Search

Tumorigenesis of bladder cancer and retinoblastoma is correlated with long non-coding RNA (lncRNA) RBAT1. However, the role of RBAT1 in ovarian carcinoma (OC) is unclear. Thus, the study explored the role of RBAT1 in OC. This research enrolled patients with OC (n = 68), irritable bowel disease (IBD, n = 68, females), digestive tract inflammation (DTI, n = 68, females), urinary tract infection (UTI, n = 68, females), endometriosis (EM, n = 68, females), and healthy controls (HCs, n = 68) to collect plasma sampled. OC and paired non-tumor tissues were collected from patients with OC. RBAT1 accumulation in all samples was analyzed using RT-qPCR. The role of plasma RBAT1 in OC diagnosis was examined using the ROC curves with OC patients as the true positive cases and other patients and HCs as the true negative cases. The role of RBAT1 in predicting the survival of OC patients was analyzed using the survival curve study. RBAT1 was overexpressed in both OC plasma and tissues. Plasma RBAT1 levels were correlated with RBAT1 levels in OC tissues but not in non-tumor tissues. Plasma RBAT1 could distinguish OC patients from other patients and HCs. Patients with high plasma RBAT1 levels had a shorter survival. RBAT1 is overexpressed in OC and might be applied to improve the diagnosis and prognosis of OC.

Background: This study aimed to explore the role of lncRNA DLGAP1-AS2 in ovarian cancer (OC). Methods: Expression of DLGAP1-AS2, mature miR-16 and miR-16 precursor in paired OC tissues and non-tumor tissues collected from 62 OC patients was determined by RT-qPCR. Correlations were analyzed by Pearson’s correlation coefficient. Overexpression of DLGAP1-AS2 was achieved in OC cell line UWB1.289 to explore the effects of DLGAP1-AS2 overexpression on the expression of mature miR-16 and miR-16 precursor by RT-qPCR. Transwell assays were performed to analyze the role of DLGAP1-AS2 and miR-16 in regulating the invasion and migration of UWB1.289 cells.Results: DLGAP1-AS2 was upregulated in OC and inversely correlated with mature miR-16, but not miR-16 precursor. In OC cells, DLGAP1-AS2 overexpression resulted in downregulated mature miR-16, but failed to significantly affect the expression of miR-16 precursor. Cell invasion and migration analysis showed that DLGAP1-AS2 overexpression reduced the inhibitory effects of miR-16 overexpression on cell invasion and migration.Conclusions: DLGAP1-AS2 may suppress the maturation of miR-16 to suppress cell invasion and migration of OC cells.

Light fields suffer from a fundamental resolution tradeoff between the angular and the spatial domain. In this paper, we present a novel cross-scale light field super-resolution approach (up to $\text{8}\times$ resolution gap) to super-resolve low-resolution (LR) light field images that are arranged around a high-resolution (HR) reference image. To bridge the enormous resolution gap between the cross-scale inputs, we introduce an intermediate view denoted as single image super-resolution (SISR) image, i.e., super-resolving LR input via single image based super-resolution scheme, which owns identical resolution as HR image yet lacks high-frequency details that SISR scheme cannot recover under such significant resolution gap. By treating the intermediate SISR image as the low-frequency part of our desired HR image, the remaining issue of recovering high-frequency components can be effectively solved by the proposed high-frequency compensation super-resolution (HCSR) method. Essentially, HCSR works by transferring as much as possible the high-frequency details from the HR reference view to the LR light field image views. Moreover, to solve the nontrivial warping problem that induced by the significant resolution gaps between the cross-scale inputs, we compute multiple disparity maps from the reference image to all the LR light field images, followed by a blending strategy to fuse for a refined disparity map; finally, a high-quality super-resolved light field can be obtained. The superiority of our proposed HCSR method is validated on extensive datasets including synthetic, real-world and challenging microscope scenes.

This paper addresses the problem of disparity map accurate estimation in the cross-scale reference-based light field, which consists several low-quality images arranged around one central high-resolution (HR) image. In the framework, we use a HR image-guidance CNN (HRIG-CNN) for estimating the disparity map in the HR level. Specifically, we first calculate the coarse disparity map using our cross-pattern strategy, which can blend the multiple disparity maps. And then, we refine this coarse disparity map using HRIG-CNN for obtaining high-quality disparity map, which contains detail information and preserve edge information. With the HR image guidance, our HRIG-CNN achieves state-of-the-art for obtaining disparity map in such hybrid light field condition. In the end, we provide both quantitative and qualitative evaluations on different methods, and demonstrate the high performance and robustness of the proposed framework compared with the state-of-the-arts algorithms.

With the development of consumer light field cameras, the light field imaging has become an extensively used method for capturing the three-dimensional appearance of a scene. The depth estimation often requires a dense sampled light field in the angular domain or a high resolution in the spatial domain. However, there is an inherent trade-off between the angular and spatial resolutions of the light field. Recently, some studies for super-resolving the trade-off light field have been introduced. Rather than the conventional approaches that optimize the depth maps, these approaches focus on maximizing the quality of the super-resolved light field. In this article, we investigate how the depth estimation can benefit from these super-resolution methods. Specifically, we compare the qualities of the estimated depth using (a) the original sparse sampled light fields and the reconstructed dense sampled light fields, and (b) the original low-resolution light fields and the high-resolution light fields. Experiment results evaluate the enhanced depth maps using different super-resolution approaches.

Summary Future exascale systems are expected to adopt compute nodes that incorporate many accelerators. To shed some light on the upcoming software challenge, this paper investigates the particular topic of programming clusters that have multiple Xeon Phi coprocessors in each compute node. A new offload approach is considered for intra-node communication, which combines Intel's APIs of coprocessor offload infrastructure (COI) and symmetric communication interface (SCIF) for achieving low latency. While the conventional pragma-based offload approach allows simpler programming, the COI-SCIF approach has three advantages in (1) lower overhead associated with launching offloaded code, (2) higher data transfer bandwidths, and (3) more advanced asynchrony between computation and data movement. The low-level COI-SCIF approach is also shown to have benefits over the MPI-OpenMP counterpart, which belongs to the symmetric usage mode. Moreover, a hybird programming strategy based on COI-SCIF is presented for joining the computational force of all CPUs and coprocessors, while realizing communication hiding. All the programming approaches are tested by a real-world 3D application, for which the COI-SCIF-based approach shows a performance advantage on Tianhe-2. Copyright © 2015 John Wiley & Sons, Ltd.

This paper addresses the problem of generating a super-resolution (SR) image from a low-resolution (LR) image assisted by nearby high-resolution (HR) image. The scaling factor of the super-resolved image is up to 8 times and even more, which is much larger than the ordinary super-resolution scaling factor. Combined patch match and learning based method for image super-resolution using a cross-resolution input. The method is used to super-resolve the images captured by a hybrid light field system consisting of a standard LF camera and a HR DSLR camera. We take the central high-resolution image as a reference, to deal with the around low-resolution images. Unlike other relative algorithm, the proposed method is exploited for the existence of large parallax between the captured images. The main process of our method is a combination of patch based (i.e., example based) algorithm and learning based (e.g., convolutional neural network) method, and does not require any calibration information. Experimental results show that our proposed method performs better than existing method on challenging scenes containing complex texture, specularity and large parallax. Both accuracy and visual improvements in our results are noticeable.

With the development of consumer light field cameras, the light field imaging has become an extensively used method for capturing the 3D appearance of a scene. The depth estimation often require a dense sampled light field in the angular domain. However, there is an inherent trade-off between the angular and spatial resolution of the light field. Recently, some studies for novel view synthesis or angular super-resolution from a sparse set of have been introduced. Rather than the conventional approaches that optimize the depth maps, these approaches focus on maximizing the quality of synthetic views. In this paper, we investigate how the depth estimation can benefit from these angular super-resolution methods. Specifically, we compare the qualities of the estimated depth using the original sparse sampled light fields and the reconstructed dense sampled light fields. Experiment results evaluate the enhanced depth maps using different view synthesis approaches.

We present a novel method to segment an object from multiview images using level set method. Our approach takes advantage of the unique property of level set method in the flexibility of objective energy function design and the adaptability to cut boundary with arbitrary topology. We introduce an iterating optimized 3D level set framework for view coherent segmentation and propose three forces in this framework to drive the convergence of level set to the ideal boundary. In between, the point cloud term and the edge term are designed to give an as-good-as-possible boundary indicator for the level set function, while the local color discriminative classifier is iteratively updated with the multiview silhouette and the 3D point cloud to drive the deformation of the zero level set. Extensive experimental results demonstrate that our approach can produce much more accurate edge localization and more coherent segmentation result across views, compared with the state-of-the-art methods, even for the case of very challenging foreground topologies and ambiguous foreground-background color distribution.

Strap-down inertial navigation system (SINS) is widely used in military field, to facilitate the study of SINS algorithms and various coupled navigation algorithms, a simulation system of SINS is designed. Based on modular design, with good portability and expansibility, the system consists of four independent modules: analysis module of motion state, trajectory simulator, IMU simulation module and SINS calculation module. With graphical interface, the system can control every motion state of the trajectory, which is convenient to generate various trajectories efficiently. Using rotation vector attitude algorithm to process simulation data, experiment results show that the attitude, velocity and position error is consistent with the theoretical value, which verifies the rationality of the simulation model and the availability of the simulation system.