Learning a Deep Demosaicing Network for Spike Camera With Color Filter Array.

For capturing dynamic scenes with ultra-fast motion, neuromorphic cameras with extremely high temporal resolution have demonstrated their great capability and potential. Different from the event cameras that only record relative changes in light intensity, spike camera fires a stream of spikes according to a full-time accumulation of photons so that it can recover the texture details for both static areas and dynamic areas. Recently, color spike camera has been invented to record color information of dynamic scenes using a color filter array (CFA). However, demosaicing for color spike cameras is an open and challenging problem. In this paper, we develop a demosaicing network, called CSpkNet, to reconstruct dynamic color visual signals from the spike stream captured by the color spike camera. Firstly, we develop a light inference module to convert binary spike streams to intensity estimates. In particular, a feature-based channel attention module is proposed to reduce the noises caused by quantization errors. Secondly, considering both the Bayer configuration and object motion, we propose a motion-guided filtering module to estimate the missing pixels of each color channel, without undesired motion blur. Finally, we design a refinement module to improve the intensity and details, utilizing the color correlation. Experimental results demonstrate that CSpkNet can reconstruct color images from the Bayer-pattern spike stream with promising visual quality.

Sharp needle reconstructs peripheral outflow for patients with malfunctional arteriovenous fistula.

OBJECTIVE: To investigate the feasibility and efficacy of combining ultrasound-guided sharp needle technique with percutaneous transluminal angioplasty (PTA) for treating outflow stenosis or dysfunction in arteriovenous fistula (AVF) among hemodialysis patients. METHODS: From October 2021 to March 2023, patients with occluded or malfunctional fistula veins not amenable to regularly angioplasty were retrospectively enrolled in the study. They underwent ultrasound-guided sharp needle intervention followed by PTA. Data on the location and length between the two veins, technical success, clinical outcomes, and complications were collected. Patency rates post-angioplasty were calculated through Kaplan-Meier analysis. RESULTS: A total of 23 patients were included. The mean length of the reconstructed extraluminal segment was 3.18 cm. The sharp needle opening was performed on the basilic vein (60.9%), brachial vein (26.1%), or upper arm cephalic vein (13%) to create outflow channels. Postoperatively, all cases presented with mild subcutaneous hematomas around the tunneling site and minor diffuse bleeding. The immediate patency rate for the internal fistulas was 100%, with 3-month, 6-month, and 12-month patency rates at 91.3%, 78.3%, and 43.5%, respectively. CONCLUSION: Sharp needle technology merged with PTA presents an effective and secure minimally invasive method for reconstructing the outflow tract, offering a new solution for recanalizing high-pressure or occluded fistulas.

DVSRNet: Deep Video Super-Resolution Based on Progressive Deformable Alignment and Temporal-Sparse Enhancement.

Video super-resolution (VSR) is used to compose high-resolution (HR) video from low-resolution video. Recently, the deformable alignment-based VSR methods are becoming increasingly popular. In these methods, the features extracted from video are aligned to eliminate the motion error targeting high super-resolution (SR) quality. However, these methods often suffer from misalignment and the lack of enough temporal information to compose HR frames, which accordingly induce artifacts in the SR result. In this article, we design a deep VSR network (DVSRNet) based on the proposed progressive deformable alignment (PDA) module and temporal-sparse enhancement (TSE) module. Specifically, the PDA module is designed to accurately align features and to eliminate artifacts via the bidirectional information propagation. The TSE module is constructed to further eliminate artifacts and to generate clear details for the HR frame. In addition, we construct a lightweight deep optical flow network (OFNet) to obtain the bidirectional optical flows for the implementation of the PDA module. Moreover, two new loss functions are designed for our proposed method. The first one is adopted in OFNet and the second one is constructed to guarantee the generation of sharp and clear details for the HR frames. The experimental results demonstrate that our method performs better than the state-of-the-art methods.

F-Box and Leucine-Rich Repeat Protein 7 Is a Prognostic Biomarker and Is Correlated with the Immunosuppressive Microenvironment in Colorectal Cancer.

Background: Colorectal cancer (CRC) is a common malignancy of the digestive system, but its specific mechanisms of occurrence and development remain incompletely understood. F-Box and leucine-rich repeat protein 7 (FBXL7) is a subunit of the Skp-cullin-F-box ubiquitin ligase, involved in cell cycle regulation, endothelial cell damage, and inflammatory immunological responses. However, the role of FBXL7 in CRC remains unknown. In this study, we investigated the clinical significance and potential mechanism of FBXL7 expression in CRC progression. Methods: We utilized data from The Cancer Genome Atlas (TCGA) and the University of California Santa Cruz Xena (UCSC Xena) database for bioinformatic analyses. Clinical CRC samples were used to confirm FBXL7 expression. Gene set enrichment analysis (GSEA) and various databases, such as TCGA, UCSC Xena, cBioPortal, University of ALabama at Birmingham CANcer data analysis portal, MethSurv, Tumor Immune Estimation Resource (TIMER), TIMER2.0, Tumor-Immune System Interaction Database, and Tumor Immune Dysfunction and Exclusion Database (TIDB), were used to investigate the role of FBXL7 in CRC. Statistical analysis was performed using R (v.3.6.3) or GraphPad Prism 8.0. Results: Our findings revealed the predictive significance of FBXL7 in CRC patients. FBXL7 expression was associated with tumor stage, lymph node stage, pathological stage, perineural invasion, and lymphatic invasion. GSEA analysis identified associations between FBXL7 and extracellular matrix organization, as well as immune-related pathways. Immunological analysis revealed a correlation between high FBXL7 expression and the development of an immunosuppressive microenvironment. Conclusion: Identifying FBXL7 as a novel biomarker for CRC could shed light on the promotion of CRC development by the immune environment.

Exogenous galanin alleviates hepatic steatosis by promoting autophagy via the AMPK-mTOR pathway.

Defective autophagy-induced intracellular lipid degradation is causally associated with non-alcoholic fatty liver disease (NAFLD) development. Therefore, agents that can restore autophagy may have potential clinical application prospects on this public health issue. Galanin (GAL) is a pleiotropic peptide that regulates autophagy and is a potential drug for the treatment of NAFLD. In this study, we used an MCD-induced NAFLD mouse model in vivo and an FFA-induced HepG2 hepatocyte model in vitro to evaluate the anti-NAFLD effect of GAL. Exogenous GAL supplementation significantly attenuated lipid droplet accumulation and suppressed hepatocyte TG levels in mice and cell models. Mechanistically, Galanin-mediated reduction of lipid accumulation was positively correlated with upregulated p-AMPK, as evidenced by upregulated protein expressions of fatty acid oxidation-related gene markers (PPAR-α and CPT1A), upregulated expressions of the autophagy-related marker (LC3B), and downregulated autophagic substrate p62 levels. In FFA-treated HepG2 cells, activation of fatty acid oxidation and autophagy-related proteins by galanin was reversed by autophagy inhibitors, chloroquine, and the AMPK inhibitor. Galanin ameliorates hepatic fat accumulation by inducing autophagy and fatty acid oxidation via the AMPK/mTOR pathway.

Automatic Multilabel Classification of Multiple Fundus Diseases Based on Convolutional Neural Network With Squeeze-and-Excitation Attention.

Purpose: Automatic multilabel classification of multiple fundus diseases is of importance for ophthalmologists. This study aims to design an effective multilabel classification model that can automatically classify multiple fundus diseases based on color fundus images. Methods: We proposed a multilabel fundus disease classification model based on a convolutional neural network to classify normal and seven categories of common fundus diseases. Specifically, an attention mechanism was introduced into the network to further extract information features from color fundus images. The fundus images with eight categories of labels were applied to train, validate, and test our model. We employed the validation accuracy, area under the receiver operating characteristic curve (AUC), and F1-score as performance metrics to evaluate our model. Results: Our proposed model achieved better performance with a validation accuracy of 94.27%, an AUC of 85.80%, and an F1-score of 86.08%, compared to two state-of-the-art models. Most important, the number of training parameters has dramatically dropped by three and eight times compared to the two state-of-the-art models. Conclusions: This model can automatically classify multiple fundus diseases with not only excellent accuracy, AUC, and F1-score but also significantly fewer training parameters and lower computational cost, providing a reliable assistant in clinical screening. Translational Relevance: The proposed model can be widely applied in large-scale multiple fundus disease screening, helping to create more efficient diagnostics in primary care settings.

Contactless WiFi Sensing and Monitoring for Future Healthcare - Emerging Trends, Challenges, and Opportunities.

WiFi sensing has received recent and significant interest from academia, industry, healthcare professionals, and other caregivers (including family members) as a potential mechanism to monitor our aging population at a distance without deploying devices on users' bodies. In particular, these methods have the potential to detect critical events such as falls, sleep disturbances, wandering behavior, respiratory disorders, and abnormal cardiac activity experienced by vulnerable people. The interest in such WiFi-based sensing systems arises from practical advantages including its ease of operation indoors as well as ready compliance from monitored individuals. Unlike other sensing methods, such as wearables, camera-based imaging, and acoustic-based solutions, WiFi technology is easy to implement and unobtrusive. This paper reviews the current state-of-the-art research on collecting and analyzing channel state information extracted using ubiquitous WiFi signals, describing a range of healthcare applications and identifying a series of open research challenges, including untapped areas of research and related trends. This work aims to provide an overarching view in understanding the technology and discusses its use-cases from a perspective that considers hardware, advanced signal processing, and data acquisition.

Automatic generation of retinal optical coherence tomography images based on generative adversarial networks.

SIGNIFICANCE: The automatic generation algorithm of optical coherence tomography (OCT) images based on generative adversarial networks (GAN) can generate a large number of simulation images by a relatively small number of real images, which can effectively improve the classification performance. AIM: We proposed an automatic generation algorithm for retinal OCT images based on GAN to alleviate the problem of insufficient images with high quality in deep learning, and put the diagnosis algorithm toward clinical application. APPROACH: We designed a generation network based on GAN and trained the network with a data set constructed by 2014_BOE_Srinivasan and OCT2017 to acquire three models. Then, we generated a large number of images by the three models to augment age-related macular degeneration (AMD), diabetic macular edema (DME), and normal images. We evaluated the generated images by subjective visual observation, Fréchet inception distance (FID) scores, and a classification experiment. RESULTS: Visual observation shows that the generated images have clear and similar features compared with the real images. Also, the lesion regions containing similar features in the real image and the generated image are randomly distributed in the image field of view. When the FID scores of the three types of generated images are lowest, three local optimal models are obtained for AMD, DME, and normal images, indicating the generated images have high quality and diversity. Moreover, the classification experiment results show that the model performance trained with the mixed images is better than that of the model trained with real images, in which the accuracy, sensitivity, and specificity are improved by 5.56%, 8.89%, and 2.22%. In addition, compared with the generation method based on variational auto-encoder (VAE), the method improved the accuracy, sensitivity, and specificity by 1.97%, 2.97%, and 0.99%, for the same test set. CONCLUSIONS: The results show that our method can augment the three kinds of OCT images, not only effectively alleviating the problem of insufficient images with high quality but also improving the diagnosis performance.

Rethinking the Competition Between Detection and ReID in Multiobject Tracking.

Due to balanced accuracy and speed, one-shot models which jointly learn detection and identification embeddings, have drawn great attention in multi-object tracking (MOT). However, the inherent differences and relations between detection and re-identification (ReID) are unconsciously overlooked because of treating them as two isolated tasks in the one-shot tracking paradigm. This leads to inferior performance compared with existing two-stage methods. In this paper, we first dissect the reasoning process for these two tasks, which reveals that the competition between them inevitably would destroy task-dependent representations learning. To tackle this problem, we propose a novel reciprocal network (REN) with a self-relation and cross-relation design so that to impel each branch to better learn task-dependent representations. The proposed model aims to alleviate the deleterious tasks competition, meanwhile improve the cooperation between detection and ReID. Furthermore, we introduce a scale-aware attention network (SAAN) that prevents semantic level misalignment to improve the association capability of ID embeddings. By integrating the two delicately designed networks into a one-shot online MOT system, we construct a strong MOT tracker, namely CSTrack. Our tracker achieves the state-of-the-art performance on MOT16, MOT17 and MOT20 datasets, without other bells and whistles. Moreover, CSTrack is efficient and runs at 16.4 FPS on a single modern GPU, and its lightweight version even runs at 34.6 FPS. The complete code has been released at https://github.com/JudasDie/SOTS.

A New Polyphase Down-Sampling Based Multiple Description Image Coding.

Multiple description coding (MDC) is an efficient source coding technique for error-prone transmission over multiple channels. In this paper, we focus on the design of a new polyphase down-sampling based MDC (NPDS-MDC) for image signals. The encoding of our proposed NPDS-MDC consists of three steps. First, we perform down-sampling on each N×N image block according to the quincunx down-sampling pattern. Second, we propose a new transform and apply it to the down-sampled pixels to produce the side descriptions. Third, we develop an error compensation algorithm to reduce the compression distortion occurring on the down-sampled pixels. In our scheme, the side decoding is performed posterior to image interpolation with reference to the down-sampled compressed pixels. Moreover, the central decoding is achieved by interlacing the side descriptions. We also propose a compression-constrained central deblocking algorithm to further improve the efficiency of the central decoding. The experimental results indicate that our proposed MDC scheme offers clearly superior performance, especially at high bit rates, as compared to the state-of-the-art methods for various types of images.

Compression-Dependent Transform-Domain Downward Conversion for Block-Based Image Coding.

Transform-domain downward conversion (TDDC) for image coding is usually implemented by discarding some high-frequency components from each transformed block. As a result, a block of fewer coefficients is formed, and a lower compression cost is achieved due to the coding of only a few low-frequency coefficients. In this paper, we focus on the design of a new TDDC-based coding method by using our proposed interpolation-compression directed filtering (ICDF) and error-compensated scalar quantization (ECSQ), leading to the compression-dependent TDDC (CDTDDC)-based coding. More specifically, ICDF is first used to convert each macro-block into an coefficient block. Then, this coefficient block is compressed with ECSQ, resulting in a smaller compression distortion for those pixels that locate at some specific positions of a macro-block. We select these positions according to the 4:1 uniform sub-sampling lattice and use the pixels locating at them to reconstruct the whole macro-block through an interpolation. The proposed CDTDDC-based coding can be applied to compress both grayscale and color images. More importantly, when it is used in the color image compression, it offers not only a new solution to reduce the data-size of chrominance components but also a higher compression efficiency. Experimental results demonstrate that applying our proposed CDTDDC-based coding to compress still images can achieve a significant quality gain over the existing compression methods.

CodingFlow: Enable Video Coding for Video Stabilization.

Video coding focuses on reducing the data size of videos. Video stabilization targets at removing shaky camera motions. In this paper, we enable video coding for video stabilization by constructing the camera motions based on the motion vectors employed in the video coding. The existing stabilization methods rely heavily on image features for the recovery of camera motions. However, feature tracking is time-consuming and prone to errors. On the other hand, nearly all captured videos have been compressed before any further processing and such a compression has produced a rich set of block-based motion vectors that can be utilized for estimating the camera motion. More specifically, video stabilization requires camera motions between two adjacent frames. However, motion vectors extracted from video coding may refer to non-adjacent frames. We first show that these non-adjacent motions can be transformed into adjacent motions such that each coding block within a frame contains a motion vector referring to its adjacent previous frame. Then, we regularize these motion vectors to yield a spatially-smoothed motion field at each frame, named as CodingFlow, which is optimized for a spatially-variant motion compensation. Based on CodingFlow, we finally design a grid-based 2D method to accomplish the video stabilization. Our method is evaluated in terms of efficiency and stabilization quality, both quantitatively and qualitatively, which shows that our method can achieve high-quality results compared with the state-of-the-art methods (feature-based).

Repairing bad co-segmentation using its quality evaluation and segment propagation.

In this paper, we improve co-segmentation performance by repairing bad segments based on their quality evaluation and segment propagation. Starting from co-segmentation results of the existing co-segmentation method, we first perform co-segmentation quality evaluation to score each segment. Good segments can be filter out based on the scores. Then, a propagation method is designed to transfer good segments to the rest bad ones so as to repair the bad segmentation. In our method, the quality evaluation is implemented by the measurements of foreground consistency and segment completeness. Two propagation methods such as global propagation and local region propagation are then defined to achieve the more accurate propagation. We verify the proposed method using four state-of-the-arts co-segmentation methods and two public datasets such as ICoseg dataset and MSRC dataset. The experimental results demonstrate the effectiveness of the proposed quality evaluation method. Furthermore, the proposed method can significantly improve the performance of existing methods with larger intersection-over-union score values.

A randomised, double-blind, placebo-controlled trial on the effect of Pycnogenol on the climacteric syndrome in peri-menopausal women.

BACKGROUND: French maritime pine bark extract (Pycnogenol) was found to alleviate menstrual pain and reduce hyperactivity in clinical studies. These results suggest the possibility to observe positive effects in treating climacteric syndrome. OBJECTIVE: Clinical investigation of the effect of Pycnogenol, French maritime pine bark extract, on the climacteric syndrome. METHODS: Some 200 peri-menopausal women were enrolled in a double-blind, placebo-controlled study, and treated with Pycnogenol (200mg) daily. Climacteric symptoms were evaluated by the Women's Health Questionnaire (WHQ), patients were checked for antioxidative status and routine chemistry. A total of 155 women completed the study. RESULTS: All climacteric symptoms improved, antioxidative status increased and LDL/HDL ratio was favourably altered by Pycnogenol. No side effects were reported. CONCLUSION: Pycnogenol may offer an alternative method to reducing climacteric symptoms without unwanted effects.