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Evaluation of Tier One Exposure Assessment Models (ETEAM): Project Overview and Methods.

Annals of work exposures and health

Tischer M, Lamb J, Hesse S, van Tongeren M.
PMID: 29028257
Ann Work Expo Health. 2017 Oct 01;61(8):911-920. doi: 10.1093/annweh/wxx066.

No abstract available.

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Tischer M, Lamb J, Hesse S, et al. Evaluation of Tier One Exposure Assessment Models (ETEAM): Project Overview and Methods. Ann Work Expo Health. 2017;61(8):911-920doi: 10.1093/annweh/wxx066.
Tischer, M., Lamb, J., Hesse, S., & van Tongeren, M. (2017). Evaluation of Tier One Exposure Assessment Models (ETEAM): Project Overview and Methods. Annals of work exposures and health, 61(8), 911-920. https://doi.org/10.1093/annweh/wxx066
Tischer, Martin, et al. "Evaluation of Tier One Exposure Assessment Models (ETEAM): Project Overview and Methods." Annals of work exposures and health vol. 61,8 (2017): 911-920. doi: https://doi.org/10.1093/annweh/wxx066
Tischer M, Lamb J, Hesse S, van Tongeren M. Evaluation of Tier One Exposure Assessment Models (ETEAM): Project Overview and Methods. Ann Work Expo Health. 2017 Oct 01;61(8):911-920. doi: 10.1093/annweh/wxx066. PMID: 29028257.
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Metacognition and evidence analysis instruction: an educational framework and practical experience.

Systematic reviews

Parrott JS, Rubinstein ML.
PMID: 26293452
Syst Rev. 2015 Aug 21;4:112. doi: 10.1186/s13643-015-0101-8.

The role of metacognitive skills in the evidence analysis process has received little attention in the research literature. While the steps of the evidence analysis process are well defined, the role of higher-level cognitive operations (metacognitive strategies) in integrating...

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Parrott JS, Rubinstein ML. Metacognition and evidence analysis instruction: an educational framework and practical experience. Syst Rev. 2015;4:112doi: 10.1186/s13643-015-0101-8.
Parrott, J. S., & Rubinstein, M. L. (2015). Metacognition and evidence analysis instruction: an educational framework and practical experience. Systematic reviews, 4112. https://doi.org/10.1186/s13643-015-0101-8
Parrott, J Scott, and Rubinstein, Matthew L. "Metacognition and evidence analysis instruction: an educational framework and practical experience." Systematic reviews vol. 4 (2015): 112. doi: https://doi.org/10.1186/s13643-015-0101-8
Parrott JS, Rubinstein ML. Metacognition and evidence analysis instruction: an educational framework and practical experience. Syst Rev. 2015 Aug 21;4:112. doi: 10.1186/s13643-015-0101-8. PMID: 26293452; PMCID: PMC4546034.
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Principal metabolic flux mode analysis.

Bioinformatics (Oxford, England)

Bhadra S, Blomberg P, Castillo S, Rousu J.
PMID: 29420676
Bioinformatics. 2018 Jul 15;34(14):2409-2417. doi: 10.1093/bioinformatics/bty049.

MOTIVATION: In the analysis of metabolism, two distinct and complementary approaches are frequently used: Principal component analysis (PCA) and stoichiometric flux analysis. PCA is able to capture the main modes of variability in a set of experiments and does...

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Bhadra S, Blomberg P, Castillo S, et al. Principal metabolic flux mode analysis. Bioinformatics. 2018;34(14):2409-2417doi: 10.1093/bioinformatics/bty049.
Bhadra, S., Blomberg, P., Castillo, S., & Rousu, J. (2018). Principal metabolic flux mode analysis. Bioinformatics (Oxford, England), 34(14), 2409-2417. https://doi.org/10.1093/bioinformatics/bty049
Bhadra, Sahely, et al. "Principal metabolic flux mode analysis." Bioinformatics (Oxford, England) vol. 34,14 (2018): 2409-2417. doi: https://doi.org/10.1093/bioinformatics/bty049
Bhadra S, Blomberg P, Castillo S, Rousu J. Principal metabolic flux mode analysis. Bioinformatics. 2018 Jul 15;34(14):2409-2417. doi: 10.1093/bioinformatics/bty049. PMID: 29420676; PMCID: PMC6041797.
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Review and perspective on mathematical modeling of microbial ecosystems.

Biochemical Society transactions

Succurro A, Ebenhöh O.
PMID: 29540507
Biochem Soc Trans. 2018 Apr 17;46(2):403-412. doi: 10.1042/BST20170265. Epub 2018 Mar 14.

Understanding microbial ecosystems means unlocking the path toward a deeper knowledge of the fundamental mechanisms of life. Engineered microbial communities are also extremely relevant to tackling some of today's grand societal challenges. Advanced meta-omics experimental techniques provide crucial insights...

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Succurro A, Ebenhöh O. Review and perspective on mathematical modeling of microbial ecosystems. Biochem Soc Trans. 2018;46(2):403-412doi: 10.1042/BST20170265.
Succurro, A., & Ebenhöh, O. (2018). Review and perspective on mathematical modeling of microbial ecosystems. Biochemical Society transactions, 46(2), 403-412. https://doi.org/10.1042/BST20170265
Succurro, Antonella, and Ebenhöh, Oliver. "Review and perspective on mathematical modeling of microbial ecosystems." Biochemical Society transactions vol. 46,2 (2018): 403-412. doi: https://doi.org/10.1042/BST20170265
Succurro A, Ebenhöh O. Review and perspective on mathematical modeling of microbial ecosystems. Biochem Soc Trans. 2018 Apr 17;46(2):403-412. doi: 10.1042/BST20170265. Epub 2018 Mar 14. PMID: 29540507; PMCID: PMC5906705.
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Density-dependence interacts with extrinsic mortality in shaping life histories.

PloS one

Dańko MJ, Burger O, Kozłowski J.
PMID: 29049399
PLoS One. 2017 Oct 19;12(10):e0186661. doi: 10.1371/journal.pone.0186661. eCollection 2017.

The role of extrinsic mortality in shaping life histories is poorly understood. However, substantial evidence suggests that extrinsic mortality interacts with density-dependence in crucial ways. We develop a model combining Evolutionarily Stable Strategies with a projection matrix that allows...

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Dańko MJ, Burger O, Kozłowski J. Density-dependence interacts with extrinsic mortality in shaping life histories. PLoS One. 2017;12(10):e0186661doi: 10.1371/journal.pone.0186661.
Dańko, M. J., Burger, O., & Kozłowski, J. (2017). Density-dependence interacts with extrinsic mortality in shaping life histories. PloS one, 12(10), e0186661. https://doi.org/10.1371/journal.pone.0186661
Dańko, Maciej Jan, et al. "Density-dependence interacts with extrinsic mortality in shaping life histories." PloS one vol. 12,10 (2017): e0186661. doi: https://doi.org/10.1371/journal.pone.0186661
Dańko MJ, Burger O, Kozłowski J. Density-dependence interacts with extrinsic mortality in shaping life histories. PLoS One. 2017 Oct 19;12(10):e0186661. doi: 10.1371/journal.pone.0186661. eCollection 2017. PMID: 29049399; PMCID: PMC5648222.
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Managing variance: Key policy challenges for the Anthropocene.

Proceedings of the National Academy of Sciences of the United States of America

Anderies JM.
PMID: 26578808
Proc Natl Acad Sci U S A. 2015 Nov 24;112(47):14402-3. doi: 10.1073/pnas.1519071112. Epub 2015 Nov 17.

No abstract available.

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Anderies JM. Managing variance: Key policy challenges for the Anthropocene. Proc Natl Acad Sci U S A. 2015;112(47):14402-3doi: 10.1073/pnas.1519071112.
Anderies, J. M. (2015). Managing variance: Key policy challenges for the Anthropocene. Proceedings of the National Academy of Sciences of the United States of America, 112(47), 14402-3. https://doi.org/10.1073/pnas.1519071112
Anderies, John M. "Managing variance: Key policy challenges for the Anthropocene." Proceedings of the National Academy of Sciences of the United States of America vol. 112,47 (2015): 14402-3. doi: https://doi.org/10.1073/pnas.1519071112
Anderies JM. Managing variance: Key policy challenges for the Anthropocene. Proc Natl Acad Sci U S A. 2015 Nov 24;112(47):14402-3. doi: 10.1073/pnas.1519071112. Epub 2015 Nov 17. PMID: 26578808; PMCID: PMC4664313.
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BioNSi: A Discrete Biological Network Simulator Tool.

Journal of proteome research

Rubinstein A, Bracha N, Rudner L, Zucker N, Sloin HE, Chor B.
PMID: 27354160
J Proteome Res. 2016 Aug 05;15(8):2871-80. doi: 10.1021/acs.jproteome.6b00278. Epub 2016 Jul 13.

Modeling and simulation of biological networks is an effective and widely used research methodology. The Biological Network Simulator (BioNSi) is a tool for modeling biological networks and simulating their discrete-time dynamics, implemented as a Cytoscape App. BioNSi includes a...

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Rubinstein A, Bracha N, Rudner L, et al. BioNSi: A Discrete Biological Network Simulator Tool. J Proteome Res. 2016;15(8):2871-80doi: 10.1021/acs.jproteome.6b00278.
Rubinstein, A., Bracha, N., Rudner, L., Zucker, N., Sloin, H. E., & Chor, B. (2016). BioNSi: A Discrete Biological Network Simulator Tool. Journal of proteome research, 15(8), 2871-80. https://doi.org/10.1021/acs.jproteome.6b00278
Rubinstein, Amir, et al. "BioNSi: A Discrete Biological Network Simulator Tool." Journal of proteome research vol. 15,8 (2016): 2871-80. doi: https://doi.org/10.1021/acs.jproteome.6b00278
Rubinstein A, Bracha N, Rudner L, Zucker N, Sloin HE, Chor B. BioNSi: A Discrete Biological Network Simulator Tool. J Proteome Res. 2016 Aug 05;15(8):2871-80. doi: 10.1021/acs.jproteome.6b00278. Epub 2016 Jul 13. PMID: 27354160.
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Evolution of dietary diversity and a starvation driven cross-diffusion system as its singular limit.

Journal of mathematical biology

Brocchieri E, Corrias L, Dietert H, Kim YJ.
PMID: 34731313
J Math Biol. 2021 Nov 03;83(5):58. doi: 10.1007/s00285-021-01679-y.

We rigorously prove the passage from a Lotka-Volterra reaction-diffusion system towards a cross-diffusion system at the fast reaction limit. The system models a competition of two species, where one species has a more diverse diet than the other. The...

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Brocchieri E, Corrias L, Dietert H, et al. Evolution of dietary diversity and a starvation driven cross-diffusion system as its singular limit. J Math Biol. 2021;83(5):58doi: 10.1007/s00285-021-01679-y.
Brocchieri, E., Corrias, L., Dietert, H., & Kim, Y. J. (2021). Evolution of dietary diversity and a starvation driven cross-diffusion system as its singular limit. Journal of mathematical biology, 83(5), 58. https://doi.org/10.1007/s00285-021-01679-y
Brocchieri, Elisabetta, et al. "Evolution of dietary diversity and a starvation driven cross-diffusion system as its singular limit." Journal of mathematical biology vol. 83,5 (2021): 58. doi: https://doi.org/10.1007/s00285-021-01679-y
Brocchieri E, Corrias L, Dietert H, Kim YJ. Evolution of dietary diversity and a starvation driven cross-diffusion system as its singular limit. J Math Biol. 2021 Nov 03;83(5):58. doi: 10.1007/s00285-021-01679-y. PMID: 34731313.
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A Model Template Approach for Rapid Evaluation and Application of Physiologically Based Pharmacokinetic Models for Use in Human Health Risk Assessments: A Case Study on Per- and Polyfluoroalkyl Substances.

Toxicological sciences : an official journal of the Society of Toxicology

Bernstein AS, Kapraun DF, Schlosser PM.
PMID: 34077538
Toxicol Sci. 2021 Aug 03;182(2):215-228. doi: 10.1093/toxsci/kfab063.

Physiologically based pharmacokinetic (PBPK) models are commonly used in risk assessments to perform inter- and intraspecies extrapolations as well as to extrapolate between different dosing scenarios; however, they must first undergo quality assurance review, which can be a time-consuming...

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Bernstein AS, Kapraun DF, Schlosser PM. A Model Template Approach for Rapid Evaluation and Application of Physiologically Based Pharmacokinetic Models for Use in Human Health Risk Assessments: A Case Study on Per- and Polyfluoroalkyl Substances. Toxicol Sci. 2021;182(2):215-228doi: 10.1093/toxsci/kfab063.
Bernstein, A. S., Kapraun, D. F., & Schlosser, P. M. (2021). A Model Template Approach for Rapid Evaluation and Application of Physiologically Based Pharmacokinetic Models for Use in Human Health Risk Assessments: A Case Study on Per- and Polyfluoroalkyl Substances. Toxicological sciences : an official journal of the Society of Toxicology, 182(2), 215-228. https://doi.org/10.1093/toxsci/kfab063
Bernstein, Amanda S, et al. "A Model Template Approach for Rapid Evaluation and Application of Physiologically Based Pharmacokinetic Models for Use in Human Health Risk Assessments: A Case Study on Per- and Polyfluoroalkyl Substances." Toxicological sciences : an official journal of the Society of Toxicology vol. 182,2 (2021): 215-228. doi: https://doi.org/10.1093/toxsci/kfab063
Bernstein AS, Kapraun DF, Schlosser PM. A Model Template Approach for Rapid Evaluation and Application of Physiologically Based Pharmacokinetic Models for Use in Human Health Risk Assessments: A Case Study on Per- and Polyfluoroalkyl Substances. Toxicol Sci. 2021 Aug 03;182(2):215-228. doi: 10.1093/toxsci/kfab063. PMID: 34077538.
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A Network Immuno-Epidemiological HIV Model.

Bulletin of mathematical biology

Gupta C, Tuncer N, Martcheva M.
PMID: 33452941
Bull Math Biol. 2021 Jan 16;83(3):18. doi: 10.1007/s11538-020-00855-3.

In this paper we formulate a multi-scale nested immuno-epidemiological model of HIV on complex networks. The system is described by ordinary differential equations coupled with a partial differential equation. First, we prove the existence and uniqueness of the immunological...

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Gupta C, Tuncer N, Martcheva M. A Network Immuno-Epidemiological HIV Model. Bull Math Biol. 2021;83(3):18doi: 10.1007/s11538-020-00855-3.
Gupta, C., Tuncer, N., & Martcheva, M. (2021). A Network Immuno-Epidemiological HIV Model. Bulletin of mathematical biology, 83(3), 18. https://doi.org/10.1007/s11538-020-00855-3
Gupta, Churni, et al. "A Network Immuno-Epidemiological HIV Model." Bulletin of mathematical biology vol. 83,3 (2021): 18. doi: https://doi.org/10.1007/s11538-020-00855-3
Gupta C, Tuncer N, Martcheva M. A Network Immuno-Epidemiological HIV Model. Bull Math Biol. 2021 Jan 16;83(3):18. doi: 10.1007/s11538-020-00855-3. PMID: 33452941.
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Evolution of iPSC disease models.

Protein & cell

Zhang W, Ding Z, Liu GH.
PMID: 22259122
Protein Cell. 2012 Jan;3(1):1-4. doi: 10.1007/s13238-012-2005-x.

No abstract available.

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Zhang W, Ding Z, Liu GH. Evolution of iPSC disease models. Protein Cell. 2012;3(1):1-4doi: 10.1007/s13238-012-2005-x.
Zhang, W., Ding, Z., & Liu, G. H. (2012). Evolution of iPSC disease models. Protein & cell, 3(1), 1-4. https://doi.org/10.1007/s13238-012-2005-x
Zhang, Weiqi, et al. "Evolution of iPSC disease models." Protein & cell vol. 3,1 (2012): 1-4. doi: https://doi.org/10.1007/s13238-012-2005-x
Zhang W, Ding Z, Liu GH. Evolution of iPSC disease models. Protein Cell. 2012 Jan;3(1):1-4. doi: 10.1007/s13238-012-2005-x. PMID: 22259122; PMCID: PMC4875220.
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Strengthening policy coding methodologies to improve COVID-19 disease modeling and policy responses: a proposed coding framework and recommendations.

BMC medical research methodology

Lane J, Garrison MM, Kelley J, Sarma P, Katz A.
PMID: 33292170
BMC Med Res Methodol. 2020 Dec 08;20(1):298. doi: 10.1186/s12874-020-01174-w.

BACKGROUND: In recent months, multiple efforts have sought to characterize COVID-19 social distancing policy responses. These efforts have used various coding frameworks, but many have relied on coding methodologies that may not adequately describe the gradient in social distancing...

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Lane J, Garrison MM, Kelley J, et al. Strengthening policy coding methodologies to improve COVID-19 disease modeling and policy responses: a proposed coding framework and recommendations. BMC Med Res Methodol. 2020;20(1):298doi: 10.1186/s12874-020-01174-w.
Lane, J., Garrison, M. M., Kelley, J., Sarma, P., & Katz, A. (2020). Strengthening policy coding methodologies to improve COVID-19 disease modeling and policy responses: a proposed coding framework and recommendations. BMC medical research methodology, 20(1), 298. https://doi.org/10.1186/s12874-020-01174-w
Lane, Jeff, et al. "Strengthening policy coding methodologies to improve COVID-19 disease modeling and policy responses: a proposed coding framework and recommendations." BMC medical research methodology vol. 20,1 (2020): 298. doi: https://doi.org/10.1186/s12874-020-01174-w
Lane J, Garrison MM, Kelley J, Sarma P, Katz A. Strengthening policy coding methodologies to improve COVID-19 disease modeling and policy responses: a proposed coding framework and recommendations. BMC Med Res Methodol. 2020 Dec 08;20(1):298. doi: 10.1186/s12874-020-01174-w. PMID: 33292170; PMCID: PMC7721792.
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Showing 1057 to 1068 of 1077 entries