Quantitative coronary CT angiography: absolute lumen sizing rather than %stenosis predicts hemodynamically relevant stenosis

This full day tutorial will use lectures and demonstrations from leading researchers and museum practitioners to present the principles and practices for robust photography-based digital techniques in museum contexts. The tutorial will present many examples of existing and cutting-edge uses of photography-based imaging including Reflectance Transformation Imaging (RTI), Algorithmic Rendering (AR), camera calibration, and methods of imaged-based generation of textured 3D geometry. The tutorial will also explore a framework for Leading museums are now adopting the more mature members of this family of robust digital imaging practices. These practices are part of the emerging science known as Computational Photography (CP). The imaging family’s common feature is the purpose-driven selective extraction of information from sequences of standard digital photographs. The information is extracted from the photographic sequences by computer algorithms. The extracted information is then integrated into a new digital representations containing knowledge not present in the original photogs, examined either alone or sequentially. The tutorial will examine strategies that promote widespread museum adoption of empirical acquisition technologies, generate scientifically reliable digital representations that are ‘born archival’, assist this knowledge’s long-term digital preservation, enable its future reuse for novel purposes, aid the physical conservation of the digitally represented museum materials, and enable public access and research

MEDFORD: A HUMAN AND MACHINE READABLE METADATA MARKUP LANGUAGE

Reproducibility of research is essential for science. However, in the way modern computational biology research is done, it is easy to lose track of small, but extremely critical, details. Key details, such as the specific version of a software used or iteration of a genome can easily be lost in the shuffle, or perhaps not noted at all. Much work is being done on the database and storage side of things, ensuring that there exists a space to store experiment-specific details, but current mechanisms for recording details are cumbersome for scientists to use. We propose a new metadata description language, named MEDFORD, in which scientists can record all details relevant to their research. Human-readable, easily-editable, and templatable, MEDFORD serves as a collection point for all notes that a researcher could find relevant to their research, be it for internal use or for future replication. MEDFORD has been applied to coral research, documenting research from RNA-seq analyses to photo collections

Mortalidade hospitalar como indicador de qualidade: uma revisão Hospital mortality as an indicator of clinical performance: a review

Este artigo visa a discutir as principais questões metodológicas relacionadas à mortalidade hospitalar como indicador de qualidade. Variações nos valores deste indicador se devem a inúmeros fatores, associados ao doente e à doença, que devem ser examinados para que possamos utilizá-lo como medida de desempenho. Presença de comorbidades e a gravidade do caso estão associadas à chance de morrer . Aspectos metodológicos, relevantes para a construção deste indicador, incluem a qualidade das fontes de dados, o intervalo de tempo no qual elas são calculadas e os diferentes tipos de agregação. São discutidos diversos modelos, tanto para classificação da gravidade, quanto para o ajuste das taxas de mortalidade entre serviços. São examinados ainda modelos explicativos para a variação de mortalidade. Conclui-se que nas condições em que a morte não é um evento raro, o emprego de taxas de mortalidade hospitalar representa uma ferramenta útil para indicar serviços com eventuais problemas de qualidade.<br>This article discusses the principal methodological problems related to hospital mortality as an indicator of clinical performance. Hospital mortality rates variation are due to various factors associated with patients' characteristics and to the specific diseases they are suffering. Socio-demographic variables, presence of comorbidity and severity may define case-mixes were chances of dying are not associated to technology deployed or quality of care. Relevant methodological aspects for calculating the rates include the quality of the source of data, time period and aggregation criteria. Various models that exist both for classifying severity of cases and for risk adjustment are presented and discussed. Explanatory models for mortality rates variation are also examined. The authors conclude that outcome indicators can be used as tool for health care service evaluation. For those conditions which death is not a rare event hospital mortality rates may constitute an useful tool for indicating services with low than expected quality of care