Prediction of Fatalities in Vehicle Collisions in Canada

Traffic collisions affect millions around the world and are the leading cause of death for children and young adults. Thus, Canada’s road safety plan is to reduce collision injuries and fatalities with a vision of making the safest roads in the world. We aim to predict fatalities of collisions on Canadian roads, and to discover causation of fatalities through exploratory data analysis and machine learning techniques. We analyse the vehicle collisions from Canada’s National Collision Database (1999–2017.) Through data mining methodologies, we investigate association rules and key contributing factors that lead to fatalities. Then, we propose two supervised learning classification models, Lasso Regression and XGBoost, to predict fatalities. Our analysis shows the deadliness of head-on collisions, especially in non-intersection areas with lacking traffic control systems. We also reveal that most collision fatalities occur in non-extreme weather and road conditions. Our prediction models show that the best classifier of fatalities is XGBoost with 83% accuracy. Its most important features are “collision configuration” and “used safety devices” elements, outnumbering attributes such as vehicle year, collision time, age, or sex of the individual. Our exploratory and predictive analysis reveal the importance of road design and traffic safety education

Discovery of 6‑(Fluoro-¹⁸<i>F</i>)‑3-(1<i>H</i>‑pyrrolo[2,3‑<i>c</i>]pyridin-1-yl)isoquinolin-5-amine ([¹⁸F]-MK-6240): A Positron Emission Tomography (PET) Imaging Agent for Quantification of Neurofibrillary Tangles (NFTs)

Neurofibrillary tangles (NFTs) made up of aggregated tau protein have been identified as the pathologic hallmark of several neurodegenerative diseases including Alzheimer’s disease. In vivo detection of NFTs using PET imaging represents a unique opportunity to develop a pharmacodynamic tool to accelerate the discovery of new disease modifying therapeutics targeting tau pathology. Herein, we present the discovery of 6-(fluoro-¹⁸<i>F</i>)-3-(1<i>H</i>-pyrrolo­[2,3-<i>c</i>]­pyridin-1-yl)­isoquinolin-5-amine, <b>6</b> ([¹⁸F]-MK-6240), as a novel PET tracer for detecting NFTs. <b>6</b> exhibits high specificity and selectivity for binding to NFTs, with suitable physicochemical properties and in vivo pharmacokinetics