A Tree Augmented Classifier Based on Extreme Imprecise Dirichlet Model

In this paper we present TANC, i.e., a tree-augmented naive credal classifier based on imprecise probabilities; it models prior near-ignorance via the Extreme Imprecise Dirichlet Model (EDM) (Cano et al., 2007) and deals conservatively with missing data in the training set, without assuming them to be missing-at-random. The EDM is an approximation of the global Imprecise Dirichlet Model (IDM), which considerably simplifies the computation of upper and lower probabilities; yet, having been only recently introduced, the quality of the provided approximation needs still to be verified. As first contribution, we extensively compare the output of the naive credal classifier (one of the few cases in which the global IDM can be exactly implemented) when learned with the EDM and the global IDM; the output of the classifier appears to be identical in the vast majority of cases, thus supporting the adoption of the EDM in real classification problems. Then, by experiments we show that TANC is more reliable than the precise TAN (learned with uniform prior), and also that it provides better performance compared to a previous (Zaffalon, 2003) TAN model based on imprecise probabilities. TANC treats missing data by considering all possible completions of the training set, but avoiding an exponential increase of the computational times; eventually, we present some preliminary results with missing data.

NP-hardness of MAP in Ternary Tree Bayesian Networks

This paper strengthens the NP-hardness result for the (partial) maximum a posteriori (MAP) problem in Bayesian networks with topology of trees (every variable has at most one parent) and variable cardinality at most three. MAP is the problem of querying the most probable state configuration of some (not necessarily all) of the network variables given evidence. It is demonstrated that the problem remains hard even in such simplistic networks.

Biomass allocation, phosphorus nutrition and vesicular-arbuscular mycorrhizal infection in clones of Yorkshire Fog, Holcus lanatus L. (Poaceae) that differ in their phosphate uptake kinetics and tolerance to arsenate

Biomass and phosphorus allocation were determined in arsenate tolerant and non-tolerant clones of the grass Holcus lanatus L. in both solution culture and in soil. Arsenate is a phosphate analogue and is taken up by the phosphate uptake system. Tolerance to arsenate in this grass is achieved by suppression of arsenate (and phosphate) influx. When clones differing in their arsenate tolerance were grown in solution culture with a range of phosphate levels, a tolerant clone did not fare as well as a non-tolerant at low levels of phosphate nutrition in that it had reduced shoot biomass production, increased biomass allocation to the roots and lower shoot phosphorus concentration. At a higher level of phosphate nutrition there was little or no difference in these parameters, suggesting that differences at lower levels of phosphate nutrition were due solely to differences in the rates of phosphate accumulation. In experiments in sterile soil (potting compost) the situation was more complicated with tolerant plants having lower growth rates but higher phosphorus concentrations. The gene for arsenate tolerance is polymorphic in arsenate uncontaminated populations. When phosphorus concentration of tolerant phenotypes was determined in one such population, again tolerants had a higher phosphorus status than non-tolerants. Tolerants also had higher rates of vesicular-arbuscular mycorrhizal (VAM) infection. The ecological implications of these results are that it appears that suppression of the high affinity uptake system, is at least in part, compensated by increased mycorrhizal infection. © 1994 Kluwer Academic Publishers.

Phosphorus nutrition of arsenate-tolerant and nontolerant phenotypes of velvetgrass

Velvetgrass (Holcus lanatus L.), also known as Yorkshire fog grass, has evolved tolerance to high levels of arsenate, and this adaptation involves reduced accumulation of arsenate through the suppression of the high affinity phosphate-arsenate uptake system. To determine the role of P nutrition in arsenate tolerance, inhibition kinetics of arsenate influx by phosphate were determined. The concentration of inhibitor required to reduce maximum influx (V(max)) by 50%, K₁, of phosphate inhibition of arsenate influx was 0.02 mol m^-3 in both tolerant and nontolerant clones. This was compared with the concentration where influx is 50% of maximum, a K(m), for arsenate influx of 0.6 mol m^-3 for tolerants and 0.025 mol m^-3 for nontolerants and, therefore, phosphate was much more effective at inhibiting arsenate influx in tolerant genotypes. The high affinity phosphate uptake system is inducible under low plant phosphate status, this increasing plant phosphate status should increase tolerance by decreasing arsenate influx. Root extension in arsenate solutions of tolerant and nontolerant tillers grown under differing phosphate nutritional regimes showed that indeed, increased plant P status increased the tolerance to arsenate of both tolerant and nontolerant clones. That plant P status increased tolerance again argues that P nutrition has a critical role in arsenate tolerance. To determine if short term flux and solution culture studies were relevant to As and P accumulation in soils, soil and plant material from a range of As contaminated sites were analyzed. As predicted from the short-term competition studies, P was accumulated preferentially to As in arsenate tolerant clones growing on mine spoil soils even when acid extractable arsenate in the soils was much greater than acid extractable phosphate. Though phosphate was much more efficient at competing with arsenate for uptake, plants growing on arsenate contaminated land still accumulated considerable amounts of As. Plants from the differing habitats showed large variation in plant phosphate status, pasture plants having much higher P levels than plants growing on the most contaminated mine spoil soils. The selectivity of the phosphate-arsenate uptake system for phosphate compared with arsenate, coupled with the suppression of this uptake system enabled tolerant clones of the grass velvetgrass to grow on soils that were highly contaminated with arsenate and deficient in phosphate.

Impacts of Milk Fraud on Food Safety and Nutrition with Special Emphasis on Developing Countries

Milk in its natural form has a high food value, since it is comprised of a wide variety of nutrients which are essential for proper growth and maintenance of the human body. In recent decades, there has been an upsurge in milk consumption worldwide, especially in developing countries, and it is now forming a significant part of the diet for a high proportion of the global population. As a result of the increased demand, in addition to the growth in competition in the dairy market and the increasing complexity of the supply chain, some unscrupulous producers are indulging in milk fraud. This malpractice has become a common problem in the developing countries, which lack strict vigilance by food safety authorities. Milk is often subjected to fraud (by means of adulteration) for financial gain, but it can also be adulterated due to ill-informed attempts to improve hygiene conditions. Water is the most common adulterant used, which decreases the nutritional value of milk. If the water is contaminated, for example, with chemicals or pathogens, this poses a serious health risk for consumers. To the diluted milk, inferior cheaper materials may be added such as reconstituted milk powder, urea, and cane sugar, even more hazardous chemicals including melamine, formalin, caustic soda, and detergents. These additions have the potential to cause serious health-related problems. This review aims to investigate the impacts of milk fraud on nutrition and food safety, and it points out the potential adverse human health effects associated with the consumption of adulterated milk.

Averaged Extended Tree Augmented Naive Classifier

This work presents a new general purpose classifier named Averaged Extended Tree Augmented Naive Bayes (AETAN), which is based on combining the advantageous characteristics of Extended Tree Augmented Naive Bayes (ETAN) and Averaged One-Dependence Estimator (AODE) classifiers. We describe the main properties of the approach and algorithms for learning it, along with an analysis of its computational time complexity. Empirical results with numerous data sets indicate that the new approach is superior to ETAN and AODE in terms of both zero-one classification accuracy and log loss. It also compares favourably against weighted AODE and hidden Naive Bayes. The learning phase of the new approach is slower than that of its competitors, while the time complexity for the testing phase is similar. Such characteristics suggest that the new classifier is ideal in scenarios where online learning is not required.

Learning Extended Tree Augmented Naive Structures

This work proposes an extended version of the well-known tree-augmented naive Bayes (TAN) classifier where the structure learning step is performed without requiring features to be connected to the class. Based on a modification of Edmonds' algorithm, our structure learning procedure explores a superset of the structures that are considered by TAN, yet achieves global optimality of the learning score function in a very efficient way (quadratic in the number of features, the same complexity as learning TANs). We enhance our procedure with a new score function that only takes into account arcs that are relevant to predict the class, as well as an optimization over the equivalent sample size during learning. These ideas may be useful for structure learning of Bayesian networks in general. A range of experiments shows that we obtain models with better prediction accuracy than naive Bayes and TAN, and comparable to the accuracy of the state-of-the-art classifier averaged one-dependence estimator (AODE). We release our implementation of ETAN so that it can be easily installed and run within Weka.

Learning Bounded Tree-Width Bayesian Networks via Sampling

Learning Bayesian networks with bounded tree-width has attracted much attention recently, because low tree-width allows exact inference to be performed efficiently. Some existing methods [12, 14] tackle the problem by using k-trees to learn the optimal Bayesian network with tree-width up to k. In this paper, we propose a sampling method to efficiently find representative k-trees by introducing an Informative score function to characterize the quality of a k-tree. The proposed algorithm can efficiently learn a Bayesian network with tree-width at most k. Experiment results indicate that our approach is comparable with exact methods, but is much more computationally efficient.

Nutrition and the eye

The topic "nutrition and the eye" cannot adequately be covered in a single review article; indeed, dozens of books and hundreds of articles have been written on the subject. This review concentrates on three areas in which specific nutrients are known or theorized to have a major impact on vision and the visual system: vitamin A deficiency; antioxidants and their proposed role in the prevention of age-related cataract and macular degeneration; and nutritional optic neuropathies, including those of the recent Cuban epidemic. In addition, this article touches on nutritional treatments that have been suggested for several less common eye diseases and, finally, considers several less prevalent conditions in which deficiency of or excess exposure to a particular nutrient has been associated with ocular pathology.

Public perceptions of personalised nutrition through the lens of Social Cognitive Theory

Social Cognitive Theory has been used to explain findings derived from focus group discussions (N = 4) held in the United Kingdom with the aim of informing best practice in personalised nutrition. Positive expectancies included weight loss and negative expectancies surrounded on-line security. Monitoring and feedback were crucial to goal setting and progress. Coaching by the service provider, family and friends was deemed important for self-efficacy. Paying for personalised nutrition symbolised commitment to behaviour change. The social context of eating, however, was perceived a problem and should be considered when designing personalised diets. Social Cognitive Theory could provide an effective framework through which to deliver personalised nutrition.