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Browsing by Author "Liu, Y."
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Item Gene Expression Patterns in Bone Following Mechanical Loading(Office of the Vice Chancellor for Research, 2010-04-09) Mantila Roosa, S.M.; Liu, Y.; Turner, C.H.Mechanical loading is a potent anabolic stimulus that substantially strengthens bones, and the time course of bone formation after initiating mechanical loading is well characterized. However, the time sequence for gene expression in a bone subjected to mechanical loading, over an extended period of time, has not been established. The advent of high-throughput measurements of gene expression and bioinformatics analysis methods offers new ways to study gene expression patterns. The primary goal of this study was to determine the time sequence for gene expression in a bone subjected to mechanical loading, during key periods of the bone formation process, including expression of matrix-related genes, the appearance of active osteoblasts, and bone desensitization. We evaluated loading-induced gene expression over a time course of 4 hours to 32 days. We then used bioinformatics tools to cluster genes into similar expression patterns and created groups of genes with common functions or signaling pathways.Item Genetic variants in the folate metabolic pathway genes predict melanoma-specific survival(Oxford University Press, 2020-10) Dai, W.; Liu, H.; Liu, Y.; Xu, X.; Qian, D.; Luo, S.; Cho, E.; Zhu, D.; Amos, C.I.; Fang, S.; Lee, J.E.; Li, X.; Nan, H.; Li, C.; Wei, Q.; Epidemiology, School of Public HealthBackground: Folate metabolism plays an important role in DNA methylation and nucleic acid synthesis and thus may function as a regulatory factor in cancer development. Genome-wide association studies (GWASs) have identified some single-nucleotide polymorphisms (SNPs) associated with cutaneous melanoma-specific survival (CMSS), but no SNPs were found in genes involved in the folate metabolic pathway. Objectives: To examine associations between SNPs in folate metabolic pathway genes and CMSS. Methods: We comprehensively evaluated 2645 (422 genotyped and 2223 imputed) common SNPs in folate metabolic pathway genes from a published GWAS of 858 patients from The University of Texas MD Anderson Cancer Center and performed the validation in another GWAS of 409 patients from the Nurses' Health Study and Health Professionals Follow-up Study, in which 95/858 (11·1%) and 48/409 (11·7%) patients died of cutaneous melanoma, respectively. Results: We identified two independent SNPs (MTHFD1 rs1950902 G>A and ALPL rs10917006 C>T) to be associated with CMSS in both datasets, and their meta-analysis yielded an allelic hazards ratio of 1·75 (95% confidence interval 1·32-2·32, P = 9·96 × 10-5 ) and 2·05 (1·39-3·01, P = 2·84 × 10-4 ), respectively. The genotype-phenotype correlation analyses provided additional support for the biological plausibility of these two variants' roles in tumour progression, suggesting that variation in SNP-related mRNA expression levels is likely to be the mechanism underlying the observed associations with CMSS. Conclusions: Two possibly functional genetic variants, MTHFD1 rs1950902 and ALPL rs10917006, were likely to be independently or jointly associated with CMSS, which may add to personalized treatment in the future, once further validated. What is already known about this topic? Existing data show that survival rates vary among patients with melanoma with similar clinical characteristics; therefore, it is necessary to identify additional complementary biomarkers for melanoma-specific prognosis. A hypothesis-driven approach, by pooling the effects of single-nucleotide polymorphisms (SNPs) in a specific biological pathway as genetic risk scores, may provide a prognostic utility, and genetic variants of genes in folate metabolism have been reported to be associated with cancer risk. What does this study add? Two genetic variants in the folate metabolic pathway genes, MTHFD1 rs1950902 and ALPL rs10917006, are significantly associated with cutaneous melanoma-specific survival (CMSS). What is the translational message? The identification of genetic variants will make a risk-prediction model possible for CMSS. The SNPs in the folate metabolic pathway genes, once validated in larger studies, may be useful in the personalized management and treatment of patients with cutaneous melanoma.Item Predicting Hazardous Driving Events Using Multi-Modal Deep Learning Based on Video Motion Profile and Kinematics Data(IEEE, 2018-11) Gao, Z.; Liu, Y.; Zheng, J. Y.; Yu, R.; Wang, X.; Sun, P.; Computer and Information Science, School of ScienceAs the raising of traffic accidents caused by commercial vehicle drivers, more regulations have been issued for improving their safety status. Driving record instruments are required to be installed on such vehicles in China. The obtained naturalistic driving data offer insight into the causal factors of hazardous events with the requirements to identify where hazardous events happen within large volumes of data. In this study, we develop a model based on a low-definition driving record instrument and the vehicle kinematic data for post-accident analysis by multi-modal deep learning method. With a higher camera position on commercial vehicles than cars that can observe further distance, motion profiles are extracted from driving video to capture the trajectory features of front vehicles at different depths. Then random forest is used to select significant kinematic variables which can reflect the potential crash. Finally, a multi-modal deep convolutional neural network (DCNN) combined both video and kinematic data is developed to identify potential collision risk in each 12-second vehicle trip. The analysis results indicate that the proposed multi-modal deep learning model can identify hazardous events within a large volumes of data at an AUC of 0.81, which outperforms the state-of-the-art random forest model and kinematic threshold method.