A Study on the Application of Machine and Deep Learning Using the Impact Response Test to Detect Defects on the Piston Rod and Steering Rack of Automobiles.

The main parts of automobiles are the piston rod of the shock absorber and the steering rack of the steering gear, and their quality control is critical in the product process. In the process line, these products are normally inspected through visual inspection, sampling, and simple tensile tests; however, if there is a problem or abnormality, it is difficult to identify the type and location of the defect. Usually, these defects are likely to cause surface cracks during processing, which in turn accelerate the deterioration of the shock absorber and steering, causing serious problems in automobiles. As a result, the purpose of this study was to present, among non-destructive methods, a shock response test method and an analysis method that can efficiently and accurately determine the defects of the piston rod and steering rack. A test method and excitation frequency range that can measure major changes according to the location and degree of defects were proposed. A defect discrimination model was constructed using machine and deep learning through feature derivation in the time and frequency domains for the collected data. The analysis revealed that it was possible to effectively distinguish the characteristics according to the location as well as the presence or absence of defects in the frequency domain rather than the time domain. The results indicate that it will be possible to quickly and accurately check the presence or absence of defects in the shock absorber and steering in the automobile manufacturing process line in the future. It is expected that this will play an important role as a key factor in building a smart factory.

Assessment of synergistic combination potential of probiotic and bacteriophage against antibiotic-resistant Staphylococcus aureus exposed to simulated intestinal conditions.

This study was designed to evaluate the combined effect of probiotic Lactobacillus rhamnosus and bacteriophage SA11 on the control of antibiotic-sensitive Staphylococcus aureus (ASSA) and antibiotic-resistant S. aureus (ARSA) under the simulated intestinal conditions. The survivability of ASSA and ARSA were determined in the simulated phosphate-buffered saline (PBS)-, trypticase soy broth (TSB)-, and milk-based gastric juices adjusted to pH 2.0, 3.0, and 5.0 at 37 °C for 30 min. The inhibitory effect of bacteriophage SA11 and probiotic on the growth of ASSA and ARSA was evaluated in the simulated intestinal juices at 37 °C for 20 h. The least reductions in the numbers of ASSA and ARSA were observed in the milk-based gastric juices at pH 2.0 (<1 log). No significant changes in the teichoic acid-mediated sliding motility were observed for ASSA and ARSA after 30-min exposure to the simulated gastric juices (pH 2.0, 3.0, and 5.0), responsible for the enhanced bacterial attachment to the epithelial cells. The bacteriophage SA11 was stable down to pH 5.0 and up to 0.06 % bile salts. The bacteriophage SA11 combined with probiotic effectively inhibited the growth of ASSA and ARSA in the simulated intestinal conditions, showing more than 4 log reduction. The relative expression levels of adhesion-related genes (clfA, eno, and fnbA) and efflux-related genes (mdeA, norB, and norC) were less decreased in ARSA than in ASSA after exposure to the simulated gastrointestinal conditions. These results might shed light on the application of bacteriophage to control the ingested antibiotic-resistant foodborne pathogens in the intestinal tract.

Blanket antimicrobial resistance gene database with structural information, BOARDS, provides insights on historical landscape of resistance prevalence and effects of mutations in enzyme structure.

Antimicrobial resistance (AMR) in pathogenic bacteria poses a significant threat to public health, yet there is still a need for development in the tools to deeply understand AMR genes based on genetic or structural information. In this study, we present an interactive web database named Blanket Overarching Antimicrobial-Resistance gene Database with Structural information (BOARDS, sbml.unist.ac.kr), a database that comprehensively includes 3,943 reported AMR gene information for 1,997 extended spectrum beta-lactamase (ESBL) and 1,946 other genes as well as a total of 27,395 predicted protein structures. These structures, which include both wild-type AMR genes and their mutants, were derived from 80,094 publicly available whole-genome sequences. In addition, we developed the rapid analysis and detection tool of antimicrobial-resistance (RADAR), a one-stop analysis pipeline to detect AMR genes across whole-genome sequencing (WGSs). By integrating BOARDS and RADAR, the AMR prevalence landscape for eight multi-drug resistant pathogens was reconstructed, leading to unexpected findings such as the pre-existence of the MCR genes before their official reports. Enzymatic structure prediction-based analysis revealed that the occurrence of mutations found in some ESBL genes was found to be closely related to the binding affinities with their antibiotic substrates. Overall, BOARDS can play a significant role in performing in-depth analysis on AMR.IMPORTANCEWhile the increasing antibiotic resistance (AMR) in pathogen has been a burden on public health, effective tools for deep understanding of AMR based on genetic or structural information remain limited. In this study, a blanket overarching antimicrobial-resistance gene database with structure information (BOARDS)-a web-based database that comprehensively collected AMR gene data with predictive protein structural information was constructed. Additionally, we report the development of a RADAR pipeline that can analyze whole-genome sequences as well. BOARDS, which includes sequence and structural information, has shown the historical landscape and prevalence of the AMR genes and can provide insight into single-nucleotide polymorphism effects on antibiotic degrading enzymes within protein structures.

Comparative Genomic Analysis and BTEX Degradation Pathways of a Thermotolerant Cupriavidus cauae PHS1.

Volatile organic compounds such as benzene, toluene, ethylbenzene, and isomers of xylenes (BTEX) constitute a group of monoaromatic compounds that are found in petroleum and have been classified as priority pollutants. In this study, based on its newly sequenced genome, we reclassified the previously identified BTEX-degrading thermotolerant strain Ralstonia sp. PHS1 as Cupriavidus cauae PHS1. Also presented are the complete genome sequence of C. cauae PHS1, its annotation, species delineation, and a comparative analysis of the BTEX-degrading gene cluster. Moreover, we cloned and characterized the BTEX-degrading pathway genes in C. cauae PHS1, the BTEX-degrading gene cluster of which consists of two monooxygenases and meta-cleavage genes. A genome-wide investigation of the PHS1 coding sequence and the experimentally confirmed regioselectivity of the toluene monooxygenases and catechol 2,3-dioxygenase allowed us to reconstruct the BTEX degradation pathway. The degradation of BTEX begins with aromatic ring hydroxylation, followed by ring cleavage, and eventually enters the core carbon metabolism. The information provided here on the genome and BTEX-degrading pathway of the thermotolerant strain C. cauae PHS1 could be useful in constructing an efficient production host.