Evaluation of a predictive model for Mycosphaerella graminicola for economic and environmental benefits

A method was developed to evaluate crop disease predictive models for their economic and environmental benefits. Benefits were quantified as the value of a prediction measured by costs saved and fungicide dose saved. The value of prediction was defined as the net gain made by using predictions, measured as the difference between a scenario where predictions are available and used and a scenario without prediction. Comparable 'with' and 'without' scenarios were created with the use of risk levels. These risk levels were derived from a probability distribution fitted to observed disease severities. These distributions were used to calculate the probability that a certain disease induced economic loss was incurred. The method was exemplified by using it to evaluate a model developed for Mycosphaerella graminicola risk prediction. Based on the value of prediction, the tested model may have economic and environmental benefits to growers if used to guide treatment decisions on resistant cultivars. It is shown that the value of prediction measured by fungicide dose saved and costs saved is constant with the risk level. The model could also be used to evaluate similar crop disease predictive models.

A predictive model for early-warning of Septoria leaf blotch on winter wheat

Disease-weather relationships influencing Septoria leaf blotch (SLB) preceding growth stage (GS) 31 were identified using data from 12 sites in the UK covering 8 years. Based on these relationships, an early-warning predictive model for SLB on winter wheat was formulated to predict the occurrence of a damaging epidemic (defined as disease severity of 5% or > 5% on the top three leaf layers). The final model was based on accumulated rain > 3 mm in the 80-day period preceding GS 31 (roughly from early-February to the end of April) and accumulated minimum temperature with a 0A degrees C base in the 50-day period starting from 120 days preceding GS 31 (approximately January and February). The model was validated on an independent data set on which the prediction accuracy was influenced by cultivar resistance. Over all observations, the model had a true positive proportion of 0.61, a true negative proportion of 0.73, a sensitivity of 0.83, and a specificity of 0.18. True negative proportion increased to 0.85 for resistant cultivars and decreased to 0.50 for susceptible cultivars. Potential fungicide savings are most likely to be made with resistant cultivars, but such benefits would need to be identified with an in-depth evaluation.

Automatic nonlinear predictive model-construction algorithm using forward regression and the PRESS statistic

An automatic nonlinear predictive model-construction algorithm is introduced based on forward regression and the predicted-residual-sums-of-squares (PRESS) statistic. The proposed algorithm is based on the fundamental concept of evaluating a model's generalisation capability through crossvalidation. This is achieved by using the PRESS statistic as a cost function to optimise model structure. In particular, the proposed algorithm is developed with the aim of achieving computational efficiency, such that the computational effort, which would usually be extensive in the computation of the PRESS statistic, is reduced or minimised. The computation of PRESS is simplified by avoiding a matrix inversion through the use of the orthogonalisation procedure inherent in forward regression, and is further reduced significantly by the introduction of a forward-recursive formula. Based on the properties of the PRESS statistic, the proposed algorithm can achieve a fully automated procedure without resort to any other validation data set for iterative model evaluation. Numerical examples are used to demonstrate the efficacy of the algorithm.

Towards cost-sensitive adaptation: when is it worth updating your predictive model?

Our digital universe is rapidly expanding,more and more daily activities are digitally recorded, data arrives in streams, it needs to be analyzed in real time and may evolve over time. In the last decade many adaptive learning algorithms and prediction systems, which can automatically update themselves with the new incoming data, have been developed. The majority of those algorithms focus on improving the predictive performance and assume that model update is always desired as soon as possible and as frequently as possible. In this study we consider potential model update as an investment decision, which, as in the financial markets, should be taken only if a certain return on investment is expected. We introduce and motivate a new research problem for data streams ? cost-sensitive adaptation. We propose a reference framework for analyzing adaptation strategies in terms of costs and benefits. Our framework allows to characterize and decompose the costs of model updates, and to asses and interpret the gains in performance due to model adaptation for a given learning algorithm on a given prediction task. Our proof-of-concept experiment demonstrates how the framework can aid in analyzing and managing adaptation decisions in the chemical industry.

Use of a predictive model for food insecurity estimates in Brazil

In 2004 the National Household Survey (Pesquisa Nacional par Amostras de Domicilios - PNAD) estimated the prevalence of food and nutrition insecurity in Brazil. However, PNAD data cannot be disaggregated at the municipal level. The objective of this study was to build a statistical model to predict severe food insecurity for Brazilian municipalities based on the PNAD dataset. Exclusion criteria were: incomplete food security data (19.30%); informants younger than 18 years old (0.07%); collective households (0.05%); households headed by indigenous persons (0.19%). The modeling was carried out in three stages, beginning with the selection of variables related to food insecurity using univariate logistic regression. The variables chosen to construct the municipal estimates were selected from those included in PNAD as well as the 2000 Census. Multivariate logistic regression was then initiated, removing the non-significant variables with odds ratios adjusted by multiple logistic regression. The Wald Test was applied to check the significance of the coefficients in the logistic equation. The final model included the variables: per capita income; years of schooling; race and gender of the household head; urban or rural residence; access to public water supply; presence of children; total number of household inhabitants and state of residence. The adequacy of the model was tested using the Hosmer-Lemeshow test (p=0.561) and ROC curve (area=0.823). Tests indicated that the model has strong predictive power and can be used to determine household food insecurity in Brazilian municipalities, suggesting that similar predictive models may be useful tools in other Latin American countries.

A predictive model of avian natal dispersal distance provides prior information for investigating response to landscape change

1. Informative Bayesian priors can improve the precision of estimates in ecological studies or estimate parameters for which little or no information is available. While Bayesian analyses are becoming more popular in ecology, the use of strongly informative priors remains rare, perhaps because examples of informative priors are not readily available in the published literature.
2. Dispersal distance is an important ecological parameter, but is difficult to measure and estimates are scarce. General models that provide informative prior estimates of dispersal distances will therefore be valuable.
3. Using a world-wide data set on birds, we develop a predictive model of median natal dispersal distance that includes body mass, wingspan, sex and feeding guild. This model predicts median dispersal distance well when using the fitted data and an independent test data set, explaining up to 53% of the variation.
4. Using this model, we predict a priori estimates of median dispersal distance for 57 woodland-dependent bird species in northern Victoria, Australia. These estimates are then used to investigate the relationship between dispersal ability and vulnerability to landscape-scale changes in habitat cover and fragmentation.
5. We find evidence that woodland bird species with poor predicted dispersal ability are more vulnerable to habitat fragmentation than those species with longer predicted dispersal distances, thus improving the understanding of this important phenomenon.
6. The value of constructing informative priors from existing information is also demonstrated. When used as informative priors for four example species, predicted dispersal distances reduced the 95% credible intervals of posterior estimates of dispersal distance by 8-19%. Further, should we have wished to collect information on avian dispersal distances and relate it to species' responses to habitat loss and fragmentation, data from 221 individuals across 57 species would have been required to obtain estimates with the same precision as those provided by the general model.

A predictive model of avian natal dispersal distance provides prior information for investigating response to landscape change

1. Informative Bayesian priors can improve the precision of estimates in ecological studies or estimate parameters for which little or no information is available. While Bayesian analyses are becoming more popular in ecology, the use of strongly informative priors remains rare, perhaps because examples of informative priors are not readily available in the published literature.

2. Dispersal distance is an important ecological parameter, but is difficult to measure and estimates are scarce. General models that provide informative prior estimates of dispersal distances will therefore be valuable.

3. Using a world-wide data set on birds, we develop a predictive model of median natal dispersal distance that includes body mass, wingspan, sex and feeding guild. This model predicts median dispersal distance well when using the fitted data and an independent test data set, explaining up to 53% of the variation.

4. Using this model, we predict a priori estimates of median dispersal distance for 57 woodland-dependent bird species in northern Victoria, Australia. These estimates are then used to investigate the relationship between dispersal ability and vulnerability to landscape-scale changes in habitat cover and fragmentation.

5. We find evidence that woodland bird species with poor predicted dispersal ability are more vulnerable to habitat fragmentation than those species with longer predicted dispersal distances, thus improving the understanding of this important phenomenon.

6. The value of constructing informative priors from existing information is also demonstrated. When used as informative priors for four example species, predicted dispersal distances reduced the 95% credible intervals of posterior estimates of dispersal distance by 8-19%. Further, should we have wished to collect information on avian dispersal distances and relate it to species' responses to habitat loss and fragmentation, data from 221 individuals across 57 species would have been required to obtain estimates with the same precision as those provided by the general model.

Rebound or resignation: developing a predictive model of return to subjective wellbeing set-point

Though subjective wellbeing (SWB) is generally stable and consistent over time, it can fall below its set-point in response to adverse life events. However, deviations from set-point levels are usually only temporary, as homeostatic processes operate to return SWB to its normal state. Given that income and close interpersonal relationships have been proposed as powerful external resources that are coincident with higher SWB, access to these resources may be an important predictor of whether or not a person is likely to recover their SWB following a departure from their set-point. Under the guiding framework of SWB Homeostasis Theory, this study considers whether access to a higher income and a committed partner can predict whether people who score lower than normal for SWB at baseline will return to normal set-point levels of SWB (rebound) or remain below the normal range (resigned) at follow-up. Participants were 733 people (53.3 % female) from the Australian Unity Longitudinal Wellbeing Study who ranged in age from 20 to 92 years (M = 59.65 years; SD = 13.15). Logistic regression analyses revealed that participants’ demographic characteristics were poor predictors of whether they rebounded or resigned. Consistent with homeostasis theory, the extent of departure from the proposed normal SWB set-point at baseline was significantly associated with rebound or resignation at time 2. These findings have implications for the way that SWB measures can be used in professional practice to identify people who are particularly vulnerable to depression and to guide the provision of appropriate and effective therapeutic interventions.

Establishing the risk of neonatal mortality using a fuzzy predictive model

O objetivo do artigo foi avaliar o uso da lógica fuzzy para estimar possibilidade de óbito neonatal. Desenvolveu-se um modelo computacional com base na teoria dos conjuntos fuzzy, tendo como variáveis peso ao nascer, idade gestacional, escore de Apgar e relato de natimorto. Empregou-se o método de inferência de Mamdani, e a variável de saída foi o risco de morte neonatal. Criaram-se 24 regras de acordo com as variáveis de entrada, e a validação do modelo utilizou um banco de dados real de uma cidade brasileira. A acurácia foi estimada pela curva ROC; os riscos foram comparados pelo teste t de Student. O programa MATLAB 6.5 foi usado para construir o modelo. Os riscos médios foram menores para os que sobreviveram (p < 0,001). A acurácia do modelo foi 0,90. A maior acurácia foi com possibilidade de risco igual ou menor que 25% (sensibilidade = 0,70, especificidade = 0,98, valor preditivo negativo = 0,99 e valor preditivo positivo = 0,22). O modelo mostrou acurácia e valor preditivo negativo bons, podendo ser utilizado em hospitais gerais.

Chimpanzee: A predictive model for the human cytochrome P450 3A subfamily

The human cytochrome P450 3A (CYP3A) subfamily is responsible for most of the metabolism of therapeutic drugs; however, an adequate in vivo model has yet to be discovered. This study begins with an investigation of a controversial topic surrounding the human CYP3As--estrogen regulation. A novel approach to this topic was used by defining expression in the estrogen-responsive endometrium. This study shows that estrogen down-regulates CYP3A4 expression in the endometrium. On the other hand, analogous studies showed an increase in CYP3A expression as age increases in liver tissue. Following the discussion of estrogen regulation, is an investigation of the cross-species relationships among all of the CYP3As was completed. The study compares isoforms from piscines, avians, rodents, canines, ovines, bovines, and primates. Using the traditional phylogenetic analyses and employing a novel approach using exon and intron lengths, the results show that only another primate could be the best animal model for analysis of the regulation of the expression of the human CYP3As. This analysis also demonstrated that the chimpanzee seems to be the best available human model. Moreover, the study showed the presence and similarities of one additional isoform in the chimpanzee genome that is absent in humans. Based on these results, initial characterization of the chimpanzee CYP3A subfamily was begun. While the human genome contains four isoforms--CYP3A4, CYP3A5, CYP3A7, and CYP3A43--the chimpanzee genome has five, the four previously mentioned and CYP3A67. Both species express CYP3A4, CYP3A5, and CYP3A43, but humans express CYP3A7 while chimpanzees express CYP3A67. In humans, CYP3A4 is expressed at higher levels than the other isoforms, but some chimpanzee individuals express CYP3A67 at higher levels than CYP3A4. Such a difference is expected to alter significantly the total CYP3A metabolism. On the other hand, any study considering individual isoforms would still constitute a valid method of study for the human CYP3A4, CYP3A5, and CYP3A43 isoforms. ^

Visualization of a student predictive model for 3D virtual environments driven to procedural training

This document presents theimplementation ofa Student Behavior Predictor Viewer(SBPV)for a student predictive model. The student predictive model is part of an intelligent tutoring system, and is built from logs of students’ behaviors in the “Virtual Laboratory of Agroforestry Biotechnology”implemented in a previous work.The SBPVis a tool for visualizing a 2D graphical representationof the extended automaton associated with any of the clusters ofthe student predictive model. Apart from visualizing the extended automaton, the SBPV supports the navigation across the automaton by means of desktop devices. More precisely, the SBPV allows user to move through the automaton, to zoom in/out the graphic or to locate a given state. In addition, the SBPV also allows user to modify the default layout of the automaton on the screen by changing the position of the states by means of the mouse. To developthe SBPV, a web applicationwas designedand implementedrelying on HTML5, JavaScript and C#.

Siting the Superconducting Super Collider in Illinois : an overview and predictive model of cultural and paleobiological resources in the SSC study area, northern Illinois /

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