Environmental factors shaping the distribution of common wintering waterbirds in a lake ecosystem with developed shoreline

In this study, we tested whether the spatial distribution of waterbirds is influenced by shoreline urbanization or other habitat characteristics. We conducted monthly censuses along shoreline sections of a continental lake (Lake Balaton, Hungary) to assess the abundance of 11 common species that use this lake as a feeding and staging area during migration and winter. We estimated the degree of urbanization of the same shoreline sections and also measured other habitat characteristics (water depth, extent of reed cover, biomass of zebra mussels, distances to waste dumps and to other wetlands). We applied linear models and model averaging to identify habitat variables with high relative importance for predicting bird distributions. Bird abundance and urbanization were strongly related only in one species. Other habitat variables exhibited stronger relationships with bird distribution: (1) diving ducks and coots preferred shoreline sections with high zebra mussel biomass, (2) gulls preferred sites close to waste dumps, and (3) the abundances of several species were higher on shoreline sections close to other wetlands. Our findings suggest that the distribution of waterbirds on Lake Balaton is largely independent of shoreline urbanization and influenced by food availability and connectivity between wetlands

Controlling complexity: the clinical relevance of mouse complex genetics.

Experimental animal models are essential to obtain basic knowledge of the underlying biological mechanisms in human diseases. Here, we review major contributions to biomedical research and discoveries that were obtained in the mouse model by using forward genetics approaches and that provided key insights into the biology of human diseases and paved the way for the development of novel therapeutic approaches

Estimation of protein secondary structure from FTIR spectra using neural networks.

Secondary structure of proteins have been predicted using neural networks (NN) from their Fourier transform infrared spectra. Leave-one-out approach has been used to demonstrate the applicability of the method. A form of cross-validation is used to train NN to prevent the overfitting problem. Multiple neural network outputs are averaged to reduce the variance of predictions. The networks realized have been tested and rms errors of 7.7% for alpha -helix, 6.4% for beta -sheet and 4.8% for turns have been achieved. These results indicate that the methodology introduced is effective and estimation accuracies are in some cases better than those previously reported in the literature

Using artificially generated spectral data to improve protein secondary structure prediction from Fourier transform infrared spectra of proteins.

Secondary structures of proteins have been predicted using neural networks from their Fourier transform infrared spectra. To improve the generalization ability of the neural networks, the training data set has been artificially increased by linear interpolation. The leave-one-out approach has been used to demonstrate the applicability of the method. Bayesian regularization has been used to train the neural networks and the predictions have been further improved by the maximum-likelihood estimation method. The networks have been tested and standard error of prediction (SEP) of 4.19% for alpha helix, 3.49% for beta sheet, and 3.15% for turns have been achieved. The results indicate that there is a significant decrease in the SEP for each type of structure parameter compared to previous works

Effects of lipoic acid supplementation on rat brain tissue: An FTIR spectroscopic and neural network study

The unfortunate increase in exposure to free radicals justifies antioxidant supplementation. Therefore, in the current study, the effect of exogenously administered lipoic acid, a natural amphipathic antioxidant, on rat brain tissue was investigated via Fourier transform infrared spectroscopy in order to understand its interactions with biological molecules. The results suggest that lipoic acid slightly disorders the acyl chains of phospholipids as observed from the frequency of the CH, stretching vibrations while it strengthens the hydrogen bonding of the interfacial region of phospholipids as indicated by the C=O stretching band. Moreover, lipoic acid seems to cause an increase in the quantity of proteins, without affecting the protein secondary structure revealed by neural network predictions based on FTIR data. These slight variations in the lipid structure and the unaltered protein secondary structure may suggest that lipoic acid is non-toxic and thus support the usage of lipoic acid as an antioxidant supplement