Identification Of Metabolite Biomarkers In Epilepsy Using 1h Mrs

Epilepsy is a serious neurological disorder that affects 1% percent of the global population. Despite its status as one of the oldest neurological disorders known to man, its mechanisms remain poorly understood. Available medications are not curative but provide symptomatic management and do not work for well for more than 30 percent of patients. Because it is nearly impossible to predict on an individual level who will eventually develop epilepsy, it is also a disorder that can only be diagnosed after the patient has experienced established seizure activity, eliminating any possibility of stopping the disorder in its prodromal phase, before the patients are symptomatic. Availability of a reliable and non-invasive biomarker tool that can predict and identify the development of epilepsy would dramatically change how the disorder is detected, monitored, managed, and treated. In this project, we tested the potential of 1H MRS to provide metabolite biomarkers of epilepsy and epileptogenesis, both in human neocortical tissue obtained from intractable epilepsy patients who underwent resective surgery and also in a longitudinal rat model of epileptogenesis, using interictal epileptiform discharges as a surrogate indicator of disease progression. Using 1H MRS, we found unique metabolite differences that are highly predictive of epileptic and non-epileptic neocortex in humans that also partially overlaps with findings from our rat model. These findings provide evidence that 1H MRS is capable of identifying metabolite changes specific to epilepsy and may lead to reliable and non-invasive biomarkers of epilepsy and epileptogenesis in the future

Variable selection for the multicategory SVM via adaptive sup-norm regularization

The Support Vector Machine (SVM) is a popular classification paradigm in machine learning and has achieved great success in real applications. However, the standard SVM can not select variables automatically and therefore its solution typically utilizes all the input variables without discrimination. This makes it difficult to identify important predictor variables, which is often one of the primary goals in data analysis. In this paper, we propose two novel types of regularization in the context of the multicategory SVM (MSVM) for simultaneous classification and variable selection. The MSVM generally requires estimation of multiple discriminating functions and applies the argmax rule for prediction. For each individual variable, we propose to characterize its importance by the supnorm of its coefficient vector associated with different functions, and then minimize the MSVM hinge loss function subject to a penalty on the sum of supnorms. To further improve the supnorm penalty, we propose the adaptive regularization, which allows different weights imposed on different variables according to their relative importance. Both types of regularization automate variable selection in the process of building classifiers, and lead to sparse multi-classifiers with enhanced interpretability and improved accuracy, especially for high dimensional low sample size data. One big advantage of the supnorm penalty is its easy implementation via standard linear programming. Several simulated examples and one real gene data analysis demonstrate the outstanding performance of the adaptive supnorm penalty in various data settings.Comment: Published in at http://dx.doi.org/10.1214/08-EJS122 the Electronic Journal of Statistics (http://www.i-journals.org/ejs/) by the Institute of Mathematical Statistics (http://www.imstat.org

Evolutionary Development of the Simulation by Logical Modeling System (SIBYL)

Through the evolutionary development of the Simulation by Logical Modeling System (SIBYL) we have re-engineered the expensive and complex IBM mainframe based Long-term Hardware Projection Model (LHPM) to a robust cost-effective computer based mode that is easy to use. We achieved significant cost reductions and improved productivity in preparing long-term forecasts of Space Shuttle Main Engine (SSME) hardware. The LHPM for the SSME is a stochastic simulation model that projects the hardware requirements over 10 years. SIBYL is now the primary modeling tool for developing SSME logistics proposals and Program Operating Plan (POP) for NASA and divisional marketing studies

Crack breathing behavior of unbalanced rotor system: A Quasi-static numerical analysis

Crack opening and closing during shaft rotation of a cracked rotor system have long been a focus of many previous studies. Previously published modeling work in the literature uses weight-governed crack breathing model for very large rotor systems. However, for lightweight or vertical or lightly damped rotors the opening and closing statuses of a crack are not always weight dominated as there is significant influence from dynamic loads. Further, the dependence of the breathing mechanism on the crack location has not been investigated yet. In this paper, the crack breathing behavior of an unbalanced shaft at the different crack location of a rotating shaft is investigated. A three-dimensional finite element model, consisting of a two-disk rotor with a transverse crack, is used. Finite element model is simulated using ABAQUS/Standard. Crack breathing behavior is found to strongly depend on its axial position, angular position, depth ratio, unbalanced force ratio and angular position. Two different crack breathing regions along the shaft length are identified, where unbalanced shaft stiffness may be larger or smaller than the balanced shaft, depending on the unbalance force orientation, magnitude and crack location. Further, four specific crack locations along the shaft length have been identified, where the crack remains fully closed or open or just behaves like in the balanced shaft. The results suggest that more accurate prediction of the dynamic response of cracked rotors can be expected when the effects of unbalance force and individual rotor physical properties on the crack breathing have been taken into account

The moderation effect of social factors on marketing factors in consumer research

Consumer research tends to isolate the impact of marketing and social factors. Little has been done to include both. This paper aimed to find out what would happen when these two sets of factors are included. Two models were built in this paper, Model I with the marketing factors only and Model II with both the marketing and social factors. Data was collected in Ireland among more than 1473 transition year students in a personal survey regarding their willingness to learn Chinese. Data were analysed by using structural equation modelling (SEM). Results showed the two social variables, acculturation and intergenerational influence, significantly consolidated the effects of brand awareness on both brand trust and purchase behaviour; and they diminished the impact of brand trust on purchase behaviour. Empirical evidence suggested the worthiness for marketing researchers to examine both marketing and social factors in consumer research

An asymmetric arcsecond radio jet from Circinus X-1

In observations with the Australia Telescope Compact Array we have resolved the radio counterpart of the unusual X-ray binary Cir X-1 into an asymmetric extended structure on arcsecond scales. In order to quantify the asymmetry we have redetermined as accurately as possible both the optical and radio coordinates of the source. The extended emission can be understood as a compact, absorbed core at the location of the X-ray binary, and extended emission up to 2 arcsec to the southeast of the core. The arcsec-scale extended emission aligns with the larger, more symmetric arcmin-scale collimated structures in the surrounding synchrotron nebula. This suggests that the transport of mass and/or energy from the X-ray binary to the synchroton nebula is occurring via the arcsec-scale structures. The ratio of extended flux from the southeast to that from the northwest of the core is at least 2:1. Interpreted as relativistic aberration of an intrinsically symmetric jet from the source, this implies a minimum outflow velocity of 0.1 c. Alternatively, the emission may be intrinsically asymmetric, perhaps as a result of the high space velocity of the system.Comment: Accepted for publication in ApJ Letters. Three figure