The heart and its cellular components are profoundly altered by missions to space and injury on Earth. Further research, however, is needed to characterize and address the molecular substrates of such changes. For this reason, neonatal and adult human cardiovascular progenitor cells (CPCs) were cultured aboard the International Space Station. Upon return to Earth, we measured changes in the expression of microRNAs and of genes related to mechanotransduction, cardiogenesis, cell cycling, DNA repair, and paracrine signaling. We additionally assessed endothelial-like tube formation, cell cycling, and migratory capacity of CPCs. Changes in microRNA expression were predicted to target extracellular matrix interactions and Hippo signaling in both neonatal and adult CPCs. Genes related to mechanotransduction (YAP1, RHOA) were downregulated, while the expression of cytoskeletal genes (VIM, NES, DES, LMNB2, LMNA), non-canonical Wnt ligands (WNT5A, WNT9A), and Wnt/calcium signaling molecules (PLCG1, PRKCA) was significantly elevated in neonatal CPCs. Increased mesendodermal gene expression along with decreased expression of mesodermal derivative markers (TNNT2, VWF, and RUNX2), reduced readiness to form endothelial-like tubes, and elevated expression of Bmp and Tbx genes, were observed in neonatal CPCs. Both neonatal and adult CPCs exhibited increased expression of DNA repair genes and paracrine factors, which was supported by enhanced migration. While spaceflight affects cytoskeletal organization and migration in neonatal and adult CPCs, only neonatal CPCs experienced increased expression of early developmental markers and an enhanced proliferative potential. Efforts to recapitulate the effects of spaceflight on Earth by regulating processes described herein may be a promising avenue for cardiac repair.

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Identifying driving styles using classification models with in-vehicle data can provide automated feedback to drivers on their driving behavior, particularly if they are driving safely. Although several classification models have been developed for this purpose, there is no consensus on which classifier performs better at identifying driving styles. Therefore, more research is needed to evaluate classification models by comparing performance metrics. In this paper, a data-driven machine-learning methodology for classifying driving styles is introduced. This methodology is grounded in well-established machine-learning (ML) methods and literature related to driving-styles research. The methodology is illustrated through a study involving data collected from 50 drivers from two different cities in a naturalistic setting. Five features were extracted from the raw data. Fifteen experts were involved in the data labeling to derive the ground truth of the dataset. The dataset fed five different models (Support Vector Machines (SVM), Artificial Neural Networks (ANN), fuzzy logic, k-Nearest Neighbor (kNN), and Random Forests (RF)). These models were evaluated in terms of a set of performance metrics and statistical tests. The experimental results from performance metrics showed that SVM outperformed the other four models, achieving an average accuracy of 0.96, F1-Score of 0.9595, Area Under the Curve (AUC) of 0.9730, and Kappa of 0.9375. In addition, Wilcoxon tests indicated that ANN predicts differently to the other four models. These promising results demonstrate that the proposed methodology may support researchers in making informed decisions about which ML model performs better for driving-styles classification.Sensors 2020, 20, 1692 2 of 21 and ultimately minimizing traffic incidents [8]. Many potential applications that utilize driving-style recognition are being developed [9]. For example, insurance companies are developing automatic ways to ascertain driving styles of their clients in order to offer the right policy [10]. In addition, in the area of advanced driver-assistance systems (ADAS), researchers are interested in the development of intelligent tools to provide tailored feedback according to the driver's behavior [11].Current studies have reported different methodologies to classify driving styles using in-vehicle data. These methodologies mostly depend on (i) the inputs that can be extracted and derived from the data gathered (e.g., acceleration, deceleration, brake) [12,13]; (ii) the computational models to classify driving styles [14,15]; (iii) driving-styles outputs (e.g., calm, normal, aggressive) [16,17]; and (iv) the performance metrics that evaluate these models [18,19]. Although these studies show a growing interest in classifying driving styles empirically, few studies have systematically evaluated which inputs and models are better predictors of a particular driving style's output.The existing body of research has attempted to evaluate several computational models to identify driving styles usin...