Enhancing Low-Light Light Field Images With a Deep Compensation Unfolding Network.

This paper presents a novel and interpretable end-to-end learning framework, called the deep compensation unfolding network (DCUNet), for restoring light field (LF) images captured under low-light conditions. DCUNet is designed with a multi-stage architecture that mimics the optimization process of solving an inverse imaging problem in a data-driven fashion. The framework uses the intermediate enhanced result to estimate the illumination map, which is then employed in the unfolding process to produce a new enhanced result. Additionally, DCUNet includes a content-associated deep compensation module at each optimization stage to suppress noise and illumination map estimation errors. To properly mine and leverage the unique characteristics of LF images, this paper proposes a pseudo-explicit feature interaction module that comprehensively exploits redundant information in LF images. The experimental results on both simulated and real datasets demonstrate the superiority of our DCUNet over state-of-the-art methods, both qualitatively and quantitatively. Moreover, DCUNet preserves the essential geometric structure of enhanced LF images much better. The code is publicly available at https://github.com/lyuxianqiang/LFLL-DCU.

Dynamic 3D Point Cloud Sequences as 2D Videos.

Dynamic 3D point cloud sequences serve as one of the most common and practical representation modalities of dynamic real-world environments. However, their unstructured nature in both spatial and temporal domains poses significant challenges to effective and efficient processing. Existing deep point cloud sequence modeling approaches imitate the mature 2D video learning mechanisms by developing complex spatio-temporal point neighbor grouping and feature aggregation schemes, often resulting in methods lacking effectiveness, efficiency, and expressive power. In this paper, we propose a novel generic representation called Structured Point Cloud Videos (SPCVs). Intuitively, by leveraging the fact that 3D geometric shapes are essentially 2D manifolds, SPCV re-organizes a point cloud sequence as a 2D video with spatial smoothness and temporal consistency, where the pixel values correspond to the 3D coordinates of points. The structured nature of our SPCV representation allows for the seamless adaptation of well-established 2D image/video techniques, enabling efficient and effective processing and analysis of 3D point cloud sequences. To achieve such re-organization, we design a self-supervised learning pipeline that is geometrically regularized and driven by self-reconstructive and deformation field learning objectives. Additionally, we construct SPCV-based frameworks for both low-level and high-level 3D point cloud sequence processing and analysis tasks, including action recognition, temporal interpolation, and compression. Extensive experiments demonstrate the versatility and superiority of the proposed SPCV, which has the potential to offer new possibilities for deep learning on unstructured 3D point cloud sequences. Code will be released at https://github.com/ZENGYIMING-EAMON/SPCV.

Deep Diversity-Enhanced Feature Representation of Hyperspectral Images.

In this paper, we study the problem of efficiently and effectively embedding the high-dimensional spatio-spectral information of hyperspectral (HS) images, guided by feature diversity. Specifically, based on the theoretical formulation that feature diversity is correlated with the rank of the unfolded kernel matrix, we rectify 3D convolution by modifying its topology to enhance the rank upper-bound. This modification yields a rank-enhanced spatial-spectral symmetrical convolution set (ReS 3-ConvSet), which not only learns diverse and powerful feature representations but also saves network parameters. Additionally, we also propose a novel diversity-aware regularization (DA-Reg) term that directly acts on the feature maps to maximize independence among elements. To demonstrate the superiority of the proposed ReS 3-ConvSet and DA-Reg, we apply them to various HS image processing and analysis tasks, including denoising, spatial super-resolution, and classification. Extensive experiments show that the proposed approaches outperform state-of-the-art methods both quantitatively and qualitatively to a significant extent. The code is publicly available at https://github.com/jinnh/ReSSS-ConvSet.

PointVST: Self-Supervised Pre-Training for 3D Point Clouds Via View-Specific Point-to-Image Translation.

The past few years have witnessed the great success and prevalence of self-supervised representation learning within the language and 2D vision communities. However, such advancements have not been fully migrated to the field of 3D point cloud learning. Different from existing pre-training paradigms designed for deep point cloud feature extractors that fall into the scope of generative modeling or contrastive learning, this paper proposes a translative pre-training framework, namely PointVST, driven by a novel self-supervised pretext task of cross-modal translation from 3D point clouds to their corresponding diverse forms of 2D rendered images. More specifically, we begin with deducing view-conditioned point- wise embeddings through the insertion of the viewpoint indicator, and then adaptively aggregate a view-specific global codeword, which can be further fed into subsequent 2D convolutional translation heads for image generation. Extensive experimental evaluations on various downstream task scenarios demonstrate that our PointVST shows consistent and prominent performance superiority over current state-of-the-art approaches as well as satisfactory domain transfer capability. Our code will be publicly available at https://github.com/keeganhk/PointVST.

Light Field Depth Estimation via Stitched Epipolar Plane Images.

Depth estimation is a fundamental problem in light field processing. Epipolar-plane image (EPI)-based methods often encounter challenges such as low accuracy in slope computation due to discretization errors and limited angular resolution. Besides, existing methods perform well in most regions but struggle to produce sharp edges in occluded regions and resolve ambiguities in texture-less regions. To address these issues, we propose the concept of stitched-EPI (SEPI) to enhance slope computation. SEPI achieves this by shifting and concatenating lines from different EPIs that correspond to the same 3D point. Moreover, we introduce the half-SEPI algorithm, which focuses exclusively on the non-occluded portion of lines to handle occlusion. Additionally, we present a depth propagation strategy aimed at improving depth estimation in texture-less regions. This strategy involves determining the depth of such regions by progressing from the edges towards the interior, prioritizing accurate regions over coarse regions. Through extensive experimental evaluations and ablation studies, we validate the effectiveness of our proposed method. The results demonstrate its superior ability to generate more accurate and robust depth maps across all regions compared to state-of-the-art methods. The source code will be publicly available at https://github.com/PingZhou-LF/Light-Field-Depth-Estimation-Based-on-Stitched-EPIs.

GQE-Net: A Graph-Based Quality Enhancement Network for Point Cloud Color Attribute.

In recent years, point clouds have become increasingly popular for representing three-dimensional (3D) visual objects and scenes. To efficiently store and transmit point clouds, compression methods have been developed, but they often result in a degradation of quality. To reduce color distortion in point clouds, we propose a graph-based quality enhancement network (GQE-Net) that uses geometry information as an auxiliary input and graph convolution blocks to extract local features efficiently. Specifically, we use a parallel-serial graph attention module with a multi-head graph attention mechanism to focus on important points or features and help them fuse together. Additionally, we design a feature refinement module that takes into account the normals and geometry distance between points. To work within the limitations of GPU memory capacity, the distorted point cloud is divided into overlap-allowed 3D patches, which are sent to GQE-Net for quality enhancement. To account for differences in data distribution among different color components, three models are trained for the three color components. Experimental results show that our method achieves state-of-the-art performance. For example, when implementing GQE-Net on a recent test model of the geometry-based point cloud compression (G-PCC) standard, 0.43 dB, 0.25 dB and 0.36 dB Bjφntegaard delta (BD)-peak-signal-to-noise ratio (PSNR), corresponding to 14.0%, 9.3% and 14.5% BD-rate savings were achieved on dense point clouds for the Y, Cb, and Cr components, respectively. The source code of our method is available at https://github.com/xjr998/GQE-Net.

EGRC-Net: Embedding-Induced Graph Refinement Clustering Network.

Existing graph clustering networks heavily rely on a predefined yet fixed graph, which can lead to failures when the initial graph fails to accurately capture the data topology structure of the embedding space. In order to address this issue, we propose a novel clustering network called Embedding-Induced Graph Refinement Clustering Network (EGRC-Net), which effectively utilizes the learned embedding to adaptively refine the initial graph and enhance the clustering performance. To begin, we leverage both semantic and topological information by employing a vanilla auto-encoder and a graph convolution network, respectively, to learn a latent feature representation. Subsequently, we utilize the local geometric structure within the feature embedding space to construct an adjacency matrix for the graph. This adjacency matrix is dynamically fused with the initial one using our proposed fusion architecture. To train the network in an unsupervised manner, we minimize the Jeffreys divergence between multiple derived distributions. Additionally, we introduce an improved approximate personalized propagation of neural predictions to replace the standard graph convolution network, enabling EGRC-Net to scale effectively. Through extensive experiments conducted on nine widely-used benchmark datasets, we demonstrate that our proposed methods consistently outperform several state-of-the-art approaches. Notably, EGRC-Net achieves an improvement of more than 11.99% in Adjusted Rand Index (ARI) over the best baseline on the DBLP dataset. Furthermore, our scalable approach exhibits a 10.73% gain in ARI while reducing memory usage by 33.73% and decreasing running time by 19.71%. The code for EGRC-Net will be made publicly available at https://github.com/ZhihaoPENG-CityU/EGRC-Net.

Knowledge mapping and current trends of global research on snoRNA in the field of cancer.

Cancer is a major health hazard for humans. Recent studies have indicated the involvement of small nucleolar RNAs (snoRNAs) in the occurrence and development of cancer and indicated its potential role as a diagnostic/prognostic marker and therapeutic target. The purpose of this study was to use the bibliometrics method to analyze the published literature on this subject. We collected articles pertaining to the field of snoRNA and cancer from the Web of Science Core Collection database. The data were analyzed to identify the research hotspots and frontiers. The number of articles in this field was low in the early period. Chu Liang and Montanaro Lorenzo were the most prolific authors on this subject, while Jiang and Feng were the most frequently cited authors. In China, three institutions published the most articles, namely Wuhan Univ, China Med Univ, and Guangxi Med Univ. The journal with the highest number of articles on this subject was Oncotarget. The country with the most published articles was China. Analysis of keywords and burst words indicated that early studies mainly focused on molecular mechanisms. Available evidence suggests the involvement of snoRNAs in the molecular mechanism of cancer development and their potential role as a diagnostic and prognostic biomarker.

Knowledge-map analysis of bladder cancer immunotherapy.

This study aimed to conduct a bibliometric analysis in the field of bladder cancer (BC) immunotherapy, and explore the research trends, hotspots and frontiers from 2000 to 2022. VOSviewer software was used to analyze the collaborative relationships between authors, institutions, countries/regions, and journals through citation, co-authorship, and co-citation analysis, to identify research hotspots and frontiers in this field. Researchers based in the United States of America have published a total of 627 papers with 27,308 citations. Indeed, the USA ranked first among the top 10 most active countries and showed the most extensive collaboration with other countries. The University of Texas MD Anderson CANC CTR has published 58 articles, making it the top most institution in terms of published articles and active collaborative research. Kamat AM and Lamm DL were the most active and co-cited authors with 28 papers and 980 co-citations, respectively. Chang Yuan and Xu le were the most active collaborative authors with a total link strength of 195. The J UROLOGY was the most active and frequently co-cited journal, with 100 papers and 6,668 co-citations. Studies of BC immunotherapy can be broadly classified into three categories: "basic research", "clinical trial", and "prognosis". Our findings provide an overview of the research priorities and future directions of BC immunotherapy. Tumor microenvironment and immune checkpoint inhibitors (ICIs) of BC, as well as the combination of ICIs with other drugs, may become the main direction of future research.

Learning a Locally Unified 3D Point Cloud for View Synthesis.

In this paper, we explore the problem of 3D point cloud representation-based view synthesis from a set of sparse source views. To tackle this challenging problem, we propose a new deep learning-based view synthesis paradigm that learns a locally unified 3D point cloud from source views. Specifically, we first construct sub-point clouds by projecting source views to 3D space based on their depth maps. Then, we learn the locally unified 3D point cloud by adaptively fusing points at a local neighborhood defined on the union of the sub-point clouds. Besides, we also propose a 3D geometry-guided image restoration module to fill the holes and recover high-frequency details of the rendered novel views. Experimental results on three benchmark datasets demonstrate that our method can improve the average PSNR by more than 4 dB while preserving more accurate visual details, compared with state-of-the-art view synthesis methods. The code will be publicly available at https://github.com/mengyou2/PCVS.

Light Field Reconstruction via Deep Adaptive Fusion of Hybrid Lenses.

This paper explores the problem of reconstructing high-resolution light field (LF) images from hybrid lenses, including a high-resolution camera surrounded by multiple low-resolution cameras. The performance of existing methods is still limited, as they produce either blurry results on plain textured areas or distortions around depth discontinuous boundaries. To tackle this challenge, we propose a novel end-to-end learning-based approach, which can comprehensively utilize the specific characteristics of the input from two complementary and parallel perspectives. Specifically, one module regresses a spatially consistent intermediate estimation by learning a deep multidimensional and cross-domain feature representation, while the other module warps another intermediate estimation, which maintains the high-frequency textures, by propagating the information of the high-resolution view. We finally leverage the advantages of the two intermediate estimations adaptively via the learned confidence maps, leading to the final high-resolution LF image with satisfactory results on both plain textured areas and depth discontinuous boundaries. Besides, to promote the effectiveness of our method trained with simulated hybrid data on real hybrid data captured by a hybrid LF imaging system, we carefully design the network architecture and the training strategy. Extensive experiments on both real and simulated hybrid data demonstrate the significant superiority of our approach over state-of-the-art ones. To the best of our knowledge, this is the first end-to-end deep learning method for LF reconstruction from a real hybrid input. We believe our framework could potentially decrease the cost of high-resolution LF data acquisition and benefit LF data storage and transmission. The code will be publicly available at https://github.com/jingjin25/LFhybridSR-Fusion.

Content-Aware Warping for View Synthesis.

Existing image-based rendering methods usually adopt depth-based image warping operation to synthesize novel views. In this paper, we reason the essential limitations of the traditional warping operation to be the limited neighborhood and only distance-based interpolation weights. To this end, we propose content-aware warping, which adaptively learns the interpolation weights for pixels of a relatively large neighborhood from their contextual information via a lightweight neural network. Based on this learnable warping module, we propose a new end-to-end learning-based framework for novel view synthesis from a set of input source views, in which two additional modules, namely confidence-based blending and feature-assistant spatial refinement, are naturally proposed to handle the occlusion issue and capture the spatial correlation among pixels of the synthesized view, respectively. Besides, we also propose a weight-smoothness loss term to regularize the network. Experimental results on light field datasets with wide baselines and multi-view datasets show that the proposed method significantly outperforms state-of-the-art methods both quantitatively and visually. The source code is publicly available at https://github.com/MantangGuo/CW4VS.

Flattening-Net: Deep Regular 2D Representation for 3D Point Cloud Analysis.

Point clouds are characterized by irregularity and unstructuredness, which pose challenges in efficient data exploitation and discriminative feature extraction. In this paper, we present an unsupervised deep neural architecture called Flattening-Net to represent irregular 3D point clouds of arbitrary geometry and topology as a completely regular 2D point geometry image (PGI) structure, in which coordinates of spatial points are captured in colors of image pixels. Intuitively, Flattening-Net implicitly approximates a locally smooth 3D-to-2D surface flattening process while effectively preserving neighborhood consistency. As a generic representation modality, PGI inherently encodes the intrinsic property of the underlying manifold structure and facilitates surface-style point feature aggregation. To demonstrate its potential, we construct a unified learning framework directly operating on PGIs to achieve diverse types of high-level and low-level downstream applications driven by specific task networks, including classification, segmentation, reconstruction, and upsampling. Extensive experiments demonstrate that our methods perform favorably against the current state-of-the-art competitors.

Functions and mechanisms of lncRNA MALAT1 in cancer chemotherapy resistance.

Chemotherapy is one of the most important treatments for cancer therapy. However, chemotherapy resistance is a big challenge in cancer treatment. Due to chemotherapy resistance, drugs become less effective or no longer effective at all. In recent years, long non-coding RNA metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) has been found to be associated with the development of chemotherapy resistance, suggesting that MALAT1 may be an important target to overcome chemotherapy resistance. In this review, we introduced the main mechanisms of chemotherapy resistance associated with MALAT1, which may provide new approaches for cancer treatment.

Knowledge-map analysis of percutaneous nephrolithotomy (PNL) for urolithiasis.

Percutaneous nephrolithotomy (PNL) has been used in the treatment of urolithiasis for more than 20 years. However, bibliometric analysis of the global use of PNL for urolithiasis is rare. We retrieved the literatures on PNL and urolithiasis from Web of science core collection database. VOSviewer was used to analyze keywords, citations, publications, co-authorship, themes, and trend topics. A total of 3103 articles were analyzed, most of which were original ones. The most common keywords were "percutaneous nephrology" and "urolithiasis", both of which were closely related to "ureteroscopy". Journal of Urology and Zeng Guohua from the First Affiliated Hospital of Guangzhou Medical University were the most published journal and author in this field. The most productive country was the United States, and its closest partners were Canada, China, and Italy. The five hot topics were the specific application methods and means, risk factors of urolithiasis, the development of treatment technology of urolithiasis, the characteristics, composition, and properties of stones, and the evaluation of curative effect. This study aimed to provide a new perspective for PNL treatment of urolithiasis and provided valuable information for urologic researchers to understand their research hotspots, cooperative institutions, and research frontiers.

Effect of circular RNAs and N6-methyladenosine (m6A) modification on cancer biology.

N6-methyladenosine (m6A), as the most abundant and well-known RNA modification, has been found to play an important role in cancer. Circular RNAs (circRNAs) are a class of single-stranded covalently closed RNA molecules generated by the reverse splicing process. Recent studies have revealed the vital roles of circRNAs in many diseases, including tumorigenesis. Accumulating evidence also shows an association between m6A modification and circRNAs. This study aimed to review the interactions between m6A modification and circRNAs and illustrate their roles in tumorigenesis. m6A modification can modulate the biogenesis, translation, cytoplasmic export, degradation, and other functions of circRNAs in different tumors. circRNAs can also modulate m6A modification by affecting writers, erasers, and readers. We focused on the potential regulatory mechanisms and the biological consequences of m6A modification of circRNAs, as well as the interactions in tumors of different systems. Finally, we listed the possible development directions of m6A modification and circRNAs, which might facilitate the clinical application of tumor therapy. AVAILABILITY OF DATA AND MATERIALS: Not applicable. Keywords.

CircTMEM30A/hsa-miR-130a-3p regulates TNFα and promotes the malignant progression of COPD with primary lung cancer.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) and lung cancer is the most common respiratory diseases among smokers. Lung cancer is one of the most common malignancy. COPD patients are at high risk of lung cancer, but the specific pathogenesis is still unclear. In recent years, circRNAs have become the focus of lung cancer research. This study investigated the effect of CircTMEM30A on COPD and lung cancer and its possible molecular mechanisms. METHODS: QPCR was used to detect the expression of CircTMEM30A in COPD patients, lung cancer patients, and COPD patients with lung cancer. CircTMEM30A was transfected with PPF, HFL1, A549 and NCI-H446 cells, and the transfection efficiency was verified by real-time quantitative PCR. The effects of CircTMEM30A on the proliferation, migration and invasion of fibroblasts and lung cancer cells were detected by MTT, Transwell and scratch tests. The target gene of CircTMEM30A was verified by luciferase reporter assay. Real-time quantitative PCR was used to detect the expression level of CircTMEM30A target gene. RESULTS: CircTMEM30A was the most highly expressed in COPD patients with lung cancer. Overexpression of CircTMEM30A enhanced the effects of proliferation, migration and invasion of fibroblasts and lung cancer cells, down-regulated the expression of E-cadherin, up-regulated the expression of Vimentin, and promoted EMT. After knocking down CircTMEM30A, the migration and invasion ability of lung cancer cells was significantly reduced. Bioinformatics retrieval and luciferase reporter gene confirmed that CircTMEM30A could be bind to miR-130a, while TNFα gene was the target gene of miR-130a. Further studies showed that overexpression of miR-130a and knockdown of TNFα reversed the promoting effect of CircTMEM30A on lung cancer cells. CONCLUSIONS: CircTMEM30A was highly expressed in COPD patients with lung cancer, and miR-130a was low expressed in lung cancer cells. CircTMEM30A regulated the expression of TNFα through miR-130a, thereby affecting the progression of COPD and lung cancer. CircTMEM30A may be a therapeutic target for COPD patients with lung cancer.

PU-Flow: A Point Cloud Upsampling Network With Normalizing Flows.

Point cloud upsampling aims to generate dense point clouds from given sparse ones, which is a challenging task due to the irregular and unordered nature of point sets. To address this issue, we present a novel deep learning-based model, called PU-Flow, which incorporates normalizing flows and weight prediction techniques to produce dense points uniformly distributed on the underlying surface. Specifically, we exploit the invertible characteristics of normalizing flows to transform points between euclidean and latent spaces and formulate the upsampling process as ensemble of neighbouring points in a latent space, where the ensemble weights are adaptively learned from local geometric context. Extensive experiments show that our method is competitive and, in most test cases, it outperforms state-of-the-art methods in terms of reconstruction quality, proximity-to-surface accuracy, and computation efficiency. The source code will be publicly available at https://github.com/unknownue/puflow.

PUFA-GAN: A Frequency-Aware Generative Adversarial Network for 3D Point Cloud Upsampling.

We propose a generative adversarial network for point cloud upsampling, which can not only make the upsampled points evenly distributed on the underlying surface but also efficiently generate clean high frequency regions. The generator of our network includes a dynamic graph hierarchical residual aggregation unit and a hierarchical residual aggregation unit for point feature extraction and upsampling, respectively. The former extracts multiscale point-wise descriptive features, while the latter captures rich feature details with hierarchical residuals. To generate neat edges, our discriminator uses a graph filter to extract and retain high frequency points. The generated high resolution point cloud and corresponding high frequency points help the discriminator learn the global and high frequency properties of the point cloud. We also propose an identity distribution loss function to make sure that the upsampled points remain on the underlying surface of the input low resolution point cloud. To assess the regularity of the upsampled points in high frequency regions, we introduce two evaluation metrics. Objective and subjective results demonstrate that the visual quality of the upsampled point clouds generated by our method is better than that of the state-of-the-art methods.