Glabridin Functions as a Quorum Sensing Inhibitor to Inhibit Biofilm Formation and Swarming Motility of Multidrug-Resistant Acinetobacter baumannii.

Objective: Acinetobacter baumannii is a hazardous bacterium that causes hospital-acquired nosocomial infections, and the advent of multidrug-resistant A. baumannii (MDR-AB) strains is concerning. Novel antibacterial therapeutic strategies must be developed. The biological effects of glabridin on MDR-AB were investigated in this study. Methods: The minimum inhibitory concentrations (MICs) of glabridin against eight clinical MDR-AB strains were determined using the broth microdilution technique. Crystal violet staining was used to assess biofilm development, which has significant contribution to bacterial resistance. Swarming motility was measured according to surface growth zone of MDR-AB on LB agar medium. qRT-PCR was used to evaluate the expression of quorum sensing genes abaI and abaR. Glabridin and routinely used therapeutic antimicrobial agents were tested for synergistic action using the checkerboard method. Results: According to our findings, glabridin suppressed MDR-AB growth at high doses (512-1024 µg/mL). The 1/4 MIC of glabridin significantly decreased MDR-AB biofilm formation by 19.98% (P < 0.05), inhibited MDR-AB motility by 44.27% (P < 0.05), whereas the 1/2 MIC of glabridin dramatically reduced MDR-AB biofilm development by 27.43% (P < 0.01), suppressed MDR-AB motility by 50.64% (P < 0.05). Mechanistically, glabridin substantially downregulated the expression of quorum sensing-related genes abaI and abaR by up to 39.12% (P < 0.001) and 25.19% (P < 0.01), respectively. However, no synergistic effect between glabridin and antibacterial drugs was found. Conclusion: Glabridin might be a quorum sensing inhibitor that inhibits MDR-AB biofilm development and swarming motility.

A Social Group-Based Information-Motivation-Behavior Skill Intervention to Promote Acceptability and Adoption of Wearable Activity Trackers Among Middle-Aged and Older Adults: Cluster Randomized Controlled Trial.

BACKGROUND: Wearable activity trackers offer potential to optimize behavior and support self-management. To assist older adults in benefiting from mobile technologies, theory-driven deployment strategies are needed to overcome personal, technological, and sociocontextual barriers in technology adoption. OBJECTIVE: To test the effectiveness of a social group-based strategy to improve the acceptability and adoption of activity trackers by middle-aged and older adults. METHODS: A cluster randomized controlled trial was conducted among 13 groups of middle-aged and older adults (≥45 years) performing group dancing (ie, square dancing) as a form of exercise in Guangzhou from November 2017 to October 2018. These dancing groups were randomized 1:1 into two arms, and both received wrist-worn activity trackers and instructions at the baseline face-to-face assessment. Based on the Information-Motivation-Behavior Skill framework, the intervention arm was also given a tutorial on the purpose of exercise monitoring (Information), encouraged to participate in exercise and share their exercise records with their dancing peers (Motivation), and were further assisted with the use of the activity tracker (Behavior Skill). We examined two process outcomes: acceptability evaluated by a 14-item questionnaire, and adoption assessed by the uploaded step count data. Intention-to-treat analysis was applied, with the treatment effects estimated by multilevel models. RESULTS: All dancing groups were followed up for the postintervention reassessment, with 61/69 (88%) participants of the intervention arm (7 groups) and 56/80 (70%) participants of the control arm (6 groups). Participants' sociodemographic characteristics (mean age 62 years, retired) and health status were comparable between the two arms, except the intervention arm had fewer female participants and lower cognitive test scores. Our intervention significantly increased the participants' overall acceptability by 6.8 points (95% CI 2.2-11.4), mainly driven by promoted motivation (adjusted group difference 2.0, 95% CI 0.5-3.6), increased usefulness (adjusted group difference 2.5, 95% CI 0.9-4.1), and better perceived ease of use (adjusted group difference 1.2, 95% CI 0.1-2.4), whereas enjoyment and comfort were not increased (adjusted group difference 0.9, 95% CI -0.4-2.3). Higher adoption was also observed among participants in the intervention arm, who were twice as likely to have valid daily step account data than their controlled counterparts (adjusted incidence relative risk [IRR]=2.0, 95% CI 1.2-3.3). The average daily step counts (7803 vs 5653 steps/day for the intervention and control, respectively) were similar between the two arms (adjusted IRR=1.4, 95% CI 0.7-2.5). CONCLUSIONS: Our social group-based deployment strategy incorporating information, motivation, and behavior skill components effectively promoted acceptability and adoption of activity trackers among community-dwelling middle-aged and older adults. Future studies are needed to examine the long-term effectiveness and apply this social engagement strategy in other group settings or meeting places. TRIAL REGISTRATION: Chinese Clinical Trial Registry ChiCTR-IOC-17013185; https://tinyurl.com/vedwc7h.

[A cloud detection algorithm for MODIS images combining Kmeans clustering and multi-spectral threshold method].

An improved method for detecting cloud combining Kmeans clustering and the multi-spectral threshold approach is described. On the basis of landmark spectrum analysis, MODIS data is categorized into two major types initially by Kmeans method. The first class includes clouds, smoke and snow, and the second class includes vegetation, water and land. Then a multi-spectral threshold detection is applied to eliminate interference such as smoke and snow for the first class. The method is tested with MODIS data at different time under different underlying surface conditions. By visual method to test the performance of the algorithm, it was found that the algorithm can effectively detect smaller area of cloud pixels and exclude the interference of underlying surface, which provides a good foundation for the next fire detection approach.

[An improved method for forest fire spot detection based on variance between-class].

An improved method using variance between-class and smoke plume mask is described. The brightness temperature threshold of potential fire pixels was adjusted to be 305 K. Based on the variance between-class of TIR channel brightness temperature and a smoke plume detection algorithm, the improved algorithm can separate the hot fire spots from the background and seek out the cool fire spots, respectively, with suitable thresholds of variance between-class. This algorithm has been used in the forest fires that happened in Fujian province and Heilongjiang province. Study shows that detection results with the algorithm are more satisfactory. It is adapted in different environments and can be more accurately detected the high-temperature fire spot and the smoder at low temperature. It increases the ability and accuracy to detect fire spots.