Detection of regional blood perfusion changes in epileptic seizures with dynamic brain perfusion CT--a pilot study

BACKGROUND AND PURPOSE: Perfusion CT (P-CT) is used for acute stroke management, not, however, for evaluating epilepsy. To test the hypothesis that P-CT may identify patients with increased regional cerebral blood flow during subtle status epilepticus (SSE), we compared P-CT in SSE to different postictal conditions. METHODS: Fifteen patients (mean age 47 years, range 21-74) underwent P-CT immediately after evaluation in our emergency room. Asymmetry indices between affected and unaffected hemispheres were calculated for regional cerebral blood volume (rCBV), regional cerebral blood flow (rCBF), and mean transit time (MTT). Regional perfusion changes were compared to EEG findings. RESULTS: Three patients in subtle status epilepticus (group 1) had increased regional perfusion with electro-clinical correlate. Six patients showed postictal slowing on EEG corresponding to an area of regional hypoperfusion (group 2). CT and EEG were normal in six patients with a first epileptic seizure (group 3). Cluster analysis of asymmetry indices separated SSE from the other two groups in all three parameters, while rCBF helped to distinguish between chronic focal epilepsies and single events. CONCLUSION: Preliminary results indicate that P-CT may help to identify patients with SSE during emergency workup. This technique provides important information to neurologists or emergency physicians in the difficult clinical differential diagnosis of altered mental status due to subtle status epilepticus.

Effects of native pollinator specialization, selfcompatibility and flowering duration of European plant species on their invasiveness elsewhere

1. When entomophilous plants are introduced to a new region, they may leave behind their usual pollinators. In particular, plant species with specialized pollination may then be less likely to establish and spread (i.e. become invasive). Moreover, other reproductive characteristics such as self-compatibility and flowering duration may also affect invasion success. 2. Here, we specifically asked whether plant species' specialization towards pollinator species and families, respectively, as measured in the native range, self-compatibility, flowering duration and their interactions are related to the degree of invasion (i.e. a measure of regional abundance) in non-native regions. 3. We used plant–pollinator interaction data from 119 German grassland sites to calculate unbiased indices of plant specialization towards pollinator species and families for 118 European plant species. We related these specialization indices, flowering duration, self-compatibility and their interactions to the degree of invasion of each species in seven large countries on four non-Eurasian continents. 4. In all models, plant species with long flowering durations had the highest degree of invasion. The best model included the specialization index based on pollinator species instead of the one based on pollinator families. Specialization towards pollinator species had a marginally significant positive effect on the degree of invasion in non-native regions for self-compatible, but not for self-incompatible species. 5. Synthesis. We showed that long flowering duration is related to the degree of invasion in other parts of the world, and a trend that pollinator generalization in the native range may interact with self-compatibility in determining the degree of invasion. Therefore, we conclude that such reproductive characteristics should be considered in risk assessment and management of introduced plant species.

Expanding the cone location and magnitude index to include corneal thickness and posterior surface information for the detection of keratoconus

PURPOSE To extend the capabilities of the Cone Location and Magnitude Index algorithm to include a combination of topographic information from the anterior and posterior corneal surfaces and corneal thickness measurements to further improve our ability to correctly identify keratoconus using this new index: ConeLocationMagnitudeIndex_X. DESIGN Retrospective case-control study. METHODS Three independent data sets were analyzed: 1 development and 2 validation. The AnteriorCornealPower index was calculated to stratify the keratoconus data from mild to severe. The ConeLocationMagnitudeIndex algorithm was applied to all tomography data collected using a dual Scheimpflug-Placido-based tomographer. The ConeLocationMagnitudeIndex_X formula, resulting from analysis of the Development set, was used to determine the logistic regression model that best separates keratoconus from normal and was applied to all data sets to calculate PercentProbabilityKeratoconus_X. The sensitivity/specificity of PercentProbabilityKeratoconus_X was compared with the original PercentProbabilityKeratoconus, which only uses anterior axial data. RESULTS The AnteriorCornealPower severity distribution for the combined data sets are 136 mild, 12 moderate, and 7 severe. The logistic regression model generated for ConeLocationMagnitudeIndex_X produces complete separation for the Development set. Validation Set 1 has 1 false-negative and Validation Set 2 has 1 false-positive. The overall sensitivity/specificity results for the logistic model produced using the ConeLocationMagnitudeIndex_X algorithm are 99.4% and 99.6%, respectively. The overall sensitivity/specificity results for using the original ConeLocationMagnitudeIndex algorithm are 89.2% and 98.8%, respectively. CONCLUSIONS ConeLocationMagnitudeIndex_X provides a robust index that can detect the presence or absence of a keratoconic pattern in corneal tomography maps with improved sensitivity/specificity from the original anterior surface-only ConeLocationMagnitudeIndex algorithm.

New insights into the reconstructed temperature in Portugal over the last 400 years

The consistency of an existing reconstructed annual (December–November) temperature series for the Lisbon region (Portugal) from 1600 onwards, based on a European-wide reconstruction, with (1) five local borehole temperature–depth profiles; (2) synthetic temperature– depth profiles, generated from both reconstructed temperatures and two regional paleoclimate simulations in Portugal; (3) instrumental data sources over the twentieth century; and (4) temperature indices from documentary sources during the late Maunder Minimum (1675–1715) is assessed. The low-frequency variability in the reconstructed temperature in Portugal is not entirely consistent with local borehole temperature–depth profiles and with the simulated response of temperature in two regional paleoclimate simulations driven by reconstructions of various climate forcings. Therefore, the existing reconstructed series is calibrated by adjusting its low-frequency variability to the simulations (first-stage adjustment). The annual reconstructed series is then calibrated in its location and scale parameters, using the instrumental series and a linear regression between them (second-stage adjustment). This calibrated series shows clear footprints of the Maunder and Dalton minima, commonly related to changes in solar activity and explosive volcanic eruptions, and a strong recent-past warming, commonly related to human-driven forcing. Lastly, it is also in overall agreement with annual temperature indices over the late Maunder Minimum in Portugal. The series resulting from this post-reconstruction adjustment can be of foremost relevance to improve the current understanding of the driving mechanisms of climate variability in Portugal.

Hierarchical clustering of flow cytometry data for the study of conventional central chondrosarcoma

We have investigated the use of hierarchical clustering of flow cytometry data to classify samples of conventional central chondrosarcoma, a malignant cartilage forming tumor of uncertain cellular origin, according to similarities with surface marker profiles of several known cell types. Human primary chondrosarcoma cells, articular chondrocytes, mesenchymal stem cells, fibroblasts, and a panel of tumor cell lines from chondrocytic or epithelial origin were clustered based on the expression profile of eleven surface markers. For clustering, eight hierarchical clustering algorithms, three distance metrics, as well as several approaches for data preprocessing, including multivariate outlier detection, logarithmic transformation, and z-score normalization, were systematically evaluated. By selecting clustering approaches shown to give reproducible results for cluster recovery of known cell types, primary conventional central chondrosacoma cells could be grouped in two main clusters with distinctive marker expression signatures: one group clustering together with mesenchymal stem cells (CD49b-high/CD10-low/CD221-high) and a second group clustering close to fibroblasts (CD49b-low/CD10-high/CD221-low). Hierarchical clustering also revealed substantial differences between primary conventional central chondrosarcoma cells and established chondrosarcoma cell lines, with the latter not only segregating apart from primary tumor cells and normal tissue cells, but clustering together with cell lines from epithelial lineage. Our study provides a foundation for the use of hierarchical clustering applied to flow cytometry data as a powerful tool to classify samples according to marker expression patterns, which could lead to uncover new cancer subtypes.

A 350 ka record of climate change from Lake El'gygytgyn, Far East Russian Arctic: refining the pattern of climate modes by means of cluster analysis

Abstract. Rock magnetic, biochemical and inorganic records of the sediment cores PG1351 and Lz1024 from Lake El’gygytgyn, Chukotka peninsula, Far East Russian Arctic, were subject to a hierarchical agglomerative cluster analysis in order to refine and extend the pattern of climate modes as defined by Melles et al. (2007). Cluster analysis of the data obtained from both cores yielded similar results, differentiating clearly between the four climate modes warm, peak warm, cold and dry, and cold and moist. In addition, two transitional phases were identified, representing the early stages of a cold phase and slightly colder conditions during a warm phase. The statistical approach can thus be used to resolve gradual changes in the sedimentary units as an indicator of available oxygen in the hypolimnion in greater detail. Based upon cluster analyses on core Lz1024, the published succession of climate modes in core PG1351, covering the last 250 ka, was modified and extended back to 350 ka. Comparison to the marine oxygen isotope (�18O) stack LR04 (Lisiecki and Raymo, 2005) and the summer insolation at 67.5� N, with the extended Lake El’gygytgyn parameter records of magnetic susceptibility (�LF), total organic carbon content (TOC) and the chemical index of alteration (CIA; Minyuk et al., 2007), revealed that all stages back to marine isotope stage (MIS) 10 and most of the substages are clearly reflected in the pattern derived from the cluster analysis.

A Concept of Semantics Extraction from Web Data by Induction of Fuzzy Ontologies

Traditionally, ontologies describe knowledge representation in a denotational, formalized, and deductive way. In addition, in this paper, we propose a semiotic, inductive, and approximate approach to ontology creation. We define a conceptual framework, a semantics extraction algorithm, and a first proof of concept applying the algorithm to a small set of Wikipedia documents. Intended as an extension to the prevailing top-down ontologies, we introduce an inductive fuzzy grassroots ontology, which organizes itself organically from existing natural language Web content. Using inductive and approximate reasoning to reflect the natural way in which knowledge is processed, the ontology’s bottom-up build process creates emergent semantics learned from the Web. By this means, the ontology acts as a hub for computing with words described in natural language. For Web users, the structural semantics are visualized as inductive fuzzy cognitive maps, allowing an initial form of intelligence amplification. Eventually, we present an implementation of our inductive fuzzy grassroots ontology Thus,this paper contributes an algorithm for the extraction of fuzzy grassroots ontologies from Web data by inductive fuzzy classification.

The FORA Framework - A Fuzzy Grassroots Ontology for Online Reputation Management

Online reputation management deals with monitoring and influencing the online record of a person, an organization or a product. The Social Web offers increasingly simple ways to publish and disseminate personal or opinionated information, which can rapidly have a disastrous influence on the online reputation of some of the entities. This dissertation can be split into three parts: In the first part, possible fuzzy clustering applications for the Social Semantic Web are investigated. The second part explores promising Social Semantic Web elements for organizational applications,while in the third part the former two parts are brought together and a fuzzy online reputation analysis framework is introduced and evaluated. Theentire PhD thesis is based on literature reviews as well as on argumentative-deductive analyses.The possible applications of Social Semantic Web elements within organizations have been researched using a scenario and an additional case study together with two ancillary case studies—based on qualitative interviews. For the conception and implementation of the online reputation analysis application, a conceptual framework was developed. Employing test installations and prototyping, the essential parts of the framework have been implemented.By following a design sciences research approach, this PhD has created two artifacts: a frameworkand a prototype as proof of concept. Bothartifactshinge on twocoreelements: a (cluster analysis-based) translation of tags used in the Social Web to a computer-understandable fuzzy grassroots ontology for the Semantic Web, and a (Topic Maps-based) knowledge representation system, which facilitates a natural interaction with the fuzzy grassroots ontology. This is beneficial to the identification of unknown but essential Web data that could not be realized through conventional online reputation analysis. Theinherent structure of natural language supports humans not only in communication but also in the perception of the world. Fuzziness is a promising tool for transforming those human perceptions intocomputer artifacts. Through fuzzy grassroots ontologies, the Social Semantic Web becomes more naturally and thus can streamline online reputation management.

FORA - A Fuzzy Set Based Framework for Online Reputation Management

The Social Web offers increasingly simple ways to publish and disseminate personal or opinionated information, which can rapidly exhibit a disastrous influence on the online reputation of organizations. Based on social Web data, this study describes the building of an ontology based on fuzzy sets. At the end of a recurring harvesting of folksonomies by Web agents, the aggregated tags are purified, linked, and transformed to a so-called fuzzy grassroots ontology by means of a fuzzy clustering algorithm. This self-updating ontology is used for online reputation analysis, a crucial task of reputation management, with the goal to follow the online conversation going on around an organization to discover and monitor its reputation. In addition, an application of the Fuzzy Online Reputation Analysis (FORA) framework, lesson learned, and potential extensions are discussed in this article.

Spatial-temporal clustering of companion animal enteric syndrome: detection and investigation through the use of electronic medical records from participating private practices

SUMMARY There is interest in the potential of companion animal surveillance to provide data to improve pet health and to provide early warning of environmental hazards to people. We implemented a companion animal surveillance system in Calgary, Alberta and the surrounding communities. Informatics technologies automatically extracted electronic medical records from participating veterinary practices and identified cases of enteric syndrome in the warehoused records. The data were analysed using time-series analyses and a retrospective space-time permutation scan statistic. We identified a seasonal pattern of reports of occurrences of enteric syndromes in companion animals and four statistically significant clusters of enteric syndrome cases. The cases within each cluster were examined and information about the animals involved (species, age, sex), their vaccination history, possible exposure or risk behaviour history, information about disease severity, and the aetiological diagnosis was collected. We then assessed whether the cases within the cluster were unusual and if they represented an animal or public health threat. There was often insufficient information recorded in the medical record to characterize the clusters by aetiology or exposures. Space-time analysis of companion animal enteric syndrome cases found evidence of clustering. Collection of more epidemiologically relevant data would enhance the utility of practice-based companion animal surveillance.