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Xinyi Li,1,* Tao Wang,2,* Li Jin,3 Zhiqiang Li,4 Cheng Hu,3 Hongliang Yi,1 Jian Guan,1 Huajun Xu,1 Xiaolin Wu5 1Department of Otorhinolaryngology Head and Neck Surgery, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Key Laboratory of Sleep Disordered Breathing, Otorhinolaryngology Institute of Shanghai JiaoTong University, Shanghai, People’s Republic of China; 2Department of Endocrinology, The Second Affiliated Hospital of Soochow University, Suzhou, People’s Republic of China; 3Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Clinical Centre for Diabetes, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai, People’s Republic of China; 4Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Bio-X Institutes, Ministry of Education, Shanghai Jiao Tong University, Shanghai, People’s Republic of China; 5Central Laboratory of Shanghai Eighth People’s Hospital, Xuhui Branch of Shanghai Sixth People’s Hospital, Shanghai, People’s Republic of China*These authors contributed equally to this workCorrespondence: Huajun Xu, Department of Otorhinolaryngology Head and Neck Surgery, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Key Laboratory of Sleep Disordered Breathing, Otorhinolaryngology Institute of Shanghai JiaoTong University, Shanghai, 200233, People’s Republic of China, Email sunnydayxu2010@163.com Xiaolin Wu, Central Laboratory of Shanghai Eighth People’s Hospital, Xuhui Branch of Shanghai Sixth People’s Hospital, Shanghai, People’s Republic of China, Email wuxiaolin999@hotmail.comObjective: Both obstructive sleep apnea (OSA) and obesity are highly prevalent worldwide, and are intrinsically linked. Previous studies showed that obesity is one of the major risk factors for OSA, but the causality of the relationship is sti

Purpose: Intraventricular blood flow dynamics are associated with cardiac function. Accurate, noninvasive, and easy assessments of hemodynamic quantities (such as velocity, vortex, and pressure) could be an important addition to the clinical diagnosis and treatment of heart diseases. However, the complex time-varying flow brings many challenges to the existing noninvasive image-based hemodynamic assessments. The development of reliable techniques and analysis tools is essential for the application of hemodynamic biomarkers in clinical practice. Methods: In this study, a time-resolved particle tracking method, Shake-the-Box, was applied to reconstruct the flow in a realistic left ventricle (LV) silicone model with biological valves. Based on the obtained velocity, 4D pressure field was calculated using a Poisson equation-based pressure solver. Furthermore, flow analysis by proper orthogonal decomposition (POD) of the 4D velocity field has been performed. Results: As a result of the Shake-the-Box algorithm, we have extracted: (i) particle positions, (ii) particle tracks, and finally, (iii) 4D velocity fields. From the latter, the temporal evolution of the 3D pressure field during the full cardiac cycle was obtained. The obtained maximal pressure difference extracted along the base-to-apex was about 2.7 mmHg, which is in good agreement with those reported in vivo. The POD analysis results showed a clear picture of different scale of vortices in the pulsatile LV flow, together with their time-varying information and corresponding kinetic energy content. To reconstruct 95% of the kinetic energy of the LV flow, only the first six POD modes would be required, leading to significant data reduction. Conclusions: This work demonstrated Shake-the-Box is a promising technique to accurately reconstruct the left ventricle flow field in vitro. The good spatial and temporal resolutions of the velocity measurements enabled a 4D reconstruction of the pressure field in the left ven

Primary liver cancer is a rising cause of cancer deaths in the US. Although immunotherapy with immune checkpoint inhibitors induces a potent response in a subset of patients, response rates vary among individuals. Predicting which patients will respond to immune checkpoint inhibitors is of great interest in the field. In a retrospective arm of the National Cancer Institute Cancers of the Liver: Accelerating Research of Immunotherapy by a Transdisciplinary Network (NCI-CLARITY) study, we use archived formalin-fixed, paraffin-embedded samples to profile the transcriptome and genomic alterations among 86 hepatocellular carcinoma and cholangiocarcinoma patients prior to and following immune checkpoint inhibitor treatment. Using supervised and unsupervised approaches, we identify stable molecular subtypes linked to overall survival and distinguished by two axes of aggressive tumor biology and microenvironmental features. Moreover, molecular responses to immune checkpoint inhibitor treatment differ between subtypes. Thus, patients with heterogeneous liver cancer may be stratified by molecular status indicative of treatment response to immune checkpoint inhibitors.

Ultra high resolution (UHR) images are almost always downsampled to fit small displays of mobile end devices and upsampled to its original resolution when exhibited on very high-resolution displays. This observation motivates us on jointly optimizing operation pairs of downsampling and upsampling that are spatially adaptive to image contents for maximal rate-distortion performance. In this paper, we propose an adaptive downsampled dual-layer (ADDL) image compression system. In the ADDL compression system, an image is reduced in resolution by learned content-adaptive downsampling kernels and compressed to form a coded base layer. For decompression the base layer is decoded and upconverted to the original resolution using a deep upsampling neural network, aided by the prior knowledge of the learned adaptive downsampling kernels. We restrict the downsampling kernels to the form of Gabor filters in order to reduce the complexity of filter optimization and also reduce the amount of side information needed by the decoder for adaptive upsampling. Extensive experiments demonstrate that the proposed ADDL compression approach of jointly optimized, spatially adaptive downsampling and upconversion outperforms the state of the art image compression methods.

Recently, numerous end-to-end optimized image compression neural networks have been developed and proved themselves as leaders in rate-distortion performance. The main strength of these learnt compression methods is in powerful nonlinear analysis and synthesis transforms that can be facilitated by deep neural networks. However, out of operational expediency, most of these end-to-end methods adopt uniform scalar quantizers rather than vector quantizers, which are information-theoretically optimal. In this paper, we present a novel Lattice Vector Quantization scheme coupled with a spatially Adaptive Companding (LVQAC) mapping. LVQ can better exploit the inter-feature dependencies than scalar uniform quantization while being computationally almost as simple as the latter. Moreover, to improve the adaptability of LVQ to source statistics, we couple a spatially adaptive companding (AC) mapping with LVQ. The resulting LVQAC design can be easily embedded into any end-to-end optimized image compression system. Extensive experiments demonstrate that for any end-to-end CNN image compression models, replacing uniform quantizer by LVQAC achieves better rate-distortion performance without significantly increasing the model complexity., Comment: Accepted by CVPR 2023

Zhaoyang Guo,1,* Yuanye Ma,1,* Yaqing Wang,2 Hongfei Xiang,1 Shang-You Yang,3 Zhu Guo,1 Ronghuan Wang,1 Wujun Chen,4 Dongming Xing,4,5 Bohua Chen,1 Hao Tao,1 Xiaolin Wu1,4 1Department of Orthopedics, The Affiliated Hospital of Qingdao University, Qingdao, People’s Republic of China; 2Department of Nephrology, The First Affiliated Hospital of China Medical University, Shenyang, People’s Republic of China; 3School of Medicine-Wichita, University of Kansas, Wichita, KS, USA; 4Cancer Institute, The Affiliated Hospital of Qingdao University, Qingdao University, Qingdao Cancer Institute, Qingdao, People’s Republic of China; 5School of Life Sciences, Tsinghua University, Beijing, People’s Republic of China*These authors contributed equally to this workCorrespondence: Xiaolin Wu; Hao Tao, Department of Orthopedics, The Affiliated Hospital of Qingdao University, Qingdao, People’s Republic of China, Email fyqs01@qdu.edu.cn; taohaob2008@163.comBackground: It is well established that discogenic low back pain (DLBP) is often caused by the inflammatory response during intervertebral disc degeneration (IDD). However, it remains unclear how inflammatory mediators such as Interleukin-6 (IL-6) are involved in discogenic pain caused by degeneration and intervertebral disc (IVD) metabolism. The purpose of this study is to study the relationship between IL-6 and Transmembrane protein 100 (TMEM100), and to analyze the different metabolites and metabolic pathways in various rat intervertebral discs by metabonomics.Methods: We established a rat model of IDD-DLBP by disc punctures and PBS infusion to examine the rat pain behaviors. Intervertebral disc tissues were harvested for molecular biology experiments to explore the relationship between IL-6 and TMEM100. High-resolution mass spectrometry (HRMS) was performed for untargeted metabolomics, and nuclear magnetic resonance spectroscopy metabolomics (MRS) for differential metabolites and metabolic pathways.R

Image signal processing (ISP) pipeline plays a fundamental role in digital cameras, which converts raw Bayer sensor data to RGB images. However, ISP-generated images usually suffer from imperfections due to the compounded degradations that stem from sensor noises, demosaicing noises, compression artifacts, and possibly adverse effects of erroneous ISP hyperparameter settings such as ISO and gamma values. In a general sense, these ISP imperfections can be considered as degradations. The highly complex mechanisms of ISP degradations, some of which are even unknown, pose great challenges to the generalization capability of deep neural networks (DNN) for image restoration and to their adaptability to downstream tasks. To tackle the issues, we propose a novel DNN approach to learn degradation-independent representations (DiR) through the refinement of a self-supervised learned baseline representation. The proposed DiR learning technique has remarkable domain generalization capability and consequently, it outperforms state-of-the-art methods across various downstream tasks, including blind image restoration, object detection, and instance segmentation, as verified in our experiments., Comment: Accepted by CVPR 2024