ALBERT

All Library Books, journals and Electronic Records Telegrafenberg

X
feed icon rss

Your email was sent successfully. Check your inbox.

An error occurred while sending the email. Please try again.

Proceed reservation?

Export
  • 1
    facet.materialart.
    Unknown
    Metastasis as an evolutionary process (2016)
    American Association for the Advancement of Science (AAAS)
    Details
    Publication Date: 2016-04-29
    Description: Therapeutic advances in oncology have not fully translated to the treatment of metastatic disease, which remains largely incurable. Metastatic subclones can emerge both early and late in the life of the primary tumor. A better understanding of the genetic evolution of metastatic disease has the potential to reveal differences in the therapeutic vulnerabilities of primary and metastatic tumors, shed light on the temporal patterns of and routes to metastatic colonization, and provide insight into the biology of the metastatic process. Here we review recent comparative studies of primary and metastatic tumors, including data suggesting that macroevolutionary shifts (the onset of chromosomal instability) contribute to the evolution of metastatic disease. We also discuss the practical challenges associated with these studies and how they might be overcome.〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Turajlic, Samra -- Swanton, Charles -- C50947/A18176/Cancer Research UK/United Kingdom -- Department of Health/United Kingdom -- New York, N.Y. -- Science. 2016 Apr 8;352(6282):169-75. doi: 10.1126/science.aaf2784.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉The Francis Crick Institute, 44 Lincoln's Inn Fields, London WC2A 3LY2, UK. Renal and Skin Units, The Royal Marsden Hospital, London SW3 6JJ, UK. ; The Francis Crick Institute, 44 Lincoln's Inn Fields, London WC2A 3LY2, UK. University College London Hospitals and Cancer Institute, Cancer Research UK Lung Cancer Centre of Excellence, Huntley Street, London WC1, UK. charles.swanton@crick.ac.uk.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/27124450" target="_blank"〉PubMed〈/a〉
    Keywords: Clone Cells/pathology ; *Evolution, Molecular ; Genetic Variation ; Humans ; Neoplasm Metastasis/*genetics/*pathology ; Neoplasms/classification/genetics/pathology ; Phylogeny
    Print ISSN: 0036-8075
    Electronic ISSN: 1095-9203
    Topics: Biology , Chemistry and Pharmacology , Computer Science , Medicine , Natural Sciences in General , Physics
    Published by American Association for the Advancement of Science (AAAS)
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
    PAPER CURRENT
  • 2
    Electronic Resource
    Electronic Resource
    A new approach to speech segmentation based on the maximum likelihood (1995)
    Springer
    Circuits, systems and signal processing 14 (1995), S. 615-632 
    Details
    ISSN: 1531-5878
    Source: Springer Online Journal Archives 1860-2000
    Topics: Electrical Engineering, Measurement and Control Technology
    Notes: Abstract Successful speech recognition is highly dependent on appropriate speech segmentation. The poor efficiency of the sequential detection of abrupt changes in the signals with relatively short stationary intervals, as is the case with speech signals, can be improved by the off-line maximum likelihood segmentation algorithm. In this paper the new segmentation algorithm is presented. For the a priori known number of segments, the algorithm determines such signal partitions for which the sum of segment distortion is minimal. The generalized maximum likelihood distortion measure has been introduced, and has proven to be particularly efficient on short signal segments. In the case of an unknown number of segments, its estimate is obtained comparing the reduction of the distortion. The asymptotic properties of the distortion sequence have been analyzed, which led to the definition of the presented segmentation algorithm. The introduced measure can be applied both to the AR and ARMA models. The segmentation algorithm is verified on test signals as well as on the natural speech signal, for which the pitch synchronous framing scheme is applied. The experimental results also include a comparison of the AR and ARMA model-based segmentations. The first results show that ARMA model-based segmentation gives somewhat better results than the AR model algorithm.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF01213958
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
    Paper (German National Licenses)
    Fulltext
Close ⊗
This website uses cookies and the analysis tool Matomo. More information can be found here...