Bacterial genomes have diverged during evolution, resulting in clearcut differences in their nucleotide composition, such as their GC content. The analysis of complete sequences of bacterial genomes also reveals the presence of nonrandom sequence variation, manifest in the frequency profile of specific short oligonucleotides. These frequency profiles constitute highly specific genomic signatures. Based on these differences in oligonucleotide frequency between bacterial genomes, we investigated the possibility of predicting the genome of origin for a specific genomic sequence. To this end, we developed a naïve Bayesian classifier and systematically analyzed 28 eubacterial and archaeal genomes. We found that sequences as short as 400 bases could be correctly classified with an accuracy of 85%. We then applied the classifier to the identification of horizontal gene transfer events in whole-genome sequences and demonstrated the validity of our approach by correctly predicting the transfer of both the superoxide dismutase (sodC) and the bioC gene from Haemophilus influenzae to Neisseria meningitis, correctly identifying both the donor and recipient species. We believe that this classification methodology could be a valuable tool in biodiversity studies.
A prerequisite for all higher level information extraction tasks is the identification of unknown names in text. Today, when large corpora can consist of billions of words, it is of utmost importance to develop accurate techniques for the automatic detection, extraction and categorization of named entities in these corpora. Although named entity recognition might be regarded a solved problem in some domains, it still poses a significant challenge in others. In this work we focus on one of the more difficult tasks, the identification of protein names in text. This task presents several interesting difficulties because of the named entities variant structural characteristics, their sometimes unclear status as names, the lack of common standards and fixed nomenclatures, and the specifics of the texts in the molecular biology domain in which they appear. We describe how we approached these and other difficulties in the implementation of Yapex, a system for the automatic identification of protein names in text. We also evaluate Yapex under four different notions of correctness and compare its performance to that of another publicly available system for protein name recognition.
HIV-1 infection causes functional defects in T cells. It also leads to a progressive reduction in numbers of such cells and both CD4+ and CD8+ cells have been reported to undergo apoptosis in culture. A corresponding reduction in B cells has not been described, but these cells are also functionally altered, with reports of polyclonal activation and hyporesponsiveness to antigenic and mitogenic stimuli. Here we investigated B cells from HIV-1-seropositive individuals and found that these cells, which are not the target for virus infection, died of apoptosis on culturing. We could also confirm previous findings that CD4+ cells from HIV-1-infected individuals undergo apoptosis in culture. Apoptosis of both B cells and CD4+ cells correlated inversely with CD4 cell counts. B cells from HIV-1-infected individuals were found to express Fas ligand, and the expression of this protein correlated with the levels of apoptosis in the same cells. Non-B cells, on the other hand, expressed increased levels of Fas but low levels of Fas ligand. These results are in line with suggestions that the Fas/Fas ligand pathway may trigger the increased levels of apoptosis observed in cells from HIV-1-infected individuals.
The concerted and self-organizing behavior of spinal cord segments in generating locomotor patterns is modulated by afferent sensory information and controlled by descending pathways from the brainstem, cerebellum, or cortex. The purpose of this study was to define a minimal set of parameters that could control a similar self-organizing behavior in a two-dimensional neural network. When we implemented synaptic depression and active membrane repolarization as two properties of the neurons, the two-dimensional neural network generated traveling waves. Their wavelength and angle of propagation could be independently controlled by two parameters that modulated excitatory premotor neurons and inhibitory commissural neurons. It is further demonstrated that the selection of wave parameters corresponds to the selection of quadruped gaits.
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