Uncontrolled Hypertension Increases Risk of All-Cause and Cardiovascular Disease Mortality in Us Adults: The NHANES III Linked Mortality Study

Clinical trials had provided evidence for the benefit effect of antihypertensive treatments in preventing future cardiovascular disease (CVD) events; however, the association between hypertension, whether treated/untreated or controlled/uncontrolled and risk of mortality in US population has been poorly understood. A total of 13,947 US adults aged ≥18 years enrolled in the Third National Health and Nutrition Examination Survey (1988-1994) were used to conduct this study. Mortality outcome events included all-cause, CVD-specific, heart disease-specific and cerebrovascular disease-specific deaths, which were obtained from linked 2011 National Death Index (NDI) files. During a median follow-up of 19.1 years, there were 3,550 all-cause deaths, including 1,027 CVD deaths. Compared with normotensives, treated but uncontrolled hypertensive patients were at higher risk of all-cause (HR = 1.62, 95%CI = 1.35-1.95), CVD-specific (HR = 2.23, 95%CI = 1.66-2.99), heart disease-specific (HR = 2.19, 95%CI = 1.57-3.05) and cerebrovascular disease-specific (HR = 3.01, 95%CI = 1.91-4.73) mortality. Additionally, untreated hypertensive patients had increased risk of all-cause (HR = 1.40, 95%CI = 1.21-1.62), CVD-specific (HR = 1.77, 95%CI = 1.34-2.35), heart disease-specific (HR = 1.69, 95%CI = 1.23-2.32) and cerebrovascular disease-specific death (HR = 2.53, 95%CI = 1.52-4.23). No significant differences were identified between normotensives, and treated and controlled hypertensives (all p \u3e 0.05). Our study findings emphasize the benefit of secondary prevention in hypertensive patients and primary prevention in general population to prevent risk of mortality later in life

3D Matting: A Soft Segmentation Method Applied in Computed Tomography

Three-dimensional (3D) images, such as CT, MRI, and PET, are common in medical imaging applications and important in clinical diagnosis. Semantic ambiguity is a typical feature of many medical image labels. It can be caused by many factors, such as the imaging properties, pathological anatomy, and the weak representation of the binary masks, which brings challenges to accurate 3D segmentation. In 2D medical images, using soft masks instead of binary masks generated by image matting to characterize lesions can provide rich semantic information, describe the structural characteristics of lesions more comprehensively, and thus benefit the subsequent diagnoses and analyses. In this work, we introduce image matting into the 3D scenes to describe the lesions in 3D medical images. The study of image matting in 3D modality is limited, and there is no high-quality annotated dataset related to 3D matting, therefore slowing down the development of data-driven deep-learning-based methods. To address this issue, we constructed the first 3D medical matting dataset and convincingly verified the validity of the dataset through quality control and downstream experiments in lung nodules classification. We then adapt the four selected state-of-the-art 2D image matting algorithms to 3D scenes and further customize the methods for CT images. Also, we propose the first end-to-end deep 3D matting network and implement a solid 3D medical image matting benchmark, which will be released to encourage further research.Comment: 12 pages, 7 figure

RACE: An Efficient Redundancy-aware Accelerator for Dynamic Graph Neural Network

Dynamic Graph Neural Network (DGNN) has recently attracted a significant amount of research attention from various domains, because most real-world graphs are inherently dynamic. Despite many research efforts, for DGNN, existing hardware/software solutions still suffer significantly from redundant computation and memory access overhead, because they need to irregularly access and recompute all graph data of each graph snapshot. To address these issues, we propose an efficient redundancy-aware accelerator, RACE, which enables energy-efficient execution of DGNN models. Specifically, we propose a redundancy-aware incremental execution approach into the accelerator design for DGNN to instantly achieve the output features of the latest graph snapshot by correctly and incrementally refining the output features of the previous graph snapshot and also enable regular accesses of vertices\u27 input features. Through traversing the graph on the fly, RACE identifies the vertices that are not affected by graph updates between successive snapshots to reuse these vertices\u27 states (i.e., their output features) of the previous snapshot for the processing of the latest snapshot. The vertices affected by graph updates are also tracked to incrementally recompute their new states using their neighbors\u27 input features of the latest snapshot for correctness. In this way, the processing and accessing of many graph data that are not affected by graph updates can be correctly eliminated, enabling smaller redundant computation and memory access overhead. Besides, the input features, which are accessed more frequently, are dynamically identified according to graph topology and are preferentially resident in the on-chip memory for less off-chip communications. Experimental results show that RACE achieves on average 1139× and 84.7× speedups for DGNN inference, with average 2242× and 234.2× energy savings, in comparison with the state-of-the-art software DGNN running on Intel Xeon CPU and NVIDIA A100 GPU, respectively. Moreover, for DGNN inference, RACE obtains on average 13.1×, 11.7×, 10.4×, and 7.9× speedup and 14.8×, 12.9×, 11.5×, and 8.9× energy savings over the state-of-the-art Graph Neural Network accelerators, i.e., AWB-GCN, GCNAX, ReGNN, and I-GCN, respectively