Academic literature on the topic 'Additivity of the Effects'

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Journal articles on the topic "Additivity of the Effects"

1

Mayr, C. "Intervention Effects and Additivity." Journal of Semantics 31, no. 4 (2013): 513–54. http://dx.doi.org/10.1093/jos/fft010.

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2

Wells, James A. "Additivity of mutational effects in proteins." Biochemistry 29, no. 37 (1990): 8509–17. http://dx.doi.org/10.1021/bi00489a001.

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3

HOFMEISTER, PHILIP, LAURA STAUM CASASANTO, and IVAN A. SAG. "Processing effects in linguistic judgment data: (super-)additivity and reading span scores." Language and Cognition 6, no. 1 (2014): 111–45. http://dx.doi.org/10.1017/langcog.2013.7.

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abstractLinguistic acceptability judgments are widely agreed to reflect constraints on real-time language processing. Nonetheless, very little is known about how processing costs affect acceptability judgments. In this paper, we explore how processing limitations are manifested in acceptability judgment data. In a series of experiments, we consider how two factors relate to judgments for sentences with varying degrees of complexity: (1) the way constraints combine (i.e., additively or super-additively), and (2) the way a comprehender’s memory resources influence acceptability judgments. Results indicate that multiple sources of processing difficulty can combine to produce super-additive effects, and that there is a positive linear relationship between reading span scores and judgments for sentences whose unacceptability is attributable to processing costs. These patterns do not hold for sentences whose unacceptability is attributable to factors other than processing costs, e.g., grammatical constraints. We conclude that tests of (super)-additivity and of relationships to reading span scores can help to identify the effects of processing difficulty on acceptability judgments, although these tests cannot be used in contexts of extreme processing difficulty.
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4

Tolstoguzov A. B., Mazarov P. A., Ieshkin A. E., Meyer F., and Fu D. J. "Nonlinear effects in the sputtering of gallium arsenide and silicon by bismuth cluster ions." Technical Physics Letters 48, no. 3 (2022): 18. http://dx.doi.org/10.21883/tpl.2022.03.52875.19071.

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An experimental study on the influence of the energy and the number of atoms in the bombarding ions Bin+ (n=1-4) on the sputter yield of GaAs was carried out. It was shown that the specific sputter yield Ysp non-additively increases with increasing n and specific kinetic energy Esp per an atom in the bombarding ion, and the efficiency of energy transfer from bombarding ions to target atoms also increases with increasing n. A comparison was made with the previously obtained results for Si targets. Keywords: ion sputtering, non-additivity factor, cluster ions, bismuth.
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5

Russ, D., and R. Kishony. "Additivity of inhibitory effects in multidrug combinations." Nature Microbiology 3, no. 12 (2018): 1339–45. http://dx.doi.org/10.1038/s41564-018-0252-1.

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6

Schäfer, Lothar, Ming Cao, Michael Ramek, Brian J. Teppen, Susan Q. Newton, and Khamis Siam. "Conformational geometry functions: additivity and cooperative effects." Journal of Molecular Structure 413-414 (September 1997): 175–204. http://dx.doi.org/10.1016/s0022-2860(97)00023-9.

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7

Du, Di, Chia-Hua Chang, Yumeng Wang, et al. "Response envelope analysis for quantitative evaluation of drug combinations." Bioinformatics 35, no. 19 (2019): 3761–70. http://dx.doi.org/10.1093/bioinformatics/btz091.

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Abstract Motivation The concept of synergy between two agents, over a century old, is important to the fields of biology, chemistry, pharmacology and medicine. A key step in drug combination analysis is the selection of an additivity model to identify combination effects including synergy, additivity and antagonism. Existing methods for identifying and interpreting those combination effects have limitations. Results We present here a computational framework, termed response envelope analysis (REA), that makes use of 3D response surfaces formed by generalized Loewe Additivity and Bliss Independence models of interaction to evaluate drug combination effects. Because the two models imply two extreme limits of drug interaction (mutually exclusive and mutually non-exclusive), a response envelope defined by them provides a quantitatively stringent additivity model for identifying combination effects without knowing the inhibition mechanism. As a demonstration, we apply REA to representative published data from large screens of anticancer and antibiotic combinations. We show that REA is more accurate than existing methods and provides more consistent results in the context of cross-experiment evaluation. Availability and implementation The open-source software package associated with REA is available at: https://github.com/4dsoftware/rea. Supplementary information Supplementary data are available at Bioinformatics online.
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8

Nord-Larsen, Thomas, Henrik Meilby, and Jens Peter Skovsgaard. "Simultaneous estimation of biomass models for 13 tree species: effects of compatible additivity requirements." Canadian Journal of Forest Research 47, no. 6 (2017): 765–76. http://dx.doi.org/10.1139/cjfr-2016-0430.

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A desirable feature of biomass models distinguishing different tree components is compatible additivity of the component functions. Due to forcing of parameter estimates, such additivity is achieved at an expense of precision of the component functions. This study aimed to analyse the loss of precision incurred by forcing of parameters in tree biomass models due to (i) additivity constraints, (ii) combining global and species-specific parameters, and (iii) estimating component functions simultaneously as a system instead of as individual equations. Based on biomass data from 697 trees including 13 different species, we estimated a set of compatibly additive, nonlinear biomass models using simultaneous estimation and compared these with less restricted model systems. In line with other similar studies, the overall model system explained 88%–99% of the variation in individual biomass components. Compared with the unrestricted model, restricting parameters to obtain compatible additivity resulted in a change in RMSE of –0.6% to 5.4%. When restricting parameter estimates using both species-specific and global parameters, RMSE increased by 1.7%–13.1%. Estimating model parameters using simultaneous estimation (nonlinear iterated seemingly unrelated regression, NSUR) increased model bias compared with ordinary least squares estimation (OLS) for most biomass components. Contrary to expectations, NSUR estimation did not lead to a reduction in the standard error of estimates.
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9

DiLabio, Gino A., Derek A. Pratt, and James S. Wright. "Theoretical Calculation of Ionization Potentials for Disubstituted Benzenes: Additivity vs Non-Additivity of Substituent Effects." Journal of Organic Chemistry 65, no. 7 (2000): 2195–203. http://dx.doi.org/10.1021/jo991833e.

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10

Hwang, Jungwun, Ping Li, William R. Carroll, Mark D. Smith, Perry J. Pellechia, and Ken D. Shimizu. "Additivity of Substituent Effects in Aromatic Stacking Interactions." Journal of the American Chemical Society 136, no. 40 (2014): 14060–67. http://dx.doi.org/10.1021/ja504378p.

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