Identification of mutations in patients with amyotrophic lateral sclerosis (ALS) in a genome-wide association study can reveal possible biomarkers of such a rapidly progressive and fatal neurodegenerative disease. It was observed that significant single nucleotide polymorphisms vary when the tested population changes from one ethnic group to another. To identify new loci associated with ALS susceptibility in the Taiwanese Han population, we performed a genome-wide association study on 94 patients with sporadic ALS and 376 matched controls. We uncovered two new susceptibility loci at 13q14.3 (rs2785946) and 11q25 (rs11224052). In addition, we analyzed the functions of all the associated genes among 54 significant single nucleotide polymorphisms using Gene Ontology annotations, and the results showed several statistically significant neural- and muscle-related Gene Ontology terms and the associated diseases.

This paper presents an automatic prediction model for ground vibration induced by Taiwan high-speed trains on embankment structures. The prediction model is developed using different field-measured ground vibration data. The main characteristics that affect the overall vibration level are established based on the database of measurement results. The influence factors include train speed, ground condition, measurement distance, and supported structure. Support vector machine (SVM) algorithm, a widely used prediction model, is adopted to predict the vibration level induced by high-speed trains on embankments. The measured and predicted vibration levels are compared to verify the reliability of the prediction model. Analysis results show that the developed SVM model can reasonably predict vibration level with an accuracy rate of 72% to 84% for four types of vibration level, including overall, low, middle, and high frequency ranges. The methodology in developing the automatic prediction system for ground vibration level is also presented in this paper.