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A group of rat monoclonal antibodies recognizing the six different α chains of human type IV collagen have been established by our novel method. The method is designated the rat lymph node method in which enlarged medial iliac lymph nodes of a rat injected with an antigen emulsion via hind footpads are used as a source of B cells for cell fusion to produce hybridomas. The immunogens used were synthetic peptides having non-consensus amino acid sequences near the carboxyl termini of type IV collagen α chains. Hybridomas were screened both by ELISA with synthetic peptides and by indirect immunofluorescence with cryostat sections of human kidneys. Because the epitopes of all antibodies were determined by multipin-peptide scanning, they were confirmed to be isoform-specific. They are useful for identification of α chains of type IV collagen at the protein level in normal and abnormal conditions. The combined use of synthetic peptides as immunogens, the rat lymph node method as making monoclonal antibodies, and the multipin-peptide scanning as epitope mapping is found to be a strong tool for identification of peptides and proteins whose amino acid sequences are known or have been deduced.
We discuss methods for dealing with incomplete-data in the United Kingdom Women’s Cohort Study. We demonstrate by example how important it is to address the issues related to missing data with statistical integrity, illustrate the deficiencies of a data-reduction and a single-imputation method, and discuss how the method of multiple imputation overcomes them. Although the method entails some complexity, the computational activities can be organized in such a way that efficient analyses can be conducted by analysts who are not acquainted with all the details of the imputation method and who wish to rely on software they use and regard as standard.
LCA is often applied for decision-making that concerns actions reaching near or far into the future. However, traditional life cycle assessment methodology must be adjusted for the prospective and change-oriented purposes, but no standardised way of doing this has emerged yet. In this chapter some challenges are described and some learnings are derived. Many of the future-orientedFuture-oriented LCAs published so far perform relatively short-term prediction of simple comparisons. But for more long-term time horizonsTime horizon foresightForesight methods can be of help. ScenariosScenarios established by qualified experts about future technological and economic developments are indispensable in future technology assessments. The uncertaintiesUncertainties in future-oriented LCAs are to a large extent qualitative and it is important to emphasise that LCA of future technologies will provide a set of answers and not ‘the’ answer.
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