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Journal articles on the topic 'Individual variability'

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Author: Grafiati
Published: 4 June 2021
Last updated: 10 February 2024

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1

Cohen, J. John. "Individual Variability and Immunity." Brain, Behavior, and Immunity 13, no. 2 (June 1999): 76–79. http://dx.doi.org/10.1006/brbi.1999.0561.

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2

Gatzia, Dimitria Electra. "The Individual Variability Problem." Philosophia 38, no. 3 (February 2, 2010): 533–54. http://dx.doi.org/10.1007/s11406-009-9234-0.

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3

Galster, Jason. "Individual Variability in Aided Outcomes." Seminars in Hearing 34, no. 02 (April 22, 2013): 065–66. http://dx.doi.org/10.1055/s-0033-1341343.

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4

Green, David W. "Individual variability and neuroplastic changes." Applied Psycholinguistics 35, no. 5 (August 19, 2014): 910–12. http://dx.doi.org/10.1017/s0142716414000228.

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An important proposal in the insightful Keynote Article by Baum and Titone is that the field of bilingualism research needs to attend more closely to intersubject variability in order to understand the nature of neuroplastic changes in the brains of bilingual speakers as they age. I agree. Understanding such variability and its drivers (such as the contexts of language use that Baum and Titone nicely comment on in the Montreal milieu) will help us develop theoretical accounts of the cognitive control processes recruited in bilingual speakers and establish how adaptive changes to these processes mediate the effects of normal aging; yield protective effects against neurodegenerative disease, such as Alzheimer disease; and modulate language recovery poststroke in bilingual speakers. In this commentary I explore some aspects of this variability and commend, in line with the views expressed in the Keynote Article, the value of relating behavioral indices to whole brain structural magnetic resonance imaging for enriching our understanding of experience-dependent changes. Allied to tractography studies, such research can help us develop a rich picture of the major drivers of neuroplastic changes.
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5

Zilles, Karl, and Katrin Amunts. "Individual variability is not noise." Trends in Cognitive Sciences 17, no. 4 (April 2013): 153–55. http://dx.doi.org/10.1016/j.tics.2013.02.003.

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6

Uchmański, Janusz. "Individual variability and population regulation: an individual-based model." Oikos 90, no. 3 (September 2000): 539–48. http://dx.doi.org/10.1034/j.1600-0706.2000.900312.x.

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7

Uchmański, Janusz. "Individual variability and metapopulation dynamics: An individual-based model." Ecological Modelling 334 (August 2016): 8–18. http://dx.doi.org/10.1016/j.ecolmodel.2016.04.019.

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8

Haar, Shlomi, Opher Donchin, and Ilan Dinstein. "Individual Movement Variability Magnitudes Are Explained by Cortical Neural Variability." Journal of Neuroscience 37, no. 37 (August 18, 2017): 9076–85. http://dx.doi.org/10.1523/jneurosci.1650-17.2017.

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9

Henriksen, Rie, Andrey Höglund, Jesper Fogelholm, Robin Abbey-Lee, Martin Johnsson, Niels J. Dingemanse, and Dominic Wright. "Intra-Individual Behavioural Variability: A Trait under Genetic Control." International Journal of Molecular Sciences 21, no. 21 (October 29, 2020): 8069. http://dx.doi.org/10.3390/ijms21218069.

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When individuals are measured more than once in the same context they do not behave in exactly the same way each time. The degree of predictability differs between individuals, with some individuals showing low levels of variation around their behavioural mean while others show high levels of variation. This intra-individual variability in behaviour has received much less attention than between-individual variability in behaviour, and very little is known about the underlying mechanisms that affect this potentially large but understudied component of behavioural variation. In this study, we combine standardized behavioural tests in a chicken intercross to estimate intra-individual behavioural variability with a large-scale genomics analysis to identify genes affecting intra-individual behavioural variability in an avian population. We used a variety of different anxiety-related behavioural phenotypes for this purpose. Our study shows that intra-individual variability in behaviour has a direct genetic basis that is largely unique compared to the genetic architecture for the standard behavioural measures they are based on (at least in the detected quantitative trait locus). We identify six suggestive candidate genes that may underpin differences in intra-individual behavioural variability, with several of these candidates having previously been linked to behaviour and mental health. These findings demonstrate that intra-individual variability in behaviour appears to be a heritable trait in and of itself on which evolution can act.
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10

Spahr, Kimberly S., Christopher D. Wickens, Benjamin A. Clegg, and C. A. P. Smith. "Individual Differences in Estimating Numeric Variability." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 64, no. 1 (December 2020): 796. http://dx.doi.org/10.1177/1071181320641184.

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11

Hric, Kathleen A., Karen A. Wixson, Margaret Kunji, and Anita B. Bosky. "INDIVIDUAL VARIABILITY AMONG LESS ABLE READERS." Journal of Reading, Writing, and Learning Disabilities International 4, no. 1 (January 1988): 49–67. http://dx.doi.org/10.1080/0748763880040107.

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12

Rahmouni, Sohir, and Laurent Madelain. "Inter-individual variability in saccadic adaptation." Journal of Vision 15, no. 12 (September 1, 2015): 1281. http://dx.doi.org/10.1167/15.12.1281.

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13

De Bacquer, D., G. De Backer, L. Braeckman, and G. Baele. "Intra-individual variability of fibrinogen levels." Journal of Clinical Epidemiology 50, no. 4 (April 1997): 393–99. http://dx.doi.org/10.1016/s0895-4356(97)00044-9.

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14

Angiolillo, Dominick J., Antonio Fernandez-Ortiz, Esther Bernardo, Fernando Alfonso, Carlos Macaya, Theodore A. Bass, and Marco A. Costa. "Variability in Individual Responsiveness to Clopidogrel." Journal of the American College of Cardiology 49, no. 14 (April 2007): 1505–16. http://dx.doi.org/10.1016/j.jacc.2006.11.044.

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15

Perkins, Kenneth A. "Individual variability in responses to nicotine." Behavior Genetics 25, no. 2 (March 1995): 119–32. http://dx.doi.org/10.1007/bf02196922.

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16

Dauman, N., S. Erlandsson, L. Lundlin, and R. Dauman. "Intra-individual variability in tinnitus patients." HNO 63, no. 4 (April 2015): 302–6. http://dx.doi.org/10.1007/s00106-014-2978-2.

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17

Blanck, Paul, Martin Stoffel, Hinrich Bents, Beate Ditzen, and Johannes Mander. "Heart Rate Variability in Individual Psychotherapy." Journal of Nervous and Mental Disease 207, no. 6 (June 2019): 451–58. http://dx.doi.org/10.1097/nmd.0000000000000994.

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18

Rothstein, Jules M. "Individual Differences, Variability, and Student Research." Physical Therapy 78, no. 6 (June 1, 1998): 564–65. http://dx.doi.org/10.1093/ptj/78.6.564.

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19

Ayroles, Julien F., Sean M. Buchanan, Chelsea O’Leary, Kyobi Skutt-Kakaria, Jennifer K. Grenier, Andrew G. Clark, Daniel L. Hartl, and Benjamin L. de Bivort. "Behavioral idiosyncrasy reveals genetic control of phenotypic variability." Proceedings of the National Academy of Sciences 112, no. 21 (May 7, 2015): 6706–11. http://dx.doi.org/10.1073/pnas.1503830112.

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Quantitative genetics has primarily focused on describing genetic effects on trait means and largely ignored the effect of alternative alleles on trait variability, potentially missing an important axis of genetic variation contributing to phenotypic differences among individuals. To study the genetic effects on individual-to-individual phenotypic variability (or intragenotypic variability), we used Drosophila inbred lines and measured the spontaneous locomotor behavior of flies walking individually in Y-shaped mazes, focusing on variability in locomotor handedness, an assay optimized to measure variability. We discovered that some lines had consistently high levels of intragenotypic variability among individuals, whereas lines with low variability behaved as although they tossed a coin at each left/right turn decision. We demonstrate that the degree of variability is itself heritable. Using a genome-wide association study (GWAS) for the degree of intragenotypic variability as the phenotype across lines, we identified several genes expressed in the brain that affect variability in handedness without affecting the mean. One of these genes, Ten-a, implicates a neuropil in the central complex of the fly brain as influencing the magnitude of behavioral variability, a brain region involved in sensory integration and locomotor coordination. We validated these results using genetic deficiencies, null alleles, and inducible RNAi transgenes. Our study reveals the constellation of phenotypes that can arise from a single genotype and shows that different genetic backgrounds differ dramatically in their propensity for phenotypic variabililty. Because traditional mean-focused GWASs ignore the contribution of variability to overall phenotypic variation, current methods may miss important links between genotype and phenotype.
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20

Wang, Lijuan (Peggy), Ellen Hamaker, and C. S. Bergeman. "Investigating inter-individual differences in short-term intra-individual variability." Psychological Methods 17, no. 4 (2012): 567–81. http://dx.doi.org/10.1037/a0029317.

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21

Anderson, Sean C., Eric J. Ward, Andrew O. Shelton, Milo D. Adkison, Anne H. Beaudreau, Richard E. Brenner, Alan C. Haynie, Jennifer C. Shriver, Jordan T. Watson, and Benjamin C. Williams. "Benefits and risks of diversification for individual fishers." Proceedings of the National Academy of Sciences 114, no. 40 (September 18, 2017): 10797–802. http://dx.doi.org/10.1073/pnas.1702506114.

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Individuals relying on natural resource extraction for their livelihood face high income variability driven by a mix of environmental, biological, management, and economic factors. Key to managing these industries is identifying how regulatory actions and individual behavior affect income variability, financial risk, and, by extension, the economic stability and the sustainable use of natural resources. In commercial fisheries, communities and vessels fishing a greater diversity of species have less revenue variability than those fishing fewer species. However, it is unclear whether these benefits extend to the actions of individual fishers and how year-to-year changes in diversification affect revenue and revenue variability. Here, we evaluate two axes by which fishers in Alaska can diversify fishing activities. We show that, despite increasing specialization over the last 30 years, fishing a set of permits with higher species diversity reduces individual revenue variability, and fishing an additional permit is associated with higher revenue and lower variability. However, increasing species diversity within the constraints of existing permits has a fishery-dependent effect on revenue and is usually (87% probability) associated with increased revenue uncertainty the following year. Our results demonstrate that the most effective option for individuals to decrease revenue variability is to participate in additional or more diverse fisheries. However, this option is expensive, often limited by regulations such as catch share programs, and consequently unavailable to many individuals. With increasing climatic variability, it will be particularly important that individuals relying on natural resources for their livelihood have effective strategies to reduce financial risk.
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22

Real, Leslie, and Beverly J. Rathcke. "Patterns of Individual Variability in Floral Resources." Ecology 69, no. 3 (June 1988): 728–35. http://dx.doi.org/10.2307/1941021.

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23

Zorina, Z. A., I. M. Catereniuc, and Yu M. Kiselevskiy. "INDIVIDUAL VARIABILITY OF THE AXILLARY ARTERY ARCHITECTONICS." Journal of the Grodno State Medical University 17, no. 2 (2019): 192–98. http://dx.doi.org/10.25298/2221-8785-2019-17-2-192-198.

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24

Sharda, Megha, Nicholas E. V. Foster, and Krista L. Hyde. "Imaging Brain Development: Benefiting from Individual Variability." Journal of Experimental Neuroscience 9s1 (January 2015): JEN.S32734. http://dx.doi.org/10.4137/jen.s32734.

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Human brain development is a complex process that evolves from early childhood to young adulthood. Major advances in brain imaging are increasingly being used to characterize the developing brain. These advances have further helped to elucidate the dynamic maturational processes that lead to the emergence of complex cognitive abilities in both typical and atypical development. However, conventional approaches involve categorical group comparison models and tend to disregard the role of widespread interindividual variability in brain development. This review highlights how this variability can inform our understanding of developmental processes. The latest studies in the field of brain development are reviewed, with a particular focus on the role of individual variability and the consequent heterogeneity in brain structural and functional development. This review also highlights how such heterogeneity might be utilized to inform our understanding of complex neuropsychiatric disorders and recommends the use of more dimensional approaches to study brain development.
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25

Sugie, Yoriyasu, Tetsuzo Ashizawa, and Atsuto Furuyabu. "Day-to-day Variability of Individual Trips." Journal of the City Planning Institute of Japan 24 (October 25, 1989): 307–12. http://dx.doi.org/10.11361/journalcpij.24.307.

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26

MASUYAMA, Motosaburo. "QUASI-CONSTANCY OF HUMAN BIOCHEMICAL INDIVIDUAL VARIABILITY." Japanese Journal of Biometrics 7 (1986): 23–24. http://dx.doi.org/10.5691/jjb.7.23.

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27

Morris, Aled, Luca Börger, and Elaine Crooks. "Individual Variability in Dispersal and Invasion Speed." Mathematics 7, no. 9 (September 1, 2019): 795. http://dx.doi.org/10.3390/math7090795.

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We model the growth, dispersal and mutation of two phenotypes of a species using reaction–diffusion equations, focusing on the biologically realistic case of small mutation rates. Having verified that the addition of a small linear mutation term to a Lotka–Volterra system limits it to only two steady states in the case of weak competition, an unstable extinction state and a stable coexistence state, we exploit the fact that the spreading speed of the system is known to be linearly determinate to show that the spreading speed is a nonincreasing function of the mutation rate, so that greater mixing between phenotypes leads to slower propagation. We also find the ratio at which the phenotypes occur at the leading edge in the limit of vanishing mutation.
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28

Howes, L. G., and H. Krum. "Intra-individual variability of plasma cholesterol concentrations." Clinical Chemistry 34, no. 9 (September 1, 1988): 1930–31. http://dx.doi.org/10.1093/clinchem/34.9.1930a.

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29

Allen, Jont, and Andrea Trevino. "Individual Variability of Hearing-Impaired Consonant Perception." Seminars in Hearing 34, no. 02 (April 22, 2013): 074–85. http://dx.doi.org/10.1055/s-0033-1341345.

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30

Kahan, B. D., and P. Kelly. "35 INTRA-INDIVIDUAL VARIABILITY OF CYCLOSPORINE EXPOSURE." Therapeutic Drug Monitoring 19, no. 5 (October 1997): 556. http://dx.doi.org/10.1097/00007691-199710000-00046.

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31

Penner, Louis A., Saul Shiffman, Jean A. Paty, and Barbara A. Fritzsche. "Individual differences in intraperson variability in mood." Journal of Personality and Social Psychology 66, no. 4 (1994): 712–21. http://dx.doi.org/10.1037/0022-3514.66.4.712.

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32

R. C. Bautista and T. J. Siebenmorgen. "INDIVIDUAL RICE KERNEL MOISTURE CONTENT VARIABILITY TRENDS." Applied Engineering in Agriculture 21, no. 4 (2005): 637–43. http://dx.doi.org/10.13031/2013.18552.

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33

Todd, A. C., P. J. Parsons, S. Tang, and E. L. Moshier. "Individual variability in human tibia lead concentration." Environmental Health Perspectives 109, no. 11 (November 2001): 1139–43. http://dx.doi.org/10.1289/ehp.011091139.

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34

Buckley, R., and D. B. Goldstein. "Infectious Disease Genomics: Individual Variability, New Opportunities." MD Conference Express 11, no. 12 (November 1, 2011): 4–5. http://dx.doi.org/10.1177/155989771112001.

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35

Rowland, Benjamin, John Vaughan, and Barry Stein. "Individual Variability in Real-Time Multisensory Integration." Journal of Vision 16, no. 12 (September 1, 2016): 154. http://dx.doi.org/10.1167/16.12.154.

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36

Van Dongen, Hans P. A. "Analysis of inter- and intra-individual variability." Journal of Sleep Research 14, no. 2 (June 2005): 201–3. http://dx.doi.org/10.1111/j.1365-2869.2005.00453.x.

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37

Frey, Danielle J., Pietro Badia, and Kenneth P. Wright. "Analysis of inter- and intra-individual variability." Journal of Sleep Research 14, no. 2 (June 2005): 205–6. http://dx.doi.org/10.1111/j.1365-2869.2005.00454.x.

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38

de Ribaupierre, Anik, and Laurence Rieben. "Individual and situational variability in cognitive development." Educational Psychologist 30, no. 1 (January 1995): 5–14. http://dx.doi.org/10.1207/s15326985ep3001_1.

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39

Zhou, Yang, Jean-Claude Thill, Yang Xu, and Zhixiang Fang. "Variability in individual home-work activity patterns." Journal of Transport Geography 90 (January 2021): 102901. http://dx.doi.org/10.1016/j.jtrangeo.2020.102901.

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40

Van Leeuwen, Peter, Dirk Cysarz, Friedrich Edelhäuser, and Dietrich Grönemeyer. "Heart rate variability in the individual fetus." Autonomic Neuroscience 178, no. 1-2 (November 2013): 24–28. http://dx.doi.org/10.1016/j.autneu.2013.01.005.

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41

Simard, Yvan, John K. Horne, Diane Lavoie, and Ian McQuinn. "Capelin TS: Effect of individual fish variability." Journal of the Acoustical Society of America 108, no. 5 (November 2000): 2458. http://dx.doi.org/10.1121/1.4743055.

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42

Huwang, Longcheen, Arthur B. Yeh, and Chien-Wei Wu. "Monitoring Multivariate Process Variability for Individual Observations." Journal of Quality Technology 39, no. 3 (July 2007): 258–78. http://dx.doi.org/10.1080/00224065.2007.11917692.

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43

Markowitz, John S. "Transforming Events: Hepatic Metabolism and Individual Variability." Clinical Therapeutics 35, no. 3 (March 2013): 202–4. http://dx.doi.org/10.1016/j.clinthera.2013.02.016.

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44

Piatkov, Irina, Colin Rochester, Trudi Jones, and Steven Boyages. "Warfarin Toxicity and Individual Variability—Clinical Case." Toxins 2, no. 11 (October 28, 2010): 2584–92. http://dx.doi.org/10.3390/toxins2112584.

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45

Arazi, Ayelet, Nitzan Censor, and Ilan Dinstein. "Neural Variability Quenching Predicts Individual Perceptual Abilities." Journal of Neuroscience 37, no. 1 (November 14, 2016): 97–109. http://dx.doi.org/10.1523/jneurosci.1671-16.2016.

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46

Arazi, Ayelet, Nitzan Censor, and Ilan Dinstein. "Neural Variability Quenching Predicts Individual Perceptual Abilities." Journal of Neuroscience 37, no. 1 (January 4, 2017): 97–109. http://dx.doi.org/10.1523/jneurosci.1671-16.2017.

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47

Saadeh, Rami, and James Klaunig. "Children's Inter-Individual Variability and Asthma Development." International Journal of Health Sciences 9, no. 4 (2015): 447–58. http://dx.doi.org/10.12816/0031236.

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48

Ford, Jessica H., Mark V. Bravington, and Jooke Robbins. "Incorporating individual variability into mark-recapture models." Methods in Ecology and Evolution 3, no. 6 (October 1, 2012): 1047–54. http://dx.doi.org/10.1111/j.2041-210x.2012.00243.x.

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49

Picou, Erin M., Todd A. Ricketts, and Benjamin W. Y. Hornsby. "Visual Cues and Listening Effort: Individual Variability." Journal of Speech, Language, and Hearing Research 54, no. 5 (October 2011): 1416–30. http://dx.doi.org/10.1044/1092-4388(2011/10-0154).

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50

Ma, Qiang, and Anthony Y. H. Lu. "Origins of Individual Variability in P4501A Induction." Chemical Research in Toxicology 16, no. 3 (March 2003): 249–60. http://dx.doi.org/10.1021/tx0200919.

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