The occurrence of parallel speciation strongly implies the action of natural selection. However, it is unclear how general a phenomena parallel speciation is since it was only shown in a small number of animal species. In particular, the adaptive process and mechanisms underlying the process of parallel speciation remain elusive. Here, we used an integrative approach incorporating population genomics, common garden, and crossing experiments to investigate parallel speciation of the wild rice species Oryza nivara from O. rufipogon . We demonstrated that O. nivara originated multiple times from different O. rufipogon populations and revealed that different O. nivara populations have evolved similar phenotypes under divergent selection, a reflection of recurrent local adaptation of ancient O. rufipogon populations to dry habitats. Almost completed premating isolation was detected between O. nivara and O. rufipogon in the absence of any postmating barriers between and within these species. These results suggest that flowering time is a “magic” trait that contributes to both local adaptation and reproductive isolation in the origin of wild rice species. Our study thus demonstrates a convincing case of parallel ecological speciation as a consequence of adaptation to new environments.

These findings indicate that intrauterine administration of autologous PBMC activated by HCG in vitro effectively improves embryo implantation in patients with three or more IVF failures.

BackgroundComputational prediction of protein subcellular localization can greatly help to elucidate its functions. Despite the existence of dozens of protein localization prediction algorithms, the prediction accuracy and coverage are still low. Several ensemble algorithms have been proposed to improve the prediction performance, which usually include as many as 10 or more individual localization algorithms. However, their performance is still limited by the running complexity and redundancy among individual prediction algorithms.ResultsThis paper proposed a novel method for rational design of minimalist ensemble algorithms for practical genome-wide protein subcellular localization prediction. The algorithm is based on combining a feature selection based filter and a logistic regression classifier. Using a novel concept of contribution scores, we analyzed issues of algorithm redundancy, consensus mistakes, and algorithm complementarity in designing ensemble algorithms. We applied the proposed minimalist logistic regression (LR) ensemble algorithm to two genome-wide datasets of Yeast and Human and compared its performance with current ensemble algorithms. Experimental results showed that the minimalist ensemble algorithm can achieve high prediction accuracy with only 1/3 to 1/2 of individual predictors of current ensemble algorithms, which greatly reduces computational complexity and running time. It was found that the high performance ensemble algorithms are usually composed of the predictors that together cover most of available features. Compared to the best individual predictor, our ensemble algorithm improved the prediction accuracy from AUC score of 0.558 to 0.707 for the Yeast dataset and from 0.628 to 0.646 for the Human dataset. Compared with popular weighted voting based ensemble algorithms, our classifier-based ensemble algorithms achieved much better performance without suffering from inclusion of too many individual predictors.ConclusionsWe proposed a method for rational design of minimalist ensemble algorithms using feature selection and classifiers. The proposed minimalist ensemble algorithm based on logistic regression can achieve equal or better prediction performance while using only half or one-third of individual predictors compared to other ensemble algorithms. The results also suggested that meta-predictors that take advantage of a variety of features by combining individual predictors tend to achieve the best performance. The LR ensemble server and related benchmark datasets are available at http://mleg.cse.sc.edu/LRensemble/cgi-bin/predict.cgi.