Recent technological developments allow us to measure the status of dozens of proteins in individual cells. This opens the way to understand the heterogeneity of complex multi-signaling networks across cells and cell types, with important implications to understand and treat diseases such as cancer. These technologies are, however, limited to proteins for which antibodies are available and are fairly costly, making predictions of new markers and of existing markers under new conditions a valuable alternative. To assess our capacity to make such predictions and boost further methodological development, we organized the Single Cell Signaling in Breast Cancer DREAM challenge. We used a mass cytometry dataset, covering 36 markers in over 4,000 conditions totaling 80 million single cells across 67 breast cancer cell lines. Through four increasingly difficult subchallenges, the participants predicted missing markers, new conditions, and the time-course response of single cells to stimuli in the presence and absence of kinase inhibitors. The challenge results show that despite the stochastic nature of signal transduction in single cells, the signaling events are tightly controlled and machine learning methods can accurately predict new experimental data.
To perform realistic air traffic control (ATC) simulation in various air traffic situations, an aircraft dynamic model that is accurate and efficient is required. In this research, an improved five degree of freedom (5-DOF) dynamic model with feedback control and guidance law is developed, which utilizes selected performance data and operational specifications from the base of aircraft data (BADA) and estimations using aircraft design techniques to improve the simulation fidelity. In addition, takeoff weight is estimated based on the aircraft type and flight plan to improve simulation accuracy. The dynamic model is validated by comparing the simulation results with recorded flight trajectories. An ATC simulation system using this 5-DOF model can be used for various ATC related research.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.