Potential energy surfaces for cluster emitting nuclei

Potential energy surfaces are calculated by using the most advanced asymmetric two-center shell model allowing to obtain shell and pairing corrections which are added to the Yukawa-plus-exponential model deformation energy. Shell effects are of crucial importance for experimental observation of spontaneous disintegration by heavy ion emission. Results for 222Ra, 232U, 236Pu and 242Cm illustrate the main ideas and show for the first time for a cluster emitter a potential barrier obtained by using the macroscopic-microscopic method.Comment: 10 pages, 21 figures, revtex

Ecklonia Maxima Extract Effect in Tissue Regeneration of Symbionts at in Vivo Heteroplasmic Grafting of Some Tomatoes

The research was conducted to determine the Ecklonia maxima extract effect in the symbiont accretion at the in vivo heteroplasmic grafting of some tomatoes. E. maxima or sea bamboo is a seaweed used for obtaining of organic extracts used as stimulators in horticulture because consists the natural plant hormones such as auxins and cytokinins which have optimal role in cell division, important activity for tissue regeneration. The experimental variants were grafted plants, combinations between different symbionts, cultivar fragments from Lycopersicon esculentum specie. The symbionts were two scions, F1 hybrids, 'Siriana' (Romanian cultivar), 'Abellus' (Dutch cultivar) and two rootstocks, 'Buzău' variety (Romanian cultivar), 'Emperador' F1 hybrid (Dutch cultivar). The algae extract used had auxins (11 mg/L) and cytokinins (0.3 mg/L). Two treatments were applied before grafting on scion and rootstock, 1 mL/500 mL water and a treatment at grafting on soil, 5 mL/L water. Control variant was without hormone extract. Determinations, observations and interpretations of the algae effect were made on symbionts. The best results on tissue regeneration were obtained in plants treated with sea bamboo extract compared to untreated control plants. The E. maxima extract influenced the tissue regeneration

Cystic fibrosis mice carrying the missense mutation G551D replicate human genotype phenotype correlations

We have generated a mouse carrying the human G551D mutation in the cystic fibrosis transmembrane conductance regulator gene (CFTR) by a one-step gene targeting procedure. These mutant mice show cystic fibrosis pathology but have a reduced risk of fatal intestinal blockage compared with 'null' mutants, in keeping with the reduced incidence of meconium ileus in G551D patients. The G551D mutant mice show greatly reduced CFTR-related chloride transport, displaying activity intermediate between that of cftr(mlUNC) replacement ('null') and cftr(mlHGU) insertional (residual activity) mutants and equivalent to approximately 4% of wild-type CFTR activity. The long-term survival of these animals should provide an excellent model with which to study cystic fibrosis, and they illustrate the value of mouse models carrying relevant mutations for examining genotype-phenotype correlations

Plasma Resonance in Layered Normal Metals and Superconductors

A microscopic theory of the plasma resonance in layered metals is presented. It is shown that electron-impurity scattering can suppress the plasma resonance in the normal state and sharpen it in the superconducting state. Analytic properties of the conductivity for the electronic transport perpendicular to the layers are investigated. The dissipative part of the electromagnetic response in c-direction has been found to depend on frequency in a highly non-trivial manner. This sort of behavior cannot be incorporated in the widely used phenomenological Gorter-Kazimir model.Comment: 34 pages including 12 figures in uuencoded.file. A revised version. Several formulas and a number of misprints are corrected. A problem with printing of figures is fixe

Methods for cost-sensitive learning

Many approaches for achieving intelligent behavior of automated (computer) systems involve components that learn from past experience. This dissertation studies computational methods for learning from examples, for classification and for decision making, when the decisions have different non-zero costs associated with them. Many practical applications of learning algorithms, including transaction monitoring, fraud detection, intrusion detection, and medical diagnosis, have such non-uniform costs, and there is a great need for new methods that can handle them. This dissertation discusses two approaches to cost-sensitive classification: input data weighting and conditional density estimation. The first method assigns a weight to each training example in order to force the learning algorithm (which is otherwise unchanged) to pay more attention to examples with higher misclassification costs. The dissertation discusses several different weighting methods and concludes that a method that gives higher weight to examples from rarer classes works quite well. Another algorithm that gave good results was a wrapper method that applies Powell's gradient-free algorithm to optimize the input weights. The second approach to cost-sensitive classification is conditional density estimation. In this approach, the output of the learning algorithm is a classifier that estimates, for a new data point, the probability that it belongs to each of the classes. These probability estimates can be combined with a cost matrix to make decisions that minimize the expected cost. The dissertation presents a new algorithm, bagged lazy option trees (B-LOTs), that gives better probability estimates than any previous method based on decision trees. In order to evaluate cost-sensitive classification methods, appropriate statistical methods are needed. The dissertation presents two new statistical procedures: BLOTs provides a confidence interval on the expected cost of a classifier, and BDELTACOST provides a confidence interval on the difference in expected costs of two classifiers. These methods are applied to a large set of experimental studies to evaluate and compare the cost-sensitive methods presented in this dissertation. Finally, the dissertation describes the application of the B-LOTs to a problem of predicting the stability of river channels. In this study, B-LOTs were shown to be superior to other methods in cases where the classes have very different frequencies a situation that arises frequently in cost-sensitive classification problems