Realize Facilitated Targeted Poverty Elimination with Targeted Poverty——Alleviation: A New Anti-Poverty Approach for Rural China

Since the Reform and Opening-up, remarkable achievements in poverty alleviation have been made in China. China's success in mass poverty reduction can be attributed to its rapid economic growth, large-scale regional poverty alleviation and development, sound social security policies, widereaching farmer-benefiting policies and the equal land allotment system. However, with income inequality on the rise, poverty reduction effect made by the economic growth is declining: More targeted poverty alleviation policies are needed by China. Targeted poverty alleviation aims to improve the effect and efficiency of poverty alleviation through precise identification of poverty-stricken populations and comprehensive supportive measures. To tackle a variety of challenges such as the poor cultivation among povertystricken populations, the complex causes for poverty, and inflexible capital management, China should improve its poverty alleviation mechanism by innovating poverty identification methods, support approaches, capital management and performance assessment.

MicroRNA target prediction based on second-order Hidden Markov Model

MicroRNAs are one class of small singlestranded RNA of about 22 nt serving as important negative gene regulators.In animals,miRNAs mainly repress protein translation by binding itself to the 3'UTR regions of mRNAs with imperfect complementary pairing.Although bioinformatics investigations have resulted in a number of target prediction tools,all of these have a common shortcoming—a high false positive rate.Therefore,it is important to further filter the predicted targets.In this paper,based on miRNA:target duplex,we construct a second-order Hidden Markov Model,implement Baum-Welch training algorithm and apply this model to further process predicted targets.The model trains the classifier by 244 positive and 49 negative miRNA:target interaction pairs and achieves a sensitivity of 72.54%,specificity of 55.10%and accuracy of 69.62%by 10-fold crossvalidation experiments.In order to further verify the applicability of the algorithm,previously collected datasets,including 195 positive and 38 negative,are chosen to test it,with consistent results.We believe that our method will provide some guidance for experimental biologists,especially in choosing miRNA targets for validation.