Evidence of clonal budding in a radial cluster of Paraconularia crustula (White) (Pennsylvanian: ?Cnidaria)

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/73153/1/j.1502-3931.1992.tb01645.x.pd

New data on the morphology of Sphenothallus Hall: implications for its affinities

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/73676/1/j.1502-3931.1992.tb01378.x.pd

Functional morphology and systematic position of tabulatomorphs

Tabulatomorph functional morphology being a reliable guide to locate the group among Metazoa, the authors, who were supplied with an abundant North-African material, offer evidence particularly to compare them with sponges. They provisionally conclude that most of them were allied to primitive sponges and even algae but achieved various differentiations more advanced than any of those

Characterization of new proteins found by analysis of short open reading frames from the full yeast genome

We have analysed short open reading frames (between 150 and 300 base pairs long) of the yeast genome (Saccharomyces cerevisiae) with a two-step strategy. The first step selects a candidate set of open reading frames from the DNA. sequence based on statistical evaluation of DNA and protein sequence properties. The second step filters the candidate set by selecting open reading frames with high similarity to other known sequences (from any organism). As a result, we report ten new predicted proteins not present in the current sequence databases. These include a new alcohol dehydrogenase, a protein probably related to the cell cycle, as well as a homolog of the prokaryotic ribosomal protein L36 likely to be a mitochondrial ribosomal protein coded in the nuclear genome. We conclude that the analysis of short open reading frames leads to biologically interesting discoveries, even though the quantitative yield of new proteins is relatively low

Adaptive XML Tree Classification on evolving data streams

Abstract. We propose a new method to classify patterns, using closed and maximal frequent patterns as features. Generally, classification requires a previous mapping from the patterns to classify to vectors of features, and frequent patterns have been used as features in the past. Closed patterns maintain the same information as frequent patterns using less space and maximal patterns maintain approximate information. We use them to reduce the number of classification features. We present a new framework for XML tree stream classification. For the first component of our classification framework, we use closed tree mining algorithms for evolving data streams. For the second component, we use state of the art classification methods for data streams. To the best of our knowledge this is the first work on tree classification in streaming data varying with time. We give a first experimental evaluation of the proposed classification method.