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Pancreatic adenocarcinoma upregulated factor (PAUF) was initially identified as a secreted protein that is substantially expressed in pancreatic ductal adenocarcinoma (PDAC). PAUF also affects invasiveness, motility, and the proliferation of cells in several types of cancer. Recently, PAUF was reported to play a pivotal role in the TLR4-mediated migration and invasion of PDAC cells. However, the mechanism inducing PAUF expression and its functional role in TGF-β-stimulated PDAC cells have not yet been studied. Thus, we first assessed whether TGF-β regulates PAUF expression in several PDAC cell lines and found a significant increase in PAUF expression in Smad signaling-positive Panc-1 cells treated with TGF-β. We also found that the PAUF promoter region contains a Smad-binding element. TGF-β-treated Panc-1 cells showed an increase in PAUF promoter activity, but this effect was not observed in TGF-β-stimulated Smad4-null BxPC-3 cells. Restoring Smad4 expression increased the PAUF promoter activity and expression in Smad4-overexpressing BxPC-3 cells treated with TGF-β. We further found that PAUF aggravated the TGF-β-induced epithelial–mesenchymal transition (EMT) in Panc-1 and BxPC-3 cells via the activation of MEK-ERK signaling. These results indicate that TGF-β/Smad signaling-mediated upregulation of PAUF plays a crucial role in EMT progression by activating the TGF-β-mediated MEK-ERK signaling pathway. [ABSTRACT FROM AUTHOR]

One of the major challenges that existing spatial data is facing is the fragmentation of its representation of indoor and outdoor space. As studies in the use of omnidirectional images in representing space and providing Location-based Services (LBS) has been increasing, the representation of the different scales of space, both in indoors and outdoors, has yet to be addressed. This study aims to develop a data model for generating a multi-scale image-based representation of space using omnidirectional images based spatial relationships. This paper identifies the different scales of space that are represented in spatial data and extends previous approaches of using omnidirectional images in providing indoor LBS towards representing the other scales of space, particularly in outdoor space. Using a sample data, we present an experimental implementation to demonstrate the potential of the proposed data model. Results show that apart from the realistic visualization that image data provides, basic spatial functions can be performed on the image data constructed based on the proposed data model. [ABSTRACT FROM AUTHOR]

Advancements in data-acquisition technology have led to the increasing demand for high-precision road data for autonomous driving. Specifically, road boundaries and linear road markings, like edge and lane markings, provide fundamental guidance for various applications. Unfortunately, their extraction usually requires labor-intensive manual work, and the automatic extraction, which can be applied universally for diverse curved road types, presents a challenge. Given this context, this study proposes a method to automatically extract road boundaries and linear road markings by applying an oriented bounding box (OBB) collision-detection algorithm. The OBBs are generated from a reference line using the point cloud data's position and intensity values. By applying the OBB collision-detection algorithm, road boundaries and linear road markings can be extracted efficiently and accurately in straight and curved roads by adjusting search length and width to detect OBB collision. This study assesses horizontal position accuracy using automatically extracted and manually digitized data to verify this method. The resulting RMSE for extracted road boundaries is +4.8 cm and +5.3 cm for linear road markings, indicating that high-accuracy road boundary and road marking extraction was possible. Therefore, our results demonstrate that the automatic extraction adjusting OBB detection parameters and integrating the OBB collision-detection algorithm enables efficient and precise extraction of road boundaries and linear road markings in various curving types of roads. Finally, this enhances its practicality and simplifies the implementation of the extraction process. [ABSTRACT FROM AUTHOR]

To purchase or authenticate to the full-text of this article, please visit this link: http://dx.doi.org/10.1111/j.1467-8306.2007.00561.x Byline: Jiyeong Lee (*[dagger]) Keywords: 3D GIS; 3D Navigable Data Model; building evacuation; emergency response Abstract: Since the 11 September 2001 attacks in the United States and the 7 July 2005 London bombings, geospatial researchers have attempted to utilize GIScience technologies in response to disasters occurring in the microspace of multilevel structures (such as the interior of buildings) in urban areas. Such applications require 3D geographic information systems (GIS) functionalities to represent the three-dimensional structures of urban environments and to conduct 3D GIS-based spatial analyses. These requirements motivate this study to represent the complex internal structure of buildings at a three-dimensional subunit level so as to analyze human behavior in an emergency situation. This article discusses the development of a 3D Navigable Data Model (3D NDM) based on the 3D Geometric Network Data Model and representing pedestrian access within buildings or urban built-environments, which can be modeled as a network of walkway sections and connections. Additionally, this article discusses the design of a geospatial database created to manage the physical and environmental factors of disaster sites that are essential for emergency response. To improve planning and facilitate rescue operations in a decision support system, this article presents (1) a 3D geo-coding method to locate rescue personnel and disaster sites within the reference data (a network representation of a building), (2) a 3D map matching method to define the correlation between the location of bottlenecks or disaster sites and the nearest location on the 3D NDM, which represents the internal structure of the built-environment as a network representation, and (3) an indoor navigation model, based on the Dijkstra algorithm, to identify optimal routes within a multilevel structure by measuring relative pedestrian accessibility and to provide navigation guidance for rescue personnel. Lastly, this article presents the results of an experimental implementation of the 3D NDM using GIS data for a section of the University of North Carolina at Charlotte campus. Author Affiliation: (*)Department of Geography and Earth Sciences, The University of North Carolina at Charlotte ([dagger])Department of Geoinformatics, The University of Seoul, South Korea Article History: Initial submission, December 2005; revised submission, June 2006; final acceptance, June 2006 Article note: Correspondence: Department of Geography and Earth Sciences, The University of North Carolina at Charlotte, Charlotte, NC 28223, e-mail: jlee68@uncc.edu, or Department of Geoinformatics, The University of Seoul, South Korea, e-mail: jlee@uos.ac.kr.