The macroeconomic effects of oil price and risk-premium shocks on Vietnam: Evidence from an over-identifying SVAR analysis

This paper studies the macroeconomic effects of oil price shocks in Vietnam. It expands Kilian’s (2009) framework to simultaneously consider risk-premium shocks and comprehensively assess their consequences on international competitiveness and the State Bank management of the monetary policy. Methodologically, this implies dealing with an over-identified structural vector autoregression (SVAR) model. Data wise, the analysis is performed on a unique dataset with variables defined at a monthly frequency running from 1998:01 to 2018:12. Demand-side, global-, and specific-oil price shocks determine inflation and international competitiveness, and play an essential role in explaining the long-run variations of several Vietnamese macroeconomic indicators (mainly the trade balance, three-month interest rates, and the inflation rate). Vietnam’s Dong pegging to the US Dollar results in a stronger impact of these shocks when real exchange rates and the rate of exports are modelled, than when real effective exchange rates and the trade balance are modelled. In the latter case, shock absorption is quicker given the multilateral trade context in which no single pegging holds. In association to the strong tie between Vietnam’s Dong and the U.S. dollar, we also uncover remarkable effects of risk-premium (or U.S. Federal Fund rate) shocks. Supply-side oil price shocks have little impact on inflation and international competitiveness but condition the monetary policy. Neglecting such influence in the past may have resulted in an excessively conservative monetary policy

Digital droplet PCR and IDAA for the detection of CRISPR indel edits in the malaria species <i>Anopheles stephensi</i>

CRISPR/Cas9 technology is a powerful tool for the design of gene-drive systems to control and/or modify mosquito vector populations; however, CRISPR/Cas9-mediated nonhomologous end joining mutations can have an important impact on generating alleles resistant to the drive and thus on drive efficiency. We demonstrate and compare the insertions or deletions (indels) detection capabilities of two techniques in the malaria vector mosquito Anopheles stephensi: Indel Detection by Amplicon Analysis (IDAA™) and Droplet Digital™ PCR (ddPCR™). Both techniques showed accuracy and reproducibility for indel frequencies across mosquito samples containing different ratios of indels of various sizes. Moreover, these techniques have advantages that make them potentially better suited for high-throughput nonhomologous end joining analysis in cage trials and contained field testing of gene-drive mosquitoes

Mosquito Population Modification for Malaria Control

Malaria is a mosquito-borne disease that kills millions of people every year. Existing control tools have been insufficient to eliminate the disease in many endemic regions and additional approaches are needed. Novel vector-control strategies using genetic engineering to create malaria-resistant mosquitoes (population modification) can potentially contribute a new set of tools for mosquito control. Here we review the current mosquito control strategies and the development of transgenic mosquitoes expressing anti-parasite effector genes, highlighting the recent improvements in mosquito genome editing with CRISPR-Cas9 as an efficient and adaptable tool for gene-drive systems to effectively spread these genes into mosquito populations

GIS-based modeling of rainfall-induced landslides using data mining-based functional trees classifier with AdaBoost, Bagging, and MultiBoost ensemble frameworks

The main objective of this study is to propose and verify a novel ensemble methodology that could improve prediction performances of landslide susceptibility models. The proposed methodology is based on the functional tree classifier and three current state-of-the art machine learning ensemble frameworks, Bagging, AdaBoost, and MultiBoost. According to current literature, these methods have been rarely used for the modeling of rainfall-induced landslides. The corridor of the National Road 32 (Vietnam) was selected as a case study. In the first stage, the landslide inventory map with 262 landslide polygons that occurred during the last 20 years was constructed and then was randomly partitioned into a ratio of 70/30 for training and validating the models. Second, ten landslide conditioning factors were prepared such as slope, aspect, relief amplitude, topographic wetness index, topographic shape, distance to roads, distance to rivers, distance to faults, lithology, and rainfall. The model performance was assessed and compared using the receiver operating characteristic and statistical evaluation measures. Overall, the FT with Bagging model has the highest prediction capability (AUC = 0.917), followed by the FT with MultiBoost model (AUC = 0.910), the FT model (AUC = 0.898), and the FT with AdaBoost model (AUC = 0.882). Compared with those derived from popular methods such as J48 decision trees and artificial neural networks, the performance of the FT with Bagging model is better. Therefore, it can be concluded that the FT with Bagging is promising and could be used as an alternative in landslide susceptibility assessment. The result in this study is useful for land use planning and decision making in landslide prone areas