| 1. |
- Xu, Caihua, et al.
(författare)
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WT1 promotes cell proliferation in non-small cell lung cancer cell lines through up-regulating cyclin D1 and p-pRb in vitro and in vivo
- 2013
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Ingår i: PLOS ONE. - San Francisco : PLoS, Public Library of Science. - 1932-6203. ; 8:8
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Tidskriftsartikel (refereegranskat)abstract
- The Wilms' tumor suppressor gene (WT1) has been identified as an oncogene in many malignant diseases such as leukaemia, breast cancer, mesothelioma and lung cancer. However, the role of WT1 in non-small-cell lung cancer (NSCLC) carcinogenesis remains unclear. In this study, we compared WT1 mRNA levels in NSCLC tissues with paired corresponding adjacent tissues and identified significantly higher expression in NSCLC specimens. Cell proliferation of three NSCLC cell lines positively correlated with WT1 expression; moreover, these associations were identified in both cell lines and a xenograft mouse model. Furthermore, we demonstrated that up-regulation of Cyclin D1 and the phosphorylated retinoblastoma protein (p-pRb) was mechanistically related to WT1 accelerating cells to S-phase. In conclusion, our findings demonstrated that WT1 is an oncogene and promotes NSCLC cell proliferation by up-regulating Cyclin D1 and p-pRb expression.
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| 2. |
- Jin, Qiangguo, et al.
(författare)
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DUNet : A deformable network for retinal vessel segmentation
- 2019
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Ingår i: Knowledge-Based Systems. - : Elsevier. - 0950-7051 .- 1872-7409. ; 178, s. 149-162
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Tidskriftsartikel (refereegranskat)abstract
- Automatic segmentation of retinal vessels in fundus images plays an important role in the diagnosis of some diseases such as diabetes and hypertension. In this paper, we propose Deformable U-Net (DUNet), which exploits the retinal vessels’ local features with a U-shape architecture, in an end to end manner for retinal vessel segmentation. Inspired by the recently introduced deformable convolutional networks, we integrate the deformable convolution into the proposed network. The DUNet, with upsampling operators to increase the output resolution, is designed to extract context information and enable precise localization by combining low-level features with high-level ones. Furthermore, DUNet captures the retinal vessels at various shapes and scales by adaptively adjusting the receptive fields according to vessels’ scales and shapes. Public datasets: DRIVE, STARE, CHASE_DB1 and HRF are used to test our models. Detailed comparisons between the proposed network and the deformable neural network, U-Net are provided in our study. Results show that more detailed vessels can be extracted by DUNet and it exhibits state-of-the-art performance for retinal vessel segmentation with a global accuracy of 0.9566/0.9641/0.9610/0.9651 and AUC of 0.9802/0.9832/0.9804/0.9831 on DRIVE, STARE, CHASE_DB1 and HRF respectively. Moreover, to show the generalization ability of the DUNet, we use another two retinal vessel data sets, i.e., WIDE and SYNTHE, to qualitatively and quantitatively analyze and compare with other methods. Extensive cross-training evaluations are used to further assess the extendibility of DUNet. The proposed method has the potential to be applied to the early diagnosis of diseases.
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| 3. |
- Lii, Neal Y., et al.
(författare)
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Exodex Adam—A Reconfigurable Dexterous Haptic User Interface for the Whole Hand
- 2022
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Ingår i: Frontiers Robotics AI. - : Frontiers Media SA. - 2296-9144. ; 8
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Tidskriftsartikel (refereegranskat)abstract
- Applications for dexterous robot teleoperation and immersive virtual reality are growing. Haptic user input devices need to allow the user to intuitively command and seamlessly “feel” the environment they work in, whether virtual or a remote site through an avatar. We introduce the DLR Exodex Adam, a reconfigurable, dexterous, whole-hand haptic input device. The device comprises multiple modular, three degrees of freedom (3-DOF) robotic fingers, whose placement on the device can be adjusted to optimize manipulability for different user hand sizes. Additionally, the device is mounted on a 7-DOF robot arm to increase the user’s workspace. Exodex Adam uses a front-facing interface, with robotic fingers coupled to two of the user’s fingertips, the thumb, and two points on the palm. Including the palm, as opposed to only the fingertips as is common in existing devices, enables accurate tracking of the whole hand without additional sensors such as a data glove or motion capture. By providing “whole-hand” interaction with omnidirectional force-feedback at the attachment points, we enable the user to experience the environment with the complete hand instead of only the fingertips, thus realizing deeper immersion. Interaction using Exodex Adam can range from palpation of objects and surfaces to manipulation using both power and precision grasps, all while receiving haptic feedback. This article details the concept and design of the Exodex Adam, as well as use cases where it is deployed with different command modalities. These include mixed-media interaction in a virtual environment, gesture-based telemanipulation, and robotic hand–arm teleoperation using adaptive model-mediated teleoperation. Finally, we share the insights gained during our development process and use case deployments.
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