Background Pecan ( Carya illinoinensis ) and Chinese hickory ( C. cathayensis ) are important commercially cultivated nut trees in the genus Carya (Juglandaceae), with high nutritional value and substantial health benefits. Results We obtained >187.22 and 178.87 gigabases of sequence, and ∼288× and 248× genome coverage, to a pecan cultivar (“Pawnee”) and a domesticated Chinese hickory landrace (ZAFU-1), respectively. The total assembly size is 651.31 megabases (Mb) for pecan and 706.43 Mb for Chinese hickory. Two genome duplication events before the divergence from walnut were found in these species. Gene family analysis highlighted key genes in biotic and abiotic tolerance, oil, polyphenols, essential amino acids, and B vitamins. Further analyses of reduced-coverage genome sequences of 16 Carya and 2 Juglans species provide additional phylogenetic perspective on crop wild relatives. Conclusions Cooperative characterization of these valuable resources provides a window to their evolutionary development and a valuable foundation for future crop improvement.

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BackgroundThe purpose of this study was to build a model of machine learning (ML) for the prediction of mortality in patients with isolated moderate and severe traumatic brain injury (TBI).MethodsHospitalized adult patients registered in the Trauma Registry System between January 2009 and December 2015 were enrolled in this study. Only patients with an Abbreviated Injury Scale (AIS) score ≥ 3 points related to head injuries were included in this study. A total of 1734 (1564 survival and 170 non-survival) and 325 (293 survival and 32 non-survival) patients were included in the training and test sets, respectively.ResultsUsing demographics and injury characteristics, as well as patient laboratory data, predictive tools (e.g., logistic regression [LR], support vector machine [SVM], decision tree [DT], naive Bayes [NB], and artificial neural networks [ANN]) were used to determine the mortality of individual patients. The predictive performance was evaluated by accuracy, sensitivity, and specificity, as well as by area under the curve (AUC) measures of receiver operator characteristic curves. In the training set, all five ML models had a specificity of more than 90% and all ML models (except the NB) achieved an accuracy of more than 90%. Among them, the ANN had the highest sensitivity (80.59%) in mortality prediction. Regarding performance, the ANN had the highest AUC (0.968), followed by the LR (0.942), SVM (0.935), NB (0.908), and DT (0.872). In the test set, the ANN had the highest sensitivity (84.38%) in mortality prediction, followed by the SVM (65.63%), LR (59.38%), NB (59.38%), and DT (43.75%).ConclusionsThe ANN model provided the best prediction of mortality for patients with isolated moderate and severe TBI.