Impact of log parsing on deep learning-based anomaly detection.

Software systems log massive amounts of data, recording important runtime information. Such logs are used, for example, for log-based anomaly detection, which aims to automatically detect abnormal behaviors of the system under analysis by processing the information recorded in its logs. Many log-based anomaly detection techniques based on deep learning models include a pre-processing step called log parsing.

Development of a Selective Medium for Surveillance of Fusarium Head Blight Disease.

Fusarium head blight (FHB), predominantly caused by Fusarium graminearum and F. asiaticum, is a significant fungal disease impacting small-grain cereals. The absence of highly resistant cultivars underscores the need for vigilant FHB surveillance to mitigate its detrimental effects.

PRINS: scalable model inference for component-based system logs.

Behavioral software models play a key role in many software engineering tasks; unfortunately, these models either are not available during software development or, if available, quickly become outdated as implementations evolve. Model inference techniques have been proposed as a viable solution to extract finite state models from execution logs. However, existing techniques do not scale well when processing very large logs that can be commonly found in practice.

Association of HLA class I and II genes with Middle East respiratory syndrome coronavirus infection in Koreans.

Introduction: Middle East Respiratory Syndrome (MERS) caused by MERS-coronavirus (CoV) is a lower respiratory tract disease characterized by a high mortality rate. MERS-CoV spread from Saudi Arabia to other countries, including South Korea. Dysfunction of the human leukocyte antigen (HLA) system has many effects due to genetic complexity and its role in the adaptive immune response.

Allele and haplotype frequencies of human leukocyte antigen-A, -B, -C, -DRB1, -DRB3/4/5, -DQA1, -DQB1, -DPA1, and -DPB1 by next generation sequencing-based typing in Koreans in South Korea.

Allele frequencies and haplotype frequencies of HLA-A, -B, -C, -DRB1, -DRB3/4/5, -DQA1, -DQB1, -DPA1, and -DPB1 have been rarely reported in South Koreans using unambiguous, phase-resolved next generation DNA sequencing. In this study, HLA typing of 11 loci in 173 healthy South Koreans were performed using next generation DNA sequencing with long-range PCR, TruSight® HLA v2 kit, Illumina MiSeqDx platform system, and Assign™ for TruSight™ HLA software. Haplotype frequencies were calculated using the PyPop software.

The HLA-A*11:384 allele identified in a volunteer donor for hematopoietic stem cell transplant.

HLA-A*11:384 differs from HLA-A*11:01:01:01 in codon 151 in exon 3.

The HLA-DRB1*09:45 allele identified in a volunteer donor for hematopoietic stem cell transplant.

HLA-DRB1*09:45 differs from HLA-DRB1*09:01:02:01 in codon 19 in exon 2.

The HLA-A*02:954 allele identified in a volunteer donor for hematopoietic stem cell transplant.

HLA-A*02:954 differs from HLA-A*02:06:01:01 in codon 82 in exon 2.

The HLA-A*24:514N allele identified in a volunteer donor for hematopoietic stem cell transplant.

HLA-A*24:514N differs from HLA-A*24:02:01:01 in codon 167 in exon 3.

The HLA-B*13:144 allele identified in a volunteer donor for hematopoietic stem cell transplant.

HLA-B*13:144 differs from HLA-B*13:01:01:01 in codon 12 in exon 2.

Can Offline Testing of Deep Neural Networks Replace Their Online Testing?: A Case Study of Automated Driving Systems.

We distinguish two general modes of testing for Deep Neural Networks (DNNs): Offline testing where DNNs are tested as individual units based on test datasets obtained without involving the DNNs under test, and online testing where DNNs are embedded into a specific application environment and tested in a closed-loop mode in interaction with the application environment. Typically, DNNs are subjected to both types of testing during their development life cycle where offline testing is applied immediately after DNN training and online testing follows after offline testing and once a DNN is deployed within a specific application environment. In this paper, we study the relationship between offline and online testing.

Distributions of HLA-A, -B, and -DRB1 alleles typed by amplicon-based next generation sequencing in Korean volunteer donors for unrelated hematopoietic stem cell transplantation.

HLA genes play a pivotal role for successful hematopoietic stem cell transplantation (HSCT). There is an increasing need for sophisticated screening of donor HLA genotypes for unrelated HSCT. Next generation sequencing (NGS) has emerged as an alternative for classical Sanger sequence for HLA typing.

The HLA-B*35:01:64 allele identified in a volunteer donor for hematopoietic stem cell transplant.

HLA-B*35:01:64 differs from HLA-B*35:01:01:01 in codon 58 in exon 2.

The HLA-A*24:480 allele identified in a volunteer donor for hematopoietic stem cell transplant.

HLA-A*24:480 differs from HLA-A*24:02:01:01 in codon 178 in exon 3.

The HLA-A*33:03:42 allele identified in a volunteer donor for hematopoietic stem cell transplant.

HLA-A*33:03:42 differs from HLA-A*33:03:01:01 in codon 90 in exon 2.

The HLA-DRB1*04:05:21 allele identified in a volunteer donor for hematopoietic stem cell transplant.

HLA-DRB1*04:05:21 differs from HLA-DRB1*04:05:01:01 in codon 83 in exon 2.

HLA-A*02:877 allele identified in a volunteer donor for hematopoietic stem cell transplant.

HLA-A*02:877 differs from HLA-A*02:06:01:01 in codon 183 in exon 3.

The HLA-A*31:154 allele identified in a volunteer donor for hematopoietic stem cell transplant.

HLA-A*31:154 differs from HLA-A*31:01:02:01 in codon 115 in exon 3.

The HLA-B*46:01:26 allele identified in a volunteer donor for hematopoietic stem cell transplant.

HLA-B*46:01:26 differs from HLA-B*46:01:01:01 in codon 66 in exon 2.

The HLA-A*02:842 allele identified in a volunteer donor for hematopoietic stem cell transplant.

HLA-A*02:842 differs from HLA-A*02:149 in codon 99 in exon 3.

The HLA-DRB1*04:277 allele identified in a volunteer donor for hematopoietic stem cell transplant.

HLA-DRB1*04:277 differs from HLA-DRB1*04:06:01:01 in codon 62 in exon 2.

The HLA-B*51:284 allele identified in a volunteer donor for hematopoietic stem cell transplant.

HLA-B*51:284 differs from HLA-B*51:01:01:01 in codon 44 in exon 2.

The HLA-B*07:367 allele identified in a volunteer donor for hematopoietic stem cell transplant.

HLA-B*07:367 differs from HLA-B*07:02:01:01 in codon 181 in exon 3.

The HLA-B*15:529 allele identified in a volunteer donor for hematopoietic stem cell transplant.

HLA-B*15:529 differs from HLA-B*15:01:01:01 in codon 138 in exon 3.

The HLA-B*44:454 allele identified in a volunteer donor for hematopoietic stem cell transplant.

HLA-B*44:454 differs from HLA-B*44:03:02:01 in codon 81 in exon 2.