Many people die each year in the world in single vehicle roadway departure crashes caused by driver inattention, especially on the freeway. Lane Departure Warning System (LDWS) is a useful system to avoid those accident, in which, the lane detection is a key issue. In this paper, after a brief overview of existing methods, we present a robust lane detection algorithm based on geometrical model and Gabor filter. This algorithm is based on two assumptions: the road in front of vehicle is approximately planar and marked which are often correct on the highway and freeway where most lane departure accidents happen [1]. The lane geometrical model we build in this paper contains four parameters which are starting position, lane original orientation, lane width and lane curvature. The algorithm is composed of three stages: the first stage is called off-line calibration which just runs once after the camera is mounted and fixed in the vehicle. The parameters of camera used for lane detection is accurately estimated by the 2D calibration method [2]; The second stage is called lane model parameters estimation and lane model candidates construction, the first three parameters, starting position, lane original orientation and lane width will be estimated using dominant orientation estimation [3] and local Hough transform. Then the construction of lane model candidates is implemented for the final lane model matching; the third stage is model matching. The proposed lane module matching algorithm is implemented to match the best fitted lane model. The combination of these modules can overcome the universal lane detection problems due to inaccuracies in edge detection such as shadow of tree and passengers on the road. Experimental results on real road will be presented to prove the effectiveness of the proposed lane detection algorithm.

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Autonomous robotic navigation in forested environments is difficult because of the highly variable appearance and geometric properties of the terrain. In most navigation systems, researchers assume a priori knowledge of the terrain appearance properties, geometric properties, or both. In forest environments, vegetation such as trees, shrubs, and bushes has appearance and geometric properties that vary with change of seasons, vegetation age, and vegetation species. In addition, in forested environments the terrain surface is often rough, sloped, and/or covered with a surface layer of grass, vegetation, or snow. The complexity of the forest environment presents difficult challenges for autonomous navigation systems. In this paper, a self-supervised sensing approach is introduced that attempts to robustly identify a drivable terrain surface for robots operating in forested terrain. The sensing system employs both LIDAR and vision sensor data. There are three main stages in the system: feature learning, feature training, and terrain prediction. In the feature learning stage, 3D range points from LIDAR are analyzed to obtain an estimate of the ground surface location. In the feature training stage, the ground surface estimate is used to train a visual classifier to discriminate between ground and nonground regions of the image. In the prediction stage, the ground surface location can be estimated at high frequency solely from vision sensor data. C 2012 Wiley Periodicals, Inc.

Road detection is an important problem with application to driver assistance systems and autonomous, self-guided vehicles. The focus of this paper is on the problem of feature extraction and classification for front-view road detection. Specifically, we propose using Support Vector Machines (SVM) for road detection and effective approach for self-supervised online learning. The proposed road detection algorithm is capable of automatically updating the training data for online training which reduces the possibility of misclassifying road and non-road classes and improves the adaptability of the road detection algorithm. The algorithm presented here can also be seen as a novel framework for self-supervised online learning in the application of classification-based road detection algorithm on intelligent vehicle. R IEEE Intelligent Vehicles Symposium

Urethral strictures remain a reconstructive challenge, due to less than satisfactory outcomes and high incidence of stricture recurrence. An “ideal” urethral reconstruction should establish similar architecture and function as the original urethral wall. We fabricated a novel tissue-engineered bionic urethras using cell sheet technology and report their viability in a canine model. Small amounts of oral and adipose tissues were harvested, and adipose-derived stem cells, oral mucosal epithelial cells, and oral mucosal fibroblasts were isolated and used to prepare cell sheets. The cell sheets were hierarchically tubularized to form 3-layer tissue-engineered urethras and labeled by ultrasmall super-paramagnetic iron oxide (USPIO). The constructed tissue-engineered urethras were transplanted subcutaneously for 3 weeks to promote the revascularization and biomechanical strength of the implant. Then, 2 cm length of the tubularized penile urethra was replaced by tissue-engineered bionic urethra. At 3 months of urethral replacement, USPIO-labeled tissue-engineered bionic urethra can be effectively detected by MRI at the transplant site. Histologically, the retrieved bionic urethras still displayed 3 layers, including an epithelial layer, a fibrous layer, and a myoblast layer. Three weeks after subcutaneous transplantation, immunofluorescence analysis showed the density of blood vessels in bionic urethra was significantly increased following the initial establishment of the constructs and was further up-regulated at 3 months after urethral replacement and was close to normal level in urethral tissue. Our study is the first to experimentally demonstrate 3-layer tissue-engineered urethras can be established using cell sheet technology and can promote the regeneration of structural and functional urethras similar to normal urethra.

Background: The HOXA cluster antisense RNA 2 (HOXA-AS2) has recently been discovered to be involved in carcinogenesis in multiple cancers. However, the role and underlying mechanism of HOXA-AS2 in non-small cell lung cancer (NSCLC) yet need to be unraveled. Methods: HOXA-AS2 expression in NSCLC tissues and cell lines was detected using quantitative real-time PCR (qRT-PCR). Furthermore, the effects of HOXA-AS2 on NSCLC cell proliferation, apoptosis, migration, and invasion were assessed by MTS, flow cytometry, wound healing and transwell invasion assays, respectively. Starbase2.0 predicted and luciferase reporter and RNA immunoprecipitation (RIP) assays were used to validate the association of HOXA-AS2 and miR-520a-3p in NSCLC cells. Results: Our results revealed that HOXA-AS2 in NSCLC tissues were up-regulated and cell lines, and were associated with poor prognosis and overall survival. Further functional assays demonstrated that HOXA-AS2 knockdown significantly inhibited NSCLC cell proliferation, induced cell apoptosis and suppressed migration and invasion. Starbase2.0 predicted that HOXA-AS2 sponge miR-520a-3p at 3′-UTR, which was confirmed using luciferase reporter and RIP assays. miR-520a-3p expression was inversely correlated with HOXA-AS2 expression in NSCLC tissues. In addition, miR-520a-3p inhibitor attenuated the inhibitory effect of HOXD-AS2-depletion on cell proliferation, migration and invasion of NSCLC cells. Moreover, HOXA-AS2 could regulate HOXD8 and MAP3K2 expression, two known targets of miR-520a-3p in NSCLC. Conclusion: These findings implied that HOXA-AS2 promoted NSCLC progression by regulating miR-520a-3p, suggesting that HOXA-AS2 could serve as a therapeutic target for NSCLC.

Pyroptosis is a programmed cell death to enhance immunogenicity of tumor cells, but pyroptosis-based immunotherapy is limited due to the immune escape involving myeloid-derived suppressor cells (MDSCs). Therefore, designing a nanoplatform to not only trigger apoptosis-pyroptosis transformation but also combat the MDSC-based immune escape is of great significance. As a proof-of-concept study, here, we designed a metal organic framework (MOF)-based nanoplatform to tailor the pyroptosis immunotherapy through disrupting the MDSC-mediated immunosuppression. By pH-responsive zeolitic imidazolate framework-8 (ZIF-8) modified with hyaluronic acid (HA), the chemotherapeutic drug mitoxantrone (MIT) and DNA demethylating agent hydralazine (HYD) were successfully co-encapsulated into ZIF-8 for achieving (M+H)@ZIF/HA nanoparticles. This nanoplatform demonstrated a powerful apoptosis-to-pyroptosis transformation with a potent disruption of MDSC-mediated T cell paralysis via reducing immunosuppressive methylglyoxal by HYD. Overall, our two-pronged nanoplatform (M+H)@ZIF/HA can switch the cold tumor into an arsenal of antigens that stimulate robust immunological responses, while suppressing immune escape, collectively triggering vigorous cytotoxic T cell responses with remarkable tumor elimination and building a long-term immune memory response against metastasis.

Stress urinary incontinence (SUI) is a common urinary system disease that mostly affects women. Current treatments still do not solve the critical problem of urethral sphincter dysfunction. In recent years, there have been major developments in techniques to obtain, culture, and characterize autologous stem cells as well as many studies describing their applications for the treatment of SUI. In this paper, we review recent publications and clinical trials investigating the applications of several stem cell types as potential treatments for SUI and the underlying challenges of such therapy.