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Artykuły w czasopismach na temat „Additivity of the Effects”

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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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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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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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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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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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11

Gregoret, L. M., and R. T. Sauer. "Additivity of mutant effects assessed by binomial mutagenesis." Proceedings of the National Academy of Sciences 90, no. 9 (1993): 4246–50. http://dx.doi.org/10.1073/pnas.90.9.4246.

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12

Kozlov, A. V., B. N. Mordyuk, and A. V. Chernyashevsky. "On the additivity of acoustoplastic and electroplastic effects." Materials Science and Engineering: A 190, no. 1-2 (1995): 75–79. http://dx.doi.org/10.1016/0921-5093(94)09588-n.

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13

Pöch, G., P. Dittrich, R. J. Reiffenstein, W. Lenk, and A. Schuster. "Evaluation of experimental combined toxicity by use of dose–frequency curves: comparison with theoretical additivity as well as independence." Canadian Journal of Physiology and Pharmacology 68, no. 10 (1990): 1338–45. http://dx.doi.org/10.1139/y90-202.

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Dose–frequency curves of toxic effects of a substance A were evaluated in the absence and in the presence of a fixed dose of a second substance B. Data were fitted by the curve–fitting program ALLFIT. Observed combined frequencies of A + B were compared statistically with the expected frequencies of additivity and (or) independence by the χ2-square goodness-of-fit test. The theoretical dose–frequency curves expected for an additive response were obtained by a solely graphical procedure and the theoretical curves for independent effects were calculated from the effects of B and A at certain doses. In rotarod tests with trained mice, the combined deteriorating effect of ethanol and benzodiazepines were significantly over-additive. However, their lethal interaction appeared underadditive in mice. The lethal underadditive interaction of ethanol and phencyclidine (PCP) can be ascribed largely to independent actions of these compounds. Loss of righting reflex was additively enhanced by PCP, whereas PCP overadditively enhanced the effect of ethanol. The insecticidal action of the cholinesterase inhibitors malathion and parathion appeared additive and significantly different from independent interaction. A comparison of results from dose–response curves with isoboles showed good agreement. The method appears as an attractive alternative or as a complementary procedure to the isobolographic analysis. Combination experiments as described can be carried out and evaluated rather simply, with a minimum of expenditure and a maximum of information.Key words: combined toxic effects, statistical evaluation, dose–frequency curves, additivity, independence.
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14

Hyla-Kryspin, Isabella, Stefan Grimme, Heinz H. Büker, Nico M. M. Nibbering, Fabrice Cottet, and Manfred Schlosser. "The Gas Phase Acidity of Oligofluorobenzenes and Oligochlorobenzenes: About the Additivity or Non-Additivity of Substituent Effects." Chemistry - A European Journal 11, no. 4 (2005): 1251–56. http://dx.doi.org/10.1002/chem.200400967.

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15

Lopes-Pacheco, Miquéias, Mafalda Bacalhau, Sofia S. Ramalho, et al. "Rescue of Mutant CFTR Trafficking Defect by the Investigational Compound MCG1516A." Cells 11, no. 1 (2022): 136. http://dx.doi.org/10.3390/cells11010136.

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Although some therapeutic progress has been achieved in developing small molecules that correct F508del-CFTR defects, the mechanism of action (MoA) of these compounds remain poorly elucidated. Here, we investigated the effects and MoA of MCG1516A, a newly developed F508del-CFTR corrector. MCG1516A effects on wild-type (WT) and F508del-CFTR were assessed by immunofluorescence microscopy, and biochemical and functional assays both in cell lines and in intestinal organoids. To shed light on the MoA of MCG1516A, we evaluated its additivity to the FDA-approved corrector VX-661, low temperature, genetic revertants of F508del-CFTR (G550E, R1070W, and 4RK), and the traffic-null variant DD/AA. Finally, we explored the ability of MCG1516A to rescue trafficking and function of other CF-causing mutations. We found that MCG1516A rescues F508del-CFTR with additive effects to VX-661. A similar behavior was observed for WT-CFTR. Under low temperature incubation, F508del-CFTR demonstrated an additivity in processing and function with VX-661, but not with MCG1516A. In contrast, both compounds promoted additional effects to low temperature to WT-CFTR. MCG1516A demonstrated additivity to genetic revertant R1070W, while VX-661 was additive to G550E and 4RK. Nevertheless, none of these compounds rescued DD/AA trafficking. Both MCG1516A and VX-661 rescued CFTR processing of L206W- and R334W-CFTR with greater effects when these compounds were combined. In summary, the absence of additivity of MCG1516A to genetic revertant G550E suggests a putative binding site for this compound on NBD1:NBD2 interface. Therefore, a combination of MCG1516A with compounds able to rescue DD/AA traffic, or mimicking the actions of revertant R1070W (e.g., VX-661), could enhance correction of F508del-CFTR defects.
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16

Prettico, Giuseppe, and Antonio Acin. "Can bipartite classical information resources be activated?" Quantum Information and Computation 13, no. 3&4 (2013): 245–65. http://dx.doi.org/10.26421/qic13.3-4-6.

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Non-additivity is one of the distinctive traits of Quantum Information Theory: the combined use of quantum objects may be more advantageous than the sum of their individual uses. Non-additivity effects have been proven, for example, for quantum channel capacities, entanglement distillation or state estimation. In this work, we consider whether non-additivity effects can be found in Classical Information Theory. We work in the secret-key agreement scenario in which two honest parties, having access to correlated classical data that are also correlated to an eavesdropper, aim at distilling a secret key. Exploiting the analogies between the entanglement and the secret-key agreement scenario, we provide some evidence that the secret-key rate may be a non-additive quantity. In particular, we show that correlations with conjectured bound information become secret-key distillable when combined. Our results constitute a new instance of the subtle relation between the entanglement and secret-key agreement scenario.
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17

Bundschuh, Mirco, Jochen P. Zubrod, Lara L. Petschick, and Ralf Schulz. "Multiple Stressors in Aquatic Ecosystems: Sublethal Effects of Temperature, Dissolved Organic Matter, Light and a Neonicotinoid Insecticide on Gammarids." Bulletin of Environmental Contamination and Toxicology 105, no. 3 (2020): 345–50. http://dx.doi.org/10.1007/s00128-020-02926-6.

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Abstract Whether and to which extent the effects of chemicals in the environment interact with other factors remains a scientific challenge. Here we assess the combined effects of temperature (16 vs. 20°C), light conditions (darkness vs. 400 lx), dissolved organic matter (DOM; 0 vs. 6 mg/L) and the model insecticide thiacloprid (0 vs. 3 µg/L) in a full-factorial experiment on molting and leaf consumption of Gammarus fossarum. Thiacloprid was the only factor significantly affecting gammarids’ molting. While DOM had low effects on leaf consumption, temperature, light and thiacloprid significantly affected this response variable. The various interactions among these factors were not significant suggesting additivity. Only the interaction of the factors temperature and thiacloprid suggested a tendency for antagonism. As most stressors interacted additively, their joint effects may be predictable with available models. However, synergistic interactions are difficult to capture while being central for securing ecosystem integrity.
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18

Okajima, Katsunori. "Spatial additivity of surround effects in whiteness-blackness perception." JOURNAL OF THE ILLUMINATING ENGINEERING INSTITUTE OF JAPAN 77, Appendix (1993): 109–10. http://dx.doi.org/10.2150/jieij1980.77.appendix_109.

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Okajima, Katsunori. "Spatial Additivity of Surround Effects in Achromatic Pattern Perception." JOURNAL OF THE ILLUMINATING ENGINEERING INSTITUTE OF JAPAN 79, no. 2 (1995): 101–7. http://dx.doi.org/10.2150/jieij1980.79.2_101.

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OKAJIMA, Katsunori, Graham H. FIELDER, and Alan R. ROBERTSON. "Spatial Additivity of Surround Effects in Achromatic Pattern Perception." Journal of Light & Visual Environment 21, no. 2 (1997): 23–27. http://dx.doi.org/10.2150/jlve.21.2_23.

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Móréh, Ágnes, Anett Endrédi, and Ferenc Jordán. "Additivity of pairwise perturbations in food webs: Topological effects." Journal of Theoretical Biology 448 (July 2018): 112–21. http://dx.doi.org/10.1016/j.jtbi.2018.04.009.

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SUZUKI, Ikuo. "Effects of additivity and directed forgetting on false memory." Proceedings of the Annual Convention of the Japanese Psychological Association 77 (September 19, 2013): 2EV—073–2EV—073. http://dx.doi.org/10.4992/pacjpa.77.0_2ev-073.

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Richardson, David E., Lisa Lang, John R. Eyler, et al. "Additivity of Fluorine Substituent Effects in Ruthenocene Ionization Energetics." Organometallics 16, no. 1 (1997): 149–50. http://dx.doi.org/10.1021/om960758k.

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Abboud, José-Luis M., Ilmar A. Koppel, and Ivar Koppel. "Additivity of substituent effects on the acidity of alcohols." Journal of Physical Organic Chemistry 26, no. 6 (2013): 467–72. http://dx.doi.org/10.1002/poc.3110.

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Carroll, Thomas X., T. Darrah Thomas, Leif J. Sæthre, and Knut J. Børve. "Additivity of Substituent Effects. Core-Ionization Energies and Substituent Effects in Fluoromethylbenzenes." Journal of Physical Chemistry A 113, no. 15 (2009): 3481–90. http://dx.doi.org/10.1021/jp810612x.

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Baeder, Desiree Y., Guozhi Yu, Nathanaël Hozé, Jens Rolff, and Roland R. Regoes. "Antimicrobial combinations: Bliss independence and Loewe additivity derived from mechanistic multi-hit models." Philosophical Transactions of the Royal Society B: Biological Sciences 371, no. 1695 (2016): 20150294. http://dx.doi.org/10.1098/rstb.2015.0294.

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Antimicrobial peptides (AMPs) and antibiotics reduce the net growth rate of bacterial populations they target. It is relevant to understand if effects of multiple antimicrobials are synergistic or antagonistic, in particular for AMP responses, because naturally occurring responses involve multiple AMPs. There are several competing proposals describing how multiple types of antimicrobials add up when applied in combination, such as Loewe additivity or Bliss independence. These additivity terms are defined ad hoc from abstract principles explaining the supposed interaction between the antimicrobials. Here, we link these ad hoc combination terms to a mathematical model that represents the dynamics of antimicrobial molecules hitting targets on bacterial cells. In this multi-hit model, bacteria are killed when a certain number of targets are hit by antimicrobials. Using this bottom-up approach reveals that Bliss independence should be the model of choice if no interaction between antimicrobial molecules is expected. Loewe additivity, on the other hand, describes scenarios in which antimicrobials affect the same components of the cell, i.e. are not acting independently. While our approach idealizes the dynamics of antimicrobials, it provides a conceptual underpinning of the additivity terms. The choice of the additivity term is essential to determine synergy or antagonism of antimicrobials. This article is part of the themed issue ‘Evolutionary ecology of arthropod antimicrobial peptides’.
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Lambert, Ronald J. W., and Douglas A. Dawson. "New models for the time dependent toxicity of individual and combined toxicants." Toxicology Research 8, no. 4 (2019): 509–21. http://dx.doi.org/10.1039/c9tx00005d.

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POMPEI, S., E. CAGLIOTI, V. LORETO, and F. TRIA. "DISTANCE-BASED PHYLOGENETIC ALGORITHMS: NEW INSIGHTS AND APPLICATIONS." Mathematical Models and Methods in Applied Sciences 20, supp01 (2010): 1511–32. http://dx.doi.org/10.1142/s0218202510004672.

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Phylogenetic methods have recently been rediscovered in several interesting areas among which immunodynamics, epidemiology and many branches of evolutionary dynamics. In many interesting cases the reconstruction of a correct phylogeny is blurred by high mutation rates and/or horizontal transfer events. As a consequence, a divergence arises between the true evolutionary distances and the distances between pairs of taxa as inferred from the available data, making the phylogenetic reconstruction a challenging problem. Mathematically this divergence translates in the non-additivity of the actual distances between taxa and the quest for new algorithms able to efficiently cope with these effects is wide open. In distance-based reconstruction methods, two properties of additive distances were extensively exploited as antagonist criteria to drive phylogeny reconstruction: on the one hand a local property of quartets, i.e. sets of four taxa in a tree, the four-point condition; on the other hand, a recently proposed formula that allows to write the tree length as a function of the distances between taxa, the Pauplin's formula. A deeper comprehension of the effects of the non-additivity on the inspiring principles of the existing reconstruction algorithms is thus of paramount importance. In this paper we present a comparative analysis of the performances of the most important distance-based phylogenetic algorithms. We focus in particular on the dependence of their performances on two main sources of non-additivity: back-mutation processes and horizontal transfer processes. The comparison is carried out in the framework of a set of generative algorithms for phylogenies that incorporate non-additivity in a tunable way.
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Murphy, Sheila T., Jennifer L. Monahan, and R. B. Zajonc. "Additivity of nonconscious affect: Combined effects of priming and exposure." Journal of Personality and Social Psychology 69, no. 4 (1995): 589–602. http://dx.doi.org/10.1037/0022-3514.69.4.589.

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Taylor, T. L., S. E. Hall, S. E. Boehnke, and D. P. Phillips. "Additivity of perceptual channel-crossing effects in auditory gap detection." Journal of the Acoustical Society of America 105, no. 1 (1999): 563–66. http://dx.doi.org/10.1121/1.424593.

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Hwang, Jungwun, Ping Li, William R. Carroll, Mark D. Smith, Perry J. Pellechia, and Ken D. Shimizu. "Correction to “Additivity of Substituent Effects in Aromatic Stacking Interactions”." Journal of the American Chemical Society 141, no. 9 (2019): 4154. http://dx.doi.org/10.1021/jacs.9b01443.

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Ewing, David, and Daniel S. Guilfoil. "Additivity in the Sensitizing Effects of Nitrous Oxide and Oxygen." Radiation Research 120, no. 2 (1989): 294. http://dx.doi.org/10.2307/3577715.

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Laback, Bernhard, Thibaud Necciari, Peter Balazs, Sophie Savel, and Sølvi Ystad. "Simultaneous masking additivity for short Gaussian-shaped tones: Spectral effects." Journal of the Acoustical Society of America 134, no. 2 (2013): 1160–71. http://dx.doi.org/10.1121/1.4812773.

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Koszinowski, Konrad, Detlef Schröder, and Helmut Schwarz. "Additivity Effects in the Reactivities of Bimetallic Cluster Ions PtmAun+." ChemPhysChem 4, no. 11 (2003): 1233–37. http://dx.doi.org/10.1002/cphc.200300840.

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Kifer, Domagoj, Daniela Jakšić, and Maja Šegvić Klarić. "Assessing the Effect of Mycotoxin Combinations: Which Mathematical Model Is (the Most) Appropriate?" Toxins 12, no. 3 (2020): 153. http://dx.doi.org/10.3390/toxins12030153.

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In the past decades, many studies have examined the nature of the interaction between mycotoxins in biological models classifying interaction effects as antagonisms, additive effects, or synergisms based on a comparison of the observed effect with the expected effect of combination. Among several described mathematical models, the arithmetic definition of additivity and factorial analysis of variance were the most commonly used in mycotoxicology. These models are incorrectly based on the assumption that mycotoxin dose-effect curves are linear. More appropriate mathematical models for assessing mycotoxin interactions include Bliss independence, Loewe’s additivity law, combination index, and isobologram analysis, Chou-Talalays median-effect approach, response surface, code for the identification of synergism numerically efficient (CISNE) and MixLow method. However, it seems that neither model is ideal. This review discusses the advantages and disadvantages of these mathematical models.
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Españo, Erica, and Jeong-Ki Kim. "Effects of Statin Combinations on Zika Virus Infection in Vero Cells." Pharmaceutics 15, no. 1 (2022): 50. http://dx.doi.org/10.3390/pharmaceutics15010050.

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The Zika virus (ZIKV) remains a global health concern. Thus far, no antiviral or vaccine has been approved to prevent or treat ZIKV infection. In a previous study, we found that lipophilic statins can inhibit ZIKV production in Vero cells. These statins appear to have different potencies against ZIKV infection. Here, we determined whether combinations of statins would have synergistic effects to maximize the efficacy of the statins and to reduce potential side effects. Specifically, we used a modified fixed-ratio assay for the combinations of atorvastatin (ATO) or fluvastatin (FLU) with mevastatin (MEV) or simvastatin (SIM). All combinations with MEV tended towards synergy, especially with higher fractions of MEV in the combinations. The ATO + SIM combination tended towards additivity. The FLU + SIM combination also tended towards additivity except for one combination which had the highest fraction of FLU over SIM among the tested combinations. Overall, certain combinations of ATO or FLU with SIM or MEV may be synergistic. More exhaustive combinatorial assays in vitro and in vivo could help define whether combining lipophilic statins would be beneficial and safe for treating ZIKV infections.
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Wolfe, Darren, Paul Hester, and Ralph L. Keil. "Volatile Anesthetic Additivity and Specificity in Saccharomyces cerevisiae." Anesthesiology 89, no. 1 (1998): 174–81. http://dx.doi.org/10.1097/00000542-199807000-00024.

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Background In animals, combinations of volatile anesthetics are additive for inducing anesthesia. Furthermore, although there is a correlation between lipophilicity and anesthetic potency, not all volatile lipophilic compounds are anesthetic. Previously the authors demonstrated the effects of volatile anesthetics on the eukaryote Saccharomyces cerevisiae (yeast). To further relate anesthetic action in this organism to mammals, anesthetic additivity and effects of volatile, lipophilic nonanesthetics were studied. In addition, yeast pleiotropic drug-resistance (Pdr) mutants, which confer resistance to various lipophilic compounds, were tested to determine if they are involved in anesthetic response. Methods Yeast strains were grown to saturation in liquid culture, diluted, plated on various solid media, incubated, and scored for growth. Results Combinations of volatile anesthetics inhibit growth of wild-type (Zzz+) but not anesthetic-resistant (Zzz-) strains when additive concentrations equal 1 minimum inhibitory concentration (MIC). Two volatile, lipophilic compounds that are nonanesthetic in mammals do not inhibit yeast growth. Zzz- mutants remain sensitive to drugs used to identify yeast PDR genes. Conversely Pdr strains, which are resistant to various lipophilic compounds, remain sensitive to volatile anesthetics. Conclusions Yeast growth is inhibited in an additive manner by volatile anesthetics. Volatile, lipophilic compounds devoid of anesthetic activity in mammals do not inhibit yeast growth. Zzz- mutants appear to be specifically resistant to volatile anesthetics and distinct from known Pdr mutants. These results suggest that volatile anesthetics behave in a parallel manner in yeast and mammals, making yeast a useful model to investigate the molecular effects of these compounds in living cells.
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38

Della, EW, PE Pigou, DK Taylor, LB Krivdin, and RH Contreras. "Empirical Additivity of Coupling Pathways in Bicycloalkanes." Australian Journal of Chemistry 46, no. 1 (1993): 63. http://dx.doi.org/10.1071/ch9930063.

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Multipath couplings involving 1H-1H, and 13C-13C nuclei in a variety of 1-substituted bicycloalkanes are treated according to additivity principles based on empirical coupling increments derived from model hydrocarbons. The coupling constants are essentially additive in practically all series except for derivatives of bicyclo [1.1.1]pentane; non-additivities of several couplings in the latter are ascribed to the effects of contributions arising from through-space bridgehead-bridgehead interactions, as well as from mutual electronic perturbations between the different pathways. The results are compared with those derived previously from theoretical calculations, which also have some difficulty in reproducing the experimental data in the case of the bicyclo [1.1.1] pentyl system.
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39

Rosales, M., M. Gill, C. D. Wood, and A. W. Speedy. "Associative effects in vitro of mixtures of tropical fodder trees." BSAP Occasional Publication 22 (1998): 175–77. http://dx.doi.org/10.1017/s0263967x0003250x.

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Previous work in animal nutrition has focused on single foods and assumed additivity during in vitro fermentation. In the tropics, farmers are likely to offer mixtures of foods, including tree fodders, which may not be simply additive in nutritional terms. There is little information about the nutritional interactions between tropical foods. The objective of this research was to test the existence of associative effects of mixtures of tropical fodder tree leaves.
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Zhang, Xianmin, Zuohua Liu, Shixian Yang, Zuming Peng, Yali He та Liran Wei. "The Right Equivalent Integral Equation of Impulsive Caputo Fractional-Order System of Order ϵ∈(1,2)". Fractal and Fractional 7, № 1 (2022): 37. http://dx.doi.org/10.3390/fractalfract7010037.

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For the impulsive fractional-order system (IFrOS) of order ϵ∈(1,2), there have appeared some conflicting equivalent integral equations in existing studies. However, we find two fractional-order properties of piecewise function and use them to verify that these given equivalent integral equations have some defects to not be the equivalent integral equation of the IFrOS. For the IFrOS, its limit property shows the linear additivity of the impulsive effects. For the IFrOS, we use the limit analysis and the linear additivity of the impulsive effects to find its correct equivalent integral equation, which is a combination of some piecewise functions with two arbitrary constants; that is, the solution of the IFrOS is a general solution. Finally, a numerical example is given to show the equivalent integral equation and the non-uniqueness of the solution of the IFrOS.
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Krása, K., O. Vajnerová, J. Ďurišová, et al. "Simvastatin and dehydroepiandrosterone sulfate effects against hypoxic pulmonary hypertension are not additive." Physiological Research 71, no. 6 (2022): 801–10. http://dx.doi.org/10.33549/physiolres.934913.

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Pulmonary hypertension is a group of disorders characterized by elevated mean pulmonary artery pressure (mPAP) and pulmonary vascular resistance. To test our hypothesis that combining two drugs useful in experimental pulmonary hypertension, statins and dehydroepiandrosterone sulfate (DHEA S), is more effective than either agent alone, we induced pulmonary hypertension in adult male rats by exposing them to hypoxia (10%O2) for 3 weeks. We treated them with simvastatin (60 mg/l) and DHEA S (100 mg/l) in drinking water, either alone or in combination. Both simvastatin and DHEA S reduced mPAP (froma mean±s.d. of 34.4±4.4 to 27.6±5.9 and 26.7±4.8 mmHg, respectively), yet their combination was not more effective (26.7±7.9 mmHg). Differences in the degree of oxidative stress (indicated by malondialdehydeplasma concentration),the rate of superoxide production (electron paramagnetic resonance), or blood nitric oxide levels (chemiluminescence) did not explain the lack of additivity of the effect of DHEA S and simvastatin on pulmonary hypertension. We propose that the main mechanism of both drugs on pulmonary hypertension could be their inhibitory effect on 3-hydroxy-3-methyl-glutaryl-coenzyme A reductase, which could explain their lack of additivity.
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Hughes, Kimberly A., Julien F. Ayroles, Melissa M. Reedy, et al. "Segregating Variation in the Transcriptome: Cis Regulation and Additivity of Effects." Genetics 173, no. 3 (2006): 1347–55. http://dx.doi.org/10.1534/genetics.105.051474.

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Akinola, James, Charles T. Campbell, and Nirala Singh. "Effects of Solvents on Adsorption Energies: A General Bond-Additivity Model." Journal of Physical Chemistry C 125, no. 44 (2021): 24371–80. http://dx.doi.org/10.1021/acs.jpcc.1c06781.

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Moon, Jongmin, and Oh-Sang Kwon. "Additivity of attractive and repulsive sequential effects in motion direction estimation." Journal of Vision 19, no. 10 (2019): 295a. http://dx.doi.org/10.1167/19.10.295a.

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Riege, Anine H., and Karl Halvor Teigen. "Additivity neglect in probability estimates: Effects of numeracy and response format." Organizational Behavior and Human Decision Processes 121, no. 1 (2013): 41–52. http://dx.doi.org/10.1016/j.obhdp.2012.11.004.

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Platten, Florian, Jan Hansen, Johanna Milius, Dana Wagner, and Stefan U. Egelhaaf. "Additivity of the Specific Effects of Additives on Protein Phase Behavior." Journal of Physical Chemistry B 119, no. 48 (2015): 14986–93. http://dx.doi.org/10.1021/acs.jpcb.5b08078.

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Kaufman, Paul L. "Non-additivity of maximal pilocarpine and cytochalasin effects on outflow facility." Experimental Eye Research 44, no. 2 (1987): 283–91. http://dx.doi.org/10.1016/s0014-4835(87)80012-x.

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Hsu (Xu Zuyao), T. Y. "Additivity hypothesis and effects of stress on phase transformations in steel." Current Opinion in Solid State and Materials Science 9, no. 6 (2005): 256–68. http://dx.doi.org/10.1016/j.cossms.2006.02.011.

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Zhuo, Hongying, Qingzhong Li, Wenzuo Li, and Jianbo Cheng. "Non-additivity between substitution and cooperative effects in enhancing hydrogen bonds." Journal of Chemical Physics 141, no. 24 (2014): 244305. http://dx.doi.org/10.1063/1.4904294.

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Skinner, M. M., and T. C. Terwilliger. "Potential use of additivity of mutational effects in simplifying protein engineering." Proceedings of the National Academy of Sciences 93, no. 20 (1996): 10753–57. http://dx.doi.org/10.1073/pnas.93.20.10753.

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