https://www.selleckchem.com/products/brd0539.html Retention and transfer tests were both completed 1 and 7 days later. MRCPs measured training-related neural adaptations during the first visit and motor performance was assessed as time and trajectory to the target. The EXE group had better performance than CON at retention (significant 7 days post-training). MRCP amplitudes increased from early to late motor training and this amplitude change was correlated with motor performance at retention. Results suggest that moderate-intensity exercise post-motor training helps motor skill retention and that there may be a relationship with motor training-related cortical adaptations that is enhanced with post-motor training exercise. In the era of datafication, it is important that medical data are accurate and structured for multiple applications. Especially data for oncological staging need to be accurate to stage and treat a patient, as well as population-level surveillance and outcome assessment. To support data extraction from free-text radiological reports, Dutch natural language processing (NLP) algorithm was built to quantify T-stage of pulmonary tumors according to the tumor node metastasis (TNM) classification. This structuring tool was translated and validated on English radiological free-text reports. A rule-based algorithm to classify T-stage was trained and validated on, respectively, 200 and 225 English free-text radiological reports from diagnostic computed tomography (CT) obtained for staging of patients with lung cancer. The automated T-stage extracted by the algorithm from the report was compared to manual staging. A graphical user interface was built for training purposes to visualize the results of the algorithm by highlighting the extracted concepts and its modifying context. Accuracy of the T-stage classifier was 0.89 in the validation set, 0.84 when considering the T-substages, and 0.76 when only considering tumor size. Results were comparable with the