Breeding systems, hybridization and continuing evolution in Avon Gorge Sorbus.

BACKGROUND AND AIMS: Interspecific hybridization and polyploidy are key processes in plant evolution and are responsible for ongoing genetic diversification in the genus Sorbus (Rosaceae). The Avon Gorge, Bristol, UK, is a world 'hotspot' for Sorbus diversity and home to diploid sexual species and polyploid apomictic species. This research investigated how mating system variation, hybridization and polyploidy interact to generate this biological diversity. METHODS: Mating systems of diploid, triploid and tetraploid Sorbus taxa were analysed using pollen tube growth and seed set assays from controlled pollinations, and parent-offspring genotyping of progeny from open and manual pollinations. KEY RESULTS: Diploid Sorbus are outcrossing and self-incompatible (SI). Triploid taxa are pseudogamous apomicts and genetically invariable, but because they also display self-incompatibility, apomictic seed set requires pollen from other Sorbus taxa - a phenomenon which offers direct opportunities for hybridization. In contrast tetraploid taxa are pseudogamous but self-compatible, so do not have the same obligate requirement for intertaxon pollination. CONCLUSIONS: The mating inter-relationships among Avon Gorge Sorbus taxa are complex and are the driving force for hybridization and ongoing genetic diversification. In particular, the presence of self-incompatibility in triploid pseudogamous apomicts imposes a requirement for interspecific cross-pollination, thereby facilitating continuing diversification and evolution through rare sexual hybridization events. This is the first report of naturally occurring pseudogamous apomictic SI plant populations, and we suggest that interspecific pollination, in combination with a relaxed endosperm balance requirement, is the most likely route to the persistence of these populations. We propose that Avon Gorge Sorbus represents a model system for studying the establishment and persistence of SI apomicts in natural populations.

Automated development of artificial neural networks for clinical purposes: Application for predicting the outcome of choledocholithiasis surgery.

Among various expert systems (ES), Artificial Neural Network (ANN) has shown to be suitable for the diagnosis of concurrent common bile duct stones (CBDS) in patients undergoing elective cholecystectomy. However, their application in practice remains limited since the development of ANNs represents a slow process that requires additional expertize from potential users. The aim of this study was to propose an ES for automated development of ANNs and validate its performances on the problem of prediction of CBDS. Automated development of the ANN was achieved by applying the evolutionary assembling approach, which assumes optimal configuring of the ANN parameters by using Genetic algorithm. Automated selection of optimal features for the ANN training was performed using a Backward sequential feature selection algorithm. The assessment of the developed ANN included the evaluation of predictive ability and clinical utility. For these purposes, we collected data from 303 patients who underwent surgery in the period from 2008 to 2014. The results showed that the total bilirubin, alanine aminotransferase, common bile duct diameter, number of stones, size of the smallest calculus, biliary colic, acute cholecystitis and pancreatitis had the best prognostic value of CBDS. Compared to the alternative approaches, the ANN obtained by the proposed ES had better sensitivity and clinical utility, which are considered to be the most important for the particular problem. Besides the fact that it enabled the development of ANNs with better performances, the proposed ES significantly reduced the complexity of ANNs' development compared to previous studies that required manual selection of optimal features and/or ANN configuration. Therefore, it is concluded that the proposed ES represents a robust and user-friendly framework that, apart from the prediction of CBDS, could advance and simplify the application of ANNs for solving a wider range of problems.