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  • 1
    Electronic Resource
    Electronic Resource
    Springer
    Biodiversity and conservation 5 (1996), S. 897-920 
    ISSN: 1572-9710
    Keywords: Sites of Special Scientific Interest in Wales ; upland, lowland and coastal zones ; size variation ; habitat and species diversity ; conservation function
    Source: Springer Online Journal Archives 1860-2000
    Topics: Biology
    Notes: The designation of Sites of Special Scientific Interest (SSSIs) is one of the major statutory measures for wildlife protection in Britain. In this account, procedures for selecting SSSIs are outlined, and the representation of habitats and species in different taxonomic groups which qualify 731 SSSIs notified in Wales (in November 1994) are summarized. Biological SSSIs occupy approximately 9.6% of the total area of Wales. There are significant differences in the numbers and sizes of sites characteristic in the uplands (few large), lowlands (many small) and coast (intermediate). Over 70% of the biological SSSIs have more than one qualifying feature. Most sites (663, 90%) have been selected for one or more habitats, and many sites (328, 45%) have particular species attributes. In relation to their total extent in Wales, some habitats (including ombrotrophic peatland, dwarf-shrub heathland, rich fen and sand dune) have greater proportional representation in SSSIs than others (such as upland grassland, woodland and scrub). These differences reflect conservation priorities for Welsh habitats which are related to the wider British context. As expected, birds and vascular plants contribute to notification of a greater number of sites than other groups; invertebrates, lichens and bryophytes qualify in some sites and require further evaluation in others; except for bats, mammals are comparatively poorly represented as special features. Birds qualify many of the largest SSSIs in Wales (breeding assemblages in the uplands and overwintering wildfowl and waders in estuaries). Possibilities for future refinement of the SSSI series are considered. It is suggested that the establishment of conservation sites is sufficiently advanced in Britain to permit worthwhile examination of the composition and function of the network as a whole against conservation objectives.
    Type of Medium: Electronic Resource
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  • 2
    Publication Date: 2012-03-01
    Description: : High-throughput technologies can identify genes whose expression profiles correlate with specific phenotypes; however, placing these genes into a biological context remains challenging. To help address this issue, we developed nested Expression Analysis Systematic Explorer (nEASE). nEASE complements traditional gene ontology enrichment approaches by determining statistically enriched gene ontology subterms within a list of genes based on co-annotation. Here, we overview an open-source software version of the nEASE algorithm. nEASE can be used either stand-alone or as part of a pathway discovery pipeline. Availability: nEASE is implemented within the Multiple Experiment Viewer software package available at http://www.tm4.org/mev . Contact: cholmes@stats.ox.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online.
    Print ISSN: 1367-4803
    Electronic ISSN: 1460-2059
    Topics: Biology , Computer Science , Medicine
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  • 3
    Publication Date: 2012-09-30
    Description: Motivation: Meta-analysis of genomics data seeks to identify genes associated with a biological phenotype across multiple datasets; however, merging data from different platforms by their features (genes) is challenging. Meta-analysis using functionally or biologically characterized gene sets simplifies data integration is biologically intuitive and is seen as having great potential, but is an emerging field with few established statistical methods. Results: We transform gene expression profiles into binary gene set profiles by discretizing results of gene set enrichment analyses and apply a new iterative bi-clustering algorithm (iBBiG) to identify groups of gene sets that are coordinately associated with groups of phenotypes across multiple studies. iBBiG is optimized for meta-analysis of large numbers of diverse genomics data that may have unmatched samples. It does not require prior knowledge of the number or size of clusters. When applied to simulated data, it outperforms commonly used clustering methods, discovers overlapping clusters of diverse sizes and is robust in the presence of noise. We apply it to meta-analysis of breast cancer studies, where iBBiG extracted novel gene set—phenotype association that predicted tumor metastases within tumor subtypes. Availability: Implemented in the Bioconductor package iBBiG Contact: aedin@jimmy.harvard.edu
    Print ISSN: 1367-4803
    Electronic ISSN: 1460-2059
    Topics: Biology , Computer Science , Medicine
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  • 4
    Publication Date: 2015-01-16
    Description: BARD, the BioAssay Research Database ( https://bard.nih.gov/ ) is a public database and suite of tools developed to provide access to bioassay data produced by the NIH Molecular Libraries Program (MLP). Data from 631 MLP projects were migrated to a new structured vocabulary designed to capture bioassay data in a formalized manner, with particular emphasis placed on the description of assay protocols. New data can be submitted to BARD with a user-friendly set of tools that assist in the creation of appropriately formatted datasets and assay definitions. Data published through the BARD application program interface (API) can be accessed by researchers using web-based query tools or a desktop client. Third-party developers wishing to create new tools can use the API to produce stand-alone tools or new plug-ins that can be integrated into BARD. The entire BARD suite of tools therefore supports three classes of researcher: those who wish to publish data, those who wish to mine data for testable hypotheses, and those in the developer community who wish to build tools that leverage this carefully curated chemical biology resource.
    Print ISSN: 0305-1048
    Electronic ISSN: 1362-4962
    Topics: Biology
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  • 5
    Publication Date: 2015-08-08
    Description: The hypoxia-inducible factor (HIF) hydroxylases regulate hypoxia sensing in animals. In humans, they comprise three prolyl hydroxylases (PHD1–3 or EGLN1–3) and factor inhibiting HIF (FIH). FIH is an asparaginyl hydroxylase catalyzing post-translational modification of HIF-α, resulting in reduction of HIF-mediated transcription. Like the PHDs, FIH is proposed to have a hypoxia-sensing role in cells, enabling responses to changes in cellular O2 availability. PHD2, the most important human PHD isoform, is proposed to be biochemically/kinetically suited as a hypoxia sensor due to its relatively high sensitivity to changes in O2 concentration and slow reaction with O2. To ascertain whether these parameters are conserved among the HIF hydroxylases, we compared the reactions of FIH and PHD2 with O2. Consistent with previous reports, we found lower Kmapp(O2) values for FIH than for PHD2 with all HIF-derived substrates. Under pre-steady-state conditions, the O2-initiated FIH reaction is significantly faster than that of PHD2. We then investigated the kinetics with respect to O2 of the FIH reaction with ankyrin repeat domain (ARD) substrates. FIH has lower Kmapp(O2) values for the tested ARDs than HIF-α substrates, and pre-steady-state O2-initiated reactions were faster with ARDs than with HIF-α substrates. The results correlate with cellular studies showing that FIH is active at lower O2 concentrations than the PHDs and suggest that competition between HIF-α and ARDs for FIH is likely to be biologically relevant, particularly in hypoxic conditions. The overall results are consistent with the proposal that the kinetic properties of individual oxygenases reflect their biological capacity to act as hypoxia sensors.
    Print ISSN: 0021-9258
    Electronic ISSN: 1083-351X
    Topics: Biology , Chemistry and Pharmacology
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