716 resultados para Spotted owl
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One plate is printed and numbered on both sides.
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"Issued December 1994"--P. [2].
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Mode of access: Internet.
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Thesis (Master's)--University of Washington, 2016-06
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In the European lesser-spotted dogfish Scyliorhinus canicula, rectal gland mass in mg (M-Rg) followed the allometric relationship: M-Rg = 1.15 M-0.68, where M is body mass (g). The concept of allometric scaling is an important consideration in studies investigating the function Of osmoregulatory organs. (C) 2003 the Fisheries Society of the British Isles.
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Bi-sensory striped arrays are described in owl and platypus that share some similarities with the other variant of bi-sensory striped array found in primate and carnivore striate cortex: ocular dominance columns. Like ocular dominance columns, the owl and platypus striped systems each involve two different topographic arrays that are cut into parallel stripes, and interdigitated, so that higher-order neurons can integrate across both arrays. Unlike ocular dominance stripes, which have a separate array for each eye, the striped array in the middle third of the owl tectum has a separate array for each cerebral hemisphere. Binocular neurons send outputs from both hemispheres to the striped array where they are segregated into parallel stripes according to hemisphere of origin. In platypus primary somatosensory cortex (SI), the two arrays of interdigitated stripes are derived from separate sensory systems in the bill, 40,000 electroreceptors and 60,000 mechanoreceptors. The stripes in platypus SI cortex produce bimodal electrosensory-mechanosensory neurons with specificity for the time-of-arrival difference between the two systems. This thunder-and-lightning system would allow the platypus to estimate the distance of the prey using time disparities generated at the bill between the earlier electrical wave and the later mechanical wave caused by the motion of benthic prey. The functional significance of parallel, striped arrays is not clear, even for the highly-studied ocular dominance system, but a general strategy is proposed here that is based on the detection of temporal disparities between the two arrays that can be used to estimate distance. (C) 2004 Elsevier Ltd. All rights reserved.
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The potential for large-scale use of a sensitive real time reverse transcription polymerase chain reaction (RT-PCR) assay was evaluated for the detection of Tomato spotted wilt virus (TSWV) in single and bulked leaf samples by comparing its sensitivity with that of DAS-ELISA. Using total RNA extracted with RNeasy (R) or leaf soak methods, real time RT-PCR detected TSWV in all infected samples collected from 16 horticultural crop species (including flowers, herbs and vegetables), two arable crop species, and four weed species by both assays. In samples in which DAS-ELISA had previously detected TSWV, real time RT-PCR was effective at detecting it in leaf tissues of all 22 plant species tested at a wide range of concentrations. Bulk samples required more robust and extensive extraction methods with real time RT-PCR, but it generally detected one infected sample in 1000 uninfected ones. By contrast, ELISA was less sensitive when used to test bulked samples, once detecting up to I infected in 800 samples with pepper but never detecting more than I infected in 200 samples in tomato and lettuce. It was also less reliable than real time RT-PCR when used to test samples from parts of the leaf where the virus concentration was low. The genetic variability among Australian isolates of TSWV was small. Direct sequencing of a 587 bp region of the nucleoprotein gene (S RNA) of 29 isolates from diverse crops and geographical locations yielded a maximum of only 4.3% nucleotide sequence difference. Phylogenetic analysis revealed no obvious groupings of isolates according to geographic origin or host species. TSWV isolates, that break TSWV resistance genes in tomato or pepper did not differ significantly in the N gene region studied, indicating that a different region of the virus genome is responsible for this trait.
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In the field of mental health risk assessment, there is no standardisation between the data used in different systems. As a first step towards the possible interchange of data between assessment tools, an ontology has been constructed for a particular one, GRiST (Galatean Risk Screening Tool). We briefly introduce GRiST and its data structures, then describe the ontology and the benefits that have already been realised from the construction process. For example, the ontology has been used to check the consistency of the various trees used in the model. We then consider potential uses in integration of data from other sources. © 2009 IEEE.
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Clinical decision support systems (CDSSs) often base their knowledge and advice on human expertise. Knowledge representation needs to be in a format that can be easily understood by human users as well as supporting ongoing knowledge engineering, including evolution and consistency of knowledge. This paper reports on the development of an ontology specification for managing knowledge engineering in a CDSS for assessing and managing risks associated with mental-health problems. The Galatean Risk and Safety Tool, GRiST, represents mental-health expertise in the form of a psychological model of classification. The hierarchical structure was directly represented in the machine using an XML document. Functionality of the model and knowledge management were controlled using attributes in the XML nodes, with an accompanying paper manual for specifying how end-user tools should behave when interfacing with the XML. This paper explains the advantages of using the web-ontology language, OWL, as the specification, details some of the issues and problems encountered in translating the psychological model to OWL, and shows how OWL benefits knowledge engineering. The conclusions are that OWL can have an important role in managing complex knowledge domains for systems based on human expertise without impeding the end-users' understanding of the knowledge base. The generic classification model underpinning GRiST makes it applicable to many decision domains and the accompanying OWL specification facilitates its implementation.