Genomic Diversity within the Enterobacter cloacae Complex

Background: Isolates of the Enterobacter cloacae complex have been increasingly isolated as nosocomial pathogens, but phenotypic identification of the E. cloacae complex is unreliable and irreproducible. Identification of species based on currently available genotyping tools is already superior to phenotypic identification, but the taxonomy of isolates belonging to this complex is cumbersome. Methodolgy/Principal Findings: This study shows that multilocus sequence analysis and comparative genomic hybridization based on a mixed genome array is a powerful method for studying species assignment within the E. cloacae complex. The E. cloacae complex is shown to be evolutionarily divided into two clades that are genetically distinct from each other. The younger first clade is genetically more homogenous, contains the Enterobacter hormaechei species and is the most frequently cultured Enterobacter species in hospitals. The second and older clade consists of several (sub)species that are genetically more heterogonous. Genetic markers were identified that could discriminate between the two clades and cluster 1. Conclusions/Significance: Based on genomic differences it is concluded that some previously defined (clonal and heterogenic) (sub)species of the E. cloacae complex have to be redefined because of disagreements with known or proposed nomenclature. However, further improved identification of the redefined species will be possible based on novel markers presented here. © 2008 Paauw et al. Chemicals / CAS: Bacterial Proteins; DNA, Bacteria

Methicillin Resistance Transfer from Staphylocccus epidermidis to Methicillin-Susceptible Staphylococcus aureus in a Patient during Antibiotic Therapy

BACKGROUND: The mecA gene, encoding methicillin resistance in staphylococci, is located on a mobile genetic element called Staphylococcal Cassette Chromosome mec (SCCmec). Horizontal, interspecies transfer of this element could be an important factor in the dissemination of methicillin-resistant S. aureus (MRSA). Previously, we reported the isolation of a closely related methicillin-susceptible Staphylococcus aureus (MSSA), MRSA and potential SCCmec donor Staphylococcus epidermidis isolate from the same patient. Based on fingerprint techniques we hypothesized that the S. epidermidis had transferred SCCmec to the MSSA to become MRSA. The aim of this study was to show that these isolates form an isogenic pair and that interspecies horizontal SCCmec transfer occurred. METHODOLOGY/RESULTS: Whole genome sequencing of both isolates was performed and for the MSSA gaps were closed by conventional sequencing. The SCCmec of the S. epidermidis was also sequenced by conventional methods. The results show no difference in nucleotide sequence between the two isolates except for the presence of SCCmec in the MRSA. The SCCmec of the S. epidermidis and the MRSA are identical except for a single nucleotide in the ccrB gene, which results in a valine to alanine substitution. The main difference with the closely related EMRSA-16 is the presence of SaPI2 encoding toxic shock syndrome toxin and exfoliative toxin A in the MSSA-MRSA pair. No transfer of SCCmec from the S. epidermidis to the MSSA could be demonstrated in vitro. CONCLUSION: The MSSA and MRSA form an isogenic pair except for SCCmec. This strongly supports our hypothesis that the MRSA was derived from the MSSA by interspecies horizontal transfer of SCCmec from S. epidermidis O7.1

Reviews and syntheses:Remotely sensed optical time series for monitoring vegetation productivity

International audienceAbstract. Vegetation productivity is a critical indicator of global ecosystem health and is impacted by human activities and climate change. A wide range of optical sensing platforms, from ground-based to airborne and satellite, provide spatially continuous information on terrestrial vegetation status and functioning. As optical Earth observation (EO) data are usually routinely acquired, vegetation can be monitored repeatedly over time; reflecting seasonal vegetation patterns and trends in vegetation productivity metrics. Such metrics include e.g., gross primary productivity, net primary productivity, biomass or yield. To summarize current knowledge, in this paper, we systematically reviewed time series (TS) literature for assessing state-of-the-art vegetation productivity monitoring approaches for different ecosystems based on optical remote sensing (RS) data. As the integration of solar-induced fluorescence (SIF) data in vegetation productivity processing chains has emerged as a promising source, we also include this relatively recent sensor modality. We define three methodological categories to derive productivity metrics from remotely sensed TS of vegetation indices or quantitative traits: (i) trend analysis and anomaly detection, (ii) land surface phenology, and (iii) integration and assimilation of TS-derived metrics into statistical and process-based dynamic vegetation models (DVM). Although the majority of used TS data streams originate from data acquired from satellite platforms, TS data from aircraft and unoccupied aerial vehicles have found their way into productivity monitoring studies. To facilitate processing, we provide a list of common toolboxes for inferring productivity metrics and information from TS data. We further discuss validation strategies of the RS-data derived productivity metrics: (1) using in situ measured data, such as yield, (2) sensor networks of distinct sensors, including spectroradiometers, flux towers, or phenological cameras, and (3) inter-comparison of different productivity products or modelled estimates. Finally, we address current challenges and propose a conceptual framework for productivity metrics derivation, including fully-integrated DVMs and radiative transfer models here labelled as "Digital Twin". This novel framework meets the requirements of multiple ecosystems and enables both an improved understanding of vegetation temporal dynamics in response to climate and environmental drivers and also enhances the accuracy of vegetation productivity monitoring

Reviews and syntheses: Remotely sensed optical time series for monitoring vegetation productivity

Vegetation productivity is a critical indicator of global ecosystem health and is impacted by human activities and climate change. A wide range of optical sensing platforms, from ground-based to airborne and satellite, provide spatially continuous information on terrestrial vegetation status and functioning. As optical Earth observation (EO) data are usually routinely acquired, vegetation can be monitored repeatedly over time, reflecting seasonal vegetation patterns and trends in vegetation productivity metrics. Such metrics include gross primary productivity, net primary productivity, biomass, or yield. To summarize current knowledge, in this paper we systematically reviewed time series (TS) literature for assessing state-of-the-art vegetation productivity monitoring approaches for different ecosystems based on optical remote sensing (RS) data. As the integration of solar-induced fluorescence (SIF) data in vegetation productivity processing chains has emerged as a promising source, we also include this relatively recent sensor modality. We define three methodological categories to derive productivity metrics from remotely sensed TS of vegetation indices or quantitative traits: (i) trend analysis and anomaly detection, (ii) land surface phenology, and (iii) integration and assimilation of TS-derived metrics into statistical and process-based dynamic vegetation models (DVMs). Although the majority of used TS data streams originate from data acquired from satellite platforms, TS data from aircraft and unoccupied aerial vehicles have found their way into productivity monitoring studies. To facilitate processing, we provide a list of common toolboxes for inferring productivity metrics and information from TS data. We further discuss validation strategies of the RS data derived productivity metrics: (1) using in situ measured data, such as yield; (2) sensor networks of distinct sensors, including spectroradiometers, flux towers, or phenological cameras; and (3) inter-comparison of different productivity metrics. Finally, we address current challenges and propose a conceptual framework for productivity metrics derivation, including fully integrated DVMs and radiative transfer models here labelled as “Digital Twin”. This novel framework meets the requirements of multiple ecosystems and enables both an improved understanding of vegetation temporal dynamics in response to climate and environmental drivers and enhances the accuracy of vegetation productivity monitoring

Assessing the Quality of Clinical Teachers: A Systematic Review of Content and Quality of Questionnaires for Assessing Clinical Teachers

BACKGROUND: Learning in a clinical environment differs from formal educational settings and provides specific challenges for clinicians who are teachers. Instruments that reflect these challenges are needed to identify the strengths and weaknesses of clinical teachers. OBJECTIVE: To systematically review the content, validity, and aims of questionnaires used to assess clinical teachers. DATA SOURCES: MEDLINE, EMBASE, PsycINFO and ERIC from 1976 up to March 2010. REVIEW METHODS: The searches revealed 54 papers on 32 instruments. Data from these papers were documented by independent researchers, using a structured format that included content of the instrument, validation methods, aims of the instrument, and its setting. Results : Aspects covered by the instruments predominantly concerned the use of teaching strategies (included in 30 instruments), supporter role (29), role modeling (27), and feedback (26). Providing opportunities for clinical learning activities was included in 13 instruments. Most studies referred to literature on good clinical teaching, although they failed to provide a clear description of what constitutes a good clinical teacher. Instrument length varied from 1 to 58 items. Except for two instruments, all had to be completed by clerks/residents. Instruments served to provide formative feedback ( instruments) but were also used for resource allocation, promotion, and annual performance review (14 instruments). All but two studies reported on internal consistency and/or reliability; other aspects of validity were examined less frequently. CONCLUSIONS: No instrument covered all relevant aspects of clinical teaching comprehensively. Validation of the instruments was often limited to assessment of internal consistency and reliability. Available instruments for assessing clinical teachers should be used carefully, especially for consequential decisions. There is a need for more valid comprehensive instruments

Ontology-based information visualization:Toward semantic web applications

This chapter has demonstrated an elegant way to visually represent ontological data. We have described how the Cluster Map visualization can use ontologies to create expressive information visualizations, with the attractive property that classes and objects that are semantically related are also spatially close in the visualization. Another key aspect of the visualization is that it focuses on visualizing instances rather than ontological models, thereby making it very useful for information retrieval purposes. A number of applications developed in the past few years have been described that prominently incorporate the Cluster Map visualization. Based on these descriptions, we could distinguish a number of generic information retrieval tasks that are well supported by the visualization. These applications prove the usability and usefulness of the Cluster Map in real-life scenarios. Furthermore, these applications show the applicability of the visualization in Semantic Web-based environments, where lightweight ontologies are playing a crucial role in organizing and accessing heterogeneous and decentralized information sources

Supporting User Tasks through Visualisation of Light-weight Ontologies

Introduction As is abundantly clear from the other chapters in this volume, ontologies will play a central role in the development and deployment of the Semantic Web. They will be used for many di#erent purposes, ranging across information localisation, integration, querying, presentation and navigation. Experiences in other fields (Data Mining, Scientific Computing) have shown that visualisation techniques can be successfully employed to support many of these tasks in those areas. The question then naturally arises if visualisation techniques can also be successfully employed on the ontology-based Semantic Web. The answer to this question of course depends strongly on the nature of the ontologies that we expect to be deployed on the Semantic Web. In our opinion, two specific features of ontologies will be important with respect to visualisation: We expect the majority of the ontologies on the Semantic Web to be light-weight. Light-weight ontologies are typified by the fact that t

3 Ontology-based Information Visualization: Towards Semantic Web Applications

The Semantic Web is an extension of the current World Wide Web, based on the idea of exchanging information with explicit, formal and machine-accessible descriptions of meaning. Providing information with such semantics will enable the construction of applications that have an increased awareness of what is contained in th