Chronic Exposure to TDI Induces Cell Migration and Invasion via TGF-ß1 Signal Transduction.

Toluene diisocyanate (TDI) is commonly used in manufacturing, and it is highly reactive and causes respiratory damage. This study aims to identify the mechanism of tumorigenesis in bronchial epithelial cells induced by chronic TDI exposure. In addition, transcriptome analysis results confirmed that TDI increases transforming growth factor-beta 1 (TGF-ß1) expression and regulates genes associated with cancerous characteristics in bronchial cells. Our chronically TDI-exposed model exhibited elongated spindle-like morphology, a mesenchymal characteristic. Epithelial-mesenchymal transition (EMT) was evaluated following chronic TDI exposure, and EMT biomarkers increased concentration-dependently. Furthermore, our results indicated diminished cell adhesion molecules and intensified cell migration and invasion. In order to investigate the cellular regulatory mechanisms resulting from chronic TDI exposure, we focused on TGF-ß1, a key factor regulated by TDI exposure. As predicted, TGF-ß1 was significantly up-regulated and secreted in chronically TDI-exposed cells. In addition, SMAD2/3 was also activated considerably as it is the direct target of TGF-ß1 and TGF-ß1 receptors. Inhibiting TGF-ß1 signaling through blocking of the TGF-ß receptor attenuated EMT and cell migration in chronically TDI-exposed cells. Our results corroborate that chronic TDI exposure upregulates TGF-ß1 secretion, activates TGF-ß1 signal transduction, and leads to EMT and other cancer properties.

Large-scale digital forensic investigation for Windows registry on Apache Spark.

In this study, we investigate large-scale digital forensic investigation on Apache Spark using a Windows registry. Because the Windows registry depends on the system on which it operates, the existing forensic methods on the Windows registry have been targeted on the Windows registry in a single system. However, it is a critical issue to analyze large-scale registry data collected from several Windows systems because it allows us to detect suspiciously changed data by comparing the Windows registry in multiple systems. To this end, we devise distributed algorithms to analyze large-scale registry data collected from multiple Windows systems on the Apache Spark framework. First, we define three main scenarios in which we classify the existing registry forensic studies into them. Second, we propose an algorithm to load the Windows registry into the Hadoop distributed file system (HDFS) for subsequent forensics. Third, we propose a distributed algorithm for each defined forensic scenario using Apache Spark operations. Through extensive experiments using eight nodes in an actual distributed environment, we demonstrate that the proposed method can perform forensics efficiently on large-scale registry data. Specifically, we perform forensics on 1.52 GB of Windows registry data collected from four computers and show that the proposed algorithms can reduce the processing time by up to approximately 3.31 times, as we increase the number of CPUs from 1 to 8 and the number of worker nodes from 2 to 8. Because the distributed algorithms on Apache Spark require the inherent network and MapReduce overheads, this improvement of the processing performance verifies the efficiency and scalability of the proposed algorithms.