Koseki S - Search Results

1

Growth-Inhibitory Effect of d-Tryptophan on Vibrio spp. in Shucked and Live Oysters.

Chen J, Kudo H, Kan K, Kawamura S, Koseki S. Chen J, et al. Among authors: koseki s. Appl Environ Microbiol. 2018 Sep 17;84(19):e01543-18. doi: 10.1128/AEM.01543-18. Print 2018 Oct 1. Appl Environ Microbiol. 2018. PMID: 30030231 Free PMC article.

2

Calculating stochastic inactivation of individual cells in a bacterial population using variability in individual cell inactivation time and initial cell number.

Koyama K, Abe H, Kawamura S, Koseki S. Koyama K, et al. Among authors: koseki s. J Theor Biol. 2019 May 21;469:172-179. doi: 10.1016/j.jtbi.2019.01.042. Epub 2019 Mar 2. J Theor Biol. 2019. PMID: 30831174

3

Alternative approach to modeling bacterial lag time, using logistic regression as a function of time, temperature, pH, and sodium chloride concentration.

Koseki S, Nonaka J. Koseki S, et al. Appl Environ Microbiol. 2012 Sep;78(17):6103-12. doi: 10.1128/AEM.01245-12. Epub 2012 Jun 22. Appl Environ Microbiol. 2012. PMID: 22729541 Free PMC article.

4

Prediction of a required log reduction with probability for Enterobacter sakazakii during high-pressure processing, using a survival/death interface model.

Koseki S, Matsubara M, Yamamoto K. Koseki S, et al. Appl Environ Microbiol. 2009 Apr;75(7):1885-91. doi: 10.1128/AEM.02283-08. Epub 2009 Feb 6. Appl Environ Microbiol. 2009. PMID: 19201951 Free PMC article.

A probabilistic model for predicting Enterobacter sakazakii inactivation in trypticase soy broth (TSB) and infant formula (IF) by high-pressure processing was developed. The modeling procedure is based on a previous model (S. Koseki and K. Yamamoto, Int. J. Food Mic …

A probabilistic model for predicting Enterobacter sakazakii inactivation in trypticase soy broth (TSB) and infant formula (IF) by high-press …

5

Modeling of pathogen survival during simulated gastric digestion.

Koseki S, Mizuno Y, Sotome I. Koseki S, et al. Appl Environ Microbiol. 2011 Feb;77(3):1021-32. doi: 10.1128/AEM.02139-10. Epub 2010 Dec 3. Appl Environ Microbiol. 2011. PMID: 21131530 Free PMC article.

6

Evaluation of Strain Variability in Inactivation of Campylobacter jejuni in Simulated Gastric Fluid by Using Hierarchical Bayesian Modeling.

Koyama K, Ranta J, Takeoka K, Abe H, Koseki S. Koyama K, et al. Among authors: koseki s. Appl Environ Microbiol. 2021 Jul 13;87(15):e0091821. doi: 10.1128/AEM.00918-21. Epub 2021 Jul 13. Appl Environ Microbiol. 2021. PMID: 34047637 Free PMC article.

7

Modelling the invasion of human small intestinal epithelial-like cells by Salmonella enterica Typhimurium and Listeria monocytogenes using Bayesian inference.

Takahashi Y, Abe H, Koyama K, Koseki S. Takahashi Y, et al. Among authors: koseki s. Lett Appl Microbiol. 2022 Aug;75(2):388-395. doi: 10.1111/lam.13738. Epub 2022 May 25. Lett Appl Microbiol. 2022. PMID: 35575530

8

Bayesian Generalized Linear Model for Simulating Bacterial Inactivation/Growth Considering Variability and Uncertainty.

Hiura S, Abe H, Koyama K, Koseki S. Hiura S, et al. Among authors: koseki s. Front Microbiol. 2021 Jun 24;12:674364. doi: 10.3389/fmicb.2021.674364. eCollection 2021. Front Microbiol. 2021. PMID: 34248886 Free PMC article.

9

Classification of food spoilage bacterial species and their sodium chloride, sodium acetate and glycine tolerance using chemometrics analysis and Raman spectroscopy.

Yamamoto T, Taylor JN, Koseki S, Koyama K. Yamamoto T, et al. Among authors: koseki s. J Microbiol Methods. 2021 Nov;190:106326. doi: 10.1016/j.mimet.2021.106326. Epub 2021 Sep 10. J Microbiol Methods. 2021. PMID: 34517040

10

Is skipping the definition of primary and secondary models possible? Prediction of Escherichia coli O157 growth by machine learning.

Koyama K, Kubo K, Hiura S, Koseki S. Koyama K, et al. Among authors: koseki s. J Microbiol Methods. 2022 Jan;192:106366. doi: 10.1016/j.mimet.2021.106366. Epub 2021 Nov 12. J Microbiol Methods. 2022. PMID: 34774875

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