Trend of nontuberculous mycobacteria species and minimal inhibitory concentration in a referral laboratory in Korea from 2013 to 2019.

BACKGROUND: This study investigated the trends of nontuberculous mycobacterial (NTM) isolates and the minimal inhibitory concentrations (MIC) of antimicrobial agents in Korea. METHODS: Data from 2013 to 2019 were collected from 69 medical institutions through 12 branches of the Korean Institute of Tuberculosis. NTM identification was conducted using the Advansure Mycobacteria Genoblot assay. The MIC of antibiotics against NTM species were measured using the broth microdilution method according to the Clinical and Laboratory Standards Institute guidelines. RESULTS: Over seven years, 86,194 NTM identifications were requested, with an annual increase from 8034 in 2013-17,229 in 2019. The most frequently identified NTM species were M. intracellulare (33,467; 47.3%) and M. avium (19,818; 27.2%), followed by M. abscessus (6858; 9.4%) and M. massiliense (3156; 4.3%). Regarding the antimicrobial agents, imipenem exhibited the greatest difference in MIC between M. intracellulare and M. avium, whereas clarithromycin showed the most significant difference between M. abscessus and M. massiliense. No notable changes were observed in the annual MIC distribution of most antibacterial agents, except for clarithromycin in M. abscessus. CONCLUSIONS: The prevalence of NTM in Korea is gradually increasing, and follow-up studies on NTM isolates identified as the causative agents of infection are needed.

Analysis of Friction Noise Mechanism in Lead Screw System of Autonomous Vehicle Seats and Dynamic Instability Prediction Based on Deep Neural Network.

This study investigated the squeal mechanism induced by friction in a lead screw system. The dynamic instability in the friction noise model of the lead screw was derived through a complex eigenvalue analysis via a finite element model. A two degree of freedom model was described to analyze the closed solutions generated in the lead screw, and the friction noise sensitivity was examined. The analysis showed that the main source of friction noise in the lead screw was the bending mode pair, and friction-induced instability occurred when the ratio of the stiffness of the bending pair modes was 0.9-1. We also built an architecture to predict multiple outputs from a single model using deep neural networks and demonstrated that friction-induced instability can be predicted by deep neural networks. In particular, instability with nonlinearity was predicted very accurately by deep neural networks with a maximum absolute difference of about 0.035.