Relevant bibliographies by topics / Dose-Response modeling

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Dose-Response modeling'

Author: Grafiati
Published: 15 February 2025

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Journal articles on the topic "Dose-Response modeling"

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May, Susanne, and Carol Bigelow. "Modeling Nonlinear Dose-Response Relationships in Epidemiologic Studies: Statistical Approaches and Practical Challenges." Dose-Response 3, no. 4 (2005): dose—response.0. http://dx.doi.org/10.2203/dose-response.003.04.004.

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Non-linear dose response relationships pose statistical challenges for their discovery. Even when an initial linear approximation is followed by other approaches, the results may be misleading and, possibly, preclude altogether the discovery of the nonlinear relationship under investigation. We review a variety of straightforward statistical approaches for detecting nonlinear relationships and discuss several factors that hinder their detection. Our specific context is that of epidemiologic studies of exposure-outcome associations and we focus on threshold and J-effect dose response relationsh
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Hunt, Daniel L., Shesh N. Rai, and Chin-Shang Li. "Summary of Dose-Response Modeling for Developmental Toxicity Studies." Dose-Response 6, no. 4 (2008): dose—response.0. http://dx.doi.org/10.2203/dose-response.08-007.hunt.

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Developmental toxicity studies are an important area in the field of toxicology. Endpoints measured on fetuses include weight and indicators of death and malformation. Binary indicator measures are typically summed over the litter and a discrete distribution is assumed to model the number of adversely affected fetuses. Additionally, there is noticeable variation in the litter responses within dose groups that should be taken into account when modeling. Finally, the dose-response pattern in these studies exhibits a threshold effect. The threshold dose-response model is the default model for non
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COLEMAN, MARGARET, and HARRY MARKS. "Topics in Dose-Response Modeling." Journal of Food Protection 61, no. 11 (1998): 1550–59. http://dx.doi.org/10.4315/0362-028x-61.11.1550.

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Great uncertainty exists in conducting dose-response assessment for microbial pathogens. The data to support quantitative modeling of dose-response relationships are meager. Our philosophy in developing methodology to conduct microbial risk assessments has been to rely on data analysis and formal inferencing from the available data in constructing dose-response and exposure models. The probability of illness is a complex function of factors associated with the disease triangle: the host, the pathogen, and the environment including the food vehicle and indigenous microbial competitors. The epid
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Zhao, Yuchao, and Paolo F. Ricci. "Modeling dose-Response at Low dose: A Systems Biology Approach for Ionization Radiation." Dose-Response 8, no. 4 (2010): dose—response.0. http://dx.doi.org/10.2203/dose-response.09-054.zhao.

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Slob, W. "Dose-Response Modeling of Continuous Endpoints." Toxicological Sciences 66, no. 2 (2002): 298–312. http://dx.doi.org/10.1093/toxsci/66.2.298.

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Feinendegen, Ludwig E., Myron Pollycove, and Ronald D. Neumann. "Low-Dose Cancer Risk Modeling Must Recognize Up-Regulation of Protection." Dose-Response 8, no. 2 (2009): dose—response.0. http://dx.doi.org/10.2203/dose-response.09-035.feinendegen.

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Cox, Louis Anthony (Tony). "A Model of Cytotoxic Dose-Response Nonlinearities Arising from Adaptive Cell Inventory Management in Tissues." Dose-Response 3, no. 4 (2005): dose—response.0. http://dx.doi.org/10.2203/dose-response.003.04.005.

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Why do low-level exposures to environmental toxins often elicit over-compensating responses that reduce risk to an organism? Conversely, if these responses improve health, why wait for an environmental challenge to trigger them? This paper presents a mathematical modeling framework that addresses both questions using the principle that evolution favors tissues that hedge their bets against uncertain environmental challenges. We consider a tissue composed of differentiated cells performing essential functions (e.g., lung tissue, bone marrow, etc.). The tissue seeks to maintain adequate supplies
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Li, Zhenhong, Bin Sun, Rebecca A. Clewell, Yeyejide Adeleye, Melvin E. Andersen, and Qiang Zhang. "Dose-Response Modeling of Etoposide-Induced DNA Damage Response." Toxicological Sciences 137, no. 2 (2013): 371–84. http://dx.doi.org/10.1093/toxsci/kft259.

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Cox, Louis Anthony (Tony). "Universality of J-Shaped and U-Shaped Dose-Response Relations as Emergent Properties of Stochastic Transition Systems." Dose-Response 3, no. 3 (2005): dose—response.0. http://dx.doi.org/10.2203/dose-response.0003.03.006.

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Dose-response data for many chemical carcinogens exhibit multiple apparent concentration thresholds. A relatively small increase in exposure concentration near such a threshold disproportionately increases incidence of a specific tumor type. Yet, many common mathematical models of carcinogenesis do not predict such threshold-like behavior when model parameters (e.g., describing cell transition rates) increase smoothly with dose, as often seems biologically plausible. For example, commonly used forms of both the traditional Armitage-Doll and multistage (MS) models of carcinogenesis and the Mool
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Herbert, Donald E., and Colin G. Orton. "Dose/time/response modeling in radiation therapy." International Journal of Radiation Oncology*Biology*Physics 19 (January 1990): 114–15. http://dx.doi.org/10.1016/0360-3016(90)90636-x.

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Dissertations / Theses on the topic "Dose-Response modeling"

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Leininger, Thomas J. "An Adaptive Bayesian Approach to Dose-Response Modeling." Diss., CLICK HERE for online access, 2009. http://contentdm.lib.byu.edu/ETD/image/etd3325.pdf.

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Ås, Joel. "Active dose selection and dose-response modeling for quantitative high-throughput screening (qHTS)." Thesis, Uppsala universitet, Cancerfarmakologi och beräkningsmedicin, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-300682.

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This master thesis studies the potential benefit of iterative selection of the concentrations evaluated when building mathematical dose-response curves (and response surfaces when there are two drugs) using experimental measurements. The reference alternative is to use a standard two-fold dilution series or ten-fold dilution series measured in replicates. The standard 4-parameter Hill dose-response model is used as a reference and for simulations. Models to screen for synergy between two different substances are also developed in this thesis.
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Adamus-Górka, Magdalena. "Improved dose response modeling for normal tissue damage and therapy optimization." Doctoral thesis, Stockholm University, Medical Radiation Physics (together with KI), 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-7433.

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<p>The present thesis is focused on the development and application of dose response models for radiation therapy. Radiobiological models of tissue response to radiation are an integral part of the radiotherapeutic process and a powerful tool to optimize tumor control and minimize damage to healthy tissues for use in clinical trials. Ideally, the models could work as a historical control arm of a clinical trial eliminating the need to randomize patents to suboptimal therapies. In the thesis overview part, some of the basic properties of the dose response relation are reviewed and the most comm
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Adamus-Górka, Magdalena. "Improved dose response modeling for normal tissue damage and therapy optimization /." Stockholm ; Solna : Medical Radiation Physics, Stockholm University and Karolinska institutet, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-7433.

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Eklund, Karin. "Modeling Silicon Diode Dose Response in Radiotherapy Fields using Fluence Pencil Kernels." Doctoral thesis, Uppsala universitet, Avdelningen för sjukhusfysik, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-120581.

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In radiotherapy, cancer is treated with ionizing radiation, most commonly bremsstrahlung photons from electrons of several MeV. Secondary electrons produced in photon-interactions results in dose deposition. The treatment response is low for low doses, raises sharply for normal treatment doses and saturates at higher doses. This response pattern applies to both eradication of tumors and to complications in healthy tissues. Well controlled treatments require accurate dosimetry since the uncertainty in delivered dose will be magnified 1 to 5 times in treatment response variations. Techniques tha
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Sand, Salomon. "Dose-response modeling : evaluation, application, and development of procedures for benchmark dose analysis in health risk assessment of chemical substances /." Stockholm, 2005. http://diss.kib.ki.se/2005/91-7140-420-1/.

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Wessel, Michael Raymond. "Dose time response modeling of neurobehavioral screening data application of physiologically relevant parameters to describe dose dependent time of peak effects /." [Tampa, Fla.] : University of South Florida, 2005. http://purl.fcla.edu/fcla/etd/SFE0001273.

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Toyinbo, Peter A. "On Effective and Efficient Experimental Designs for Neurobehavioral Screening Tests: The Choice of a Testing Time for Estimating the Time of Peak Effects." [Tampa, Fla.] : University of South Florida, 2004. http://purl.fcla.edu/fcla/etd/SFE0000393.

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Wessel, Michael Raymond. "Dose Time Response Modeling of Neurobehavioral Screening Data: Application of Physiologically Relevant Parameters to Allow for Dose Dependent Time of Peak Effects." Scholar Commons, 2005. https://scholarcommons.usf.edu/etd/911.

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In collaboration with the United States Environmental Protection Agency (USEPA), the University of South Florida Health Risk Methodology Group has developed dose-time-response models to characterize neurobehavioral response to chemical exposure. The application of dose-time-response models to neurobehavioral screening tests on laboratory animals allows for benchmark dose estimation to establish exposure limits in environmental risk assessment. This thesis has advanced dose-time-response modeling by generalizing a published toxico diffusion model to allow for dose dependent time of peak effects
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Davidson, Sarah E. "Alternative Approach to Dose-Response Modeling of Toxicogenomic Data with an Application in Risk Assessment of Engineered Nanomaterials." University of Cincinnati / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1627666554729205.

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Books on the topic "Dose-Response modeling"

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Cooke, Roger M., ed. Uncertainty Modeling in Dose Response. John Wiley & Sons, Inc., 2009. http://dx.doi.org/10.1002/9780470481400.

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Cooke, Roger M. Uncertainty modeling in dose response: Bench testing environmental toxicity. Wiley, 2009.

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L, Sielken Robert, ed. Quantitative cancer modeling and risk assessment. Prentice Hall, 1993.

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Lin, Dan, Ziv Shkedy, Daniel Yekutieli, Dhammika Amaratunga, and Luc Bijnens, eds. Modeling Dose-Response Microarray Data in Early Drug Development Experiments Using R. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-24007-2.

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Hanford Life Sciences Symposium (26th 1987 Richland, Wash.). Modeling for scaling to man: Biology, dosimetry, and response, [proceedings of the] 26th Hanford Life Sciences Symposium. Edited by Mahaffey Judith A. Pergamon Press, 1989.

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Cooke, Roger M. Uncertainty Modeling in Dose Response. Wiley & Sons, Incorporated, John, 2009.

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Cooke, Roger M. Uncertainty Modeling in Dose Response: Bench Testing Environmental Toxicity. Wiley & Sons, Incorporated, John, 2009.

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Cooke, Roger M. Uncertainty Modeling in Dose Response: Bench Testing Environmental Toxicity. Wiley & Sons, Incorporated, John, 2008.

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Holland, Charles D. Quantitive cancer modeling and risk assessment. PTR Prentice Hall, 1993.

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Exposure-response modeling: Methods and practical implementation. CRC Press, Taylor & Francis, 2016.

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Book chapters on the topic "Dose-Response modeling"

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Dinse, Gregg E., and David M. Umbach. "Dose-Response Modeling." In Chemical Mixtures and Combined Chemical and Nonchemical Stressors. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-56234-6_8.

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Edler, Lutz, Annette Kopp-schneider, and Harald Heinzl. "Dose-Response Modeling." In Recent Advances in Quantitative Methods in Cancer and Human Health Risk Assessment. John Wiley & Sons, Ltd, 2006. http://dx.doi.org/10.1002/0470857706.ch13.

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Crawford-Brown, Douglas J. "Modeling Dose-Response Relationships." In Theoretical and Mathematical Foundations of Human Health Risk Analysis. Springer US, 1997. http://dx.doi.org/10.1007/978-1-4615-6143-9_6.

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Straetemans, Roel. "Nonlinear Modeling of Dose-Response Data." In Modeling Dose-Response Microarray Data in Early Drug Development Experiments Using R. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-24007-2_4.

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Haas, Chuck. "Dose-Response Modeling for Microbial Risk." In Food Safety Handbook. John Wiley & Sons, Inc., 2005. http://dx.doi.org/10.1002/047172159x.ch4.

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Bijnens, Luc, Hinrich W. H. Göhlmann, Dan Lin, et al. "Functional Genomic Dose-Response Experiments." In Modeling Dose-Response Microarray Data in Early Drug Development Experiments Using R. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-24007-2_5.

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Charnley, Gail. "Cancer Dose-Response Modeling and Methylene Chloride." In Oncogene and Transgenics Correlates of Cancer Risk Assessments. Springer US, 1992. http://dx.doi.org/10.1007/978-1-4615-3056-5_17.

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DeVito, Michael J., Amy Kim, Nigel J. Walker, Fred Parham, and Christopher Portier. "Dose-Response Modeling for 2,3,7,8-Tetrachlorodibenzo-p-Dioxin." In Dioxins and Health. John Wiley & Sons, Inc., 2005. http://dx.doi.org/10.1002/0471722014.ch7.

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Thakur, Ajit K. "Modeling and Risk Assessment of Carcinogenic Dose-Response." In Pharmacokinetics. Springer US, 1988. http://dx.doi.org/10.1007/978-1-4684-5463-5_11.

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Sielken, R. L. "Quantitative Cancer Dose-Response Modeling for All Ages." In Risk Analysis. Springer US, 1991. http://dx.doi.org/10.1007/978-1-4899-0730-1_32.

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Conference papers on the topic "Dose-Response modeling"

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Ewing, Lucas, Sebastian Ahn, Oliver Jonas, and Nobuhiko Hata. "Pixelwise tissue segmentation for precise local in-vivo dose response assessment in patient-derived xenografts." In Image-Guided Procedures, Robotic Interventions, and Modeling, edited by Baowei Fei and Cristian A. Linte. SPIE, 2019. http://dx.doi.org/10.1117/12.2513080.

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Witulski, A. F., M. B. Smith, N. Mahadevan, et al. "Bayesian Modeling of COTS Power MOSFET Ionizing Dose Impact on Circuit Response." In 2017 17th European Conference on Radiation and Its Effects on Components and Systems (RADECS). IEEE, 2017. http://dx.doi.org/10.1109/radecs.2017.8696104.

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Bighamian, Ramin, Sadaf Soleymani, Andrew T. Reisner, Istvan Seri, and Jin-Oh Hahn. "Modeling and System Identification of Hemodynamic Responses to Vasopressor-Inotropes." In ASME 2013 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/dscc2013-3726.

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In an effort to establish an initial step towards the ultimate goal of developing an analytic tool to optimize the vasopressor-inotrope therapy through individualized dose-response relationships, we propose a phenomenological model intended to reproduce the hemodynamic response to vasopressor-inotropes. The proposed model consists of a cardiovascular model relating blood pressure to cardinal cardiovascular parameters (stroke volume and total peripheral resistance) and the phenomenological relationships between the cardinal cardiovascular parameters and the vasopressor-inotrope dose, in such a
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Poorbahrami, K., K. J. Carey, A. Hahn, et al. "Modeling Bronchodilator Dose Response in the Central Airways of Asthmatic Lungs Using Computational Fluid Dynamics." In American Thoracic Society 2020 International Conference, May 15-20, 2020 - Philadelphia, PA. American Thoracic Society, 2020. http://dx.doi.org/10.1164/ajrccm-conference.2020.201.1_meetingabstracts.a5692.

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Affan, Affan, Jacek M. Zurada, Michael E. Brier, and Tamer Inanc. "Adaptive Individualized Drug-Dose Response Modeling from a Limited Clinical Data: Case of Warfarin Management." In 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE, 2021. http://dx.doi.org/10.1109/embc46164.2021.9630158.

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Schulmeister, Karl, Gerald Sonneck, Herbert Hoedlmoser, Frank Rattay, John Mellerio, and David H. Sliney. "Modeling of uncertainty associated with dose-response curves as applied for probabilistic risk assessment in laser safety." In BiOS 2001 The International Symposium on Biomedical Optics, edited by Bruce E. Stuck and Michael Belkin. SPIE, 2001. http://dx.doi.org/10.1117/12.426713.

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Williams, Katherine S., Ardith W. El-Kareh, and Timothy W. Secomb. "Abstract 439: Mathematical modeling of cellular dose-response for radiation and radiation-drug combinations including cell cycle effects." In Proceedings: AACR 104th Annual Meeting 2013; Apr 6-10, 2013; Washington, DC. American Association for Cancer Research, 2013. http://dx.doi.org/10.1158/1538-7445.am2013-439.

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Zhuang, Shuhan, Sheng Fang, and Xinwen Dong. "Local-Scale Atmospheric Dispersion Modelling of Radionuclides Following the Fukushima Daiichi Nuclear Accident Using SWIFT-RIMPUFF." In 2022 29th International Conference on Nuclear Engineering. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/icone29-90748.

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Abstract Air dispersion modeling is an important tool for emergency response following a nuclear accident, such as the Fukushima accident. Current researches mainly focus on global- and regional-scale modeling with wind field data derived from different methodological models and observations. However, the capability of the local-scale atmospheric dispersion model hasn’t been discussed in detail for the Fukushima accident. In this paper, the local-scale modeling of radionuclides following the Fukushima accident was investigated with the combination of the wind diagnosed model SWIFT and the radi
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Dong, Xinwen, Sheng Fang, and Shuhan Zhuang. "Interpolation Influence on the Fast Fourier Transform Based Calculation of Three-Dimensional Dose Rate Field." In 2022 29th International Conference on Nuclear Engineering. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/icone29-89244.

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Abstract The calculation of three-dimensional dose rate fields plays a key role in radiation dose rate estimation and the service for the nuclear emergency. The recent fast calculation method based on the Fast Fourier Transform (FFT) method can greatly speed up the calculation without losing accuracy, which is promising for operational usage in nuclear emergency response systems. But it can only be used for a uniform grid. Unfortunately, most atmospheric dispersion models use a non-uniform grid, which prevents the direct application of FFT-based calculation. Therefore, interpolation is require
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Patel, Krishna, Michael Stevens, Suyash Adhikari, Greg Book, Muhammad Mubeen, and Godfrey Pearlson. "Acute cannabis-related alterations in an fMRI time estimation task." In 2022 Annual Scientific Meeting of the Research Society on Marijuana. Research Society on Marijuana, 2022. http://dx.doi.org/10.26828/cannabis.2022.02.000.26.

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Introduction: Cannabis is widely popular recreational drug of choice in the US. The drug is known to alter the subjective experience of time. However, its effects on time estimation at a brain level are still largely unexplored. Our goal was to investigate acute effects of cannabis on an fMRI time estimation task by evaluating brain activation differences between cannabis and placebo conditions. We hypothesized that participants’ time estimation accuracy and corresponding BOLD response would be altered during the cannabis condition in a dose-related manner, compared to placebo. Methods: In thi
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Reports on the topic "Dose-Response modeling"

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Nottingham, Quinton J., Jeffrey B. Birch, and Barry A. Bodt. Modeling Nonmonotonic Dose-Response Curves. Defense Technical Information Center, 2001. http://dx.doi.org/10.21236/ada391664.

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NTP Research Report on National Toxicology Program Approach to Genomic Dose-Response Modeling. NIEHS, 2018. http://dx.doi.org/10.22427/ntp-rr-5.

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