In this work the results of the study on the degradation of dairy waste and lactose with use of titanium dioxide (TiO2), as photocatalyst, are presented. The ability of TiO2 to degrade dairy waste is compared either in the presence of molecular oxygen or of a bacterial hydrogenase as electron acceptor. The enzyme-mediated H2 production rates or the O2 consumption rates are used to measure electron donor degradation. The results obtained clearly indicate that dairy waste can be degraded by the TiO2 photocatalyst. Proteins present in the dairy waste have a strong inhibitory effect on the degradation process. Indeed lactose alone can easily be degraded. When the protein extract obtained from the dairy waste was added to the lactose solution, the reaction for both electron acceptors, hydrogenase and oxygen, was inhibited. When the dairy waste solutions were diluted, there was a positive effect on the reaction rate. This was particularly true in the case of hydrogenase, and to a lesser extent in the case of oxygen acceptor. The reduction of the pH from 8 to 6 also increased H2 production when the enzyme was used.

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Artificial neural networks (ANNs) are a simple and rapid system for pattern recognition. In this study they were used to classify Mössbauer spectra of penta-coordinated and octahedral Sn(iv) complexes. Mössbauer spectra recognition is a lengthy procedure requiring a great deal of experience. The application of a system such as artificial neural networks provides a rapid and accurate method for the correct classification of Mössbauer spectra. As the two categories of spectra are not linearly separable, conventional techniques like principal component analysis (PCA) or perceptron can not be used. A more complex ANN was therefore used to solve this problem. The network was built as a standard three-layer back-propagation network with 256 input neurons, 2 hidden neurons and 1 output neuron and a sigmoidal activation function. The network's performance was tested with test sets of 10, 20 and 50% of the total number of spectra. The mean square error (MSE) of the different test sets show significant differences. This type of network was able to classify correctly the spectra with an MSE of less than 0.030. Moreover, the network was even able to classify in the appropriate class a spectrum that had been deliberately inverted, demonstrating the ability of ANN to recognize objects affected by noise or distortion.

Conformational modifications and changes in the aggregation state of human alphaB-crystallin were investigated at different concentrations of SDS, KBr, urea, and NH4SCN and at different temperatures. Intrinsic fluorescence measurements indicated complete and reversible unfolding of the protein at 2 M NH4SCN, whereas the concentration of urea required for complete and irreversible unfolding was 6 M. Gel permeation chromatography indicated almost complete dissociation of the micelle-like aggregate of alphaB-crystallin in 2 M NH4SCN, but only partial dissociation into large-sized aggregates in 6 M urea. Thiocyanate-treated alphaB-crystallin recovered its chaperone-like activity upon dilution of the dissociating agent, whereas the urea-treated protein did not.