Background Recent reports reveal the presence of Wolbachia in Ae. aegypti . Our study presents additional support for Wolbachia infection in Ae. aegypti by screening field-collected adult mosquitoes using two Wolbachia -specific molecular makers. Methods A total of 672 Ae. aegypti adult mosquitoes were collected from May 2014 to January 2015 in Metropolitan Manila. Each individual sample was processed and screened for the presence of Wolbachia by selected markers, Wolbachia -specific 16S rDNA and its surface protein ( wsp ), under optimized PCR conditions and sequenced. Results Totals of 113 (16.8%) and 89 (13.2%) individual mosquito samples were determined to be infected with Wolbachia using the wsp and 16S rDNA markers, respectively. The Ae. aegpyti wsp sample sequences were similar or identical to five known Wolbachia strains belonging to supergroups A and B while the majority of 16S rDNA sample sequences were similar to strains belonging to supergroup B. Overall, 80 (11.90%) individual mosquito samples showed positive amplifications in both markers and 69% showed congruence in supergroup identification (supergroup B). Conclusions By utilizing two Wolbachia -specific molecular makers, our study demonstrated the presence of Wolbachia from individual Ae. aegypti samples. Our results showed a low Wolbachia infection rate and inferred the detected strains belong to either supergroups A and B. Electronic supplementary material The online version of this article (10.1186/s13071-019-3629-y) contains supplementary material, which is available to authorized users.

BackgroundSeveral studies have applied ecological factors such as meteorological variables to develop models and accurately predict the temporal pattern of dengue incidence or occurrence. With the vast amount of studies that investigated this premise, the modeling approaches differ from each study and only use a single statistical technique. It raises the question of whether which technique would be robust and reliable. Hence, our study aims to compare the predictive accuracy of the temporal pattern of Dengue incidence in Metropolitan Manila as influenced by meteorological factors from four modeling techniques, (a) General Additive Modeling, (b) Seasonal Autoregressive Integrated Moving Average with exogenous variables (c) Random Forest and (d) Gradient Boosting.MethodsDengue incidence and meteorological data (flood, precipitation, temperature, southern oscillation index, relative humidity, wind speed and direction) of Metropolitan Manila from January 1, 2009 – December 31, 2013 were obtained from respective government agencies. Two types of datasets were used in the analysis; observed meteorological factors (MF) and its corresponding delayed or lagged effect (LG). After which, these datasets were subjected to the four modeling techniques. The predictive accuracy and variable importance of each modeling technique were calculated and evaluated.ResultsAmong the statistical modeling techniques, Random Forest showed the best predictive accuracy. Moreover, the delayed or lag effects of the meteorological variables was shown to be the best dataset to use for such purpose. Thus, the model of Random Forest with delayed meteorological effects (RF-LG) was deemed the best among all assessed models. Relative humidity was shown to be the top-most important meteorological factor in the best model.ConclusionThe study exhibited that there are indeed different predictive outcomes generated from each statistical modeling technique and it further revealed that the Random forest model with delayed meteorological effects to be the best in predicting the temporal pattern of Dengue incidence in Metropolitan Manila. It is also noteworthy that the study also identified relative humidity as an important meteorological factor along with rainfall and temperature that can influence this temporal pattern.Electronic supplementary materialThe online version of this article (10.1186/s12879-018-3066-0) contains supplementary material, which is available to authorized users.

Biological control is considered as a promising alternative to pesticide and plant resistance to manage plant diseases, but a better understanding of the interaction of its natural and societal functions is necessary for its endorsement. The introduction of biological control agents (BCAs) alters the interaction among plants, pathogens, and environments, leading to biological and physical cascades that influence pathogen fitness, plant health, and ecological function. These interrelationships generate a landscape of tradeoffs among natural and social functions of biological control, and a comprehensive evaluation of its benefits and costs across social and farmer perspectives is required to ensure the sustainable development and deployment of the approach. Consequently, there should be a shift of disease control philosophy from a single concept that only concerns crop productivity to a multifaceted concept concerning crop productivity, ecological function, social acceptability, and economical accessibility. To achieve these goals, attempts should make to develop “green” BCAs used dynamically and synthetically with other disease control approaches in an integrated disease management scheme, and evolutionary biologists should play an increasing role in formulating the strategies. Governments and the public should also play a role in the development and implementation of biological control strategies supporting positive externality.

No abstract