Antibiotic resistance is a global concern; however, data on antibiotic-resistant Ureaplasma spp. and Mycoplasma hominis are limited in comparison to similar data on other microbes. A total of 492 Ureaplasma spp. and 13 M. hominis strains obtained in Hangzhou, China, in 2018 were subjected to antimicrobial susceptibility testing for levofloxacin, moxifloxacin, erythromycin, clindamycin, and doxycycline using the broth microdilution method. The mechanisms underlying quinolone and macrolide resistance were determined. Meanwhile, a model of the topoisomerase IV complex bound to levofloxacin in wild-type Ureaplasma spp. was built to study the quinolone resistance mutations. For Ureaplasma spp., the levofloxacin, moxifloxacin, and erythromycin resistance rates were 84.69%, 51.44%, and 3.59% in U. parvum and 82.43%, 62.16%, and 5.40% in U. urealyticum, respectively. Of the 13 M. hominis strains, 11 were resistant to both levofloxacin and moxifloxacin, and five strains showed clindamycin resistance. ParC S83L was the most prevalent mutation in levofloxacin-resistant Ureaplasma strains, followed by ParE R448K. The two mutations GyrA S153L and ParC S91I were commonly identified in quinolone-resistant M. hominis. A molecular dynamics-refined structure revealed that quinolone resistance-associated mutations inhibited the interaction and reduced affinity with gyrase or topoisomerase IV and quinolones. The novel mutations S21A in the L4 protein and G2654T and T2245C in 23S rRNA and the ermB gene were identified in erythromycin-resistant Ureaplasma spp. As fluoroquinolone resistance in Ureaplasma spp. and Mycoplasma hominis remains high in China, the rational use of antibiotics needs to be further enhanced.
Various application scenarios of a smartphone sometimes require one-handed and/or eyes-free interaction. Tilt-based interfaces have the potential to meet these requirements. Taking multiple application scenarios into account, we conducted an experiment to systematically investigate human ability in controlling tilt input of a mobile phone. Three visual feedback levels, i.e., fully visual feedback (FV ), partially visual feedback (PV ), and no visual feedback (NV ), were investigated. Under the NV condition, the participants performed a task using an eyes-free method. The results revealed that trials were performed the fastest but were the most error-prone under the NV condition. The participants could easily distinguish 4 tilt orientation levels (TOLs) and 2 tilt magnitude levels (TMLs) or 8 TOLs and 2 TMLs under the NV condition with tolerance of an error rate 10% or 15%, respectively. We also found out that the participants' abilities to control tilt input were related to tilt orientation directions. The results have some implications for non-visual interface designs using tilt as primitive input.
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