Hydrological regulation of Vibrio dynamics in a tropical monsoonal estuary: a classification and regression tree approach

Distribution and antibiotic resistance of vibrio population in an urbanized tropical lake-the Vembanad-in the southwest coast of India

Among the diverse Vibrio spp. autochthonous to coastal ecosystems, V. cholerae, V. fluvialis, V. vulnificus and V. parahaemolyticus are pathogenic to humans. Increasing sea-surface temperature, sea-level rise and water-related disasters associated with climate change have been shown to influence the proliferation of these bacteria and change their geographic distribution. We investigated the spatio-temporal distribution of Vibrio spp. in a tropical lake for 1 year at a 20-day interval. The abundance of Vibrio spp. was much higher during the south-west monsoon in 2018, when the lake experienced a once-in-a-century flood. The distribution of Vibrio spp. was influenced by salinity (r = 0.3, p < 0.001), phosphate (r = 0.18, p < 0.01) and nitrite (r = 0.16, p < 0.02) in the water. We isolated 470 colonies of Vibrio-like organisms and 341 could be revived further and identified using 16S rRNA gene sequencing. Functional annotations showed that all the 16 Vibrio spp. found in the lake could grow in association with animals. More than 60% of the isolates had multiple antibiotic resistance (MAR) index greater than 0.5. All isolates were resistant to erythromycin and cefepime. The proliferation of multiple antibiotic-resistant Vibrio spp. is a threat to human health. Our observations suggest that the presence of a diverse range of Vibrio spp. is favoured by the low-saline conditions brought about by heavy precipitation. Furthermore, infections caused by contact with Vibrio-contaminated waters may be difficult to cure due to their multiple antibiotic resistances. Therefore, continuous monitoring of bacterial pollution in the lakes is essential, as is the generation of risk maps of vibrio-infested waters to avoid public contact with contaminated waters and associated disease outbreaks.

Tools to Enumerate and Predict Distribution Patterns of Environmental Vibrio vulnificus and Vibrio parahaemolyticus

Vibrio vulnificus (Vv) and Vibrio parahaemolyticus (Vp) are water- and foodborne bacteria that can cause several distinct human diseases, collectively called vibriosis. The success of oyster aquaculture is negatively impacted by high Vibrio abundances. Myriad environmental factors affect the distribution of pathogenic Vibrio, including temperature, salinity, eutrophication, extreme weather events, and plankton loads, including harmful algal blooms. In this paper, we synthesize the current understanding of ecological drivers of Vv and Vp and provide a summary of various tools used to enumerate Vv and Vp in a variety of environments and environmental samples. We also highlight the limitations and benefits of each of the measurement tools and propose example alternative tools for more specific enumeration of pathogenic Vv and Vp. Improvement of molecular methods can tighten better predictive models that are potentially important for mitigation in more controlled environments such as aquaculture.

Short-term forecast model of taxi demand based on time and space heterogeneity

Accurate prediction of traffic flow is of great significance for alleviating urban traffic congestions. Most previous studies used historical traffic data, in which only one model or algorithm was adopted by the whole prediction space and the differences in various regions were ignored. In this context, based on time and space heterogeneity, a Classification and Regression Trees-K-Nearest Neighbor (CART-KNN) Hybrid Prediction model was proposed to predict short-term taxi demand. Firstly, a concentric partitioning method was applied to divide the test area into discrete small areas according to its boarding density level. Then the CART model was used to divide the dataset of each area according to its temporal characteristics, and KNN was established for each subset by using the corresponding boarding density data to estimate the parameters of the KNN model. Finally, the proposed method was tested on the New York City Taxi and Limousine Commission (TLC) data, and the traditional KNN model, backpropagation (BP) neural network, long-short term memory model (LSTM) were used to compare with the proposed CART-KNN model. The selected models were used to predict the demand for taxis in New York City, and the Kriging Interpolation was used to obtain all the regional predictions. From the results, it can be suggested that the proposed CART-KNN model performed better than other general models by showing smaller mean absolute percentage error (MAPE) and root mean square error (RMSE) value. The improvement of prediction accuracy of CART-KNN model is helpful to understand the regional demand pattern to partition the boarding density data from the time and space dimensions. The partition method can be extended into many models using traffic data.