/2018/4547029.

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The goal of this study was to characterize the degree of structuring of verbal and motor behaviours, unfolded during the application of an procedure called the Strange Situation. This procedure is used for assessing children’s attachment quality during early stages of their development. Many studies have demonstrated that communicative interactions share features with complex dynamic systems. In such studies, estimations of degree of structure have been used to characterize the system’s synchronization. Thus, assuming that processes of communicative interaction occur in the Strange Situation procedure, it was expected to find traces of synchronization. The metrics were estimated through a Categorical Cross-Recurrence Quantification Analysis applied to the behaviours of individuals and dyads. Two applications of the Strange Situation were implemented and recorded. Verbal and motor interactions among children, caregivers, and strangers were transcribed, categorized, and organized as time series. From each time series of original behaviours, randomized time series were created. Measures of recurrence extracted from Recurrent Plots, such as determinism, entropy, maximum line, laminarity, and trapping time, were calculated. Original and randomized time series were compared in terms of these measures. Results indicated that communicative interaction during the Strange Situation had a structure that mimics properties observed in social interactions where synchronization emerges. In our case, verbal behaviours were more prone to synchronization than motor behaviours, in both individuals and dyads, even though this pattern was more salient among caregivers and strangers than children. The relevance of having measures that can capture synchronization during the administration of the Strange Situation is discussed. Our preliminary findings allow us to point out that the application of RQA and C-RQA to the Strange Situation could not only contribute to methodology, but also contribute to emphasizing the role of coupling in communicative interaction generated by the application of this procedure to measure attachment patterns.

46

Beutel, Oliver, Friedrich Roder, Oliver Birkholz, Christian Rickert, Heinz-Jürgen Steinhoff, Michał Grzybek, Ünal Coskun, and Jacob Piehler. "Two-Dimensional Trap for Ultrasensitive Quantification of Transient Protein Interactions." ACS Nano 9, no. 10 (September 15, 2015): 9783–91. http://dx.doi.org/10.1021/acsnano.5b02696.

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