A New Method for Collecting Large Amounts of Symbiotic Gastrodermal Cells from Octocorals.

The study of cnidarian-dinoflagellate endosymbiosis in octocorals is becoming increasingly important. As symbiotic gastrodermal cells (SGCs) are the key cells in a symbiotic relationship, obtaining SGCs and studying their functions represent an urgent need. The majority of the cells dissociated from octocoral tissues consist of host cells and algal cells, and very few intact SGCs can be observed. To solve this problem, we developed a new method to collect large amounts of SGCs from octocorals. We incubated the tissue of Sinularia flexibilis in high-salinity (60‱) filtered seawater for 6 h and were able to collect more than 18 times the number of SGCs from the control group. To test the quality of the dissociated cells, we performed three assays to evaluate their cell viability. All three assays demonstrated that cell viability was good after incubating in a high-salinity solution. We also used two other octocorals, Paralemnalia thyrsoides and Sinularia compressa, to perform the same experiment, and the results were similar to those for Sinularia flexibilis. Therefore, a high-salinity-induced increase in the SGC ratio is a common phenomenon among octocorals. This method allows researchers to collect large amounts of SGCs from octocorals and helps us to better understand the complex molecular interactions in cnidarian-dinoflagellate endosymbiosis.

Using street imagery and crowdsourcing internet marketplaces to measure motorcycle helmet use in Bangkok, Thailand.

INTRODUCTION: The majority of Thailand's road traffic deaths occur on motorised two-wheeled or three-wheeled vehicles. Accurately measuring helmet use is important for the evaluation of new legislation and enforcement. Current methods for estimating helmet use involve roadside observation or surveillance of police and hospital records, both of which are time-consuming and costly. Our objective was to develop a novel method of estimating motorcycle helmet use. METHODS: Using Google Maps, 3000 intersections in Bangkok were selected at random. At each intersection, hyperlinks of four images 90° apart were extracted. These 12 000 images were processed in Amazon Mechanical Turk using crowdsourcing to identify images containing motorcycles. The remaining images were sorted manually to determine helmet use. RESULTS: After processing, 462 unique motorcycle drivers were analysed. The overall helmet wearing rate was 66.7 % (95% CI 62.6 % to 71.0 %). Taxi drivers had higher helmet use, 88.4% (95% CI 78.4% to 94.9%), compared with non-taxi drivers, 62.8% (95% CI 57.9% to 67.6%). Helmet use on non-residential roads, 85.2% (95% CI 78.1 % to 90.7%), was higher compared with residential roads, 58.5% (95% CI 52.8% to 64.1%). Using logistic regression, the odds of a taxi driver wearing a helmet compared with a non-taxi driver was significantly increased 1.490 (p<0.01). The odds of helmet use on non-residential roads as compared with residential roads was also increased at 1.389 (p<0.01). CONCLUSION: This novel method of estimating helmet use has produced results similar to traditional methods. Applying this technology can reduce time and monetary costs and could be used anywhere street imagery is used. Future directions include automating this process through machine learning.