Identification and classification of p53-regulated genes

Sequence-specific transactivation by p53 is essential to its role as a tumor suppressor. A modified tetracycline-inducible system was established to search for transcripts that were activated soon after p53 induction. Among 9,954 unique transcripts identified by serial analysis of gene expression, 34 were increased more than 10-fold; 31 of these had not previously been known to be regulated by p53. The transcription patterns of these genes, as well as previously described p53-regulated genes, were evaluated and classified in a panel of widely studied colorectal cancer cell lines. “Class I” genes were uniformly induced by p53 in all cell lines; “class II” genes were induced in a subset of the lines; and “class III” genes were not induced in any of the lines. These genes were also distinguished by the timing of their induction, their induction by clinically relevant chemotherapeutic agents, the absolute requirement for p53 in this induction, and their inducibility by p73, a p53 homolog. The results revealed substantial heterogeneity in the transcriptional responses to p53, even in cells derived from a single epithelial cell type, and pave the way to a deeper understanding of p53 tumor suppressor action.

A natural classification of the basic helix–loop–helix class of transcription factors

A natural (evolutionary) classification is provided for 242 basic helix–loop–helix (bHLH) motif-containing proteins. Phylogenetic analyses of amino acid sequences describe the patterns of evolutionary change within the motif and delimit evolutionary lineages. These evolutionary lineages represent well known functional groups of proteins and can be further arranged into five groups based on binding to DNA at the hexanucleotide E-box, the amino acid patterns in other components of the motif, and the presence/absence of a leucine zipper. The hypothesized ancestral amino acid sequence for the bHLH transcription factor family is given together with the ancestral sequences of the subgroups. It is suggested that bHLH proteins containing a leucine zipper are not a natural, monophyletic group.

On the classification of binary shifts of finite commutant index

We provide a complete classification up to conjugacy of the binary shifts of finite commutant index on the hyperfinite II1, factor. There is a natural correspondence between the conjugacy classes of these shifts and polynomials over GF(2) satisfying a certain duality condition.

Classification of mononuclear zinc metal sites in protein structures

Our study of the extended metal environment, particularly of the second shell, focuses in this paper on zinc sites. Key findings include: (i) The second shell of mononuclear zinc centers is generally more polar than hydrophobic and prominently features charged residues engaged in an abundance of hydrogen bonding with histidine ligands. Histidine–acidic or histidine–tyrosine clusters commonly overlap the environment of zinc ions. (ii) Histidine tautomeric metal bonding patterns in ligating zinc ions are mixed. For example, carboxypeptidase A, thermolysin, and sonic hedgehog possess the same ligand group (two histidines, one unibidentate acidic ligand, and a bound water), but their histidine tautomeric geometries markedly differ such that the carboxypeptidase A makes only Nδ1 contacts, thermolysin makes only Nɛ2 contacts, and sonic hedgehog uses one of each. Thus the presence of a similar ligand cohort does not necessarily imply the same topology or function at the active site. (iii) Two close histidine ligands HXmH, m ≤ 5, rarely both coordinate a single metal ion in the Nδ1 tautomeric conformation, presumably to avoid steric conflicts. Mononuclear zinc sites can be classified into six types depending on the ligand composition and geometry. Implications of the results are discussed in terms of divergent and convergent evolution.

A fully automatic evolutionary classification of protein folds: Dali Domain Dictionary version 3

The Dali Domain Dictionary (http://www.ebi.ac.uk/dali/domain) is a numerical taxonomy of all known structures in the Protein Data Bank (PDB). The taxonomy is derived fully automatically from measurements of structural, functional and sequence similarities. Here, we report the extension of the classification to match the traditional four hierarchical levels corresponding to: (i) supersecondary structural motifs (attractors in fold space), (ii) the topology of globular domains (fold types), (iii) remote homologues (functional families) and (iv) homologues with sequence identity above 25% (sequence families). The computational definitions of attractors and functional families are new. In September 2000, the Dali classification contained 10 531 PDB entries comprising 17 101 chains, which were partitioned into five attractor regions, 1375 fold types, 2582 functional families and 3724 domain sequence families. Sequence families were further associated with 99 582 unique homologous sequences in the HSSP database, which increases the number of effectively known structures several-fold. The resulting database contains the description of protein domain architecture, the definition of structural neighbours around each known structure, the definition of structurally conserved cores and a comprehensive library of explicit multiple alignments of distantly related protein families.

The COG database: new developments in phylogenetic classification of proteins from complete genomes

The database of Clusters of Orthologous Groups of proteins (COGs), which represents an attempt on a phylogenetic classification of the proteins encoded in complete genomes, currently consists of 2791 COGs including 45 350 proteins from 30 genomes of bacteria, archaea and the yeast Saccharomyces cerevisiae (http://www.ncbi.nlm.nih.gov/COG). In addition, a supplement to the COGs is available, in which proteins encoded in the genomes of two multicellular eukaryotes, the nematode Caenorhabditis elegans and the fruit fly Drosophila melanogaster, and shared with bacteria and/or archaea were included. The new features added to the COG database include information pages with structural and functional details on each COG and literature references, improvements of the COGNITOR program that is used to fit new proteins into the COGs, and classification of genomes and COGs constructed by using principal component analysis.

ICB database: the gyrB database for identification and classification of bacteria

The Identification and Classification of Bacteria (ICB) database (http:/www.mbio.co.jp/icb) contains currently available information about the DNA gyrase subunit B (gyrB) gene in bacteria. The database is designed to provide the scientific community with a reference point for using gyrB as an evolutionary and taxonomic marker. Nucleic and amino acid sequence data are currently available for over 850 strains, along with alignments at several different taxonomic levels and an exhaustive review of primer selection and background information.

Archaeal-eubacterial mergers in the origin of Eukarya: phylogenetic classification of life.

A symbiosis-based phylogeny leads to a consistent, useful classification system for all life. "Kingdoms" and "Domains" are replaced by biological names for the most inclusive taxa: Prokarya (bacteria) and Eukarya (symbiosis-derived nucleated organisms). The earliest Eukarya, anaerobic mastigotes, hypothetically originated from permanent whole-cell fusion between members of Archaea (e.g., Thermoplasma-like organisms) and of Eubacteria (e.g., Spirochaeta-like organisms). Molecular biology, life-history, and fossil record evidence support the reunification of bacteria as Prokarya while subdividing Eukarya into uniquely defined subtaxa: Protoctista, Animalia, Fungi, and Plantae.

Neural-network-based classification of cognitively normal, demented, Alzheimer disease and vascular dementia from single photon emission with computed tomography image data from brain.

Single photon emission with computed tomography (SPECT) hexamethylphenylethyleneamineoxime technetium-99 images were analyzed by an optimal interpolative neural network (OINN) algorithm to determine whether the network could discriminate among clinically diagnosed groups of elderly normal, Alzheimer disease (AD), and vascular dementia (VD) subjects. After initial image preprocessing and registration, image features were obtained that were representative of the mean regional tissue uptake. These features were extracted from a given image by averaging the intensities over various regions defined by suitable masks. After training, the network classified independent trials of patients whose clinical diagnoses conformed to published criteria for probable AD or probable/possible VD. For the SPECT data used in the current tests, the OINN agreement was 80 and 86% for probable AD and probable/possible VD, respectively. These results suggest that artificial neural network methods offer potential in diagnoses from brain images and possibly in other areas of scientific research where complex patterns of data may have scientifically meaningful groupings that are not easily identifiable by the researcher.

The aid of machine learning to overcome the classification of real health discharge reports written in Spanish

Hospitals attached to the Spanish Ministry of Health are currently using the International Classification of Diseases 9 Clinical Modification (ICD9-CM) to classify health discharge records. Nowadays, this work is manually done by experts. This paper tackles the automatic classification of real Discharge Records in Spanish following the ICD9-CM standard. The challenge is that the Discharge Records are written in spontaneous language. We explore several machine learning techniques to deal with the classification problem. Random Forest resulted in the most competitive one, achieving an F-measure of 0.876.

Morphological classification of microtidal sand and gravel beaches

A new classification of microtidal sand and gravel beaches with very different morphologies is presented below. In 557 studied transects, 14 variables were used. Among the variables to be emphasized is the depth of the Posidonia oceanica. The classification was performed for 9 types of beaches: Type 1: Sand and gravel beaches, Type 2: Sand and gravel separated beaches, Type 3: Gravel and sand beaches, Type 4: Gravel and sand separated beaches, Type 5: Pure gravel beaches, Type 6: Open sand beaches, Type 7: Supported sand beaches, Type 8: Bisupported sand beaches and Type 9: Enclosed beaches. For the classification, several tools were used: discriminant analysis, neural networks and Support Vector Machines (SVM), the results were then compared. As there is no theory for deciding which is the most convenient neural network architecture to deal with a particular data set, an experimental study was performed with different numbers of neuron in the hidden layer. Finally, an architecture with 30 neurons was chosen. Different kernels were employed for SVM (Linear, Polynomial, Radial basis function and Sigmoid). The results obtained for the discriminant analysis were not as good as those obtained for the other two methods (ANN and SVM) which showed similar success.

A classification of the Conularida / G. Winston Sinclair, Dept. of Geology, University of Michigan.

v.10:no.13(1952)

Lithological studies : a description and classification of the rocks of the Cordilleras / by M.E. Wadsworth. With eight plates.

v.11:no.1 (1884)