Distribution of (TTAGGG)n telomeric sequences in karyotypes of the Xenopus species complex

The chromosomal distribution of the conserved vertebrate telomeric (TTAGGG)(n) sequence was studied by fluorescence in situ hybridization (FISH) in four Xenopus species and the triploid Silurana tropicalis. As expected, hybridization signals were observed at the distal ends of every chromosome in all species. In addition, the hybridization pattern demonstrates varied organization of (TTAGGG)(n) sequences in the different karyotypes. In X. borealis and X. muelleri hybridization signals intensely labeled one end of a homologous chromosome pair that coincides with the sites containing ribosomal RNA gene clusters. The karyotype of X. clivii remarkably differs from other Xenopus karyotypes in displaying numerous interstitial telomeric sites (ITS). C-banding analysis shows that the non-telomeric sites appear to correspond to the interstitially located constitutive heterochromatin. This suggests that interstitial telomeric sites in X. clivii do not necessarily represent the relic of ancestral telomeres resulting from the fusion of chromosomes, but their occurrence is due to the fact that (TTAGGG)(n) repeat arrays may be a constituent of highly repetitive DNA.

Selecting a restoration technique to minimize OCR error

This paper introduces a learning problem related to the task of converting printed documents to ASCII text files. The goal of the learning procedure is to produce a function that maps documents to restoration techniques in such a way that on average the restored documents have minimum optical character recognition error. We derive a general form for the optimal function and use it to motivate the development of a nonparametric method based on nearest neighbors. We also develop a direct method of solution based on empirical error minimization for which we prove a finite sample bound on estimation error that is independent of distribution. We show that this empirical error minimization problem is an extension of the empirical optimization problem for traditional M-class classification with general loss function and prove computational hardness for this problem. We then derive a simple iterative algorithm called generalized multiclass ratchet (GMR) and prove that it produces an optimal function asymptotically (with probability 1). To obtain the GMR algorithm we introduce a new data map that extends Kesler's construction for the multiclass problem and then apply an algorithm called Ratchet to this mapped data, where Ratchet is a modification of the Pocket algorithm . Finally, we apply these methods to a collection of documents and report on the experimental results.

Explication of the coping goal construct: implications for coping and reemployment

This longitudinal study developed a reemployment coping goal construct and examined its role in a job-loss context. Several predictors of displaced workers' reemployment coping goal intensity were examined: human capital, employment commitment, internal coping resources, and anticipation of job loss. Results show that human capital, employment commitment, internal coping resources, and anticipation of job loss positively predicted a reemployment coping goal. Human capital also directly predicted reemployment. Finally, unemployed individuals' reemployment coping goal intensity positively predicted job-search effort, which positively predicted reemployment. Implications for the process of coping with job loss and future research are discussed.