Abstract-Currently there are many research focused on using smartphone as a data collection device. Many have shown its sensors ability to replace a lab test bed. These inertial sensors can be used to segment and classify driving events fairly accurately. In this research we explore the possibility of using the vehicle's inertial sensors from the CAN bus to build a profile of the driver to ultimately provide proper feedback to reduce the number of dangerous car maneuver. Braking and turning events are better at characterizing an individual compared to acceleration events. Histogramming the time-series values of the sensor data does not help performance. Furthermore, combining turning and braking events helps better differentiate between two similar drivers when using supervised learning techniques compared to seperate events alone, albeit with anemic performance.

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IntroductionAcute myeloid leukemia (AML) is a common hematologic malignancy characterized by an abnormal accumulation of myeloid precursors in the bone marrow and blood. Similar to many other types of cancer, genetic abnormalities are associated with the development of AML, particularly chromosomal translocations that result in novel fusion proteins. One of the common translocations implicated in AML is the 8q22;21q22 translocation [t(8;21)]. 1 Based on the French-American-British (FAB) classification of leukemic cells, t(8;21) is associated with nearly 40% of the AML cases with the FAB M2 phenotype. 2 t(8,21) involves the AML1 (RUNX1) gene on chromosome 21 and the ETO (MTG8, RUNX1T1) gene on chromosome 8. [3][4][5] AML1 is the DNA-binding subunit of the core binding factor (CBF) transcription factor complex. Its N-terminus contains a highly conserved DNA binding domain called the runt homology domain (RHD). t(8;21) fuses the N-terminus of AML1 including RHD in-frame with almost the entire ETO protein to form AML1-ETO. [3][4][5][6] This fusion protein acts as a dominant negative form of AML1 during embryogenesis. 7,8 It functions as a transcriptional repressor by interacting with NCoR/SMRT/HDAC. 9,10 AML1-ETO was shown to activate expression of BCL-2 and p21, possibly via interacting with p300. [11][12][13] AML1-ETO promotes stem cell renewal and blocks hematopoietic differentiation. [14][15][16] However, its role in blocking cell-cycle progression and promoting apoptosis contradicts its function in promoting leukemogenesis and therefore requires secondary mutagenic events for full transformation. 17,18 We previously identified a single nucleotide insertion that resulted in a truncated AML1-ETO protein (AML1-ETOtr or AEtr), which rapidly promoted leukemia. 19 Subsequently, we identified a C-terminally truncated variant of AML1-ETO named AML1-ETO9a (AE9a), resulting from alternative splicing and found to coexist with full-length AML1-ETO in most analyzed t(8;21) AML patients. 20 Similar to AEtr, AE9a causes a rapid onset of leukemia in mice, 20 which provides a useful mouse model to study the molecular biology of t(8;21) leukemia.To understand the molecular mechanism of AML1-ETOrelated leukemia development and to explore novel therapeutic targets to treat this type of leukemia, in this study we combined gene expression microarray and promoter occupancy (ChIP-chip) analyses to identify genes directly modulated by AE9a in primary murine leukemia cells. Among the common targets of microarray and ChIP-chip assays, approximately 30% show human t(8;21)-specific up or down-regulation compared with AML that have other chromosomal abnormalities. CD45, a protein tyrosine phosphatase and a negative regulator of cytokine/growth factor receptor and JAK/STAT signaling, 21-23 is among those targets. Its expression is down-regulated in AE9a leukemia cells. Consequently, JAK/STAT signaling is enhanced in these leukemia cells. We show that re-expression of CD45 suppresses JAK/STAT activation, delays leukemia development by AE9a an...

Epigenetics may have an important role in mood stabilizer action. Valproic acid (VPA) is a histone deacetylase inhibitor, and lithium (Li) may have downstream epigenetic actions. To identify genes commonly affected by both mood stabilizers and to assess potential epigenetic mechanisms that may be involved in their mechanism of action, we administered Li (N=12), VPA (N=12), and normal chow (N=12) to Brown Norway rats for 30 days. Genomic DNA and mRNA were extracted from the hippocampus. We used the mRNA to perform gene expression analysis on Affymetrix microarray chips, and for genes commonly regulated by both Li and VPA, we validated expression levels using quantitative real-time PCR. To identify potential mechanisms underlying expression changes, genomic DNA was bisulfite treated for pyrosequencing of key CpG island ‘shores' and promoter regions, and chromatin was prepared from both hippocampal tissue and a hippocampal-derived cell line to assess modifications of histones. For most genes, we found little evidence of DNA methylation changes in response to the medications. However, we detected histone H3 methylation and acetylation in the leptin receptor gene, Lepr, following treatment with both drugs. VPA-mediated effects on histones are well established, whereas the Li effects constitute a novel mechanism of transcriptional derepression for this drug. These data support several shared transcriptional targets of Li and VPA, and provide evidence suggesting leptin signaling as an epigenetic target of two mood stabilizers. Additional work could help clarify whether leptin signaling in the brain has a role in the therapeutic action of Li and VPA in bipolar disorder.

Abstract-Naturalistic driving studies (NDS) capture huge amounts of drive data, that is analyzed for critical information about driver behavior, driving characteristics etc. Moreover, NDS involve data collected from a wide range of sensing technologies in cars and this makes the analysis of this data a challenging task. In this paper, we propose a multimodal synergistic approach for automated drive analysis process that can be employed in analyzing large amounts of drive data. The visual information from cameras, vehicle dynamics from CAN bus, vehicle global positioning coordinates from GPS and digital road map data, that are collected during the drive, are analyzed in a collaborative and complementary manner in the approach presented in this paper. It will be shown that the proposed synergistic drive analysis approach automatically determines a wide range of critical information about the drive in varying road conditions.