895 resultados para museum specimens
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Aim-To develop an expert system model for the diagnosis of fine needle aspiration cytology (FNAC) of the breast.
Methods-Knowledge and uncertainty were represented in the form of a Bayesian belief network which permitted the combination of diagnostic evidence in a cumulative manner and provided a final probability for the possible diagnostic outcomes. The network comprised 10 cytological features (evidence nodes), each independently linked to the diagnosis (decision node) by a conditional probability matrix. The system was designed to be interactive in that the cytopathologist entered evidence into the network in the form of likelihood ratios for the outcomes at each evidence node.
Results-The efficiency of the network was tested on a series of 40 breast FNAC specimens. The highest diagnostic probability provided by the network agreed with the cytopathologists' diagnosis in 100% of cases for the assessment of discrete, benign, and malignant aspirates. A typical probably benign cases were given probabilities in favour of a benign diagnosis. Suspicious cases tended to have similar probabilities for both diagnostic outcomes and so, correctly, could not be assigned as benign or malignant. A closer examination of cumulative belief graphs for the diagnostic sequence of each case provided insight into the diagnostic process, and quantitative data which improved the identification of suspicious cases.
Conclusion-The further development of such a system will have three important roles in breast cytodiagnosis: (1) to aid the cytologist in making a more consistent and objective diagnosis; (2) to provide a teaching tool on breast cytological diagnosis for the non-expert; and (3) it is the first stage in the development of a system capable of automated diagnosis through the use of expert system machine vision.
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To compare the rejection rates of non-small cell lung cancer (NSCLC) samples obtained by differing sampling methods for testing by Sanger sequencing for epidermal growth factor receptor (EGFR) mutations. To assess the association between unsatisfactory outcomes and the quantity of DNA extracted from cytological versus histological samples.
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2007- present- National Museum of Archaeology, Gozo- text, display, academic input
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Malone, C.A.T. and S.K.F. Stoddart, Five Year Statement.1991: Cambridge University.
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Elucidation of the transcriptome and proteome of the normal retina will be difficult since it is comprised of at least 55 different cell types. However the characteristic layered cellular anatomy of the retina makes it amenable to planar sectioning, enabling the generation of enriched retinal cell populations. The aim of this study was to validate a reproducible method for preparing enriched retinal layers from porcine retina.
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Non-invasive population genetics has become a valuable tool in ecology and conservation biology, allowing genetic studies of wild populations without the need to catch, handle or even observe the study subjects directly. We address some of the concerns regarding the limitations of using non-invasive samples by comparing the quality of population genetic information gained through DNA extracted from faecal samples and biopsy samples of two elusive bat species, Myotis mystacinus and Myotis nattereri. We demonstrate that DNA extracted from faeces and tissue samples gives comparable results for frequency based population genetic analyses, despite the occurrence of genotyping errors when using faecal DNA. We conclude that non-invasive genetic sampling for population genetic analysis in bats is viable, and although more labour-intensive and expensive, it is an alternative to tissue sampling, which is particularly pertinent when specimens are rare, endangered or difficult to capture. © 2012 Museum and Institute of Zoology PAS.
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Tactility Factory Story and Panels on display as part of Showcase of Northern Irish Innovation, Pump House. Organized by Innovation Centre, NI Science Park, Belfast. Showcasing 14 of NI most innovative companies. (Jan 2013-ongoing) presented to HRH Prince Andrew (Jan 2013)