VIDA: a virus database system for the organization of animal virus genome open reading frames

VIDA is a new virus database that organizes open reading frames (ORFs) from partial and complete genomic sequences from animal viruses. Currently VIDA includes all sequences from GenBank for Herpesviridae, Coronaviridae and Arteriviridae. The ORFs are organized into homologous protein families, which are identified on the basis of sequence similarity relationships. Conserved sequence regions of potential functional importance are identified and can be retrieved as sequence alignments. We use a controlled taxonomical and functional classification for all the proteins and protein families in the database. When available, protein structures that are related to the families have also been included. The database is available for online search and sequence information retrieval at http://www.biochem.ucl.ac.uk/bsm/virus_database/VIDA.html.

Changes in Organization of the Endoplasmic Reticulum during Xenopus Oocyte Maturation and ActivationV⃞

The organization of the endoplasmic reticulum (ER) in the cortex of Xenopus oocytes was investigated during maturation and activation using a green fluorescent protein chimera, immunofluorescence, and electron microscopy. Dense clusters of ER developed on the vegetal side (the side opposite the meiotic spindle) during maturation. Small clusters appeared transiently at the time of nuclear envelope breakdown, disappeared at the time of first polar body formation, and then reappeared as larger clusters in mature eggs. The appearance of the large ER clusters was correlated with an increase in releaseability of Ca2+ by IP3. The clusters dispersed during the Ca2+ wave at activation. Possible relationships of ER structure and Ca2+ regulation are discussed.

Functional organization of the hippocampal memory system

In humans declarative or explicit memory is supported by the hippocampus and related structures of the medial temporal lobe working in concert with the cerebral cortex. This paper reviews our progress in developing an animal model for studies of cortical–hippocampal interactions in memory processing. Our findings support the view that the cortex maintains various forms of memory representation and that hippocampal structures extend the persistence and mediate the organization of these codings. Specifically, the parahippocampal region, through direct and reciprocal interconnections with the cortex, is sufficient to support the convergence and extended persistence of cortical codings. The hippocampus itself is critical to the organization cortical representations in terms of relationships among items in memory and in the flexible memory expression that is the hallmark of declarative memory.

Structure-activity relationships derived by machine learning: the use of atoms and their bond connectivities to predict mutagenicity by inductive logic programming.

We present a general approach to forming structure-activity relationships (SARs). This approach is based on representing chemical structure by atoms and their bond connectivities in combination with the inductive logic programming (ILP) algorithm PROGOL. Existing SAR methods describe chemical structure by using attributes which are general properties of an object. It is not possible to map chemical structure directly to attribute-based descriptions, as such descriptions have no internal organization. A more natural and general way to describe chemical structure is to use a relational description, where the internal construction of the description maps that of the object described. Our atom and bond connectivities representation is a relational description. ILP algorithms can form SARs with relational descriptions. We have tested the relational approach by investigating the SARs of 230 aromatic and heteroaromatic nitro compounds. These compounds had been split previously into two subsets, 188 compounds that were amenable to regression and 42 that were not. For the 188 compounds, a SAR was found that was as accurate as the best statistical or neural network-generated SARs. The PROGOL SAR has the advantages that it did not need the use of any indicator variables handcrafted by an expert, and the generated rules were easily comprehensible. For the 42 compounds, PROGOL formed a SAR that was significantly (P < 0.025) more accurate than linear regression, quadratic regression, and back-propagation. This SAR is based on an automatically generated structural alert for mutagenicity.