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. In 2023, a notable FHB outbreak occurred in the southern region of Korea. We assessed FHB disease severity by quantifying infected spikelets and grains. Isolating fungal pathogens from infected samples often encounters interference from various microorganisms. We developed a cost-effective, selective medium, named BGT (Burkholderia glumae Toxoflavin) medium, utilizing B. glumae, which is primarily known for causing bacterial panicle blight in rice. This medium exhibited selective growth properties, predominantly supporting Fusarium spp., while substantially inhibiting the growth of other fungi. Using the BGT medium, we isolated F. graminearum and F. asiaticum from infected wheat and barley samples across Korea. To further streamline the process, we used a direct PCR approach to amplify the translation elongation factor 1-α (TEF-1α) region without a separate genomic DNA extraction step. Phylogenetic analysis of the TEF-1α region revealed that the majority of the isolates were identified as F. asiaticum. Our results demonstrate that BGT medium is an effective tool for FHB diagnosis and Fusarium strain isolation.

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unfortunately, these models either are not available during software development or, if available, quickly become outdated as implementations evolve.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. In this paper, we address the scalability problem of inferring the model of a component-based system from large system logs, without requiring any extra information. Our model inference technique, called PRINS, follows a divide-and-conquer approach. The idea is to first infer a model of each system component from the corresponding logs; then, the individual component models are merged together taking into account the flow of events across components, as reflected in the logs. We evaluated PRINS in terms of scalability and accuracy, using nine datasets composed of logs extracted from publicly available benchmarks and a personal computer running desktop business applications. The results show that PRINS can process large logs much faster than a publicly available and well-known state-of-the-art tool, without significantly compromising the accuracy of inferred models.

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. We investigated the association of HLA class I and II alleles with MERS-CoV in 32 patients with MERS. METHODS: HLA-A, -B, -C, -DRB1, -DQB1, and -DPB1 were genotyped by polymerase chain reaction sequence-based typing. RESULTS: HLA-DQB1*03:02 are significantly associated with moderate/mild cases of MERS-CoV. Other alleles are no statistical significance. CONCLUSIONS: Treatment strategies based on current research on the HLA gene and MERS-CoV will provide potential therapeutic targets.

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. Direct counting methods were used to investigate the association with DRB1 for samples with only one copy of a particular secondary DRB locus. We compared these allele types with the ambiguous allele combinations of the IPD-IMGT/HLA database. We identified 20, 40, 26, 31, 19, 16, 4, and 16 alleles of HLA-A, HLA-B, HLA-C, HLA-DRB1, HLA-DQA1, HLA-DQB1, HLA-DPA1, and HLA-DPB1, respectively. The number of HLA-DRB3/4/5 alleles was 4, 5, and 3, respectively. The haplotype frequencies of most common haplotypes were as follows: A*33:03:01-B*44:03:01-C*14:03-DRB1*13:02:01-DQB1*06:04:01-DPB1*04:01:01 (2.89%), A*33:03:01-B*44:03:01-C*14:03 (4.91%), DRB1*08:03:02-DQA1*01:03:01-DQB1*06:01:01-DPA1*02:02:02-DPB1*05:01:01 (5.41%), DRB1*04:05:01-DRB4*01:03:01 (12.72%), DQA1*01:03:01-DQB1*06:01:01 (13.01%), and DPA1*02:02:02-DPB1*05:01:01 (30.83%). In samples with only one copy of a specific secondary DRB locus, we examined its association with DRB1. We, thus, resolved 10 allele ambiguities in HLA-B, -C (each exon 2+3), -DRB1, -DQB1, -DQA1, and -DPB1 (each exon 2) of the IPD-IMGT/HLA database. Korean population was geographically close to Japanese and Han Chinese populations in the genetic distances by multidimensional scaling (MDS) plots. The information obtained by HLA typing of the 11 extended loci by next generation sequencing may be useful for more exact diagnostic tests on various transplantations and the genetic population relationship studies in South Koreans.

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.

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-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-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*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.

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. Our goal is to determine how offline testing and online testing differ or complement one another and if offline testing results can be used to help reduce the cost of online testing? Though these questions are generally relevant to all autonomous systems, we study them in the context of automated driving systems where, as study subjects, we use DNNs automating end-to-end controls of steering functions of self-driving vehicles. Our results show that offline testing is less effective than online testing as many safety violations identified by online testing could not be identified by offline testing, while large prediction errors generated by offline testing always led to severe safety violations detectable by online testing. Further, we cannot exploit offline testing results to reduce the cost of online testing in practice since we are not able to identify specific situations where offline testing could be as accurate as online testing in identifying safety requirement violations.

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.

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. In this study, HLA-A, -B, and -DRB1 alleles were genotyped at the allelic (6-digit) level using MiSeqDx in 26,202 volunteers from the Korean Network for Organ Sharing. Exon 2 and 3 of HLA-A and -B and exon 2 of HLA-DRB1 were amplified by polymerase chain reaction (PCR) and each allele was determined by matching the targeted exons and the reference sequence consisting of the IPD-IMGT/HLA Database. Seventy alleles of HLA-A, 102 alleles of HLA-B, and 69 alleles of HLA-DRB1 were identified. According to common and well-documented catalogs, 34 alleles in HLA-A, 61 in HLA-B, and 45 in HLA-DRB1 locus were common alleles, and 12, 14, and 11 kinds, were well-documented alleles, respectively. Thirteen novel alleles including 3 alleles in HLA-A, 8 alleles in HLA-B, and 2 alleles in HLA-DRB1 loci were found. Ten haplotypes with a frequency of more than 1.0% accounted for 22.4% of the total haplotype frequencies. Cis/trans ambiguities of HLA-A and -B loci by combination of exons 2 and 3 were analyzed to be 0.17% of 3 and 3.95% of 22 genotypes, respectively. This information on rare and novel alleles found by accurate HLA typing with NGS may be helpful for unrelated HSCT among Koreans.

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-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.

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-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*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-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-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.

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.