Linnaeus was right all along: Ulva and Enteromorpha are not distinct genera.

Ulva, one of the first Linnaean genera, was later circumscribed to consist of green seaweeds with distromatic blades, and Enteromorpha Link was established for tubular forms. Although several lines of evidence suggest that these generic constructs are artificial, Ulva and Enteromorpha have been maintained as separate genera. Our aims were to determine phylogenetic relationships among taxa currently attributed to Ulva, Enteromorpha, Umbraulva Bae et I.K. Lee and the monotypic genus Chloropelta C.E. Tanner, and to make any nomenclatural changes justified by our findings. Analyses of nuclear ribosomal internal transcribed spacer DNA (ITS nrDNA) (29 ingroup taxa including the type species of Ulva and Enteromorpha), the chloroplast-encoded rbcL gene (for a subset of taxa) and a combined data set were carried out. All trees had a strongly supported clade consisting of all Ulva, Enteromorpha and Chloropelta species, but Ulva and Enteromorpha were not monophyletic. The recent removal of Umbraulva olivascens (P.J.L. Dangeard) Bae et I.K. Lee from Ulva is supported, although the relationship of the segregate genus Umbraulva to Ulvaria requires further investigation. These results, combined with earlier molecular and culture data, provide strong evidence that Ulva, Enteromorpha and Chloropelta are not distinct evolutionary entities and should not be recognized as separate genera. A comparison of traits for surveyed species revealed few synapomorphies. Because Ulva is the oldest name, Enteromorpha and Chloropelta are here reduced to synonymy with Ulva, and new combinations are made where necessary.

Enhanced approach for latent semantic indexing using wavelet transform

Latent semantic indexing (LSI) is a technique used for intelligent information retrieval (IR). It can be used as an alternative to traditional keyword matching IR and is attractive in this respect because of its ability to overcome problems with synonymy and polysemy. This study investigates various aspects of LSI: the effect of the Haar wavelet transform (HWT) as a preprocessing step for the singular value decomposition (SVD) in the key stage of the LSI process; and the effect of different threshold types in the HWT on the search results. The developed method allows the visualisation and processing of the term document matrix, generated in the LSI process, using HWT. The results have shown that precision can be increased by applying the HWT as a preprocessing step, with better results for hard thresholding than soft thresholding, whereas standard SVD-based LSI remains the most effective way of searching in terms of recall value.