Bacterial oligopeptide permeases are membrane-associated complexes of five proteins belonging to the ABC-transporter family, which have been found to be involved in obtaining nutrients, cell-wall metabolism, competence, and adherence to host cells. A lambda library of the strain CS101 group A streptococcal (GAS) genome was used to sequence 10,192 bp containing the five genes oppA to oppF of the GAS opp operon. The deduced amino acid sequences exhibited 50-84% homology to pneumococcal AmiA to AmiF sequences. The operon organization of the five genes was confirmed by transcriptional analysis and an additional shorter oppA transcript was detected. Insertional inactivation was used to create serotype M49 strains which did not express either the oppA gene or the ATPase genes, oppD and oppF. The mutation in oppA confirmed that the additional shorter oppA transcript originated from the opp operon and was probably due to an intra-operon transcription terminator site located downstream of oppA. While growth kinetics, binding of serum proteins, and attachment to eukaryotic cells were unaffected, the oppD/F mutants showed reduced production of the cysteine protease, SpeB, and a change in the pattern of secreted proteins. Thus, the GAS opp operon appears to contribute to both protease production and export/processing of secreted proteins.
Expression Profiler (EP, http://www.ebi.ac.uk/expressionprofiler) is a web-based platform for microarray gene expression and other functional genomics-related data analysis. The new architecture, Expression Profiler: next generation (EP:NG), modularizes the original design and allows individual analysis-task-related components to be developed by different groups and yet still seamlessly to work together and share the same user interface look and feel. Data analysis components for gene expression data preprocessing, missing value imputation, filtering, clustering methods, visualization, significant gene finding, between group analysis and other statistical components are available from the EBI (European Bioinformatics Institute) web site. The web-based design of Expression Profiler supports data sharing and collaborative analysis in a secure environment. Developed tools are integrated with the microarray gene expression database ArrayExpress and form the exploratory analytical front-end to those data. EP:NG is an open-source project, encouraging broad distribution and further extensions from the scientific community.
Ensemble-based active learning has been proven to efficiently reduce the number of training instances and thus the cost of data acquisition. To determine the utility of a candidate training instance, the disagreement about its class value among the ensemble members is used. While the disagreement for binary classification is easily determined using margins, the adaption to multi-class problems is not straightforward and little studied in the literature. In this paper we consider four approaches to measure ensemble disagreement, including margins, uncertainty sampling and entropy, and evaluate them empirically on various ensemble strategies for active learning. We show that margins outperform the other disagreement measures on three of four active learning strategies. Our experiments also show that some active learning strategies are more sensitive to the choice of disagreement measure than others.
We introduce s-kNN, a nearest neighbor based spatial data mining algorithm. It belongs to the class of vector-geometry based algorithms that reason on complex spatial objects instead of point measurements. In contrast to most methods in this class, it does on the fly spatial computations that cannot be replaced by a preprocessing step without sacrificing efficiency. The key is a partial evaluation scheme for efficient computations. The algorithm is fully integrated into an object-relational spatial database. It is the basis for traffic frequency predictions (vehicles and pedestrians) for all German cities larger than 50,000 inhabitants and is the basis for pricing of posters in Germany.
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