Support vector machine classification for large data sets via minimum enclosing ball clustering

Support vector machine (SVM) is a powerful technique for data classification. Despite of its good theoretic foundations and high classification accuracy, normal SVM is not suitable for classification of large data sets, because the training complexity of SVM is highly dependent on the size of data set. This paper presents a novel SVM classification approach for large data sets by using minimum enclosing ball clustering. After the training data are partitioned by the proposed clustering method, the centers of the clusters are used for the first time SVM classification. Then we use the clusters whose centers are support vectors or those clusters which have different classes to perform the second time SVM classification. In this stage most data are removed. Several experimental results show that the approach proposed in this paper has good classification accuracy compared with classic SVM while the training is significantly faster than several other SVM classifiers.

Low-temperature catalytic decomposition of N2O on platinum and bismuth-modified platinum: identification of active sites

Classification of the active surface sites of platinum catalysts responsible for low temperature N2O decomposition, in terms of steps, kinks and terraces, has been achieved by controlled addition of bismuth to as-received platinum/graphite catalysts.

‘Risk, Politics and the ‘Scientification’ of Political Judgement: Prisoner Release and Conflict Transformation in Northern Ireland’

Drawing on research in Northern Ireland into the process of release under the Belfast/Good Friday Agreement, this article explores the identification and classification of risk in relation to prisoners released early under the Sentences (NI) Act. The main argument is that conflict, post-conflict and transitional conditions expose more starkly the political underpinnings of risk-management strategy and the article demonstrates the particular variant of Politicized Risk Assessment (PRA) recently used in the release of prisoners in Northern Ireland

The Impact of Admissions for the Management of End-stage renal disease on hospital bed occupancy

BACKGROUND:

End-stage renal disease (ESRD) is increasingly prevalent but the inpatient costs associated with this condition are poorly defined due to limitations with data extraction and failure to differentiate between hospitalisation for renal and non-renal disease reasons. The impact of admissions primarily for the management of ESRD on hospital bed utilisation was assessed over a 5-year period in a large teaching hospital.

METHODS:

All admission episodes were reviewed and the ESRD group was identified by a primary International Classification of Diseases code for ESRD or a non-specific primary renal failure code with a secondary code for ESRD. The frequency and duration of hospitalisation and contribution to bed day occupancy of this group with ESRD was determined.

RESULTS:

There were 70,808 patients responsible for a total of 116,915 admissions and 919,212 bed days over the study period. Of these, 988 (1.4%) patients were admitted for the management of ESRD, accounting for 2,387 (2.0%) of admissions and utilisation of 23,011 (2.5%) bed days. After adjustment for age and gender, those admitted for ESRD management were significantly more likely to have a prolonged admission exceeding 30 days (odds ratio 1.46, 95% confidence interval 1.23-1.72, p < 0.001). When the admission was an emergency rather than an elective event, the patient was 4.6 times more likely to be hospitalised for over 30 days.

CONCLUSIONS:

Persons admitted for ESRD management are hospitalised more frequently and for longer than the overall inpatient population, occupying a substantial number of bed days.

Exploratory analysis of spatiotemporal patterns of cellular automata by clustering compressibility

In this paper we study the classification of spatiotemporal pattern of one-dimensional cellular automata (CA) whereas the classification comprises CA rules including their initial conditions. We propose an exploratory analysis method based on the normalized compression distance (NCD) of spatiotemporal patterns which is used as dissimilarity measure for a hierarchical clustering. Our approach is different with respect to the following points. First, the classification of spatiotemporal pattern is comparative because the NCD evaluates explicitly the difference of compressibility among two objects, e.g., strings corresponding to spatiotemporal patterns. This is in contrast to all other measures applied so far in a similar context because they are essentially univariate. Second, Kolmogorov complexity, which underlies the NCD, was used in the classification of CA with respect to their spatiotemporal pattern. Third, our method is semiautomatic allowing us to investigate hundreds or thousands of CA rules or initial conditions simultaneously to gain insights into their organizational structure. Our numerical results are not only plausible confirming previous classification attempts but also shed light on the intricate influence of random initial conditions on the classification results.

Resolution of a taxonomic conundrum: an ultrastructural and molecular description of the life cycle of Pleistophora mulleri (Pfeiffer 1895; Georgevitch 1929)

The classification of a microsporidian parasite observed in the abdominal muscles of amphipod hosts has been repeatedly revised but still remains inconclusive. This parasite has variable spore numbers within a sporophorous vesicle and has been assigned to the genera Glugea, Pleistophora, Stempellia, and Thelohania. We used electron microscopy and molecular evidence to resolve the previous taxonomic confusion and confirm its identification as Pleistophora mulleri. The life cycle of P. mulleri is described from the freshwater amphipod host Gammarus duebeni celticus. Infection appeared as white tubular masses within the abdominal muscle of the host. Light and transmission electron microscope examination revealed the presence of an active microsporidian infection that was diffuse within the muscle block with no evidence of xenoma formation. Paucinucleate merogonial plasmodia were surrounded by an amorphous coat immediately external to the plasmalemma. The amorphous coat developed into a merontogenetic sporophorous vesicle that was present throughout sporulation. Sporogony was polysporous resulting in uninucleate spores, with a bipartite polaroplast, an anisofilar polar filament and a large posterior vacuole. SSU rDNA analysis supported the ultrastructural evidence clearly placing this parasite within the genus Pleistophora. This paper indicates that Pleistophora species are not restricted to vertebrate hosts.

Identification of gene networks associated with acute myeloid leukemia by comparative molecular methylation and expression profiling

Around 80% of acute myeloid leukemia (AML) patients achieve a complete remission, however many will relapse and ultimately die of their disease. The association between karyotype and prognosis has been studied extensively and identified patient cohorts as having favourable [e.g. t(8; 21), inv (16)/t(16; 16), t(15; 17)], intermediate [e.g. cytogenetically normal (NK-AML)] or adverse risk [e.g. complex karyotypes]. Previous studies have shown that gene expression profiling signatures can classify the sub-types of AML, although few reports have shown a similar feature by using methylation markers. The global methylation patterns in 19 diagnostic AML samples were investigated using the Methylated CpG Island Amplification Microarray (MCAM) method and CpG island microarrays containing 12,000 CpG sites. The first analysis, comparing favourable and intermediate cytogenetic risk groups, revealed significantly differentially methylated CpG sites (594 CpG islands) between the two subgroups. Mutations in the NPM1 gene occur at a high frequency (40%) within the NK-AML subgroup and are associated with a more favourable prognosis in these patients. A second analysis comparing the NPM1 mutant and wild-type research study subjects again identified distinct methylation profiles between these two subgroups. Network and pathway analysis revealed possible molecular mechanisms associated with the different risk and/or mutation sub-groups. This may result in a better classification of the risk groups, improved monitoring targets, or the identification of novel molecular therapies.

Data-driven optimal filtering for phase and frequency of noisy oscillations: Application to vortex flow metering

A method for measuring the phase of oscillations from noisy time series is proposed. To obtain the phase, the signal is filtered in such a way that the filter output has minimal relative variation in the amplitude over all filters with complex-valued impulse response. The argument of the filter output yields the phase. Implementation of the algorithm and interpretation of the result are discussed. We argue that the phase obtained by the proposed method has a low susceptibility to measurement noise and a low rate of artificial phase slips. The method is applied for the detection and classification of mode locking in vortex flow meters. A measure for the strength of mode locking is proposed.

Algorithmic computation of knot polynomials of secondary structure elements of proteins

The classification of protein structures is an important and still outstanding problem. The purpose of this paper is threefold. First, we utilize a relation between the Tutte and homfly polynomial to show that the Alexander-Conway polynomial can be algorithmically computed for a given planar graph. Second, as special cases of planar graphs, we use polymer graphs of protein structures. More precisely, we use three building blocks of the three-dimensional protein structure-alpha-helix, antiparallel beta-sheet, and parallel beta-sheet-and calculate, for their corresponding polymer graphs, the Tutte polynomials analytically by providing recurrence equations for all three secondary structure elements. Third, we present numerical results comparing the results from our analytical calculations with the numerical results of our algorithm-not only to test consistency, but also to demonstrate that all assigned polynomials are unique labels of the secondary structure elements. This paves the way for an automatic classification of protein structures.

Cofilin immunolabelling correlates with depth of invasion in gastrointestinal endocrine cell tumors

Gastrointestinal endocrine cell tumors are a heterogeneous population of lesions believed to arise from neuroendocrine cells of the gastrointestinal mucosa. The current classification of these tumors is based on tumor size, microscopic features and clinical evidence of metastasis. Although diagnostic categories generally correlate with prognosis, molecular prognostic markers will be clinically useful adjuncts. Cofilin has been implicated in tumor invasion, and its immunolocalisation was studied in gastrointestinal endocrine cell tumors. The immunolocalisation of cofilin was studied by immunohistochemistry in 34 formalin-fixed, paraffin wax-embedded gastrointestinal endocrine cell tumors using a tissue microarray platform. A significant correlation was found between high cofilin immunolabelling and the depth of invasion (p

Adventures in the pharmacological analysis of P2 receptors

The pharmacological classification of P2 receptors owes its origin to the pioneering efforts of Geoff Burnstock and those who followed him, research that was conducted primarily in physiological experimental systems. Over recent years, the techniques of molecular biology have been increasingly applied in the study of P2 receptors while, at the same time, advances in their pharmacological analysis have been limited by a lack of potent and selective agonist or antagonist ligands. This has resulted in a classification scheme which is largely structural in nature, with relatively little contribution from pharmacology. Our endeavours in this area have been directed towards the discovery of ligands with which the pharmacological analysis and definition of P2 receptors could be advanced, the ultimate goal being the design of therapeutic agents. This article will describe some of our experiences in this challenging but rewarding Nea. (C) 2000 Elsevier Science B.V. All rights reserved.

The repertoire of G protein-coupled receptors in the human parasite Schistosoma mansoni and the model organism Schmidtea mediterranea.

Background

G protein-coupled receptors (GPCRs) constitute one of the largest groupings of eukaryotic proteins, and represent a particularly lucrative set of pharmaceutical targets. They play an important role in eukaryotic signal transduction and physiology, mediating cellular responses to a diverse range of extracellular stimuli. The phylum Platyhelminthes is of considerable medical and biological importance, housing major pathogens as well as established model organisms. The recent availability of genomic data for the human blood fluke Schistosoma mansoni and the model planarian Schmidtea mediterranea paves the way for the first comprehensive effort to identify and analyze GPCRs in this important phylum.

Results

Application of a novel transmembrane-oriented approach to receptor mining led to the discovery of 117 S. mansoni GPCRs, representing all of the major families; 105 Rhodopsin, 2 Glutamate, 3 Adhesion, 2 Secretin and 5 Frizzled. Similarly, 418 Rhodopsin, 9 Glutamate, 21 Adhesion, 1 Secretin and 11 Frizzled S. mediterranea receptors were identified. Among these, we report the identification of novel receptor groupings, including a large and highly-diverged Platyhelminth-specific Rhodopsin subfamily, a planarian-specific Adhesion-like family, and atypical Glutamate-like receptors. Phylogenetic analysis was carried out following extensive gene curation. Support vector machines (SVMs) were trained and used for ligand-based classification of full-length Rhodopsin GPCRs, complementing phylogenetic and homology-based classification.

Conclusions

Genome-wide investigation of GPCRs in two platyhelminth genomes reveals an extensive and complex receptor signaling repertoire with many unique features. This work provides important sequence and functional leads for understanding basic flatworm receptor biology, and sheds light on a lucrative set of anthelmintic drug targets.

Intrinsic subtypes of gastric cancer, based on gene expression pattern, predict survival and respond differently to chemotherapy

BACKGROUND & AIMS:
Gastric cancer (GC) is a heterogeneous disease comprising multiple subtypes that have distinct biological properties and effects in patients. We sought to identify new, intrinsic subtypes of GC by gene expression analysis of a large panel of GC cell lines. We tested if these subtypes might be associated with differences in patient survival times and responses to various standard-of-care cytotoxic drugs.
METHODS:
We analyzed gene expression profiles for 37 GC cell lines to identify intrinsic GC subtypes. These subtypes were validated in primary tumors from 521 patients in 4 independent cohorts, where the subtypes were determined by either expression profiling or subtype-specific immunohistochemical markers (LGALS4, CDH17). In vitro sensitivity to 3 chemotherapy drugs (5-fluorouracil, cisplatin, oxaliplatin) was also assessed.
RESULTS:
Unsupervised cell line analysis identified 2 major intrinsic genomic subtypes (G-INT and G-DIF) that had distinct patterns of gene expression. The intrinsic subtypes, but not subtypes based on Lauren's histopathologic classification, were prognostic of survival, based on univariate and multivariate analysis in multiple patient cohorts. The G-INT cell lines were significantly more sensitive to 5-fluorouracil and oxaliplatin, but more resistant to cisplatin, than the G-DIF cell lines. In patients, intrinsic subtypes were associated with survival time following adjuvant, 5-fluorouracil-based therapy.
CONCLUSIONS:
Intrinsic subtypes of GC, based on distinct patterns of expression, are associated with patient survival and response to chemotherapy. Classification of GC based on intrinsic subtypes might be used to determine prognosis and customize therapy.

Diverse pathogenicity of Burkholderia cepacia complex strains in the Caenorhabditis elegans host model

A fast screening method was developed to assess the pathogenicity of a diverse collection of environmental and clinical Burkholderia cepacia complex isolates in the nematode Caenorhabditis elegans. The method was validated by comparison with the standard slow-killing assay. We observed that the pathogenicity of B. cepacia complex isolates in C. elegans was strain-dependent but species-independent. The wide range of observed pathogenic phenotypes agrees with the high degree of phenotypic variation among species of the B. cepacia complex and suggests that the taxonomic classification of a given strain within the complex cannot predict pathogenicity.

Outer membrane protein profiles and multilocus enzyme electrophoresis analysis for differentiation of clinical isolates of Proteus mirabilis and Proteus vulgaris

Outer membrane protein (MP) profiles and multilocus enzyme electrophoresis (MEE) analysis were used as tools for differentiating clinical isolates of Proteus spp. Fourteen distinct MP profiles were established by sodium dodecyl sulfate-urea polyacrylamide gel electrophoresis in 54 clinical isolates of Proteus spp. (44 strains identified as P. mirabilis and 10 strains identified as P. vulgaris). Forty-one isolates of P. mirabilis and eight isolates of P. vulgaris were grouped within six and three MP profiles, respectively. The remaining P. mirabilis and P. vulgaris isolates had unique profiles. MEE analysis was used to further discriminate among the strains belonging to the same MP groups. Thirty-five distinct electrophoretic types (ETs) were identified among P. mirabilis isolates. The isolates of P. mirabilis from the four most common MP groups were subgrouped into 30 ETs. All of the P. vulgaris strains had unique ETs. The results suggest that upon biochemical classification of Proteus isolates as P. mirabilis or P. vulgaris, further differentiation among strains of the same species can be obtained by the initial determination of MP profiles followed by MEE analysis of strains with identical MPs.