A knowledge-driven agent-centred framework for data mining in EMG

In this paper, we present a multi-agent framework for data mining in electromyography. This application, based on a web interface, provides a set of functionalities allowing to manipulate 1000 medical cases and more than 25,000 neurological tests stored in a medical database. The aim is to extract medical information using data mining algorithms and to supply a knowledge base with pertinent information. The multi-agent platform gives the possibility to distribute the data management process between several autonomous entities. This framework provides a parallel and flexible data manipulation.     

Yeast genomic databases and the challenge of the post-genomic era

Since the completion of the yeast genome sequence in 1996, three genomic databases, the Saccharomyces Genome Database, the Yeast Proteome Database, and MIPS (produced by the Munich Information Center for Protein Sequences), have organized published knowledge of yeast genes and proteins onto the framework of the genome. Now, post-genomic technologies are producing large-scale datasets of many types, and these pose new challenges for knowledge integration. This review first examines the structure and content of the three genomic databases, and then draws from them and other resources to examine the ways knowledge from the literature, genome, and post-genomic experiments is stored, integrated, and disseminated. To better understand the impact of post-genomic technologies, 20 collections of post-genomic data were analyzed relative to a set of 243 previously uncharacterized genes. The results indicate that post-genomic technologies are providing rich new information for nearly all yeast genes, but data from these experiments is scattered across many Web sites and the results from these experiments are poorly integrated with other forms of yeast knowledge. Goals for the next generation of databases are set forth which could lead to better access to yeast knowledge for yeast researchers and the entire scientific community. Electronic Publication     

Transgenic Plant Monitor (TPM): a relational database for the management and analysis of information in plant tissue culture and transformation laboratories

Increased public concern and strict statutory regulations relating tothe generation and exploitation of genetically modified organisms, make itimperative to track accurately individual plants through DNA transformationprogrammes. The ability to rapidly retrieve information associated withspecifictransgenic events and to provide accurate reports on demand is an increasinglyimportant feature for public research laboratories. Transgenic Plant Monitor(TPM) has been developed as a database structured to allow efficient recording,monitoring and analysis of the extensive and complex data generated in planttissue culture and transformation experiments. TPM is built upon the widelyavailable Microsoft Access database engine and can be readily adoptedand/or adapted by other users. The key features and the utility of TPM as aresearch tool are discussed in this article.     

Identification and analysis of expressed resistance gene sequences in wheat

Forty-eight resistance (R) genes conferring resistance to various types of pests have been cloned from 12 plant species. Irrespective of the host or the pest type, most R genes share a strong protein sequence similarity especially for domains and motifs. The objective of this study was to identify expressed R genes of wheat, the fraction of which is expected to be very low in the genome. Using modified RNA fingerprinting and data mining approaches we identified 220 expressed R-gene candidates. Of these, 125 sequences structurally resembled known R genes. In addition to 25-87% protein sequence similarity with the known R genes, the sequence, order, and distribution of the domains and motifs were also the same. Among the remaining 95, 17 were probable R-related, 21 were a new class of nucleotide-binding kinases, 21 were probable kinases, and 36 were p-loop-containing unknown sequences. About 76% were rare including 73 novel sequences. Three new R-gene specific motifs were also identified. Physical mapping of the 164 best R-gene candidates on 339 deletion lines localized 121 mappable R-gene candidates to 26 small chromosomal regions encompassing about 16% of the genome. About 90 of the 110 phenotypically characterized wheat R genes corresponding to 18 different pests also mapped in these regions.     

Snipping polymorphisms from large EST collections in barley (<Emphasis Type="Italic">Hordeum vulgare </Emphasis>L.)

The public EST (expressed sequence tag) databases represent an enormous but heterogeneous repository of sequences, including many from a broad selection of plant species and a wide range of distinct varieties. The significant redundancy within large EST collections makes them an attractive resource for rapid pre-selection of candidate sequence polymorphisms. Here we present a strategy that allows rapid identification of candidate SNPs in barley (Hordeum vulgare L.) using publicly available EST databases. Analysis of 271,630 EST sequences from different cDNA libraries, representing 23 different barley varieties, resulted in the generation of 56,302 tentative consensus sequences. In all, 8171 of these unigene sequences are members of clusters with six or more ESTs. By applying a novel SNP detection algorithm (SNiPpER) to these sequences, we identified 3069 candidate inter-varietal SNPs. In order to verify these candidate SNPs, we selected a small subset of 63 present in 36 ESTs. Of the 63 SNPs selected, we were able to validate 54 (86%) using a direct sequencing approach. For further verification, 28 ESTs were mapped to distinct loci within the barley genome. The polymorphism information content (PIC) and nucleotide diversity () values of the SNPs identified by the SNiPpER algorithm are significantly higher than those that were obtained by random sequencing. This demonstrates the efficiency of our strategy for SNP identification and the cost-efficient development of EST-based SNP-markers.The first two authors contributed equally to this work     

Phytoplankton of the extremely acidic mining lakes of Lusatia (Germany) with pH ≤3

Most of the flooded, open-cast lignite mining lakes of Lusatia (Germany) impacted by the oxidation of iron sulphides (pyrite and marcasite) are extremely acidic. Of 32 lakes regularly studied from 1995 to 1998, 14 have a pH <3 (median pH 2.3–2.9). These lakes are typically buffered by high concentrations of Fe (III) and have high conductivity (1000–5000 S cm^–1). Concentrations of dissolved inorganic carbon (DIC) and phosphorus are typically extremely low. These factors result in a very different environment for algae than found in neutral and acid-rain impacted lakes. The planktonic algal flora is generally dominated by flagellates belonging to genera of Chlorophyta (Chlamydomonas), Heterokontophyta of the class Chrysophyceae (Ochromonas, Chromulina), Cryptophyta (Cyathomonas) and Euglenophyta (Lepocinclis, Euglena mutabilis). Near-spherical non-motile Chlorophyta (Nanochlorum sp.), Heterokontophyta of the class Bacillariophyceae (Eunotia exigua, Nitzschia), Dinophyta (Gymnodinium, Peridinium umbonatum), other Chlorophyta (Scourfieldia cordiformis) and Cryptophyta (Rhodomonas minuta) are also found.     

Mining of assembled expressed sequence tag (EST) data for protein families: application to the G protein-coupled receptor superfamily

The availability of large expressed sequence tag (EST) databases has led to a revolution in the way new genes are identified. Mining of these databases using known protein sequences as queries is a powerful technique for discovering orthologous and paralogous genes. The scientist is often confronted, however, by an enormous amount of search output owing to the inherent redundancy of EST data. In addition, high search sensitivity often cannot be achieved using only a single member of a protein superfamily as a query. In this paper a technique for addressing both of these issues is described. Assembled EST databases are queried with every member of a protein superfamily, the results are integrated and false positives are pruned from the set. The result is a set of assemblies enriched in members of the protein superfamily under consideration. The technique is applied to the G protein-coupled receptor (GPCR) superfamily in the construction of a GPCR Resource. A novel full-length human GPCR identified from the GPCR Resource is presented, illustrating the utility of the method.     

Evaluating Land-Use Impacts: Selection of Surface Area Metrics for Life-Cycle Assessment of Mining

Land use is an increasingly important component of sustainability evaluations, and numerous performance metrics have evolved to meet this need. The selection of appropriate land-use metrics for decision makers, however, remains an ongoing challenge. Additionally, life-cycle practitioners often struggle to provide meaningful impact assessment because of challenges associated with traditional land-use impact metrics. This article is intended to assist decision makers and life-cycle practitioners who wish to more effectively measure and evaluate one aspect of land use: surface area occupation. Existing performance metrics are discussed, and the specific circumstances under which each is appropriate are identified. Building on leading-edge research and analysis in the field of life-cycle impact assessment, a modified methodology for evaluating surface area occupation is proposed. This approach is demonstrated for a series of mining practices including three individual gold mines, a bauxite mine, and a copper mine. The specific data requirements and resulting equivalency factors for each mine are discussed. Results indicate that equivalency factors for gold (average of 700 acre-yr/ton) are expected to be several orders of magnitude higher than for either bauxite (0.004 acre-yr/ ton) or copper (0.03 acre-yr/ton). These dramatic differences in results demonstrate that equivalency factors are appropriate and necessary for including land-use impact potential as part of a life-cycle assessment that includes several different minerals or material requirements.     

The DING family of proteins: ubiquitous in eukaryotes,but where are the genes?

PstS and DING proteins are members of a superfamily of secreted, high‐affinity phosphate‐binding proteins. Whereas microbial PstS have a well‐defined role in phosphate ABC transporters, the physiological function of DING proteins, named after their DINGGG N termini, still needs to be determined. PstS and DING proteins co‐exist in some Pseudomonas strains, to which they confer a highly adhesive and virulent phenotype. More than 30 DING proteins have now been purified, mostly from eukaryotes. They are often associated with infections or with dysregulation of cell proliferation. Consequently, eukaryotic DING proteins could also be involved in cell–cell communication or adherence. The ubiquitous presence in eukaryotes of proteins structurally and functionally related to bacterial virulence factors is intriguing, as is the absence of eukaryotic genes encoding DING proteins in databases. DING proteins in eukaryotes could originate from unidentified commensal or symbiotic bacteria and could contribute to essential functions. Alternatively, DING proteins could be encoded by eukaryotic genes sharing special features that prevent their cloning. Both hypotheses are discussed.