Optimal management of seizures associated with tuberous sclerosis complex: current and emerging options.

Seizures are clinically significant manifestations associated with 79%-90% of patients with tuberous sclerosis complex. Often occurring within the first year of life in the form of infantile spasms, seizures interfere with neuropsychiatric, social, and cognitive development and carry significant individual and societal consequences. Prompt identification and treatment of seizures is an important focus in the overall management of tuberous sclerosis complex patients. Medical management, either after seizure onset or prophylactically in infants with electroencephalographic abnormalities, is considered first-line therapy. Vigabatrin and adrenocorticotropic hormone have emerged over the past few decades as mainstay pharmacologic modalities. Furthermore, emerging research on mammalian target of rapamycin inhibitors demonstrated promise for the management of seizures and subependymal giant cell astrocytoma. For appropriate surgical candidates with an epileptogenic zone associated with one or more glioneuronal hamartomas, ideally in noneloquent cortex, resective surgery can be considered, which provides a cure in 56% of patients. For medically refractory patients who do not meet criteria for curative surgery, palliative surgical approaches focused on reducing seizure burden, in the form of corpus callosotomy and vagus nerve stimulation, are alternative management options. Lastly, the ketogenic diet, a reemerging therapy based on the anticonvulsant effects of ketone bodies, can be utilized independently or in conjunction with other treatment modalities for the management of difficult-to-treat seizures

Interpolation of Sparse Graph Signals by Sequential Adaptive Thresholds

This paper considers the problem of interpolating signals defined on graphs. A major presumption considered by many previous approaches to this problem has been lowpass/ band-limitedness of the underlying graph signal. However, inspired by the findings on sparse signal reconstruction, we consider the graph signal to be rather sparse/compressible in the Graph Fourier Transform (GFT) domain and propose the Iterative Method with Adaptive Thresholding for Graph Interpolation (IMATGI) algorithm for sparsity promoting interpolation of the underlying graph signal.We analytically prove convergence of the proposed algorithm. We also demonstrate efficient performance of the proposed IMATGI algorithm in reconstructing randomly generated sparse graph signals. Finally, we consider the widely desirable application of recommendation systems and show by simulations that IMATGI outperforms state-of-the-art algorithms on the benchmark datasets in this application.Comment: 12th International Conference on Sampling Theory and Applications (SAMPTA 2017

Numerical investigation of Differential Biological-Models via GA-Kansa Method Inclusive Genetic Strategy

In this paper, we use Kansa method for solving the system of differential equations in the area of biology. One of the challenges in Kansa method is picking out an optimum value for Shape parameter in Radial Basis Function to achieve the best result of the method because there are not any available analytical approaches for obtaining optimum Shape parameter. For this reason, we design a genetic algorithm to detect a close optimum Shape parameter. The experimental results show that this strategy is efficient in the systems of differential models in biology such as HIV and Influenza. Furthermore, we prove that using Pseudo-Combination formula for crossover in genetic strategy leads to convergence in the nearly best selection of Shape parameter.Comment: 42 figures, 23 page

Geographic Determinants of Hi-Tech Employment Growth in U.S. Counties

This paper examines the spatial pattern of U.S. county employment growth in high-tech industries. The spatial growth dimensions examined include industry cluster effects, urbanization effects, proximity to a college, and proximity in the urban hierarchy. Growth is examined for overall high-tech employment and for employment in various high-tech sectors. Econometric analyses are conducted for a sample of all counties and for metropolitan and non-metropolitan counties separately. Among our primary findings, we do not find evidence of positive localization or cluster growth effects, generally finding negative growth effects. We instead find some evidence of positive urbanization effects and growth penalties for greater distances from larger urban areas.