Blackheaded Fireworm (Lepidoptera: Tortricidae) Larval Phenology Model Based on Diet-Specific Developmental Rates

1994 ◽  
Vol 23 (2) ◽  
pp. 406-413
Author(s):  
S. D. Cockfield ◽  
J. B. Olson ◽  
D. L. Mahr
Keyword(s):  
Model Based ◽  
Developmental Rates ◽  
Blackheaded Fireworm ◽  
Phenology Model

TEMPERATURE-DEPENDENT DEVELOPMENT OF THE SPECKLED GREEN FRUITWORM, ORTHOSIA HIBISCI GUENÉE (LEPIDOPTERA: NOCTUIDAE)

1994 ◽  
Vol 126 (5) ◽  
pp. 1263-1275 ◽  
Author(s):  
Gary J.R. Judd ◽  
Joan E. Cossentine ◽  
Mark G.T. Gardiner ◽  
Donald R. Thomson
Keyword(s):  
High Temperature ◽  
Regression Equations ◽  
Life Stages ◽  
Temperature Dependent ◽  
Larval Instars ◽  
Dependent Development ◽  
Management Actions ◽  
Development Times ◽  
Developmental Rates ◽  
Phenology Model

AbstractTemperature-dependent development of eggs, larvae, and pupae of the speckled green fruitworm, Orthosia hibisci Guenée, at constant temperatures of 5–30.0 °C, 7.5–27.5 °C, and 0.8–20.8 °C, respectively, was described. Development times decreased with increasing temperatures and minimum developmental times in eggs, larvae, and pupae occurred at ca. 27.5, 25, and 20.8 °C, respectively. Variation in development times of all life stages was modelled accurately (R2 values 0.98–0.99) with a Weibull distribution. Relationships between temperature and developmental rates of all life stages were described by linear degree-day (DD) and nonlinear poikilotherm models. There were significant differences (ANOVA, P < 0.05) among the slopes of regression equations describing developmental rates of different life stages and larval instars. Minimum developmental temperatures for eggs (3.4 °C), first- through fifth-instar larvae (4.7, 2.9, 3.6, 3.5, and 3.7 °C), and pupae (2.8 °C) were determined by extrapolation of linear regression equations to the x-intercept. Median development time of eggs, first- through fifth-instar larvae, and pupae required 99.0, 44.2, 51.5, 52.4, 57.1, 69.9, and 61.3 DD above the minimum developmental temperatures, respectively. Developmental rates of eggs and all larval instars averaged were described by six-parameter models exhibiting low- and high-temperature inhibition. Development of pupae was best described by a four-parameter model exhibiting low- but no high-temperature inhibition. This information should be useful for developing a phenology model to improve management actions against O. hibisci.

Representation, abstraction, and simple-minded sophisticates

2020 ◽  
Vol 43 ◽  
Author(s):  
Peter Dayan
Keyword(s):  
Decision Theory ◽  
Predictive Coding ◽  
Bayesian Decision Theory ◽  
Bayesian Decision ◽  
Model Based ◽  
Model Free

Abstract Bayesian decision theory provides a simple formal elucidation of some of the ways that representation and representational abstraction are involved with, and exploit, both prediction and its rather distant cousin, predictive coding. Both model-free and model-based methods are involved.

Quantitative Model-Based Image Analysis of NuMa Distribution Links Nuclear Organization with Cell Phenotype

2001 ◽  
Vol 7 (S2) ◽  
pp. 578-579
Author(s):  
David W. Knowles ◽  
Sophie A. Lelièvre ◽  
Carlos Ortiz de Solόrzano ◽  
Stephen J. Lockett ◽  
Mina J. Bissell ◽  
...  
Keyword(s):  
Image Analysis ◽  
Mammary Epithelial Cells ◽  
Nuclear Organization ◽  
Critical Role ◽  
Quantitative Model ◽  
Mammary Epithelial ◽  
Cell Phenotype ◽  
Proliferating Cells ◽  
Mitotic Apparatus ◽  
Model Based

The extracellular matrix (ECM) plays a critical role in directing cell behaviour and morphogenesis by regulating gene expression and nuclear organization. Using non-malignant (S1) human mammary epithelial cells (HMECs), it was previously shown that ECM-induced morphogenesis is accompanied by the redistribution of nuclear mitotic apparatus (NuMA) protein from a diffuse pattern in proliferating cells, to a multi-focal pattern as HMECs growth arrested and completed morphogenesis . A process taking 10 to 14 days.To further investigate the link between NuMA distribution and the growth stage of HMECs, we have investigated the distribution of NuMA in non-malignant S1 cells and their malignant, T4, counter-part using a novel model-based image analysis technique. This technique, based on a multi-scale Gaussian blur analysis (Figure 1), quantifies the size of punctate features in an image. Cells were cultured in the presence and absence of a reconstituted basement membrane (rBM) and imaged in 3D using confocal microscopy, for fluorescently labeled monoclonal antibodies to NuMA (fαNuMA) and fluorescently labeled total DNA.

Model-Based Clustering and Classification for Data Science

2019 ◽  
Author(s):  
Charles Bouveyron ◽  
Gilles Celeux ◽  
T. Brendan Murphy ◽  
Adrian E. Raftery
Keyword(s):  
Data Science ◽  
Model Based Clustering ◽  
Model Based ◽  
Clustering And Classification

Model-Based Software Testing and Analysis with C#

2007 ◽  
Author(s):  
Jonathan Jacky ◽  
Margus Veanes ◽  
Colin Campbell ◽  
Wolfram Schulte
Keyword(s):  
Software Testing ◽  
Model Based
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