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Soluble divalent and multivalent heterodimeric analogs of proteins

Specificity in immune responses is in part controlled by the selective interaction of T cell receptors with their cognate ligands, peptide / MHC molecules. The discriminating nature of this interaction makes these molecules, in soluble form, good candidates for selectively regulating immune responses. Attempts to exploit soluble analogs of these proteins has been hampered by the intrinsic low avidity of these molecules for their ligands. To increase the avidity of soluble analogs for their cognates to biologically relevant levels, divalent peptide / MHC complexes or T cell receptors (superdimers) were constructed. Using a recombinant DNA strategy, DNA encoding either the MHC class II / peptide or TCR heterodimers was ligated to DNA coding for murine Ig heavy and light chains. These constructs were subsequently expressed in a baculovirus expression system. Enzyme-linked immunosorbant assays (ELISA) specific for the Ig and polymorphic determinants of either the TCR or MHC fraction of the molecule indicated that infected insect cells secreted approximately 1 .mu.g / ml of soluble, conformnationally intact chimeric superdimers. SDS PAGE gel analysis of purified protein showed that expected molecular weight species. The results of flow cytometry demonstrated that the TCR and class II chimeras bound specifically with high avidity to cells bearing their cognate receptors. These superdimers will be useful for studying TCR / MHC interactions, lymphocyte tracking, identifying new antigens, and have possible uses as specific regulators of immune responses.
Owner:SCHNECK JONATHAN +1

Chinese text classification method based on super-deep convolution neural network structure model

The invention provides a Chinese text classification method based on a super-deep convolution neural network structure model. The method comprises the steps of collecting a training corpus of a word vector from the internet, combining a Chinese word segmentation algorithm to conduct word segmentation on the training corpus, and obtaining a word vector model; collecting news of multiple Chinese news websites from the internet, and marking the category of the news as a corpus set for text classification, wherein the corpus set is divided into a training set corpus and a test set corpus; conducting word segmentation on the training set corpus and the test set corpus respectively, and then obtaining the word vectors corresponding to the training set corpus and the test set corpus respectively by utilizing the word vector model; establishing the super-deep convolution neural network structure model; inputting the word vector corresponding to the training set corpus into the super-deep convolution neural network structure model, and conducting training and obtaining a text classification model; inputting the Chinese text which needs to be sorted into the word vector model, obtaining the word vector of the Chinese text which needs to be classified, and then inputting the word vector into the text classification model to complete the Chinese text classification.
Owner:HEBEI UNIV OF TECH
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