Characteristics of piraltin, a polyphenol concentrate, produced by freeze-drying of red wine

Moderate consumption of red wine is associated with a decreased risk for coronary heart disease. Apart from alcohol, an additive role for wine polyphenols has been suggested. However, the real contribution of these compounds can only be studied when available without the alcohol component. The objective of the study was to prepare a wine polyphenol concentrate not containing alcohol and to compare the quantitative and qualitative properties of this concentrate with those of the original wine from which the concentrate is made. This polyphenol concentrate, called piraltin, was made out of red wine by a freeze-drying technique. Both piraltin and the original red wine were analyzed quantitatively for the main polyphenols present: gallic acid, catechin, epicatechin and quercetin. The qualitative comparison comprised the inhibitory effect of the two products on LDL oxidation in vitro. In the process of freeze-drying recovery of the four determined flavonoids of red wine is fairly constant (average 68 +/- 7%). In a copper induced LDL oxidation assay both red wine and piraltin prolonged lag-times over 300% compared to controls without a significant difference between the two products. The freeze-dried polyphenol concentrate piraltin contains about 70% of the total polyphenol content of the original wine. This preparation technique does not cause a loss of antioxidative properties of the phenols. Piraltin creates the possibility to study the effects of wine polyphenols separately without the influence of alcohol both in vitro and in vivo. (C) 2003 Elsevier Inc. All rights reserved

The Crawler

The demonstrator illustrates how behaviour can be sculpted though experience by autonomous training. The underlying theory, known as Reinforcement Learning, is a view on situations involving behaviour. At the same time it is a principle that explains behaviour. Last but not least it is a computational principle: you can use it to create behaviour. The essence of the approach is that an artefact is created that can obtain rewards

Teaching machine learning to design students

Machine learning is a key technology to design and create intelligent systems, products, and related services. Like many other design departments, we are faced with the challenge to teach machine learning to design students, who often do not have an inherent affinity towards technology. We successfully used the Embodied Intelligence method to teach machine learning to our students. By embodying the learning system into the Lego Mindstorm NXT platform we provide the student with a tangible tool to understand and interact with a learning system. The resulting behavior of the tangible machines in combination with the positive associations with the Lego system motivated all the students. The students with less technology affinity successfully completed the course, while the students with more technology affinity excelled towards solving advanced problems. We believe that our experiences may inform and guide other teachers that intend to teach machine learning, or other computer science related topics, to design students

Teaching machine learning to design students

Machine learning is a key technology to design and create intelligent systems, products, and related services. Like many other design departments, we are faced with the challenge to teach machine learning to design students, who often do not have an inherent affinity towards technology. We successfully used the Embodied Intelligence method to teach machine learning to our students. By embodying the learning system into the Lego Mindstorm NXT platform we provide the student with a tangible tool to understand and interact with a learning system. The resulting behavior of the tangible machines in combination with the positive associations with the Lego system motivated all the students. The students with less technology affinity successfully completed the course, while the students with more technology affinity excelled towards solving advanced problems. We believe that our experiences may inform and guide other teachers that intend to teach machine learning, or other computer science related topics, to design students