Pre-pubertal measurements of the somatotrophic axis as predictors of milk production in Holstein-Friesian dairy cows

This study investigated possible relationships between measurements of the somatotrophic axis in pre-pubertal dairy calves and subsequent milk yields. Endogenous growth hormone (GH) release was measured through a fed and fasted period in fifty 6-month-old Holstein-Friesian heifers and they were then challenged with growth hormone-releasing factor (GRF) to assess their GH release pattern. Insulin-like growth factor-I (IGF-I), insulin and glucose concentrations were measured in relation to time of feeding. Cows were subsequently monitored through their first three lactations to record peak and 305-day milk yields. In the first lactation, milk energy output for the first 120 days of lactation was also calculated. The mean 305-day milk yield increased from 7417 +/- 191 kg in the first lactation (n = 37) to 8749 +/- 252 kg in the third (n = 25). There were no significant relationships between any measures of GH secretion and peak or 305-day yield in any lactation. A highly significant positive relationship was established between the GH peak measured 10 min post-GRF challenge and 120-day milk energy values in the first lactation. This relationship was, however, only present in the subpopulation of 12 cows culled after one or two lactations and was absent in the 25 animals remaining for the third lactation. There were no significant relationships between pre-pubertal IGF-I and fed or fasted insulin or glucose concentrations and any subsequent measurement of yield. The usefulness of GH secretagogue challenges in calves as a predictive test for future milk production is thus limited but may have some bearing on nutrient partitioning and longevity. (c) 2005 Elsevier Inc. All rights reserved.

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.

QSAR studies of multidentate nitrogen ligands used in lanthanide and actinide extraction processes

Quantitative structure activity relationships (QSARs) have been developed to optimise the choice of nitrogen heterocyclic molecules that can be used to separate the minor actinides such as americium(III) from europium(III) in the aqueous PUREX raffinate of nuclear waste. Experimental data on distribution coefficients and separation factors (SFs) for 47 such ligands have been obtained and show SF values ranging from 0.61 to 100. The ligands were divided into a training set of 36 molecules to develop the QSAR and a test set of 11 molecules to validate the QSAR. Over 1500 molecular descriptors were calculated for each heterocycle and the Genetic Algorithm was used to select the most appropriate for use in multiple regression equations. Equations were developed fitting the separation factors to 6-8 molecular descriptors which gave r(2) values of >0.8 for the training set and values of >0.7 for the test set, thus showing good predictive quality. The descriptors used in the equations were primarily electronic and steric. These equations can be used to predict the separation factors of nitrogen heterocycles not yet synthesised and/or tested and hence obtain the most efficient ligands for lanthanide and actinide separation. (C) 2003 Elsevier B.V. All rights reserved.

Influence of disaccharide structure on prebiotic selectivity in vitro

To obtain structure-function information of a range of carbohydrates, which are available only in very small quantities, an in vitro fermentation method using 7 mg of carbohydrate, 0.7 mL of basal medium, and 1% (w/v) of fecal bacteria was validated against a pH-controlled batch culture with 150 mL of basal medium and 1.5g of test carbohydrate. This method was used to determine the influence of different glycosidic linkages and monosaccharide compositions of disaccharides on the selectivity of microbial fermentation. A prebiotic index (PI) was calculated for each disaccharide. Generally, disaccharides with linkages of 1-2, 1-4, and 1-6 generated a high PI score, with kojibiose and sophorose showing the greatest values (21.62 and 18.63, respectively). Apart from 6 alpha-mannobiose, mannose-containing disaccharicles gave a low PI due to low numbers of bifidobacteria and lactobacilli and an increase in bacteroides. The structure-function information obtained in this study may lead to a predictive understanding of how specific structures are fermented by the human gut microflora.

Application of constrained predictive control on a 3D crane system

This paper describes the SIMULINK implementation of a constrained predictive control algorithm based on quadratic programming and linear state space models, and its application to a laboratory-scale 3D crane system. The algorithm is compatible with Real Time. Windows Target and, in the case of the crane system, it can be executed with a sampling period of 0.01 s and a prediction horizon of up to 300 samples, using a linear state space model with 3 inputs, 5 outputs and 13 states.

Predictive control using feedback linearization based on dynamic neural models

This paper presents a hybrid control strategy integrating dynamic neural networks and feedback linearization into a predictive control scheme. Feedback linearization is an important nonlinear control technique which transforms a nonlinear system into a linear system using nonlinear transformations and a model of the plant. In this work, empirical models based on dynamic neural networks have been employed. Dynamic neural networks are mathematical structures described by differential equations, which can be trained to approximate general nonlinear systems. A case study based on a mixing process is presented.

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.