Abstract: The invention provides an anti-C5 humanized monoclonal antibody with low immunogenicity and low ADCC/CDC function and its application. By modifying the amino acid sequence of the framework region of Eculizumab monoclonal antibody, the immunogenicity was reduced, and the antibody was replaced from IgG2 subtype to IgG1 subtype, and a flexible amino acid sequence was inserted between the CDR3 and CH2 regions of the heavy chain of the IgG1 antibody to reduce the immunogenicity. The purpose of ADCC/CDC function is to improve the stability of the antibody and prolong its half-life. The binding affinity of the monoclonal antibody of the invention to human C5 is similar to that of the original Eculizumab antibody, it can specifically block the complement hemolytic activity of C5 and the production of C5a and can be used for the preparation of C5-targeted paroxysmal nocturnal hemoglobinuria and atypical the drug for the treatment of hemolytic uremic syndrome has excellent clinical therapeutic value.

Abstract: A computer-implemented method of training, using a computer log file, an application error prediction engine to identify one or more application errors includes parsing the computer log file into a plurality of data sets. Each data set is associated with a unique computing session having a session identifier and ending in an application or. The method also includes extracting, from each data set, values for a specified set of parameters in each data set. The method also includes encoding the extracted values for each data set into a corresponding data structure. The method also includes generating, for each data structure, a corresponding vector, the corresponding vectors collectively forming a matrix. The method also includes calculating, based on the matrix, a set of clusters, each cluster corresponding to a known error type, the set of clusters used to create a model used to identify new error types.

Abstract: A computer-implemented method of training, using a computer log file, an application error prediction engine to identify one or more application errors includes parsing the computer log file into a plurality of data sets. Each data set is associated with a unique computing session having a session identifier and ending in an application or. The method also includes extracting, from each data set, values for a specified set of parameters in each data set. The method also includes encoding the extracted values for each data set into a corresponding data structure. The method also includes generating, for each data structure, a corresponding vector, the corresponding vectors collectively forming a matrix. The method also includes calculating, based on the matrix, a set of clusters, each cluster corresponding to a known error type, the set of clusters used to create a model used to identify new error types.