Cognitive Information Processing

Contains reports on three research projects.National Institutes of Health (Grant 5 PO1 GM14940-03)National Institutes of Health (Grant 5 P01 GM15006-03)Joint Services Electronics Programs (U. S. Army, U. S. Navy, and U. S. Air Force) under Contract DA 28-043-AMC-02536(E

Radio Astronomy

Contains reports on six research projects.National Aeronautics and Space Administration (Grant NsG-419)National Aeronautics and Space Administration (Contract NSR-22-009-120

Radio Astronomy

Contains reports on seven research projects.U. S. Navy (Office of Naval Research) under Contract N00014-67-A-0204-0009National Aeronautics and Space Administration (Grant NsG-419)National Science Foundation (Grant GP-7046)National Aeronautics and Space Administration (Contract NSR-22-009-120)Joint Services Electronics Programs (U. S. Army, U. S. Navy, and U.S. Air Force, Under Contract DA 28-043-AMC-02536(E

Application of Data Mining in Air Traffic Forecasting

The main goal of the study centers on developing a model for the purpose of air traffic forecasting by using off-the-shelf data mining and machine learning techniques. Although data driven modeling has been extensively applied in the aviation sector, little research has been done in the area of air traffic forecasting. This study is inspired by previous research focused on improving the Federal Aviation Administration (FAA) Terminal Area Forecasting (TAF) methodology, which historically assumed that the US air transportation system (ATS) network structure was static. Recent developments use data mining algorithms to predict the likelihood of previously un-connected airport-pairs being connected in the future, and the likelihood of connected airport-pairs becoming un-connected. Despite the innovation of this research, it does not focus on improving the FAA’s existing methodology for forecasting future air traffic levels on existing routes, which is based on relatively simple regression and growth models. We investigate different approaches for improving and developing new features within the existing data mining applications in air traffic forecasting. We focus particularly on predicting detailed traffic information for the US ATS. Initially, a 2-stage log-log model is applied to establish the significance of different inputs and to identify issues of endogeneity and multi-colinearity, while maintaining the simplicity of current models. Although the model shows high goodness of fit, it tested positive for both mentioned issues as well as presenting problems with causality. With the objective of solving these issues, a 3-stage model that is under development is introduced. This model employs logistic regression and discrete choice modelling. As part of future work, machine learning techniques such as clustering and neural networks will be applied to improve this model’s performance

Drug Repurposing: The Anthelmintics Niclosamide and Nitazoxanide Are Potent TMEM16A Antagonists That Fully Bronchodilate Airways

There is an unmet need in severe asthma where approximately 40% of patients exhibit poor β-agonist responsiveness, suffer daily symptoms and show frequent exacerbations. Antagonists of the Ca2+-activated Cl− channel, TMEM16A, offers a new mechanism to bronchodilate airways and block the multiple contractiles operating in severe disease. To identify TMEM16A antagonists we screened a library of ∼580,000 compounds. The anthelmintics niclosamide, nitazoxanide, and related compounds were identified as potent TMEM16A antagonists that blocked airway smooth muscle depolarization and contraction. To evaluate whether TMEM16A antagonists resist use- and inflammatory-desensitization pathways limiting β-agonist action, we tested their efficacy under harsh conditions using maximally contracted airways or airways pretreated with a cytokine cocktail. Stunningly, TMEM16A antagonists fully bronchodilated airways, while the β-agonist isoproterenol showed only partial effects. Thus, antagonists of TMEM16A and repositioning of niclosamide and nitazoxanide represent an important additional treatment for patients with severe asthma and COPD that is poorly controlled with existing therapies. It is of note that drug repurposing has also attracted wide interest in niclosamide and nitazoxanide as a new treatment for cancer and infectious disease. For the first time we identify TMEM16A as a molecular target for these drugs and thus provide fresh insights into their mechanism for the treatment of these disorders in addition to respiratory disease