Genome-wide identification, characterization, and evolutionary analysis of the barley TALE gene family and its expression profiles in response to exogenous hormones.

Three-amino-loop-extension (TALE) family belongs to the homeobox gene superfamily and occurs widely in plants, playing a crucial role in regulating their growth and development. Currently, genome-wide analysis of the TALE family has been completed in many plants. However, the systematic identification and hormone response analysis of the TALE gene family in barley are still lacking. In this study, 21 TALE candidate genes were identified in barley, which can be divided into KNOX and BELL subfamilies. Barley TALE members in the same subfamily of the phylogenetic tree have analogically conserved motifs and gene structures, and segmental duplications are largely responsible for the expansion of the HvTALE family. Analysis of TALE orthologous and homologous gene pairs indicated that the HvTALE family has mainly undergone purifying selective pressure. Through spatial structure simulation, HvKNOX5-HvKNOX6 and HvKNOX5-HvBELL11 complexes are all formed through hydrogen bonding sites on both the KNOX2 and homeodomain (HD) domains of HvKNOX5, which may be essential for protein interactions among the HvTALE family members. Expression pattern analyses reveal the potential involvement of most HvTALE genes in responses to exogenous hormones. These results will lay the foundation for regulation and function analyses of the barley TALE gene family in plant growth and development by hormone regulation.

Detection of Asymptomatic Carotid Artery Stenosis in High-Risk Individuals of Stroke Using a Machine-Learning Algorithm.

Objective Asymptomatic carotid stenosis (ACS) is closely associated to the incidence of severe cerebrovascular diseases. Early identifying the individuals with ACS and its associated risk factors could be beneficial for primary prevention of stroke. This study aimed to investigate a machine-learning algorithm for the detection of ACS among high-risk population of stroke based on the associated risk factors.Methods A novel model of machine learning was utilized to screen the associated predictors of ACS based on 30 potential risk factors. The algorithm of this model adopted a random forest pattern based on the training data and then was verified using the testing data. All of the original data were retrieved from the China National Stroke Screening and Prevention Project (CNSSPP), including demographic, clinical and laboratory characteristics. The individuals with high risk of stroke were enrolled and randomly divided into a training group and a testing group at a ratio of 4:1. The identification of carotid stenosis by carotid artery duplex scans was set as the golden standard. The receiver operating characteristic (ROC) curve and the area under the curve (AUC) was used to evaluate the efficacy of the model in detecting ACS.Results Of 2841 high risk individual of stroke enrolled, 326 (11.6%) were diagnosed as ACS by ultrasonography. The top five risk factors contributing to ACS in this model were identified as family history of dyslipidemia, high level of low-density lipoprotein cholesterol (LDL-c), low level of high-density lipoprotein cholesterol (HDL-c), aging, and low body mass index (BMI). Their weights were 11.8%, 7.6%, 7.1%, 6.1%, and 6.1%, respectively. The total weight of the top 15 risk factors was 85.5%. The AUC values of the model for detecting ACS with training dataset and testing dataset were 0.927 and 0.888, respectively.Conclusions This study demonstrated that the machine-learning algorithm could be used to identify the risk factors for ACS among high risk population of stroke. Family history of dyslipidemia may be the most important risk factor for ACS. This model could be a suitable tool to optimize the clinical approach for the primary prevention of stroke.