Diabetes Prevention Using a Simulation Model That Explains Individual Variability in Response to Diet Change

Diabetes ◽  
2018 ◽  
Vol 67 (Supplement 1) ◽  
pp. 1892-P
Author(s):  
JULIA H. CHEN ◽  
MOMOKO FUKASAWA ◽  
QIAN CHEN ◽  
SAMUEL P. BURNS ◽  
KEI KUMAR ◽  
...  
1998 ◽  
Vol 55 (10) ◽  
pp. 2244-2254 ◽  
Author(s):  
Susan K Lowerre-Barbieri ◽  
James M Lowerre ◽  
Luiz R Barbieri

We used an individual-based Monte Carlo simulation model to assess how aspects associated with multiple spawning (within a spawning season) affected survivorship, lifetime fecundity, cohort egg production, and yield-per-recruit of a highly exploited species. To make our model more realistic, we included and tested the effects of individual variability in growth and a seasonal growth pattern. Birth months influenced when fish first matured and became vulnerable to the fishery. There was a sixfold increase in mature fish at the beginning of their first spawning season associated with having been born early versus late the previous season. Early born fish had a lower average life-span than later born fish. Although early born fish had lower survivorship they produced the most eggs because of an early size at first maturity, low fishing mortality in the first year, and their larger size at age. These results suggest multiple spawning can have important implications for recruitment and adult population dynamics.


2003 ◽  
Vol 21 (5) ◽  
pp. 295-303 ◽  
Author(s):  
Jarmo Hahl ◽  
Tuula Simell ◽  
Antti Kupila ◽  
P??ivi Keskinen ◽  
Mikael Knip ◽  
...  

2015 ◽  
Vol 32 (12) ◽  
pp. 1580-1587 ◽  
Author(s):  
A. A. W. A. van der Heijden ◽  
T. L. Feenstra ◽  
R. T. Hoogenveen ◽  
L. W. Niessen ◽  
M. C. de Bruijne ◽  
...  

2019 ◽  
Vol 42 ◽  
Author(s):  
Emily F. Wissel ◽  
Leigh K. Smith

Abstract The target article suggests inter-individual variability is a weakness of microbiota-gut-brain (MGB) research, but we discuss why it is actually a strength. We comment on how accounting for individual differences can help researchers systematically understand the observed variance in microbiota composition, interpret null findings, and potentially improve the efficacy of therapeutic treatments in future clinical microbiome research.


2011 ◽  
Vol 44 (5) ◽  
pp. 1-28
Author(s):  
MITCHEL L. ZOLER
Keyword(s):  

1998 ◽  
Vol 94 (3) ◽  
pp. 417-433 ◽  
Author(s):  
MARTIN VAN DER HOEF ◽  
PAUL MADDEN

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