Keto acids and free amino acids during leaf growth in Bauhinia purpurea L

Summary The biosynthesis of keto acids and free amino acids was studied during the growth of Bauhinia purpurea leaves. -KGA, OAA, pyruvic acid and PEP are the important keto acids observed at various stages. The first 2 metabolites show a progressive increase and PEP OAA pathway is very active during the process of growth.     

Production of pig iron from red mud waste fines using thermal plasma technology

Red mud, an insoluble residue produced during alkali leaching of bauxite, is considered as a low-grade iron ore containing 30% to 50% iron. The present paper deals with the use of thermal plasma technology for producing pig iron from red mud waste fines. The smelting reduction of red mud was carried out in a 35 kW DC extended arc thermal plasma reactor. Red mud was properly mixed with fluxes and graphite (fixed carbon, 99%) as a reductant as per stoichiometric requirement. The effect of various process parameters like a reductant, fluxes and smelting time on iron recovery was studied and optimized. An optimum condition for the maximum recovery of iron was obtained. A new thermal plasma process applicable to direct iron making from red mud waste fines that would achieve significant utilization of red mud was proposed.     

General conditions for predictivity in learning theory

Developing theoretical foundations for learning is a key step towards understanding intelligence. 'Learning from examples' is a paradigm in which systems (natural or artificial) learn a functional relationship from a training set of examples. Within this paradigm, a learning algorithm is a map from the space of training sets to the hypothesis space of possible functional solutions. A central question for the theory is to determine conditions under which a learning algorithm will generalize from its finite training set to novel examples. A milestone in learning theory was a characterization of conditions on the hypothesis space that ensure generalization for the natural class of empirical risk minimization (ERM) learning algorithms that are based on minimizing the error on the training set. Here we provide conditions for generalization in terms of a precise stability property of the learning process: when the training set is perturbed by deleting one example, the learned hypothesis does not change much. This stability property stipulates conditions on the learning map rather than on the hypothesis space, subsumes the classical theory for ERM algorithms, and is applicable to more general algorithms. The surprising connection between stability and predictivity has implications for the foundations of learning theory and for the design of novel algorithms, and provides insights into problems as diverse as language learning and inverse problems in physics and engineering.     

Peroxidase mediated oxidation of tyrosine: Tyrosine hydroxylation in tea leaves

Zusammenfassung Untersuchungen über die Beeinflussung der Tyrosinhydroxylierung durch Peroxidase in Teeblättern (Camellia sinensis L.) mit Hilfe von Antooxydantien.