Severe congenital neutropenia (SCN) is often associated with inherited heterozygous point mutations in ELANE, which
encodes neutrophil elastase (NE). However, a lack of appropriate models to recapitulate SCN has substantially hampered
the understanding of the genetic etiology and pathobiology of this disease. To this end, we generated both normal and SCN
patient–derived induced pluripotent stem cells (iPSCs), and performed genome editing and differentiation protocols that
recapitulate the major features of granulopoiesis. Pathogenesis of ELANE point mutations was the result of promyelocyte
death and differentiation arrest, and was associated with NE mislocalization and activation of the unfolded protein
response/ER stress (UPR/ER stress). Similarly, high-dose G-CSF (or downstream signaling through AKT/BCL2) rescues
the dysgranulopoietic defect in SCN patient–derived iPSCs through C/EBPβ-dependent emergency granulopoiesis. In
contrast, sivelestat, an NE-specific small-molecule inhibitor, corrected dysgranulopoiesis by restoring normal intracellular
NE localization in primary granules; ameliorating UPR/ER stress; increasing expression of CEBPA, but not CEBPB; and
promoting promyelocyte survival and differentiation. Together, these data suggest that SCN disease pathogenesis includes
NE mislocalization, which in turn triggers dysfunctional survival signaling and UPR/ER stress. This paradigm has the
potential to be clinically exploited to achieve therapeutic responses using lower doses of G-CSF combined with targeting to
correct NE mislocalization.
This project includes a SQL Server database and a Visual Studio project that simulate the transactions at a chain of grocery stores. It has stores, employees, products, ingredients in the products, manufacturers, and brands. When the simulation runs it generates customer transactions, employee work history, store history (open/closed/on fire/etc.), coupons, coupon usage, and product price history. As this data accumulates you can use it to teach SQL programming and query design.
The source code for the simulation project is included and can be used as an object lesson for software development courses, particularly C# and OOP.
Comparison of approximate approaches to solving the Travelling Salesman Problem and its application to UAV swarming. International Journal of Unmanned Systems Engineering. 3(1): 1-16. The Travelling Salesman Problem (TSP) is a widely researched Non-deterministic Polynomial-time hard optimization problem with a range of important applications in a wide spectrum of disciplines including aerospace engineering. In this paper, a comparison of different approaches to solve the TSP and also its application towards swarming of UAVs is considered. The objective of the TSP is to determine the optimal route associated with the shortest tour connecting all targets just once. Genetic Algorithms (GA) are one of the most widely applied techniques for solving this class of optimization problems. Two other techniques, 2-opt and Particle Swarm Optimization, are used and the results are compared with those obtained using GA. The comparison is made for different numbers of targets, using salient figures of merit such as computational time required and the cost function which is the minimum solution (distance) obtained. Results show that the 2-opt approach with the closest neighbour as initial starting point for the search yields superior performance. In the Multiple Travelling Salesman Problem, we propose a cluster-first approach which allocates each specific UAV to a subset of targets. The 200 targets are divided into four clusters corresponding to the four UAVs and then TSP algorithms like 2-opt and GA are employed to solve each cluster. This approach drastically reduces the computational time and also gives much better results than the conventional technique of directly applying GA over the 200 targets.
This study introduces the technique of Genetic Fuzzy Trees (GFTs) through novel application to an air combat control problem of an autonomous squadron of Unmanned Combat Aerial Vehicles (UCAVs) equipped with next-generation defensive systems. GFTs are a natural evolution to Genetic Fuzzy Systems, in which multiple cascading fuzzy systems are optimized by genetic methods. In this problem a team of UCAV's must traverse through a battle space and counter enemy threats, utilize imperfect systems, cope with uncertainty, and successfully destroy critical targets. Enemy threats take the form of Air Interceptors (AIs), Surface to Air Missile (SAM) sites, and Electronic WARfare (EWAR) stations. Simultaneous training and tuning a multitude of Fuzzy Inference Systems (FISs), with varying degrees of connectivity, is performed through the use of an optimized Genetic Algorithm (GA). The GFT presented in this study, the Learning Enhanced Tactical Handling Algorithm (LETHA), is able to create controllers with the presence of deep learning, resilience to uncertainties, and adaptability to changing scenarios. These resulting deterministic fuzzy controllers are easily understandable by operators, are of very high performance and efficiency, and are consistently capable of completing new and different missions not trained for.
